Engineering, Technology & Applied Science Research

Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Anas Saleh Alkarim, Abdullah S. Al-Malaise Al-Ghamdi, Mahmoud Ragab — Tue, 02 Apr 2024 00:00:00 +0000

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.

Utilization of Multi-Mission Satellite Altimetry for Wave Energy with Site Suitability Analysis using the Analytic Hierarchy Process

Mat Nizam Uti, Ami Hassan Md Din, Norhakim Yusof, Syarif Abdul Asaad Jairin — Tue, 02 Apr 2024 00:00:00 +0000

Space technology advancements have enabled the acquisition of marine data that support the research on wave energy as an alternative to reduce fossil fuel dependency and mitigate climate change. Malaysia's ocean renewable energy potential lacks attention from local authorities due to insufficient in-situ data, posing challenges in investigating ocean characteristics, such as wave heights. This study investigated Malaysia's wave energy potential using extensive significant wave height data from multiple altimetry missions. The former assessed site suitability using the Analytical Hierarchy Process (AHP) multicriteria analysis, incorporating marine constraints, namely socioeconomic, physical, and environmental factors. The multicriteria findings were integrated into a Geographical Information System (GIS) to improve the site suitability analysis and generate a localized suitability index for wave energy. Validation of satellite altimeter data with in-situ measurements showed a strong correlation and low RMSE. AHP analysis indicated good consistency in the criteria analysis, with a consistency ratio of 0.045, which falls below the limit of 0.1. The coastal and offshore regions of the Malaysian seas are suitable for harnessing wave energy with energy ranges up to 4.21 kW/m. Therefore, this study provides valuable information to stakeholders and the government to increase their interest in wave energy.

Investigating the Response Variability of Statically Determined Sandwich Beams considering two Random Fields of Elastic Modulus

Dao Ngoc Tien, Tran The Hiep, Hoang Van Thanh, Nguyen Van Thuan — Tue, 02 Apr 2024 00:00:00 +0000

In this paper, the displacement variation in sandwich beams is determined by employing a semi-analytical approach. The classical displacement is calculated by integration using Mohr’s equation, although the integration is complicated due to the inclusion of random fields in the inertial moment term. Using the trapezoidal rule to compute these integrals, the random fields are discretized into random variables at the nodal point of the beam segments. Thus, the expected displacement, standard deviation, and coefficient of variation can be computed. To validate the results, the random fields are simulated using a previously described spectral method. The results of numerical examples were compared with the semi-analytical method and the Monte Carlo simulation demonstrating the high accuracy of the proposed method. The results also illustrate the influence of the parameters of the random fields of elastic modulus on the variability of displacement.

A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete

Ilham Yahya Amir, Abdinasir Mohamed Yusuf, Ikenna D. Uwanuakwa — Tue, 02 Apr 2024 00:00:00 +0000

The prediction of the asphalt dynamic modulus (E*), which measures the material's ability to withstand changes in shape or structure, is important. Previous studies indicated that the well-known Witczak 1-40D model for E* is outperformed by machine learning models. Additionally, the application of machine learning algorithms requires manual fine-tuning of their hyperparameters. In this study, the artificial Hummingbird and Harris Hawks optimization algorithms were employed in the automatic calibration of the Random Forest and Gradient Boost algorithms' hyperparameters for modeling E* using the Witczak 1-40D model and additional parameters. In addition, the model was interpreted using the Shapley value and permutation feature importance. The results indicate that the optimized artificial hummingbird algorithm model performed better, with R² reaching 0.97. The interpretability of the model suggests that the binder parameters exhibited the highest effect on the variance of E*.

Extraction of Solar Module Parameters using a Novel Optimization Technique

Hossam E. Ahmed, Yehya I. Mesalam, Shaaban M. Shaaban — Tue, 02 Apr 2024 00:00:00 +0000

The parameters of a Photovoltaic (PV) model are pivotal in gauging its efficiency under varying sunlight irradiances, temperatures, and different load scenarios. Determining these PV model parameters poses a complex non-linear optimization challenge. This study is based on a new metaheuristic optimization algorithm called the Pelican Optimization Algorithm (POA) to discern the unknown parameters of the PV model. The suggested POA algorithm underwent testing using a monocrystalline panel, encompassing its single-diode configuration. The objective function is designed to minimize the root of the mean squared errors between the predicted and actual current values, adhering to specific parameter constraints. Various statistical error metrics were utilized to emphasize the performance of the proposed algorithm. A comparative analysis with other well-established algorithms was conducted, indicating that POA stands out as highly competitive since it showcases superior efficiency in parameter identification compared to its counterparts.

An FPGA Accelerator for Real Time Hyperspectral Images Compression based on JPEG2000 Standard

Tue, 02 Apr 2024 00:00:00 +0000

Lossless hyperspectral images have the advantage of reducing the data size, hence saving on storage and transmission costs. This study presents a dynamic pipeline hardware design for compressing and decompressing images using the Joint Photographic Experts Group-Lossless (JPEG2000) algorithm. The proposed architecture was specifically tailored for implementation on a Field Programmable Gate Array (FPGA) to accomplish efficient image processing. The introduction of a pipeline pause mechanism effectively resolves the issue of coding errors deriving from parameter modifications. Bit-plane coding was employed to enhance the efficacy of image coding calculations, leading to a reduction of parameter update delays. However, the context and decision creation procedure were streamlined, resulting in a significant enhancement in throughput. A hardware module utilizing the parallel block compression architecture was developed for JPEG2000 compression/decompression, allowing for configurable block size and bringing about enhanced image, compression/decompression, throughput, and reduced times. Verification results were obtained by implementing the proposed JPEG 2000 compression on a Zynq-7000 system-on-chip. The purpose of this system was to enable on-board satellite processing of hyperspectral image cubes with a specific focus on achieving lossless compression. The proposed architecture outperformed previous approaches by using fewer resources and achieving a higher compression ratio and clock frequency.

Transformer Encoder with Protein Language Model for Protein Secondary Structure Prediction

Ammar Kazm, Aida Ali, Haslina Hashim — Tue, 02 Apr 2024 00:00:00 +0000

In bioinformatics, protein secondary structure prediction plays a significant role in understanding protein function and interactions. This study presents the TE_SS approach, which uses a transformer encoder-based model and the Ankh protein language model to predict protein secondary structures. The research focuses on the prediction of nine classes of structures, according to the Dictionary of Secondary Structure of Proteins (DSSP) version 4. The model's performance was rigorously evaluated using various datasets. Additionally, this study compares the model with the state-of-the-art methods in the prediction of eight structure classes. The findings reveal that TE_SS excels in nine- and three-class structure predictions while also showing remarkable proficiency in the eight-class category. This is underscored by its performance in Qs and SOV evaluation metrics, demonstrating its capability to discern complex protein sequence patterns. This advancement provides a significant tool for protein structure analysis, thereby enriching the field of bioinformatics.

Building Information Modeling (BIM) for Construction Project Schedule Management: A Review

Tuan Anh Nguyen, Tu Anh Nguyen, The Van Tran — Tue, 02 Apr 2024 00:00:00 +0000

Nowadays, the implementation and application of the BIM process in construction project management is a pressing need. This aligns with the global development trends in the construction sector and project information management in general. Numerous scholars and companies are actively engaged in learning, understanding, and investigating various aspects of BIM to stay up-to-date and meet the inevitable developmental requirements. This study focuses on the role and application of BIM, intending to identify limitations that hinder its fulfillment of expectations in project schedule management. In addition, it explores studies that show how other countries have effectively employed BIM in project management and progress tracking throughout the project lifecycle. The study aims to address three main objectives: (a) comprehensively examine and provide evidence related to the concept of BIM in project schedule management, (b) present the benefits of applying BIM in comparison to traditional methods in project management and operation, and (c) identify limitations stemming from various factors that may pose challenges in the application of BIM in project schedule management.

Efficient Energy Management with Emphasis on EV Charging/Discharging Strategy

Habib Kraiem, Wiem Gadri, Aymen Flah — Tue, 02 Apr 2024 00:00:00 +0000

Leveraging the Vehicle-to-Grid (V2G) concept, this research explores how a decentralized energy reserve from hybrid electric vehicles can enhance the power system, particularly in large-scale implementations. The study introduces a V2G solution designed for effective microgrid frequency control over a full day. Targeting a scenario with minimal usage, typically in spring or fall, the microgrid is scaled to represent a community of 2000 homes. This is exemplified by integrating 500 Electric Vehicles (EVs) based on a 1:4 vehicle-to-household ratio, reflecting a plausible future scenario. The research conducts a comprehensive examination of the microgrid's voltage, current, and active power. By synchronizing the management of diesel and Renewable Energy Source (RES) generation, power transactions, and EV generation, the microgrid's frequency is effectively regulated through V2G devices adjusting load demand. The implemented V2G-enriched microgrid demonstrates improved energy management and mitigates the inconsistencies and fluctuations inherent in RES power generation, showing notable performance enhancements. In various operational contexts, system parameter fluctuations have been analyzed, revealing that deviations are maintained below a 5% threshold.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Nesamony Sajitha, Srinivasan Prasanna Priya — Tue, 02 Apr 2024 00:00:00 +0000

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Thermal and Mechanical Properties Enhancement of Cement Mortar using Phosphogypsum Waste: Experimental and Modeling Study

Ehab M. Ragab, Tarek M. Awwad, Nidhal Becheikh — Tue, 02 Apr 2024 00:00:00 +0000

This research presents an in-depth investigation into the application of phosphogypsum (PG), a by-product of phosphate fertilizer plants and chemical industries, as a replacement material for cement in mortar, with a focus on enhancing its thermal and mechanical properties. The influence of PG as a partial replacement for cement on the compressive strength of mortar after 3, 7, and 28 days is investigated. Utilizing the Box-Behnken design within Response Surface Methodology, this study analyzed factors, such as sulfuric acid concentration, washing time, calcination temperature, and PG to cement ratio. Results indicate that optimal PG levels enhance mortar strength, particularly at 28 days, through sustained ettringite formation and microstructure optimization. Sulfuric acid concentration and calcination temperature were identified as the most significant elements influencing compressive strength, with the latter improving PG quality and reactivity. A PG to cement ratio up to 10% was found beneficial, while washing time had a negligible effect. The research highlights a critical synergy between the sulfuric acid concentration applied during the purification of PG and the calcination temperature. A significant improvement of 21% in compressive strength was achieved, underscoring the combined effect of chemical and thermal treatment on PG's efficacy in mortar. The increased sulfuric acid concentration is presumed to purify the PG by removing impurities, thus improving its reactivity. Concurrently, calcination alters the PG's crystalline structure and diminishes its organic composition. This interdependent optimization is instrumental in enhancing the structural integrity of PG-modified mortar. The potential for raw PG to be used as an insulating material is more pronounced at higher replacement rates (10%), while sulphuric acid treated PG (SCPG) and heat treated PG (HTPG) seem to be unable to provide a clear insulative advantage.

Rational Pile Design using Computer-based Program Coding in Matlab: A Case Study

Cao Van Hoa — Tue, 02 Apr 2024 00:00:00 +0000

Scientific approaches to pile design have made significant progress in recent years. However, despite these advancements, estimating the axial resistance and settlement of piles still heavily relies on empirical correlations. The design of resource-efficient and environmentally friendly piles is a pressing need. Yet, there is no explicit theoretical or practical experience to guide pile design rationally. Typically, determining a pile's resistance and settlement is treated as separate problems without considering the interactions between the pile and the soil. Additionally, soil data are inconsistent due to the heterogeneous and isotropic character of the soil in the half-space under the foundation. In this study, the modified Fellenius Unified method was coded in Matlab and applied to analyze pile behavior, considering the resistance and settlement of each pile, as well as interactions between piles and the soil simultaneously. The results showed that this approach is promising for practical applications. Moreover, its implementation in the evaluation of pile design for an apartment project in Binh Duong, Vietnam, suggests that the pile's length can be reduced even further than it currently is.

An IoT Smart System for Cold Supply Chain Storage and Transportation Μanagement

Tue, 02 Apr 2024 00:00:00 +0000

Cold supply chains are becoming more and more attractive due to the high demand induced by increased consumption. To fulfill standards and customers’ requirements regarding the conditions under which cold supply chain products (mainly foods and pharmaceuticals) are stored (in warehouses) and transported to the end-users, tracking those conditions is a necessity. To ensure a high level of visibility, fostering emerging technologies can improve the quality of service in supply chains in terms of delivery time, cost, and quality. In this paper, a global framework for monitoring the conditions of storage and transportation of cold products across the whole supply chain is designed and implemented practically. The proposed solution is built around low-cost and low-energy consumption devices such as sensors and microcontrollers which are connected to cloud storage to allow a high level of visibility in the supply chain allowing all parties, including the end-consumers, to follow the products during their transfer, providing a conceptual framework that monitors the performance on a real-time basis and enhances decision making. A prototype using an embedded temperature/humidity sensor, a tiny microcontroller equipped with a Wi-Fi connectivity device, and a mobile 4G/5G network is designed and implemented. The proposed system is connected to a cloud-storage platform continuously accessible by the main parties of the cold supply chain including the provider, the transporter, and the end-consumer. The proposed framework may be handled as a smart contract during which any party can assume its responsibility for the assurance of the best conditions of the supply chain operation. A small-scale real-life scenario conducted in Jeddah City, Saudi Arabia is introduced to show the feasibility of the proposed framework.

