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The demand for improved efficiency and accuracy in vaccine safety assessments is increasing. Here, we explore the application of computer vision technologies to automate the monitoring of experimental mice for potential side effects after vaccine administration. Traditional observation methods are labor-intensive and lack the capability for continuous monitoring. By deploying a computer vision system, our research aims to improve the efficiency and accuracy of vaccine safety assessments. The methodology involves training machine learning models on annotated video data of mice behaviors pre- and post-vaccination. Preliminary results indicate that computer vision effectively identify subtle changes, signaling possible side effects. Therefore, our approach has the potential to significantly enhance the monitoring process in vaccine trials in animals, providing a practical solution to the limitations of human observation., Comment: 1 figure

Cognitive impairments in older adults represent a significant public health concern, necessitating accurate diagnostic and monitoring strategies. In this study, the principal cognitive and neuropsychological evaluations employed for the diagnosis and longitudinal observation of cognitive deficits in the elderly are investigated. An analytical review of instruments including the Mini-Mental State Examination (MMSE), Digit Symbol Substitution Test (DSST), Montreal Cognitive Assessment (MoCA), and Trail Making Test (TMT) is conducted. This examination encompasses an assessment of each instrument's methodology, efficacy, advantages, and limitations. The objective is to enhance comprehension of these assessments for the early identification and effective management of conditions such as dementia and mild cognitive impairment, thereby contributing to the advancement of cognitive health within the geriatric population.

The integrity of offshore wind turbines, pivotal for sustainable energy generation, is often compromised by surface material defects. Despite the availability of various detection techniques, limitations persist regarding cost-effectiveness, efficiency, and applicability. Addressing these shortcomings, this study introduces a novel approach leveraging an advanced version of the YOLOv8 object detection model, supplemented with a Convolutional Block Attention Module (CBAM) for improved feature recognition. The optimized loss function further refines the learning process. Employing a dataset of 5,432 images from the Saemangeum offshore wind farm and a publicly available dataset, our method underwent rigorous testing. The findings reveal a substantial enhancement in defect detection stability, marking a significant stride towards efficient turbine maintenance. This study's contributions illuminate the path for future research, potentially revolutionizing sustainable energy practices., Comment: arXiv admin note: text overlap with arXiv:2306.16019

The safety of wind turbines is a prerequisite for the stable operation of offshore wind farms. However, bird damage poses a direct threat to the safe operation of wind turbines and wind turbine blades. In addition, millions of birds are killed by wind turbines every year. In order to protect the ecological environment and maintain the safe operation of offshore wind turbines, and to address the problem of the low detection capability of current target detection algorithms in low-light environments such as at night, this paper proposes a method to improve the network performance by integrating the CBAM attention mechanism and the RetinexNet network into YOLOv5. First, the training set images are fed into the YOLOv5 network with integrated CBAM attention module for training, and the optimal weight model is stored. Then, low-light images are enhanced and denoised using Decom-Net and Enhance-Net, and the accuracy is tested on the optimal weight model. In addition, the k-means++ clustering algorithm is used to optimise the anchor box selection method, which solves the problem of unstable initial centroids and achieves better clustering results. Experimental results show that the accuracy of this model in bird detection tasks can reach 87.40%, an increase of 21.25%. The model can detect birds near wind turbines in real time and shows strong stability in night, rainy and shaky conditions, proving that the model can ensure the safe and stable operation of wind turbines.