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.

In the realm of high-definition surveillance for dense traffic environments, the accurate detection and classification of vehicles remain paramount challenges, often hindered by missed detections and inaccuracies in vehicle type identification. Addressing these issues, an enhanced version of the You Only Look Once version v5s (YOLOv5s) algorithm is presented, wherein the conventional network structure is optimally modified through the partial integration of the Swin Transformer V2. This innovative approach leverages the convolutional neural networks' (CNNs) proficiency in local feature extraction alongside the Swin Transformer V2's capability in global representation capture, thereby creating a symbiotic system for improved vehicle detection. Furthermore, the introduction of the Similarity-based Attention Module (SimAM) within the CNN framework plays a pivotal role, dynamically refocusing the feature map to accentuate local features critical for accurate detection. An empirical evaluation of this augmented YOLOv5s algorithm demonstrates a significant uplift in performance metrics, evidencing an average detection precision (mAP@0.5:0.95) of 65.7%. Specifically, in the domain of vehicle category identification, a notable increase in the true positive rate by 4.48% is observed, alongside a reduction in the false negative rate by 4.11%. The culmination of these enhancements through the integration of Swin Transformer and SimAM within the YOLOv5s framework marks a substantial advancement in the precision of vehicle type recognition and reduction of target miss detection in densely populated traffic flows. The methodology's success underscores the efficacy of this integrated approach in overcoming the prevalent limitations of existing vehicle detection algorithms under complex surveillance scenarios.