bluuebunny/crossref_metadata_2025 huggingface dataset:
NADNACIONALNA I NACIONALNA PRAVNA REGULACIJA AZILA I NJEGOVO KORIŠTENJE U RJEŠAVANJU MIGRACIJSKE KRIZE
Authors: Arlović Branka
Published: December 2024

Rad se bavi izvorima pravnoga uređivanja traženja azila, njegova odobravanja i uživanja, koji su sadržani u pravnim (i drugim) aktima nadnacionalnoga i nacionalnoga prava. Povod za izradu ovoga rada je migracijska kriza koja se sve više iskazuje kao masovni dolazak migranata, ljudi u pokretu, koji su istodobno tražitelji azila, ali primarno u najbogatijim i najkonsolidiranijim zemljama Europske unije. Osim toga, suvremena europska kretanja u području azila idu u pravcu učestalih zahtjeva i aktivnosti radi njegova priznanja i reguliranja kao subjektivnoga ljudskoga prava i slobode za svaku osobu koja ga zatraži ako je izložena progonu, torturi i pogibiji. Nameće se pitanje može li se azil uopće propisati kao subjektivno pravo pod međunarodnom zaštitom a da se istodobno postignu dva cilja: a) da se postupak traženja, dodjele i uživanja vodi po postupku i pred nadležnim tijelima država članica, a tek supsidijarno pred nadležnim međunarodnim tijelima; b) da se postigne ravnoteža u odobravanju azila u njegovoj zaštiti s javnim interesima država koje ga odobravaju, poglavito u pogledu nacionalne sigurnosti. U radu će se nastojati prikazati i usustaviti najvažniji nadnacionalni i nacionalni izvori izbjegličkoga i azilnoga prava te kroz njihovu obradu dati poneki odgovor na postavljena pitanja.
Ključne riječi: azil, izbjeglice, migracije, nadnacionalni i nacionalni izvori, ljudska prava i temeljne slobode
The Idea of Historical Justice in the Thoughts of Gumar Karash and its Modern Significance
Authors: Әлжан Қуаныш , Аташ Берік
Published: December 2024

Бұл мақалада, алдымен, қазақ дүниетамындағы әділеттілік идеясы мәселесі қойылуының концептуалды негіздеріне шолу жасалады. Ғұмар Қараштың тарихи әділеттілік туралы идеялары талданады. Оның Қазақ халқының өз алдына жеке мемлекет болуының алғышарттарын көрсеткендігі зерделенеді. Білім мен еуропалық өркениетке ұмтылу қазақ халқының тарихи әділеттілік орнатуының негізі екендігін толғайтындығы туралы оның толғауларынан үзінділер келтіре отырып, оған философиялық талдаулар жасалады. Қазақ халқының бұрынғы тәуелсіз ел болғандығын аңсау толғаулары баяндалады. Сонымен қатар, Ғұмар Қараштың ел бірлігі туралы ойлары мен ана тілінің қолданыс аясын кеңейту мәселелері тарихи әділеттілікті қайта қалпына келтіру мәселесі ретінде қойылады. Ғұмар Қараштың осындай мәселелері бүгінгі заманмен байланыстыра, салыстырыла қарастырылады және оның өзекті мәселелері талданады. Атап айтқанда, тіл мен дін мәселесі, тәуелсіздікті нығайту мәселесіндегі тарихи әділеттілікті қайта қалпына келтірудің дискурсы қарастырылады. ХХ ғасыр басындағы қазақ руханияты мен ХХІ ғасыр басындағы қазақ руханияты салыстырыла көрсетіліп, аударма мәселесі, ғылыми стильдің қалыптасуы т.б. прогрессивті және прогрессивті емес қырлары ашыла көрсетіледі.
Increasing Citizens’ Trust in Law Enforcement Agencies: Political and Legal Aspects of Assessment
Authors: Имашев Берик
Published: December 2024

Доверие граждан к государственным органам вообще, правоохранительным и судебным, в частности, в современных условиях превратилось в ключевую ценность взаимоотношений народа и государства. Ее истоки коренятся в действующем Основном Законе, реализуемой президентской концепции «слышащего государства». Дефиниция «доверие» в публичной правовой сфере в достаточной степени в отечественной правовой науке не разработана и требует своего законодательного за-крепления, а также систематизации законодательства в этой части. В настоящее время «доверие» стало активно использоваться для демонстрации одного из главных на-правлений государственной деятельности и построения отношений между личностью и обществом на принципиально новой основе. Поэтому как в практическом, так и в научном плане представляется необходимым сконцентрировать вектор этой деятельности на системной и научной основах. К решению этой проблематики, необходимо привлекать не только социологов, но и юристов (прежде всего криминологов), право-защитные организации, а также СМИ как это уже давно практикуется в странах раз-витой демократии. В статье также предпринята попытка изучить зарождающуюся практику про-ведения мониторинга функционирования органов внутренних дел, включая его законодательные основы, развитые в единичных подзаконных актах, а также социологических исследований госорганов и институтов гражданского общества, их аналитические оценки в СМИ. Вносятся предложения по расширению проведения углубленных опросов граждан, встреч руководителей всех органов правоохраны с населением, а также концентрации усилий ученых различных областей гуманитарных знаний.
Investigation of the Ιmpact of HVOF Spraying Parameters on the Abrasion Resistance of Tungsten Carbide Coatings
Authors: Nguyen Hong Tien , Nguyen Tuan Linh , Nguyen Van Thien , Hoang Long
Published: December 2024