Comparative Evaluation of Different Hybrid Intelligent Load-Frequency Controllers for Interconnected Electric Power Grids

Diem-Vuong Doan, Ngoc-Khoat Nguyen — Tue, 02 Apr 2024 00:00:00 +0000

Network frequency is considered to be one of the most crucial parameters that strongly affect the stability and economic achievements of interconnected electric power grids. System frequency usually fluctuates and deviates from the nominal values due to random and continuous load changes over time, affecting the electric equipment to significantly decrease efficiency and increase instability. A Load-Frequency Control (LFC) strategy has been proposed to solve this problem. This study compared several different control strategies, namely Fuzzy Particle Swarm Optimization (Fuzzy-PSO), Proportional Integral Derivative (PID), Fuzzy-PID, Fuzzy- Proportional Integral (PI), PSO-PI, and FPID to investigate the effectiveness of intelligent hybrid LFC controllers. The above controllers were simulated on a three-area interconnected power network with the participation of renewable energy sources. Taking into account different load cases, the Fuzzy-PSO-PID controller obtained frequency deviations in the range of 0.0015 to 0.002 Hz. The settling time was about 10 s to reach zero frequency error in each area. With the above controller quality parameters, the Fuzzy-PSO-PID controller provided better quality than the other controllers. A comparative numerical simulation in MATLAB/Simulink for various load change scenarios revealed the effectiveness of hybrid smart controllers, such as the Fuzzy-PID-PSO, outperforming the traditional ones.

Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study

Ardalan F. Khalil, Sarkawt Rostam — Tue, 02 Apr 2024 00:00:00 +0000

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.

A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments

Harman Yousif Ibrahim Khalid, Najla Badie Ibrahim Aldabagh — Tue, 02 Apr 2024 00:00:00 +0000

Software Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control over the whole network and the availability of processing and storing capabilities, makes the deployment of artificial intelligence-based IDS in controllers a hot topic in the research community to resolve security issues. In order to develop effective AI-based IDSs in an SDN environment, there must be a high-quality dataset for training the model to offer effective and accurate attack prediction. There are some intrusion detection datasets used by researchers, but those datasets are either outdated or incompatible with the SDN environment. In this survey, an overview of the published work was conducted using the InSDN dataset from 2020 to 2023. Also, research challenges and future work for further research on IDS issues when deployed in an SDN environment are discussed, particularly when employing machine learning and deep learning models. Moreover, possible solutions for each issue are provided to help the researchers carry out and develop new methods of secure SDN.

A System Dynamics Approach to Feedback Processes in Project Scheduling

Babatunde Omoniyi Odedairo, Ali Alarjani — Tue, 02 Apr 2024 00:00:00 +0000

Projects, as catalysts for proactive transformation, offer a temporary and adaptable framework that effectively handles complexities (or uncertainties) within a competitive corporate landscape. Hence, the use of an effective project management framework, such as Dynamic Project Scheduling (DPS), is a method to handle intricacies in order to accomplish organizational objectives. DPS refers to a triangle interaction involving baseline scheduling, schedule risk analysis, and project control while supporting schedule adjustment in response to changes and uncertainties. However, there is a lack of information regarding studies that have investigated the feedback mechanisms among DPS components. This study was designed to examine the counterintuitive relationships between these components using system dynamics. The quantities within the DPS system were identified and defined. A casual loop diagram was used to illustrate the interactions among these quantities. Subsequently, a Stock and Flow Diagram (SFD) was created to identify the inputs, states, and flow mechanisms within the DPS. Using the SFD, a system dynamics expression was generated which was then employed to compute the rate of change of the Budgeted Cost of Work Remaining (BCWR) for two projects at different time intervals. The results properly indicated the period of idleness during project execution. The use of BCWR rather than schedule variance provides a more effective visual representation for evaluating performance and tracking progress. The BCWR and planned value exhibit contrasting trends, highlighting the importance of earned value quantities in project control. The use of system dynamics in project management can enhance the planning and scheduling phase, allow project managers to monitor pertinent performance measures, and optimize project outcomes through informed decisions.

Deflection and Elastic Modulus Assessment of Subgrade in Flexible Pavement mixed with Waste Tire Scrap Material

Sujoy Sarkar, Sumit Kumar Biswas, Saibal Chakraborty — Tue, 02 Apr 2024 00:00:00 +0000

This study aims to assess the deflection and elastic modulus (E_s) of subgrade in flexible pavements, focusing on a comparative analysis between pavements with clayey soil subgrade and subgrade modified with tire scrap. The research utilized Falling Weight Deflectometer (FWD) for measuring subgrade deflection, essential in evaluating pavement performance. The FWD applied a dynamic load to the pavement, with deflection measurements processed using the KGP-BACK software to calculate the E_s of the pavement subgrade. This approach included assessing the Lower Layer Index (LLI) and E_s of the subgrade. Findings revealed a notable reduction of 37.5% in deflection and 2.68 times increase in E_s for the tire scrap modified subgrade pavement compared to the standard clayey soil subgrade pavement. These results demonstrate significant enhancements in pavement structure, underlining the potential of recycled materials in sustainable civil engineering practices.

The Efficiency of Surface Impedance Technique in the Transverse Wave Approach for the EM-Modeling of Fractal-Like Tree Structure used in 5G Applications

Mohamed Ayari, Saleh Altowaijri — Tue, 02 Apr 2024 00:00:00 +0000

Fractal antenna technology is a promising approach for 5G applications because its complex nature offers optimization potential in terms of time and space trade-offs. However, the computational effort required to analyze such antennas is significant. This paper investigates the Advanced Transverse Wave Approach (ATWA), which utilizes the surface impedance technique to improve simulation efficiency. This study introduces and analyzes a fractal-like 5G tree structure, displaying improved computational accuracy and efficacy, as well as peak memory utilization compared to current works. The proposed approach demonstrates significant effectiveness in enhancing the performance of complex fractal antennas for 5G technology and shows promise for integration with cloud, fog, and edge computing environments. This integration could potentially optimize data processing and network efficiency in these advanced computing landscapes.

Predicting the Number of Software Faults using Deep Learning

Wahaj Alkaberi, Fatmah Assiri — Tue, 02 Apr 2024 00:00:00 +0000

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

Sensorless Maximum Power Point Control for Single-stage Grid Connected PV Systems

Mokhtar Abbassi, Abdelkarim Aouiti, Faouzi Bacha — Tue, 02 Apr 2024 00:00:00 +0000

In this paper, a novel approach for implementing the maximum power point that could be generated from a photovoltaic (PV) panel while eliminating the need for current sensors through the application of the Hill Climbing algorithm is proposed. The active power generated by the PV panel is injected into the grid via a three-phase inverter using voltage-oriented voltage control with Spatial Vector Modulation (SVM). The developed strategy ensures minimal ripples for both active and reactive power and produces a sinusoidal alternating current waveform, even under varying lighting conditions. A comprehensive description of the adopted control strategy is provided and validated through numerical simulations conducted in MATLAB/Simulink environment. Furthermore, the performance of the proposed method is assessed by analyzing the simulation results. In an attempt to validate the effectiveness of the proposed approach, an implementation of the inverter control was conducted with the DSpace 1104 board, and the results underscored the feasibility and effectiveness of the employed approach for grid-connected PV systems.

Residual Attention Augmentation Graph Neural Network for Improved Node Classification

Tue, 02 Apr 2024 00:00:00 +0000

Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.

Production of Thermoplastic Composites reinforced with Posidonia Oceanica Fibers

Faouzi Slimani, Ines Ghanmi, Samir Ghanmi, Mohamed Guedri — Tue, 02 Apr 2024 00:00:00 +0000

This study investigates the development and characterization of a new biocomposite and biodegradable material based on natural fibers. This new biocomposite is composed of commercially available biodegradable polylactic acid (PLA) as a matrix and Posidonia Oceanica (PO) fibers collected from the coasts of Tunisia as reinforcement. This new material is produced by heating and pressing the two components in a special device. The use of PO, or sea balls, will allow exploiting one of the marine residues abundant on Tunisian beaches, instead of exploited industrially, and to preserve the beaches from debris given the impact of tourist activity in the Tunisian economy. The PLA/PO coupling allowed obtaining a biocomposite with promising mechanical properties. The improvement in maximum stress and strain after the addition of PO is one of the highlights of the results of this work.

Study of the Injection of Secondary Air into the Intake Manifold of the Gas Turbine to Avoid the Compressor Surging Phenomenon

Tue, 02 Apr 2024 00:00:00 +0000

This paper presents part of the research on avoiding or reducing the surging effects that appear in the axial compressor intake manifold of a gas turbine. This research has led to an original solution validated by numerical simulations and experimental investigations. The increased amount of air suddenly required in the transient regime of the gas turbine is introduced into the intake manifold through slits arranged perpendicular to the direction of flow, on an aerodynamic profile at a certain angle to it and a certain distance from the minimum transversal section. The slits are arranged on the opposite sides of the gallery and connect with a transverse channel of the airfoil, in which there is air under pressure, from which the introduction of additional air is ordered. The numerical and experimental results extended to the influence of many geometric and mechanical parameters, proving that the proposed solution is as effective as possible compared to the classic ejector solution.

Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language

Tue, 02 Apr 2024 00:00:00 +0000

This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes.

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Tue, 02 Apr 2024 00:00:00 +0000

Software-Defined Networks (SDNs) provide a contemporary approach to networking technology, offering a versatile and dynamically efficient network architecture for enhanced surveillance and performance. However, SDN architectures may encounter flow conflicts. These conflicts arise when modifications are made to specific flow properties, such as priority, match field, and action. Despite the existence of recommended solutions, the process of resolving conflicts in SDN continues to encounter difficulties. This study proposes an Extremely Fast Decision Tree (EFDT) classification technique to detect and categorize conflicts inside the flow table. The novelty of this method is based on the development of an accurate and effective machine-learning technique implemented on the Ryu controller plane and validated using the Mininet simulator. The effectiveness and efficiency of the proposed method were evaluated using various indicators, demonstrating superior performance in recognizing and categorizing conflict flow types in all flow sizes ranging from 10,000 to 100,000.

Hydraulic Jump Characteristics Downstream of a Compound Weir consisting of Two Rectangles with a below Semicircular Gate

Majed Alsaydalani — Tue, 02 Apr 2024 00:00:00 +0000

Weirs are often used in laboratories, industries, and irrigation channels to measure discharge. The discharge capacity of a structure is vital for its safety and plays an important role in the combined gate-weir flow, which is a complicated phenomenon in hydropower. This study carried out experiments on a combined hydraulic structure, which included a compound sharp-crested weir made up of two rectangles along with an inverted semicircular sharp gate. Installed on a straight channel, this structure served as a control instrument. The study aimed to investigate the downstream hydraulic jump characteristics of this combined structure, specifically, the sequent depth ratio (y₂/y₁), the hydraulic jump height ratio (H_j/y₁), the energy loss ratio through the jump (E_L/E_u), and the jump length ratio (L_j/y₁). The width of the upper rectangle on the weir was set at 20 cm. The width of the lower rectangle (W₂) was set at 5, 7, and 9 cm, while its depths (z) were fixed at 6, 9, and 11 cm. The gate's diameters varied between 8, 12, and 15 cm. These measurements were alternated with varying initial Froude numbers (F_n1) ranging between 1.32 and 1.5. The results showed that the dimensions of both the weir and the gate influenced the hydraulic jump characteristics. Empirical formulas were developed to predict y₂/y₁, H_j/y₁, E_L/E_u, and L_j/y₁ based on the differing dimensions of the combined structure. The findings and analysis of this study are limited to the range of data that were tested.

Influence of Supplementary Oxide Layer on Solar Cell Performance

Tue, 02 Apr 2024 00:00:00 +0000

The increasing use of solar energy for electricity production has led to a directly proportional growth in the production of solar cells. Photovoltaic (PV) performance of silicon solar cells can be improved by using more efficient technologies, optimizing processes, and changing behavior in order to reduce operational costs and greenhouse gas emissions. In order to propose solutions for commercial solar cell production with better performance, this article presents an experimental assessment on Supplementary Oxide Layers (SOLs) that are deposited on the surface of a solar cell absorber layer. SOLs are typically used to improve the performance of solar cells by passivating surface defects, reducing recombination losses, and improving the electrical contact between the absorber layer and the metal electrodes. The obtained solar cells are tested under natural sunlight conditions, following a variable dynamic electronic charge profile. The experimental results along with the corresponding I-V and P-V curves, are assessed according to the process parameters, the lighting parameters, and the dynamic load scenario. SOLs have been shown to improve the Power Conversion Efficiency (PCE) of solar cells considerably. The proposed method for increasing the energy efficiency of solar cells can be applied to any type of commercial solar cell and it is easy to implement at the industrial or research level by controlling process parameters. The integration of the whole process, i.e. development of precursor solutions, deposition of thin films, and testing of electrical properties is another contribution of the current study, along with its interdisciplinary character, which involves materials science, electronics, and software programming.

