Engineering, Technology & Applied Science Research

A Study on the Enhancement of the Large-Scale Construction Project Capability of Project Management Boards

The Van Tran, Tuan Anh Nguyen, Thao Minh Hoang — Sat, 01 Jun 2024 00:00:00 +0000

This article analyzes the data collected from the general public and experts in the industry in order to assess the current situation and propose solutions to improve the capability of project management boards of large-scale construction projects in Vietnam. A dataset of 129 survey respondents, whose reliability has been validated with the Cronbach's alpha and the Exploratory Factor Analysis (EFA) test, was constructed. The remaining 24 factors were organized into 8 factor groups that influence the project management capability. Group I, which had the greatest impact, is the legal procedures group, followed by the project deployment process group, down to Group VIII, which is the project risk management group. The results of this study help large-scale construction companies find effective solutions to improve the quality of their projects and increase their competitive capacity in the domestic and international markets.

Hardware Implementation of a Deep Learning-based Model for Image Quality Assessment

Yahia Said, Yazan A. Alsariera — Sat, 01 Jun 2024 00:00:00 +0000

Image quality assessment is very important for accurate analysis and better interpretation. In reality, environmental effects and device limitations may degrade image quality. Recently, many image quality assessment algorithms have been proposed. However, these algorithms require high computation overhead, making them unsuitable for mobile devices, such as smartphones and smart cameras. This paper presents a hardware implementation of an image quality assessment algorithm based on a Lightweight Convolutional Neural Network (LCNN) model. Many advances have been made in the construction of high-accuracy LCNN models. The current study used EfficientNet V2. The model achieved state-of-the-art image classification performance on many famous benchmark datasets while having a smaller size than other models with the same performance. The model was utilized to learn human visual behavior through understanding dataset information without prior knowledge of target visual behavior. The proposed model was implemented employing a Field Programmable Gate Array (FPGA) for possible integration into mobile devices. The Xilinx ZCU 102 board was implemented to evaluate the proposed model. The results confirmed the latter’s efficiency in image quality assessment compared to existing models.

Experimental and Numerical Study of the Performance Improvement of the Solar Dryer Equipped with PVT

Mohamed Fterich, Ahmed Saadeddine Souissi, Ezzeddine Touti, Hatem Bentaher — Sat, 01 Jun 2024 00:00:00 +0000

This research addresses the improvement of the performance of a solar dryer equipped with a PVT unit by integrating a heat exchanger into the drying system. The results indicated that introducing a heat exchanger into the drying process had a positive impact on enhancing and raising the drying temperature by harnessing the amount of free energy dissipated after the drying operation. The absorbed energy ranged from 30 J/s to 275 J/s from the hot air emitted throughout the drying process during the day, depending on the drying temperature. This paper also discusses the influence of the drying room design on the thermal balance within the room. Consequently, four different designs for the drying room were developed and studied with the COMSOL software. The findings revealed that the design-4, which optimally places two air inlets (one at the bottom and one at the top) on one side, whereas the opposing side has a centralized air outlet, utilizing a fan to ensure effective air circulation, is the best solution in terms of thermal balance and distribution of the drying air inside the drying chamber.

Vertical Accuracy of Google Earth Data

Khalid L. A. El-Ashmawy — Sat, 01 Jun 2024 00:00:00 +0000

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.

Fuzzy TOPSIS and Fuzzy AHP-based MCDA for selecting the Optimal Location for a Solar PV-powered RO Desalination Unit in Visakhapatnam, India

Anantha Sai Somasi, Srichandan Kondamudi — Sat, 01 Jun 2024 00:00:00 +0000

This feasibility study explores the viability of solar PV-powered Reverse Osmosis (RO) desalination in five locations in Visakhapatnam, India. The assessment integrates technical, economic, environmental, social, and political considerations using Multi-Criteria Decision Analysis (MCDA) with Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) methods to handle uncertainties in decision-making. The study evaluates the technical feasibility of the integration, conducts economic analysis, examines environmental impacts, investigates social benefits, and challenges, and analyzes the political landscape. The former emphasizes the significance of understanding challenges and potential solutions associated with RO desalination, aiming for sustainable development aligned with local and global goals. Yarada and Bheemili were the most suitable locations selected based on Fuzzy TOPSIS and Fuzzy AHP, respectively. The study also highlighted the need for public awareness and government support for desalination projects.

Accelerated testing of the Wear Behavior of 3D-printed Spur Gears

Sat, 01 Jun 2024 00:00:00 +0000

This paper presents the results of an in-depth investigation of 3D-printed plastic gears made of ABS, PLA, and annealed PLA. Wear tests performed on a specialized rig underscore the superior wear resistance of ABS gears, while the annealing process shows a modest improvement in PLA gear durability. The novelty of this study is a comprehensive evaluation of the wear behavior of different 3D printed materials under different loading conditions. This study introduces an innovative accelerated testing method, emphasizing efficiency in product development through reduced testing durations and adaptability to various scenarios.

Modified Equivalent Compression Stress Block for Normal-Strength Concrete Flexural Design using Energy Modeling

Hamdy El-Gohary — Sat, 01 Jun 2024 00:00:00 +0000

The equivalent stress block is recommended for use in the design of reinforced concrete sections to simplify the analysis of the composite behavior of concrete and steel reinforcement. In most current codes, a rectangular equivalent stress block is provided. The design parameters of the equivalent block were recommended many years ago. Due to the importance of the equivalent stress block concept, numerous investigations have been performed to increase its accuracy. In the current paper, an exploration of the rectangular equivalent stress block has been carried out using the energy modeling approach. Energy modeling is a new general approach for studying the behavior of concrete elements. In this method, the energy consumed (work done) can be determined by integrating the force-displacement diagram (in the current study this will be the concrete stress-strain curve in compression). Schematic and equivalent stress-strain curves for concrete in uniaxial compression provided in most current codes and relevant textbooks were considered in this research. The codes taken into account in the current study are ACI-318-19, Canadian Code CSA A23.3-04, Eurocode EC-2, and Chinese standard GB 500 10 – 2002. The energy consumed by these curves for different values of concrete strength has been compared with numerous experimental results. This comparison shows that the results of the equivalent stress block provided in most of the considered current codes are conservative. Applying the energy modeling for the considered experimental stress-strain curves a modified equivalent stress block is recommended for practical use. The results of the proposed equivalent stress block are in good agreement with the experimental ones. The ratio between the predicted total energy engaging the proposed model and the total energy calculated for the experimental results ranges between 0.95 and 1.08 with a mean value equal to unity.

Experimental and Analytical Investigation of Deep Drawing Process for producing Pentacle Cups

Zainab H. Mohsein, Waleed K. Jawad, Aseel H. Abed — Sat, 01 Jun 2024 00:00:00 +0000

Sheet metal forming is a critical process in modern manufacturing, used to create both finished and semi-finished products. In this industry, there is an increasing demand for fast and cost-effective manufacturing and modification of dies. Therefore, improving theoretical and experimental engineering approaches to reduce manufacturing costs and lead-time between design and production is essential. The development of numerical methods has made Finite Element Analysis (FEA) a valuable tool for predicting product deformation. This study used three forming methods to create a pentacle cup from a low-carbon steel sheet (1008-AISI) with a thickness of 0.7 mm and a diameter of 80 mm. ANSYS Workbench 3-D modeling software was utilized to simulate the drawing procedures. The resulting product's wall thickness and strain were measured and graphed to demonstrate the impact of the different forming methods. The first method involved direct formation by drawing a circular blank metal into a pentacle shape. The second method involved redrawing a cylindrical cup into a pentagonal cup, while the third method entailed converting a pentagonal cup into a pentacle cup. The results showed that the second forming method produced the highest maximum punch load reaching approximately 42.24 kN in experimental testing and 36.66 kN in Finite Element Modeling (FEM), exceeding that of the third forming method. The maximum thinning at cup curvature was observed in the pentacle cup created by the second method, particularly in the major and minor areas, and was more pronounced than in the pentacle cups produced by the third forming method. Ultimately, the third forming method was identified as the optimal technique for producing a pentacle cup with less thinning at the cup curvature and a more uniform distribution of thickness and strain. Overall, this study highlights the importance of advancements in theoretical and experimental engineering approaches to reduce manufacturing costs and improve the efficiency of the sheet metal forming process. The findings from this study can lead to the development of optimal forming techniques for creating high-quality products.

Urban Development Analysis using GIS and Remote Sensing. The Case Study of Makkah City

Medhat M. Helal, Tarek A. Eldamaty — Sat, 01 Jun 2024 00:00:00 +0000

Makkah Al-Mukarramah has undergone significant urban transformation in recent decades, transitioning from non-urban to urban landscapes driven by fast economic growth. This study aims to analyze the increase in population, urbanization, topography, and land use of Makkah City over the past 20 years, from 2000 to 2020. Makkah holds special significance for the Saudi government due to its religious and regional prominence, resulting in remarkable developmental strides within short timeframes. This has led to a surge in population and spatial expansion towards the city's outskirts, bringing about both quantitative and qualitative changes in the city. The growth rate in Makkah was 2.453% in 2020 and 2% in 2010, indicating a trajectory conducive to future land use/land cover planning. The population has shown remarkable growth, rising from 1,294,000 in 2000 to 1,578,722 in 2010 (22% increase) and further reaching 2,017,793 in 2020 (27.81% increase), nearly doubling over the two-decade span. The city's area expanded to 465 Km² in 2020, compared to 388 Km² in 2010 and 366 Km² in 2000, attributed to a notable increase in the number of districts from 60 in 2010 to 101 in 2020, marking a substantial 68.3% rise. This study used a map scale of 1:300,000 to classify features, such as mountains, urban areas, deserts, and roads. The results indicate a decrease in mountains and deserts, while urban areas and roads have increased, aligning with the population growth observed over the two decades.

Enhancement of Perlite Concrete Properties containing Sustainable Materials by Incorporation of Hybrid Fibers

‪Ahmed Jasim Qasim, Nada Mahdi Fawzi — Sat, 01 Jun 2024 00:00:00 +0000

Utilizing waste resources in concrete manufacturing, while employing alternative components and minimizing the Ordinary Portland Cement (OPC) production, is a matter of great importance owing to several environmental and stability considerations. OPC is the fundamental component implemented in the conventional concrete production process. However, the OPC industry has raised environmental concerns since it produces mass amounts of carbon dioxide (CO₂). A more sustainable substance, utilizing metakaolin as pozzolanic material and local ash as a filler can serve as an OPC substitute, thereby reducing the CO₂release into the environment. This work examines the impact of incorporating sustainable recycled copper fibers as well as alkali resistance glass fibers on the properties of perlite structural lightweight aggregate concrete containing local, sustainable materials. The research includes slump, density, and thermal conductivity tests along with tests conducted during the 7, 28, and 60 days of curing for compressive, flexural, and split tensile strength. The concrete was reinforced with 1% hybrid fibers by volume. The results reveal that adding fibers to lightweight concrete reduces the slump and increases density and thermal conductivity, while it also increases the compressive, flexural, and split tensile strengths.

Behavior of Reactive Powder Concrete reinforced with Hybrid Fibers containing Sustainable Materials

Saif Ibrahim Hendi, Nada Mahdi Fawzi Aljalawi — Sat, 01 Jun 2024 00:00:00 +0000

This study investigates the behavior of recycled Reactive Powder Concrete (RPC), made from finely ground recycled raw materials and containing a certain percentage of recycled copper (electrical waste copper wire) and steel fibers. This concrete has a relatively low water-to-binder ratio and is composed of cement, fine aggregate, and ultrafine powders, such as quartz powder and silica fume. The properties of Fiber-Reinforced Reactive Powder Concrete (FR-RPC) containing micro-steel fibers, recycled copper fibers, and a mixture of steel-recycled and copper fibers were investigated. A micro-steel fiber RPC (MF1) was used as a reference mix, having 1% steel fibers by volume with 13 mm length and 0.2 mm diameter. Recycled copper fiber RPC (MF2) was prepared utilizing 1% recycled copper fibers by volume, with a diameter of 0.2 mm and a length of 10 mm. In addition, Hybrid FR-RPC (HFR-RPC) samples were prepared by mixing micro steel fibers and recycled copper fibers in proportions of 0.5-0.5% (MF3), 0.4-0.6% (MF4), and 0.3-0.7% (MF5), respectively. The compressive strength, flexural strength, and splitting tensile strength of these FR-RPC mixes were studied. The results displayed that MF3 achieved slightly lower compressive strength, flexural strength, and splitting tensile strength than MF1 and higher than MF2, MF4, and MF5. Although the mechanical strengths of MF3 were marginally lower than those of MF1, compressive strength, flexural strength, and splitting tensile strength were almost the same. Therefore, copper wire waste fibers can be employed along with steel fibers with excellent results.

CFD Analysis for Improving Forced Convection Heat Transfer from Newly Designed Perforated Heat Sinks

Ahmed Al-Zahrani — Sat, 01 Jun 2024 00:00:00 +0000

This study develops a 3D-CFD model to analyze the thermal performance of perforated fin heat sinks and evaluates four perforated continuous and interrupted fin heat sinks with distinct geometric patterns. Using the Finite-Volume Method (FVM) to discretize the governing equations, the SolidWorks 2019 flow simulation software was implemented to solve and validate the latter, demonstrating that the CFD simulation model employed in the current study is reliable. The performance parameters of the heat sink are presented in terms of Reynolds number and heater power. The results indicate that modules B and C achieved higher heat transfer rates, average heat transfer coefficient, and Nusselt number compared to the other modules. Module A had the highest fin efficiency and module D exhibited greater fin effectiveness than the other ones.

A Solution for Energy-Efficient Operation of Urban Electric Trains: Integrating Rooftop PV with the Active Rectifier in the Traction Substation

An Thi Hoai Thu Anh, Tran Hung Cuong — Sat, 01 Jun 2024 00:00:00 +0000

The utilization of renewable sources connected to a grid to reduce traction substation installation costs and electrified trains' operation energy is a highly promising solution in the electric transportation field. This study proposes a DC traction power supply system integrated with a solar energy system using a DC-DC boost converter and an active rectifier replacing a diode located at the traction substation. The active rectifier not only recovers regenerative braking energy when electric trains operate in braking mode but also transfers solar energy from the DC bus to the grid. With the characteristics of urban railway lines utilizing high-power traction motors and high-voltage DC bus, this paper presents the structure of the Modular Multilevel DC-DC boost converter in the solar energy system employing the Maximum Power Point Tracking (MPPT) algorithm, whereas the modular multilevel active rectifier utilizes the Voltage Oriented Control (VOC) algorithm with three loop circuits: phase-lock loop, current loop, and voltage loop. Simulation results in Matlab/Simulink with parameters collected from the Nhon-Hanoi station urban railway line in Vietnam demonstrate that the PV system produces almost 37% of the energy in the accelerating phase of electric trains.

Analyzing Cost Deviation Factors in Iraqi Construction Projects: A Comprehensive Examination of Influencing Variables

Safaa Adnan Mohammed, Afrah Hasan — Sat, 01 Jun 2024 00:00:00 +0000

Construction projects suffer from many difficulties and complications, including cost deviations due to multiple factors. The research goal of this study is to explore the factors causing cost deviation in Iraqi construction projects. Twenty-five influencing factors were identified, based on previous studies and expert opinions. One hundred questionnaires were distributed to project participants, including contracting companies, consultants, and employers, with a 73% response rate. Relative Importance Index (RII) was used to analyze the findings. The results showed that the five most influential factors are poor planning approaches, contracting companies' financial difficulties, poor site management, poor project management/poor cost control, and inaccurate cost estimates. In order to reduce cost deviations, a set of improvements was recommended based on the results of the study.

Application of Data Envelopment Analysis to Evaluate Health Regions Efficiency in Saudi Arabia

Walid Abdelfattah, Bader S. Alanazi — Sat, 01 Jun 2024 00:00:00 +0000

Efficient healthcare systems must provide quality care, ensuring equitable access and sustaining financial viability. This study employs Data Envelopment Analysis (DEA) to evaluate the efficiency of healthcare regions in Saudi Arabia over a five-year period (2017-2021). Departing from traditional hospital-centric assessments, the study takes a regional approach, offering a holistic view of the entire healthcare system. Inputs such as number of beds, physicians, and nurses, along with outputs like outpatients and inpatients, were considered. The study not only provides efficiency scores but also identifies reference health regions, benchmarks, and tangible targets for improvement. Notably, the impact of the COVID-19 pandemic on healthcare efficiency is analyzed, providing insights into adaptive strategies during crises. The findings contribute to the understanding of regional healthcare dynamics, offering actionable insights for policymakers, facilitating evidence-based resource allocation, and informing strategies for continuous improvement. Future research directions include a global benchmarking analysis and a qualitative exploration of policy implications. This study bridges the gap between academic research and practical policy considerations, emphasizing the importance of adaptability and resilience in healthcare systems.

Α Chaotic Map-based Approach to Reduce Black Hole Attacks and Authentication Computational Time in MANETs

Ahsan Saud Qadri Syed, C. Atheeq, Layak Ali, Mohammad Tabrez Quasim — Sat, 01 Jun 2024 00:00:00 +0000

The need for Mobile Ad hoc Networks (MANETs) has expanded with the development of mobile computing and wireless sensor network technologies. However, this increase has also led to a rise of the attacks on these networks. In order to ensure high Quality of Service (QoS) and maintain connectivity, MANETs require careful consideration of factors, such as power, connectivity, secure transmissions, authentication, and handovers. Handovers are necessary for seamless network connectivity and require quick authentication to ensure uninterrupted service. Although RSA (Rivest Shamir Adleman) and ECC (Elliptic Curve Cryptography) algorithms are commonly used for authentication due to their fast asymmetric key encryption-decryption and exchange, they are less effective against black hole attacks. Chaos algorithms provide a faster authentication process and are efficient against false behavior black hole attacks. This study demonstrates that the chaos algorithm is a viable option for providing fast authentication and preventing malicious nodes from disrupting the network.

