郑州幼儿师范高等专科学校

BackgroundAlthough digital exclusion has been widely discussed in relation to healthcare access and digital service use, less is known about its implications for exercise participation among middle-aged and older adults. This study examined how individual digital literacy and area-level digital exclusion jointly shape exercise participation in China.MethodsData were drawn from three waves of the China Health and Retirement Longitudinal Study (CHARLS; 2013, 2015, and 2018) and linked to city-year indicators from the China Statistical Yearbook. The analytic sample included 13,085 person-wave observations from 9,201 unique respondents across 111 cities. Exercise participation was measured as a binary indicator of engagement in exercise-oriented physical activity. Individual digital literacy was assessed using a composite index of digital access, internet use, device resources, and digital behaviors. Area-level digital exclusion was measured using a reverse-coded city-year indicator of contextual digital exclusion. Mixed-effects logistic regression models were used to estimate the associations of individual digital literacy and area-level digital exclusion with exercise participation and to test their cross-level interaction.Results37.7% of observations reported exercise participation, increasing from 27.5% in 2013 to 40.3% in 2018. In fully adjusted models, higher individual digital literacy was associated with greater odds of exercise participation (OR = 1.11, 95% CI: 1.022–1.208, p < 0.05). Area-level digital exclusion was negatively associated with exercise participation; although the association was attenuated after adjustment for demographic and socioeconomic characteristics, it remained statistically significant in the fully adjusted model (OR = 0.94, 95% CI: 0.869–0.991, p < 0.05). The interaction between individual digital literacy and area-level digital exclusion was negative and significant (OR = 0.96, 95% CI: 0.929–0.984, p < 0.01), indicating that the positive association between digital literacy and exercise participation was weaker in more digitally excluded cities.ConclusionIndividual digital literacy was positively associated with exercise participation among middle-aged and older adults in China, whereas area-level digital exclusion weakened this beneficial association. Policies promoting healthy aging should address both older adults’ digital literacy and the broader digital exclusion in which they live.

Clothing pattern design plays an important role in clothing aesthetics and market sales. However, with the advancement of technology and changes in consumer aesthetic needs, traditional hand-designed methods have been unable to meet the personalized and diversified pattern needs of contemporary young people. Therefore, it is necessary to explore and develop more efficient and innovative pattern design methods to meet the modern clothing market's demand for personalized, creative, and diversified design. To address this challenge, a f-WGRAN model based on zero-sample image classification is proposed. This model generates image features through an improved generative adversarial network and trains a classifier using the generated features to achieve clothing image classification and feature extraction. Meanwhile, the study adopts the l-system generation method for pattern drawing, and obtains the desired artistic pattern outline by selecting appropriate axioms, achieving personalized and beautiful clothing pattern design. The results showed that the f-WGRAN model achieved the highest classification accuracy of 69.8 % and 68.7 % on the AWA and FLO datasets, respectively, in the traditional zero-sample learning case. In the generalized zero-sample academic image classification experiments, the classification accuracy of the visible category samples of the f-WGRAN model was as high as 63.2 %, 75.3 %, 58.0 %, and 34.8 % on the AWA, FLO, CUB, and SUN datasets, respectively. The mean accuracy reached 60.5 %, 67.3 %, and 50.6 % on three datasets. The combination of f-WGRAN model and l-system method has strong feasibility in clothing pattern design. This method can automatically generate multiple styles of patterns according to different needs, achieving a faster design process and fully meeting the needs of young consumers for personalized and innovative patterns. This technology has promoted the intelligent and personalized development in the field of fashion design, providing effective methodological references for modern fashion design.

In vital industries including finance, energy, transportation, and meteorology, time series forecasting is essential. Prediction accuracy is frequently hampered by issues including nonlinearity, multi-scale patterns, and significant noise in real-world data. In order to tackle these problems, this study suggests a new hybrid forecasting framework called GA-VMD-RF, which integrates a real-time decomposition mechanism for deployment-ready multi-step forecasting and combines Genetic Algorithm-optimized Variational Mode Decomposition (VMD) with Random Forest (RF) regression. The proposed model introduces a permutation entropy-guided GA to dynamically tune VMD parameters, enhancing decomposition quality and preserving modal predictability. Each decomposed sub-series is modeled independently using RF, capturing distinct temporal dynamics. Unlike traditional full-series decomposition methods that risk information leakage, our real-time strategy performs VMD solely on the training data and incrementally updates decomposition during inference, ensuring fair and practical evaluations. Furthermore, an Average Window Reconstruction (AWR) mechanism is employed to fuse predictions from overlapping sliding windows, improving robustness and reducing temporal drift. Extensive experiments on diverse datasets—including U.S. Treasury yields, Brent crude oil prices, and wind speed observations—demonstrate that GA-VMD-RF outperforms classical models (SVM, LSTM) and VMD-based baselines in both short- and long-horizon tasks. Comparative results show improvements of up to 51.9% in MAPE over single-model baselines and 8.1% over traditional VMD-RF setups.

In the rapidly evolving landscape of the new energy sector, where innovation and sustainability are paramount, the integration of mindfulness practices holds promise for fostering the skills and mindset necessary for entrepreneurial ventures and employment opportunities. College and university students, as the future workforce and innovators, stand to play a pivotal role in shaping the trajectory of the new energy industry. Drawing upon a comprehensive literature review and empirical data collected through surveys and interviews with students, faculty members, and industry professionals, this paper explores mindfulness cultivation models tailored for entrepreneurship and employment among college and university students, specifically within the context of the emerging new energy industry. The research performed structural and equation modelling using Python to derive the resultant outcome. This work deploys improved fuzzy decision tree for classification of the entrepreneurial and employment behavior (EEB). The results of the study revealed that self-regulation and self-transcendence positively impact EEB, whereas self-awareness does not significantly influence EEB. The study shows that by embracing mindfulness as a foundational element of academic and professional development, stakeholders can contribute to building a more resilient, compassionate, and inclusive society.

Preschool education belongs to non-compulsory enlightenment education, and it is difficult to measure and analyze the development of preschool education in different regions because of its multiple attributes and diversity of influencing factors. In addition, decision makers will be limited by their own cognition when facing multi-attribute factors and uncertain factors, and there is a big gap between the decision results given and the actual situation. Therefore, this chapter introduces generalized orthogonal fuzzy sets and prospect theory into the measurement model of preschool education development level based on technique for order of preference by similarity to ideal solution (TOPSIS) method to improve the decision accuracy of decision makers. The experimental results show that the model can effectively deal with uncertain information and improve the analysis accuracy, and the results are more in line with the actual situation than other models. At the same time, it can intuitively compare and analyze the development of preschool education in different regions, and provide reliable data support for decision makers.