bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Data Envelopment Analysis to Evaluate Health Regions Efficiency in Saudi Arabia
Authors: Abdelfattah Walid , Alanazi Bader S.
Published: June 2024

Efficient healthcare systems must provide quality care, ensuring equitable access and sustaining financial viability. This study employs Data Envelopment Analysis (DEA) to evaluate the efficiency of healthcare regions in Saudi Arabia over a five-year period (2017-2021). Departing from traditional hospital-centric assessments, the study takes a regional approach, offering a holistic view of the entire healthcare system. Inputs such as number of beds, physicians, and nurses, along with outputs like outpatients and inpatients, were considered. The study not only provides efficiency scores but also identifies reference health regions, benchmarks, and tangible targets for improvement. Notably, the impact of the COVID-19 pandemic on healthcare efficiency is analyzed, providing insights into adaptive strategies during crises. The findings contribute to the understanding of regional healthcare dynamics, offering actionable insights for policymakers, facilitating evidence-based resource allocation, and informing strategies for continuous improvement. Future research directions include a global benchmarking analysis and a qualitative exploration of policy implications. This study bridges the gap between academic research and practical policy considerations, emphasizing the importance of adaptability and resilience in healthcare systems.
Measured and Predicted Unsaturated Permeability of Cracked Compacted Fine Soil
Authors: Mabrouk Abdelkader , Jamei Mehrez , Ahmed Anwar
Published: June 2024

The unsaturated permeability of cracked compacted fine soil is a key parameter in geotechnical engineering, particularly when analyzing water flow through the soil in various conditions. The compaction affects the saturated and unsaturated permeability by reducing porosity. However, cracks can appear by shrinkage and growth during desiccation, which obviously leads to macro-porosity (a process during which the soil acquires a high level of double porosity). The development of a crack network influences the suction (as negative water pressure) and then the unsaturated permeability. The current paper aims to analyze the role of the crack network (considered as macropores) on the unsaturated permeability, by quantifying the network based on the Crack Intensity Factor (CIF). The unsaturated permeability is given as a function, separately of CIF and suction. The experimental results may be considered constructive for soil modeling. Regarding the birth of the first crack, it occurred when the suction reached a value near to that of the air entry suction. Since the first crack appeared, primary cracks were developed and then followed by secondary cracks. The obtained experimental results of WRC and Kunsat for cracked compacted clay are beneficial in managing the design of the geotechnical structure stability and the environmental issues of water diffusion. CIF increases with suction, which is augmented during the drying process demonstrating a decrease in the moisture content. After 21 hours of desiccation, CIF ended up reaching a value of 4%. It is generally recognized that cracks create preferential pathways for water flow, whereas their geometry and distribution influence how water moves through the soil. Modeling the impact of cracks on permeability may involve considering factors like crack width, orientation, and connectivity. In this paper, a simple model was proposed to predict the unsaturated permeability as a function of suction with different CIF values with the material being assumed as a double porosity soil.
The Mechanical Behavior of High-Density Polyethylene under Short-Time Hydraulic Pressure Test
Authors: Manu Ioana Daniela , Petrescu Marius Gabriel , Zisopol Dragos Gabriel , Naim Ramadan Ibrahim , Ilinca Costin Nicolae
Published: June 2024

This paper provides a synthesis of the results of experimental research and numerical simulations on polyethylene pipes subjected to short-time hydraulic pressure testing. Also, the current paper offers basic information about the engineering behavior of High-Density Polyethylene (HDPE) under the aforementioned test. HDPE presents high levels of technical performance because it has a high-density resin, high molecular weight, and bimodal Molecular Weight Distribution (MWD). HDPE pressure pipelines are used in Drinking Water Distribution Networks (DWDNs) and are component pieces of the thermoplastic piping system. The experimental test was mainly oriented toward the comparative determinations of the burst pressure of both the defect-free pipes and those with a lack of material defects made through mechanical operations. Also, the experimental test establishes the short-time hydraulic failure pressure as well as the determination of the resistance of the polyethylene pipes to hydraulic pressure in a short time period. The numerical simulations were carried out with the purpose of validating the results obtained analytically and experimentally.
Real-Time Home Energy Management with IoT and Blockchain
Authors: Jaber Al-Reshidi Eissa , Ramadan Rabie Α. , Aboshosha Bassam W. , Salem Marwa , Alayba Abdulaziz Mohammed
Published: June 2024