Current Loop Control of Jet Fan Motors in Thu Thiem Tunnel by the Exact Linearization Method

An Thi Hoai Thu Anh, Tran Van Khoi, Lam Quang Thai — Sat, 01 Jun 2024 00:00:00 +0000

The Thu Thiem road tunnel located deep under the Saigon River is currently ventilated by a system of 12 jet fans that push dust and dirty air out into the environment. These jet fans are driven by Induction Motors (IMs) modeled by mathematical equations on the structural nonlinear dq coordinate system, so the conventional linear controllers partly fail to meet the response requirements. Therefore, this paper proposes the application of the exact linearization control method for the current loop in the Field Oriented Control (FOC) structure of an IM that drives a jet fan of the Thu Thiem road tunnel. This is a control method based on a linearization model with cascaded loops. The stator current controller controls two currents: i_sd controls the flux, and i_sq controls the torque. The control design is verified by the simulation results on Matlab/Simulink with data collected from the jet fan system of the Thu Thiem road tunnel.

Improved Tomato Disease Detection with YOLOv5 and YOLOv8

Rabie Ahmed, Eman H. Abd-Elkawy — Sat, 01 Jun 2024 00:00:00 +0000

This study delves into the application of deep learning for precise tomato disease detection, focusing on four crucial categories: healthy, blossom end rot, splitting rotation, and sun-scaled rotation. The performance of two lightweight object detection models, namely YOLOv5l and YOLOv8l, was compared on a custom tomato disease dataset. Initially, both models were trained without data augmentation to establish a baseline. Subsequently, diverse data augmentation techniques were obtained from Roboflow to significantly expand and enrich the dataset content. These techniques aimed to enhance the models' robustness to variations in lighting, pose, and background conditions. Following data augmentation, the YOLOv5l and YOLOv8l models were re-trained and their performance across all disease categories was meticulously analyzed. After data augmentation, a significant improvement in accuracy was observed for both models, highlighting its effectiveness in bolstering the models' ability to accurately detect tomato diseases. YOLOv8l consistently achieved slightly higher accuracy compared to YOLOv5l, particularly when excluding background images from the evaluation.

Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

Mohamed A. J. Al-Ani, Mohamed Ali Zdiri, Fatma Ben Salem, Nabil Derbel — Sat, 01 Jun 2024 00:00:00 +0000

This paper provides a comprehensive analysis of a grid-connected hybrid microgrid system that seamlessly integrates renewable energy sources, encompassing wind generators, solar arrays, and Fuel Cells (FCs). Emphasis is placed on the pivotal role of power electronic converters in optimizing control and energy management strategies for these diverse sources. The wind and solar subsystems employ Perturb and Observe (P&O) controllers to achieve Maximum Power Point Tracking (MPPT). Additionally, the study delves into the analysis and control design of the grid-connected hybrid system inverter, employing a Proportional-Integral (PI) control technique in the synchronous d-q frame to maximize the output voltage response and active power. In managing renewable grid energy based on artificial neural networks (ANNs), the main goal is to address grid availability concerns by prioritizing renewable sources. The hybrid system acts as a backup during grid unavailability and simultaneously produces hydrogen via electrolysis. The excess energy is seamlessly supplied to the grid upon filling the hydrogen tank. The proposed solution shows great promise for use in renewable energy management systems that combine hybrid technologies.

Enhancing Roadway Efficiency through Comprehensive Studies on Travel Time, Delays, and Spot Speeds

Md. Kamrul Islam — Sat, 01 Jun 2024 00:00:00 +0000

This study integrates travel time and delay with spot speed study, utilizing the floating car method. The correlation between increased traffic flow and elevated delay times is underscored. Notably, a significant portion of vehicles operating below anticipated speeds emerges as a pivotal factor contributing to congestion. The average journey time is approximately 4 minutes, with an average stopped delay ranging from 0.17 to 0.11 minutes. Spot speed study in four directions reveals nuanced speed dynamics. In the East-West direction, the 15th, 85th, and 98th percentile speeds are 39.14 km/h, 62.27 km/h, and 85.00 km/h and similar patterns are observed in the other directions. These results transcend routine traffic studies, offering actionable insights for urban planning, traffic regulation, and economic assessments. The study delves beyond statistical compilations, providing concrete evidence of the intricate interplay between traffic flow, delay times, and speed dynamics. It aspires to guide traffic engineers, urban planners, and decision-makers into the realms of future urban development. The comprehensive understanding derived ensures informed decision-making in transportation management and infrastructure planning.

Navigating Business Model Innovation in Chinese Manufacturing: Insights and Implications

Yao Zhang, Qaiser Mohi Ud Din, Yuan Yuan — Sat, 01 Jun 2024 00:00:00 +0000

Despite an increasing number of manufacturing companies innovating their business models in the digital economy, how innovative business models are formed has remained an under-researched area, especially in the manufacturing industry. This study addresses the particular research gap by analyzing the business model innovation process and identifies and explores five conditions that influence the business model innovation process: creative ideas, value proposition optimization, transaction structure reconstruction, profit model exploration, and dynamic potential accumulation. Then, based on the data of 238 respondents in Chinese manufacturing companies, the fuzzy-set approach is employed by conducting Qualitative Comparative Analysis (fsQCA) to explore the configurations of the innovative business model formation process. The results show that high levels of business model innovation can be achieved through different configurations: (1) creative ideas, value proposition optimization, and transaction structure reconstruction combined with dynamic potential accumulation, (2) creative ideas, value proposition optimization, and profit model exploration combined with dynamic potential accumulation, (3) value proposition optimization, and profit model exploration combined with transaction structure reconstruction, and (4) transaction structure reconstruction combined with dynamic potential accumulation. This study contributes to the theoretical literature on business model innovation and provides practical information for manufacturing companies looking to innovate their business models.

Measured and Predicted Unsaturated Permeability of Cracked Compacted Fine Soil

Abdelkader Mabrouk, Mehrez Jamei, Anwar Ahmed — Sat, 01 Jun 2024 00:00:00 +0000

The unsaturated permeability of cracked compacted fine soil is a key parameter in geotechnical engineering, particularly when analyzing water flow through the soil in various conditions. The compaction affects the saturated and unsaturated permeability by reducing porosity. However, cracks can appear by shrinkage and growth during desiccation, which obviously leads to macro-porosity (a process during which the soil acquires a high level of double porosity). The development of a crack network influences the suction (as negative water pressure) and then the unsaturated permeability. The current paper aims to analyze the role of the crack network (considered as macropores) on the unsaturated permeability, by quantifying the network based on the Crack Intensity Factor (CIF). The unsaturated permeability is given as a function, separately of CIF and suction. The experimental results may be considered constructive for soil modeling. Regarding the birth of the first crack, it occurred when the suction reached a value near to that of the air entry suction. Since the first crack appeared, primary cracks were developed and then followed by secondary cracks. The obtained experimental results of WRC and K_unsat for cracked compacted clay are beneficial in managing the design of the geotechnical structure stability and the environmental issues of water diffusion. CIF increases with suction, which is augmented during the drying process demonstrating a decrease in the moisture content. After 21 hours of desiccation, CIF ended up reaching a value of 4%. It is generally recognized that cracks create preferential pathways for water flow, whereas their geometry and distribution influence how water moves through the soil. Modeling the impact of cracks on permeability may involve considering factors like crack width, orientation, and connectivity. In this paper, a simple model was proposed to predict the unsaturated permeability as a function of suction with different CIF values with the material being assumed as a double porosity soil.

Energy-Efficient and Reliable Routing for Real-time Communication in Wireless Sensor Networks

Sat, 01 Jun 2024 00:00:00 +0000

Wireless Sensor Networks (WSN) can be part of a tremendous number of applications. Many WSN applications require real-time communication where the sensed data have to be delivered to the sink node within a predetermined deadline decided by the application. In WSNs, the sensor nodes' constrained resources (e.g. memory and power) and the lossy wireless links, give rise to significant difficulties in supporting real-time applications. In addition, many WSN routing algorithms strongly emphasize energy efficiency, while delay is not the primary concern. Thus, WSNs desperately need new routing protocols that are reliable, energy-efficient, and appropriate for real-time applications. The proposed algorithm is a real-time routing algorithm appropriate for delay-sensitive applications in WSNs. It has the ability to deliver data on time while also enabling communications that are reliable and energy-efficient. It achieves this by deciding which candidate neighbors are eligible to participate in the routing process and can deliver the packet before its deadline. In order to lessen the delay of the chosen paths, it also computes the relaying speed for each eligible candidate. Moreover, it takes into account link quality, hop count, and available buffer size of the selected relays, which leads to end-to-end delay reduction while also minimizing energy consumption. Finally, it considers the node's energy consumption rate when selecting the next forwarder to extend the network lifetime. Through simulation experiments, the proposed algorithm has shown improved performance in terms of packet delivery ratio, network lifetime packets miss ratio, average end-to-end delay, and energy imbalance factor.

Parameter Estimation of Photovoltaic Cell using Transit Search Optimizer

Hady El Said Abdel Maksoud, Shaaban M. Shaaban — Sat, 01 Jun 2024 00:00:00 +0000

In the evaluation of a Photovoltaic (PV) system's performance, precise calculation of the system's parameters is essential, as these parameters significantly influence its efficiency across various sunlight intensities, temperature ranges, and distinct load conditions. Addressing the intricate non-linear optimization problem of pinpointing these PV system parameters, the current research adopts a novel metaheuristic optimization approach, called Transit Search (TS). The proposed technique was rigorously tested on a monocrystalline solar panel, which included both single and double-diode model structures. The design of the objective function within this framework aims to diminish the square root of the average squared discrepancies between theoretical and measured current outputs, while remaining within the established parameter bounds. The proficiency of the TS algorithm was highlighted by employing a variety of statistical error indicators, underlining the latter’s effectiveness. When pitted against other established optimization algorithms through comparative analysis, TS demonstrated outstanding capabilities, evidently outperforming its contemporaries in the accurate determination of PV system parameters.

Transforming Physical Crime Scene into Geospatial-based Point Cloud Data

Sat, 01 Jun 2024 00:00:00 +0000

Terrestrial Laser Scanning (TLS) and Close-Range Photogrammetry (CRP) are advanced techniques for capturing 3D data in crime scene reconstruction, offering complementary information. Despite taking multiple scans and images from different angles to ensure a comprehensive model, limitations, such as device positioning, shadows, object distance, and laser beam angles prevent the creation of a complete crime scene model. Therefore, combining TLS and CRP data is crucial for achieving a comprehensive reconstruction. This study aims to transform a physical crime scene into a geospatial-based reconstructed model known as point clouds. The technique used was highly rich in realistic features, digitally reconstructed from TLS and CRP. The data sources were then fused via a rigid body transformation, creating a comprehensive crime scene model. The combined point cloud measurements were compared with measurements obtained from a high-precision Vernier caliper to ascertain their accuracy. The resulting Root Mean Square (RMSE) difference between the fused point cloud data and the high-precision caliper measurements was approximately ±4mm. The fusion of TLS and CRP data provides reliable and highly accurate 3D model point clouds, making it suitable for forensic applications.

Optimization of the PM-EDM Process Parameters for Ti-35Nb-7Zr-5Ta Bio Alloy

Ahmed Rabeea Hayyawi, Haydar Al-Ethari, Ali Hubi Haleem — Sat, 01 Jun 2024 00:00:00 +0000

Powder-Mixed Electrical Discharge Machining (PM-EDM) is one of the latest advancements in EDM process capability augmentation. This procedure involves effectively mixing a suitable material in fine powder form with the dielectric fluid. The dielectric fluid's breakdown properties are enhanced by the additional powder. The objective of the present research is to machine the Ti-35Nb-7Zr-5Ta alloy prepared by powder metallurgy and study the influence of process parameters, such as peak current, pulse-on time, pulse-off time, powder type (Ag, Si, Ag+Si), and powder concentration. The metal removal rate and SR represent the response parameters. The Taguchi approach was followed to design the experiments. The five-factor three-level design was chosen to use the Taguchi L27 orthogonal array. It was found that the addition of Ag, Si, or Ag+Si powders to the dielectric fluid enhanced the metal removal rate and the surface finish for this alloy. The addition of Ag powder to the dielectric fluid gave a higher Material Removal Rate (MRR) and a lower SR compared to Si or Ag+Si powders. Powder concentration and pulse current are the most effective parameters on MRR and SR followed by powder type, pulse-on, and pulse-off. The maximum Grey Relational Grade (GRG) exists at (I=5 A, T_on=9 µs, T_off=37 µs, P_T=Ag, P_C=20 g/L). These are the optimal conditions for PM-EDM of the Ti-35Nb-7Zr-5Ta alloy that give maximum MRR with minimum SR.

Prioritizing Road Maintenance: A Framework integrating Fuzzy Best-Worst Method and VIKOR for Multi-Criteria Decision Making

Ali Ezat Hasan, Firas K. Jaber — Sat, 01 Jun 2024 00:00:00 +0000

A nation’s development depends on its transport networks, particularly the road network, which plays a crucial role in the country’s economic and social advancement and well-being. However, roads are subject to deterioration due to weather conditions, traffic loading, and construction quality. If they are not maintained properly, they will quickly worsen over time, resulting in reduced mobility and accessibility. To develop and maintain a good road network, careful planning is needed, which covers all aspects of road maintenance, funding, construction, quality, and other criteria. However, due to limited budgets, not all roads can be maintained and rehabilitated at the same time. Road maintenance priority and optimal use of insufficient funding are the most challenging problems the authorities face. The development of a systematic approach is essential to formulate appropriate maintenance policies. This is why the concept of road maintenance prioritization is essential. Additionally, industry experts have also identified a lack of a Multi-Criteria Decision Making (MCDM) technique that can incorporate the views of all Decision Makers (DMs) in the road maintenance prioritization process. This study aims to propose a framework for prioritizing road maintenance using MCDM techniques in a fuzzy environment. A case study that considers 20 criteria was conducted. The study integrated two MCDM techniques, namely the Fuzzy Best-Worst Method (BWM) and VIKOR, to help DMs evaluate and rank the alternatives, on the basis of the selected maintenance criteria. The aim of this framework is to enhance the decision-making process with impartiality and reliability and to assist in reaching an optimal decision. By comparing the Q values for each alternative, A5 was revealed to have higher priority over the other roads in terms of maintenance and rehabilitation activities.

Securing Cloud Computing Services with an Intelligent Preventive Approach

Saleh M. Altowaijri, Yamen El Touati — Sat, 01 Jun 2024 00:00:00 +0000

Cloud computing is a technological marvel that transcends conventional boundaries by utilizing an Internet-based network of remote servers to store, manage, and process data and many other services. It represents a contemporary paradigm for delivering information technology services. Today, cloud computing services have become indispensable for both individuals and corporations. However, adopting cloud services presents fresh challenges in terms of service quality, resource optimization, data integration, cost governance, and operational security. The security of cloud services is of supreme importance, given the open and distributed nature of the environment, making it susceptible to various cyberattacks, such as Denial of Service (DoS) or Distributed DoS (DDoS) attacks, among others. Cyberattacks can have severe repercussions on the availability of cloud services, potentially causing complete DoS. In numerous instances, the detection of attacks is delayed, pushing cloud platforms to a breaking point. Emphasizing the importance of proactive measures, it becomes crucial to identify and alert about any suspicious access long before the latter reaches a critical stage, mitigating the risks and preventing potential service disruptions. This study introduces a preventive approach that utilizes artificial intelligence techniques to improve the security of cloud services. The proposed method aims to detect and flag potential attack behaviors well in advance before they affect service quality. To achieve this, the particular method involves periodic identification and measurement of critical information on service access and resource utilization. This can be accomplished by analyzing cloud server logs or integrating dedicated sniffing software to capture and store technical traffic details. Subsequently, the collected data are processed by analyzing traffic properties to proactively identify and report any indications of cyberattacks.

Evaluation of the Hydrodynamic Properties and Performance Efficiency of a Three-Row Permeable Vertical Breakwater

Tarek Eldamaty, Medhat Helal — Sat, 01 Jun 2024 00:00:00 +0000

Coastal protection structures reduce risks and economic losses by eliminating coastal erosion, wave damage, and flooding. Fixed breakwaters are used along the coast but are often inappropriate due to their negative environmental impact. Permeable breakwaters resemble a row of breakwaters with continuous walls and are proposed as a more environmentally friendly alternative. The wave-structure interaction and flow behavior of this type of breakwater are more complex but must be analyzed before designing it. This study develops a mathematical model of wave interaction with a permeable three-row vertical breakwater based on the least squares method. Comparison with experimental measurements of the reflection, transmission, and dissipation coefficients shows that the mathematical model adequately reproduces most of the important features of the results. This study provides a deeper understanding of the hydrodynamic performance of a permeable three-row vertical continuous wall breakwater.

Real-Time Home Energy Management with IoT and Blockchain

Sat, 01 Jun 2024 00:00:00 +0000

As a result of the exponential increase in energy consumption, energy shortages, and augmented energy costs have become a significant problem for households. Solar cells are used in many homes and/or buildings to address these problems. However, managing renewable energy sources in homes can be difficult due to the irregular nature of renewable energy production. Internet of Things (IoT) devices can provide real-time data on energy production and consumption, offering a promising solution to this issue. The current study proposes a framework based on IoT and blockchain technology for home energy management by predicting future energy consumption patterns and optimizing energy use in real time. The blockchain module facilitates peer-to-peer energy trading between renewable energy-generating homeowners and consumers. The proposed framework was tested employing a dataset based on smart homes with solar panels and wind turbines. The results manifest a reduction in energy costs and a possible 30% increase in their traditional gain.

An Online Scheme for Delayed MISO System Identification

Yamna Ghoul, Naoufel Zitouni, Aymen Flah — Thu, 01 Aug 2024 00:00:00 +0000

The issue of parametric estimation in time delay models is the main topic of this research article. Multiple-Input Single-Output (MISO) Continuous-Time (CT) systems with numerous unknown time delays characterize these models. Two different recursive parametric estimate techniques are explored in this paper, the strategic application of Sequential Nonlinear Least Squares (SNLS) to attain global convergence and the Recursive Least Squares (RLS) technique in conjunction with the Gauss-Newton algorithm with the goal of achieving local optimization. Both approaches contribute to the comprehensive understanding of the parametric estimation landscape for time delay models. In a pivotal stride towards enhancing convergence, the research proposes a hybridization of the two methods. This synergistic approach is designed to leverage the strengths of both SNLS and the RLS-Gauss-Newton combination, fostering improved overall convergence properties. To substantiate the credibility and effectiveness of the proposed methodologies, the conducted research provides comprehensive simulation results. These simulations offer concrete examples of the efficacy and practicality of the suggested techniques in real-world situations, and they make significant contributions to the field of parametric estimation for time delay models.

