Daqing Normal University

Zinc finger homeodomain (ZF-HD) proteins are critical for plant growth, development, and responses to abiotic stress, yet systematic research on the ZF-HD gene family in melon (Cucumis melo L.) remains limited. This study performed a genome-wide characterization of the melon ZF-HD family, followed by functional validation of CmZHD8 under salt stress. Thirteen CmZF-HD genes were identified, unevenly distributed across eight chromosomes, and classified into five subfamilies (ZHD I-IV, MIF), with no ZHD V members, consistent with the classification of cucumber. Family expansion was primarily driven by dispersed and tandem duplication events, with duplicated genes undergoing strong purifying selection (Ka/Ks < 1). Most CmZF-HD promoters contained ABA- and MeJA-responsive cis-acting element and displayed tissue-specific expression patterns. RT-qPCR analysis showed that CmZHD8 expression was sharply induced at 24 h under salt stress, reaching a 26-fold increase over the control, consistent with RNA-seq data. Heterologous expression in yeast confirmed that overexpression of CmZHD8 enhanced salt tolerance. These findings reveal the evolutionary features and functional mechanisms of the CmZF-HD family in salt stress response, identify CmZHD8 as a key candidate gene, and provide a theoretical foundation for stress-resistance research and molecular breeding in melon.

Promoting synergistic economic–resource–environmental development in resource-based cities (RBCs) is a fundamental requirement for ensuring national energy security and advancing regional sustainable and coordinated development. This study innovatively proposes the theoretical framework of “green transformation resilience (GTR)” based on evolutionary resilience theory, and then empirically explores the GTR of 114 RBCs in China from the perspective of urban development stages using multiple data models. The findings indicate that the GTR demonstrated an overall upward trend, though it remained at a consistently low level. Regenerative RBCs exhibited the highest GTR levels. GTR exhibits an uneven spatial distribution, primarily caused by super-variation density. The factor detection results indicate that factors such as government intervention, income level, and human capital have strong explanatory power for the spatial variation of GTR. Interaction analysis confirmed the significant nonlinear enhancement or bivariate enhancement of all pairs of factors. This study provides a basis for the differentiated development paths of GTR in China’s RBCs. Moreover, through factor interaction testing, it also offers guidance on policy combinations and prioritization for RBCs in different development stages.

Artificial intelligence (AI) has become a critical tool for risk management across industries such as insurance, healthcare, business, and finance. It enables risk quantification, improves predictive accuracy, and supports decision-making in dynamic and uncertain environments. This paper examines models, methods, and frameworks for AI-based risk assessment, while addressing concerns of ethics, regulation, and explainability. Key technologies, including machine learning, deep learning, and reinforcement learning, are highlighted for their ability to transform traditional approaches by enhancing prediction, optimization, and decision processes. The second part focuses on AI-driven risk modeling techniques. Supervised learning methods such as support vector machines, random forests, and decision trees demonstrate strong predictive capacity from historical data. Unsupervised learning, including clustering methods, uncovers hidden patterns in risk datasets. Reinforcement learning is gaining prominence for adaptive risk optimization under changing conditions. Deep learning, particularly neural networks, offers significant improvements in handling large-scale data and achieving higher predictive accuracy. Finally, the paper outlines the future of AI in risk management, recognizing both its transformative potential and persistent challenges. With the rapid advancement of AI and increasing availability of big data, risk management practices are undergoing fundamental change. Yet, successful adoption requires careful attention to ethical, legal, and technological considerations. Organizations must continue to adapt to ensure that AI technologies are deployed transparently, responsibly, and to the benefit of enterprises and society as a whole.

This paper aims to study the distribution pattern of oil and gas beneath the regional mudstone caprock at oil source faults in petroliferous basins. Based on the research of the vertical sealing evolution forms of oil source faults and their distribution locations, the study is conducted from two aspects. One is to determine the vertical sealed and unsealed positions of oil source faults during the oil and gas accumulation period according to the ancient fault juxtaposition thickness of the regional mudstone caprock and the maximum such thickness for fault connection. The other is to clarify the current vertical sealed and unsealed positions of oil source faults by analyzing the mixed displacement pressure of fault rocks and the current displacement pressure of underlying reservoir rocks. By integrating these two aspects, a prediction method is established. Applied to the Dazhangtuo Fault in Qikou Sag of Bohai Bay Basin, it reveals 4 forms of vertical sealing evolution. The sealed-sealed form distributes in the west, center and east; the sealed-unsealed form is in one west location; the unsealed-sealed form is in two east locations; the unsealed-unsealed form is in one location. The distribution of sealed-sealed and unsealed-sealed forms is beneficial for oil and gas accumulation, proving the method’s feasibility and providing a new perspective for related studies.But this method can only be used in basins or sags with a large number of measured displacement pressures of surrounding rocks.

The influence of the device's structure and operating parameters, along with the material properties of millet, on threshing and separation performance forms the theoretical basis for designing and researching a single longitudinal axial flow threshing and separation device specifically adapted to millet. Therefore, a theoretical model for grain threshing and separation in a single longitudinal axial flow threshing device was established based on variable mass theory. To validate the theoretical model, single-factor tests were conducted on the feeding rate, rotational speed, and water content of Longgu 31 millet. The error analysis between the experimental and calculated values indicates that within a moisture content range of 17.14% to 32.93%, feeding rates varying from 1 to 3 kg/s, and rotational speeds ranging from 700 to 1000 r/min, the R-squared values consistently exceed 0.97. This indicates an excellent fit of the theoretical model. The theoretical model will serve as a valuable reference for the design and investigation of the single longitudinal axial flow separation device.