Convolutional Neural Networks (CNNs) have recently been introduced to Alzheimer's Disease (AD) diagnosis. Despite their encouraging prospects, most of the existing models only process AD-related brain atrophy on a single spatial scale, and have high computational complexity. Here, we propose a novel Attention-based 3D Multi-scale CNN model (AMSNet), which can better capture and integrate multiple spatial-scale features of AD, with a concise structure. For the binary classification between 384 AD patients and 389 Cognitively Normal (CN) controls using sMRI scannings, AMSNet achieves remarkable overall performance (91.3% accuracy, 88.3% sensitivity, and 94.2% specificity) with fewer parameters and lower computational load, generally surpassing seven comparative models. Furthermore, AMSNet generalizes well in other AD-related classification tasks, such as the three-way classification (AD-MCI-CN). Our results manifest the feasibility and efficiency of the proposed multi-scale spatial feature integration and attention mechanism used in AMSNet for AD classification, and provide potential biomarkers to explore the neuropathological causes of AD.

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science.

Brain network based on resting-state functional magnetic resonance (rs-fMRI) is the most popular method for brain disease diagnosis, which is expected to provide accurate and effective biomarkers. The original fully connected resting-state networks (RSNs) are too dense and must be filtered to get the real network model. In this study, orthogonal minimum spanning trees (OMSTs) was used to filter the connection matrix of 49 age-matched healthy controls (HC) and 50 patients with mild cognitive impairment (MCI). At the same time, we also used global cost efficiency (GCE) algorithm to filter brain network for comparison with OMSTs. We calculated the topological metrices of brain network. Fisher score was used to select features, and the optimal feature subset was used to construct SVM classifier. The classification accuracy of OMSTs was 87%, while that of GCE algorithm was 81%. The experimental results show that the classification accuracy is greatly improved by using OMSTs, which is an effective brain network filtering method.