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Our mission is to inspire and empower the scientific exchange between scholars around the world, especially those from emerging countries. We provide a virtual library for knowledge seekers, a global showcase for academic researchers, and an open science platform for potential partners.

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Open Access
Research article
Evaluating Alternative Propulsion Systems for Urban Public Transport in Niš: A Multicriteria Decision-Making Approach
nikola petrović ,
saša marković ,
boban nikolić ,
vesna jovanović ,
marijana petrović
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Available online: 05-27-2024

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In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings.
Open Access
Research article
Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis
shankha shubhra goswami ,
surajit mondal ,
rohit halder ,
jibangshu nayak ,
arnabi sil
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Available online: 05-23-2024

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The rapid advancement of technology has correspondingly escalated the sophistication of cyber threats. In response, the integration of artificial intelligence (AI) into cybersecurity (CS) frameworks has been recognized as a crucial strategy to bolster defenses against these evolving challenges. This analysis scrutinizes the effects of AI implementation on CS effectiveness, focusing on a case study involving company XYZ's adoption of an AI-driven threat detection system. The evaluation centers on several pivotal metrics, including False Positive Rate (FPR), Detection Accuracy (DA), Mean Time to Detect (MTTD), and Operational Efficiency (OE). Findings from this study illustrate a marked reduction in false positives, enhanced DA, and more streamlined security operations. The integration of AI has demonstrably fortified CS resilience and expedited incident response capabilities. Such improvements not only underscore the potential of AI-driven solutions to significantly enhance CS measures but also highlight their necessity in safeguarding digital assets within a continuously evolving threat landscape. The implications of these findings are profound, suggesting that leveraging AI technologies is imperative for effectively mitigating cyber threats and ensuring robust digital security in contemporary settings.
Open Access
Research article
Design and Economic Analysis of a Solar-Powered Charging Station for Personal Electric Vehicles in Indonesia
singgih d. prasetyo ,
alvyan n. rizandy ,
anom r. birawa ,
farrel j. regannanta ,
zainal arifin ,
mochamad s. mauludin ,
sukarman
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Available online: 05-23-2024

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Indonesia, known for its abundant renewable resources, especially solar energy, presents a substantial potential for developing solar-powered solutions to meet its increasing electricity demands. This study explores the feasibility of a Solar Power Plant (PLTS) as the energy source for a personal Electric Vehicle Charging Station (SPKL), facilitating the transition from fuel-based to electric vehicles. Using a simulation-based approach, a hypothetical daily electricity load of 12,711 kW was considered. The simulations indicate that an On-Grid PLTS is the most economically viable option, offering significant investment returns. The annual energy output of the PLTS was calculated to be 30,767 kWh. Financial projections suggest a substantial profit by the 25th year, amounting to IDR 374,450,204.39. This research underscores the strategic importance of integrating hybrid technologies in developing renewable energy infrastructures, particularly in regions like Indonesia, where solar irradiance is high. The findings advocate for broader implementation of such systems aligned with national energy sustainability and economic efficiency goals.
Open Access
Research article
A Novel Approach for Systematic Literature Reviews Using Multi-Criteria Decision Analysis
Vilmar Steffen ,
Maiquiel Schmidt de Oliveira ,
flavio trojan
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Available online: 05-22-2024

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This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature.
Open Access
Research article
Fuzzy Logic-Based Fault Detection in Industrial Production Systems: A Case Study
imen driss ,
ines dafri ,
samy ilyes zouaoui
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Available online: 05-20-2024

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The burgeoning application of artificial intelligence (AI) technologies for the diagnosis and detection of defects has marked a significant area of interest among researchers in recent years. This study presents a fuzzy logic-based approach to identify failures within industrial systems, with a focus on operational anomalies in a real-world context, particularly within the competitive landscape of Omar Benamour, in Al-Fajjouj region, Guelma, Algeria. The analysis has been started with the employment of the Activity-Based Costing (ABC) method to identify the critical machinery within the K-short dough production line. Subsequently, an elaborate failure tree analysis has been conducted on the pressing machine, enabling the deployment of a fuzzy logic approach for the detection of failures in the dough cutter of AMOR BENAMOR's K production line press. The effectiveness of the proposed method has been validated through an evaluation conducted with an authentic and real-time data from the facility, where the study took place. The results underscore the efficacy of the fuzzy logic approach in enhancing fault detection within industrial systems, offering substantial implications for the advancement of defect diagnosis methodologies. The study advocates for the integration of fuzzy logic principles in the operational oversight of industrial machinery, aiming to mitigate potential failures and optimize production efficiency.
Open Access
Research article
The Moderating Role of Trust in the Adoption of Self-Service Payment Systems by Consumers
nguyen le ,
ngoc thi bich mai ,
nhan trong ngo ,
hien thu thi dang
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Available online: 05-16-2024

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In an age where online shopping and innovative services are rapidly evolving, consumer adaptation to shopping trends, store layouts, and payment modalities is critical. Among these adaptations, self-service checkout systems have been introduced in Vietnamese supermarkets to streamline the post-shopping payment process and alleviate cashier counter congestion. This research was conducted to assess factors influencing consumer intentions towards using self-service payment systems. Data from 497 consumers were collected through non-probability sampling and analyzed using the Smart PLS 4.0 software to test various hypotheses. It was found that consumers’ perceptions of usefulness and ease of use, along with their attitudes towards usage, significantly influence their intention to adopt these systems. Importantly, trust was identified as a positive moderator, enhancing the relationship between consumers’ attitudes towards usage and their intentions to engage with self-service payment systems. These findings suggest managerial implications for increasing system acceptance and understanding consumer needs related to self-service payment options in Vietnamese markets. The results contribute to the broader discourse on technology acceptance, particularly within the framework of the Technology Readiness and Acceptance Model, and underscore the importance of trust in the successful deployment of technological solutions in retail settings.

