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.
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.
The increasing demand for electricity, coupled with the limitations of centralised power generation, has necessitated the transition towards smart grid technologies as a critical evolution of traditional power systems. The smart grid represents a significant transformation from the conventional grid, offering a pathway towards modernising energy infrastructure. This review aims to present a comprehensive analysis of the advantages and challenges of smart grid implementation, particularly within the context of the Kurdistan Region of Iraq. Key benefits such as improved grid intelligence, enhanced reliability, and sustainability were highlighted. However, several challenges were identified, including cybersecurity risks, regulatory complexities, and issues of interoperability, which collectively pose obstacles to widespread adoption. Furthermore, the review examines the current energy network in the Kurdistan region and proposes a framework for integrating smart grid technologies. Strategies for addressing the identified challenges were discussed, emphasising the importance of overcoming these barriers to facilitate the region's transition to a more advanced and efficient energy infrastructure.
In order to better understand the competitive dynamics between e-commerce platforms and traditional retail outlets, a Stackelberg game model was developed. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to determine the Pareto solution set for this multi-objective optimization problem. The findings reveal that: a) The effect of consumer reference quality can lead enterprises to adjust their strategy levels downwards, potentially resulting in profit loss under certain conditions. b) When the influence of competitive intensity on market demand is minimal, a reduction in enterprise profits occurs in both centralized and cost-sharing decision-making frameworks, with more significant detriment observed in the cost-sharing mode; conversely, when the influence is substantial, enhancements in competitive intensity can significantly increase overall system profits. c) The model's validity was confirmed through the application of the NSGA-II.
The integration of artificial intelligence (AI) and robotics into the warehouse management system (WMS) has substantially advanced supply chain (SC) operations, offering notable improvements in efficiency, accuracy, and economic resilience. In warehousing environments, AI algorithms and robotized systems enable rapid and precise product retrieval from storage while optimizing routing and packaging, thereby reducing order preparation time and enhancing delivery reliability. The implementation of these advanced technologies also results in fewer errors, improved customer satisfaction, and streamlined SC processes, empowering organizations to better manage inventory and respond swiftly to fluctuating market demands. Such innovations allow for reduced operating costs, enhanced productivity, and increased sustainability. Autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and drones, among other cutting-edge solutions, are increasingly incorporated into the WMS to minimize physical labor and mitigate workplace injuries. Despite these benefits, considerable challenges remain, including the high initial costs and requisite technical expertise for ongoing maintenance. The integration of new AI and robotic technologies into pre-existing systems necessitates careful evaluation, substantial employee training, and process adaptation. Nonetheless, these technologies play a crucial role in fostering environmentally and socially sustainable operations within warehouses and broader SCs, contributing to reduced carbon emissions and the elimination of hazardous tasks for human workers. This study aims to identify the most effective AI and robotic technologies for a sustainable WMS, with recommendations tailored to maximize SC value through automation. A detailed examination of existing warehouse practices is essential to pinpoint areas where automation can yield the most substantial impact and deliver long-term resilience and value for SCs.
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