The internal translucent color light box serves as both a display and behavioral guidance tool in exhibitions. However, its functionality can be compromised by variations in light intensity, temperature, humidity, bolt fastening integrity, and door lock status. Conventional Internet of Things (IoT)-based systems, while effective, often involve the installation of expensive sensors, control units, and network infrastructure within each light box. Given the large number of light boxes typically used in exhibitions, the high cost and slow response time of such systems remain significant limitations. This study proposes a novel approach utilizing a Peripheral Component Interconnect (PCI) bus structure to form a network of interconnected light boxes. By sequentially collecting voltage and current data from photosensitive resistors across adjacent groups of four light boxes, faults can be rapidly identified through a hierarchical comparison method. This method enables precise fault localization with minimal cost and at significantly reduced time. Simulations and physical prototypes were developed using Multisim to model the changes in light intensity during the fault detection process. Experimental results demonstrate the system's ability to accurately pinpoint malfunctioning light boxes when light levels fall below 1000 lx. The detection accuracy reaches 100% under these conditions. Notably, the proposed system requires no complex control processing, and offers an over 90% reduction in detection time and cost compared to traditional manual inspections and IoT-based fault detection systems. This approach presents a highly cost-effective and efficient solution for exhibition light box fault localization, facilitating maintenance by enabling visual identification of malfunctioning units.

This study investigates the optimization of wrench time to improve maintenance efficiency and reliability within a chemical processing plant. Wrench time, defined as the proportion of time spent directly performing maintenance tasks, was quantified through random observations of maintenance technicians. The findings revealed an average wrench time of 28% across the site, with variations between individual crew groups ranging from 20% to 35% and craft-specific wrench times varying from 13.3% to 45.5%. Several inefficiencies were identified, including prolonged wait times for equipment isolation, safety clearance, job planning, and parts procurement. Key contributing factors to these inefficiencies were found to include poor coordination between maintenance and production, insufficient work prioritization, inadequate adherence to schedules, a high volume of emergency tasks, and the absence of essential tools such as bills of materials (BOMs), equipment data, and troubleshooting checklists. To address these challenges, a range of improvement initiatives were implemented. These included enhancing coordination between maintenance and production by refining process steps, introducing additional planning tools for effective work prioritization, providing job aids, developing generic troubleshooting checklists, leveraging Industrial Internet of Things (IIoT) technologies, and establishing metrics to monitor progress. Early indications suggest that these initiatives have led to a reduction in maintenance backlog and gradual improvements in overall equipment effectiveness (OEE). It is anticipated that these changes will result in increased wrench time, enhanced maintenance quality and reliability, reduced downtime, and lower operational costs. For maintenance managers and engineers, the findings offer actionable insights into optimizing workflows and resource allocation, thereby contributing to the improvement of operational efficiency and reliability.

This study evaluates the potential of nitrogen and hydrogen as alternative working fluids in air conditioning systems to improve thermal comfort and optimize energy efficiency, using computational fluid dynamics (CFD) simulations. A controlled indoor environment measuring 6 m $\times$ 4.5 m $\times$ 3 m was simulated, with nitrogen and hydrogen tested at inlet velocities of 0.7 m/s, 0.8 m/s, 0.9 m/s, 1.0 m/s, and 1.1 m/s, and an inlet temperature fixed at 293 K (20℃). The analysis focused on the impact of these gases on room and outlet temperatures to assess airflow distribution, heat transfer, and thermal comfort compared to traditional air-based systems. Results indicated that nitrogen improved airflow uniformity and facilitated heat transfer but exhibited limitations in effectively reducing room temperature due to its thermal properties. In contrast, hydrogen demonstrated stable outlet temperatures across all velocities, benefiting from its higher thermal conductivity; however, room temperatures showed significant variation, particularly at higher inlet velocities. Temperature prediction errors in the CFD model ranged from 0.003% to 2.78%, suggesting high accuracy yet underscoring the need for refinement in simulation methods. The findings highlight the promise of nitrogen and hydrogen in optimizing air conditioning system performance but emphasize the necessity for further investigation into the practical implications, specifically regarding operational safety, energy efficiency, and environmental impacts.

In the realm of Wireless Sensor Networks (WSNs), energy efficiency emerges as a paramount concern due to the inherent limitations in the energy capacity of sensor nodes. The extension of network lifespan is critically dependent on the strategic selection of Cluster Heads (CHs), a process that necessitates a nuanced approach to optimize communication, resource allocation, and network performance overall. This study proposes a novel methodology for CH selection, integrating Multiple Criteria Decision Making (MCDM) with the K-Means algorithm to facilitate a more discerning aggregation and forwarding of data to the network sink. Central to this approach is the application of the Einstein Weighted Averaging Aggregation (EWA) operator, which introduces a layer of sophistication in handling the uncertainties inherent in WSN deployments. The efficiency of CH selection is vital, as CHs serve as pivotal nodes within the network, their selection and operational efficiency directly influencing the network's energy consumption and data processing capabilities. By employing a meticulously designed clustering process via the K-Means algorithm and selecting CHs based on a comprehensive set of parameters, including, but not limited to, residual energy and node proximity, this methodology seeks to substantially enhance the energy efficiency of WSNs. Comparative analysis with the Low-Energy Adaptive Cluster Hierarchy (LEACH)-Fuzzy Clustering (FC) algorithm underscores the efficacy of the proposed approach, demonstrating a 15% improvement in network lifespan. This advancement not only ensures optimal utilization of limited resources but also promotes the sustainability of WSN deployments, a critical consideration for the widespread application of these networks in various fields. The findings of this study underscore the significance of adopting sophisticated, algorithmically driven strategies for CH selection, highlighting the potential for significant enhancements in WSN longevity through methodical, data-informed decision-making processes.