For components operating under high pressure and high values of friction conditions, rapid wear is a common issue. Surface treatment measures are often employed to enhance the working lifespan of such components by improving their resistance to abrasion. This paper utilizes the Taguchi experimental design method in conjunction with Analysis of Variance (ANOVA) to assess the impact of spraying parameters on the abrasion resistance of the coating when applying the HVOF method. The sprayed material is WC HMSP1060-00+60% 4070, primarily consisting of Nickel and Tungsten Carbide, whereas the material for manufacturing the pressing screws is 1045 steel. The investigated spraying parameters include the spray Flow Rate (F), Spray Distance (D), and Oxygen/Propane Ratio (R). The experimental results are analyzed to determine the most suitable spraying parameters to achieve the highest abrasion resistance, and thereby improve the working lifespan of the components.
Utilizing Chaotic Logistic Keys for LSB1 and LSB2 Message Steganography
Authors: Sharadqh Ahmad A. , Alkasassbeh Jawdat S. , Alawneh Tareq A. , Al-Qaisi Aws , Makableh Yahia F. , Al Adwan Safaa
Published: December 2024

This paper introduces a novel hybrid data steganography method that combines the new techniques of LSB1 and LSB2. The proposed method simplifies data-hiding and extraction operations by utilizing a patch method. A unique Private Key (PK) divides the message into two parts. The first part is processed using the LSB1 method, while the second one is treated with the LSB2 method. The PK information is utilized to create secret keys, namely key1 and key2. The keys are generated by converting two Chaotic Logistic Keys (CLKs) and establishing the sequence of cover Stego bytes for concealing and revealing data. The secret message is protected within a secure key area by utilizing the PK, which improves security by preventing unwanted access. The secret message's effective extraction depends significantly on the PK's content. Any alterations to the key during the extraction step will be deemed unlawful, possibly leading to a compromized secret key. Moreover, the suggested approach is followed and assessed by using different messages. The results are comprehensively studied, ensuring a robust evaluation of the quality, efficiency, and security improvements of the data steganography process. The experimental results confirm the data steganography's quality, efficiency, and security enhancements.
Navigating the Clouds: An Exploratory Research of Cloud Computing Adoption in Saudi Arabia's Small and Medium Enterprises
Authors: Alamri Nada , Alzahrani Saeed
Published: December 2024

The adoption of cloud computing has become essential for SMEs, especially in the e-commerce industry, as it has the potential to improve the efficiency of operations. However, the existing literature indicates significant gaps, including a general lack of comprehensive research focused on cloud computing adoption for SMEs and very limited research on the Kingdom of Saudi Arabia (KSA) context. This study aimed to fill these gaps by evaluating the current status of cloud computing adoption by SMEs in KSA and identifying the challenges facing its implementation in the e-commerce sector. To achieve these objectives, the Technology-Organization-Environment (TOE) framework was employed, analyzing the technological, organizational, and environmental factors that affect cloud service adoption. This study incorporates insights from government agencies, service providers, and SMEs to provide a holistic view. Through a mixed-method approach, combining qualitative and quantitative data, this study offers a detailed understanding of the cloud computing landscape in KSA, providing valuable insights for stakeholders and policymakers.
A Novel Hybrid Approach for Global Maximum Power Point Tracking in Standalone PV Systems
Authors: Prasanth Sai C. , Vijaya Kumar M.
Published: December 2024

The efficiency of Photo-Voltaic (PV) systems is highly dependent on their ability to accurately track the Global Maximum Power Point (GMPP) under varying environmental conditions. Traditional Maximum Power Point Tracking (MPPT) methods often struggle with issues such as slow tracking speed, susceptibility to local maxima, and the need for complex parameter tuning, particularly in dynamically changing environments with Partial Shading Conditions (PSCs) and rapid irradiation changes. To address these challenges, this study introduces a hybrid approach that combines a modified Rao algorithm with the Perturb and Observe (P&O) method. The modified Rao algorithm was employed in the initial tracking stages to quickly locate the global vicinity, benefiting from its simplicity and the absence of algorithm-specific parameters, whereas the P&O method ensured precise tracking in the final stages. The performance of the proposed method was assessed on a PV array subjected to PSCs and compared with several well-known MPPT algorithms, such as Gray Wolf Optimization (GWO), JayaDE, and the Slime Mould Algorithm (SMO). The proposed approach was implemented and analyzed using the MATLAB/Simulink software.
Improving the RSA Encryption for Images by Introducing DNA Sequence Encoding
Authors: Hennache Ali , Hennache Mamoune Lyes , Ghaly Sidi Mohamed Ahmed
Published: December 2024