Evaluation of the Effect of Access Point Density on the Safety of Primary Roads. A Case Study

Ashar Ahmed, Md. Kamrul Islam, Ahmad Farhan Mohd Sadullah, Uneb Gazder — Tue, 02 Apr 2024 00:00:00 +0000

This paper investigates the nuanced exploration of access point density's influence on accident frequency, specifically focusing on primary roads in Malaysia. The analysis is multifaceted, considering geographic variations, land use patterns, and the density of access points per km. This investigation scrutinizes the direct relationship between the number of access points per km and the corresponding accident frequency. A critical threshold value for access point density is identified, revealing its consequential impact on average accident frequency. The observed direct proportionality between access point density and accident frequency is a pivotal discovery. Moreover, the role of land use parameters emerges as a key determinant in understanding how accident frequency varies with access point density, particularly on specific road types. This establishes eight access points per km as a potential threshold value for ensuring optimal access point density within a road network. In summary, this study provides insights into the intricate dynamics of access point density and its consequential impact on road safety. The identified threshold value and the recognition of the role of land use contribute valuable perspectives for informed decision-making in road network planning and management.

Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning

Asmaa Alayed, Rehab Alidrisi, Ekram Feras, Shahad Aboukozzana, Alaa Alomayri — Tue, 02 Apr 2024 00:00:00 +0000

The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning techniques, a detection model was trained to visually inspect fire extinguishers and identify defects. Fire extinguisher images were collected, annotated, and augmented to create a dataset of 7,633 images with 16,092 labeled instances. Then, experiments were carried out using YOLOv5, YOLOv7, YOLOv8, and RT-DETR. Pre-trained models were used for transfer learning. A comparative analysis was performed to evaluate these models in terms of accuracy, speed, and model size. The results of YOLOv5n, YOLOv7, YOLOv8n, YOLOv8m, and RT-DETR indicated satisfactory accuracy, ranging between 83.1% and 87.2%. YOLOv8n was chosen as the most suitable due to its fastest inference time of 2.7 ms, its highest mAP0.5 of 87.2%, and its compact model size, making it ideal for real-time mobile applications.

Improving Electric Vehicle Autonomy in the Smart City Concept

Ahmed Saad Eddine Souissi, Habib Kraiem, Aymen Flah, Amjad El Madani — Tue, 02 Apr 2024 00:00:00 +0000

Organizing automobiles in a city is challenging due to the sensitive data that need to be disclosed. Information that can be utilized to identify a car and provide some useful characteristics about it is among the large amount of data that can be collected from an automobile. This operation will be easier if the vehicles are placed on a specific platform based on the smart city concept. Even if sensors and cameras are installed around the roads and the city, having the vehicle information will be more useful. The current study tries to demonstrate how it is feasible to improve vehicle autonomy by initially enhancing the vehicle's energetic performance, based on the smart city idea. Intelligent control topology serves as the foundation for the exposed energy management protocol. The suggested concept is created and the associated results are displayed using the Matlab Simulink platform.

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

Sahbi Marrouchi, Moez ben Hessine, Souad Chebbi — Tue, 02 Apr 2024 00:00:00 +0000

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.

Influence of Crumb Rubber and Recycled Steel Fibers on Hybrid Layered Reinforced Concrete Columns under Compression Load

Hasan Alasmari — Tue, 02 Apr 2024 00:00:00 +0000

Waste tires pose an environmental issue that causes health problems when discarded by either land burial or burning. The current study investigated the properties and characteristics of different-length hybrid layered columns of Recycled Steel Fibers (RSF) at a fixed ratio (0.6%) of the volume fraction with utilized fixed content (15%) of Crumb Rubber (CR). Nine square column specimens were prepared and tested under axial compression load to reveal the effect of RSF and CR content on hybrid layered reinforcement columns. The results revealed that as the RSF content increased concrete’s properties were enhanced. However, the inclusion of CR at the top layer resulted in performance reduction. Additionally, the layered structure with CR and RSF has higher characteristic properties, including higher load capacity and displacement. Moreover, adding 0.6% RSF to both layers and CR at the top led to a 120% increase in the toughness of the concrete loading capacity. This was followed by a reasonable improvement in displacement and ductility.

Effect of Ground Granulated Blast Slag and Temperature Curing on the Strength of Fly Ash-based Geopolymer Concrete

Anil Kumar, . Rajkishor, Niraj Kumar, Anil Kumar Chhotu, Bhushan Kumar — Tue, 02 Apr 2024 00:00:00 +0000

Concrete is used most extensively after water to meet construction requirements. Since the population is increasing day by day, the demand for concrete will always increase, hence, the demand for cement will also increase. The production of cement requires a lot of energy and emits greenhouse gases into the environment. Therefore, an alternative material for cement concrete is required. Geopolymer concrete (GPC) is an alternative to cement made of aluminosilicate materials such as fly ash, Ground Granulated Blast Slag (GGBS), silica fume, metakaolin, etc. If these materials are activated with an alkaline activator, then a bond that is responsible for the strength develops. GPC made with fly ash needs temperature curing to develop its strength, which limits its use on a large scale. In this study, a mix ratio of GPC equivalent to conventional M20 concrete was obtained at ambient curing conditions. The effect of temperature curing was also studied. GPC was prepared in three different mixes. In each mix, the binder content was changed by varying the fly ash and GGBS content. Two sets of cube, beam, and cylindrical samples were prepared from each mixture. One set was cured at ambient temperatures and the other at increased temperatures. The temperature-cured specimens provided higher strength than the ambient-cured. If a strength equivalent to conventional M20 concrete is required for ambient curing, then the mix should be 70% fly ash and 30% GGBS, and the ratio of binder, fine aggregate, and coarse aggregate should be 1:1.5:3.

Experimental Study of the Flame Retardancy of PMMA-Graphene Composite Materials

Tue, 02 Apr 2024 00:00:00 +0000

In this paper, Polymethyl methacrylate (PMMA)-graphene nano-composites were prepared and tested with the use of a cone calorimeter. Graphene was added to PMMA in limited weight percentages to improve the flame retardancy of PMMA. Two samples of PMMA-graphene, namely 1 and 3 wt%, were investigated. The combustion properties of the tested samples of PMMA-graphene composites, mass loss rate, heat release rate, and time to ignition were measured and calculated. It was found that the peak heat release rate of PMMA-graphene composites reduced by 17% when 3 wt% graphene was added to pure PMMA. Adding graphene to PMMA improves the thermal stability of PMMA by reducing the time of ignition. Also, the presence of graphene enhanced the formation of a continuous carbonized layer at the surface of the burned PMMA.

Interleaved Bidirectional DC-DC Converter for Renewable Energy Application based on a Multiple Storage System

Yehya I. Mesalam, Shaaban Awdallh, Hajer Gaied, Aymen Flah — Tue, 02 Apr 2024 00:00:00 +0000

Due to its fewer components, the DC-DC three-phase converter has a simpler design and could be less expensive. However, it can present challenges in terms of precise voltage regulation and current balancing, due to the limited number of switching phases. On the other hand, the three-phase converter offers more precise voltage regulation and improved current balance owing to its higher number of phases. Although this results in increased design complexity and potentially higher cost, it allows for a more uniform distribution of current load among MOSFETs. The particular needs of the application, acceptable trade-offs between complexity, cost, and performance, as well as the requirement for precise voltage regulation and ideal current balancing, can determine which option is the best. This work investigates a three-phase interlaced DC converter with a parallel MOSFET. A two-way DC-DC converter was used to assess PWM when charging and discharging a battery. The results demonstrate a great DC voltage gain without a very high cycle load.

Statistical Analysis of the Factors influencing the In Situ U-Value of Walls

Smita Rashmi, Ravish Kumar — Tue, 02 Apr 2024 00:00:00 +0000

Building thermal performance testing requires in situ measurement techniques that are well supported and validated by simulation with statistics to improve the accuracy of the results. Local on-site performance of building components is different from the theoretical one, influenced by factors affecting the building's thermal conditions. The current paper reviews the factors influencing the measured U-value results in the heat flux method based on quantitative findings of other studies through regression and correlation statistics. The findings regarding the current status of knowledge are limited to in situ methods without detailed insights of response time, sensitivity analysis, and thermal boundary conditions in the local context. Regression analysis between wall characteristics, time duration, temperature difference, and the measured U-value shows a very strong and statistically significant impact of these variables on the accuracy of the measured U-value of low transmittance walls. The R2 value indicates that three variables can collectively explain 91% of the variance in the measured U-value. There is a linear correlation between the wall characteristics and the measured U-value and a non-linear correlation between the time duration, temperature difference, and the measured U-value. Future work will focus on developing a measurement framework that considers time-dependent variables, dynamic weather, and uncertainty with high accuracy for different boundary conditions.

Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection

Erhan Yilmaz, Ozgu Can — Tue, 02 Apr 2024 00:00:00 +0000

Insider threats pose a significant risk to organizations, necessitating robust detection mechanisms to safeguard against potential damage. Traditional methods struggle to detect insider threats operating within authorized access. Therefore, the use of Artificial Intelligence (AI) techniques is essential. This study aimed to provide valuable insights for insider threat research by synthesizing advanced AI methodologies that offer promising avenues to enhance organizational cybersecurity defenses. For this purpose, this paper explores the intersection of AI and insider threat detection by acknowledging organizations' challenges in identifying and preventing malicious activities by insiders. In this context, the limitations of traditional methods are recognized, and AI techniques, including user behavior analytics, Natural Language Processing (NLP), Large Language Models (LLMs), and Graph-based approaches, are investigated as potential solutions to provide more effective detection mechanisms. For this purpose, this paper addresses challenges such as the scarcity of insider threat datasets, privacy concerns, and the evolving nature of employee behavior. This study contributes to the field by investigating the feasibility of AI techniques to detect insider threats and presents feasible approaches to strengthening organizational cybersecurity defenses against them. In addition, the paper outlines future research directions in the field by focusing on the importance of multimodal data analysis, human-centric approaches, privacy-preserving techniques, and explainable AI.

Enhancing Cognitive Radio WSN Communication through Cluster Head Selection Technique

Shraddha Panbude, Prachi Deshpande, Brijesh Iyer, A. B. Nandgaonkar — Tue, 02 Apr 2024 00:00:00 +0000

The demand for frequency spectrum is increasing rapidly with the wide growth of wireless communications. Spectrum sensing issues present in Cognitive Radio Sensor Networks (CRSN) are detected dynamically using spectral sensing techniques, which also help to utilize frequency bands more effectively. The study proposes a novel Cosine Sand Cat Optimization (CSCO) protocol to address spectral sensing problems by selecting the optimal Cluster Head (CH) in a CRSN. The CRSN is simulated, and spectral allocation is performed using LeNet to extract signal components. Then, Primary User (PU) aware optimal CH selection is performed using the proposed CSCO by taking account of multi-objective fitness parameters. Finally, data communication is performed between nodes after CH selection using the CSCO protocol. The simulation results of CSCO were validated to determine its superiority concerning Secondary User (SU) density, and it attained residual energy, network lifetime, Packet Delivery Ratio (PDR), normalized throughput, and delay of 69.457 J, 77, 75.89%, 74.473, and 4.782ms, respectively.

Investigation of Maximum Mid-Span Displacement and Reaction Forces in Fiber-reinforced Concrete Beams subjected to Impact

Zena Mahmoud, Muhannad Aldosary, Abdulkader Ismail Al-Hadithi — Tue, 02 Apr 2024 00:00:00 +0000

Self-Compacting Fiber-Reinforced Concrete (SCFRC) is a specialized type of concrete that combines the properties of Self-Compacting Concrete (SCC) with the addition of fibers for reinforcement. SCFRC is designed to have excellent flowability and self-leveling characteristics while providing enhanced tensile strength, ductility, and crack resistance. This paper presents a discussion on the topic of SCFRC and the impact load behavior of SCFRC beams reinforced with Waste Plastic Fibers (WPFs). A comparison with reinforced concrete beams without fibers is also conducted. This study aims to predict the maximum mid-span displacement and the maximum reaction force of the fiber concrete beams under impact load. Twelve beams that represent the total adopted parameters were tested under impact loading. The beams were divided into three main groups according to the longitudinal steel ratio. The steel ratio was varied by using steel bars of 10, 8, and 6 mm diameter, with PET waste fibers with different volume ratios V_f% of 0, 0.5, 0.75, and 1%. The results showed that the use of beams is reinforced with ρ_max, ρ_max< ρ< ρ_min, and ρ_min having reduced maximum deflection by 24.23%, 35.9%, and 46.28%, respectively, when using WPFs with a volumetric value of 1%. This paper also covers work steps, model details, and the tests that were carried out on the specimens, which were made from materials available in local markets.