Predicting the Severity of Accidents at Highway Railway Level Crossings of the Eastern Zone of Indian Railways using Logistic Regression and Artificial Neural Network Models

Anil Kumar Chhotu, Sanjeev Kumar Suman — Sat, 01 Jun 2024 00:00:00 +0000

Road-railroad level crossing accidents pose serious safety risks to road users, and their significant increase requires more research efforts to propose substitute solutions. Such a solution must consider the impact of intersection geometry, user perception, traffic characteristics, driver behavior, environment, and seasonal variations on accidents. This study explores the considerable number of such accidents and develops a predictive model using all the factors that influence them. For these objectives, data were collected from databases maintained by the zonal head office of the East Central Railway (ECR) in India. Data included 175 level crossings that experienced at least one accident between 2006 and 2021 in the ECR region. This study presents two accident prediction models using logistic regression and ANN for the predominant factors of accidents in the ECR zone of Indian railways. The accuracy of fatal accident prediction was 96% for logistic regression and 98% for ANN.

Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques

Moussa S. Elbisy — Sat, 01 Jun 2024 00:00:00 +0000

Coastal defense structures are of paramount importance in protecting coastal communities from the adverse impacts of severe weather events and flooding. This study uses machine learning techniques, specifically Decision Tree (DT), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) models, to estimate wave overtopping discharge at coastal structures with combined slopes employing the recently built EurOtop database. The models were evaluated by deploying statistical metrics and Taylor diagram visualization. The GBT model demonstrated a high level of accuracy in predicting wave-overtopping discharge. Compared to the other models, the scatter index of GBT (0.392) was lower than that of DT (0.512) and SVM (0.823). In terms of the R-index, GBT (0.991) was superior to DT (0.977) and SVM (0.943). The GBT results were also compared with those of previous works. The findings showed that the GBT model significantly decreased the overall error and provided accurate estimations of the wave-overtopping discharge.

Effect of Sustainable Materials on Some Properties of Pervious Concrete

Demoa Jawad Kazem, Nada Mahdi Fawzi — Sat, 01 Jun 2024 00:00:00 +0000

Recycling and sustainability are important topics nowadays due to the increased environmental problems and waste accumulation. In this research, focus was given on the production of sustainable pervious concrete. Four mixes were prepared: a reference mixture, a mixture with 30% of the volume of coarse aggregates replaced with ceramic waste, the third mixture was similar to the second, but it contained 10% metakaolin instead of cement, and the fourth mixture was similar to the third, but carbon fibers were added. Thermal conductivity and density tests were carried out after 28 days of curing on the casting samples, while flexural tests were performed after 7 and 28 days. The results showed that flexural strength, density, and thermal conductivity were decreased in the second and third mixtures compared to the reference mixture, but in different decreasing percentages, so the good effect of metakaolin became clear. The addition of carbon fibers to the fourth mixture led to an increase in flexural strength and thermal conductivity, whereas density was lower than in the reference and third mixtures.

A Comparison of RSM-DA and PSO-TOPSIS in optimizing the Finishing Turning of 9XC Steel under MQL Conditions

Thuy Duong Nguyen, Khanh Huyen Nguyen, Long Nguyen Ha — Sat, 01 Jun 2024 00:00:00 +0000

This study compares the effectiveness of RSM-DA and PSO-TOPSIS in optimizing the finishing turning of 9XC steel under Minimum Quantity Lubrication. Experiments using the Box-Behnken Design were conducted. Depth of Cut (a_p), Feed Per Tooth (f_z), and Cutting Speed (V_c) served as the input parameters while the Material Removal Rate (MRR) and Surface Roughness (R_a) as the output responses. Fifteen experiments based on the Box-Behnken orthogonal array design were carried out. The RSM-DA method yielded optimized values of 3.35999 cm³/min for MRR and 0.25367 μm for R_a when V_c, a_p, and f_z were set at 180 m/min, 0.2999 mm, and 0.06 mm/rev respectively. The Pareto solutions were obtained by PSO and TOPSIS identified the optimum set. The optimized MRR and R_aroughness values were found to be 4.320 cm³/min and 0.474 μm, respectively when V_c = 180 m/min, a_p = 0.3 mm, and f_z = 0.10 mm/rev. The research results showed the suitability, strengths, and weaknesses of PSO-TOPSIS and RSM-DA for multi-objective optimization of the turning process of 9XC alloy under Minimum Quantity Lubrication conditions.

Enhancing Rendering Performance in Complex Visualizations by using Optimization Techniques and Algorithms in Browser Environments

Sanja Brekalo, Klaudio Pap, Bruno Trstenjak — Sat, 01 Jun 2024 00:00:00 +0000

This research is based on the hypothesis that optimization techniques can significantly improve the performance of complex visualizations in web browsers. The aim of the former was to determine to which extent the optimization can be achieved. Optimizations were coded to improve visualization, reduce the need for visual rendering, and decrease script execution time as well as the needed resources. To test the hypothesis, various optimization methods and algorithms were implemented on the initial visualization script and were tested. The main goal of this implementation was to assess how optimization methods, including quadtrees, spatial hashing, binning, LOD adjustments, and the use of the map data structure, affect the performance of web visualization. The obtained results confirmed the hypothesis and the original animation was significantly improved. The implementation of optimizations had a positive effect on the performance of visualizations. The conducted tests gave concrete evidence confirming the validity of the initial hypothesis. This led to certain conclusions regarding which methods provide the best results when optimizing complex visualizations. Key recommendations for code optimization, which can be used in the development of complex visualizations in web browsers, were derived.

Investigation of Influencing Factors on Surface Quality during Low-Speed Cutting of Steels with a Hardness exceeding 50 HRC for forging Dies

Chaiyakron Sukkam, Seksan Chaijit — Sat, 01 Jun 2024 00:00:00 +0000

This study investigates the factors that affect surface quality in low-speed milling of steel with a hardness greater than 50 HRC, specifically for forming molds. The material used in the experiment was SKT4 mold steel with a hardness of 50 HRC, which is commonly employed to form molds, with dimensions of 100×100×50 mm. The cutting tools put into service were carbide ball end mills of the HARD Series 5R×10×60L. This study examines changes in the surface roughness values of the milled workpiece material based on the feed rate and cutting depth. The constant spindle speed deployed was 1,200 rpm, and heat dissipation was achieved by air cooling. The results revealed that the feed rate and the interaction between the feed rate and the cutting depth had a p-value of 0.000. This considerably influences the average surface roughness (R_a) value at the 0.05 significance level. However, the cutting depth had a p-value of 0.061, which is greater than the significance level of 0.05 and thus does not substantially affect the average surface roughness.

The Mechanical Behavior of High-Density Polyethylene under Short-Time Hydraulic Pressure Test

Sat, 01 Jun 2024 00:00:00 +0000

This paper provides a synthesis of the results of experimental research and numerical simulations on polyethylene pipes subjected to short-time hydraulic pressure testing. Also, the current paper offers basic information about the engineering behavior of High-Density Polyethylene (HDPE) under the aforementioned test. HDPE presents high levels of technical performance because it has a high-density resin, high molecular weight, and bimodal Molecular Weight Distribution (MWD). HDPE pressure pipelines are used in Drinking Water Distribution Networks (DWDNs) and are component pieces of the thermoplastic piping system. The experimental test was mainly oriented toward the comparative determinations of the burst pressure of both the defect-free pipes and those with a lack of material defects made through mechanical operations. Also, the experimental test establishes the short-time hydraulic failure pressure as well as the determination of the resistance of the polyethylene pipes to hydraulic pressure in a short time period. The numerical simulations were carried out with the purpose of validating the results obtained analytically and experimentally.

Condenser Pressure Influence on Ideal Steam Rankine Power Vapor Cycle using the Python Extension Package Cantera for Thermodynamics

Osama A. Marzouk — Sat, 01 Jun 2024 00:00:00 +0000

This study investigates the Rankine vapor power thermodynamic cycle using steam/water as the working fluid, which is common in commercial power plants for power generation as the source of the rotary shaft power needed to drive electric generators. The four-process cycle version, which comprises a water pump section, a boiler/superheater section, a steam turbine section, and a condenser section, was considered. The performance of this thermodynamic power cycle depends on several design parameters. This study varied a single independent variable, the absolute pressure of the condenser, by a factor of 256, from 0.78125 to 200 kPa. The peak pressure and peak temperature in the cycle were fixed at 50 bar (5,000 kPa) and 600°C, respectively, corresponding to a base case with a base value for the condenser's absolute pressure of 12.5 kPa (0.125 bar). The analysis was performed using the thermodynamics software package Cantera as an extension of the Python programming language. The results suggest that over the range of condenser pressures examined, a logarithmic function can be deployed to describe the dependence of input heat, the net output work, and cycle efficiency on the absolute pressure of the condenser. Each of these three performance metrics decreases as the absolute pressure of the condenser increases. However, a power function is a better choice to describe how the steam dryness (steam quality) at the end of the turbine section increases as the absolute pressure of the condenser rises.

A Forensic Framework for gathering and analyzing Database Systems using Blockchain Technology

Sat, 01 Jun 2024 00:00:00 +0000

A blockchain is a distributed database that contains the records of transactions that are shared among all members of a community. Most members must confirm each and every transaction in order for a fraudulent transaction to fail to occur. As a rule, once a record is created and accepted by the blockchain, it cannot be altered or deleted by anyone. This study focuses on improving the investigation task in the database forensics field by utilizing blockchain technology. To this end, a novel conceptual framework is proposed for the forensic analysis of data from database systems engaging blockchain technology. This is the first time that blockchain technology is followed in database forensics for the purpose of tracing digital evidence. The design science research method was adopted to accomplish the objectives of the present study. The findings displayed that with the developed forensics framework, the data regarding database incidents could be gathered and analyzed in a more efficient manner.

Weqaa: An Intelligent Mobile Application for Real-Time Inspection of Fire Safety Equipment

Rehab Alidrisi, Ekram Feras, Shahad Aboukozzana, Alaa Alomayri, Asmaa Alayed — Sat, 01 Jun 2024 00:00:00 +0000

Fire safety is an important consideration, particularly in buildings where there are significant risks linked to a possible fire breakout. Therefore, it is crucial to implement procedures and regulations in buildings to minimize fire damage. Despite the installation of various pieces of Fire Safety Equipment (FSE), over time their effectiveness may be reduced due to factors, such as failure, damage, and insufficient maintenance. For this reason, the fire safety inspection process came to ensure the FSE availability and efficiency. Visual fire safety inspection conducted by civil defense is found to be time-consuming and inefficient, primarily due to manual procedures and difficulty in identifying defects, leading to inaccurate results and low performance. The purpose of this research is to enhance and automate fire safety inspection by implementing deep learning and computer vision techniques in a mobile application, thus addressing the challenges associated with visual inspection. Weqaa application allows the inspector to point their mobile phone camera at the fire extinguisher, then determine the condition of the extinguisher, document it, and report it to the relevant authority to quickly determine the appropriate action procedure. Interviews with expert inspectors were performed to outline the required functions of the application. The mobile application was developed using Flutter and being integrated with the detection model to permit the user to inspect fire extinguishers. Initial testing of the application has exhibited promising results, with inspectors noting its competence in detecting violations and improving inspection processes. The use of the particular application enabled the inspectors to perform the required functions faster, more accurately, and with fewer errors compared to the visual inspection deployment, indicating the application's effectiveness in detecting violations.

New Scheduling Scheme in Cellular V2X Communication

Wahida Ali Mansouri, Somia Asklany, Salwa Hamda Othman, Abdulbasit A. Darem — Sat, 01 Jun 2024 00:00:00 +0000

The enormous increase in mobile data traffic and the heterogeneity and stringent Quality of Service (QoS) requirements of different applications have placed a substantial strain on the underlying network infrastructure and represent a challenge for Cellular Vehicle-to-Everything (Cellular V2X). V2X communication is a key enabler for the realization of smart and connected transportation systems, offering a wide range of applications, such as enhanced road safety, traffic management, and autonomous driving. In this context, the best way to provide great flexibility and address both the present and future QoS concerns is to use intelligent Radio Resource Management (RRM) in general and creative packet scheduling in particular. The diverse QoS requirements of multiple application classes under dynamic network conditions present substantial challenges for conventional scheduling algorithms given the increasing demand for bandwidth-hungry applications. This study proposes a scheduling system for V2X communications based on traffic prioritization that manages QoS provisioning for different types of traffic considering channel quality, remaining payload, and delay. Simulation results demonstrate the highly promising performance of the proposed New Scheduling V2X Communications (NSVC) algorithm that leads to significantly lower latencies, as the average delay scheme did not exceed 0.001 ms for 100 users.

A Novel PIFA Design for SAR Reduction in 5G Networks to Analyze the RF Shield Ιmpact

Ashok Kumar Penta, Ch. R. Phani Kumar — Sat, 01 Jun 2024 00:00:00 +0000

Fifth Generation (5G) Technology, representing the latest advancement in wireless communication networks, has brought attention to the rising concerns regarding Specific Absorption Rate (SAR) due to temperature fluctuations. The negative impacts of SAR, particularly in the context of mobile users' head exposure, have prompted the exploration of effective mitigation strategies. This article introduces a novel approach, employing a Planar Inverted F-Antenna (PIFA) operating at 26 GHz, with the integration of RF shields, specifically a flexible ferrite sheet and a foam absorber, aimed at reducing SAR in the human head. Dosimetry investigations, conducted at frequencies exceeding 26 GHz, reveal that SAR values without shielding materials (1.59 W/kg) approach the safety limit of SAR. The incorporation of ferrite and foam absorber leads to SAR reductions of 1.53 and 1.48 W/kg, respectively. Notably, the proposed antenna demonstrates significant SAR Reduction Factor (SRF) values, particularly at 5G network frequencies (26 GHz). Comparative analysis highlights the superior performance of the foam absorber across various parameters. The prototype of the proposed antenna has been fabricated and subjected to testing, affirming its potential for alleviating SAR in the context of 5G technology.

IoT Security Model for Smart Cities based on a Metamodeling Approach

Daifallah Zaid Alotaibe — Sat, 01 Jun 2024 00:00:00 +0000

Security solutions for the Internet of Things (IoT) in smart cities are complex and require a comprehensive approach to success. Several models and frameworks have been developed focusing on IoT security. Some deal with access controls and security and some with authentication and authorization in various forms. Literature still lacks a comprehensive IoT security model for smart cities, which can support the implementation of IoT. Accordingly, this study has set two objectives: to explore the present studies in IoT security for smart cities and to develop an IoT security model for smart cities based on the metamodeling approach. According to the findings of the study, the existing IoT security models for smart cities consider seven security aspects: authentication and authorization, device management, intrusion detection and prevention, device integrity, secure communication, secure data storage, and response to security incidents. The model developed in this study, called IoT Security Metamodel (IoTSM), combines these aspects. IoTSM captures the main qualities of IoT security practices in smart cities through domain security processes.

Association between the Analytical Technique and Finite Element Method for designing SPMSMs with Inner Rotor Type for Electric Vehicle Applications

Duc-Quang Nguyen, Thanh Nguyen Thai, Cuc Le Thi, Dinh Bui Minh, Vuong Dang Quoc — Sat, 01 Jun 2024 00:00:00 +0000

This paper focuses on computing the electromagnetic parameters of inner rotor-configuration Surface-mounted Permanent Magnet Synchronous Motors (SPMSMs) via the combination of analytical and finite element methods with an application to electric bicycles. The analytical method is, first, developed in detail to calculate the essential parameters of the inner rotor-configuration SPMSM. Then, a Finite Element Method (FEM) is introduced to verify the analytical parameters found. Further, a simulation of magnetic flux density, torque, cogging torque, torque ripple, back Electromagnetic Force (EMF), linkage flux, and temperature is performed. The end result is validated with a real 2.2 kW inner rotor-configuration SPMSM.

Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective

Muhammad Nafees Khan, Zhen Shao — Sat, 01 Jun 2024 00:00:00 +0000

This study aims to systematically review the literature on the impact of big data and knowledge management on customer interactions and consumption patterns from an applied science perspective. A comprehensive search strategy was implemented in seven scientific publication databases. The inclusion criteria consisted of original research articles published in English, excluding gray literature, book chapters, and conference proceedings. A total of 400 articles were retrieved, and 40 articles met the inclusion criteria after two rounds of screening. The selected articles were analyzed following a mixed-method approach incorporating qualitative and quantitative data analysis techniques. Thematic analysis was deployed to identify recurring themes and patterns in the articles, while descriptive statistics were used to summarize the study characteristics. The data analysis showed that big data and knowledge management significantly affect customer interactions and consumption patterns, with most studies focusing on the retail and banking sectors. The findings of this study have several theoretical and practical implications. From a theoretical point of view, this review contributes to the growing body of literature on the intersection of big data, knowledge management, and consumer behavior. From a practical perspective, the results can inform policymakers and practitioners on leveraging big data and knowledge management in order to improve customer interactions and consumption patterns.

Theoretical Assessment of Different Aviation Fuel Blends based on their Physical-Chemical Properties

Radu Mirea, Grigore Cican — Sat, 01 Jun 2024 00:00:00 +0000

The current study focuses on the theoretical assessment of Sustainable Aviation Fuels (SAFs) obtained by blending traditional jet fuel (Jet A) and different liquids (biodiesel and alcohols) from an analytical point of view. Aeroshell 500 oil was added (5% vol.) to ensure the lubrication of the turbo engine. An in-depth analysis of the physical-chemical properties of Jet A fuel blended with different biodiesels and alcohols was performed. The considered blends consisted of Jet A fuel and biodiesel from palm oil, pork fat, and sunflower and methanol, ethanol, and butanol. All six liquids were mixed with Jet A by 10, 20 and 30%. Flash point, kinematic viscosity, density, freezing point, elemental analysis, and FTIR analysis were conducted for all the blends. The acquired results show the influence of each component on the physical-chemical properties of the blends. Based on the physical-chemical analysis of the blends, conclusions on the latter’s behavior during burning were drawn and the gaseous pollutants resulting from the burning process were examined.