As a result of the exponential increase in energy consumption, energy shortages, and augmented energy costs have become a significant problem for households. Solar cells are used in many homes and/or buildings to address these problems. However, managing renewable energy sources in homes can be difficult due to the irregular nature of renewable energy production. Internet of Things (IoT) devices can provide real-time data on energy production and consumption, offering a promising solution to this issue. The current study proposes a framework based on IoT and blockchain technology for home energy management by predicting future energy consumption patterns and optimizing energy use in real time. The blockchain module facilitates peer-to-peer energy trading between renewable energy-generating homeowners and consumers. The proposed framework was tested employing a dataset based on smart homes with solar panels and wind turbines. The results manifest a reduction in energy costs and a possible 30% increase in their traditional gain.
Economic Viability of Distribution Network Upgrade Deferral through BESS Sizing from K-Means Clustered Annual Load Profile Data
Authors: Ondigo Edwin , Wekesa Cyrus
Published: June 2024

The augmented electricity demand requires electrical infrastructure upgrades with system operators instituting strategies to increase Distribution Network (DN) capacity in tandem with load growth. In this study, a simple method of deploying Li-ion Battery Energy Storage Systems (BESSs) to defer DN upgrades is presented by utilizing historical load profiles. The k-means algorithm is employed to cluster the annual load profiles obtained from a substation in groups of 15-minute intervals. The load data are min-max scaled and fed as input to the K-means algorithm. The NPV financial analysis method is followed in the DN upgrade deferral benefit determination with the acquired benefit depending on Li-ion BESS price and feeder upgrade cost. The results indicate economic viability of up to four years with a Net Present Value (NPV) of US$10k for Li-ion 2000kW/3000kWh BESS. More benefits and deferral years are achieved by varying Li-ion BESS and feeder upgrade costs to 80% and 120%, respectively with deferral years increased to six with an NPV of US$110k for Li-ion BESS of 3100kW/6000kWh.
Behavior of Reinforced Concrete Beams with Vertically Penetrated Holes across the Cross ‐ Sectional Depth
Authors: Khadhair ‪Aws Yaseen‬‏ , Izzet Amer F.
Published: June 2024

In Iraq and other countries, RC beams are frequently used as alternative small slab penetrations in low-rise constructions. The structure is capable of transferring stresses effectively and the existence of minor gaps normally has little impact on the structural performance. However, special consideration must be given to the analysis and design of such beams depending on the effect of the openings. Hence, the objective of this paper is to address this research gap by evaluating and enhancing the behavior of RC beams with a vertical opening that penetrates the entire depth of the beams and obstructs the reinforcement bars since all the building design codes and guidelines introduce very limited recommendations for beams with vertical openings. To achieve that, five specimens were fabricated as RC beams with vertical openings and one specimen as a reference beam. The six specimens were tested under two-point loads over a clear span length of 180 cm and a total length of 200 cm. It was found that by adopting a vertical opening into the RC beam without any strengthening methods, the ultimate carrying load capacity decreased and the mid-span deflection increased compared with the reference solid beam.
Effect of Sustainable Materials on Some Properties of Pervious Concrete
Authors: Kazem Demoa Jawad , Fawzi Nada Mahdi
Published: June 2024

Recycling and sustainability are important topics nowadays due to the increased environmental problems and waste accumulation. In this research, focus was given on the production of sustainable pervious concrete. Four mixes were prepared: a reference mixture, a mixture with 30% of the volume of coarse aggregates replaced with ceramic waste, the third mixture was similar to the second, but it contained 10% metakaolin instead of cement, and the fourth mixture was similar to the third, but carbon fibers were added. Thermal conductivity and density tests were carried out after 28 days of curing on the casting samples, while flexural tests were performed after 7 and 28 days. The results showed that flexural strength, density, and thermal conductivity were decreased in the second and third mixtures compared to the reference mixture, but in different decreasing percentages, so the good effect of metakaolin became clear. The addition of carbon fibers to the fourth mixture led to an increase in flexural strength and thermal conductivity, whereas density was lower than in the reference and third mixtures.
Hardware Implementation of a Deep Learning-based Model for Image Quality Assessment
Authors: Said Yahia , Alsariera Yazan A.
Published: June 2024

Image quality assessment is very important for accurate analysis and better interpretation. In reality, environmental effects and device limitations may degrade image quality. Recently, many image quality assessment algorithms have been proposed. However, these algorithms require high computation overhead, making them unsuitable for mobile devices, such as smartphones and smart cameras. This paper presents a hardware implementation of an image quality assessment algorithm based on a Lightweight Convolutional Neural Network (LCNN) model. Many advances have been made in the construction of high-accuracy LCNN models. The current study used EfficientNet V2. The model achieved state-of-the-art image classification performance on many famous benchmark datasets while having a smaller size than other models with the same performance. The model was utilized to learn human visual behavior through understanding dataset information without prior knowledge of target visual behavior. The proposed model was implemented employing a Field Programmable Gate Array (FPGA) for possible integration into mobile devices. The Xilinx ZCU 102 board was implemented to evaluate the proposed model. The results confirmed the latter’s efficiency in image quality assessment compared to existing models.
Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity
Authors: Kumar Ankit , Sharma Richa , Bedi Punam
Published: June 2024