Recent research is focused on the exploitation of DNA-based molecules for data encryption due to their high capacity to store larger volumes of data and lower computation requirements [1, 2]. This study proposes a Hybrid Image Encryption method (HIE) that convolves DNA sequence encoding with the Rivest–Shamir–Adleman (RSA) algorithm to enhance the security of image encryption. The proposed scheme uses small prime numbers to encrypt the image, which is then encoded as a DNA sequence. Subsequently, the encrypted DNA sequence is stored in a physical medium. The encrypted DNA sequence can then be decrypted using the RSA algorithm and the corresponding private key to recover the original image. The results show that using small prime numbers for RSA encryption of an image and encoding it as a DNA sequence can enhance security and reduce computational time.
Decentralized Payment Framework for Low-Connectivity Areas Using Ethereum Blockchains
Authors: Khan Burhan Ul Islam , Shah Asadullah , Goh Khang Wen , Putra Rusnardi Rahmat , Khan Abdul Raouf , Chaimanee Mesith
Published: December 2024

This paper presents a pioneering analytical framework for a secure payment system leveraging blockchain technology tailored to regions with suboptimal network connectivity. Contemporary payment mechanisms utilizing Ethereum are predominantly optimized for areas with robust network infrastructure, neglecting regions with less connectivity. To address this gap, the proposed model integrates novel security attributes and employs an analytical method to design a decentralized payment system. The framework facilitates communication between low-connectivity zones and Internet service providers through auxiliary nodes, creating a local blockchain network for residents, merchants, and auditors. A mathematical model quantifies operational costs, transaction processing, and synchronization of auxiliary nodes, ensuring a resilient and secure payment architecture. A unique aspect of the proposed approach is its robustness against auditor outages and network variability, coupled with an empirical analysis of incentive structures for auditors' block validation activities. Moreover, it delineates the minimum requirements for secure transaction completion. Empirical findings showed a significant improvement in system efficiency, including a 79% reduction in block time, a 28% increase in transaction throughput, a 30% decrease in energy consumption, a 68% shorter confirmation time, a 63% reduction in execution time, a 46% increase in block production rate, and 82% reduced network variability. This study's significant contribution lies in introducing a sustainable, cost-effective, and secure payment system for regions with inadequate network services.
Physical and Mechanical Properties of Abaca Fiber Reinforced Polymer Composites for Sustainable Structural Application
Authors: Fakhruddin . , Mawaddah Nur Ainun , Irmawaty Rita , Ngeljaratan Luna Nurdianti
Published: December 2024

This study evaluates the physical and mechanical properties of Abaca Fiber Reinforced Polymer (AbFRP) laminates for sustainable structural application. The research examines the impact of alkali treatment on the weight and diameter of the abaca fibers, as well as the tensile strength of both individual fibers and AbFRP laminates. The results indicate that the application of 0.5 wt% NaOH treatment reduced the weight and diameter of abaca fibers by 5.7% and 4.2%, respectively. Alkali treatment enhanced the tensile strength of single fibers, increasing it from 977.8 MPa to 1978.6 MPa, while the tensile strength of AbFRP laminates decreased from 78.1 MPa to 67.3 MPa. In terms of elastic modulus, the untreated and treated AbFRP laminates exhibited values of 14.9 GPa and 18.5 GPa, respectively. These findings demonstrate that AbFRP laminates possess notable mechanical characteristics, making them suitable for implementation for structural application.
Use of Geomatic Techniques for Mapping Suspended Solids in Aquatic Ecosystems: The Case Study of Guayas River, Ecuador
Authors: Yepez Ramírez Jennyffer Rebeca , Ricaurte Parraga Rayner Reynaldo , Verdugo Arcos Jesus Armando
Published: December 2024