A New Approach for Wireless Sensor Networks based on Tree-based Routing using Hybrid Fuzzy C-Means with Genetic Algorithm

Neetu Sikarwar, Ranjeet Singh Tomar — Sat, 01 Jun 2024 00:00:00 +0000

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio.

Enhancing Data Security through Machine Learning-based Key Generation and Encryption

Abhishek Saini, Ruchi Sehrawat — Sat, 01 Jun 2024 00:00:00 +0000

In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations.

Digital Shadow-based Control of Temperature and Fault Classification in Shell and Tube Heat Exchanger using Fuzzy Logic Technique

Surendran T. Jeyarajah, G. Joselin Retna Kumar — Sat, 01 Jun 2024 00:00:00 +0000

In this study, the Digital Shadow (DS) of the Shell and Tube Heat Exchanger (STHE) is designed and analyzed for numerous disturbances that occur when the system is in a running condition. The disruptive segregation of the heat exchanger is related to the DS for its operation, and thus a realistic DS was developed for the STHE. Fuzzy Logic (FL) was used to identify and segregate the disturbance signals from the process output. The Response Optimization (RO) algorithm was adopted and modified to work on the STHE. The observer-based residual generator design was implemented to prevent system failure and defective conditions. Model Predictive Controller (MPC), Transposed System Controller (TSC), and a looping-based control technique called Unity Response Loop (URL) were also implemented, and the results are discussed. The findings of this study contribute to the improvement of the overall performance of non-linear systems in industrial processes and the avoidance of defects.

Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques

Jihen Bouslimi, Sahbi Boubaker, Kais Tissaoui — Sat, 01 Jun 2024 00:00:00 +0000

This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.

Influential Time and Cost Factors for Commercial Projects in the Malaysian Construction Industry

Sat, 01 Jun 2024 00:00:00 +0000

Time and cost play an important role in project completion for both developing and developed countries. This study aims to identify the critical factors that influence the time and cost of commercial projects in Malaysia. A detailed questionnaire survey was conducted with industry professionals, and the results were analyzed based on the survey responses. The average index method was used to determine critical factors based on responses from professionals. The critical factors that affect time are poor contract management, client fund shortages, late drawing submissions, land acquisition problems, and inadequate surveying before construction. The critical factors identified for cost are delayed client payments, shortage of skilled workers, design changes, errors in the construction process, and changes in top management. This study raises awareness by identifying critical factors to minimize their impact so that construction can be completed on time and according to the defined budget, and maximize the benefits of future projects.

3D Numerical Study and Parametric Analysis of PV/T Design Effect on Thermal and Electrical Performance

Sat, 01 Jun 2024 00:00:00 +0000

This paper explores the influence of design variations on the electrical and thermal efficiencies of PV/T (Photovoltaic-Thermal) systems. Utilizing COMSOL Multiphysics, three different PVT configurations with varying air duct designs were studied. The results demonstrated significant enhancements in both electrical and thermal efficiencies, with the PVT-3 configuration outperforming PVT-1 and PVT-2. Specifically, PVT-3, incorporating fin-shaped air ducts, exhibited the lowest recorded panel temperature of 55 °C, indicating improved electrical efficiency and thermal performance. Also, PVT-3 achieved the highest average thermal efficiency of 46.35% and the best electrical performance of 13.91%. Furthermore, the study highlights ameliorated airflow dynamics and uniformity within the ducts, particularly with the redesigned air inlet. These findings underscore the importance of design innovations in optimizing temperature management and energy output in PVT systems. It is worth noting that the tests were conducted under identical operating conditions, including air velocity, inlet temperature, ambient temperature, and solar irradiation.

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

Yujiang Wang, Marshima Mohd Rosli, Norzilah Musa, Feng Li — Sat, 01 Jun 2024 00:00:00 +0000

Multi-class data classification is distinguished as a significant and challenging research topic in contemporary machine learning, particularly when concerning imbalanced data sets. Hence, a thorough investigation of multi-class imbalanced data classification is becoming increasingly pertinent. In this paper, an overview of multi-class imbalanced data classification was generated via conducting a systematic mapping study, which endeavors to analyze the state of contemporary multi-class imbalanced data classification, with the primary goal of ascertaining the corpus of research undertaken in machine learning. To achieve this aim, 7,164 papers were assessed and the 147 prominent ones were selected from five digital libraries, which were further categorized according to techniques, issues, and types of datasets. After a thorough review of these papers, a taxonomy of multi-class imbalanced data classification techniques is proposed. Based on the results, researchers widely employ algorithmic-level, ensemble, and oversampling strategies to address the issue of multi-class imbalance in medical datasets, primarily to mitigate the impact of challenging data factors. This research highlights an urgent need for more studies on multi-class imbalanced data classification.

Leveraging Techniques of Epistemic Network Analysis to Discover Behaviors of Student Learning Reflections in Online Learning Environments

Sahar Alqahtani — Sat, 01 Jun 2024 00:00:00 +0000

In the domain of learning analytics, reflective writing has introduced trends to enhance the learning and teaching experience. Epistemic Network Analysis (ENA), is a recent development in the techniques of learning analytics regarding handling huge amounts of text and visualizing learners’ interactions in the form of network graphs. In this context, 43 students participated in 10 tasks over a 16-week semester on a blended course. The current article aims to explore their reflective behaviors through this new learning methodology and establish via the ENA technique whether there is any relationship between such behaviors and course performance. The findings show the effectiveness of ENA in investigating students’ overall learning reflection patterns and revealing the frequencies of each reflection type for both high- and low-performing students. The group of high performers demonstrated a stronger connection with positive feelings regarding the learning experience, whereas the low performers exhibited a negative attitude toward the learning process. The obtained results provide insights into students' impressions of specific teaching or learning methods. Linking the reflection behavior to the level of student performance enables teachers to improve course design and provide appropriate interventions, which may be reflected in enhanced student performance.

A Novel Architecture Design of a USB Module in Wireless Modem for IOT Applications

Sat, 01 Jun 2024 00:00:00 +0000

Embedded micro-electro-mechanical technologies and network connectivity allow for the integration of sensing, identification, and communication capabilities into a variety of smart devices. These intelligent devices can automatically link to create the Internet of Things (IoT). The greater power consumption of a scan-based test has been one of the biggest problems since Very Large-Scale Integration (VLSI) architecture was introduced. There are too many switches made during the scan shifting procedures due to the enormous number of the scan cells. The design and implementation of an IoT access point are presented in this paper using the Logic Vision tool. In the semiconductor sector, scan chains are frequently employed for structural testing following fabrication or production. In this paper, a new architecture was designed with USB protocol, which reduces dynamic power, and fault-free circuits were constructed. The proposed architecture can work with the current one without changing the decompression architecture. Experimental findings on commercial circuits demonstrate that this strategy minimizes the scan shifting power.

Precision Agriculture based on Machine Learning and Remote Sensing Techniques

Fahad Alaieri — Sat, 01 Jun 2024 00:00:00 +0000

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices.

Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing

Khalil Alharbi, Mohd Anul Haq — Sat, 01 Jun 2024 00:00:00 +0000

This study investigates the effectiveness of the DistilBERT model in classifying tweets related to disasters. This study achieved significant predictive accuracy through a comprehensive analysis of the dataset and iterative refinement of the model, including adjustments to hyperparameters. The benchmark model developed highlights the benefits of DistilBERT, with its reduced size and improved processing speed contributing to greater computational efficiency while maintaining over 95% of BERT's capabilities. The results indicate an impressive average training accuracy of 92.42% and a validation accuracy of 82.11%, demonstrating the practical advantages of DistilBERT in emergency management and disaster response. These findings underscore the potential of advanced transformer models to analyze social media data, contributing to better public safety and emergency preparedness.

Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity

Ankit Kumar, Richa Sharma, Punam Bedi — Sat, 01 Jun 2024 00:00:00 +0000

This study carries out a comprehensive comparison of fine-tuned GPT models (GPT-2, GPT-3, GPT-3.5) and LLaMA-2 models (LLaMA-2 7B, LLaMA-2 13B, LLaMA-2 70B) in text classification, addressing dataset sizes, model scales, and task diversity. Since its inception in 2018, the GPT series has been pivotal in advancing NLP, with each iteration introducing substantial enhancements. Despite its progress, detailed analyses, especially against competitive open-source models like the LLaMA-2 series in text classification, remain scarce. The current study fills this gap by fine-tuning these models across varied datasets, focusing on enhancing task-specific performance in hate speech and offensive language detection, fake news classification, and sentiment analysis. The learning efficacy and efficiency of the GPT and LLaMA-2 models were evaluated, providing a nuanced guide to choosing optimal models for NLP tasks based on architectural benefits and adaptation efficiency with limited data and resources. In particular, even with datasets as small as 1,000 rows per class, the F1 scores for the GPT-3.5 and LLaMA-2 models exceeded 0.9, reaching 0.99 with complete datasets. Additionally, the LLaMA-2 13B and 70B models outperformed GPT-3, demonstrating their superior efficiency and effectiveness in text classification. Both the GPT and LLaMA-2 series showed commendable performance on all three tasks, underscoring their ability to handle a diversity of tasks. Based on the size, performance, and resources required for fine-tuning the model, this study identifies LLaMA-2 13B as the most optimal model for NLP tasks.

The Effect of Different Curing Methods on the Properties of Reactive Powder Concrete Reinforced with Various Fibers

Ahmed A. Luti, Zena K. Abbas — Sat, 01 Jun 2024 00:00:00 +0000

The current study explores the effects of four curing methods on the strength of Reactive Powder Concrete (RPC) reinforced with different fibers. Four mixtures of RPC, reference (RM-RPC), wavy fiber reinforced (WF-RPC), carbon fiber reinforced (CF-RPC), and micro steel fiber reinforced (MF-RPC) mixes were prepared and cured following four curing methods (normal, autogenous, coating, and warm water). The results revealed that warm water curing achieved the highest values of compressive, flexural, and splitting strength, attaining 138.9 MPa 22.4 MPa, and 20.89 MPa, respectively. The results of using different fiber reinforcement displayed that the compressive strength of fiber-reinforced RPC mixes was notably higher than that of the RM-RPC. The compressive strength increase results were 9.04% for WF-RPC, 24% for CF-RPC, and 27.96% for MF-RPC regardless of the curing method adopted. Flexural strength increased by 21.2%, 38.47%, and 55.86% for WF-RPC, CF-RPC, and MF-RPC, accordingly in autogenous curing, whereas the change in flexural strength was 30.65%, 39.14%, and 36.59%, correspondingly in coating curing and 21.27%, 29.22%, and 39.55%, respectively, for warm water curing. The optimum flexural values were mainly obtained for MF-RPC regardless of the kind of curing used. CF-RPC almost achieved the same results as MF-RPC with slightly lower values. It can be concluded that fiber reinforcement had a more positive influence on the flexural and splitting strength of RPC than on the compressive strength.

Fingerprint Sequencing: An Authentication Mechanism that Integrates Fingerprints and a Knowledge-based Methodology to Promote Security and Usability

Mohammad Η. Algarni — Sat, 01 Jun 2024 00:00:00 +0000

Biometric authentication stands at the forefront of modern security measures, offering a highly sophisticated and reliable method for identity verification. Biometrics aims to identify an individual’s identity by comparing specific characteristics against a stored template. Unlike traditional passwords or PINs, which can be forgotten, shared, or stolen, biometric authentication relies on unique biological or behavioral traits that are inherent to each individual. The current article introduces the innovative concept of multi-fingerprint sequence authentication process to verify users. In contrast to the traditional, single fingerprint methods, this multifactor technique combines the use of multiple fingerprints along with a sequence pattern for enhanced usability and security. Furthermore, this study presents a comprehensive evaluation of an innovative authentication system utilizing a multiple fingerprint sequence pattern as an alternative to biometric usernames and textual passwords, named BioPass. By leveraging an established framework, the research focuses on assessing the proposed system's usability and security aspects, as well as its potential benefits.

Dermatological Decision Support Systems using CNN for Binary Classification

Rajendra Dev Dondapati, Thangaraju Sivaprakasam, Kollati Vijaya Kumar — Sat, 01 Jun 2024 00:00:00 +0000

Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.

Magnesium AZ63 Alloy Protective Coatings by Plasma Electrolytic Oxidation in Mixed Aqueous Electrolytes

Sat, 01 Jun 2024 00:00:00 +0000

Ceramic protective coatings, primarily composed of spinel (MgAl₂O₄), magnesia (MgO), and trimagnesium phosphate (Mg₃(PO₄)₂), were produced on magnesium AZ63 alloy through Plasma Electrolytic Oxidation (PEO) in mixed sodium phosphate/aluminate electrolytes with varying aluminate concentrations and constant processing time. The morpho-structural and compositional characteristics of the coatings were studied using X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Their functional mechanical and anti-corrosive properties were assessed through tribological testing, electrochemical impedance spectroscopy, and potentiodynamic bias tests. The findings indicated that the samples processed through PEO exhibited significantly enhanced properties compared to the AZ63 magnesium alloy. The best tribological properties were observed for the lowest aluminate concentration. Optimum corrosion resistance properties were obtained for coatings produced in a mixed electrolyte of 10 g/L sodium phosphate and 20 g/L sodium aluminate.

The Effect of Expansion Ratio, Opening Size, and Prestress Strand on the Flexural Behavior of Steel Beams with Expanded Web using FEA

Ausama Ahmed, Abdul Muttailb I. Said — Sat, 01 Jun 2024 00:00:00 +0000

The expanded web depth of steel beams leads to improved strength and stiffness. In some design scenarios, such as cellular, castellated, or expanded steel beams, increasing the depth of the web improves the strength and performance of the steel beam. The expanded web of steel beams can be accomplished by creating a horizontal cutting in the web and then adding a plate between the two web section halves, which are called spacer plates. This method leads to improved stiffness and strength. Numerical models have been developed to accurately predict the properties of these beams. This study investigates the use of incremental plates to increase the depth of hot-rolled wide-flange steel beams. The experimental results were validated first with the Finite Element (FE) numerical model created by ABAQUS software, and then Finite Element Analysis (FEA) methods were used to create and analyze new numerical models by considering parameters that provided more models at a lower cost. The load-mid-span deflection curve behavior of both models (experimental and theoretical) was similar. Also, the load-deflection behavior of steel beams with two types of openings was studied. For the first type of opening (B1 NUM), with a smaller opening width of 30 mm and a higher hole number (24), the ultimate load increased by 53%, 111%, and 184%, the deflection at 0.95 Pu increased by 160%, 293%, and 81% of beams with ratio 150%, 200%, and 250% compared with the reference beam. For the second type of opening (B2 NUM), with a larger opening width of 60 mm and smaller hole number (12), the ultimate load capacity increased by 51%, 147%, and 177%, for beams with a ratio of 150%, 200%, and 250%, compared with the reference beam. The deflection at 0.95 Pu increased by 46% and 15% for beams with a ratio of 150%, and 200%, and decreased by 8% for beams with a ratio of 250%. Accordingly, for the first type of opening, the expanded ratios of 150% and 200% performed best with a reduction of only 1%-12% in the ultimate load capacity. However, for the second type of opening, the beam with a ratio of 250% performed better than the first type. Using prestress strands may highly improve steel beams' performance with the expanded webs containing openings by creating a stronger section that can withstand higher loads and exhibit improved structural performance, especially from beams with a ratio of 150%.

An Investigation of AI-Based Ensemble Methods for the Detection of Phishing Attacks

Yazan A. Alsariera, Meshari H. Alanazi, Yahia Said, Firas Allan — Sat, 01 Jun 2024 00:00:00 +0000

Phishing attacks remain a significant cybersecurity threat in the digital landscape, leading to the development of defense mechanisms. This paper presents a thorough examination of Artificial Intelligence (AI)-based ensemble methods for detecting phishing attacks, including websites, emails, and SMS. Through the screening of research articles published between 2019 and 2023, 37 relevant studies were identified and analyzed. Key findings highlight the prevalence of ensemble methods such as AdaBoost, Bagging, and Gradient Boosting in phishing attack detection models. Adaboost emerged as the most used method for website phishing detection, while Stacking and Adaboost were prominent choices for email phishing detection. The majority-voting ensemble method was frequently employed in SMS phishing detection models. The performance evaluation of these ensemble methods involves metrics, such as accuracy, ROC-AUC, and F-score, underscoring their effectiveness in mitigating phishing threats. This study also underscores the availability of credible open-access datasets for the progressive development and benchmarking of phishing attack detection models. The findings of this study suggest the development of new and optimized ensemble methods for phishing attack detection.

Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

Hoa Cao Van — Sat, 01 Jun 2024 00:00:00 +0000

Piles are typically designed to ensure the bearing capacity and settlement elastic behavior. However, some projects seem over-designed, leading to unnecessary waste, whereas others experience excessive settlement. This could be caused by various factors, such as site investigation, sampling and testing methods, selection of soil behavior model, and calculation programs. To achieve a successful pile design, engineers must consider, among others, the loads applied to the pile, the resistance capacity of the piles, the pile material's bearing capacity, the pile's displacement, and the soil's settlement. On the other hand, the input parameters for geotechnical problems, in general, and pile design problems, in particular, often do not reflect the actual behavior of the soil due to its heterogeneous and anisotropic nature. To address these challenges, an Artificial Neural Network (ANN) approach is proposed for pile design, using a relatively wide range of soil input data. This study establishes a numerical program for pile design combined with the ANN approach, validated by verifying the pile design of a project constructed in Vietnam. The results indicate that the proposed program can reasonably simulate pile group behavior and assist engineers in deploying appropriate safety factors.

Influence of Slenderness Ratio and Sectional Geometry on the Behavior of Steel braced Frames

Diyar Yousif Ali, Raid Ahmed Mahmood — Sat, 01 Jun 2024 00:00:00 +0000

Diagonal bracings are installed in frame structures, functioning as members for lateral resistance and energy dissipation. The objective of this study is to assess the hysteresis response behavior of circular hollow steel bracing. Energy dissipation, a key consideration in choosing brace parameters, plays a crucial role in enhancing seismic performance. This study highlights the cyclic response of three Finite Element (FE) modeled steel braces with variable steel diameter and wall thickness. The design method is additionally confirmed through FE models experiencing hysteresis loadings, suggesting that this approach can secure the overall stability of bracing and is well-suited for practical engineering implementations.

An sEMG Signal-based Robotic Arm for Rehabilitation applying Fuzzy Logic

Sat, 01 Jun 2024 00:00:00 +0000

The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population.