This study carries out a comprehensive comparison of fine-tuned GPT models (GPT-2, GPT-3, GPT-3.5) and LLaMA-2 models (LLaMA-2 7B, LLaMA-2 13B, LLaMA-2 70B) in text classification, addressing dataset sizes, model scales, and task diversity. Since its inception in 2018, the GPT series has been pivotal in advancing NLP, with each iteration introducing substantial enhancements. Despite its progress, detailed analyses, especially against competitive open-source models like the LLaMA-2 series in text classification, remain scarce. The current study fills this gap by fine-tuning these models across varied datasets, focusing on enhancing task-specific performance in hate speech and offensive language detection, fake news classification, and sentiment analysis. The learning efficacy and efficiency of the GPT and LLaMA-2 models were evaluated, providing a nuanced guide to choosing optimal models for NLP tasks based on architectural benefits and adaptation efficiency with limited data and resources. In particular, even with datasets as small as 1,000 rows per class, the F1 scores for the GPT-3.5 and LLaMA-2 models exceeded 0.9, reaching 0.99 with complete datasets. Additionally, the LLaMA-2 13B and 70B models outperformed GPT-3, demonstrating their superior efficiency and effectiveness in text classification. Both the GPT and LLaMA-2 series showed commendable performance on all three tasks, underscoring their ability to handle a diversity of tasks. Based on the size, performance, and resources required for fine-tuning the model, this study identifies LLaMA-2 13B as the most optimal model for NLP tasks.
Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping
Authors: Khalil Ragab , Khan Mohammad Shahiq , Hasan Yassin , Nacer Nacer , Khan Sheroz
Published: June 2024

Temporal-vegetation mapping bearing temporal-related features is important because it helps to understand the global climate changes that drive resource management and habitat conservation. This paper presents a Supervised Normalized Difference Vegetation Index (SNDVI) approach for mapping the vegetation cover in arid environment regions. The NDVI is used to extract features to classify land as a vegetation cover, water body, or bare soil. Through the use of Normalized Difference Vegetation Index (NDVI), regions can be categorized as dry or sandy, based on the soil reflectance values. NDVI is the most commonly deployed index for accurate vegetation cover estimates. The NDVI values lie in a range from -1 to +1, depending on the environmental region and vegetation conditions. It is difficult to assign a specific threshold value to distinguish between vegetation and non-vegetation for all the eco-regions under a specific landscape and ecological conditions. The proposed approach is based on the quantitative verification of the samples as well as the supervised classification method followed to categorize the images. The SNDVI approach has been applied to three different locations in three different seasons in arid ecoregions to extract features for vegetation mapping. The results disclose that SNDVI is a very reliable parameter in extracting true vegetation cover in arid regions. An accuracy evaluation matrix has been performed for each case study and the overall obtained accuracy value ranged from 82% to 100%, depending on the season of the area under investigation. The utility of the proposed method is determined by bench-marking the results with those of the techniques recently utilized by contemporary researchers.
Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective
Authors: Khan Muhammad Nafees , Shao Zhen
Published: June 2024

This study aims to systematically review the literature on the impact of big data and knowledge management on customer interactions and consumption patterns from an applied science perspective. A comprehensive search strategy was implemented in seven scientific publication databases. The inclusion criteria consisted of original research articles published in English, excluding gray literature, book chapters, and conference proceedings. A total of 400 articles were retrieved, and 40 articles met the inclusion criteria after two rounds of screening. The selected articles were analyzed following a mixed-method approach incorporating qualitative and quantitative data analysis techniques. Thematic analysis was deployed to identify recurring themes and patterns in the articles, while descriptive statistics were used to summarize the study characteristics. The data analysis showed that big data and knowledge management significantly affect customer interactions and consumption patterns, with most studies focusing on the retail and banking sectors. The findings of this study have several theoretical and practical implications. From a theoretical point of view, this review contributes to the growing body of literature on the intersection of big data, knowledge management, and consumer behavior. From a practical perspective, the results can inform policymakers and practitioners on leveraging big data and knowledge management in order to improve customer interactions and consumption patterns.
Study on Topology Optimization Design for Additive Manufacturing
Authors: Anh Nguyen Thi , Quynh Nguyen Xuan , Tung Tran Thanh
Published: June 2024