Satellite images cover large remote areas and are useful for detecting and monitoring water bodies. In Ecuador, since 1950, the lower Guayas River basin has undergone significant natural and anthropogenic changes that have impacted its dynamics and sustainability. This study aims to analyze through in situ data and geomatic techniques the change that the river has undergone in a decade by mapping the Suspended Sediment Concentration (SSC). Increasing levels of pollution in the river have raised concerns, prompting various approaches to measure and mitigate sedimentation to maintain the sustainable quality of the watershed. The spatiotemporal variations of SSC in the Guayas River revealed a remarkable variability, influenced by the operation of reservoirs, changes in land use, erosion, and sedimentation causing SSC in 2013 to range from 64.82 to 707.06 mg/l in the satellite image of 9/16/2013 and from 87.58 to 933.36 mg/l in the image of 7/26/2023. Understanding this distribution is crucial for the environmental protection and sustainability of aquatic ecosystems. This study used Landsat 8 data, an atmospheric pre-correction, and a remote sensing model. The results indicate a creasing trend of SSC in the stretches of the Guayas River between 2013 and 2023, which allows the understanding of the spatiotemporal dynamics of suspended sediment transport.
A Potential Pozzolanic Material consisting of Rice Straw Ash and Fly Ash for Geopolymer Mortar Production-based Cementitious System
Authors: Amiruddin A. Arwin , Tumpu M. , Rangan Parea R. , Irmawaty Rita , Bakri Bambang , Mansyur .
Published: December 2024

Rice straw waste, among other issues, is a significant source of air pollution and methane emissions from biological decomposition. This study examines the use of Rice Straw Ash (RSA), when combined with Fly Ash (FA) and Laterite Soil (LS), as a pozzolan in cementitious systems. The study's purpose is to examine the microstructure and compressive strength of a geopolymer mortar composed of FA, RSA, and LS. The RSA is activated with sodium hydroxide (NaOH), an alkaline activator, with concentrations of 6, 12, and 15 M NaOH. After air and water curing for 3, 7, and 28 days, the compression strength of the geopolymer mortar was tested. To determine the dominant compound of the pozzolan reactions that were generated in cementitious systems, Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) were employed. When geopolymer mortar is cured in air and water, its compressive strength increases with age. This is due to the fact that RSA, FA, and LS have the ability to form iron oxide (Fe3O4) in the amorphous phase and have a strong bond with alumina (Al2O3) and silica (SiO2). The material's fineness affects its compressive strength as well. This study intends to replace cement in mortar and concrete utilizing environmentally friendly materials. Furthermore, the creation of geopolymer material usually requires the use of oven heat to enhance the geopolymerization procedure. However, this study shows that this method does not require oven heat.
Monocular Camera Calibration based on Genetic Simulated Annealing Algorithms
Authors: Khrouch Hafsa , Mahdaoui Abdelaaziz , Merras Mostafa , Hsaini Abdellah Marhraoui , Chana Idriss , Bouazi Aziz
Published: December 2024

This study presents a nonlinear camera calibration approach based on combining genetic and simulated annealing algorithms. This is a global optimization technique, which combines simulated annealing with genetic algorithms to find the optimal camera's intrinsic and extrinsic parameters. Since this matter is considered an optimization problem by several studies, a novel hybrid approach was developed and studied based on two powerful nature-inspired techniques to find the intrinsic and extrinsic parameters of the camera. Numerous experiments were conducted to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed hybrid approach is robust, reliable, and accurate.
A Research on Two-Stage Facial Occlusion Recognition Algorithm based on CNN
Authors: Zhe Wang , Batumalay Malathy , Thinakaran Rajermani , Chan Choon Kit , Wen Goh Khang , Yu Zhang Jing , Wei Li Jian , Raman Jeyagopi
Published: December 2024

In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.
An Empirical Study on the Development and Assessment of a Supplier Selection Model based on the Analytical Hierarchy Process in the Al Kharj Industrial Sector
Authors: Alam Teg , AlArjani Ali
Published: December 2024

Selecting a supplier is a critical strategic decision for supply chain management in today's global context. The process involves evaluating suppliers based on core competencies, pricing, delivery timeframes, location, data gathering, and related risks. Suppliers play a crucial role in an organization's profitability and stability. Finding the most optimal supplier can help industries reduce material expenses and maintain their competitive advantage. The supplier not only impacts the organization's profit margin but also its economic strength. Choosing a supplier requires considering qualitative and quantitative elements, making it a decision issue with several criteria. This study aims to create and evaluate a supplier selection model using the analytical hierarchy approach, focusing on a specific case study. When selecting the best supplier, it is crucial to consider tangible and intangible elements that may conflict. The supplier selection process considers several criteria, including qualitative and quantitative variables. The proposed methodology involved a literature review and informal interviews with industry experts and academics to establish the selection criteria. "Quality Supplier Co." was chosen due to the paramount importance of their quality. This research will comprehensively analyze several criteria to identify suppliers accurately.
Vessel Detection in Satellite Images using Deep Learning
Authors: Sankhe Darshana , Bhosale Snehal
Published: December 2024

