In the modern steel rolling industry, rolls are crucial components for ensuring the efficient and stable operation of steel rolling equipment, and their service performance is of vital importance. However, the wear and thermal stress during operation lead to a high scrapping rate of rolls, making it urgent to seek effective surface strengthening techniques. In this paper, laser quenching and laser alloying techniques were used to treat the surface of roll samples. It was found that with the increase of laser energy density, the depth of the hardened layer increased (from 0.16 mm to 0.62 mm). When the laser energy density was 857.1 J/cm², the hardness of the hardened layer reached a peak value of 873 Hv, and the hardness curve was stable. When the laser energy density exceeded 900 J/cm²,although the hardness of the hardened layer was still high, microcracks appeared on the surface. To solve this problem, the laser alloying technique optimized the surface composition by introducing alloy powders such as WC and Cr. At a energy density 2500 J/cm²,the comprehensive performance was significantly improved: the depth of the hardened layer increased to 0.89 mm, the peak hardness increased to 937 Hv, and the hardness distribution was more uniform, while effectively suppressing crack defects.

When a disaster such as a flu pandemic occurs, humanity always comes to the fore, especially morality. In the past, the study of epidemic and moral motivation always started from the macro level, and few discussed the relationship between epidemic and moral motivation from the individual level. In order to explore the relationship between epidemic risk perception, epidemic cognition and moral motivation (Epidemic cognition refers to individual cognition and emotional expression of epidemic periodicity. The perception of epidemic risk is mainly an individual's judgment on whether he or she is susceptible to COVID-19 ). In this study, 496 college students were selected as research objects, and the self-compiled Epidemic Risk Perception Scale, the Epidemic Cognition Scale and the Moral Motivation Scale translated by Chinese scholars were used as research tools. SPSS23.0 was used to explore the structure and test the reliability and validity of the self-compiled Epidemic Risk Perception Scale. The collected data were processed by common method deviation test, descriptive statistical analysis, correlation analysis, regression analysis, mediation effect and other statistical methods. The results showed that the self-compiled epidemic risk Perception Scale had good reliability and validity. There was a significant positive correlation between epidemic risk perception and moral motivation, and a significant negative correlation between epidemic cognition and moral motivation. At the same time, the influence mechanism of the three factors was explored, and it was found that the epidemic risk perception could positively predict the moral motivation, while the masking effect appeared when the variable epidemic cognition was added.

This study investigates the synergistic effects and mechanisms of biochar-loaded microorganisms in tetracycline degradation. By preparing biochar under various conditions and screening tetracycline-degrading microorganisms, we employed adsorption methods and immobilization techniques to immobilize these microorganisms on biochar surfaces. Through systematic degradation experiments and environmental condition optimization, we evaluated the degradation efficiency of tetracycline by biochar-loaded microorganisms. The results demonstrate that biochar not only adsorbs and enriches tetracycline but also provides colonization space for microorganisms, significantly enhancing their degradation capacity. Microorganisms directly degrade tetracycline through secreted degrading enzymes, while biochar acts as an electron donor during the degradation process to promote microbial activity. Additionally, biochar effectively mitigates tetracycline's toxicity to microorganisms and regulates microbial community structure.

As a crystallization of the deep integration of technology and agriculture, smart agriculture, with Internet of Things (IoT), artificial intelligence (AI), big data, and other core technologies, is vigorously reshaping traditional agricultural production models. From an accounting perspective, it has a profound impact on agricultural cost structure, resource allocation efficiency, and financial decision-making. This article adheres to the theory of "multi-terminal collaboration, intelligent control, and data empowerment" to deeply analyze smart agriculture. Traditional agriculture is constrained by issues such as population expansion, climate change, and labor shortage, resulting in severe resource waste and declining economic benefits. Smart agriculture emerged as a response to these challenges. In terms of core technologies, the collaborative application of IoT, AI, big data analysis, automation and robotics, and cloud computing technologies provides comprehensive perception, precise decision-making, and efficient execution for agricultural production. For example, in practical cases such as intelligent management of greenhouse facilities, smart agriculture significantly improves production efficiency, optimizes resource allocation, and brings considerable economic benefits. At the market level, the global smart agriculture market is growing rapidly, and the Chinese market holds great potential. Policy support, technological advancements, and cost reductions provide momentum for its development, but issues such as high initial investment, difficulties in technology promotion, data security risks, and talent shortages also increase financial risks and uncertainties. From an accounting perspective, smart agriculture faces challenges such as high technology promotion costs, lack of unified cost accounting and benefit evaluation standards, and long equipment investment return cycles. Therefore, it is necessary to strengthen policy support, improve the cost accounting and technical standard system, and enhance talent cultivation. Looking ahead, smart agriculture will develop towards intelligence, precision, industrial chain collaboration, and green sustainability, continuously improving agricultural economic benefits and financial performance, and providing solid financial support for agricultural modernization.

