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.
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.
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.
The rapid advancement of the internet industry and the emergence of intelligent production models necessitate a transformative approach to talent cultivation in global universities. The Outcomes-Based Education (OBE) model demonstrates distinct advantages and adaptability within this evolving landscape. By defining explicit learning outcomes, incorporating flexible curriculum designs, emphasizing practical skills, adopting a philosophy of continuous improvement, implementing multi-dimensional evaluation mechanisms, and employing student-centered teaching methods, OBE establishes a robust theoretical framework and practical methodology for developing high-quality artificial intelligence (AI) talents suited to the demands of the new era. This study, centered on graduate students at the Capital University of Economics and Business, proposes three strategic dimensions for curriculum reform grounded in the OBE concept: the objectives of curriculum reform, innovative teaching models, and the implementation of the curriculum. The investigation highlights the significance of value cultivation in discipline construction, the establishment of a diversified talent training system, and the optimization of a scientifically integrated teaching framework. This research offers valuable insights, ranging from policy recommendations to practical applications, aimed at advancing the high-quality development of computer science disciplines in a contemporary context.
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.
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.
The pressing need to reduce reliance on petroleum in the energy sector and the increasing demand for environmental protection are driving research and practical endeavors in the management of renewable supply chains. Professionals, global institutions and scholars have widely acknowledged the importance of studying the correlation, between the performance of supply chains and renewable energy sources. It's important to delve into the articles in terms of the methodologies that have been used, the principal concerns addressed, the specific renewable energy sources focused on, and the performance indicators employed to optimize supply chains for renewable energies. This paper provides an analysis that improves the understanding of research in the realm of quantitative decision making for renewable energy supply chains. The analysis commences by searching for articles published. Subsequently, they are narrowed down to those that are most relevant. The article also addresses knowledge gaps in the literature. The findings provide a reference for researchers who are considering conducting studies in this area.
This investigation was conducted to assess the impact of effort, interest, and cognitive competence on statistics achievement, mediated by self-concept among students. The study engaged 453 students enrolled in a statistics course at Yarmouk University, Jordan, who completed a self-report questionnaire. Path analysis facilitated the examination of both direct and indirect influences exerted by effort, interest, and cognitive competence on statistics achievement, with self-concept serving as a mediator. It was found that effort, interest, and cognitive competence significantly directly affected statistics achievement. Furthermore, self-concept was observed to partially mediate the relationships between each of effort, interest, cognitive competence, and statistics achievement. These results underscore the critical roles of effort, interest, and cognitive competence as predictors of success in statistics. The partial mediation by self-concept suggests its important but not exclusive role in enhancing academic outcomes. This study contributes to educational strategies by highlighting the potential of interventions focused on self-concept enhancement to improve academic performance in statistical education. Implications for educators and policy-makers are discussed in terms of designing effective educational interventions.
This paper has several aims. First, it seeks to answer whether a portfolio comprised of top innovators outperforms the S&P 500 index. To achieve this, a strategy was developed to invest long in top innovators based on their ranking, and its performance was compared to that of the broad-based index. Secondly, the paper aims to assess the volatility associated with innovative stocks, given the common belief that higher innovativeness carries higher risk. Additionally, it seeks to analyse the impact of sector factors on the portfolio's performance. Finally, the paper conducts a comparative analysis between the portfolio's performance and that of the ARK Innovation ETF (ARKK), which specifically focuses on investing in companies relevant to the theme of disruptive innovation.
Waste management poses a significant challenge in large urban areas, demanding meticulous logistical planning and scientific insight to balance environmental impact and cost-effectiveness. Ali Mendjeli, a newly established city in Constantine, Algeria, exemplifies this challenge without a mapped and documented inventory. This study employs a Geographic Information System (GIS) approach to develop a management application aimed at identifying key factors in solid waste management. Traditional waste management practices typically rely on manual methods prone to incomplete or inaccurate outcomes. In contrast, GIS tools facilitate the creation, organization, and modeling of comprehensive spatially referenced databases, integrating data on waste collection operators and disposal points hosted in cloud computing environments. This approach enhances precision and efficiency in waste management decision-making processes.
Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Regency, has reported a notable burden of the disease, evidenced by 102 confirmed cases in 2022 as per the annual parasite incidence (API) data, highlighting indigenous transmissions in specific locales. This research was conducted to assess the susceptibility to malaria within the operational area of the Hila Perawatan Primary Healthcare Centre (Puskesmas), situated in the Leihitu sub-district of Ambon Island, through an examination of $Anopheles$ mosquito breeding sites, larval densities, and habitat indices. Employing a descriptive research design, this cross-sectional observational study was carried out on October 26-27, 2023, to meticulously document the ecological footprint of the $Anopheles$ mosquito, particularly $Anopheles$ $farauti$. Findings reveal a habitat index (HI) of 33% in Kaitetu village with a larval density of 20%, indicating a significant presence of Anopheles farauti larvae. These findings suggest that environmental and behavioral factors within households, such as the use of gauze and ceilings, nocturnal activities, application of mosquito repellents, wearing of long-sleeved clothing, and utilization of mosquito nets, are pivotal in influencing malaria transmission dynamics. This study underscores the imperative of integrating environmental management with community engagement strategies to mitigate malaria transmission in endemic regions. The results not only provide a nuanced understanding of the $Anopheles$ mosquito's breeding patterns and its implications for malaria transmission but also offer a foundational basis for tailoring targeted interventions aimed at reducing the malaria burden in the Central Maluku District.
Open Access
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