Maritime surveillance and monitoring have emerged as crucial components, serving various purposes such as security, environmental protection, and economic activities. This paper focuses on utilizing Synthetic Aperture Radar (SAR) satellite imagery to detect and track vessels in maritime regions. SAR technology provides notable advantages in imaging capabilities, enabling effective vessel detection under diverse weather conditions and during both day and night. Deep learning (DL) models are trained employing annotated SAR images, including multiple vessel patterns, sizes, and orientations. The enhancement of model generalization and robustness is accomplished by applying transfer learning techniques and data augmentation strategies, ensuring reliable detection performance across different environmental conditions and vessel types. By leveraging SAR imagery, this paper aims to contribute to enhanced maritime situational awareness, enabling timely identification of small vessels, including those involved in illegal fishing, smuggling, or other illicit activities. The results of this research hold promise for bolstering maritime security, aiding search and rescue operations, and facilitating effective regulation of maritime traffic.
A Study on the Influence of Silicon Content on Wear and Mechanical Properties of Cast Nickel-Aluminum Bronze
Authors: Thongyothee Chawanan , Chareonvilisiri Sombun
Published: December 2024

Although aluminum bronze exhibits considerable wear and mechanical properties, nickel-aluminum bronze displays superior performance. A potential issue is the resistance to wear, particularly whether the addition of silicon (Si) enhances anti-wear properties. Samples with varying silicon content (0.2%, 0.4%, and 0.6%) were prepared in three conditions for the purpose of investigating the effects of silicon on wear and mechanical properties. The materials were forged and cast in accordance with the specifications set forth in JIS H 5120 for the production of specimens. The wear and mechanical properties were evaluated through a series of tests, including tensile strength, hardness, impact, and ball-on-disc wear tests. The results demonstrated that, in comparison to ASTM B148-52 or the precursor AlBC-3, as observed in nickel-aluminum bronze, the addition of silicon during the melting of the substrate resulted in a transformation of the precipitation of the kappa phase to α+κ. The intermetallic compounds were introduced to enhance the formation of pearlite. This resulted in an increase in the tensile strength and hardness of the metal, but a simultaneous decline in its ability to absorb impact. Furthermore, the wear test results indicated that the surface friction coefficient between the nickel-aluminum-bronze disc and the SUS304 stainless steel ball was associated with the amount of silicon added and the hardness. In other words, the workpiece friction coefficient would decrease with increasing hardness due to the rising silicon content. Additionally, it was observed that no adhesive wear was evident between the stainless-steel balls on the nickel-aluminum-bronze disc.
Optimal Index Selection using Optimized Deep Deterministic Policy Gradient for NoSQL Database
Authors: Sumalatha V. , Pabboju Suresh
Published: December 2024

As big data technology has developed, so have complex applications that require increasing resources. The need for high-performance reading and writing increases the usage of NoSQL (MongoDB) databases. As the number of queries in a given amount of time negatively affects the performance of the database, an automated index selection strategy should be used to improve the database performance. This study proposes an Optimized Deep Deterministic Policy Gradient (ODDPG) to select the optimal index. The Adaptive Crocodile Optimization Algorithm (ACOA) is used to improve DDPG's decision-making performance. The ACOA algorithm is used to receive the best action sequences of a DQN. Simulation results showed that the proposed method achieved better results than the existing DDPG model by 2.3% in Average Time Of Query (ATQ) executed, 10% in Query Per Hour (QPH), and 11% in throughput.
Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools
Authors: Thasana Wiroj , Wattanawichit Karn , Kaewdook Don , Thermsuk Somkiat
Published: December 2024

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.
Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset
Authors: Bollampally Anupama , Kavitha J. , Sumanya P. , Rajesh D. , Jaffar Amar Y. , Eid Wesam N. , Albarakati Hussain M. , Aldosari Fahd M. , Alharbi Ayman A.
Published: December 2024

This study investigates the application of a Bidirectional Long Short-Term Memory (BiLSTM) model for Human Activity Recognition (HAR) using the PAMAP2 dataset. The aim was to enhance the accuracy and efficiency of recognizing daily activities captured by wearable sensors. The proposed BiLSTM-based model achieved outstanding performance, with 98.75% training accuracy and 99.27% validation accuracy. It also demonstrated high precision, recall, and F1 scores (all 0.99). Comparative analysis with state-of-the-art models, including Deep-HAR and CNN-BiLSTM-BiGRU, revealed that the proposed BiLSTM model surpassed their performance. These results highlight the potential of the proposed approach for real-time HAR applications in edge computing, particularly where accurate and efficient activity recognition is crucial.
Enhancing Information Technology Governance in Universities: A Smart Chatbot System based on Information Technology Infrastructure Library
Authors: Ahriz Souad , Gharbaoui Hiba , Benmoussa Nezha , Chahid Abdelilah , Mansouri Khalifa
Published: December 2024

