This study proposes an optimized genetic algorithm-based wavelet image fusion technique for printed circuit board (PCB) detection, incorporating an improved Genetic Algorithm (GA) with the Elite Strategy and integrating it with discrete wavelet transform (DWT). The proposed method aims to enhance both the accuracy and efficiency of image fusion, which is crucial for defect detection in PCB inspection. A DWT is utilized to decompose images into multiple frequency components, where the low-frequency band preserves the structural integrity of the image, and the high-frequency band retains essential fine details such as edges and textures, which are critical for identifying defects. An improved genetic algorithm is applied to optimize the fusion process, incorporating the Elite Strategy to retain the best solutions in each evolutionary iteration. This strategy prevents the loss of optimal wavelet decomposition weights, and ensures steady convergence towards the global optimum. By maintaining superior solutions throughout the evolutionary process, the algorithm effectively enhances the fusion quality and computational efficiency. Experimental evaluations validate the effectiveness of the proposed approach, demonstrating superior performance over conventional fusion methods. The enhanced algorithm achieves significant improvements in key performance metrics, including relative standard deviation (RSD), peak signal-to-noise ratio (PSNR), image clarity, and processing efficiency. The team developed a prototype system and conducted simulations in a relatively realistic environment to validate the proposed method’s potential for high-precision PCB detection. The results demonstrate that the approach offers a robust solution for automated defect detection and quality assessment.

Prompt engineering provides an efficient way to adapt large language models (LLMs) to downstream tasks without retraining model parameters. However, designing effective prompts can be challenging, especially when model gradients are unavailable and human expertise is required. Existing automated methods based on gradient optimization or heuristic search exhibit inherent limitations under black box or limited-query conditions. We propose Domain-Aware Reinforcement Learning for Prompt Optimization (DA-RLPO), which treats prompt editing as a sequential decision process and leverages structured domain knowledge to constrain candidate edits. Our experimental results show that DA-RLPO achieves higher accuracy than baselines on text classification tasks and maintains robust performance with limited API calls, while also demonstrating effectiveness on text-to-image and reasoning tasks.

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization.

Recent advances in deep learning have intensified the need for robust low-light image processing in critical applications like autonomous driving, where single-photon cameras (SPCs) offer high photon sensitivity but produce noisy outputs requiring specialized enhancement. This work addresses this challenge through a unified framework integrating three key components: an SNR-guided adaptive enhancement framework that dynamically processes regions with varying noise levels using spatial-adaptive operations and intelligent feature fusion; a specialized self-attention mechanism optimized for low-light conditions; and a conditional autoregressive generation approach applied to robust depth estimation from enhanced SPC images. Our comprehensive evaluation across multiple datasets demonstrates improved performance over state-of-the-art methods, achieving a PSNR of 24.61 dB on the LOL-v1 dataset and effectively recovering fine-grained textures in depth estimation, particularly in real-world SPC applications, while maintaining computational efficiency. The integrated solution effectively bridges the gap between single-photon sensing and practical computer vision tasks, facilitating more reliable operation in photon-starved environments through its novel combination of adaptive noise processing, attention-based feature enhancement, and generative depth reconstruction.

Object detection in UAV aerial imagery plays a vital role in applications such as traffic surveillance, urban management, and low-altitude inspection. However, aerial images typically present challenges including small object scales, dense distributions, severe occlusion, and cluttered backgrounds. Existing YOLO-series detectors still exhibit limitations in small-object feature representation, multi-scale contextual modeling, and downsampling detail preservation. To address these issues, this paper proposes MD-YOLO, an improved object detection model tailored for UAV scenarios, built upon the YOLO26 baseline. MD-YOLO incorporates three lightweight modules—IMO, DS-SPPF, and HPConv to optimize backbone feature extraction, multi-scale contextual aggregation, and Neck downsampling, respectively, thereby enhancing the model’s detection capability for small objects in complex UAV scenarios. Experimental results demonstrate that, compared with the baseline model, MD-YOLO achieves improvements of 3.9, 3.2, 2.9, and 3.1 percentage points in Precision, Recall, mAP50, and mAP50-95 on the VisDrone-2019 dataset, while maintaining a parameter count of only 9.3 M, thereby striking a favorable balance between accuracy and complexity. Independent evaluations on the UAVDT and NWPU VHR-10 datasets further confirm the consistent effectiveness of the model across diverse UAV imaging conditions, with mAP50 improvements of 8.3 and 1.4 percentage points, respectively.