A Framework for Efficient and Accurate Automated CLO and PLO Assessment

Tue, 02 Apr 2024 00:00:00 +0000

Accurate and efficient learning outcome assessment is crucial for ensuring high-quality education, but traditional methods can be time-consuming, error-prone, and inconsistent. We developed a novel Excel Macro-enabled framework for automating the evaluation of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) in higher education. The framework consists of two Excel Macro-enabled workbooks. The course section workbook guides instructors through the assessment process, automatically calculates CLO achievement levels, and generates reports for the coordinators and the Head of Department (HoD). The course-level workbook aggregates data from all course sections and calculates CLO and PLO achievement levels relative to the course. Proven successful in three FCIT (Faculty of Computer and Information Technology) programs at NBU (Northern Border University), the framework demonstrably reduces assessment time and errors, improves consistency, and facilitates data-driven program improvement, making it a valuable tool for enhancing program quality.

Effect of Fire Exposure on the Properties of Self-Compacting Concrete reinforced by Glass Fibers

Rawaa K. Aboud, Hadeel K. Awad, Shatha D. Mohammed — Tue, 02 Apr 2024 00:00:00 +0000

The optimal design of any structural elements requires examining all environmental risks, emergency accidents, and standard load cases. Exposure to fire is one of the most common safety threats. Nowadays wide developments are achieved in the field of concrete technology, therefore, experimental and theoretical investigations should be performed on the characteristics of such developed materials under different loading conditions. This study investigates the impact of fire exposure on the mechanical characteristics of self-compacting concrete, specifically compressive and tensile strength, modulus of elasticity, and stress-strain relation. The adopted fire exposure consisted of six steady-state temperatures (300, 400, 500, 600, 700, and 800°C) for one hour and a sudden cooling method. Four glass fiber volume fractions were adopted: 0, 0.5, 1, and 1.5%. The glass fiber volume fractions considered (0.5-1.5%) improved the mechanical properties investigated. Two states were detected for the effect of fire exposure. The effect of fire exposure was inversely proportional to fiber content in burning temperatures of 300-700°C, while the reduction in mechanical properties of 1.5% fiber content was greater than those of 0.5 and 1% when the temperature increased to 800°C. Furthermore, the addition of glass fiber changed the brittle mode stress-strain relation to semi-ductile for the non-burned and burned up to 600°C specimens, whereas a brittle behavior was detected when the temperature increased above 600°C. In general, a similar effect was noticed for all the glass fiber ratios considered regarding the slope of the stress-strain linear stage compared to the non-burned specimens, which was more salient when the burning temperature increased.

Design and Development of a Wheelchair Prototype

Van-Tinh Nguyen, Tran Thanh Tung — Tue, 02 Apr 2024 00:00:00 +0000

The rate of people with disabilities in Vietnam is about 7% out of a total of 98 million. Wheelchairs are popular assistive devices for disabled people and are often found in many places around the world, including Vietnam. This study proposes a novel design of a prototype electric wheelchair to support people with disabilities in Vietnam. The electric wheelchair model was successfully simulated and manufactured, fully meeting the proposed technical requirements. The proposed model works well and is suitable for the shape and physical strength of Vietnamese people.

Structural Behavior of Concrete One-Way Slab with Mixed Reinforcement of Steel and Glass Fiber Polymer Bars under Fire Exposure

Mohammed R. Rasheed, Shatha D. Mohammed — Tue, 02 Apr 2024 00:00:00 +0000

Steel Reinforced Concrete (RC) frequently faces durability problems. In certain areas, Glass Fiber-Reinforced Polymer (GFRP) rebars are considered a non-corrodible substitute for steel reinforcement. Elevated temperatures have a significant impact on the mechanical characteristics and the adhesiveness of GFRP rebars to concrete, particularly when the polymeric matrix's glass transition temperature is approached or surpassed. Three simply supported reinforced concrete slabs were considered in the experimental program. Each specimen had identical dimensions of 1500×540×120 mm. For the fire resistance requirements, a 45 mm clear concrete cover and an exception of a 200 mm unexposed (cool) anchor zone at the ends were considered. The GFRP replacement ratio was 0, 20, and 40%. The burning procedure involved fire exposure for an hour with a steady-state temperature of 500 °C in accordance with ASTM E-119 regarding the temperature time elevation and a sudden cooling condition. The optimal concrete cover was detected by testing a fire-exposed small model reinforced by GFRP bars of varying concrete cover. The specimen was tested under static intense loads. The reference slab and the slab with a replacement percentage of 20% failed due to flexural failure, whereas the slab with a replacement percentage of 40% failed due to shear failure. The influence of the GFRP replacement ratio was extended to include toughness and ultimate load. A replacement percent of 20% increased them by 18.30, and 2.62%, respectively, while a replacement percent of 40% decreased them by 28.16, and 3.13%, accordingly. It was also shown that the location of replacing the GFRP and 200 mm of unexposed (cold) installation area at the ends with a 45 mm concrete cover has a significant impact. The more the GFRP is located in the middle, away from the ends, the better the fire resistance is.

Wind Resource Evaluation in Libya: A Comparative Study of Ten Numerical Methods for the Estimation of Weibull Parameters using Multiple Datasets

Youssef Kassem, Huseyin Camur, Almonsef Alhadi Salem Mosbah — Tue, 02 Apr 2024 00:00:00 +0000

This study examines Libya's pursuit of sustainable wind energy solutions, using nine sites with mast measurements before the 2011 civil war and six gridded datasets, including CFSR, ERA5, EAR5-Ag, MERRA2, EAR5-Land, and TerraClimate. Employing the Weibull distribution function with ten methods, the empirical method of Justus proved to be optimal for calculating Weibull parameters across datasets. Al Bayda and Darnah exhibit substantial wind power potential (116.80-123.00 W/m²) based on MERRA2 data, making them ideal for large-scale wind turbine deployment. Furthermore, the results showed that wind power density was estimated below 100 W/m² for all selected locations according to CFSR, ERA5, EAR5-Ag, EAR5-Land, and TerraClimate. This study emphasizes the need for new mast measurements to refine dataset selection, which is crucial for accurate assessments and large wind farm planning. Consequently, this study provides key insights into optimizing wind energy utilization in diverse Libyan regions, addressing both the potential and the challenges in sustainable energy development.

Fostering Sustainability through the Integration of Renewable Energy in an Agricultural Hydroponic Greenhouse

Aymen Lachheb, Rym Marouani, Chabakata Mahamat, Safa Skouri, Salwa Bouadila — Tue, 02 Apr 2024 00:00:00 +0000

This research explores the feasibility of integrating renewable energy sources, such as solar and wind, to power a hydroponic greenhouse. In this way, the latter’s energy autonomy is ensured. The study begins by evaluating the annual electricity consumption of the examined system. A renewable energy system capable of meeting its energy requirements throughout the year is also designed. The main objective is to assess the efficiency of two types of renewable energy sources, namely photovoltaic panels and wind turbines, and to improve their integration within the agricultural chamber by implementing a model simulation. Two scenarios were examined: the first one represents a photovoltaic power plant with storage, connected to the grid, while the second scenario presents a wind power plant connected to the grid. This numerical analysis is supplemented by a one-year experimental study of a photovoltaic installation connected to the network with storage, which in turn is connected to the experimental device. To handle energy within the renewable energy greenhouse, an energy management system was developed based on a fuzzy logic controller. This system aims to maintain energy balance and ensure continuous power supply. The energy management system optimizes energy flow to minimize consumption, reduce grid dependence, and improve overall system efficiency, resulting in cost savings and certain environmental benefits.

A Hybrid Intelligent Controller for Extended-Range Electric Vehicles

Tue, 02 Apr 2024 00:00:00 +0000

A smart battery electric vehicle control framework is proposed in this paper. The specific controller empowers ceaseless observation and management of the battery's state with the scope of extending the vehicle's driving range under varying temperature and driving pattern conditions. The proposed method utilizes an incorporated scheme for dealing with a crossover energy stockpiling framework to expand a battery's lifespan while further ensuring its smooth activity.

Optimal Placement of Superconducting Magnetic Energy Storages in a Distribution Network with Embedded Wind Power Generation

Steven Foday Sesay, Cyrus Wabuge Wekesa, Livingstone M. H. Ngoo — Tue, 02 Apr 2024 00:00:00 +0000

The prevalence of distributed generation in most power grids can negatively affect their performance in terms of power loss, voltage deviation, and voltage stability. Superconducting Magnetic Energy Storages (SMESs) can help in addressing this problem as long as they are optimally placed in the distribution network. This paper presents a hybrid Grasshopper Optimization Algorithm and a Simulated Annealing (GOA-SA) method to determine the optimal placement of SMESs in a distribution network with an embedded wind power generation system. The optimization was formulated as a multi-objective problem to minimize active power losses, reactive power losses, and voltage deviation and maximize the voltage stability index. An IEEE 57-node distribution network was employed and simulations were performed using MATLAB R2020b. Based on simulations using 200 kW SMESs in discharge mode, the active power loss decreased by 82.57%, the reactive power loss decreased by 80.71%, the average voltage deviation index decreased by 66.91%, and the voltage stability index improved by 34.97%. In the charging operation mode, the active power loss increased by 24.86%, the reactive power loss increased by 8.21%, the average voltage deviation increased by 12.86%, and the voltage stability index increased by 12.79%. These results show that SMESs can improve the technical performance of a distribution network.

Efficiency Assessment of an Inverter based on Solar PV Energy in Baghdad

Bilal Nasir — Tue, 02 Apr 2024 00:00:00 +0000

The yearly energy yield of a Solar Photovoltaic (SPV) system is a rendition pointer utilized by the erector to determine the output energy generated by it. From the energy speculation, the payback period and the return on investment can be contemplated. The system energy yield formula consists of many parameters, the most important of which is the SPV inverter efficiency. The European and peak (maximum) efficiency factors from the inverter data sheet are typically utilized, but this utilization is unsound because the SPV does not always work at the peak of its effectiveness due to varying irradiance. The inverter's weighted efficiency is considered more sound as it deems the inverter output power peculiarities. The European weighted efficiency is the most widely accepted inverter efficiency determination. Since it is derived and documented on a rimmed European irradiance profile, it may not be appropriate for inverters constructed in different climatic conditions, especially in the equatorial and subtropical environmental regions. This work aims to formulate a fangled weighted efficiency equation for the inverter's work in the Iraqi environment (especially in Baghdad city as a case study) documented on the IEC 61683: 1999 Standard and Irradiance-Duration curve. The sophisticated formula is endorsed on experimental data from the field using an SMA-SB-4000-TL inverter. It was found that the speculated energy yield using the derived efficiency formula for the Baghdad environment closely matches the energy yield of an original 4.0 KW SPV inverter system with only 1% difference between the determined and acquired values. This means that the employment of the Baghdad weighted efficiency in place of the European or peak weighted efficiency will result in a sounder speculation of the system energy yield, return on investment, and payback duration of the SPV system project.

Temperature Dependency of Photoelectronic Properties of Group III-V Arsenide Solar Cell

Tue, 02 Apr 2024 00:00:00 +0000

This study explores the effect of temperature on different characteristics of Solar Cells (SC) composed of a structured III-V arsenide group. The temperature dependence of the SC characteristics was investigated numerically and by simulation. In both approaches, each characteristic was compared with a conventional Si SC. InAs showed superior stability and lower temperature sensitivity, as it has a negligible decrease of 0.098 eV in the energy bandgap, while the energy bandgaps of Si, AlAs, and GaAs are 0.129, 0.186, and 0.200 eV, respectively. Moreover, with a decay rate of 81.911 mV/°K, InAs exhibited the lowest temperature sensitivity in open-circuit voltage. InAs additionally demonstrated the least increase in degradation rate, while the SC power output is still a cause of concern. AlAs, Si, and GaAs had a total accumulative gradient change of 0.162, 0.136, and 0.034% in the degradation rate, respectively, while InAs showcased the highest stability by displaying a change of only 0.008%. A comparative analysis illustrated that among these III-V arsenide compounds, InAs had a rock-bottom sensitivity to temperature changes and better temperature stability in both numerical and simulation approaches.

Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home

Talal Alshammari — Tue, 02 Apr 2024 00:00:00 +0000

The acceleration of house technology via the use of mobile phones has made it easier to control houses, where occupants (especially older people) spend most of their time. The climate of Saudi Arabia, especially in the northern area, is too hot during summer and cold during winter. Control of the indoor environment in a smart home is a preferable choice that can reduce power consumption to operate heating, ventilation, and air-conditioning. Machine learning algorithms have been used to predict physical variables of indoor environment, such as temperature and humidity. The model can be trained, learn, and make predictions using historical data. Machine learning techniques can automate temperature monitoring and control. This paper proposes an algorithm that combines Artificial Neural Networks (ANNs) and GridSearchCV to predict physical variables in indoor environments in Saudi Arabia. GridSearchCV was utilized to tune the parameters of the machine learning algorithm. The assessment of the proposed algorithm involved its performance comparison to state-of-the-art machine learning algorithms. A real-world dataset was generated to estimate the performance of the considered algorithms. The room data were collected every 5 min for 31 days during July 2022. The dataset contains 6 columns and 8,910 records from 6 sensors (timestamps, light, temperature, humidity, pressure, and altitude). Random Forest (RF), Decision Tree (DT), and ANN methods were compared with the proposed algorithm. The RF had the highest R² value of 0.84 and the lowest Mean Square Error (MSE) of 0.43. The DT achieved an R² score of 0.78, while the ANN achieved R²score of 0.61, MSE of 1.04, and Mean Absolute Error (MAE) of 0.75. The proposed algorithm achieved an R² of 0.69, MSE of 0.87, and MAE of 0.67.

Numerical and Experimental Investigation of Performance and Flooding Phenomena of a PEM Fuel Cell with and without Micro-Porous Layers

Nguyen Ha Hiep, Vu Duong — Tue, 02 Apr 2024 00:00:00 +0000

This work presents the results of manufacturing a single Proton Exchange Membrane Fuel Cell (PEMFC) with Micro-Porous Layers (MPLs) and an active area of 25 cm², and the experimental study required to build its polarization curve. Based on the physical model data, a numerical model of this PEMFC is created in the ANSYS PEM Fuel Cell module. Numerical simulations were performed with boundary conditions consistent with the experimental conditions on the test station. The calculation and experimental result comparison of the polarization curves for voltages ranging from 0.29 V to 0.94 V proved that the utilized numerical model is highly reliable. The simulation of PEMFC without MPLs was conducted according to other stable input parameters and boundary conditions. The results show that the PEMFC performance decreases significantly due to the flooding phenomenon inside PEMFC without MPLs compared to PEMFC with MPLs. Such phenomena are challenging to observe experimentally. Numerical modeling can be further used to optimize the fuel cell components.

Thermal Potential of a Twin-Screw Compressor as Thermoelectric Energy Harvesting Source

Tue, 02 Apr 2024 00:00:00 +0000

This study evaluates the potential of a twin-screw compressor as a heat source to harness thermal energy. Thermoelectric generators are a feasible solution for microenergy harvesting from waste heat based on the Seebeck effect. Thermographic infrared images of the compressor were used to assess potential installation spots. The physical mounting of the thermoelectric modules must consider certain hindering aspects. At first, the compressor skid is subject to standards and authorizations for its components, leaving only a couple of spots for screw-mounted module installations. Another inconvenience is the bonds in any thermoelectric material causing them not to withstand lateral mechanical stress in other directions except the c-axis perpendicular to the layers. Therefore, vibration measurements have to be performed beforehand. Numerical simulations were conducted, relying on the acquired thermoelectric modules as well as on the temperature and vibration data measured on the compressor. The thermoelectric generators studied are part of a multisource piezoelectric and thermoelectric energy harvesting system under research and development.

Experimental Analysis of Twin Screw Compressor's Energetic Efficiency Depending on Volume Ratio

Tue, 02 Apr 2024 00:00:00 +0000

The current paper presents the results of the experimental analysis to assess and optimize the twin-screw compressor’s efficiency by varying the volume ratio. The experimental tests are conducted on the compressor's test bench, with a dedicated automation system inside the control console. The control and monitoring software allows parameter recording for subsequent visualization, data curation, and post-processing. The evaluation of screw compressor's performance requires a simultaneous analysis of the thermodynamic and flow processes, both of which depend on the compressor’s geometry. The obtained volumetric and adiabatic efficiencies are good, with values over 0.88 and 0.69, respectively.

Efficient Hardware Accelerator and Implementation of JPEG 2000 MQ Decoder Architecture

Tue, 02 Apr 2024 00:00:00 +0000

Due to the extensive use of multimedia technologies, there is a pressing need for advancements and enhanced efficiency in picture compression. JPEG 2000 standard aims to meet the needs for encoding still pictures. JPEG 2000 is an internationally recognized standard for compressing still images. It provides a wide range of features and offers superior compression ratios and interesting possibilities when compared to traditional JPEG approaches. Nevertheless, the MQ decoder in the JPEG 2000 standard presents a substantial obstacle for real-time applications. In order to fulfill the demands of real-time processing, it is imperative to meticulously devise a high-speed MQ decoder architecture. This work presents a novel MQ decoder architecture that is both high-speed and area-efficient, making it comparable to previous designs and well-suited for chip implementation. The design is implemented using the VHDL hardware description language and is synthesized with Xilinx ISE 14.7 and Vivado 2015.1. The implementation findings show that the design functions at a frequency of 438.5 MHz on Virtex-6 and 757.5 MHz on Zync7000. For these particular frequencies, the calculated frame rate is 63.1 frames per second.

Smart Grid 2.0: Modeling Peer-to-Peer Trading Community and Incentives for Prosumers in the Transactive Energy Grid

Manal Mahmoud Khayyat, Sami Ben Slama — Tue, 02 Apr 2024 00:00:00 +0000

Smart Grid 2.0 (SG 2.0) implementation constitutes an additional challenge in the industry and research fields. Energy consumption decreases when producers exchange excess energy consumers, including intelligent consumers, Distributed Generation (DG), such as wind and solar, and Electric Vehicles (EVs). By utilizing Demand Response (DR) based on Real-Time Pricing (RTP), the operation of every device in a smart home can be scheduled. Allowing users to trade energy directly with other energy producers (prosumers) rather than exclusively relying on the grid, peer-to-peer (P2P) energy trading in smart homes lowers energy prices for users. This article focuses on how the DR P2P energy trading affects consumers. The study conducted utilizes a two-stage scheduling technique to reduce consumers' electricity expenses. The initial stage involves arranging each device in the smart home based on RTP employing a deep learning method. The P2P energy trading between consumers in the second phase is made more accessible by the DR and the simulation results exhibit that energy trading decreases electricity bills in smart homes. Utility companies can reduce load during peak hours using DR-based P2P energy trading.

Exploring the Mechanical Behavior of Concrete enhanced with Fibers derived from recycled Plastic Bottles

Lana Ayad Abdulateef, Sara Hikmat Hassan, Ahmed Mohamed Ahmed — Tue, 02 Apr 2024 00:00:00 +0000

The increasing issue of plastic waste has become detrimental to human society, particularly with the increase in disposable plastic bottles in many countries. This study investigates the impact of incorporating plastic bottle waste fibers on the slump, density, compressive strength, split tensile strength, and flexural strength of concrete. This material was selected for its cost-effectiveness and wide availability, addressing the prevalent global concern of environmental pollution resulting from inadequate waste management practices. This study describes a systematic plan to fabricate and test cubes, cylinders, and beams using Fiber-Reinforced Concrete (FRC). A comparative analysis was performed between concrete reinforced with plastic bottle waste fibers, in varying ratios of 1, 2, and 3%, and plain concrete. The results showed a positive impact on concrete strength with fiber addition, although at the expense of reduced workability and decreased concrete density. In particular, a significant improvement in the ductility of the concrete was observed. The analysis shows that a fiber ratio of 2% emerges as the most optimal dosage to achieve improved concrete properties. This study provides valuable insights into the imperative pursuit of sustainable concrete production and the environmental challenges posed by plastic waste.

A Study on the Influence of FDM Parameters on the Tensile Behavior of Samples made of PET-G

Dragos Gabriel Zisopol, Mihail Minescu, Dragos Valentin Iacob — Tue, 02 Apr 2024 00:00:00 +0000

This experimental study investigated the influence of FDM 3D printing parameters on the tensile behavior of PET-G-made parts. In this context, 27 test specimens were produced using FDM on the Anycubic 4 Max Pro 2.0 printer with layer heights applied in one pass L_h = 0.10/0.15/0.20 mm and filling percentages I_d = 50/75/100 %. All these samples were tensile tested on the Barrus White 20 kN universal testing machine. The experimental results determined maximum tensile strength, elongation percentage at break, and Young's modulus. The two parameters considered, I_d and L_h, influence the maximum tensile strength, the elongation percentage at break, and Young's modulus. The findings demonstrated that the filling percentage has a strong influence on the maximum tensile strength and the elongation percentage at the break of the PET-G samples, and L_h has a decisive influence on Young's modulus.

Shoreline Changes and Sediment Transport along Nhat Le Coast, Vietnam

Vu Dinh Cuong, Nguyen Thanh Hung, Tran Dinh Hoa, Nguyen Tien Thanh — Tue, 02 Apr 2024 00:00:00 +0000

One of the most beautiful beaches in Northern Vietnam, Nhat Le, has recently experienced severe erosion as a result of the ensemble interaction of natural factors, such as tropical cyclones, extreme weather events, and human activities. Consequently, negative impacts on tourism and social and economic development have been recorded. This paper aims to provide a deep understanding of the changes in shoreline and longshore sediment transport at Nhat Le estuary based on two modules of LITDRIFT and LITLINE of the LITPACK software package combined with geospatial analysis. The rate of change statistics is calculated using the Digital Shoreline Analysis System (DSAS) from 30-year multi-temporal satellite data (1989-2019) for multiple historical shoreline positions. The Module of LITDRIFT is employed to estimate sediment transport and the shoreline position calculated from the LITLINE module. These data are then compared with measured topographic data and satellite images. Wave climate conditions are incorporated into the LITDRIFT module to identify the volume of sediment transport along the coast on seasonal and annual bases. The results illustrate that a mean erosion rate of about 2 m per year was observed in the southern sandspit of Nhat Le from 1989 to 2019. This rate reaches 4.5 m per year during 2009-2019.

Characterization of Pure and Doped ZnO Nanostructured Powders elaborated in Solar Reactor

Tue, 02 Apr 2024 00:00:00 +0000

The synthesis of nano-oxides is an important field of nanotechnology, as these materials possess unique properties and applications. Several methods have been developed for synthesizing nano-oxides, each offering advantages and disadvantages depending on the desired material characteristics. Solar energy focused on solar reactors can be utilized for nano-oxide elaboration, offering a sustainable and environmentally friendly approach. The current article presents the research carried out for the elaboration of pure and doped nanostructured zinc oxides using solar energy. The morphostructural characteristics were determined by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and the Brunauer-Emmett-Teller method. The attenuated total reflectance Fourier transform infrared spectroscopy confirmed the synthesis of pure and doped nanostructured ZnO. The optical properties were highlighted by UV-VIS Spectroscopy. The research points out that crystallite sizes vary between 37 and 51 nm due to the influence of doping metal. The morphology associated with these particles is predominantly whiskers with elongated parts between 0.18 and 1.4 um. Doping with Fe, Si, Yb, and Ce causes a wider band gap compared to pure ZnO nanoparticles. As solar energy becomes more accessible and efficient, solar-driven synthesis of pure and doped ZnO is poised to be a crucial factor in shaping the future of material science and technology.

Quicklime-stabilized Tuff and Clayey Soils for Highway A3 Construction in Northern Tunisia

Nejib Ghazouani — Tue, 02 Apr 2024 00:00:00 +0000

This study presents a comprehensive examination of the effects of quicklime (QL) addition on the stabilization of two distinct clayey soils with high (CH) and low plasticity (CL-tuff). The results showed that incorporating QL into the soils substantially improves their stabilization characteristics. Specifically, the addition of QL results in a notable decrease in the final water content of both soils, as shown by a reduction from 23.04 to 19.06% in CH and from 18.07 to 17.1% in CL-tuff at 4% QL addition. Furthermore, this study reveals a transformation in the plasticity properties of soils. Liquid Limit (LL) and Plasticity Index (PI) were reduced, with CH-tuff exhibiting a significant decrease in PI from 48 to 12 and an increase in Plastic Limit (PL) from 21.8 to 55 at 4% QL. CL-tuff also showed reduced plasticity, with PI decreasing to 8.33 at 4% QL. Additionally, the Immediate Bearing Index (IBI) was improved for both soil samples, indicating improved load-bearing capacities. For CH samples, IBI improved from 6.37 to 11.99 at 4% QL addition, while for CL-tuff, it increased dramatically from 4.5 to 23.6 for the same QL percentage. The findings underscore the effectiveness of QL in improving soil properties crucial for chemical stabilization, providing evidence that QL addition can be a key technique in soil stabilization, especially for soils with high plasticity or those requiring increased bearing strength.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Tue, 02 Apr 2024 00:00:00 +0000

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

A Real Test and Simulation Result Comparison of Selected Properties of Hybrid Composite Materials

Naqib Daneshjo, Dusan Sabadka, Peter Malega — Tue, 02 Apr 2024 00:00:00 +0000

In this study, the notion of composite materials is thoroughly assessed. Actual and simulation in a specific computer software stress testing of hybrid composites are investigated. The paper deals with the mechanics of rigid bodies, their elasticity, strength, and stiffness. In addition to a general overview of the former’s behavior and properties, this paper presents the possibility of calculating the bearing capacity of various materials in relevant computer programs. The production and testing process of the composite samples are described. The latter are then subjected to simulated tests in computer software. The main objective of this study is to compare real test results of hybrid composites, namely combined carbon fibers, glass fibers, aramid-carbon fibers, aramid honeycomb, and metal mesh with the simulation findings.

