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Introduction: Global warming has profoundly transformed the natural environment and significantly impacted people's production methods, thereby promoting low-carbon consumption behaviors. While numerous scholars have examined the factors influencing low-carbon consumption behavior, their analyses predominantly rely on classical theoretical frameworks such as planned behavior theory, value-belief-norm theory and The ABC attitude theory. However, there is a notable scarcity of studies investigating the relationship between the social environment and low-carbon behaviors among youth groups. To address this gap in the literature, we aim to explore how the social environment influences youth groups' low-carbon consumption behaviors, as well as identify the mechanisms through which this influence may manifest. Methods: This study examines the factors influencing low-carbon consumption behavior from three perspectives: the social environment, low-carbon cognition, and conformity consumption. A theoretical model of low-carbon consumption has been developed, and data were collected through a questionnaire survey involving 600 young individuals in the Xizang Autonomous Region. The hypothesized relationships were tested using structural equation modeling techniques. Results: The findings indicate that the social environment has a significant positive impact on both low-carbon cognition and behavior. Furthermore, low-carbon cognition is shown to positively influence low-carbon behavior. In terms of the relationship between the social environment and low-carbon behavior, it is found that low-carbon cognition acts as a mediating variable. Additionally, it was observed that lower levels of conformity consumption negatively moderate both the relationship between the social environment and low-carbon behavior as well as that between low-carbon cognition and behavior. Discussion: These findings suggest that engaging young individuals not only fosters environmental awareness but also promotes sustainable consumption, thereby establishing a solid foundation for the protection and enhancement ofour ecological environment. Furthermore, it is essential to disseminate the concept of low-carbon consumerism through various media channels and methodologies. This approach aims to enhance young individuals' understanding of low-carbon principles, guiding them toward more scientifically informed consumption habits while reducing tendencies for blind conformity. [ABSTRACT FROM AUTHOR]

Phenolic compounds were separated and identified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and analyzed with OPLS-DA and metabolomics in eight cashew apples. The results showed that the phenolic compound content in Bra4 was the highest, while Gua had the lowest levels. The content of 39 phenolic compounds (nine types) showed significant variability across the 8 cashew apples. Sixteen phenolic compounds with significant differences were excavated and could be used to differentiate and identify cashew apple varieties. The 18 differentially significant phenolic compounds present in Bra4 and Gua were mainly distributed in 6 metabolic pathways, and the metabolic pathways of flavonoid biosynthesis and phenylpropanoid biosynthesis were elucidated to regulate the synthesis of luteolin and syringin, respectively. These studies provide a scientific basis for selecting and breeding cashew varieties and developing products. [ABSTRACT FROM AUTHOR]

Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to extract graph-level representations, which are then used for the detection task. However, the inherent limited receptive field of GNNs may exclude crucial anomalous information embedded within the graph. Moreover, the inadequate modeling of cross-graph relationships limits the exploration of connections between different graphs, thus restricting the model's ability to uncover inter-graph anomalous patterns. In this paper, we propose a novel approach called Dual-View Graph-of-Graph Representation Learning Network for unsupervised GLAD, which takes into account both intra-graph and inter-graph perspectives. Firstly, to enhance the capability of mining intra-graph information, we introduce a Graph Transformer that enhances the receptive field of the GNNs by considering both attribute and structural information. This augmentation enables a comprehensive exploration of the information encoded within the graph. Secondly, to explicitly capture the cross-graph dependencies, we devise a Graph-of-Graph-based dual-view representation learning network to explicitly capture cross-graph interdependencies. Attribute and structure-based graph-of-graph representations are induced, facilitating a comprehensive understanding of the relationships between graphs. Finally, we utilize anomaly scores from different perspectives to quantify the extent of anomalies present in each graph. This multi-perspective evaluation provides a more comprehensive assessment of anomalies within the graph data. Extensive experiments conducted on multiple benchmark datasets demonstrate the effectiveness of our proposed method in detecting anomalies within graph data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive and real-time prediction of protein and oil content in rapeseed by inputting rapeseed images. Among the tested models, FasterNet-L showed the optimal performance, with predicted coefficients of determination (R p 2 ) of 0.9366 for oil content and 0.8828 for protein content. The mean square error of prediction (RMSEP) was 0.6982 and 0.6498, and the residual predictive deviation (RPD) was 3.88 and 2.92 for oil and protein content, respectively. Furthermore, three pruning methods were employed, and neural pruning via growth regularization proved to be the most effective, with a 13.18 % improvement in prediction speed and a 15.79 % reduction in model size. Finally, this method can be expanded and applied to other oilseed crops for rapid quality identification and detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Image classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. We demonstrate that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status. LMMs stand out from traditional single-modal classification approaches, which often require large training datasets and offer limited interpretability.