怀化学院

Low-alloy wear-resistant steel is critical for mechanical transmission components serving under reciprocating friction and corrosive environments. To meet the service requirements of core transmission components of archery training robots, two Sn-microalloyed low-alloy wear-resistant steels (0.04 wt.% and 0.20 wt.% Sn) were designed based on commercial NM450 steel in this work. The effects of Sn on the continuous cooling transformation, microstructure evolution, mechanical properties, and corrosion-wear performance of the steels were systematically investigated, and the optimal heat treatment process was determined. The results show that both steels have a critical cooling rate of 20 °C/s for full martensitic structure with excellent hardenability. The optimized heat treatment enables a well-balanced strength, hardness and toughness of the steels. The 0.20 wt.% Sn steel exhibits significantly improved corrosion resistance and corrosion-wear performance, which is attributed to Sn-induced formation of dense and stable rust layer that inhibits corrosion-wear synergy. This study provides theoretical and data support for composition design and heat treatment optimization of low-alloy wear-resistant steel for archery training robots.

This study is dedicated to developing a novel bio-based flexible epoxy resin composite to address the issues of brittleness and inadequate corrosion resistance in traditional epoxy resins. First, a vulcanized eucommia rubber-tung oil composite (S-ERTO) was prepared. This involved a two-step process: the Diels-Alder (D-A) reaction followed by vulcanization. Subsequently, different contents of S-ERTO were introduced into the epoxy resin system to obtain modified epoxy resins. After that, a comprehensive evaluation of the epoxy resins was carried out, with a particular focus on their flexibility and corrosion resistance. The results indicated that once the S-ERTO was cured with the epoxy resin, a distinctive cross-linked network structure was formed. This structure endowed the system with enhanced comprehensive properties. Specifically, when the S-ERTO content was 15%, the flexibility of the modified epoxy resin was remarkably improved. The elongation at break reaching 58.04% served as clear evidence of this improvement. Moreover, the electrochemical impedance of the composite reached 2.69 & times; 1012 Omega & centerdot;cm2, and the water contact angle increased from 75 degrees to 110.01 degrees. In the neutral salt-spray test, the composite also demonstrated excellent corrosion-resistant performance. In conclusion, the epoxy resin modification strategy based on bio-based materials proposed in this study offers a new approach for preparing high-performance, flexible, and corrosion-resistant epoxy resin composites. It holds broad application prospects in fields such as flexible electronics and marine anti-corrosion.

Within the framework of control-value theory (CVT), this study employed latent profile analysis to identify distinct academic emotion profiles (encompassing enjoyment, anxiety, boredom and foreign language peace of mind) among 1088 secondary school ethnic minority English-as-a-foreign-language (EFL) learners in China. Four emotion groups were identified: negative emotions, moderate-level mixed emotions, high-level mixed emotions and positive emotions. Wald chi-square tests revealed significant differences in engagement across the four groups in behavioural and cognitive engagement, with the positive emotion group scoring the highest, followed sequentially by the high-level mixed emotion, moderate-level mixed emotion and negative emotion groups. In emotional engagement, the positive emotion group still ranked highest and the negative emotion group lowest, while no significant difference was observed between the two mixed emotion groups. Multinomial logistic regression analysis indicated that perceived classroom affordance significantly predicted emotion group memberships. Higher affordance perception increased the likelihood of belonging to the positive emotion group while reducing the probability of classification into the high-level mixed, moderate-level mixed and negative emotion groups in sequence. These findings extend the CVT by illuminating the complex emotional experiences of underrepresented ethnic minority EFL learners in China and their correlates with environmental and behavioural factors.

This study investigates relationship between system construction, cultural tourism empowerment, and citizens' participation in context of sustainable rural tourism development in South Central China. The research employs a structural equation model (SEM) in its analysis of data collected from rural communities in Hunan, Hubei, and Jiangxi. The results present significant positive relationships between system construction and cultural tourism empowerment (beta = 0.493, p < 0.001), cultural tourism empowerment and community participation (beta = 0.221, p < 0.01), and system construction and community participation (beta = 0.221, p < 0.01). Cultural tourism empowerment is further identified as a partial mediator of the relationship between system construction and community participation (mediating effect: 0.108, p < 0.01). The study also identifies that participation capacity acts as a mediator between system construction and cultural tourism empowerment (beta = 0.120, p < 0.05). These findings illustrate that not only an organized tourism system empower communities, but also generates active community participation in tourism development. The study offers important insights into the importance of correlating system construction with cultural tourism empowerment for effective sustainable rural tourism. The research findings show that enhanced community involvement through empowerment and change of the system can lead to fairer distribution of tourism benefits, thus enabling sustainable long-term rural tourism destinations. The research offers policy implications to local governments and tourism stakeholders who aim to improve inclusive and sustainable tourism practice in rural areas.

Existing face deepfake detection methods mainly rely on extracting specific forgery patterns (such as facial features, noise characteristics, and frequency domain information) to enhance the performance of detection models. However, these learning strategies, which depend on specific forgery patterns, limit the model's generalisation ability when facing diverse forgery types, making it difficult to effectively identify fake images with unknown forgery patterns. To address this issue, a novel deepfake detection framework FARL is proposed based on adaptive feature aggregation and reconstruction learning, which enhances the model's sensitivity to forgery traces, thereby significantly improving detection performance. Specifically, a cross feature aggregation encoder (CFAE) is introduced to enhance the model's sensitivity to forgery traces through an attention refinement block and a cross fusion strategy. Additionally, a block feature aggregation decoder (BFAD) is implemented to aggregate high-resolution feature maps from encoder blocks and low-resolution feature maps from the previous decoder block. Finally, the differences between the original image and the reconstructed image are utilised as a guide. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method for deepfake detection.