The rapid evolution of information and communication technologies has created a pressing need for higher education institutions to modernize their Information Technology (IT) governance practices. This article proposes an innovative solution to enhance the governance and efficiency of IT services while optimizing and personalizing user experience. The proposed solution consists of a chatbot using Artificial Intelligence (AI) and Natural Language Processing (NLP) combined with the Information Technology Infrastructure Library (ITIL) standard to automate the management of IT services in the digital work environment (ENT). Intended for students, teachers, and administrators, this chatbot provides reactive support by responding to requests, reducing waiting times, and improving satisfaction. It also helps decrease operational costs and the workload of support teams by autonomously handling recurring requests. Beyond efficiency improvements, the chatbot contributes significantly to IT governance by providing structured service management, improving decision-making through real-time data, and supporting compliance with governance frameworks. An online survey conducted among 120 students revealed slow processing of requests and unavailability of services, justifying the need for this chatbot. The chatbot was designed with advanced NLP and Machine Learning (ML) technologies. Preliminary tests demonstrate the chatbot’s response reliability, with an accuracy rate of 96% and a response time decrease to an average of 4.17 seconds. The use of chatbots has considerable potential for universities to improve the efficiency of digital services offered to students.
A Systematic Literature Review on Automatic Sexism Detection in Social Media
Authors: Lei Wang , Abdullah Nur Atiqah Sia , Syed Aris Syaripah Ruzaini
Published: December 2024

Sexist content has become increasingly prevalent on social media platforms, underscoring the critical need for the development of efficient Automatic Sexism Detection methods. Previous literature reviews have not encompassed the new advancements in Automatic Sexism Detection observed over the past three years. Hence, the present study conducted a Systematic Literature Review (SLR) that examined 48 primary studies published between 2014 and 17th Sept. 2024, retrieved from six bibliographic databases. This paper aims to present a comprehensive literature review on Automatic Sexism Detection, encompassing the datasets, preprocessing techniques, feature extraction methods, text representations, classification approaches, and evaluation models employed in Automatic Sexism Detection research. The paper includes a discussion of the findings, limitations, and future research directions of the chosen articles. Additionally, it provides an overview of the conclusions drawn from the conducted research. The performed analysis reveals a lack of corpus beyond the English and Spanish language encountered in datasets, with most of the latter being annotated for either misogyny or non-misogyny. Common preprocessing techniques analyzed in the current study include lowercase conversion, text removal, tokenization, stemming, and rewriting. Discrete representations, such as TF-IDF, N-grams, and BoW, are frequently utilized, while distributed representations, like Bert and GloVe, are prominent. Bert is the predominant classification model utilized while combining lexical features can enhance the results in the majority of the discussed scenarios. Accuracy (A) and F1 score (F1) are the most widely deployed evaluation metrics in this field.
A CFD Analysis to Investigate the Effect of Inserts on the Overall Heat Transfer Coefficient in a Concentric Tube Heat Exchanger
Authors: Syed Naveedul Hasan , Khan Naseer Ahmed , Ahmad Naveed , Khan Murad , Ahmad Farooq , Humayun Fiza , Haq Samiul , Alsayer Ibrahim Ali , Altuwair Ibrahim Abdullah
Published: December 2024

In the present study, a U-bend concentric tube heat exchanger has been modeled using Computational Fluid Dynamics (CFD) through ANSYS Fluent software to study the influence of different variables on the overall heat transfer coefficient (U). Following the successful validation of the CFD model, an analysis was conducted to examine the impact of five distinct star-shaped inserts on U enhancement in the U-bend concentric tube heat exchanger. The analysis demonstrated that the maximum U values, which were 381.21 W/m²K and 468.96 W/m²K, were attained at a hot water flow rate of 0.007 L/s when 14 mm plain and twisted star-shaped inserts were, respectively, employed. The incorporation of the inserts resulted in the generation of a secondary fluid motion within the tube, which in turn induced turbulence and consequently enhanced the heat transfer rate. However, the turbulence generated within the tube was attributed to the high pressure drop occurring there. The pressure drop within the inner tube was found to be 129.27 Pa and 149.44 Pa for the plain and twisted star-shaped inserts, respectively. The impact of elevated pressure drops for all five star-shaped insert types was examined and revealed to be the greatest for the 7 mm twisted insert, which was identified as the optimal choice for operational use. This conclusion was based on the observation that the twisted insert exhibited the highest U (390.89 W/m²K) at a pressure drop of 35.30 Pa, achieved at a hot water flow rate of 0.007 L/s.
Multi-Step Wind Speed Forecasting by Secondary Decomposition Algorithm and LSTM
Authors: Tahir Ari Shawkat , Abdulazeez Adnan Mohsin , Ali Ismail Ali
Published: December 2024

