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This study explores the relationship between self-compassion and mental health of postgraduates based on the perspective of the dual-factor model (DFM) of mental health and the mediating role of help-seeking behavior. A total of 605 postgraduates were investigated with a questionnaire. The results showed that the DFM of mental health was better than the one-factor model for the mental health status of postgraduates. Among them, those with complete mental health accounted for the highest proportion (63.3%), followed by vulnerable (25.1%), troubled (9.1%), and symptomatic but content (2.5%). Self-compassion and non-professional help-seeking behavior had a positive predictive effect on positive mental health factors of the postgraduates, while self-compassion had a negative predictive effect on their psychological symptoms. Non-professional help-seeking behavior played a partial mediating role between self-compassion and positive mental health factors.

Thioredoxin (TRX) is a small protein with REDOX activity that plays a crucial role in a plant's growth, development, and stress resistance. The TRX family has been extensively studied in Arabidopsis, rice, and wheat, and so it is likely that its members have similar biological functions in Liriodendron that have not been reported in Liriodendron. In this study, we performed the genome-wide identification of the TRX gene family based on the Liriodendron chinense genome, leading to a total of 42 LcTRX gene members. A phylogenetic analysis categorized these 42 LcTRX proteins into 13 subfamilies. We further characterized their chromosome distributions, gene structures, conserved protein motifs, and cis-elements in the promoter regions. In addition, based on the publicly available transcriptome data for Liriodendron hybrid and following RT-qPCR experiments, we explored the expression patterns of LhTRXs to different abiotic stressors, i.e., drought, cold, and heat stress. Notably, we found that several LhTRXs, especially LhTRX-h3, were significantly upregulated in response to abiotic stress. In addition, the subcellular localization assay showed that LhTRX-h3 was mainly distributed in the cytoplasm. Subsequently, we obtained LhTRX-h3 overexpression (OE) and knockout (KO) callus lines in Liriodendron hybrid. Compared to the wild type (WT) and LhTRX-h3-KO callus proliferation of LhTRX-h3-OE lines was significantly enhanced with reduced reactive oxygen species (ROS) accumulation under drought stress. Our findings that LhTRX-h3 is sufficient to improve drought tolerance. and underscore the significance of the TRX gene family in environmental stress responses in Liriodendron. [ABSTRACT FROM AUTHOR]

Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process. [ABSTRACT FROM AUTHOR]