Topology optimization is an advanced technique for structural optimization that aims to achieve an optimally efficient structure by redistribution materials while ensuring fulfillment of load-carrying, performance, and initial boundary. One of the obstacles in the process of optimizing structures for mechanical parts is that these optimized structures sometimes encounter difficulties during the manufacturing process. Additive Manufacturing (AM), also known as 3D printing technology, is a method of manufacturing machine parts through joining layers of material. AM opens up the possibility of fabricating complex structures, especially for structures that have been subjected to topology optimization techniques. This project aims to compare the initial shape of a box under static load and its shape after optimization. The subsequent produced models have reduced weights of 43%, 59%, 70%, 73%, and 77%, respectively, weighing 491.45 g, 357.42 g, 261.31 g, 235.56 g, and 203.87 g. All models are capable of supporting a 10 kg load, demonstrating the ability of the structure to meet technical specifications. The results show that combining structural optimization and additive manufacturing can take advantage of both approaches and show significant potential for modern manufacturing.
3D Numerical Study and Parametric Analysis of PV/T Design Effect on Thermal and Electrical Performance
Authors: Souissi Ahmed Saad Eddine , Masmali Majed , Fterich Mohamed , Toutti Ezzeddinne , Chouikhi Houssam
Published: June 2024

This paper explores the influence of design variations on the electrical and thermal efficiencies of PV/T (Photovoltaic-Thermal) systems. Utilizing COMSOL Multiphysics, three different PVT configurations with varying air duct designs were studied. The results demonstrated significant enhancements in both electrical and thermal efficiencies, with the PVT-3 configuration outperforming PVT-1 and PVT-2. Specifically, PVT-3, incorporating fin-shaped air ducts, exhibited the lowest recorded panel temperature of 55 °C, indicating improved electrical efficiency and thermal performance. Also, PVT-3 achieved the highest average thermal efficiency of 46.35% and the best electrical performance of 13.91%. Furthermore, the study highlights ameliorated airflow dynamics and uniformity within the ducts, particularly with the redesigned air inlet. These findings underscore the importance of design innovations in optimizing temperature management and energy output in PVT systems. It is worth noting that the tests were conducted under identical operating conditions, including air velocity, inlet temperature, ambient temperature, and solar irradiation.
Weqaa: An Intelligent Mobile Application for Real-Time Inspection of Fire Safety Equipment
Authors: Alidrisi Rehab , Feras Ekram , Aboukozzana Shahad , Alomayri Alaa , Alayed Asmaa
Published: June 2024

Fire safety is an important consideration, particularly in buildings where there are significant risks linked to a possible fire breakout. Therefore, it is crucial to implement procedures and regulations in buildings to minimize fire damage. Despite the installation of various pieces of Fire Safety Equipment (FSE), over time their effectiveness may be reduced due to factors, such as failure, damage, and insufficient maintenance. For this reason, the fire safety inspection process came to ensure the FSE availability and efficiency. Visual fire safety inspection conducted by civil defense is found to be time-consuming and inefficient, primarily due to manual procedures and difficulty in identifying defects, leading to inaccurate results and low performance. The purpose of this research is to enhance and automate fire safety inspection by implementing deep learning and computer vision techniques in a mobile application, thus addressing the challenges associated with visual inspection. Weqaa application allows the inspector to point their mobile phone camera at the fire extinguisher, then determine the condition of the extinguisher, document it, and report it to the relevant authority to quickly determine the appropriate action procedure. Interviews with expert inspectors were performed to outline the required functions of the application. The mobile application was developed using Flutter and being integrated with the detection model to permit the user to inspect fire extinguishers. Initial testing of the application has exhibited promising results, with inspectors noting its competence in detecting violations and improving inspection processes. The use of the particular application enabled the inspectors to perform the required functions faster, more accurately, and with fewer errors compared to the visual inspection deployment, indicating the application's effectiveness in detecting violations.
Exploring Sentiment Analysis on Social Media Texts
Authors: Alabdulkarim Najeeb Abdulazez , Haq Mohd Anul , Gyani Jayadev
Published: June 2024

Sentiment analysis is a critical component in understanding customer opinions and reactions. This study explores the application of sentiment analysis using Python on the Amazon Fine Food Reviews dataset to classify customer reviews as positive or negative, enabling businesses to gain valuable insight into customer sentiments. This study used and compared the efficiency of Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LSTM, and ALBERT. The comparison results showed that the LSTM and ALBERT classifiers stand out with remarkable accuracy (96%) and substantial support for positive and negative reviews. On the other hand, although the Random Forest classifier had similar accuracy (96%), it exhibited lower support for positive and negative sentiments.
Pile Design using the Modified Unified Method combined with Monte Carlo Simulation
Authors: Cao Van Hoa
Published: June 2024

