南充职业技术学院

Bioremediation of uranium-contaminated environments using native bacteria shows great promise. While Shewanella putrefaciens (S. putrefaciens) is a known uranium reducing bacterium, the mechanisms and adaptability of indigenous strains from uranium mine tailings remain unexplored. This study isolated a dominant indigenous strain of S. putrefaciens and employed a combined genomic and spectroscopic approach to elucidate its unique uranium fixation mechanism. Microbial diversity analysis confirmed the dominance of Shewanella in the oligotrophic and radioactive tailings. Whole-genome sequencing revealed a significant enrichment of genes related to energy metabolism and stress resistance, providing a genetic basis for its survival and activity. Crucially, by combining advanced spectroscopic techniques with an MtrA gene knockout experiment, we deciphered the specific role of the Mtr pathway in extracellular electron transfer for uranium reduction. Remarkably, the isolated strain achieved a uranium removal efficiency of up to 93% under experimental conditions, demonstrating its high potential for uranium bioremediation. This work not only provides a robust indigenous candidate for bioremediation but also delivers novel mechanistic insights into the uranium transformation processes of indigenous Shewanella, advancing strategies for the application of tailored microbiomes in radioactive waste management.

Abstract Management of a reservoir is essential for maximizing hydropower output, especially as climate change changes the patterns. In this research, we introduce the Improved Dandelion Optimizer Algorithm (IDOA), which is a tool which adds to the database of tools which promote enhancements in hydropower potential with changes in climate. The IDOA works by using previous climate data, climate variables, and operational constraints to identify optimal reservoir storage options. The IDOA’s primary goal is to improve hydroelectricity generation while still meeting objectives around environmental sustainability. The efficiencies and adaptability of the IDOA will be demonstrated through case studies and sensitivity analysis, thus giving reservoir managers a useful tool for navigating the changing environment for hydropower advantages. The IDOA outperformed suboptimal reservoir storage strategies because it reduced storage volume reductions and improved flexibility, risk management, and overall effectiveness. Key advantages include climate-sensitive operational adjustment, use of interpretable performance indicators, and robust solution quality under uncertainty. These results demonstrate the practical potential of the proposed framework for adaptive reservoir management in the face of climate change. The optimized reservoir storage levels improved optimally by ~37.6 mm under the SSP1 and 26 mm under the SSP5 cases. The research also indicated the varying levels of power output between the suboptimal and optimal situations, where power output was reduced and increased under the suboptimal and optimal conditions respectively. Lastly, the research identified that exploration of optimization of power generation strategies could result in a greater return on improvement under case SSP5.

The increasing population and food demand require a timely and accurate large-scale rice yield estimation method to ensure food security and the development of global food trade. However, traditional methods suffer from insufficient spatio-temporal feature extraction and inability of county- and pixel-scale yield estimation in large-area yield prediction, limiting the accuracy of yield estimation. Taking Northeast China as our study area, we propose a deep learning model, zoned CNN-transformer (ZCT) combining a convolutional neural network (CNN) with a transformer module. We divided Northeast China into four environmentally similar regions through cluster analysis. In each region, the features in remote sensing data, meteorology, and soil properties were extracted and modeled. We compared the estimation accuracy of ZCT with that of CNN-transformer, zoned CNN-ALSTM, and zoned ANN-transformer. The results show that the ZCT model performs the best in the end-of-season model performance assessment, achieving an R2 of 0.89 and RMSE of 617.34 kg/ha at the county scale, and an R2 of 0.87 and RMSE of 678.04 kg/ha at the pixel scale. In addition, the ZCT model also performs well in the mid-season dynamics assessment, in which August is a critical period. We also suggest that normalized difference vegetation index, enhanced vegetation index, solar induced fluorescence, organic carbon content, and cation exchange capacity are the five most important feature variables in yield estimation through feature importance analysis. In conclusion, our ZCT model not only extracts features related to rice yield, but also reduces the impact of environmental differences on performance of the model in large regions through zonal modeling. This model realizes yield estimation at both county and pixel scales, providing an efficient and accurate solution for rice yield estimation in large regions and offering technical support for food security.

IntroductionIn the dynamic realm of public health policy, the valuation of enterprise data assets is increasingly pivotal. This paper introduces a comprehensive methodological framework for multi heterogeneous data fusion, aimed at enhancing data asset valuation in this context. Traditional methods often struggle with integrating diverse data types, structured, unstructured, and semi structured, into a cohesive analytical structure.MethodsTo address this challenge, this study proposes a novel framework composed of FusionNet and an Innovative Fusion Strategy. FusionNet employs machine learning techniques to fuse varied data sources, while the Innovative Fusion Strategy aligns data integration with policy oriented objectives. The framework further incorporates an Adaptive Data Synthesis mechanism that applies domain knowledge to optimize valuation accuracy.Results and discussionExperimental results on four public health datasets demonstrate that the proposed method improves valuation accuracy by 3.6% and AUC by 2.2% compared to strong baselines. These findings suggest the proposed framework enhances decision support for public health policy through improved data integration and valuation modeling.