Parkinson's Disease (PD) is the most common senile disease. With the develop of artificial intelligent and information technology, wearable medical devices have made rapid development in the diagnosis and treatment of PD on the basis of traditional diagnostic treatment methods. According to the main symptoms and judgment criteria of PD, wearable medical devices are divided into two categories: monitoring type and treatment type. The monitoring type is mainly to use various sensors to collect and organize data on biological signals for PD, such as tremor, postural gait, muscle rigidity and bradykinesia. On this basis, the treatment devices can provide real-time treatment methods to reduce the disease. This paper classifies, summarizes and expounds the current research on wearable medical devices, monitoring and treatment of PD, and prospects the future research and challenges of wearable medical devices in PD.

Scheduling agricultural machinery across multiple cooperatives is often inefficient because existing rigid, discrete assignment models fail to flexibly coordinate shared resources under tight time windows. To address this limitation, we develop a simulation-based framework for the Multi-cooperative Agricultural Machinery Scheduling Problem (MAMSP) underpinned by a Continuous Collaborative Workload Sharing (CWS) formulation. To mitigate the solution fragmentation inherent in continuous optimization, we propose a Hybrid Particle Swarm Optimization with Sparsity Repair (HPSO-SR). The algorithm integrates a stochastic initialization strategy to enhance global exploration, a mutation injection mechanism to avoid swarm stagnation, and a sparsity repair operator that prunes uneconomical fractional assignments, yielding operationally feasible sparse schedules. A real-world case study from Liyang, China, augmented by synthetic instances of varying scales (small, medium, and large), was conducted to benchmark the proposed approach against a rule-based heuristic, a Genetic Algorithm (GA-CWS), and Simulated Annealing (SA-CWS) under a unified decoding scheme. The results show that HPSO-SR consistently achieves the lowest objective values, reducing the total cost by 74.43% relative to GA-CWS and 59.20% relative to SA-CWS in the medium-scale case. By deliberately trading off minimal additional transfer cost against improved timeliness, the obtained schedules nearly eliminate delay penalties. Sensitivity analysis and mechanism ablation studies further confirm that the sparse solutions exhibit structural resilience and that the proposed repair strategy is essential for algorithmic convergence, supporting the reliability of the proposed approach for time-critical, high-stakes agricultural operations.

In order to enhance the efficacy of mobile robots, a new dung beetle optimization algorithm (NewDBO) is proposed. It is improved using four strategies to improve the search capability and convergence speed of the algorithm. The efficacy of the enhanced algorithm is evaluated through the use of test functions and its application to the path planning problem. The results indicate that the improved algorithm exhibits faster convergence speed and better optimization ability than the comparison algorithm, and the shortest path can be obtained while ensuring the minimum number of turns in the path planning problem, which verifies the algorithm's feasibility and robustness.