Research Influence of Flux Air Gaps on Electromagetic Components of Shunt Reactors

Dang Chi Dung, Doan Thanh Bao, Phan Hoai Nam, Pham Minh Tu, Vuong Dang Quoc — Tue, 02 Apr 2024 00:00:00 +0000

This paper introduces an evaluation of the flux air gaps of Shunt Reactors (SRs) to effectively mitigate fringing and leakage fluxes along the height of the iron core. The assessment of these discretely distributed flux air gaps in SRs is a rigorous and challenging process. To define their exact number, the case of one flux air gap is analyzed and investigated to observe/simulate the influence of the flux density distribution and the leakage flux along the air gaps on the reactive power and the operation conditions of the SR. Based on that, to reduce leakage flux, a large flux air gap is divided into smaller ones. Initially, an analytic model is presented to define the main parameters of the SRs. Then, a finite element method is developed to simulate electromagnetic quantities, such as the magnetic flux density, leakage flux, and electromagnetic force. The obtained results can help manufacturers define the exact number of flux air gaps along the iron core of the SR. From that, a suitable technology can be given in manufacturing high voltage SRs applied to high or super high voltage transmission lines.

Impact of Safety Management Practices on Safety Performance in Workplace Environment: A Case Study in Iraqi Electricity Production Industry

Omar Munaf Tawfeeq, Sivadass A. L. Thiruchelvam, Izham Bin Zainal Abidin — Tue, 02 Apr 2024 00:00:00 +0000

Organizations are becoming more aware of the need to ensure a safe working environment for their staff. Technological advancements and industrial growth have enhanced efficiency, however, they present new challenges and risks for employees. Accidents remain a concern despite International Labor Organization (ILO) guidelines, governmental bodies, and industry institutions promoting workplace safety. Therefore, it is crucial to evaluate the determinants of workplace safety performance, particularly in the electrical power industry. This study formulates a theoretical model to assess the predictors of safety practices of managers and staff in the Iraqi electricity sector, extending the safety climate model with four external constructs and a moderating variable. Data were collected from 374 participants using an online questionnaire and the PLS-SEM method for analysis. The factor loadings exceeded the recommended value of 0.7 and internal consistencies were greater than the threshold value of 0.8. The findings showed that the safety performance in the Iraqi electric power sector is influenced by safety communication, safety policy, safety control, prevention planning, and safety commitment. Safety commitment is affected by safety policy, prevention planning, control, and communication, while safety training and safety control were found to be insignificant. Furthermore, safety communication had the most significant effect. The results of this study provide some theoretical and practical implications for employees' safety performance toward their overall safety in the electric power industry.

Model-based Design of a High-Throughput Canny Edge Detection Accelerator on Zynq-7000 FPGA

Tue, 02 Apr 2024 00:00:00 +0000

This paper presents a novel approach for fast FPGA prototyping of the Canny edge detection algorithm using High-Level Synthesis (HLS) based on the HDL Coder. Traditional RTL-based design methodologies for implementing image processing algorithms on FPGAs can be time-consuming and error-prone. HLS offers a higher level of abstraction, enabling designers to focus on algorithmic functionality while the tool automatically generates efficient hardware descriptions. This advantage was exploited by implementing the Canny edge detection algorithm in MATLAB/Simulink and utilizing the HDL Coder to automatically convert it into synthesizable VHDL code. This design flow significantly reduces development time and complexity compared to the traditional RTL approach. The experimental results showed that the HLS-based Canny edge detector achieved real-time performance on a Xilinx FPGA platform, showcasing the effectiveness of the proposed approach for fast FPGA prototyping in image processing applications.

A Data Acquisiton System with sEMG Signal and Camera Images for Finger Classification with Machine Learning Algorithms

Ismail Mersinkaya, Ahmet Resit Kavsaoglu — Tue, 02 Apr 2024 00:00:00 +0000

Advances in robotics and biomedical engineering have expanded the possibilities of Human-Computer Interaction (HCI) in the last few years. The identification of hand movements is the accurate and real-time signal acquisition of hand movements through the use of image-based systems and surface electromyography sensors. This study uses multithreading to record motion signals from the forearm muscles in conjunction with a surface electromyography (sEMG) sensor and a camera image. The finger movement information labels were tabulated and analyzed along with the simultaneous acquisition of surface electromyography signals and these gestures through the camera. After the acquisition, signal processing techniques were applied to the sEMG signal markered from the camera. Therefore, once the interface is established, data sets suitable for machine learning can be generated.

Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems

C. Atheeq, Ruhiat Sultana, Syeda Asfiya Sabahath, Murtuza Ahmed Khan Mohammed — Tue, 02 Apr 2024 00:00:00 +0000

Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.

Optimizing the Supercritical Carbon Dioxide Extraction of Hibiscus Flower Essential Oil using Response Surface Analysis

Tahani Y. A. Alanazi — Tue, 02 Apr 2024 00:00:00 +0000

The development of Supercritical Fluid Extraction (SFE) has opened the door to the harvesting of plants for a wide range of chemical compounds. This study distilled essential oil from hibiscus flowers utilizing the supercritical CO₂ method. Different extraction parameters, including pressure (100-300 bar) and temperature (300-350 K), were studied to visualize how they affected oil recovery. Response surface analysis was used to fine-tune the extraction process. The chemical composition of the recovered oil was analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). According to the findings, 13.11% per 80 g of dry flowers is the ideal oil extracted from Hibiscus flowers, using SFE at 200 bar pressure and 325 K extraction temperature. Six compounds were provisionally identified in the extracted oil from hibiscus flowers under optimum SFE conditions.

An MCDM Approach for Evaluating Construction-Related Risks using a Combined Fuzzy Grey DEMATEL Method

Rana Jabbar Kasid Jalhoom, Ahmed Mohammed Raoof Mahjoob — Tue, 02 Apr 2024 00:00:00 +0000

There is a need for more research into prioritizing project risks based on a sound technique due to the complicated and disorganized character of this stage. The project risk management process typically begins with the identification of critical hazards. This study presents a Grey Fuzzy Decision-Making Trial and Evaluation Laboratory (FGDEMATEL) approach to prioritize potential causes of project risks within Multi-Criteria Decision-Making (MCDM). This framework organizes the numerous risks using the Risk Breakdown Structure (RBS) of the Project Management Institute (PMI). The risk information used in this analysis comes mostly from the views and choices of project experts. Grey theory, which takes language phrases for preference collections and translates them into numerical intervals, is responsible for controlling uncertainty and variance in experts' preferences. As each expert has unique skills and experiences, it evaluates the significance of their opinions using a fuzzy number system that incorporates three dimensions. In the end, the FGDEMATEL approach devised a method to rank various project risks.

Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia

Rabie A. Ramadan, Sahbi Boubaker — Tue, 02 Apr 2024 00:00:00 +0000

Water scarcity is considered a major problem in dry regions, such as the northern areas of Saudi Arabia and especially the city of Hail. Water resources in this region come mainly from groundwater aquifers, which are currently suffering from high demand and severe climatic conditions. Forecasting water consumption as accurately as possible may contribute to a high level of sustainability of water resources. This study investigated different Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting (GB), to efficiently predict water consumption in such areas. These models were evaluated using a set of performance measures, including Mean Squared Error (MSE), R-squared (R²), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (MedAE). Two datasets, water consumption and weather data, were collected from different sources to examine the performance of the ML algorithms. The novelty of this study lies in the integration of both weather and water consumption data. After examining the most effective features, the two datasets were merged and the proposed algorithms were applied. The RF algorithm outperformed the other models, indicating its robustness in capturing water usage behavior in dry areas such as Hail City. The results of this study can be used by local authorities in decision-making, water consumption analysis, new project construction, and consumer behavior regarding water usage habits in the region.

Deep Learning for Tomato Disease Detection with YOLOv8

Tue, 02 Apr 2024 00:00:00 +0000

Tomato production plays a crucial role in Saudi Arabia, with significant yield variations due to factors such as diseases. While automation offers promising solutions, accurate disease detection remains a challenge. This study proposes a deep learning approach based on the YOLOv8 algorithm for automated tomato disease detection. Augmenting an existing Roboflow dataset, the model achieved an overall accuracy of 66.67%. However, class-specific performance varies, highlighting challenges in differentiating certain diseases. Further research is suggested, focusing on data balancing, exploring alternative architectures, and adopting disease-specific metrics. This work lays the foundation for a robust disease detection system to improve crop yields, quality, and sustainable agriculture in Saudi Arabia.

A Study on the Influence of FDM Parameters on the Compressive Behavior of PET-G Parts

Dragos Gabriel Zisopol, Mihail Minescu, Dragos Valentin Iacob — Tue, 02 Apr 2024 00:00:00 +0000

This article presents the results of a study on the influence of Fused Deposition Modeling (FDM) 3D printing parameters on the compressive behavior of test specimens made of PET-G. In this context, 45 test specimens, made by FDM on the Anycubic 4 Max Pro 2.0 printer, were compressive tested on a universal testing machine Barrus White 20 kN, with the height of the layer applied in one pass being L_h = 0.10/0.15/0.20 mm and filling percentage I_d = 50/75/100%. The two considered variable parameters, L_h and I_d influence the compression resistance of the PET-G parts, with I_d having a more significant influence. The scope and novelty of this work is to find the optimal parameters for maximum compressive strength (C_s) of PET-G samples made of FDM.

Using a Chaotic Digital System to Generate Random Numbers for Secure Communication on 5G Networks

Haider Th. Salim Alrikabi, Ibtisam A. Aljazaery, Abdul Hadi Mohammed Alaidi — Tue, 02 Apr 2024 00:00:00 +0000

There are several encryption system applications in 5G networks where rapid response is needed, particularly in the military, health sector, traffic, and vehicular movement. This article presents a proposed data security system for 5G networks that fortifies the security of the network through the use of synchronized chaotic systems to produce pseudo-random numbers. The technique by which random numbers are generated during the encryption procedures is closely associated with 5G network security. Many synchronized chaotic systems are used to produce chaotic random models which are used as encryption bases for a wide variety of data. In this study, the encryption was carried out using a variety of data, including two and three-dimensional color images and audio signals of varying lengths, in addition to the use of Fast Fourier Transform (FFT) for encryption of the ingredient energy wave. The results revealed that the algorithm deployed in the process of encryption performed well. Simulations were performed in MATLAB.

A Spectrotemporal EEG Mapping Approach for Decoding Imagined Marathi Language Phonemes

Umesh Mhapankar, Milind Shah — Tue, 02 Apr 2024 00:00:00 +0000

Individuals facing verbal communication impairments resulting from brain disorders like paralysis or autism encounter significant challenges when unable to articulate speech. This research proposes the design and development of a wearable system capable of decoding imagined speech using electroencephalogram (EEG) signals obtained during the mental process of speech generation. The system’s main objective is to offer an alternative communication method for individuals who can hear and think but face challenges in articulating their thoughts verbally. The design suggested includes user-friendliness, wearability, and comfort for seamless integration into daily life. A minimal number of electrodes are strategically placed on the scalp to minimize invasiveness. Achieving precise localization of the cortical areas responsible for generating the EEG patterns during imagined speech is vital for accurate decoding. Literature studies are utilized to determine the cortical positions associated with speech processing. Due to the inherent limitations in EEG spatial resolution, meticulous experiments are conducted to map the scalp positions onto their corresponding cortical counterparts. Specifically, we focus on identifying the scalp location over the superior temporal gyrus (T3) using the internationally recognized 10-20 electrode placement system by employing a circular periphery movement with a 2 cm distance increment. Our research involves nine subjects spanning various age groups, with the youngest being 23 and the oldest 65. Each participant undergoes ten iterations, during which they imagine six Marathi syllables. Our work contributes to the development of wearable assistive technology, enabling mute individuals to communicate effectively by translating their imagined speech into actionable commands. This innovation ultimately enhances their social participation and overall well-being.

A Low-Profile Electrically Small Serrated Rectangular Patch Antenna for RFID Applications

Tue, 02 Apr 2024 00:00:00 +0000

This paper presents the design and analysis of a global system for mobile communication applications, as well as the design and analysis of a compact, bidirectional Electrically Small Antenna (ESA) at 0.9 GHz for Radio Frequency Identification (RFID) applications. In order to attain results at lower frequencies while keeping a compact size, the proposed design consists of a microstrip patch antenna in which an SRR and a semicircular-shaped SRR were subtracted from the ground plane. The dimensions of the FR4 substrate, on which this ESA was designed, were 20 × 18 × 1.6 mm. Ansys HFSS was used for the design and simulation of the antenna. Chemical etching was implemented to fabricate the ESA and MS2037C Anritsu Combinational Analyzer was applied for testing. The simulated results show that at 0.9 GHz, the ESA achieves a bandwidth of 300 MHz (700 MHz-1000 MHz). At the resonance frequency, a bidirectional radiation pattern with a 80% radiation efficiency is obtained in both H and E planes. A 90% agreement between the simulated and the fabricated results has been achieved.

