Electroencephalogram (EEG) signals, as a reliable biological indicator, have been widely used in fatigue driving detection due to their capacity to reflect a driver's cognitive and neural response state. However, EEG signals have problems such as imbalanced data distribution, significant differences between subjects, and complex scenes, which affect the detection effect. Small commonalities between input objects can be interpreted as important information about an entire sample. Therefore, to retain as much information as possible, We design a new approach for integrating fuzzy features, comprehensive adaptive interpretable TSK fuzzy classifier(CAI-TSK-FC). It not only captures the features of multiple subclassifiers more efficiently and alleviates the dataset imbalance problem. Also, it can reduce the accumulation of error information by randomly retaining fuzzy rules as well as normalization. Finally, we linearly combine the results of multiple subclassifiers to comprehensively consider the learning effect of multiple subclassifiers to adapt to different subjects and datasets. Experiments conducted on both self-made and public datasets (SEED-VIG) show that CAI-TSK-FC has good performance and interpretability on different EEG fatigue driving datasets. In comparison to existing methods, it achieves an accuracy improvement of 3.15% and 1.52%, respectively, as well as a specificity improvement of 4.72% and 0.91%, respectively.

Neuroscience research indicates that the interaction among different functional regions of the brain plays a crucial role in driving various cognitive tasks. Existing studies have primarily focused on constructing either local or global functional connectivity maps within the brain, often lacking an adaptive approach to fuse functional brain regions and explore latent relationships between localization during different cognitive tasks. This paper introduces a novel approach called the Local-Ascending-Global Learning Strategy (LAG) to uncover higher-level latent topological patterns among functional brain regions. The strategy initiates from the local connectivity of individual brain functional regions and develops a K-Level Self-Adaptive Ascending Network (SALK) to dynamically capture strong connectivity patterns among brain regions during different cognitive tasks. Through the step-by-step fusion of brain regions, this approach captures higher-level latent patterns, shedding light on the progressively adaptive fusion of various brain functional regions under different cognitive tasks. Notably, this study represents the first exploration of higher-level latent patterns through progressively adaptive fusion of diverse brain functional regions under different cognitive tasks. The proposed LAG strategy is validated using datasets related to fatigue (SEED-VIG), emotion (SEED-IV), and motor imagery (BCI_C_IV_2a). The results demonstrate the generalizability of LAG, achieving satisfactory outcomes in independent-subject experiments across all three datasets. This suggests that LAG effectively characterizes higher-level latent patterns associated with different cognitive tasks, presenting a novel approach to understanding brain patterns in varying cognitive contexts.

Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic “hill-climbing” procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We high-light the advantage of conversational feedback that incor-porates both positive and negative prompts, suggesting that LLMs can utilize the implicit “gradient” direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

Sports can cause the consumption of energy materials in the body. The rational use of nutritional supplements can maintain the homeostasis of the organism, which plays a very important role in improving the competitive performance of sports athletes. The purpose of this study is to explore the effect of nutritional supplements on basketball sports fatigue. The method of this study is as follows: first of all, 15 basketball players in our city were selected as the experimental objects, and they were randomly divided into the experimental group and the control group. The members of the experimental group took nutrients. After the training, 6 days a week, 3 hours in the morning and 3 hours in the afternoon, and the rest was adjusted on Sunday. Before training, four weeks and eight weeks of training, the blood routine indexes and body functions of athletes were tested. The results showed that the number of red blood cells, hemoglobin concentration, and average hemoglobin concentration of ligustilide supplement of the athletes were at the level of 0.05 after 4 weeks and 8 weeks, and the difference was significant (P < 0.05). The nutritional supplements were used in sprint (3.4 s less), long‐distance running (12.8 s less), and weight lifting (6.2 kg more) to a certain extent. Nutritional supplements are used as an auxiliary means of diet to supplement the amino acids, trace elements, vitamins, minerals, etc. required by the human body. The conclusion is that nutrition supplement can effectively improve the indexes of athletes’ body in about four weeks, but the effect is not obvious after a long time. This study provides a certain method for the research of nutritional supplements in the field of sports.

This paper has focused on a new automated detection and removal of cloud shadows of remotely sensed data to service quantitative interval of atmospheric parameters, using the FengYun-3 (FY-3), moderate spatial resolution and HuanJing-1 (HJ-1), high spatial resolution satellite data. Taking into account interaction between neighboring pixels in remote sensing images, this research aims at the special context relationships that are used to detect cloud shadows and then being carried out on related pixel match and cloud shadow removal. The tests show that the algorithm of detection and removal of cloud shadow is very simple and valid with the high discrimination accuracy and low leaking discrimination rate and so on, as well as yet associated with scale and scaling effect.