As a treasure of China's outstanding traditional culture, calligraphy art has become a representative of Chinese national culture with its long history, rich cultural connotations, diverse expressive techniques, and unique artistic charm. Higher Normal Universities (hereinafter referred to as "HNUs") bear the mission of cultivating basic education teachers for the nation. Compared with other institutions, HNU calligraphy courses shoulder the dual responsibility of inheriting calligraphy culture and integrating moral education through cultural influence. Therefore, the objectives of HNU calligraphy education extend beyond disseminating artistic culture and skills, including fostering patriotic ideals, emphasizing character cultivation, tempering strong willpower, and promoting national culture through moral education. This paper primarily explores the integration of moral education in HNU calligraphy courses from the perspectives of artistic inheritance and ideological infiltration. By analyzing the content and implementation paths of moral education in HNU calligraphy curricula, it aims to uncover moral elements within these educational practices and explore new approaches for integrating ideological-political education into HNU calligraphy courses under current educational reforms.

Corporate cohesion serves as the driving force behind business development, emerging as a strategic resource in modern enterprises. While enhancing management efficiency relies on institutional frameworks and technological tools, it fundamentally depends on fostering intrinsic collaboration among team members. As an intangible "soft power" within organizations, cohesion can accelerate decision implementation and optimize resource allocation, yet may create counterproductive constraints through excessive homogeneity. This paper elucidates the core concepts of cohesion and management efficiency, analyzes its multidimensional composition and operational mechanisms, examines its dynamic impact on management effectiveness, and proposes targeted improvement strategies. These insights provide theoretical references for optimizing management practices in contemporary enterprises.

Accurately predicting the Olympic medal count is crucial for national Olympic committees' decision-making as the Games become more international. This paper establishes a medal prediction model using historical data and machine learning, analyzing factors affecting medal distribution.DBO-LSTM-attention modeling based on historical data projects U.S. and Chinese dominance in 2028 gold and total medals, with China, Germany, and South Korea demonstrating improvement while Hungary and Austria decline.The model further predicts approximately 10 nations, predominantly developing economies, will achieve inaugural Olympic medals in 2028.Correlation analysis reveals a significant positive relationship between event quantity and medal attainment; the U.S. excels in swimming/athletics while China dominates table tennis, gymnastics, and diving. Host nations exhibit event-specific advantages, with event selection significantly influencing medal outcomes.Decision tree modeling with SHAP value analysis quantitatively confirms that elite coaching substantially enhances gold and total medal acquisition capabilities.In summary, this paper offers a scientific framework and decision support for predicting Olympic medals, optimizing coach resources, and allocating sports resources, providing valuable references for national sports committees and stakeholders.

Currently, artificial intelligence (AI) is developing rapidly, and related language service products have begun to be widely adopted in higher education institutions. These products have brought new opportunities for college English oral teaching and promoted the reform and innovation of teaching models. Beginning with an examination of the technological support that AI provides for oral English teaching in college, this paper combines relevant theoretical frameworks to explore its specific empowering manifestations in teaching practice. By analyzing the limitations and problems in the application process, corresponding optimization paths and development directions are proposed. This study aims to clarify the integration logic of artificial intelligence and oral English teaching in college, as well as provide theoretical reference for improving teaching quality and efficiency.

Objective To explore the specific effects of anemia on the growth and development of early children, and to compare and analyze the growth and development of children with different degrees of anemia. Methods Independent sample t test is used to analyze the difference of height and weight between children with mild, moderate and severe anemia and children without anemia. Results With the increase in the degree of anemia, the height and weight of children aged 8 and 12 months will decrease. There is no significant difference in height and weight between children with mild anemia and those without anemia, while the height and weight of children with moderate or severe anemia are significantly different from those without anemia. Conclusion: Anemia has a significant impact on children's growth and development, especially moderate or severe anemia has a more serious effect on children's development. The more severe the anemia , the lower the height and weight of children in the early stage compared with those without anemia. Based on the gradual improvement of children's health care development in China in recent years, children's health care practitioners should attach importance to education and intervention to correct anemia in early childhood and avoid growth and development disorders caused by anemia. Conclusions Anemia has a significant impact on early children's growth and development, especially the impact of moderate and higher anemia on children's development is more serious, the more severe the degree of anemia, the lower the height and weight of early children than children without anemia. Based on the gradual improvement of the development level of child health care in China in recent years, child health practitioners should pay attention to education and intervention to correct anemia in children and avoid growth and development obstacles caused by anemia.

In the context of the reconstruction of the education governance system and the transformation of educational space, local undergraduate colleges and universities are faced with the problems of ineffective operation of the school-family-community collaborative education mechanism, closed spatial structure, and decentralized resource allocation. Based on the perspective of "spatial empowerment", this paper integrates educational ecology and collaborative governance theory, and proposes a three-dimensional coupling collaborative mechanism construction framework of "physical space-social space-institutional space". By analyzing the phenomenon of spatial coordination mismatch in local universities, a "three-level spatial grid" organizational structure, a "resource co-construction and sharing platform" and an "embedded communication mechanism" are constructed to explore an operational and scalable spatial collaborative education mechanism. The study shows that spatial reconstruction not only enhances the depth and collaborative efficiency of multi-subject participation, but also provides path support and institutional solutions for local universities to achieve modernization in education governance.

