信阳职业技术学院

These days, exploring information retrieval models is one of the most essential aspects of English sentence retrieval research. These models are driven by diverse retrieval mechanisms that offer varying similarity calculations and directly influence the final result ranking. However, despite decades of work due to technical constraints, deep semantic analysis has been challenging. This gap emphasizes the importance of a precise semantic understanding of information acquisition through learning approaches. Based on the above opening, this paper establishes a fast retrieval model of English sentences based on the statistical language model (SLM). First, the proposed method utilizes SLM to extract significant feature words from the corpus. These feature words are identified by analyzing co-occurrence patterns and frequency distributions within the standard. Second, it employs the N-gram model to calculate the probabilities of word occurrences based on their contextual dependencies. This framework represents feature words and their associated probabilities in a structured manner by capturing the intricate nuances of language semantics. Third, the model integrates ontology to bridge the gap between human language and machine understanding by enabling the mapping natural language expressions to conceptual entities. Finally, the suggested model retrieves English sentences through semantic matching by leveraging the comprehensive semantic framework and ontology-based search. The experimental study revealed that the proposed model demonstrated an impressive retrieval ratio of 98.5% by outperforming existing models in the comparison. Moreover, these results show that the proposed algorithm performs better than the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the accuracy of this algorithm is improved by 7.52% compared with TF-IDF. When the labelled corpus is very small and the unlabeled corpus is relatively large, the algorithm enhances the classifier’s performance by 12.6%. This shows that the algorithm used in this paper reduces the influence of the synonym processing stage on the overall performance while retaining the advantages of high precision and accuracy of calculation results.

This paper examines the impact of generative artificial intelligence on alleviating FC for small and medium-sized listed companies(SMEs) in China's A-share market, based on data from 2013 to 2023. The results indicate a significant negative relationship between the level of generative artificial intelligence(AI) and financing constraints (FC). Furthermore, the development of e-commerce supply chain finance plays a mediating role between the level of generative AI and FC. The level of digital inclusive finance (DIF) has a moderating effect between generative AI and FC. Additionally, the negative impact of generative AI on FC exhibits heterogeneity between state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs), as well as among enterprises in the eastern, central, and western regions of China. The conclusions of this study provide new empirical evidence for understanding how AI technology and supply chain finance can collaboratively alleviate FC faced by small and medium-sized enterprises.

IntroductionLocated in central China, the Yellow River Basin (Henan section) is a vital grain-producing region with extensive farmland. Its strategic central location also means its economic development is crucial for the stable growth of both Henan Province and the national economy. Nevertheless, persistent drought has long presented significant challenges to the region’s agriculture, ecology, and economic development.MethodsBased on TVDI (Temperature Vegetation Dryness Index) data from 2003 to 2022, this study investigated historical drought dynamics through Theil–Sen Median and Mann–Kendall trend analyses. Future drought tendencies were assessed using R/S analysis. Spatiotemporal influencing factors were further examined using Geographic Detector.ResultsResearch results indicated that most areas of the region were experiencing drought conditions. From 2003 to 2022, drought intensified in the northeastern counties of Yuanyang, Fengqiu, Changyuan, Puyang, Fanxian, and Taiqian within the Yellow River Basin (Henan section), while drought conditions eased in the southwestern counties of Lingbao, Lushi, Luoning, Shanzhou District, and Jiyuan. Future trends in drought variation indicated that the southwestern part of the region showed potential for improvement in drought conditions, while drought conditions in the western areas were likely to worsen. The primary factors influencing the spatiotemporal variation of drought in this region were evapotranspiration, temperature, and rainfall.DiscussionThis study aimed to provide an in-depth analysis of the drought situation in the Yellow River Basin (Henan section), offering a reference to assist stakeholders in implementing timely drought mitigation measures, thereby reducing the impact of drought on agricultural production and the ecological environment.

In Industry 5.0, integrating advanced technologies like generative artificial intelligence (Gen-AI) is driven by user and system factors. While previous research has predominantly focused on user factors in AI adoption, limited attention has been given to system attributes. This study empirically examines the impact of both user and system factors on employees' attitudes, behavioural intentions and actual use of Gen-AI. Using the meta-UTAUT and Task Technology Fit (TTF) frameworks, our structural analysis of 247 respondents from multinational corporations operating in Turkey reveals that user and system factors positively influence attitudes and behavioural intentions towards Gen-AI adoption, whereas performance expectancy directly strengthens Gen-AI usage. Additionally, system factors significantly enhance user-related factors, highlighting the reciprocal relationship between the two. However, performance expectancy insignificantly relates to employees' attitudes towards Gen-AI. Our findings underscore the importance of how employees perceive and engage with Gen-AI, influencing their willingness to adopt and utilise it, which in turn fosters creativity. This research provides a comprehensive understanding of the interplay between user and system factors in Gen-AI adoption, offering insights into how their integration can enhance creative outcomes, drive employee innovation and promote widespread engagement with Gen-AI in Industry 5.0.

Purpose:This study aimed to examine the relationship among physical exercise, self-efficacy, and cognitive frailty, and to test the mediating role of self-efficacy in the association between physical exercise and cognitive frailty among community-dwelling older adults. Methods:This cross-sectional survey employed a convenience sampling method to recruit community-dwelling older adults from nine communities in Henan Province, China. Data were collected from January to May 2026. The measurements included a general information questionnaire, the Fried Phenotype (FP), the Montreal Cognitive Assessment (MoCA), the Physical Activity Rating Scale-3 (PARS-3), and the General Self-Efficacy Scale (GSES). SPSS 27.0 and R software were used for statistical analysis. Result:A total of 570 questionnaires were distributed, and after excluding invalid responses, 563 valid questionnaires were retained. The prevalence of cognitive frailty among community-dwelling older adults was 21.3%. Univariate analysis revealed that age, marital status, education level, smoking, drinking, average monthly income, sleep quality, chronic diseases, subjective cognitive decline (SCD), physical exercise score, and self-efficacy score were significantly associated with cognitive frailty (p < 0.05). Mediation analysis further showed that self-efficacy significantly mediated the association between physical exercise and cognitive frailty, with the mediation effect accounting for 35.17% of the total effect. Conclusion:The prevalence of cognitive frailty among community-dwelling older adults was relatively high. Physical exercise was directly associated with cognitive frailty and also indirectly associated with it through self-efficacy, with the indirect effect accounting for 35.17% of the total effect.