Enhancing the reliability of wind speed forecasting is vital for efficient wind power generation. Given the wind's stochastic nature, preprocessing is crucial to obtain a clean wind speed series. This study introduces an innovative wind speed prediction model that integrates Variational Mode Decomposition (VMD), Symplectic Geometry Mode Decomposition (SGMD), and Long Short-Term Memory (LSTM). The model begins with VMD dividing the series into low- and high-frequency parts, then the SGMD further analyzes the high-frequency segment, and LSTM predicts results based on these components. Collaborative use of VMD and SGMD enables thorough decomposition of intricate wind speed data, while LSTM boosts the model's ability to capture patterns and dependencies. This hybrid model addresses the challenges posed by wind power uncertainty, aiming to efficiently integrate wind energy into power systems. The proposed hybrid model was compared to some benchmark models and outperformed them, reducing MAPE by 58% and RMSE by 31% for Dataset 1, and improving MAPE by 14% and RMSE by 36% for Dataset 2. The results confirm the competitive strength of the proposed strategy. Furthermore, the suggested two-stage decomposition technique demonstrates suitability for the examination of nonlinear characteristics in wind speed patterns.
A Blockchain Semantic-based Approach for Secure and Traceable Agri-Food Supply Chain
Authors: Annane Boubakeur , Alti Adel , Lakehal Abderrahim
Published: December 2024

Ensuring food security is crucial for maintaining food quality and enhancing consumer services by guaranteeing both safety and satisfaction. However, traditional methods to ensure food security are often susceptible to various forms of fraud and require significant processing overhead, making them inefficient for the evolving demands of modern food supply chains. To address these shortcomings, blockchain technology has emerged as a robust and efficient solution to enhance food security. This paper presents a novel lightweight blockchain-based signature mechanism designed for the rapid detection of food fraud. It also includes a domain-specific ontology to serve as a structured knowledge model, allowing systematic analysis and detection of different types of fraud within the food supply chain. This approach uses smart contracts built on lightweight blockchain technology to initiate and manage transactions related to food fraud. Then, semantic rules are applied to detect and identify fraudulent activities. Once fraud is detected, associated transactions are encrypted and tracked, ensuring visibility and traceability among consortium members. Experimental results based on large-scale transaction data demonstrated ~7.5× speed improvement over iterative search algorithms while maintaining high transaction traceability and significantly reducing storage costs.
ResNet-based Gender Recognition on Hand Images
Authors: Yildirim Eren
Published: December 2024

The use of biometric features for the surveillance and recognition of certain classes, such as gender, age, and race, is widespread and popular among researchers. Various studies have focused on gender recognition using facial, gait, or audial features. This study aimed to recognize people's gender by analyzing their hand images using a deep learning model. Before training, the images were subjected to several preprocessing stages. In the first stage, the joint points on either side of the hand were detected using the MediaPipe framework. Using the detected points, the orientation of the hands was corrected and rotated so that the fingers pointed upwards. In the last preprocessing stage, the images were smoothened while the edges were preserved by a guided filter. The processed images were used to train and test different versions of the ResNet model. The results were compared with those of some other studies on the same dataset. The proposed method achieved 96.67% recognition accuracy.
Retinal Image Augmentation using Composed GANs
Authors: Alghamdi Manal , Abdel-Mottaleb Mohamed
Published: December 2024

Medical image analysis faces a significant challenge in the scarcity of annotated data, which is crucial for developing generalizable Deep Learning (DL) models that require extensive training data. Consequently, the field of medical image generation has garnered substantial interest and potential for further exploration. Besides widely employed data augmentation techniques, such as rotation, reflection, and scaling, Generative Adversarial Networks (GANs) have demonstrated the ability to effectively leverage additional information from datasets by generating synthetic samples from real images. In the context of retinal image synthesis, an image-to-image translation approach is frequently adopted to generate retinal images from available vessel maps, which can be scarce and resource-intensive to obtain. Deviating from prior work reliant on pre-existing vessel maps, this study proposes a learning-based model that is independent of vessel maps, utilizing Progressive Growing GAN (PGGAN) to generate vascular networks from random noise. The visual and quantitative evaluations conducted suggest that the majority of the images generated by the proposed model are substantially distinct from the training set while maintaining a high proportion of true image quality, underscoring the model's potential as a powerful tool for data augmentation.
A New Hybrid Energy Storage System for Electric Vehicle Drive System
Authors: Reddy S. V. Pratap Simha , Kumar M. Vijaya
Published: December 2024

In this paper, a new Hybrid Energy Storage System (HESS) for Electric Vehicle (EV) drive systems is proposed to increase their battery lifespan, with the potential to meet peak power demands without heavily straining the batteries. The developed feedback control circuit works as a controller to maintain the voltage of the Supercapacitor (SC) at a value higher than the battery voltage during the high acceleration periods of the driving cycle, creating a relatively constant load profile for the battery. The battery is not used to directly harvest energy from the regenerative braking, being, thus, isolated from frequent charges during high acceleration/deceleration periods, which increases its lifespan. The simulation results demonstrate HESS's effectiveness in significantly reducing battery discharge/charge rates compared to a standalone system.
An Ensemble Forecasting Method based on optimized LSTM and GRU for Temperature and Humidity Forecasting
Authors: Saleem Maryam , Saleem Muhammad Majid , Waseem Fareena , Bashir Muhammad Adnan
Published: December 2024

