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Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Many scholars have studied the influencing factors of mobile government users' usage intention. Reviewing a large amount of literature, we found that the existing empirical research results in this field are inconsistent. Therefore, we need to clarify further the primary factors and moderating variables that affect mobile government users' willingness and reveal the specific reasons for the differences in results. Finally, we will try to obtain the universal conclusion and provide a reference for improving the relevant theories and researches on user information behavior. [Method/Process] We selected 8 antecedent variables and 4 moderating variables affecting users' usage intention, 45 Chinese and foreign literatures, 27 795 samples, and 139 independent effect values in the meta-analysis. [Result/Conclusion] Performance expectation (r=0.586), effort expectation (r=0.538), trust (r=0.590), social influence (r=0.549), safety (r=0.489), compatibility (r=0.645), quality of service (r=0.588), and information quality (r=0.549) have significant positive effects on the usage intention of mobile government users. Group category, education level, social culture, and mobile government category moderate the relationship between the above 8 variables and mobile government users' usage intention.

A deep learning‐powered VS approach combined with two free docking programs are proposed and evaluated for screening an ultra‐large compound library to obtain diverse potential active compounds rapidly and efficiently. We found that it is a practical and transferable strategy to significantly reduce computational cost. Background: Molecular docking‐based virtual screening (VS) aims to choose ligands with potential pharmacological activities from millions or even billions of molecules. This process could significantly cut down the number of compounds that need to be experimentally tested. However, during the docking calculation, many molecules have low affinity for a particular protein target, which waste a lot of computational resources. Methods: We implemented a fast and practical molecular screening approach called DL‐DockVS (deep learning dock virtual screening) by using deep learning models (regression and classification models) to learn the outcomes of pipelined docking programs step‐by‐step. Results: In this study, we showed that this approach could successfully weed out compounds with poor docking scores while keeping compounds with potentially high docking scores against 10 DUD‐E protein targets. A self‐built dataset of about 1.9 million molecules was used to further verify DL‐DockVS, yielding good results in terms of recall rate, active compounds enrichment factor and runtime speed. Conclusions: We comprehensively evaluate the practicality and effectiveness of DL‐DockVS against 10 protein targets. Due to the improvements of runtime and maintained success rate, it would be a useful and promising approach to screen ultra‐large compound libraries in the age of big data. It is also very convenient for researchers to make a well‐trained model of one specific target for predicting other chemical libraries and high docking‐score molecules without docking computation again. [ABSTRACT FROM AUTHOR]

The delivery of targeted family education guidance services is essential for establishing a three-in-one education model that encompasses families, schools, and communities. It is also the key to creating a favorable educational environment that promotes the comprehensive and healthy development of individual students. To gain a comprehensive understanding of family education characteristics and trends in Chinese counties, accurately tap into the educational needs of parents, and effectively identify strengths and weaknesses in family education guidance services, this paper employed a quantitative research method based on a questionnaire survey and interview data from J district of C city after years of continuous monitoring of the quality of family education in the area, aiming to explore these issues and put forward several suggestions for improvement. The results indicate that the family learning environment and atmosphere in the district are generally favorable, while parental educational competence requires further improvement. Parents express a strong desire for interactive, systematic, and fragmented family education guidance from schools. Most parents acknowledge the significance of receiving guidance on family education, yet they are disinclined to actively engage in learning-related activities. While the operational mechanisms of county-level family education guidance services have been continuously improved, there is a need for greater overall coordination. Furthermore, the potential of school-led family education guidance services has yet to be fully tapped, and the scientific and professional standards of such guidance need to be further enhanced. Based on the aforementioned findings, we propose recommendations for improvement. Using J district's effective improvement practices in management, curriculum, and teams as an example, we intend to explore practical ways to promote county-level family education guidance services and provide a valuable reference for the establishment of a comprehensive and effective system to improve such services. [ABSTRACT FROM AUTHOR]