Piles are typically designed to ensure the bearing capacity and settlement elastic behavior. However, some projects seem over-designed, leading to unnecessary waste, whereas others experience excessive settlement. This could be caused by various factors, such as site investigation, sampling and testing methods, selection of soil behavior model, and calculation programs. To achieve a successful pile design, engineers must consider, among others, the loads applied to the pile, the resistance capacity of the piles, the pile material's bearing capacity, the pile's displacement, and the soil's settlement. On the other hand, the input parameters for geotechnical problems, in general, and pile design problems, in particular, often do not reflect the actual behavior of the soil due to its heterogeneous and anisotropic nature. To address these challenges, an Artificial Neural Network (ANN) approach is proposed for pile design, using a relatively wide range of soil input data. This study establishes a numerical program for pile design combined with the ANN approach, validated by verifying the pile design of a project constructed in Vietnam. The results indicate that the proposed program can reasonably simulate pile group behavior and assist engineers in deploying appropriate safety factors.
The Effect of Expansion Ratio, Opening Size, and Prestress Strand on the Flexural Behavior of Steel Beams with Expanded Web using FEA
Authors: Ahmed Ausama , Said Abdul Muttailb I.
Published: June 2024

The expanded web depth of steel beams leads to improved strength and stiffness. In some design scenarios, such as cellular, castellated, or expanded steel beams, increasing the depth of the web improves the strength and performance of the steel beam. The expanded web of steel beams can be accomplished by creating a horizontal cutting in the web and then adding a plate between the two web section halves, which are called spacer plates. This method leads to improved stiffness and strength. Numerical models have been developed to accurately predict the properties of these beams. This study investigates the use of incremental plates to increase the depth of hot-rolled wide-flange steel beams. The experimental results were validated first with the Finite Element (FE) numerical model created by ABAQUS software, and then Finite Element Analysis (FEA) methods were used to create and analyze new numerical models by considering parameters that provided more models at a lower cost. The load-mid-span deflection curve behavior of both models (experimental and theoretical) was similar. Also, the load-deflection behavior of steel beams with two types of openings was studied. For the first type of opening (B1 NUM), with a smaller opening width of 30 mm and a higher hole number (24), the ultimate load increased by 53%, 111%, and 184%, the deflection at 0.95 Pu increased by 160%, 293%, and 81% of beams with ratio 150%, 200%, and 250% compared with the reference beam. For the second type of opening (B2 NUM), with a larger opening width of 60 mm and smaller hole number (12), the ultimate load capacity increased by 51%, 147%, and 177%, for beams with a ratio of 150%, 200%, and 250%, compared with the reference beam. The deflection at 0.95 Pu increased by 46% and 15% for beams with a ratio of 150%, and 200%, and decreased by 8% for beams with a ratio of 250%. Accordingly, for the first type of opening, the expanded ratios of 150% and 200% performed best with a reduction of only 1%-12% in the ultimate load capacity. However, for the second type of opening, the beam with a ratio of 250% performed better than the first type. Using prestress strands may highly improve steel beams' performance with the expanded webs containing openings by creating a stronger section that can withstand higher loads and exhibit improved structural performance, especially from beams with a ratio of 150%.
A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization
Authors: Ouderni Ramia , Bouaziz Bechir , Bacha Faouzi
Published: June 2024

Due to their small size and isolated energy systems, islands face a significant energy supply challenge. To develop sustainable energy systems, Hybrid Renewable Energy Systems (HRES) help in the generation of electricity in island zones, as they are a clean and inexhaustible source of energy. The purpose of this study is to optimize the allocation of Renewable Energy Sources (RES) on an island in Tunisia. To ensure efficient management between the total power generation and the total community load demand, an Energy Management System (EMS) is required. This paper presents the integration of an optimal EMS using Particle Swarm Optimization (PSO) to directly allocate and optimize the energy generated by an HRES. In addition, the PSO algorithm is applied to regulate energy production, consumption, and storage to maximize the utilization of the available renewable sources while satisfying load requirements. The results exhibit that this approach is effective for the dynamic optimization of energy management in an HRES, contributing to a more efficient and sustainable utilization of energy resources.
Improved Tomato Disease Detection with YOLOv5 and YOLOv8
Authors: Ahmed Rabie , Abd-Elkawy Eman H.
Published: June 2024

This study delves into the application of deep learning for precise tomato disease detection, focusing on four crucial categories: healthy, blossom end rot, splitting rotation, and sun-scaled rotation. The performance of two lightweight object detection models, namely YOLOv5l and YOLOv8l, was compared on a custom tomato disease dataset. Initially, both models were trained without data augmentation to establish a baseline. Subsequently, diverse data augmentation techniques were obtained from Roboflow to significantly expand and enrich the dataset content. These techniques aimed to enhance the models' robustness to variations in lighting, pose, and background conditions. Following data augmentation, the YOLOv5l and YOLOv8l models were re-trained and their performance across all disease categories was meticulously analyzed. After data augmentation, a significant improvement in accuracy was observed for both models, highlighting its effectiveness in bolstering the models' ability to accurately detect tomato diseases. YOLOv8l consistently achieved slightly higher accuracy compared to YOLOv5l, particularly when excluding background images from the evaluation.
An Investigation of AI-Based Ensemble Methods for the Detection of Phishing Attacks
Authors: Alsariera Yazan A. , Alanazi Meshari H. , Said Yahia , Allan Firas
Published: June 2024