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Recent advancements have seen the integration of nanocomposites, composed of clay minerals and polymers, into cementitious materials to enhance their mechanical properties. This investigation focuses on the dynamics of clay-based cementitious nanofluids along a vertical plate, adopting a Jeffrey fluid model to encompass various phenomena. The effects of a first-order chemical reaction and heat generation/absorption are considered, alongside slip velocity and Newtonian heating conditions. The governing equations, represented as partially coupled partial differential equations, have been extended using a constant proportional Caputo (CPC) fractional derivative. Exact solutions were derived employing the Laplace transform technique. A detailed graphical analysis was conducted to elucidate the influence of pertinent flow parameters on the velocity, temperature, and concentration profiles. It was observed that the incorporation of clay nanoparticles results in a reduction of the fluid's heat transfer rate by 10.17%, and a decrease in the mass transfer rate by 1.31% at a nanoparticle volume fraction of 0.04. These findings underscore the nuanced role of nanoparticle concentration in modifying fluid dynamics under the studied conditions, providing a validated and precise understanding of nanofluid behavior in construction-related applications. This research not only supports the potential of nanotechnology in improving cementitious materials but also contributes to the broader field of fluid mechanics by integrating complex heating and slip conditions into the study of nanoparticle-enhanced fluids.

Open Access
Research article
Characterization and Risk Assessment of Cyber Security Threats in Cloud Computing: A Comparative Evaluation of Mitigation Techniques
oludele awodele ,
chibueze ogbonna ,
emmanuel o. ogu ,
johnson o. hinmikaiye ,
jide e. t. akinsola
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Available online: 05-15-2024

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Advancements in information technology have significantly enhanced productivity and efficiency through the adoption of cloud computing, yet this adoption has also introduced a spectrum of security threats. Effective cybersecurity mitigation strategies are imperative to minimize the impact on cloud infrastructure and ensure reliability. This study seeks to categorize and assess the risk levels of cybersecurity threats in cloud computing environments, providing a comprehensive characterization based on eleven major causes, including natural disasters, loss of encryption keys, unauthorized login access, and others. Using fuzzy set theory to analyze uncertainties and model threats, threats were identified, prioritized, and categorized according to their impact on cloud infrastructure. A high level of data loss was revealed in five key features, such as encryption key compromise and unauthorized login access, while a lower impact was observed in unknown cloud storage and exposure to sensitive data. Seven threat features, including encryption key loss and operating system failure, were found to significantly contribute to data breaches. In contrast, others like virtual machine sharing and impersonation, exhibited lower risk levels. A comparative analysis of threat mitigation techniques determined Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege (STRIDE) as the most effective methodology with a score of 59, followed by Quality Threat Modeling Methodology (QTMM) (57), Common Vulnerability Scoring System (CVSS) (51), Process for Attack Simulation and Threat Analysis (PASTA) (50), and Persona non-Grata (PnG) (47). Attack Tree and Hierarchical Threat Modeling Methodology (HTMM) each achieved 46, while Linkability, Identifiablility, Nonrepudiation, Detectability, Disclosure of Information, Unawareness and Noncompliance (LINDDUN) scored 45. These findings underscore the value of fuzzy set theory in tandem with threat modeling to categorize and assess cybersecurity risks in cloud computing. STRIDE is recommended as an effective modeling technique for cloud environments. This comprehensive analysis provides critical insights for organizations and security experts, empowering them to proactively address recurring threats and minimize disruptions to daily operations.

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Dental implants (DIs) are prone to failure due to uncommon mechanical complications and fractures. Precise identification of implant fixture systems from periapical radiographs is imperative for accurate diagnosis and treatment, particularly in the absence of comprehensive medical records. Existing methods predominantly leverage spatial features derived from implant images using convolutional neural networks (CNNs). However, texture images exhibit distinctive patterns detectable as strong energy at specific frequencies in the frequency domain, a characteristic that motivates this study to employ frequency-domain analysis through a novel multi-branch spectral channel attention network (MBSCAN). High-frequency data obtained via a two-dimensional (2D) discrete cosine transform (DCT) are exploited to retain phase information and broaden the application of frequency-domain attention mechanisms. Fine-tuning of the multi-branch spectral channel attention (MBSCA) parameters is achieved through the modified aquila optimizer (MAO) algorithm, optimizing classification accuracy. Furthermore, pre-trained CNN architectures such as Visual Geometry Group (VGG) 16 and VGG19 are harnessed to extract features for classifying intact and fractured DIs from panoramic and periapical radiographs. The dataset comprises 251 radiographic images of intact DIs and 194 images of fractured DIs, meticulously selected from a pool of 21,398 DIs examined across two dental facilities. The proposed model has exhibited robust accuracy in detecting and classifying fractured DIs, particularly when relying exclusively on periapical images. The MBSCA-MAO scheme has demonstrated exceptional performance, achieving a classification accuracy of 95.7% with precision, recall, and F1-score values of 95.2%, 94.3%, and 95.6%, respectively. Comparative analysis indicates that the proposed model significantly surpasses existing methods, showcasing its superior efficacy in DI classification.

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