Investigating the Impact of Domestic Sewage on Asphalt Concrete Pavement Strength

Afzal Ahmed, Sajjad Ali, Ashar Ahmed, Farah Khan — Tue, 02 Apr 2024 00:00:00 +0000

This study evaluates the impact of exposing asphalt pavement to sewage and fresh water. In total, 87 samples were prepared, where half of them were immersed in sewage and the others were immersed in freshwater. The Marshall mix design method was adopted for the preparation of samples. Three immersed samples were tested for stability and flow every 24 hours in both fresh water and sewage, comparing the results with a control sample. The samples immersed in fresh water lost their stability and flow after 11 days, while the ones immersed in sewage water lost their stability and flow after 9 days. Furthermore, the loss in stability for samples immersed in fresh water and sewage after 14 days was found to be 38.8 and 55.6%, respectively. The results revealed that sewage water affects asphalt concrete pavement more severely than freshwater. Finally, it was concluded that proper drainage and adequate supplemental sewerage systems are necessary to maintain the desired strength of the pavement throughout its design life.

A Novel Computational Mathematical Model for Team and Route Selection of the Emergency Response Operations

Dalvana Lopes Ribeiro, Andre Andrade Longaray — Tue, 02 Apr 2024 00:00:00 +0000

During the daily operations of emergency response, the decision maker is faced with the complex challenge of selecting a team and route in a short time period to respond and attend to the emergency. This study presents the combined use of the Analytic Hierarchy Process (AHP), the Élimination Et Choix Traduisant la Realité II (ELECTRE II), and the Dijkstra algorithm to deal with such situations. First, the AHP method is implemented to rank the aspects that are most relevant to a given emergency. Subsequently, this ranking is employed in ELECTRE-II to determine which emergency response team is best prepared to provide support. In the last stage of the proposed model, regarding the geographic coordinates of the team and the emergency, the Geographic Information System (GIS) utilizes the Dijkstra algorithm to regulate the most suitable route for assistance.

Development and Characterization of a PLA Biocomposite reinforced with Date Palm Fibers

Ines Ghanmi, Faouzi Slimani, Samir Ghanmi, Mohamed Guedri — Tue, 02 Apr 2024 00:00:00 +0000

Despite the promising potential of bio-composites derived from plant fibers due to their ecological and economic benefits, challenges persist in their preparation, restricting their commercial applications. These challenges are primarily associated with developing suitable methods, acquiring appropriate equipment for treating plant fibers, and addressing the time constraints in preparation. This study aims to contribute to the development and characterization of a new biocomposite and biodegradable material based on natural fibers produced through hot compression. The newly developed biocomposite comprises commercial biodegradable poly-lactic acid (PLA) as a matrix and untreated fiber fabric extracted from date palms as reinforcement. The use of untreated fiber fabric has successfully overcome the preparation difficulties. Experimental results on the new biocomposite reveal the strong adhesion between its fibers and the matrix, emphasizing the significant impact of choosing the right manufacturing conditions on the developed mechanical properties.

Microstructure and Mechanical Properties of Carbon Fiber Phenolic MatrixComposites containing Carbon Nanotubes and Silicon Carbide

Tayyab Subhani — Tue, 02 Apr 2024 00:00:00 +0000

A novel class of hybrid composites was prepared containing carbon fibers along with carbon nanotubes and silicon carbide particles in phenolic resin for improved mechanical performance. The loading of carbon fibers was ~60 wt% while carbon nanotubes and silicon carbide particles were reinforced in the fractions of 0.1 wt% and 5 wt%, respectively. Individually reinforced composites containing 0.1 wt% carbon nanotubes and 5 wt% silicon carbide particles were also manufactured for comparison with hybrid composites. Microstructural and mechanical property characterization was performed using electron microscopy and mechanical testing, respectively. Uniform dispersion of nanometer-scale carbon nanotubes and micrometer-scale silicon carbide particles was observed under microscopy. The pooled effect of carbon nanotubes and silicon carbide particles significantly increased the tensile, compressive, and flexural performance of composites while carbon nanotubes offered greater weight fraction value improvement than silicon carbide particles.

The Influence of Plasma Nitriding Technology Parameters on the Hardness of 18XГT Steel Parts

Nguyen Thai Van, Le Hong Ky — Tue, 02 Apr 2024 00:00:00 +0000

This article presents the results of the research on the influence of plasma nitriding technology parameters on the working surface hardness of machine parts made of previously hardened 18XГT steel. A total of 27 experiments were conducted on the H4580 Eltrolab instrument. Minitab software was used to process the experimental results. The regression function set up with visual charts was utilized as the basis for analysis of the influence of temperature, time, and gas permeation concentration on the working surface hardness. Analysis of variance (ANOVA) showed that all the nitriding technology parameters influenced the regression function. The permeation temperature TL had the greatest influence on hardness, while the permeation time h and the gas permeation concentration G1 had less influence. When the double interaction between the parameters was considered, it was shown that these pairs also had a large influence on the surface hardness, but at different levels.

Innovative Technological Solutions for Environmental Sustainability in Chinese Engineering Practices

Azhar Ud Din, Yang Yang, Muhammad Inam Makki Khan, Waqas Khuram — Tue, 02 Apr 2024 00:00:00 +0000

The Chinese government announced the clear goal of attaining carbon neutrality by 2060, in order to gradually achieve net-zero carbon dioxide (CO₂) emissions, whose impact on global warming needs to be reduced while also a sustainable industry needs to be promoted. Recognizing the critical role of Green Human Resource Management (GHRM) in supporting green innovation and achieving the carbon neutrality agenda, this study aims to fill a research gap by emphasizing this overlooked nexus. The former examines the influence of GHRM, green innovation, and carbon neutrality on environmental performance by carefully analyzing the current literature on China's achievement of carbon neutrality and its implications for environmentally friendly performances. The current study assesses the planning frameworks of the country, explores the concept of achieving carbon neutrality, and evaluates the practical implications.

Short Empirical Insight: Leadership and Artificial Intelligence in the Pharmaceutical Industry

Chunjia Hu, Qaiser Mohi Ud Din, Li Zhang — Tue, 02 Apr 2024 00:00:00 +0000

This study aims to analyze the importance of the emerging idea of green talent management and its effect on employees' innovative work behavior. In addition, the study examines how ethical leadership and artificial intelligence influence Pakistan's pharmaceutical industry. Four hundred and seven (407) survey forms were gathered from the management departments of five pharmaceutical industries in the twin cities of Pakistan (Islamabad and Rawalpindi). The data collected were analyzed using PLS-SEM with the help of Smart PLS. The empirical evidence presented in this study supports the notion that green talent management significantly affects employees' innovative work behavior. Furthermore, the results reveal that ethical leadership and artificial intelligence are crucial in regulating the connection between green talent management and innovative work behavior. This study provides managerial and theoretical implications derived from its results. These implications can help leaders in pharmaceutical industries effectively leverage green talent management to stimulate innovative work behaviors of their employees and attain a competitive edge in their respective marketplaces. Several studies focused on addressing the difficulties faced by organizational leaders in cultivating and maintaining people who can make valuable contributions to their companies and help gain a competitive edge in their markets. However, studies that investigate these risks are limited.

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Souad Kamel, Abeer Al Qahtani, Abdullah Saad Musaed Al-Shahrani — Tue, 02 Apr 2024 00:00:00 +0000

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection

Ahmed Alzahrani — Tue, 02 Apr 2024 00:00:00 +0000

Images sent across internet platforms are frequently subject to modifications, including simple alterations, such as compression, scaling, and filtering, which can mask possible changes. These modifications significantly limit the usefulness of digital image forensics analysis methods. As a result, precise classification of authentic and forged images becomes critical. In this study, a system for augmented image forgery detection is provided. Previous research on identifying counterfeit images revealed unexpected outcomes when using conventional feature encoding techniques and machine learning classifiers. Deep neural networks have been also utilized in these efforts, however, the gradient vanishing problem was ignored. A DenseNet model was created to tackle limitations inherent in typical Convolutional Neural Networks (CNNs), such as gradient vanishing and unnecessary layer requirements. The proposed DenseNet model architecture, which is composed of densely connected layers, is designed for precise discrimination between genuine and altered images. A dataset of forged images was implemented to compare the proposed DenseNet model to state-of-the-art deep learning methods, and the results showed that it outperformed them. The recommended enhanced DenseNet model has the ability to detect modified images with an astonishing accuracy of 92.32%.

Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques

Mohamed Ali Zdiri, Bilel Dhouib, Zuhair Alaas, Hsan Hadj Abdallah — Tue, 02 Apr 2024 00:00:00 +0000

This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.

Feature Imputation using Neutrosophic Set Theory in Machine Learning Regression Context

Yamen El Touati, Walid Abdelfattah — Tue, 02 Apr 2024 00:00:00 +0000

The prediction context of machine learning aims to discern the underlying patterns that dictate the characteristics to forecast the output. This prediction lacks precision when the input data is not accurate or precise. This study focuses on feature imputation through the application of the neutrosophic set theory. The primary concept involves substituting feature data, which may have accuracy and correctness issues, with neutrosophic variables considering the degrees of truth, indeterminacy, and falsity to produce more precise and resilient predictions. The proposed method was implemented in a specific case study, and the results are analyzed.

The Role of Machine Learning in Managing and Organizing Healthcare Records

Ahmed Mohammed Alghamdi, Mahmoud Ahmad Al-Khasawneh, Ala Alarood, Eesa Alsolami — Tue, 02 Apr 2024 00:00:00 +0000

With the exponential growth of medical data, Machine Learning (ML) algorithms are becoming increasingly important to the management and organization of healthcare information. This study aims to explore the role that ML can play in optimizing the management and organization of healthcare records, by identifying the challenges, advantages, and limitations associated with this technology. Consequently, the current study will contribute to the understanding of how ML might be applied to the healthcare industry in a variety of circumstances. Using the findings of this study, healthcare professionals, researchers, and policymakers will be able to make informed decisions regarding the adoption and implementation of ML techniques for regulating healthcare records. The findings of this paper revealed that ML can play an important role in efficiently directing and classifying healthcare records using different perspectives.

A Systematic Literature Review on Construction Management Productivity Enhancement by utilizing Business Information Modeling

Abd Alrazaq Khamees Saja, Rasheed Mohammed Sawsan — Tue, 02 Apr 2024 00:00:00 +0000

The systematic review of Business Information Modeling (BIM) plays a crucial role in understanding its significance and impact. This review allows for a comprehensive examination of the existing literature, highlighting the benefits, challenges, and success factors associated with BIM. There is a scarcity of studies dealing with this subject, and so a question about the most important advantages that will be obtained by the construction industry, especially the construction companies, as a result of the BIM application arises. Relevant previous studies were reviewed and their quality was evaluated using a systematic methodology. The current study was characterized by the use of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis technique. As far as is known, the current study is the first of its kind in the field of Iraqi project management. The results suggest that the BIM benefits include firm's growth, organizational performance, enhanced market value, employee motivation, and service quality.

Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence

Hind Moutaz Al-Dabbas, Raghad Abdulaali Azeez, Akbas Ezaldeen Ali — Tue, 02 Apr 2024 00:00:00 +0000

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.

Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection

Ecem Iren — Tue, 02 Apr 2024 00:00:00 +0000

Deep learning is a concept of artificial neural networks and a subset of machine learning. It deals with algorithms that train and process datasets to make inferences for future samples, imitating the human process of learning from experiences. In this study, the YOLOv5 and YOLOv6 object detection models were compared on a plant dataset in terms of accuracy and time metrics. Each model was trained to obtain specific results in terms of mean Average Precision (mAP) and training time. There was no considerable difference in mAP between both models, as their results were close. YOLOv5, having 63.5% mAP, slightly outperformed YOLOv6, while YOLOv6, having 49.6% mAP50-95, was better in detection than YOLOv5. Furthermore, YOLOv5 trained data in a shorter time than YOLOv6, since it has fewer parameters.

Performance Enhancement of Distributed Processing Systems Using Novel Hybrid Shard Selection Algorithm

Praveen M. Dhulavvagol, Sashikumar G. Totad — Tue, 02 Apr 2024 00:00:00 +0000

Distributed processing systems play a crucial role in query search operations, where large-scale data are partitioned across multiple nodes using shard selection algorithms. However, the existing shard selection algorithms pose significant challenges, such as shard ranking, shard cut-off estimation, high latency, low throughput, and high processing costs. These limitations become more pronounced as the data size increases, affecting the efficiency and effectiveness of search operations. To address these challenges, the novel Hybrid Shard Selection Algorithm (HSSA) is proposed as a solution in this paper, designed specifically to enhance the effectiveness and efficiency of search operations within distributed processing systems. HSSA employs an advanced sharding approach that adeptly navigates and targets pertinent shards based on specific queries. This not only curtails search-related overhead but also enhances operational efficiency. Through rigorous testing using the Gov2 dataset, the HSSA algorithm has proven its merits. When set against well-established algorithms like CORI, Rank-S, and SHiRE, HSSA stands out, registering remarkable gains in average throughput by 21%, 16%, and 12%, while also slashing latency by 14.2%, 9.4%, and 8.2%, respectively. The insights gained from this research underscore HSSA's capability to effectively bridge the gaps inherent in traditional shard selection strategies. Furthermore, its exemplary efficacy with datasets of varied sizes amplifies its relevance for practical integration within distributed processing landscapes.