Phishing attacks remain a significant cybersecurity threat in the digital landscape, leading to the development of defense mechanisms. This paper presents a thorough examination of Artificial Intelligence (AI)-based ensemble methods for detecting phishing attacks, including websites, emails, and SMS. Through the screening of research articles published between 2019 and 2023, 37 relevant studies were identified and analyzed. Key findings highlight the prevalence of ensemble methods such as AdaBoost, Bagging, and Gradient Boosting in phishing attack detection models. Adaboost emerged as the most used method for website phishing detection, while Stacking and Adaboost were prominent choices for email phishing detection. The majority-voting ensemble method was frequently employed in SMS phishing detection models. The performance evaluation of these ensemble methods involves metrics, such as accuracy, ROC-AUC, and F-score, underscoring their effectiveness in mitigating phishing threats. This study also underscores the availability of credible open-access datasets for the progressive development and benchmarking of phishing attack detection models. The findings of this study suggest the development of new and optimized ensemble methods for phishing attack detection.
Securing Cloud Computing Services with an Intelligent Preventive Approach
Authors: Altowaijri Saleh M. , El Touati Yamen
Published: June 2024

Cloud computing is a technological marvel that transcends conventional boundaries by utilizing an Internet-based network of remote servers to store, manage, and process data and many other services. It represents a contemporary paradigm for delivering information technology services. Today, cloud computing services have become indispensable for both individuals and corporations. However, adopting cloud services presents fresh challenges in terms of service quality, resource optimization, data integration, cost governance, and operational security. The security of cloud services is of supreme importance, given the open and distributed nature of the environment, making it susceptible to various cyberattacks, such as Denial of Service (DoS) or Distributed DoS (DDoS) attacks, among others. Cyberattacks can have severe repercussions on the availability of cloud services, potentially causing complete DoS. In numerous instances, the detection of attacks is delayed, pushing cloud platforms to a breaking point. Emphasizing the importance of proactive measures, it becomes crucial to identify and alert about any suspicious access long before the latter reaches a critical stage, mitigating the risks and preventing potential service disruptions. This study introduces a preventive approach that utilizes artificial intelligence techniques to improve the security of cloud services. The proposed method aims to detect and flag potential attack behaviors well in advance before they affect service quality. To achieve this, the particular method involves periodic identification and measurement of critical information on service access and resource utilization. This can be accomplished by analyzing cloud server logs or integrating dedicated sniffing software to capture and store technical traffic details. Subsequently, the collected data are processed by analyzing traffic properties to proactively identify and report any indications of cyberattacks.
Optical Flow-Based Feature Selection with Mosaicking and FrIFrO Inception V3 Algorithm for Video Violence Detection
Authors: Vijayakumar Elakiya , Puviarasan Aruna , Natarajan Puviarasan , Ganesan Suresh Kumar Ramu
Published: June 2024

Violence in recent years poses the biggest threat to society, which needs to be addressed by all means. Video-based Violence detection is very tough to discern when the person or things that are recipients of a violent act are in motion. Detection of violence in video content is a critical task with applications spanning security surveillance, content moderation, and public safety. Leveraging the power of deep learning, the Violence Guard Freeze-In Freeze-Out Inception V3(VGFrIFrOI3) deep learning model in conjunction with optical flow-based characteristics proposes an effective solution for automated violence detection in videos. This architecture is known for its efficiency and accuracy in image classification tasks and in extracting meaningful features from video frames. By fine-tuning Inception V3 on video datasets annotated for violent and non-violent actions, the network can be permitted to learn discriminative features that simplify the detection of any violent behavior. Furthermore, the aforementioned model incorporates temporal information by processing video frames sequentially and aggregating features across multiple frames using techniques, such as temporal convolutional networks or recurrent neural networks. To assess the performance of this approach, a performance comparison of the proposed model against already existing methods was conducted, demonstrating the model’s superior accuracy and robustness in detecting violent actions. The recommended approach not only offers a highly accurate solution for violence detection in video content but also provides insights into the potential of deep learning architectures like Inception V3 in addressing real-world challenges in video analysis and surveillance. The Mosaicking processing, additionally carried out in the pre-processing step, improves the algorithm performance by deploying space search minimization and optical flow-based feature extraction, aiming to extemporize accuracy.
Leveraging Techniques of Epistemic Network Analysis to Discover Behaviors of Student Learning Reflections in Online Learning Environments
Authors: Alqahtani Sahar
Published: June 2024