Temperature and humidity predictions play a crucial role in various sectors such as energy management, agriculture, and climate science. Accurate forecasting of these meteorological parameters is essential for optimizing crop yields, managing energy consumption, and effectively mitigating the impact of climate change. In this context, this paper proposes an enhanced ensemble forecasting method for day-ahead temperature and humidity predictions. The proposed method integrates a Long Short-Term Memory (LSTM) network, a Gated Recurrent Unit (GRU), Particle Swarm Optimization (PSO) and Bayesian Model Averaging (BMA). PSO is employed to optimize the parameters of the LSTM and GRU, thereby improving forecasting accuracy. The method is implemented using Python 3.10 with TensorFlow. Additionally, the proposed approach is compared with ensemble-1, LSTM, and GRU models to demonstrate its effectiveness. The simulation results confirm the superior performance of the proposed method over existing competitive approaches.
Research on the Estimation of the Flexural Capacity of EPS Lightweight Concrete Panels
Authors: Dao Sy-Dan
Published: December 2024

Expanded Polystyrene (EPS) lightweight concrete possesses numerous special characteristics, including exceptionally low weight, good sound insulation, effective heat insulation, great fire resistance, and low water absorption. Consequently, it has garnered significant attention for research and practical applications in the construction industry, particularly in the development of soundproofing and thermal insulation components, as well as lightweight elements to reduce the superstructure weight, such as external wall panels, floor slabs, roof panels, and base materials. The primary objective of this study is to analyze and estimate the flexural capacity of EPS lightweight concrete panels through both experimental and numerical methods. Furthermore, the influence of the longitudinal reinforcement ratio, strength, and thickness of EPS lightweight aggregate concrete panels on their flexural capacity is thoroughly investigated and evaluated. The findings demonstrate that the Abaqus software, with an appropriate material model for EPS lightweight aggregate concrete, can reliably predict the flexural capacity of EPS lightweight concrete panels reinforced with longitudinal rebars.
Geospatial Modeling of Aeolian Dynamics in the Algerian Steppe from Zahrez Chergui to Hodna
Authors: Rerboudj Abdelmalek , Guettouche Mohamed-Said , Callot Yann
Published: December 2024

Assessing the hazards associated with aeolian geomorphological processes requires a fundamental understanding of their spatial distribution. These phenomena often have detrimental impacts on the environment, economy, and society. This problem is prevalent in the Algerian steppe, encompassing the Zahrez, Chergui, and Hodna regions. This study proposes a research method for developing more accurate and simpler indices to evaluate the extent and directionality of sand migration. Specifically, it examines surface characteristics, such as altitude, slope, and slope exposure. However, some tools used for spatial modeling of wind dynamics necessitate corrections to account for the effects of topography and surface features on wind, which for this study are implemented using spatial techniques. The results are incorporated into the model developed by Fryberger, which requires wind data and a Digital Surface Model (DSM) to estimate the factors included in this model. The findings indicate that the average potential quantity of sand movement is 64 t m-1 yr-1 over the entire study area, with 37.3% of the region experiencing severe deflation of 140 t m-1 yr-1. This result can be utilized to enhance the understanding of the direction and magnitude of sand movement in any region.
Effect of Elevated Temperature on Microstructure and Mechanical Properties of Hot-Rolled Steel
Authors: Saadoon Ali Malik , Al Gharawi Mohanned , Al-Mosawe Alaa
Published: December 2024

The mechanical properties and microstructure of hot-rolled steel are critical in determining its performance in industrial applications, particularly when exposed to elevated temperatures. This study examines the effects of varying temperatures and soaking times on these properties through a series of controlled experiments. The primary objective was to optimize the key response parameters, including tensile strength, yield strength, and elongation, by analyzing the influence of temperature and time. A full factorial design approach was used, applying the desirability function theory to explore all possible combinations and identify optimal processing conditions. The experimental results showed that the soaking time played a critical role, significantly influencing the mechanical properties with an impact ratio of 62%. The microstructural analysis displayed that higher temperatures and longer soaking times resulted in the formation of coarser ferrite and pearlite grains, contributing to a decrease in strength and an increase in ductility. The optimum process condition - 650 °C for 60 min - produced the highest values for tensile strength (400.32 MPa), elongation (36.78%) and yield strength (288.52 MPa). The study also highlighted the temperature-dependent nature of the mechanical behavior of hot-rolled steel. While tensile strength and yield strength initially increase with temperature, prolonged exposure, particularly at 600 °C and 750 °C, results in significant grain coarsening and a corresponding degradation of these properties. Conversely, elongation improves at moderate temperatures (150 °C to 300 °C) but decreases with prolonged exposure, especially at higher temperatures. These findings underscore the importance of precise control of thermal processing parameters to optimize the mechanical properties of hot-rolled steel. The findings offer significant insights that can be leveraged to optimize material performance in industrial applications, where thermal exposure is a critical consideration.