Object detection systems are central to the autonomy and safety of intelligent transportation systems. Yet, the accuracy of object detection models can suffer under environmental noise or adverse weather. This paper tested the robustness of four object detection architectures: YOLOv5s, YOLOv8m, YOLOv10n, and Faster R-CNN, to visual degradation (real-world weather and artificial noise). We utilize the DAWN dataset, a benchmark of 1,000 high-resolution traffic images captured under fog, rain, snow, and sandstorms, with further augmentations of Gaussian noise, salt-and-pepper noise, blurriness, and overlays applied with artificial fog. We standardized all annotations to YOLO and COCO annotation formats for multi-framework interoperability. Our quantitative analysis used mAP@0.5, mAP@0.5:0.95, Precision, and Recall to compare the models, alongside some qualitative analysis through visual overlays and plotting training loss. The findings of our analysis showed YOLOv8m achieved the highest baseline accuracy on clean data, while Faster R-CNN proved resilient in noisy environments. YOLOv10n achieves a good trade-off between efficient and robust detection. The results of this study highlight the necessity of adaptive training pipelines and environment-aware benchmarks to enhance the real-world reliability of vision-based detection systems.

The evolving energy landscape, driven by increasing demands and the growing integration of renewables, necessitates a dynamic adjustment of the energy grid. To enhance the grid’s resilience and accommodate the surging influx of green energy. Energy storage solutions have emerged as crucial components. Despite considerable research, there remains a notable gap in systematically assessing the suitability of different storage devices across diverse stationary applications. This review endeavors to bridge this gap by thoroughly examining the current landscape of energy storage and discerning its aptness for various grid support applications. Through an exploration of technical, economic, and environmental considerations, the study aims to elucidate the optimal storage technologies for different contexts. Among electrochemical storage options, lithium-ion batteries emerge as optimal choices for both low- and medium-scale applications, owing to their robust power and energy densities. Meanwhile, capacitors, supercapacitors, and superconductive magnetic energy storages exhibit promise for high-power demands within the electrical storage domain. Additionally, thermal energy storage presents a viable solution for seasonal and bulk energy requirements. This review suggests using a mix of technologies in hybrid solutions to better meet the unique needs of different applications.

This paper presents a multi-task language-based framework for procedural content generation through reinforcement learning, which aims to improve the semantic alignment between linguistic commands and quantitative game surface features. While most previous methods in PCGRL have relied on numerical conditioning, the proposed approach, using a DeBERTa encoder and a multi-objective training scheme including regression, contrastive alignment, and hybrid learning, attempts to extract meaningful, generalizable, and structured representations of natural commands. To evaluate this framework, a structured dataset consisting of over 14,000 command-level pairs in the Super Mario environment is designed, which allows for the examination of single-task, collective, combinatorial, paraphrase, and extra-domain generalization. Experimental results show that the proposed model outperforms BERT-based methods in command following, semantic stability, and structural diversity of generated levels. The findings show that separating the semantic components of language and multi-objective training can be an effective step towards producing controllable, interpretable content that is aligned with human intent in PCGRL systems.