In the domain of learning analytics, reflective writing has introduced trends to enhance the learning and teaching experience. Epistemic Network Analysis (ENA), is a recent development in the techniques of learning analytics regarding handling huge amounts of text and visualizing learners’ interactions in the form of network graphs. In this context, 43 students participated in 10 tasks over a 16-week semester on a blended course. The current article aims to explore their reflective behaviors through this new learning methodology and establish via the ENA technique whether there is any relationship between such behaviors and course performance. The findings show the effectiveness of ENA in investigating students’ overall learning reflection patterns and revealing the frequencies of each reflection type for both high- and low-performing students. The group of high performers demonstrated a stronger connection with positive feelings regarding the learning experience, whereas the low performers exhibited a negative attitude toward the learning process. The obtained results provide insights into students' impressions of specific teaching or learning methods. Linking the reflection behavior to the level of student performance enables teachers to improve course design and provide appropriate interventions, which may be reflected in enhanced student performance.
New Scheduling Scheme in Cellular V2X Communication
Authors: Mansouri Wahida Ali , Asklany Somia , Othman Salwa Hamda , Darem Abdulbasit A.
Published: June 2024

The enormous increase in mobile data traffic and the heterogeneity and stringent Quality of Service (QoS) requirements of different applications have placed a substantial strain on the underlying network infrastructure and represent a challenge for Cellular Vehicle-to-Everything (Cellular V2X). V2X communication is a key enabler for the realization of smart and connected transportation systems, offering a wide range of applications, such as enhanced road safety, traffic management, and autonomous driving. In this context, the best way to provide great flexibility and address both the present and future QoS concerns is to use intelligent Radio Resource Management (RRM) in general and creative packet scheduling in particular. The diverse QoS requirements of multiple application classes under dynamic network conditions present substantial challenges for conventional scheduling algorithms given the increasing demand for bandwidth-hungry applications. This study proposes a scheduling system for V2X communications based on traffic prioritization that manages QoS provisioning for different types of traffic considering channel quality, remaining payload, and delay. Simulation results demonstrate the highly promising performance of the proposed New Scheduling V2X Communications (NSVC) algorithm that leads to significantly lower latencies, as the average delay scheme did not exceed 0.001 ms for 100 users.
Condenser Pressure Influence on Ideal Steam Rankine Power Vapor Cycle using the Python Extension Package Cantera for Thermodynamics
Authors: Marzouk Osama A.
Published: June 2024

This study investigates the Rankine vapor power thermodynamic cycle using steam/water as the working fluid, which is common in commercial power plants for power generation as the source of the rotary shaft power needed to drive electric generators. The four-process cycle version, which comprises a water pump section, a boiler/superheater section, a steam turbine section, and a condenser section, was considered. The performance of this thermodynamic power cycle depends on several design parameters. This study varied a single independent variable, the absolute pressure of the condenser, by a factor of 256, from 0.78125 to 200 kPa. The peak pressure and peak temperature in the cycle were fixed at 50 bar (5,000 kPa) and 600°C, respectively, corresponding to a base case with a base value for the condenser's absolute pressure of 12.5 kPa (0.125 bar). The analysis was performed using the thermodynamics software package Cantera as an extension of the Python programming language. The results suggest that over the range of condenser pressures examined, a logarithmic function can be deployed to describe the dependence of input heat, the net output work, and cycle efficiency on the absolute pressure of the condenser. Each of these three performance metrics decreases as the absolute pressure of the condenser increases. However, a power function is a better choice to describe how the steam dryness (steam quality) at the end of the turbine section increases as the absolute pressure of the condenser rises.
Smart PV Hydroponic Greenhouse for Sustainable Agriculture in Tunisia
Authors: Marouani Rym , Mahamat Chabakata , Khachroumi Sofiane , Bouadila Salwa , Cherif Adnen
Published: June 2024

This study introduces smart tools and algorithms for controlling and monitoring Sustainable Agricultural Greenhouses (SHG). Through the implementation of solar energy, Internet of Things (IoT) sensor-actuator networks, and artificial intelligence, an SHG with a low carbon footprint has been designed. The former makes minimal use of water resources, resulting in the reduction of costs while optimizing crops and harvests. After choosing the structure and architecture of the system introduced, optimized PID controllers based on Artificial Neural Networks (ANN) are proposed, for the maximum power to be derived from the Photovoltaic (PV) solar source and the efficiency of the pump to be improved. Additionally, an IoT-based remote control system has been created using an ESP32 microcontroller with a Wi-Fi interface along with sensors for monitoring solar irradiation, soil moisture, indoor temperature, humidity, lighting, ventilation, and water flow. The system collects sensor data in real-time and employs a built-in algorithm to update the information in the cloud. The experimental measurements carried out in the SHG allowed for the verification of the chosen models and simulation results. Thanks to the hybridization of renewable energies, hydroponic techniques, smart technologies, and sustainable practices, this cutting-edge greenhouse creates an ideal microclimate for year-round cultivation while preserving the ecosystem's energy and water resources.
Advancing Eye Disease Assessment through Deep Learning: A Comparative Study with Pre-Trained Models
Authors: Alzamil Zamil S.
Published: June 2024