Adaptive Method for Feature Selection in the Machine Learning Context

Yamen El Touati, Jihane Ben Slimane, Taoufik Saidani — Sat, 01 Jun 2024 00:00:00 +0000

Feature selection is a fundamental aspect of machine learning that is crucial for improving the accuracy and efficiency of models. It carefully analyzes the abundance of data to identify the most significant characteristics, hence improving the accuracy of predictions and minimizing the likelihood of model overfitting. This technique not only optimizes model training by reducing computational requirements, but also enhances the model's interpretability, resulting in more transparent and reliable predictions. The deliberate omission of unnecessary variables is a process of improving the model and also constitutes a crucial measure toward achieving more flexible and comprehensible results in machine learning. An analysis to assess the effectiveness of feature selection on regression models was conducted. The impact was measured using Mean Squared Error (MSE) metrics. A variety of regression algorithms were evaluated, and then feature selection techniques, including statistical and algorithmic methods, such as SelectKBest, PCA, and RFE with Linear Regression and Random Forest, were applied. After selecting the features, linear models demonstrated improvements in mean squared error (MSE), highlighting the value of removing unnecessary data. This study emphasizes the subtle impact of feature selection on model performance, calling for a tailored strategy to maximize prediction accuracy.

Behavior of Reinforced Concrete Beams with Vertically Penetrated Holes across the Cross ‐ Sectional Depth

‪Aws Yaseen‬‏ Khadhair, Amer F. Izzet — Sat, 01 Jun 2024 00:00:00 +0000

In Iraq and other countries, RC beams are frequently used as alternative small slab penetrations in low-rise constructions. The structure is capable of transferring stresses effectively and the existence of minor gaps normally has little impact on the structural performance. However, special consideration must be given to the analysis and design of such beams depending on the effect of the openings. Hence, the objective of this paper is to address this research gap by evaluating and enhancing the behavior of RC beams with a vertical opening that penetrates the entire depth of the beams and obstructs the reinforcement bars since all the building design codes and guidelines introduce very limited recommendations for beams with vertical openings. To achieve that, five specimens were fabricated as RC beams with vertical openings and one specimen as a reference beam. The six specimens were tested under two-point loads over a clear span length of 180 cm and a total length of 200 cm. It was found that by adopting a vertical opening into the RC beam without any strengthening methods, the ultimate carrying load capacity decreased and the mid-span deflection increased compared with the reference solid beam.

An Effective Heuristic Optimizer with Deep Learning-assisted Diabetic Retinopathy Diagnosis on Retinal Fundus Images

Cinnappan Nithyeswari, Ganesan Karthikeyan — Sat, 01 Jun 2024 00:00:00 +0000

Diabetic Retinopathy (DR), a common diabetes complication affecting retinal blood vessels, may result in vision damage if not addressed promptly. Early and accurate detection is crucial for effective management, and Deep Learning (DL) techniques offer promising tools for the automated screening of Retinal Fundus Images (RFIs). This approach enhances objectivity, reduces inter-observer variability, and has the potential to extend the DR diagnoses to regions with limited access to specialized medical professionals. This manuscript presents the design of the Beluga Whale Optimizer (BWO) with Deep Learning (DL)-assisted DR Diagnosis on RFIs (BWODL-DRDRFI) technique in the Internet of Things (IoT) platform. The proposed technique automatically examines the RFIs for identifying and classifying DR. During the IoT-based data-gathering procedure the patient utilizes a head-mounted camera for capturing the RFI and sends it to a cloud server. Median Filtering (MF)-based image preprocessing is performed to eradicate noise. Next, the BWODL-DRDRFI technique exploits the ShuffleNet-v2 approach to derive feature vectors. For DR recognition, the BWODL-DRDRFI technique applies a deep Stacked AutoEncoder (SAE) model. Finally, the BWO model optimally adjusts the hyperparameter values of the DSAE model for greater classification performance. The simulation output of the BWODL-DRDRFI approach can be examined on a standard image dataset and the outputs are computed on discrete measures. The simulation result highlighted the enhanced performance of the BWODL-DRDRFI approach in the DR diagnosis process.

The Impact of Motorcycle Positioning on Start-Up Lost Time: The Empirical Case Study of Signalized Intersections in Marrakech using VISSIM

Ayoub Charef, Zahi Jarir, Mohamed Quafafou — Sat, 01 Jun 2024 00:00:00 +0000

This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach adopted for data collection was based on mobile phone video recording at a representative signalized intersection in Marrakech, capturing traffic behaviors during four distinct time periods. Then the YOLOv8 algorithm was employed for real-time object detection and analysis, allowing precise monitoring of motorcycle positioning and examining its influence on the start-up lost time. Afterwards, VISSIM simulations were implemented, on the basis of the collected data, to explore various scenarios, such as motorcycles sharing lanes with cars or dedicated motorcycle lanes. The results reveal a compelling correlation between motorcycle proximity to cars and traffic congestion, with closer positioning leading to increased congestion, longer travel times, reduced average vehicle speeds, and extended queue lengths at intersections. On the contrary, scenarios with dedicated motorcycle lanes consistently show reduced congestion and smoother traffic flow.

Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks

Ketaki Ghodinde, Uttam Chaskar — Sat, 01 Jun 2024 00:00:00 +0000

Milk is a major food constituent. However, the existing discrepancy between milk demand and supply leads to adulteration, which can be dangerous since it causes detrimental effects on health implicating lethal diseases. Although classical methods for adulteration detection are very accurate, their implementation requires skilled technicians as well as expensive and sophisticated instruments. These reasons trigger the need for improved techniques in uncovering adulteration. Urea is a natural component in milk and accounts for a substantial share of adulteration in the non-protein content of milk. The current research proposes and employs a sensor system utilizing the Electrical Impedance Spectroscopy (EIS) method to determine the presence of urea. The classification system was developed using different machine learning algorithms. Three classifiers, Extreme Gradient Boosting (XGBoost), Extreme Learning Machines (ELM), and Deep Neural Networks (DNN) were considered for various levels of urea adulteration. Milk samples were assessed by deploying the developed EIS sensor assembly and the results derived were employed in the training of the machine learning algorithms. The estimated classifiers displayed promising outcomes, involving up to 98.33% classification accuracies, outshining frequently used existing learning approaches like logistic regression.

G-GANS for Adaptive Learning in Dynamic Network Slices

Meshari Huwaytim Alanazi — Sat, 01 Jun 2024 00:00:00 +0000

This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.

Theoretical and Experimental Analysis of Group Piles of Jet and Concrete Columns using the Double Grouting Technique Subjected to Axial Loading on Sandy Soil

Rana M. Al-Khadaar, Mahmood D. Ahmed — Sat, 01 Jun 2024 00:00:00 +0000

This research deals with modifying improvement techniques by using a new technology depending on the properties of the subsoil and the surrounding soil. Jet grouting is one of these techniques utilized instead of normal deep foundations, such as piles, peers, and raft foundations, because it increases the bearing capacity, reduces the settlement, and decreases permeability. In this research, the effect of double-pile and (2*2) jet column piles was studied in the laboratory by employing a jet grouting machine for sand soil, and the results were compared with those of similarly distributed concrete piles. Moreover, the two groups were theoretically analyzed with the 3D ABAQUS finite element software. It was found that with the jet pile, the applied load is greater and the settlement is smaller than that with the concrete pile. The ultimate pile ratio obtained through laboratory tests in the (2*1) jet pile and the concrete pile groups was 71.2% and 75%, respectively. The settlement ranged from 0.00135 to 0.00148 m with the jet pile and ranged from 0.034 to 0.035 m with the concrete pile.

A CONV-EGBDNN Model for the Classification and Detection of Mango Diseases on Diseased Mango Images utilizing Transfer Learning

Ramalingam Kalaivani, Arunachalam Saravanan — Sat, 01 Jun 2024 00:00:00 +0000

Mango fruits are highly valued for their taste, flavor, and nutritional value, making them a popular choice among consumers. However, mango fruits are susceptible to various diseases that can significantly affect their yield and quality. Therefore, accurate and timely detection of these diseases is crucial for effective disease management and minimizing losses in mango production. Computer-aided diagnosis techniques have emerged as a promising tool for disease detection and classification in mango fruits. This study adopts an image classification approach to identify various diseases in mangos and distinguish them from healthy specimens. The pre-processing phase involves a Wiener filter for noise removal, followed by Otsu's threshold-based segmentation as a crucial operation. Subsequently, features are extracted by implementing the ResNet50 model. The proposed model was experimentally verified and validated, demonstrating optimal results with an accuracy of 98.25%. This high accuracy rate highlights the effectiveness of the XG-Boost classifier in accurately categorizing mango images into different disease categories. The experimental results strongly support the potential practical application of the model in the agricultural industry for disease detection in mango crops.

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Maha Ibrahim Khaleel, Musab Ahmed Mohammed, Maryam Qays — Sat, 01 Jun 2024 00:00:00 +0000

In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter.

Congestion Management using the Circulatory System Based Optimization Algorithm

Gia Tue Tang, Nguyen Duc Huy Bui, Duong Thanh Long — Sat, 01 Jun 2024 00:00:00 +0000

Congestion management is one of the most important issues in power system operation, especially in competitive electricity markets. The main aim of Congestion Management (CM) is to eliminate congestion in transmission lines. The most common technique to deal with the CM problem is re-dispatching the generator. However, finding an optimal solution for the CM problem constitutes a challenge for many researchers. Recently, a new biologically inspired metaheuristic algorithm, called Circulatory System Based Optimization (CSBO), was developed and proven to be effective in handling optimization issues. The CSBO algorithm was applied to solve the CM problem for the IEEE-30 bus system in two different cases. The former was compared with the Crayfish Optimization Algorithm (COA), Artificial Rabbits Optimization (ARO), Improved Grey Wolf Optimizer (I-GWO), and other existing methods. The simulation results revealed that the cost obtained from the proposed CSBO algorithm was lower than 14.5%, 11.31%, 9.97%, and 4% compared to PSO, FPA, FFA, and ALO. In addition, the stability of the proposed algorithm was higher than that of the other methods after 30 trials.

A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization

Ramia Ouderni, Bechir Bouaziz, Faouzi Bacha — Sat, 01 Jun 2024 00:00:00 +0000

Due to their small size and isolated energy systems, islands face a significant energy supply challenge. To develop sustainable energy systems, Hybrid Renewable Energy Systems (HRES) help in the generation of electricity in island zones, as they are a clean and inexhaustible source of energy. The purpose of this study is to optimize the allocation of Renewable Energy Sources (RES) on an island in Tunisia. To ensure efficient management between the total power generation and the total community load demand, an Energy Management System (EMS) is required. This paper presents the integration of an optimal EMS using Particle Swarm Optimization (PSO) to directly allocate and optimize the energy generated by an HRES. In addition, the PSO algorithm is applied to regulate energy production, consumption, and storage to maximize the utilization of the available renewable sources while satisfying load requirements. The results exhibit that this approach is effective for the dynamic optimization of energy management in an HRES, contributing to a more efficient and sustainable utilization of energy resources.

Investigation of an Antireflective Coating System for Solar Cells based on Thin Film Multilayers

Hammadi Khmissi, Bilel Azeza, Mohamed Bouzidi, Zainab Al-Rashidi — Sat, 01 Jun 2024 00:00:00 +0000

The optical loss due to reflection is a significant barrier to the quantum efficiency of solar cells. In this work, an antireflective coating based on multilayers of metal oxides (TiO₂, SiO₂, ZnO) was prepared with the spin coating method. The coatings' antireflective, hydrophobic, and photocatalytic properties were examined. Based on the requirements met by the refractive index, a methodical selection of material and thickness for each layer was made in order to achieve near-zero reflection. The performance of different coating systems was examined by evaluating the percentage transmittance in the visible light range (400 nm - 800 nm). The optical properties of the obtained samples were studied with regard to transmittance and reflectance. The surface wettability of antireflective coating films was assessed by measuring the Water Contact Angle (WCA). The photocatalytic characteristics were evaluated by analyzing of the degradation of 0.02 mM Methylene Blue (MB) solutions after sunlight exposure for varying durations at midday.

Improved and Efficient Object Detection Algorithm based on YOLOv5

Sat, 01 Jun 2024 00:00:00 +0000

Object detection is a fundamental and impactful area of exploration in computer vision and video processing, with wide-ranging applications across diverse domains. The advent of the You Only Look Once (YOLO) paradigm has revolutionized real-time object identification, particularly with the introduction of the YOLOv5 architecture. Specifically designed for efficient object detection, YOLOv5 has enhanced flexibility and computational efficiency. This study systematically investigates the application of YOLOv5 in object identification, offering a comprehensive analysis of its implementation. The current study critically evaluates the architectural improvements and additional functionalities of YOLOv5 compared to its previous versions, aiming to highlight its unique advantages. Additionally, it comprehensively evaluates the training process, transfer learning techniques, and other factors, advocating the integration of these features to significantly enhance YOLOv5's detection capabilities. According to the results of this study, YOLOv5 is deemed an indispensable technique in computer vision, playing a key role in achieving accurate object recognition. The experimental data showed that YOLOv5-tiny performed better than anticipated, with a mean Average Precision (mAP) of 60.9% when evaluated using an Intersection Over Union (IoU) criterion of 0.5. Compared to other approaches, the proposed framework is distinguished by significant improvements in the mean average accuracy, computational flexibility, and dependability. As a result, YOLOv5 is suitable for a wide range of real-world applications, since it is both sophisticated and resilient in addressing present issues in the fields of computer vision and video processing.

Influence of Deflection Deformations on the Sustainability of the Landfill Cover: Analysis and Recommendations

Mehrez Jamei, Abdelkader Mabrouk, Yahya Alassaf — Sat, 01 Jun 2024 00:00:00 +0000

The design of cover landfill requires an optimum thickness of the compacted fine soil layer with small permeability. In general, the objective is to reduce the thickness of the landfill cover. However, for a thin layer, and under natural evaporation, denser crack network growths occur during the desiccation by drying. Cracks change the geometrical properties during the drying and wetting cycles and significantly compromise the role of the cover layer, by inducing an infiltration water flow and gas migration. An important differential flexure deformation occurs. The landfill cover, where stiffness and tensile and shear strengths were reduced is being progressively damaged. Thus, this paper aims 1) to quantify the flexural deformation and 2) to provide a methodology and a guideline for studying the integrity of a cover landfill. So, a mechanical model is proposed and implemented in Code Bright software. The methodology starts from the calibration and the validation of the mechanical model based on 1) four-point flexural beam tests and 2) on existing published results. A physical prototype was employed to demonstrate the flexure deformation, and the crack development. Moreover, short natural fibers were mixed and embedded in the soil to make the soil reinforcement and delay crack propagation. In addition to the experimental investigation, mathematical constitutive equations were proposed, in which the contribution of short fibers in terms of increase of tensile strength was introduced. Finally, a simple case study was considered to demonstrate the role of the fiber-soil composite on flexural deformation and tensile stress distribution across the cover layer. An analysis of the numerical results was conducted to support the use of short fibers as reinforcement, which is an environmentally friendly and sustainable solution.

Comparative Analysis of Bipolar and Unipolar SPWM Techniques in PIC-Based Pure Sine Wave Single-Phase Inverters

Aymen Chaaira, Habib Kraiem, Rabiaa Gamoudi, Lassaad Sbita — Sat, 01 Jun 2024 00:00:00 +0000

This paper provides a comparative analysis of bipolar versus unipolar Sinusoidal Pulse Width Modulation (SPWM) in DC-AC inverters, focusing on Total Harmonic Distortion (THD) across modulation indices and the latter’s effects on the R-L loads. Using the PIC18F2431 microcontroller for its efficiency, a single-phase inverter accomplished to deliver a high-fidelity sine wave. This study discovered that while both SPWM methods reduce harmonics, the unipolar approach yields more uniform THD reduction and superior performance, particularly noticeable in RL load conditions, where minimal harmonic distortion is crucial. The bipolar inverter, despite a higher initial THD, shows a considerable improvement at higher indices, significantly enhanced by an LC low-pass filter. This filter is a key component in achieving sub-1% THD levels at full modulation, ensuring optimal sine wave quality. The findings highlight the operational differences between the SPWM techniques and the importance of the LC filters in ameliorating the inverter output for various power applications.

Robust and Secure Routing Protocol Based on Group Key Management for Internet of Things Systems

Salwa Othmen, Wahida Mansouri, Somia Asklany — Sat, 01 Jun 2024 00:00:00 +0000

The Internet of Things (IoT) has significantly altered our way of life, being integrated into many application types. These applications require a certain level of security, which is always a top priority when offering various services. It is particularly difficult to protect the information produced by IoT devices from security threats and protect the exchanged data as they pass through various nodes and gateways. Group Key Management (GKM) is an essential method for controlling the deployment of keys for network access and safe data delivery in such dynamic situations. However, the huge volume of IoT devices and the growing subscriber base present a scalability difficulty that is not addressed by the current IoT authentication techniques based on GKM. Moreover, all GKM models currently in use enable the independence of participants. They only concentrate on dependent symmetrical group keys for each subgroup, which is ineffective for subscriptions with very dynamic behavior. To address these issues, this study proposes a unique Decentralized Lightweight Group Key Management (DLGKM) framework integrated with a Reliable and Secure Multicast Routing Protocol (REMI-DLGKM), which is a reliable and efficient multicast routing system for IoT networks. REMI-DLGKM is a cluster-based routing protocol that qualifies for faster multiplex message distribution within the system. According to simulation results, this protocol is more effective than cutting-edge protocols in terms of end-to-end delay, energy consumption, and packet delivery ratio. The packet delivery ratio of REMI-DLGKM was 99.21%, which is 4.395 higher than other methods, such as SRPL, QMR, and MAODV. The proposed routing protocol can reduce energy consumption in IoT devices by employing effective key management strategies.

