Mobility management associating all user equipments (UEs) to proper base stations (BSs) (also known as handover, HO) to achieve the designated performance optimization is one of the most crucial functions in mobile networks. Although effective mobility management has received considerable research attentions, existing schemes follow event-driving operations, in which HO decisions are made based on events of performance degradation that BSs/UEs passively suffer from or proactively foresee. However, the performance and decisions of these schemes are highly subject to the identified events, to lose generalization and better performance under unidentified events. To address this issue, we propose a foundation model (FM) based mobility management for the sixth generation (6G) mobile networks inherently supporting artificial intelligence (AI) computing, in which the FM generates the HO decisions for all UEs to maximize the overall throughput under the constraints of ping-pong rate and HO failure (HOF) rate by implicitly taking moving trajectories, traffic demands, channel conditions of all UEs and available resources of BSs into account. To this end, a hierarchical model structure composed of Long Short-Term Memory (LSTM) networks with multi-head attention (MHA) is adopted, which is trained by emulated datasets with augmentation. The performance evaluation results show that the proposed scheme outperforms the state-of-the-art schemes in terms of the average throughput over all UEs while satisfying the required ping-pong rate and HOF rate, and justify the robustness of the proposed FM under different network deployment scenarios.

Pedestrian detection in agricultural environments remains an underexplored area in computer vision, particularly in the context of tea plantation management. This study proposes a vision-based personnel detection system customized for tea garden scenarios, aiming to enhance on-site monitoring and labor management. We adopted the YOLOv5 object detection framework and integrated it with the DeepSORT tracking algorithm to develop a real-time pedestrian recognition system. The model was initially trained on the MOT20 dataset and tested on video data collected from real-world tea plantations.Experimental results revealed that models trained on conventional urban pedestrian datasets struggle to maintain detection accuracy in tea garden environments due to occlusions by tea plants, varying lighting conditions, and target sizes. Recognition performance significantly decreased in steep-angle or top-down camera views, highlighting the need for optimized camera deployment and domain-adapted training. To address this, we conducted data augmentation and parameter tuning tailored to the tea plantation context, improving the model’s mean Average Precision (mAP) and tracking consistency. This work contributes a practical framework for customized-area personnel detection in agriculture and underscores the necessity of developing domain-specific datasets for better generalization. The findings provide valuable technical guidance for intelligent agricultural surveillance and pave the way for further research into vision-based monitoring in crop-specific environments.

As chronic diseases continue to rise globally, public health systems are under increasing pressure to find innovative and scalable care solutions. In response, this study introduces an AI-powered health advisory system that leverages generative AI techniques, specifically retrieval-augmented generation (RAG) and integrates seamlessly with LINE and the ChatGPT API. Designed to support individuals managing chronic conditions, the system provides real-time, personalized recommendations, including dietary guidance, medication reminders, and interpretations of health checkup results. Experimental evaluations show that the system achieves over$\mathbf{9 0 \%}$accuracy across key functions, underscoring its potential to enhance self-management and support preventive healthcare strategies.

It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

According to our studies, the current contention resolution algorithm adapted in wireless ad-hoc networks, binary exponential backoff scheme, does not function well in multi-hop environments due to its several performance issues and technical limitations. For example, unfair channel access, intensive collision, and throughput degradation are several widely known issues. Besides, BEB cannot support multimedia traffic since it does not include any priority mechanism. In this paper, we put forth a simple, fair channel access, priority provision, and well performed contention resolution algorithm for multi-hop wireless ad-hoc networks. Simulations are conducted to evaluate the performance scheme. As it turns out, the results show that the proposed algorithm can effectively alleviate the fairness problem and support multimedia traffic in multi-hop wireless ad-hoc networks.