The significant global challenges in eye care are treatment, preventive quality, rehabilitation services for eye patients, and the shortage of qualified eye care professionals. Early detection and diagnosis of eye diseases could allow vision impairment to be avoided. One barrier to ophthalmologists when adopting computer-aided diagnosis tools is the prevalence of sight-threatening uncommon diseases that are often overlooked. Earlier studies have classified eye diseases into two or a small number of classes, focusing on glaucoma, and diabetes-related and age-related vision issues. This study employed three well-established and publicly available datasets to address these limitations and enable automatic classification of a wide range of eye disorders. A Deep Neural Network for Retinal Fundus Disease Classification (DNNRFDC) model was developed, evaluated based on various performance metrics, and compared with four established pre-trained models (EfficientNetB7, EfficientNetB0, UNet, and ResNet152) utilizing transfer learning techniques. The results showed that the proposed DNNRFDC model outperformed these pre-trained models in terms of overall accuracy across all three datasets, achieving an impressive accuracy of 94.10%. Furthermore, the DNNRFDC model has fewer parameters and lower computational requirements, making it more efficient for real-time applications. This innovative model represents a promising avenue for further advancements in the field of ophthalmological diagnosis and care. Despite these promising results, it is essential to acknowledge the limitations of this study, namely the evaluation conducted by using publicly available datasets that may not fully represent the diversity and complexity of real-world clinical scenarios. Future research could incorporate more diverse datasets and explore the integration of additional diagnostic modalities to further enhance the model's robustness and clinical applicability.
A Failure Criterion of Weak and Crushable Limestone Rock in Mining Field containing Karstic Cavities
Authors: Alassaf Yahya , Mabrouk Abdelkader , Jamei Mehrez , Ahmed Anwar
Published: June 2024

This study aims to correlate the mechanical properties measured in the laboratory and the field for weak and crushable limestone in a mining site containing random karstic cavities. Compressive tests were performed in the laboratory to obtain Unconfined Compressive Strength (UCS) and rock mass modulus (Erm). Field tests were: i) boring and drilling cores that allowed obtaining Rock Quantification Distribution (RQD) and recovery rock parameter (REC), and ii) Ground Penetration Radar (GPR) to detect and locate random cavities in the underground limestone deposit. The correlation between the Em/UCS rate and the RQD was determined and analyzed. Based on the role of the new interpretation of the Geological Strength Index (GSI) and its relationship with the Erm/UCS rate, a mathematical relationship was determined to link GSI and RQD. This relationship was a basis for modifying the generalized Hoek-Brown criterion, involving the amplitude of reflected electromagnetic waves (EM) provided by GPR field tests.
Magnesium AZ63 Alloy Protective Coatings by Plasma Electrolytic Oxidation in Mixed Aqueous Electrolytes
Authors: Patrascu Ion , Negrea Aurelian Denis , Malinovschi Viorel , Lungu Cristian Petrica , Cimpoesu Ramona , Ducu Marian Catalin , Schiopu Adriana-Gabriela , Moga Sorin Georgian
Published: June 2024

Ceramic protective coatings, primarily composed of spinel (MgAl2O4), magnesia (MgO), and trimagnesium phosphate (Mg3(PO4)2), were produced on magnesium AZ63 alloy through Plasma Electrolytic Oxidation (PEO) in mixed sodium phosphate/aluminate electrolytes with varying aluminate concentrations and constant processing time. The morpho-structural and compositional characteristics of the coatings were studied using X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Their functional mechanical and anti-corrosive properties were assessed through tribological testing, electrochemical impedance spectroscopy, and potentiodynamic bias tests. The findings indicated that the samples processed through PEO exhibited significantly enhanced properties compared to the AZ63 magnesium alloy. The best tribological properties were observed for the lowest aluminate concentration. Optimum corrosion resistance properties were obtained for coatings produced in a mixed electrolyte of 10 g/L sodium phosphate and 20 g/L sodium aluminate.
A Mathematical Model to Predict Optimal Risk Response Budget using Genetic Algorithm
Authors: Aljorany Hiba Omer Ghaeb , Mahjoob Ahmed Mohammed Raoof
Published: June 2024

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