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The rule of law in foreign-related affairs in China operates under two paradigms: the national paradigm and the global paradigm. The national paradigm is dedicated to elevating the nation’s rule of law standards, promoting legalization of foreign-related work across all aspects and sectors, and improving legal methodologies in international engagements. It aims to safeguard China’s sovereignty, security, and development interests, protect the lawful rights and interests of Chinese citizens and legal entities overseas and enhance the nation’s rule of law image. The global paradigm demonstrates the sense of responsibility of China for actively participating in global governance, aiming to advance the international rule of law, contribute to world peace and development, and actively promote the building of a human community with a shared future. These are two complementary yet occasionally conflicting paradigms. A nation must firstly ensure its own independence, autonomy and sound development in order to contribute to the realization of the vision of a good global order. The traditional Chinese view of righteousness and interests helps to effectively guide the mutual promotion and restriction of the national paradigm and the global paradigm. With the national paradigm acting as a prerequisite for the global paradigm, we can properly allocate resources and form a structured, rigorous and reliable timeline and roadmap for the rule of law in foreign-related affairs.

A simple and effective method based on in situ infrared spectroscopy and two-dimensional (2D) correlation analysis was applied to research the chemical changes and curing reaction mechanism of epoxy resin and amine curing agents. It is generally agreed that the epoxy groups in epoxy resin react with amino groups to form new C–N and hydroxyl groups during the curing reaction process. However, detailed information about the curing reaction mechanism of epoxy resin and amine curing agents has rarely been reported. In this work, the curing reaction mechanism can be deeply understood from the results of 2D correlation analysis. Due to the nucleophilic addition reaction of amino and epoxy groups, the nitrogen atoms of primary amines easily combine with the carbon atoms in epoxy groups, which forms new C–N groups. Then, the C–O bonds in epoxy groups break; finally, as the N–H bonds in primary amines break, the hydrogen atoms combine with the oxygen atoms to form new hydroxyl groups.In situ infrared spectroscopy and two-dimensional (2D) correlation analysis was applied to research the chemical changes and curing reaction mechanism of epoxy resin and amine curing agents. The curing reaction mechanism can be deeply understood from the results. Due to the nucleophilic addition reaction of amino and epoxy groups, the nitrogen atoms easily combine with the carbon atoms, which forms new C-N groups. Then, the C-O bonds break; finally, as the N-H bonds in primary amines break, the hydrogen atoms combine with the oxygen atoms to form new hydroxyl groups.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model.

With the continuous development of portable noninvasive human sensor technologies such as brain–computer interfaces (BCI), multimodal emotion recognition has attracted increasing attention in the area of affective computing. This paper primarily discusses the progress of research into multimodal emotion recognition based on BCI and reviews three types of multimodal affective BCI (aBCI): aBCI based on a combination of behavior and brain signals, aBCI based on various hybrid neurophysiology modalities and aBCI based on heterogeneous sensory stimuli. For each type of aBCI, we further review several representative multimodal aBCI systems, including their design principles, paradigms, algorithms, experimental results and corresponding advantages. Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI.

Drone-based target detection using visible and thermal (RGB-T) images is critical in disaster rescue, intelligent transportation, and wildlife monitoring. However, persons typically occupy fewer pixels and exhibit more varied postures than vehicles or large animals, making them difficult to detect in unmanned aerial vehicle (UAV) remote sensing images with complex backgrounds. We propose a novel progressive target-aware network (PTANet) for person detection using RGB-T images. A global adaptive feature fusion module (GAFFM) is designed to fuse the texture and thermal features of persons. A progressive focusing strategy is used. Specifically, we incorporate a person segmentation auxiliary branch (PSAB) during training to enhance target discrimination, while a cross-modality background mask (CMBM) is applied in the inference phase to suppress irrelevant background regions. Extensive experiments demonstrate that the proposed PTANet achieves high accuracy and generalization performance, reaching 79.5%, 47.8%, and 97.3% mean average precision (mAP)@50 on three drone-based person detection benchmarks (VTUAV-det, RGBTDronePerson, and VTSaR), with only 4.72 M parameters. PTANet deployed on an embedded edge device with TensorRT acceleration and quantization achieves an inference speed of 11.177 ms (640 × 640 pixels), indicating its promising potential for real-time onboard person detection. The source code is publicly available on GitHub.

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.