Smart PV Hydroponic Greenhouse for Sustainable Agriculture in Tunisia

Sat, 01 Jun 2024 00:00:00 +0000

This study introduces smart tools and algorithms for controlling and monitoring Sustainable Agricultural Greenhouses (SHG). Through the implementation of solar energy, Internet of Things (IoT) sensor-actuator networks, and artificial intelligence, an SHG with a low carbon footprint has been designed. The former makes minimal use of water resources, resulting in the reduction of costs while optimizing crops and harvests. After choosing the structure and architecture of the system introduced, optimized PID controllers based on Artificial Neural Networks (ANN) are proposed, for the maximum power to be derived from the Photovoltaic (PV) solar source and the efficiency of the pump to be improved. Additionally, an IoT-based remote control system has been created using an ESP32 microcontroller with a Wi-Fi interface along with sensors for monitoring solar irradiation, soil moisture, indoor temperature, humidity, lighting, ventilation, and water flow. The system collects sensor data in real-time and employs a built-in algorithm to update the information in the cloud. The experimental measurements carried out in the SHG allowed for the verification of the chosen models and simulation results. Thanks to the hybridization of renewable energies, hydroponic techniques, smart technologies, and sustainable practices, this cutting-edge greenhouse creates an ideal microclimate for year-round cultivation while preserving the ecosystem's energy and water resources.

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Ragab Khalil, Mohammad Shahiq Khan, Yassin Hasan, Nacer Nacer, Sheroz Khan — Sat, 01 Jun 2024 00:00:00 +0000

Temporal-vegetation mapping bearing temporal-related features is important because it helps to understand the global climate changes that drive resource management and habitat conservation. This paper presents a Supervised Normalized Difference Vegetation Index (SNDVI) approach for mapping the vegetation cover in arid environment regions. The NDVI is used to extract features to classify land as a vegetation cover, water body, or bare soil. Through the use of Normalized Difference Vegetation Index (NDVI), regions can be categorized as dry or sandy, based on the soil reflectance values. NDVI is the most commonly deployed index for accurate vegetation cover estimates. The NDVI values lie in a range from -1 to +1, depending on the environmental region and vegetation conditions. It is difficult to assign a specific threshold value to distinguish between vegetation and non-vegetation for all the eco-regions under a specific landscape and ecological conditions. The proposed approach is based on the quantitative verification of the samples as well as the supervised classification method followed to categorize the images. The SNDVI approach has been applied to three different locations in three different seasons in arid ecoregions to extract features for vegetation mapping. The results disclose that SNDVI is a very reliable parameter in extracting true vegetation cover in arid regions. An accuracy evaluation matrix has been performed for each case study and the overall obtained accuracy value ranged from 82% to 100%, depending on the season of the area under investigation. The utility of the proposed method is determined by bench-marking the results with those of the techniques recently utilized by contemporary researchers.

Quasi-Continuous Tidal Datum for Peninsular Malaysia using Tide Gauge, Satellite Altimetry, and Tide Model Driver (TMD) Data

Sat, 01 Jun 2024 00:00:00 +0000

Conventionally, information from the tide gauge stations was used to establish the localized tidal datum. However, limitations in coverage, due to the sparse station distribution along the coast, have caused insufficient tidal datum information in some areas. Therefore, this study aims to develop the Peninsular Malaysia Quasi-Continuous Tidal Datum (PMQCTD) by integrating tide gauges, satellite altimetry, and Tide Model Driver (TMD) data. The research methodology includes data acquisition from 12 Departments of Survey and Mapping Malaysia (DSMMs) tide gauge stations along the coast of Peninsular Malaysia, satellite altimetry data of TOPEX, Jason-1, Jason-2, and GEOSAT Follow-On (GFO) from Radar Altimeter Database System (RADS), and the global hydrodynamic model from TMD. The tide gauge, satellite altimetry, and TMD data encompass 23 years of tidal observation data from 1993 to 2015. For the derivation of the tidal datum, tide gauge, and satellite altimetry data were analyzed following a harmonic analysis approach in the Unified Tidal Analysis and Prediction (UTide) software. Meanwhile, for the TMD data, the tidal datum was determined based on the tidal prediction from the 11 extracted major tidal constituents. For compatibility in data integration, the derived Lowest and Highest Astronomical Tide (LAT and HAT) from tide gauge, satellite altimetry, and TMD data were referenced to the Mean Sea Level (MSL), denoted as LAT_MSLand HAT_MSL, respectively. Next, the LAT_MSLand HAT_MSL were interpolated employing Inverse Distance Weighting (IDW) to develop the PMQCTD (LAT_MSLand HAT_MSL) with the ArcGIS software. The statistical assessment indicated that the established PMQCTD (LAT_MSLand HAT_MSL) has a better agreement with the DSMM tide gauges with a Root Mean Square Error (RMSE) of ± 0.228 m for LAT_MSL and ± 0.159 m for HAT_MSL In conclusion, the establishment of PMQCTD (LAT_MSLand HAT_MSL) has led to the availability of the tidal datum at any location along the coast of Peninsular Malaysia.

Study on Topology Optimization Design for Additive Manufacturing

Nguyen Thi Anh, Nguyen Xuan Quynh, Tran Thanh Tung — Sat, 01 Jun 2024 00:00:00 +0000

Topology optimization is an advanced technique for structural optimization that aims to achieve an optimally efficient structure by redistribution materials while ensuring fulfillment of load-carrying, performance, and initial boundary. One of the obstacles in the process of optimizing structures for mechanical parts is that these optimized structures sometimes encounter difficulties during the manufacturing process. Additive Manufacturing (AM), also known as 3D printing technology, is a method of manufacturing machine parts through joining layers of material. AM opens up the possibility of fabricating complex structures, especially for structures that have been subjected to topology optimization techniques. This project aims to compare the initial shape of a box under static load and its shape after optimization. The subsequent produced models have reduced weights of 43%, 59%, 70%, 73%, and 77%, respectively, weighing 491.45 g, 357.42 g, 261.31 g, 235.56 g, and 203.87 g. All models are capable of supporting a 10 kg load, demonstrating the ability of the structure to meet technical specifications. The results show that combining structural optimization and additive manufacturing can take advantage of both approaches and show significant potential for modern manufacturing.

Exploring Sentiment Analysis on Social Media Texts

Najeeb Abdulazez Alabdulkarim, Mohd Anul Haq, Jayadev Gyani — Sat, 01 Jun 2024 00:00:00 +0000

Sentiment analysis is a critical component in understanding customer opinions and reactions. This study explores the application of sentiment analysis using Python on the Amazon Fine Food Reviews dataset to classify customer reviews as positive or negative, enabling businesses to gain valuable insight into customer sentiments. This study used and compared the efficiency of Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LSTM, and ALBERT. The comparison results showed that the LSTM and ALBERT classifiers stand out with remarkable accuracy (96%) and substantial support for positive and negative reviews. On the other hand, although the Random Forest classifier had similar accuracy (96%), it exhibited lower support for positive and negative sentiments.

Efficient On-Board Charger to Improve the Life Time of Electric Vehicle Battery

Swathi Karike, Kuthuri Narasimha Raju, Sudha Rani Donepudi — Sat, 01 Jun 2024 00:00:00 +0000

Internal combustion engines produce about 10% of the world’s greenhouse gas emissions. Electric vehicles generate 17-30% lower emissions than the internal combustion engines. However, the formers entail certain drawbacks, namely the few available charging stations, the high charging cost, and the limited battery life. The purpose of this paper is to propose the best suitable converter for the on-board charger, which will be able to decrease the charging cost by improving the power factor and the battery life span. This enhancement will be accomplished through the reduction of the charging current either at a very high or very low State of Charge (SOC). Isolated and non-isolated converter topologies were studied to identify the most suitable converter for the on-board charger that will be able to ameliorate the efficiency and the input power factor as well as control the charging current limits. A non-isolated buck converter with switched inductors is used for the power factor adjustment along with the current control approach to achieve a highly efficient on-board charger. Compared to the isolated converter with transformers, the non-isolated hybrid switched inductor buck converter has a wider current control range. MATLAB/Simulink output results were analyzed to validate the performance of the designed on-board charger with a non-isolated converter.

BlockEstate: Revolutionizing Real Estate Transactions through Hyperledger-based Blockchain Technology

Laviza Falak Naz, Rohail Qamar, Raheela Asif, Saad Ahmed, Muhammad Imran — Sat, 01 Jun 2024 00:00:00 +0000

This study introduces BlockEstate, an innovative platform to revolutionize real estate transactions through the application of Hyperledger blockchain technology. BlockEstate presents novel contributions in the form of a pioneering compensation request mechanism and a sophisticated chaincode for real estate transaction management. These advancements address long-standing challenges in traditional real estate transactions by leveraging the decentralization, immutability, and transparency of blockchain technology. By ensuring secure and transparent financial transactions and automating property ownership conveyances, BlockEstate sets a new standard for efficiency and safety in the real estate industry. This study comprehensively investigates the design, functionality, and impact of BlockEstate, highlighting its unique contributions and potential to transform the real estate market.

An Effective Method for the Detection of Wall Brick Defects using Machine Vision

Ngoc-Tien Tran, Ngoc-Duy Le, Van-Nghia Le — Sat, 01 Jun 2024 00:00:00 +0000

The production lines for wall bricks have achieved a high level of automation. Most brick production lines in developing countries have automated the steps up to placing the bricks in the kiln. However, the manual loading and unloading of bricks after firing still remains. This manual process reduces labor productivity and increases the cost of the final product. To address this issue, this study aims to utilize machine vision algorithms to detect cracks in bricks, thereby facilitating the automation of the brick loading and unloading process. A comprehensive image processing method is developed, which combines square detection and moment algorithms to analyze image properties. This integrated approach enables the accurate detection of cracks and the determination of their respective areas, ensuring precise and reliable results. By detecting defects in the bricks, we can replace faulty ones and employ robots to automatically handle rows of bricks. The study's results demonstrate the proposed method's ability to accurately identify brick defects. These findings are significant as they contribute to the automation of brick loading and unloading, which can be implemented in large-scale brick factories, leading to a safer and more efficient working environment.

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Sat, 01 Jun 2024 00:00:00 +0000

Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.

Optical Flow-Based Feature Selection with Mosaicking and FrIFrO Inception V3 Algorithm for Video Violence Detection

Sat, 01 Jun 2024 00:00:00 +0000

Violence in recent years poses the biggest threat to society, which needs to be addressed by all means. Video-based Violence detection is very tough to discern when the person or things that are recipients of a violent act are in motion. Detection of violence in video content is a critical task with applications spanning security surveillance, content moderation, and public safety. Leveraging the power of deep learning, the Violence Guard Freeze-In Freeze-Out Inception V3(VGFrIFrOI3) deep learning model in conjunction with optical flow-based characteristics proposes an effective solution for automated violence detection in videos. This architecture is known for its efficiency and accuracy in image classification tasks and in extracting meaningful features from video frames. By fine-tuning Inception V3 on video datasets annotated for violent and non-violent actions, the network can be permitted to learn discriminative features that simplify the detection of any violent behavior. Furthermore, the aforementioned model incorporates temporal information by processing video frames sequentially and aggregating features across multiple frames using techniques, such as temporal convolutional networks or recurrent neural networks. To assess the performance of this approach, a performance comparison of the proposed model against already existing methods was conducted, demonstrating the model’s superior accuracy and robustness in detecting violent actions. The recommended approach not only offers a highly accurate solution for violence detection in video content but also provides insights into the potential of deep learning architectures like Inception V3 in addressing real-world challenges in video analysis and surveillance. The Mosaicking processing, additionally carried out in the pre-processing step, improves the algorithm performance by deploying space search minimization and optical flow-based feature extraction, aiming to extemporize accuracy.

A Cloud Forensics Framework to Identify, Gather, and Analyze Cloud Computing Incidents

Rafef Al-mugern, Siti Hajar Othman, Arafat Al-Dhaqm, Abdulalem Ali — Sat, 01 Jun 2024 00:00:00 +0000

The focus of cloud forensics is cyber-crime cases, no matter the object, the subject, or the environment involved. Each cloud computing environment has a variety of features that make it unique. Challenges associated with cloud forensics can be found at every stage of the digital forensics process. We need to begin by understanding the cloud forensics landscape (the cloud) in order to provide a holistic solution to overcome these challenges. While designing the cloud forensics framework, the elements that make up the cloud should be taken into consideration, which also impact the forensics process within the cloud. An extensive survey of the current state of research in cloud forensics is presented in this paper. Also, a conceptual cloud forensics framework that facilitates the identification, gathering, and analysis of cloud computing events is proposed, utilizing the design science approach. The proposed conceptual cloud forensics framework consists of six stages: identifying incidents, gathering evidence, preserving evidence, analyzing incidents, documenting incidents, and investigating post-incident events. Each stage has several activities and tasks to assist investigators dealing with cloud computing events. Unlike traditional approaches to cloud forensic investigations, the conceptual framework developed in this study is highly applicable.

A Mathematical Model to Predict Optimal Risk Response Budget using Genetic Algorithm

Hiba Omer Ghaeb Aljorany, Ahmed Mohammed Raoof Mahjoob — Sat, 01 Jun 2024 00:00:00 +0000

Construction projects encounter a variety of risks, which require a thorough risk response phase to identify, evaluate, and determine solutions. This study presents a methodology for effectively choosing an appropriate risk response strategy and allocating suitable funds for the risk response phase of building projects. The framework employs optimization approaches and evolutionary principles, specifically utilizing the Genetic Algorithm technique. The objective of the model is to reduce costs and determine a suitable fund allocation strategy with minimum risk. The effectiveness of the framework is assessed in an actual building project involving a ventilation and air conditioning system, demonstrating its ability to optimize risk response and assist decision-makers in making well-informed choices.

Ransomware Early Detection Techniques

Sat, 01 Jun 2024 00:00:00 +0000

Ransomware has become a significant threat to individuals and organizations worldwide, causing substantial financial losses and disruptions. Early detection of ransomware is crucial to mitigate its impact. The significance of early detection lies in the capture of ransomware in the act of encrypting sample files, thus thwarting its progression. A timely response to ransomware is crucial to prevent the encryption of additional files, a scenario not adequately addressed by current antivirus programs. This study evaluates the performance of six machine-learning algorithms for ransomware detection, comparing the accuracy, precision, recall, and F1-score of Logistic Regression, Decision Tree, Naive Bayes, Random Forest, AdaBoost, and XGBoost. Additionally, their computational performance is evaluated, including build time, training time, classification speed, computational time, and Kappa statistic. This analysis provides insight into the practical feasibility of the algorithms for real-world deployment. The findings suggest that Random Forst, Decision Tree, and XGBoost are promising algorithms for ransomware detection due to their high accuracy of 99.37%, 99.42%, and 99.48%, respectively. These algorithms are also relatively efficient in terms of classification speed, which makes them suitable for real-time detection scenarios, as they can effectively identify ransomware samples even in the presence of noise and data variations.

A High-Gain Dual-Band Slotted Microstrip Patch Antenna For 5G Cellular Mobile Phones

Mouaaz Nahas — Sat, 01 Jun 2024 00:00:00 +0000

Microstrip patch antennas have been widely used in contemporary mobile communication technology, including 5G. Previous studies in the area have shown that such antennas can be optimized to operate in different bands of 5G. This study proposes a microstrip patch antenna designed to operate at 26 and 28 GHz and aimed at improving the gain and other radiation characteristics by adding a combination of different slot shapes to a single rectangular patch that is very common and popular in 5G antennas. The results show that the gain is noticeably increased by inserting two hammer slots and a rectangular slot in the middle between them. The dimensions of the slots are optimized using the CST Studio Suite simulator. A comparative analysis was performed to demonstrate the superiority of the proposed over previous designs in terms of gain value and other radiation parameters. The results suggest that such a very simple and low-profile antenna can be a good candidate for 5G mobile applications.

Big Data and Cloud Computing Opportunities and Application Areas

Aws I. Abueid — Sat, 01 Jun 2024 00:00:00 +0000

The exponential growth of digital data has created new challenges and opportunities to process, store, and analyze large datasets. Cloud computing has transformed the way businesses manage their IT infrastructure by providing a scalable and cost-effective platform for data storage and processing. Because of this, businesses now have access to new opportunities to exploit big data and thus acquire insights into the behavior of their customers, increase the efficiency of their operations, and drive innovation. Furthermore, big data analysis enables the exploration of links between a set of independent data, revealing many aspects that enable the prediction of correct decisions that can aid the achievement of desired goals. With this in mind, the purpose of this article is to investigate the ways in which big data analysis can be put to use in fields such as healthcare, presenting specific instances of effective use along the way. The purpose of this study is to provide a comprehensive overview of the opportunities and application areas offered by big data and cloud computing, as well as to highlight the need for businesses and organizations to adopt these technologies to maintain their competitive edge in the digital age. Furthermore, this paper emphasizes the importance of big data analysis in facilitating decision-making and goal achievement, and it encourages businesses and organizations to adopt these technologies to stay competitive in an increasingly data-driven world. It also discusses the ethical, legal, and security issues that arise when dealing with large amounts of data, as well as ways to address these challenges.

Economic Viability of Distribution Network Upgrade Deferral through BESS Sizing from K-Means Clustered Annual Load Profile Data

Edwin Ondigo, Cyrus Wekesa — Sat, 01 Jun 2024 00:00:00 +0000

The augmented electricity demand requires electrical infrastructure upgrades with system operators instituting strategies to increase Distribution Network (DN) capacity in tandem with load growth. In this study, a simple method of deploying Li-ion Battery Energy Storage Systems (BESSs) to defer DN upgrades is presented by utilizing historical load profiles. The k-means algorithm is employed to cluster the annual load profiles obtained from a substation in groups of 15-minute intervals. The load data are min-max scaled and fed as input to the K-means algorithm. The NPV financial analysis method is followed in the DN upgrade deferral benefit determination with the acquired benefit depending on Li-ion BESS price and feeder upgrade cost. The results indicate economic viability of up to four years with a Net Present Value (NPV) of US$10k for Li-ion 2000kW/3000kWh BESS. More benefits and deferral years are achieved by varying Li-ion BESS and feeder upgrade costs to 80% and 120%, respectively with deferral years increased to six with an NPV of US$110k for Li-ion BESS of 3100kW/6000kWh.