Modeling of Mass Transfer and Reaction Kinetics in ZnO Nanoparticle Micro-Reactor Systems for AMX and DOX Degradation

Nidhal Becheikh — Tue, 02 Apr 2024 00:00:00 +0000

This study aims to model the coupled phenomena of photocatalytic reaction and mass transfer in the degradation of Amoxicillin (AMX) and Doxycycline (DOX) using Zinc oxide (ZnO) nanoparticles within microreactor systems. The objective is to gain a comprehensive understanding of the dynamic interaction between the photocatalytic degradation kinetics and the mass transfer processes to optimize the conditions for efficient antibiotic removal from contaminated water. This involves characterizing the reaction kinetics via the Langmuir-Hinshelwood model, estimating the mass transfer coefficients, and analyzing the effects of axial dispersion to ensure the accurate determination of intrinsic kinetic constants and minimize mass transfer limitations. This study used a syringe pump to ensure a consistent flow of antibiotic solution into the microreactor. The results indicate that AMX reaches adsorption equilibrium more rapidly than DOX, corresponding to its faster photocatalytic degradation kinetics and higher final conversion rate (89% for AMX, 86% for DOX). The mass transfer coefficient (k_d) was estimated using the Sherwood number, derived from three different models, with the constant Sherwood model best fitting the R1 microreactor data. An analysis of the Damköhler number (D_aII) indicates that high flow rates minimize mass transfer limitations in the R1 microreactor, allowing the determination of near-intrinsic kinetic constants. On the contrary, at low flow rates, kinetic constants are apparent as a result of mass-transfer limitations. The study concludes that higher flow rates (≥ 10 mL/h) in the R1 microreactor are preferable to approach intrinsic kinetics and reduce mass transfer limitations during photocatalytic degradation of antibiotics. These findings underscore the potential of ZnO-based oxidation processes in treating antibiotic-contaminated water with optimized conditions, providing a pathway for efficient and sustainable wastewater treatment.

Digital Forensics Readiness Framework (DFRF) to Secure Database Systems

Ahmed Albugmi — Tue, 02 Apr 2024 00:00:00 +0000

Database systems play a significant role in structuring, organizing, and managing data of organizations. In this regard, the key challenge is how to protect the confidentiality, integrity, and availability of database systems against attacks launched from within and outside an organization. To resolve this challenge, different database security techniques and mechanisms, which generally involve access control, database monitoring, data encryption, database backups, and strong passwords have been proposed. These techniques and mechanisms have been developed for certain purposes but fall short of many industrial expectations. This study used the design science research method to recommend a new Digital Forensic Readiness Framework, named DFRF, to secure database systems. DFRF involves risk assessments, data classification, database firewalls, data encryption, strong password policies, database monitoring and logging, data backups and recovery, incident response plans, forensic readiness, as well as education and awareness. The proposed framework not only identifies threats and responds to them more effectively than existing models, but also helps organizations stay fully compliant with regulatory requirements and improve their security. The design of the suggested framework was compared with existing models, confirming its superiority.

Evaluation and Improvement of the Accuracy of Reanalysis and Analysis Datasets for Wind Resource Assessment in Sudan

Youssef Kassem, Huseyin Camur, Mohamedalmojtba Hamid Ali Abdalla — Tue, 02 Apr 2024 00:00:00 +0000

Wind speed datasets are used to evaluate wind resources and energy production of wind farms. In locations where measured data are not available, reanalysis and analysis datasets can be used as an alternative to assess wind resources. This study evaluated the accuracy of wind speed data collected from reanalysis and analysis datasets against mast-measured data between 1975 and 1985 in Sudan, using monthly statistical analyses. Three bias correction methods, based on Measure-Correlate-Predict (MCP) and Linear Adaptation (LA1 and LA2), were applied to determine the original wind speed. The results indicate that LA1 outperformed MCP and LA2. Furthermore, the Weibull distribution function was employed to analyze the wind speed characteristics. In addition, wind power density was calculated using data from different sources. The findings show that although the wind power potential of the chosen locations is not suitable for large wind turbines, wind power can still be exploited with small wind turbines. Consequently, this study introduces a wind energy roadmap to attract investors in clean energy for sustainable development in Sudan, address energy problems, and meet domestic demands. The study also identifies the most important grid datasets for assessing the country's wind potential, enhancing the accuracy of assessments for investors and policymakers.

Magnesium Oxide (MgO) as a Sustainable Catalyst for Biodiesel Production from Waste Cooking Oil: A Comparative Study with KOH

Aboulbaba Eladeb — Tue, 02 Apr 2024 00:00:00 +0000

The present study investigates the efficiency of magnesium oxide (MgO) as a heterogeneous catalyst in the production of biodiesel from waste cooking oil (WCO), putting an emphasis on its environmental benefits, cost-effectiveness, and operational efficacy. Through a series of experiments, we optimized the reaction conditions, including catalyst concentration, reaction temperature, and ethanol to WCO molar ratio, to achieve a high biodiesel yield. The results indicate that an optimal MgO concentration of 3 wt%, a reaction temperature of 65 °C, and a molar ratio of 9:1 result in the highest biodiesel production efficiency. Additionally, MgO demonstrated significant reusability without a decrease in performance, underscoring its economic and environmental advantages. Comparative analysis revealed that MgO outperforms conventional KOH catalysts in terms of yield, purity, and sustainability. Our study suggests future research directions, including the optimization of MgO preparation methods and the exploration of co-catalyst systems to further enhance biodiesel production from WCO. This research contributes to the development of sustainable biodiesel production methods, aligning with global energy and environmental goals.

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Tue, 02 Apr 2024 00:00:00 +0000

Today, automatic emotion recognition in speech is one of the most important areas of research in signal processing. Identifying emotional content in Arabic speech is regarded as a very challenging and intricate task due to several obstacles, such as the wide range of cultures and dialects, the influence of cultural factors on emotional expression, and the scarcity of available datasets. This study used a variety of artificial intelligence models, including Xgboost, Adaboost, KNN, DT, and SOM, and a deep-learning model named SERDNN. ANAD was employed as a training dataset, which contains three emotions, "angry", "happy", and "surprised", with 844 features. This study aimed to present a more efficient and accurate technique for recognizing emotions in Arabic speech. Precision, accuracy, recall, and F1-score metrics were utilized to evaluate the effectiveness of the proposed techniques. The results showed that the Xgboost, SOM, and KNN classifiers achieved superior performance in recognizing emotions in Arabic speech. The SERDNN deep learning model outperformed the other techniques, achieving the highest accuracy of 97.40% with a loss rate of 0.1457. Therefore, it can be relied upon and deployed to recognize emotions in Arabic speech.

The Effect of Waste Marble Dust and Corncob Ash on the Engineering and Micro-Structural Properties of Expansive Soil for Use in Road Subgrades

Leonardo Z. Wongbae, Charles Kabubo, Alphonce Owayo — Tue, 02 Apr 2024 00:00:00 +0000

This research investigated the effect of Waste Marble Dust (WMD) and Corncob Ash (CCA) on expansive soil's engineering and microstructural properties. Various laboratory experiments were performed on the natural soil to ascertain its characteristics. The corncobs underwent pre-water treatment for fourteen days to remove excess potassium and increase their silica content, resulting in a rise in the silica level from 0% to 50%. At first, only WMD was added to the soil in increments of 5% to 30% using compaction and California bearing tests. The optimum dosage of 15% WMD addition yielded the best result. CCA was then incorporated by the weight of the soil from 2% to 10% in increments of 2% to the first optimum (15% WMD) to obtain the overall optimum for the study (15% WMD and 8% CCA). Stabilization of the natural soil using both materials led to the modification and solidification of the soil mass, evident by the rise in California bearing ratio values from 1.68% to 15.53% and unconfined compressive strength from 41.33 kN/m² to 174.68 kN/m². There was also a decrease in the soil's free swell from 120% to 15% as well as reductions in the liquid limits from 56.23% to 36.01% and in the plasticity index from 29.74% to 8.72%, respectively. The microstructural images showed the formation of cementitious compounds in the form of calcium silicate hydrate and calcium aluminate hydrate gels. The findings indicate that using WMD and CCA as a unit has great potential in enhancing engineering properties, like strength parameters and the swell potential of expansive soils.

Assessing the Influence of Various Work Breakdown Structures on Project Completion Time

Babatunde Omoniyi Odedairo — Tue, 02 Apr 2024 00:00:00 +0000

In project management, a clear definition of the objective is required for the success of a project. Scope management is a performance indicator used to ascertain compliance with predefined project boundaries. The Work Breakdown Structure (WBS) is an essential part of the scope management process and a tool in project planning. Although there is much research on WBS, there is a lack of information regarding the relationship between the selection of WBS orientation and project completion time. In this paper, the influence of alternative WBS orientations on project completion time is assessed. The Project Life Cycle (PLC) and technology (T) WBS were applied across two projects—the construction of a Liquefied Petroleum Gas (LPG) facility and the Renovation of an Office Complex (ROC)—using a top-down decomposition methodology. The PLC-WBS and T-WBS were created utilizing Figma software. The project duration was determined using the critical path method, which was implemented in the Python programming language. Based on WBS selection, differences were discovered in the definition of the project deliverables, network construction, and aggregation of work packages. These discrepancies had an impact on the technological relationships between activities by reducing opportunities for parallel processing. The LPG project was completed in 86 days using the PLC-WBS and in 80 days using the T-WBS orientation. For ROC, the project can be accomplished within 128 and 126 days, using the PLC-WBS and T-WBS orientation, respectively. This outcome suggested that there might be an association between the WBS and the project objective. Therefore, an assessment of different WBSs in project scope management demonstrated their potential influence on decision-making in activity planning and scheduling, network construction, and project objectives.

An Approach to Determine and Categorize Mental Health Condition using Machine Learning and Deep Learning Models

B. H. Bhavani, N. C. Naveen — Tue, 02 Apr 2024 00:00:00 +0000

The mental health of the human population, particularly in India during and after the COVID-19 pandemic is a major concern. All age groups have undergone mental stress during and after COVID-19, especially college students in urban areas and individuals belonging to the age group from 16 to 25. Early detection of mental stress among urban students will help in the resolution of major related issues that may hurt one's career. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled the prediction of mental health status. Numerous studies have been conducted using various approaches, but there is still no agreement on how to predict mental symptoms across age groups. In the current study, proposed DL, Long Short-Term Memory (LSTM), and ML models, namely Support Vector Machine (SVM), ADA Boost, Random Forest (RF), K-Nearest Neighbor (K-NN), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) are trained and tested on a real-world dataset. The DL LSTM model outperformed the conventional ML models with an accuracy of 100%.

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Gaseb Alotibi — Tue, 02 Apr 2024 00:00:00 +0000

Social engineering addresses a broad category of techniques aiming to persuade someone to reveal data or perform actions for criminal purposes, such as disclosing personal information about a particular target. Cybersecurity awareness is required to raise people’s understanding of how these social engineering techniques are being used and so their capacity to exploit them. To accomplish this objective, primary focus is given to educating and training individuals on how to recognize such incidents and respond to them effectively. To protect people against social engineering threats, various cybersecurity models and approaches have been proposed. There are, however, a few differences between these models, since they are developed for specific purposes. Thus, the main objective of this study is to develop a cybersecurity awareness model specifically designed for Saudi students to protect them from social engineering attacks. The design science methodology was utilized in this study. The proposed model consists of four main stages: education and training, developing policies and guidelines, improving Saudi schools’ security, as well as monitoring and evaluation. The model introduced can ensure the safety and privacy of students, teachers, and staff across different social platforms.

Assessing Real-Time Health Impacts of outdoor Air Pollution through IoT Integration

Tue, 02 Apr 2024 00:00:00 +0000

Air pollution constitutes a significant global challenge in both public health and the environment, particularly for countries undergoing industrialization and transitioning from low- to middle-income economies. This study aims to investigate the feasibility and effectiveness of a real-time air quality prediction system based on data collected from Internet of Things (IoT) sensors to help people and public institutions track and manage atmospheric pollution. The primary objective of this study was to investigate whether an IoT-based approach can provide accurate and continuous real-time air quality forecasting. The standard dataset provided by the Indian government was analyzed using regression, traditional Long-Short-Term Memory (LTSM), and bidirectional LSTM (BLSTM) models to evaluate their performance on multivariate air quality features. The results show that the proposed BLSTM model outperformed the other models in minimizing RMSE errors and avoiding overfitting.