Anoplophora horsfieldii Hope, a potential pest of the Cerambycidae family, is widely distributed throughout China, where it can cause damage to various living tree species. It has emerged as a critical invasive organism threatening China’s agricultural and forestry production as well as ecological security. This study comprehensively analyzed the key environmental factors influencing the geographical distribution of A. horsfieldii and its spatiotemporal dynamics by integrating multi-source environmental data and employing ecological niche modeling. Model validation demonstrated high reliability and accuracy of our predictions, with an area under the receiver operating characteristic curve (AUC) value of 0.933, Kappa coefficient of 0.704, and true skill statistic (TSS) reaching 0.960. Our analysis identified four dominant environmental factors governing the distribution of A. horsfieldii: mean diurnal range (Bio2), temperature annual range (Bio7), precipitation of driest quarter (Bio17), and precipitation of coldest quarter (Bio19). Under current climatic conditions, the total potential suitable distribution area for A. horsfieldii was estimated at 212.394 × 10⁴ km2, primarily located in central, southern, eastern, southwestern, and northwestern China. Future projections under three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) suggest significant reductions in highly and moderately suitable habitats, while low-suitability areas may expand into central, eastern, and southwestern regions, with Chongqing, Henan, and Anhui potentially becoming new suitable habitats. Concurrently, the centroid coordinates of suitable habitats exhibited a directional shift toward Guangdong Province, with the overall distribution pattern demonstrating a spatial transition characterized by movement from inland to coastal areas and from higher to lower latitudes. This study provides scientific theoretical support for forestry authorities in controlling the spread of A. horsfieldii, while establishing a solid foundation for future ecological conservation and biosecurity strategies. The findings offer both theoretical insights and practical guidance for pest management and ecosystem protection.

Background and objectives Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes. This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients. Methods and materials Retrospectively collected general clinical data, planning CT images, and dose files from 205 patients with advanced cervical cancer who underwent chemoradiotherapy, and classified them according to the severity of HT. Radiomics and dosiomics features were extracted from the same region of interest, and feature selection was performed using a random forest algorithm. Radiomics models, dosiomics models, and hybrid models were then constructed based on extreme gradient boosting trees. Sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the classification performance of the models. Finally, SHAP values were used to perform interpretability analysis on the best model to enhance the transparency and practicality of the model. Results The sensitivity, specificity, and AUC values for the radiomics model were 0.42, 0.86, and 0.78, respectively, while those for the dosiomics model were 0.50, 0.90, and 0.74. In contrast, the hybrid model exhibited superior classification performance with sensitivity, specificity, and AUC values of 0.50, 0.83, and 0.83, respectively. Compared to the standalone radiomics and dosiomics models, the hybrid model demonstrated enhanced classification capability. Interpretability analysis based on SHAP values not only provided a ranking of feature importance and the distribution of feature impacts on model outputs but also elucidated the specific decision-making processes influenced by these features and the interactions between them. This enables clinicians to gain a more intuitive understanding of the model's decisions. Conclusions For patients with advanced cervical cancer undergoing chemoradiotherapy, the integration of radiomics and dosiomics features can significantly enhance the classification performance of predictive models, thereby holding considerable potential for refining patient treatment strategies. Interpretability analysis based on SHAP values can aid clinicians in more readily understanding the model's decisions, thus promoting the effective implementation of the model in clinical practice.

Hippophae rhamnoides, a temperate species with a transcontinental distribution spanning Eurasia, demonstrates preferential establishment in water-limited ecosystems (arid/semi-arid zones), particularly occupying high-elevation niches with skeletal soils and high solar flux. This ecologically significant plant, prized for dual ecological provisioning and economic services, shows biogeographic concentration in China's northern desertification belts, northwestern Loess Plateau, and southwestern montane corridors. Studying the possible areas where H. rhamnoides may be found can offer a scientific foundation for the protection and sustainable management of its resources. This study utilized the biomod2 software to assess an integrated model based on 312 distribution points and 23 environmental factors. Furthermore, a modeling analysis was conducted to examine how the geographical distribution of H. rhamnoides changes over time under the SSP245 scenario. The findings show that the distribution of H. rhamnoides is primarily affected by three factors: annual mean temperature, temperature seasonality and mean temperature of the coldest quarter. Currently, H. rhamnoides is predominantly distributed in the provinces of Shanxi, Shaanxi, Gansu, Hebei, Yunnan, Xinjiang, Tibet, Sichuan, Qinghai, and Ningxia. The suitable habitat covers an area of 212.89×10⁴ km², which represents 22.15% of China's total land area. Within this region, high, medium, and low suitability areas make up 23.15%, 22.66%, and 54.20% of the suitable habitat, respectively. In the future, the centroid of the suitable habitat for H. rhamnoides is expected to gradually shift northwest, with a trend of increasing suitability in the west and decreasing suitability in the east. This study aims to provide an in-depth exploration of the distribution of H. rhamnoides and the influence of environmental factors on it from a geographical perspective. These results are important for improving the conservation, management, cultivation, and propagation of H. rhamnoides, while also offering a scientific foundation for the research of other valuable plant species.