Design of Symmetrical Voltage Multiplier High Gain Interleaved DC to DC Converter for Photovoltaic Applications

Edara Sreelatha, Alagappan Pandian, Pullacheri Sarala, Chilakala Rami Reddy — Sat, 01 Jun 2024 00:00:00 +0000

High voltage gain interleaved DC to DC boost converters are employed in Photovoltaic (PV) energy conversion for their structural advantage. The proposed converter builds upon the existing two-phase interleaved DC to DC boost converter, which is commonly used in utility grid integration circuits to minimize ripple current from the PV. The aim is to enhance the output voltage of the currently installed PV array in order to cater to high-power applications or grid integration. The key requirements include achieving high-efficiency power conversion and fully utilizing the potential of the PV system. The methods being proposed to increase the PV output voltage suffer from drawbacks such as low efficiency, complexity, and cost. In contrast, the suggested DC-DC converter boasts a remarkable efficiency of 96% and is capable of converting voltage from 25 V to 400 V for a power output of 400 W. The designed converter has been simulated in MATLAB software and the performance is compared to existing converters related to voltage stress, voltage gain against given duty cycle, and efficiency.

Determination of Optimum Test Parameter Level Ranges for Machining Processes

Sat, 01 Jun 2024 00:00:00 +0000

In this study, the ideal experimental design planning for the machining process was investigated. Two experimental designs were created by differentiating the parameter levels considered in the drilling process of stainless-steel. Close and far-level designs were obtained by creating 20% and 40% differences between the parameter levels. In the experimental system prepared according to the Taguchi method, surface roughness and cutting forces were measured as the output parameters. The results were analyzed statistically by optimization, analysis of variance and correlation analysis, and visually by chip morphology examination. According to the findings, it was determined that a 20% difference between the parameter levels was more appropriate in terms of experimental system stability, statistical data significance, and chip morphology.

Effect on Fatigue Behavior of Connecting Rod in Gasoline Engine

Yodnapha Ketmuang, Bundit Wongthong — Sat, 01 Jun 2024 00:00:00 +0000

This article reports the failure analysis of a connecting rod that is broken into 3 pieces and is used in the gasoline engine of a sedan. The connecting rod is made of JIS-S50C medium alloyed steel. Fractography was performed to characterize the failure mode on the fracture surface of this connecting rod through the examination of the macroscopic and microscopic morphologies of the fracture surface, chemical composition, metallographic analysis, mechanical properties of the material, and numerical simulation. The fracture surface of this connecting rod is caused by fatigue, which was the dominant mechanism of failure. This type of crack is indicative of shear failure in the ductile fracture mode, whereas no abnormalities were found in the composite elements of the connecting rod. The microstructure is composed of perlite-ferrite. The results of the numerical simulation and the calculated crushing stress (s_c) were compared and were found to be in accordance and within the acceptable values.

A Study on the Prediction of Apartment Prices using the GBRT model: A Case Study in Vinh City, Vietnam

Sat, 01 Jun 2024 00:00:00 +0000

This study aims to propose an efficient Machine Learning (ML) model, namely Gradient Boosting Regression Trees (GBRT), to predict apartment prices considering the fluctuation of construction material prices and the annual inflation index. For developing the ML model, 480 apartments in Vinh City (Vietnam) were considered. The input parameters employed while training the ML model were the area of the apartments, the number of bedrooms/restrooms, the apartment class, nearby health or education services, investment potential, and parking, whereas the apartment price was the output of the model. The results show that the GBRT model predicts the apartment price accurately with a high value of 0.997 and a small RMSE of 0.26. Additionally, the obtained a20-index is very high, almost 1.0. Finally, a practical graphical user interface was developed to facilitate the prediction of the apartment price in terms of usability.

Advanced Android Malware Detection through Deep Learning Optimization

Ahmed Alhussen — Sat, 01 Jun 2024 00:00:00 +0000

Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide.

Control of a Grid-connected Inverter using Sliding Mode Control

Quang-Tho Tran — Sat, 01 Jun 2024 00:00:00 +0000

The rising popularity of grid-connected multilevel inverters with photovoltaic panels underscores the importance of effective modulation and control strategies for ensuring optimal power quality. The performance of these inverters hinges significantly on modulation and control approaches, specifically addressing issues like common mode voltage, harmonics, switching loss, and dynamic response. This study introduces a novel approach to mitigate current harmonics in these inverters by employing sliding mode control. Notably, this technique achieves harmonic reduction without necessitating an increase in the switching count. The presented technique eliminates phase-locked loop, current controllers, and carrier waves, thereby easing hardware computation. Beyond computational efficiency, this approach contributes to enhanced power quality and dynamic response within the inverter system. Simulation results affirm the efficacy of the proposed method when compared to the use of the phase opposite disposition modulation combined with the current controllers. In the nominal operational mode, the proposed method reduces the current Total Harmonic Distortion (THD), the highest magnitude of individual harmonics, and the switching count by 43.6%, 73.5%, and 19.6% respectively, compared with those of the method using the phase opposite disposition modulation combined with current controllers.

Study of the Tensile Strength and Shore Hardness Behavior of PE100 SDR11 Electrofusion Welded and Artificially aged Pipes

Dragos Gabriel Zisopol, Mihail Minescu, Dragos Valentin Iacob, Nicoleta Voicu — Sat, 01 Jun 2024 00:00:00 +0000

This paper presents the tensile strength and Shore D hardness behavior of electrofusion-welded and artificially aged polyethylene (PE) pipes of the PE100 SDR11 classification with a nominal diameter of 125 mm and a wall thickness of 11.40 mm. For the study, 12 samples were taken from the body of a PE100 SDR11 pipe (9 of which were obtained from the fusion-welded joint). Subsequently, the 12 samples were divided into 3 groups of 4 pieces (1 unwelded sample and 3 welded samples). Following the Arrhenius method, the samples of the 2 groups (group II and group III) were artificially aged, those belonging to group II were aged 10 for years and those belonging to group III were aged for 20 years. Subsequently, all 12 samples were tested for tensile strength and Shore D hardness. The 10-year aging of the welded samples increased the tensile strength by 12.31% and the 20-year artificial aging increased the tensile strength by 18.44%. For the unwelded samples, artificial aging for 10 years increased the tensile strength by 11.12%, whereas aging for 20 years increased the tensile strength by 12.63%. Artificial aging of the PE100 SDR 11 pipes does not have a significant influence on the Shore D hardness, which was found within the high range of hardnesses. The results show that the PE100 SDR pipes welded by electrofusion can be used for 20 years with safety.

Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods

Muteb Zarraq Alotaibi, Mohd Anul Haq — Sat, 01 Jun 2024 00:00:00 +0000

This study investigates customer churn, which is a challenge in the telecommunications sector. Using a dataset of telecom customer churn, multiple classifiers were employed, including Random Forest, LGBM, XGBoost, Logistic Regression, Decision Trees, and a custom ANN model. A rigorous evaluation was conducted deploying cross-validation techniques to capture nuanced customer behavior. The models were optimized by hyperparameter tuning, improving their customer churn prediction results. An ensemble averaging method was also adopted, achieving an accuracy of 0.79 and a recall of 0.72 in the test data, which was slightly lower than that of the LGBM, XGBoost, and Logistic Regression. These findings contribute to the development of more reliable churn prediction models to ameliorate the customer retention rates and the operational performance of the service providers.

Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models

Zamil S. Alzamil — Sat, 01 Jun 2024 00:00:00 +0000

The significant global challenges in eye care are treatment, preventive quality, rehabilitation services for eye patients, and the shortage of qualified eye care professionals. Early detection and diagnosis of eye diseases could allow vision impairment to be avoided. One barrier to ophthalmologists when adopting computer-aided diagnosis tools is the prevalence of sight-threatening uncommon diseases that are often overlooked. Earlier studies have classified eye diseases into two or a small number of classes, focusing on glaucoma, and diabetes-related and age-related vision issues. This study employed three well-established and publicly available datasets to address these limitations and enable automatic classification of a wide range of eye disorders. A Deep Neural Network for Retinal Fundus Disease Classification (DNNRFDC) model was developed, evaluated based on various performance metrics, and compared with four established pre-trained models (EfficientNetB7, EfficientNetB0, UNet, and ResNet152) utilizing transfer learning techniques. The results showed that the proposed DNNRFDC model outperformed these pre-trained models in terms of overall accuracy across all three datasets, achieving an impressive accuracy of 94.10%. Furthermore, the DNNRFDC model has fewer parameters and lower computational requirements, making it more efficient for real-time applications. This innovative model represents a promising avenue for further advancements in the field of ophthalmological diagnosis and care. Despite these promising results, it is essential to acknowledge the limitations of this study, namely the evaluation conducted by using publicly available datasets that may not fully represent the diversity and complexity of real-world clinical scenarios. Future research could incorporate more diverse datasets and explore the integration of additional diagnostic modalities to further enhance the model's robustness and clinical applicability.

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Madhava Rao Maganti, Kurra Rajashekar Rao — Sat, 01 Jun 2024 00:00:00 +0000

The rise of 5G technology has brought with it a surge in diverse services with demanding and varying requirements. Network fragmentation has emerged as a critical technique to address this challenge, enabling the creation of virtual network segments on a shared infrastructure, allowing for efficient resource utilization and improved performance. This paper investigates the potential of network fragmentation, combined with optimized resource allocation, to enhance the performance of 5G core networks. A novel framework that integrates these two techniques is proposed. The former takes into account factors, such as network traffic patterns, service requirements, and resource availability. The framework aims to optimize network performance metrics, namely throughput, latency, and resource utilization. The experimental results demonstrate the effectiveness of the proposed framework, showcasing a significant improvement in overall network performance, paving thus the way for efficient and robust 5G service delivery.

Effect of Porosity on Combustion Performance in Packed Bed Porous Media

Sat, 01 Jun 2024 00:00:00 +0000

This study investigates the effect of packed bed material porosity and air-to-fuel ratio on the combustion stabilization of a premixed gaseous mixture. An experimental work was carried out in a single-layer concept of a packed bed on a constant cross-sectional area tubular burner. Two types of materials, Alumina (Al₂O₃) and Zirconia (ZrO₂), with different porosities, namely 0.36, 0.4, 0.44, and 0.46, were tested. The results showed that porosity has a significant effect on the position of the reaction zones. As porosity decreases, the reaction zone moves downstream of the packed bed. The excess air ratio does not affect the position of the reaction zone but has an impact on the temperature distribution inside the porous medium. The packed bed material affects the volume of the reaction zone and the temperature distribution inside the porous media, where Zirconia has a reaction zone volume higher than Alumina. The concentration of NO_x was reduced with increasing porosity. Zirconia media exhibits a lower level of NO_x emission compared to Alumina. For an excess air ratio of 1.6, the maximum NO_x values were 22.5 and 17.5 ppm for Alumina and Zirconia, respectively.

Driving Innovation: Prosumer Incentives in Peer-to-Peer Energy Trading

Marwan Mahmoud, Sami Ben Slama — Sat, 01 Jun 2024 00:00:00 +0000

Peer-to-peer energy trading is an innovative idea that overcomes several technological and industrial hurdles. It allows industries and consumers, including knowledgeable prosumers, to trade excess energy with distributed generation sources, such as solar, wind, and electric vehicles, thus promoting a significant reduction in overall energy consumption. Real-Time Pricing (RTP) is increasingly essential in integrating smart home device Demand Response (DR) strategies. RTP improves energy management and enables customers to respond intelligently to price fluctuations. In this vein, this paper proves how DR and peer-to-peer (P2P) energy trading could affect energy prices by allowing producers (consumers) and smart home users to interact directly rather than through the traditional grid. The two-pronged planning approach substantially contributes to the reduction of electricity costs. DR enables P2P energy trading, while deep learning algorithms adapt smart home devices to RTP dynamics. Simulation results show that using P2P energy trading and DR in smart homes can significantly eliminate costs. This hybrid approach increases the energy efficiency of Smart Grid (SG) 2.0 technology and makes it more adaptable and cost-effective.

hFedLAP: A Hybrid Federated Learning to Enhance Peer-to-Peer

Ismail Elshair, Tariq J. S. Khanzada — Sat, 01 Jun 2024 00:00:00 +0000

The concept of Federated Learning (FL) is a branch of Machine Learning (ML) that enables localized training of models without transferring data from local devices to a central server. FL can be categorized into two main topologies: Aggregation Server Topology (AST) and Peer-to-Peer (P2P). While FL offers advantages in terms of data privacy and decentralization, it also exhibits certain limitations in efficiency and bottleneck. However, the P2P topology does not require a server and allows only for a small number of devices. To overcome these limitations, this study proposes a hybrid FL Aggregation of P2P (hFedLAP) that mitigates some of the limitations of AST by combining it with P2P. This fusion model helps to remove the bottleneck and combines the advantages of both topologies. In the proposed hFedLAP model, clients are organized into 49 groups, each consisting of 51 clients, including one in each group serving as a client and an admin node in a P2P setup. In these groups, communication is restricted to admin nodes, supporting a maximum of 2,495 devices. Platform accuracy is maintained by implementing measures to prevent new devices with inadequate accuracy levels from joining until they attain the minimum required accuracy. The experimental results of hFedLAP were compared with AST and P2P using the MNIST dataset, showing that hFedLAP outperformed AST and P2P, achieving remarkable accuracy and scalability, with accuracy levels reaching 98.81%.

A New Design and Implementation of a Three-Phase Four-Wire Shunt Active Power Filter for Mitigating Harmonic Problems caused by Compact Fluorescent Lamps

Mohamed Hajjej, Lassaad Sbita — Sat, 01 Jun 2024 00:00:00 +0000

The massive embedding of nonlinear loads in industrial, commercial, and residential applications has created severe power quality problems in modern power distribution systems. Compact Fluorescent Lamps (CFLs), which have been designed to replace Incandescent Lamps (ILs), due to their lower energy consumption and longer lifetime, are among the most used non-linear loads. These electric devices, equipped with ballasts and power electronic converters, inject harmonic currents, reactive powers, and create unbalance in the electrical system. Active filters are widely implemented to overcome these issues and improve power quality. In this sense, a Shunt Active Power Filter (SAPF) is developed in this paper to eliminate the under-wanted harmonics caused by multiple CFLs and ameliorate the global power factor in 3-phase 4-wire systems. The suggested SAPF is connected in parallel with the loads and it consists of three main blocks, the reference current calculation block, the Voltage Source Inverter (VSI), and the VSI control block. The reference currents are calculated following the Synchronous Reference Frame (SRF) theory. Meanwhile, Pulse Width Modulation (PWM) based control is adopted for controlling the switching signals. In order to investigate the efficiency and applicability of the developed 3-phase 4-wire SAPF, different simulations and experimental tests are carried out. The measurements are performed by employing a power analyzer and are analyzed with the Power Pad III software. The obtained results disclosed that the proposed SAPF reduced the Total Harmonic Distortion (THD) of the CFL current from 89.6% to 1.62% and improved the power factor.

Metaheuristic Optimization of Maximum Power Point Tracking in PV Array under Partial Shading

Mohammed Qasim Taha, Mohammed Kareem Mohammed, Bamba El Haiba — Sat, 01 Jun 2024 00:00:00 +0000

Optimal energy harvesting is dependent on the efficient extraction of energy from photovoltaic (PV) arrays. Maximum Power Point Tracking (MPPT) algorithms are crucial in achieving the maximum power harvest from the PV systems. Therefore, in response to a fluctuating power generation rate due to shading of the PV, the MPPT algorithms must dynamically adapt to the PV array's Maximum Power Point (MPP). In this article, three metaheuristic optimization MPPT techniques, utilized in DC converters connected to the array of 4 PV panels, are compared. The Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are used to optimize MPPT in the converter, are compared. This research evaluates the efficiency of each optimization method in converging to MPP under 2 s after partial shading of the PV with respect to velocity and accuracy. All algorithms exhibit fast MPPT optimization. However, among the evaluated algorithms, the PSO was distinguished for its higher stability and efficiency.

Enhancing Neural Network Resilence against Adversarial Attacks based on FGSM Technique

Mohamed Ben Ammar, Refka Ghodhbani, Taoufik Saidani — Sat, 01 Jun 2024 00:00:00 +0000

The robustness and reliability of neural network architectures are put to the test by adversarial attacks, resulting in inaccurate findings and affecting the efficiency of applications operating on Internet of Things (IoT) devices. This study investigates the severe repercussions that might emerge from attacks on neural network topologies and their implications on embedded systems. In particular, this study investigates the degree to which a neural network trained in the MNIST dataset is susceptible to adversarial attack strategies such as FGSM. Experiments were conducted to evaluate the effectiveness of various attack strategies in compromising the accuracy and dependability of the network. This study also examines ways to improve the resilience of a neural network structure through the use of adversarial training methods, with particular emphasis on the APE-GAN approach. The identification of the vulnerabilities in neural networks and the development of efficient protection mechanisms can improve the security of embedded applications, especially those on IoT chips with limited resources.

An Improved Form of Hazen-Williams Equation for Pressurized Flow

Sat, 01 Jun 2024 00:00:00 +0000

This study performed a sensitivity analysis to correlate the frictional head loss calculated by the Darcy-Weisbach (D-W) and the Hazen-Williams (H-W) formulas. For a broad variety of fluid temperatures, velocities, and pipeline diameters, this study considered an extensive discussion and analysis to determine friction loss within pressurized pipelines using Microsoft Excel. Regression analysis and statistical tools were applied to improve the relationship between the two equations. A more accurate expression was developed to calculate the friction loss in terms of the H-W equation. The estimated values were compared with previous experimental and numerical studies, and a good agreement was observed. The proposed model was evaluated using WaterGEMS software in an application example of a water supply system against the D-W and H-W equations. Good agreements were recorded between predicted values and previous studies, with an error of less than 1%. These findings can be used to improve the hydraulic design of engineering applications.

Anomaly Detection in IIoT Transactions using Machine Learning: A Lightweight Blockchain-based Approach

Mayar Ibrahim Hasan Okfie, Shailendra Mishra — Sat, 01 Jun 2024 00:00:00 +0000

The integration of secure message authentication systems within the Industrial Internet of Things (IIoT) is paramount for safeguarding sensitive transactions. This paper introduces a Lightweight Blockchain-based Message Authentication System, utilizing k-means clustering and isolation forest machine learning techniques. With a focus on the Bitcoin Transaction Network (BTN) as a reference, this study aims to identify anomalies in IIoT transactions and achieve a high level of accuracy. The feature selection coupled with isolation forest achieved a remarkable accuracy of 92.90%. However, the trade-off between precision and recall highlights the ongoing challenge of minimizing false positives while capturing a broad spectrum of potential threats. The system successfully detected 429,713 anomalies, paving the way for deeper exploration into the characteristics of IIoT security threats. The study concludes with a discussion on the limitations and future directions, emphasizing the need for continuous refinement and adaptation to the dynamic landscape of IIoT transactions. The findings contribute to advancing the understanding of securing IIoT environments and provide a foundation for future research in enhancing anomaly detection mechanisms.

Comparison of Modular Stator 1-phase per Module with Switched Reluctance Motor

Sat, 01 Jun 2024 00:00:00 +0000

Conventional motors have the advantage of robustness, high torque output capability, and power performance compared to modular motors. However, traditional motor structure inhibits fault tolerance. For that reason, this paper proposes the structure of a modular stator. It focuses on the performance of modular stator outer rotor flux switching permanent magnet motor (MSOR-FSPM) and Segmental Stator Hybrid Excitation Switched Reluctance Motor (SS-HESRM) by simulation using 2D-FEA in no-load and load conditions. Based on the results, the maximum flux linkage of MSOR-FSPM is 0.02 Wb and 0.05 Wb for SS-HESRM. The average torque output for MSOR-FSPM at maximum armature density is 108.43 Nm and 45.26 Nm for SS-HESRM. Therefore, the torque density for MSOR-FSPM and SS-HESRM is 3.78 Nm/kg and 10.63 Nm/kg, respectively. As for the conclusion, a modular stator motor is capable of inherent fault tolerance compared to a conventional motor structure. Moreover, a modular stator motor produces a higher torque and power density because of the low iron core and optimum flux linkage.

Using Advanced "Birth and Death" APDL Code to Analyze the Thermal Transient Problem of Mass Concrete during Construction Phases

Anh Kiet Bui, Trong Chuc Nguyen, Thi Thuy Bich Nguyen — Sat, 01 Jun 2024 00:00:00 +0000

For finite element analysis of the thermal transfer problem, solving the Boundary Condition (BC) change versus time appropriately, according to the concrete construction phases, is an important factor affecting the accuracy of the analysis result. The contact BC may change versus time from the convective boundary to the contact boundary between two bodies. In this paper, a technique using the "Birth and Death" element is applied to the heat transfer boundary of a mass concrete pier versus the time of construction phases. From the obtained results, it was concluded that the temperature distribution in the pier body can be determined according to the phases of construction. The achieved temperature field gives an input to the stress analysis allowing the determination of the possibility of thermal cracking of the structure and give appropriate alternatives to prevent thermal cracks.

Development of a Collaborative Intelligent Individual Education Program System using a Prototyping Approach

Sat, 01 Jun 2024 00:00:00 +0000

The current paper describes the development of an online Collaborative Intelligent Individual Education Platform (CIIP) that is specifically designed for children with ASD based on experts' assessments and progress reports. The online platform facilitates the progress of children with special needs as it is established on their individual needs and can be accessible anywhere. The CIIP system was developed following a prototyping model approach that comprised initial requirements, design, prototyping, customer evaluation, review and refinement, development, testing, and maintenance. Two cycles of prototyping evaluation were conducted to confirm the final requirements. The results of the prototype evaluation by the stakeholders indicated that 29 changes were required before progressing to the final development of CIIP. System testing was carried out with expert testers to ensure the CIIP functions and the satisfaction of the expected requirements. The results showed that 22% of the test cases failed due to difficulties with complicated interconnections in several modules. Despite these challenges, CIIP was able to meet the requirement specifications and perform as expected.

Steel Collar Strengthening of a Slab-Column Connection under Eccentric Load

Hanaa Abdulbaset Ali, Mohannad H. Al-Sherrawi — Sat, 01 Jun 2024 00:00:00 +0000

The current study focuses on the punching shear resistance of reinforced concrete flat slabs with steel collars, examining it both experimentally and numerically. Six square flat slab specimens were casted and tested under static load, axial load, and eccentric load. The effects of the steel collars and eccentricity on the load-displacement behavior, ultimate load capacity, cracking load, failure mode, stiffness, failure angle, and ductility, were investigated. The results demonstrated that using steel collars in slab-column connection greatly increases the shear capacity of the slab under eccentric loads and moments. The strengthened slabs' ultimate capacity increased by 34% and 61%, respectively, compared to that of the slabs without collars. ABAQUS simulation results were in good accordance with the experiments. The findings underline the efficiency of the steel collars in increasing the efficiency of slab-column connections with punching shear, which is a cost-effective strengthening technique. This research provides knowledge about slab-column connections and offers relevant indications for the design and strengthening of the construction.

Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards

Hoang-Giang Vu, Duc Nguyen Huu — Sat, 01 Jun 2024 00:00:00 +0000

The goal of reducing greenhouse gas emissions and energy transition has created many favorable conditions to promote solar power generation technology. However, from a technical perspective, integrating solar power into the power grid poses many challenges in grid operation. This study investigates the impact of rooftop solar power in terms of power quality in the urban distribution grid in Vietnam. The current study simulated a typical low-voltage distribution grid with single-phase and three-phase loads to evaluate important power quality issues, such as Total Harmonic Distortion (THD) of voltage and current, voltage unbalance, and voltage rises. A grid simulation grid was developed in Matlab/Simulink under different conditions for the penetration level of solar power and the daily variation of loads. These indexes were compared with the limits specified in Vietnamese standards and the relevant international standards of IEEE and IEC. Compliance with the provisions of these standards was provided, even in the case of solar power systems with a high level of penetration into Vietnam's urban distribution grid.

A Failure Criterion of Weak and Crushable Limestone Rock in Mining Field containing Karstic Cavities

Yahya Alassaf, Abdelkader Mabrouk, Mehrez Jamei, Anwar Ahmed — Sat, 01 Jun 2024 00:00:00 +0000

This study aims to correlate the mechanical properties measured in the laboratory and the field for weak and crushable limestone in a mining site containing random karstic cavities. Compressive tests were performed in the laboratory to obtain Unconfined Compressive Strength (UCS) and rock mass modulus (E_rm). Field tests were: i) boring and drilling cores that allowed obtaining Rock Quantification Distribution (RQD) and recovery rock parameter (REC), and ii) Ground Penetration Radar (GPR) to detect and locate random cavities in the underground limestone deposit. The correlation between the E_m/UCS rate and the RQD was determined and analyzed. Based on the role of the new interpretation of the Geological Strength Index (GSI) and its relationship with the E_rm/UCS rate, a mathematical relationship was determined to link GSI and RQD. This relationship was a basis for modifying the generalized Hoek-Brown criterion, involving the amplitude of reflected electromagnetic waves (EM) provided by GPR field tests.

Tweet Prediction for Social Media using Machine Learning

Mohammed Fattah, Mohd Anul Haq — Sat, 01 Jun 2024 00:00:00 +0000

Tweet prediction plays a crucial role in sentiment analysis, trend forecasting, and user behavior analysis on social media platforms such as X (Twitter). This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances within tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-related tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust predictive outcomes. The SVM model showed significantly higher performance with 82% accuracy and an F1 score of 81%, whereas LR, XGBoost, and RF achieved 79% accuracy with average F1-scores of 78%.

Behavior of Modified Reactive Powder Concrete Containing Sustainable Materials and Reinforced with Micro-Steel Fibers

Rusul Hussein Saeed, Nada Mahdi Fawzi — Sat, 01 Jun 2024 00:00:00 +0000

Reusing and recycling construction debris offers intriguing opportunities for resource conservation and waste disposal site economies. This study investigates the feasibility of using 10 mm crushed brick as coarse aggregate in Modified Reactive Powder Concrete. Natural sand was substituted with crushed brick aggregate by 25, 50, and 100%. Up to 7 and 28 days of age, the tensile strength, absorption, and void content of the mixtures were compared with those of a mixture without coarse aggregate. According to the test results, it is feasible to produce Modified Reactive Powder Concrete (MRPC) with coarse aggregate or shattered bricks. Compared to the reference mixture, the tensile strength of MRPC decreased as the replacement ratio of broken bricks increased. At 7 and 28 days of testing, the tensile strength increased by 10.2 and 12.06 with 25% crushed bricks compared to normal reactive powder concrete. Tensile strength decreased by 7.2% and 6.27% at 7 days and by 9.89% and 8.87% at 28 days when replacing fine sand with crushed brick aggregate at rates of 50% and 100%, respectively. Compared to the reference mixture, the absorption and void content of MRPC with 25, 50, and 100% crushed brick increased by 13.6, 61.2, and 116% and 15.9, 62.1, and 136%, respectively.

Review of Ti3C2Tx MXene Nanofluids: Synthesis, Characterization, and Applications

Sat, 01 Jun 2024 00:00:00 +0000

MXene-based nanofluids are important because of their thermal and rheological properties, influencing scientific and industrial applications. MXenes, made of titanium carbides and nitrides, are investigated for nanofluid enhancement. This review covers MXene nanofluid creation, characterization, and application. To produce nanoscale MXene particles, two-dimensional materials are dissolved and dispersed in a base fluid. The stability and efficacy of MXene nanofluids depend on production methods, such as chemical exfoliation, electrochemical etching, and mechanical delamination. Improved heat transfer coefficients and thermal conductivity from MXene nanofluids help resolve heat transfer, energy efficiency, and thermal control problems. This extensive review also addresses long-term safety and the necessity for standardized characterization methodologies, helping researchers optimize MXene-based nanofluids in many technological fields

Optimal Surface Grinding Regression Model Determination with the SRP Method

Hoang Xuan Thinh, Tran Van Dua — Sat, 01 Jun 2024 00:00:00 +0000

The construction of the regression models used to control machining processes is the objective of many experimental studies. Therefore, the effectiveness of the machining process control largely depends on the regression model’s accuracy. This study was conducted to determine the optimal regression model of surface grinding. Accordingly, eight different surface grinding regression models were constructed, including one model without data transformation and seven models that utilized various data transformations. The seven data transformations employed entailed square root transformation, logarithmic transformation, inverse transformation, exponential transformation, asinh transformation, Box-Cox transformation, and Johnson transformation. The process of determining the optimal model was carried out considering five parameters: R², R²(adj), R²(pred) (predicted R²), MAE (Mean Absolute Error), and MSE (Mean Squared Error). SRP (Simple Ranking Process) was the optimization method followed to identify the best regression model. The Box-Cox transformation was recognized as the most accurate surface grinding regression model.

Investigating the Feasibility of Integrating Vegetation into Solar Chimney Power Plants in the Tamanrasset Region

Sellami Ali, Benlahcene Djaouida — Sat, 01 Jun 2024 00:00:00 +0000

This work investigates integrating vegetation into solar chimney power plants (SCPPs) using numerical simulations of an SCPP prototype in Spain. A 2D axisymmetric computational fluid dynamics model with radiation heat transfer was employed to evaluate the impact of vegetation beneath the solar collector roof on system performance. Different SCPP configurations were analyzed: a standard design, one with a secondary collector roof, and another with secondary and tertiary collector roofs. Results indicate the secondary and tertiary roof configuration exhibited the highest annual electricity generation capacity of 34-80 kW. While introducing vegetation under the collector appears feasible, it is likely to reduce the overall energy output. In summary, simulations suggest that vegetation influences SCPP operation, decreasing power production, while incorporating multiple collector roofs enhances the generation capacity.

Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement

SaiTeja Chopparapu, Gowthami Chopparapu, Divija Vasagiri — Sat, 01 Jun 2024 00:00:00 +0000

In this paper, a novel approach to enhance image quality in real-time using Deep Reinforcement Learning (DRL) is introduced. The adopted method utilizes a Convolutional Neural Network (CNN) within a Q-learning framework to dynamically apply various image enhancement filters. These filters are selected based on their impact on the Structural Similarity Index Measure (SSIM), which serves as the primary metric for evaluating enhancements. The effectiveness of the proposed approach is demonstrated through extensive experiments, where improvements in image quality are measured by employing metrics such as SSIM, Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The results exhibit a significant potential for DRL in automating complex image-processing tasks in various real-world applications.

Drivers of Renewable Energy Use in Saudi Arabia: Evidence from Wavelet Local Multiple Correlation Approach

Chaker Aloui, Hela Ben Hamida, Salem Hathroubi — Sat, 01 Jun 2024 00:00:00 +0000

This study examines the impact of various factors, including oil rents, government effectiveness, economic complexity, and economic growth, on the use of renewable energy in Saudi Arabia. Employing a novel time-localized wavelet multiple regression correlation framework, the unique approach followed reveals significant and positive interconnections between these factors and promotes renewable energy utilization in the long run. However, the aforementioned factors’ short-term correlations are substantially lower and insignificant for some time intervals. Importantly, the analysis performed shows that oil rents and government effectiveness play a dominant role among the other factors. These findings have crucial policy implications, highlighting the need for effective governance and the potential for diversifying energy sources in Saudi Arabia.

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Sat, 01 Jun 2024 00:00:00 +0000

Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care.

Establishing a Budget for Optimal Response Strategies for Risks Categorized into Distinct Groups by using a Mathematical Model and Genetic Algorithm

Hiba Omer Aljorany, Ahmed Mohammed Raoof Mahjoob — Sat, 01 Jun 2024 00:00:00 +0000

Construction projects may be subjected to various risks which must be identified, evaluated, and a suitable response to each risk must be determined. The risk response stage is a crucial and significant phase in risk management that requires particular attention. This paper proposes an effective mathematical model for determining the most suitable strategy and action in dealing with both primary and secondary risk events in different risk categories that may arise in a construction project. It also provides a method for estimating or forecasting the anticipated budget for a risk response plan. Another contribution of this study is the development of an innovative approach that combines binary programming with the genetic algorithm. The efficacy of the proposed methodology was examined by its implementation in a real geothermal project. The results demonstrated that the proposed framework serves as a useful tool to tackle the challenges related to the selection and optimization of risk response strategies, as well as setting an appropriate budget for the risk response plan. The suggested model can help decision-makers to assess the variety of viable risk response actions and strategies and arrive at a more well-informed decision.

Unlocking Success: Exploring the Impact of Human Resource Competence on Job Performance in Pakistan's Engineering Sector

Eimad Hafeez Gogia, Zhen Shao, Ali Raza Akhter — Sat, 01 Jun 2024 00:00:00 +0000

Based on innovation diffusion theory, this paper develops a research model to investigate the influence mechanism of Human Resources (HR) competency on employees' job performance through the mediation effect of HR analytics and the positive moderation effect of Τechnology Αdoption (ΤΑ). A survey was carried out in civil engineering firms in Pakistan, adapting measures with good reliability and validity from different sources, and collecting data through social media platforms and questionnaires from HR professionals. The responses of 297 respondents were collected and structural equation modeling was applied. The results show that there is a positive and significant relationship between HR competency and employee job performance, a significant partial mediation of HR analytics in the relationship between HR competency and employee job performance, and a significant positive moderation of ΤΑ in the mediation of HR analytics in the relationship between HR competency and employee job performance. This study provides an essential contribution to the diffusion of innovation theory, as ΤΑ is slow compared to other countries, and practical guidelines for the fast adoption of technology and HR analytics in the HR departments of civil engineering organizations to enhance talented employees' performance.

A Comprehensive Literature Review on the Elastic Modulus of Rock-filled Concrete

Muhammad Ibrar Ihteshaam, Feng Jin — Sat, 01 Jun 2024 00:00:00 +0000

Rock-Filled Concrete (RFC) is formed by pouring High-performance Self-Compacting Concrete (HSCC) into gaps between pre-placed rocks (that form a strong rock skeleton) in the formwork. An in-depth analysis of RFC's elastic modulus must focus on its static and elastic modulus behavior, strength characteristics, and sustainability aspects. Mesoscopic finite element modeling effectively incorporates pre-positioned rocks, Self-Compacting Concrete (SCC), and the Interfacial Transition Zone (ITZ) to correctly predict performance. RFC is a promising alternative to traditional construction methods, offering combined advantages for masonry and concrete techniques while reducing cement usage. Studies continue to examine the creep properties of reinforced fiber composites, with promising signs of their effectiveness in reducing hydration heat and concrete shrinkage. Subaquatic conservation agents enhance environmental stewardship in wet situations. The elastic modulus of rock-filled concrete increases logarithmically, mostly influenced by the rock-fill composition. It is crucial to study the shape, size, and rock-fill ratio of rocks in RFC that impact its stability, strength, and resistance to static and dynamic loads. Irregularly shaped rocks can enhance interlocking and mechanical properties, while a well-graded mix of sizes improves compaction and uniformity. Studying these properties enables engineers to optimize design and construction for durability and performance.

Cascaded and Separate Channel Estimation based on CNN for RIS-MIMO Systems

Sat, 01 Jun 2024 00:00:00 +0000

With the dramatic increase in mobile users and wireless devices accessing the network, the performance of 5G wireless communication systems is severely challenged. Reconfigurable Intelligent Surface (RIS) has received much attention as one of the promising technologies for 6G due to its ease of deployment, low power consumption, and low price. This study aims to improve accuracy, reliability, and the capacity to estimate channel characteristics between transmitter and receiver. However, this is practically challenging for the following reasons. Due to the lack of active components for baseband signal processing, low-cost passive RIS elements can only reflect incident signals but without the capability to transmit/receive pilot signals for channel estimation as active transceivers in conventional wireless communication systems. This study presents different channel estimation methods for RIS-MIMO systems that use deep learning techniques.

A Genetic Programming-Assisted Analytical Formula for Predicting the Permeability of Pervious Concrete

Ba-Anh Le, Thai Son Vu, Hoang-Quan Nguyen, Viet Hung Vu — Sat, 01 Jun 2024 00:00:00 +0000

This study proposes a new approach to construct predictive formulas for the permeability of Pervious Concrete (PC), which depends on PC mixture and porosity. To achieve this, a dataset of 195 samples collected from different sources was used. In the dataset the permeability is dependent on porosity, aggregate-to-cement ratio (AC), maximum nominal sizes (MS) of coarse aggregate, and water-to-cement or binder ratios (WC). From the dataset and through applying simple regression techniques, several analytical functions based on the Kozeny-Carman model were constructed and evaluated for their effectiveness in implementing independent datasets and similar analytical functions. Furthermore, for the first time, the Genetic Programming-based Symbolic Regression method was adopted to construct hybrid models combined with the Kozeny-Carman analytical model. The equation of the hybrid model ensures both basic physical conditions and efficiency while being simple enough for engineering-level applications.