ZnBi₃₈O₅₈ were successfully prepared by hydrothermal synthesis. The optical properties of ZnBi₃₈O₅₈ photocatalysts were investigated by X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). Then we measured the adsorption dye-degradation performance of rhodamine. The results exhibited that the photocatalytic degradation efficiency under 532 nm irradiation first increased then decreased with increasing PPy compounding, achieving achieving optimal performance at 15% compounding. The PPy was compounded on the surface of the ZnBi₃₈O₅₈ photocatalyst by in-situ oxidative polymerization, Further verified its successful compounding through XRD and SEM. Then the Degradation of rhodamine by PPy/ZnBi₃₈O₅₈ under 532 nm laser irradiation. The results showed that the compounding by PPy signifaicantly enhances its photocatalytic activity. It Provides a new idea for photocatalytic composite materials.

With the rapid growth of the smart watch market, user reviews have become critical factors influencing consumer decision-making; however, the proliferation of malicious reviews seriously threatens the credibility of e-commerce ecosystems. This study proposes a hybrid feature engineering approach for detecting malicious smart watch reviews through multi-dimensional linguistic pattern fusion techniques to construct comprehensive feature representations. The method integrates four dimensions—basic statistical features, lexical features, sentiment features, and grammatical features—forming a compact and efficient feature space. Experimental results demonstrate that the proposed hybrid feature engineering approach achieves 98.58±0.32% classification accuracy, 98.59% F1-score, and 94.86% AUC on Random Forest classifiers, representing improvements of 9.0%, 6.4%, and 70.6% respectively compared to traditional TF-IDF baseline methods. Simultaneously, feature dimensionality was optimized from 50 to 10 dimensions, achieving an 80% dimensionality reduction effect while significantly improving computational efficiency. This method provides an effective technical solution for smart watch review quality monitoring with significant practical application value.

This study explores the cognitive construction of metaphors and their role in cross-cultural communication, focusing on how Large Language Models (LLMs) can facilitate the analysis and comparison of metaphors across languages. Metaphors, as fundamental elements of human thought, are deeply embedded in cultural contexts, shaping how individuals from different societies conceptualize abstract concepts like time, life, and education. Traditional metaphor analysis methods, while insightful, are limited by their small scale and subjectivity. This study proposes a computational approach using LLMs to identify and compare metaphors across languages and cultures. By leveraging LLMs, this research offers a more objective, scalable, and dynamic method for studying metaphors, enabling cross-lingual analysis and revealing cognitive differences between cultures. The findings highlight the potential of LLMs in enhancing our understanding of how metaphors evolve and how they shape communication in a globalized world. Furthermore, the study emphasizes the importance of integrating theoretical frameworks from cognitive linguistics with modern computational tools to deepen our comprehension of metaphorical structures in cross-cultural contexts.

This paper analyzes the marketing strategies and growth of UNF, a youth-focused sportswear brand in China’s digital retail market. It highlights how UNF uses social commerce, influencer campaigns, and data-driven personalization to engage Gen Z consumers. The study identifies both strengths—such as niche positioning and digital agility—and challenges like licensing dependence and offline limitations. A phased strategy is proposed to support sustainable expansion.

This study, using a public childcare institution in Chengdu as a case example, analyzes parents' satisfaction with childcare services through questionnaires and interviews across six dimensions: management services, environmental facilities, health care, educational activities, teaching staff, and parent-child interaction. The research found that overall parental satisfaction is relatively high, but issues such as inflexible service management, suboptimal environmental facilities, and insufficient professional capabilities of the teaching staff still exist. To address these issues, strategies such as optimizing service management, upgrading.

As pipeline leaks will cause huge safety hidden trouble and early alert for pipeline leaks is urgently needed for the safety, efficiency operation of the industrial infrastructure. In order to address the problem mentioned above, this paper introduces an automatic pipeline leakage fault detection framework using Multi-Scale Attention CNN (MSA-CNN). (1)A multi-scale convolutional feature extraction module was designed to extract hierarchical spatial-temporal features from multi-source signals such as acoustics and pressure. (2) The channel-space dual attention mechanism is introduced to adaptively strengthen the important feature responses related to leakage at different scales and suppress irrelevant noise interference. (3) The residual fusion encoder structure is constructed to effectively integrate multi-layer feature representation to improve the model's ability to distinguish between small leakage signals and complex backgrounds. Compared with the traditional CNN and single-scale attention methods, the proposed model significantly enhances the sensitivity to small-scale leakage events and the robustness to complex environments while maintaining the computational efficiency, and has been deployed in a real-time detection platform that supports edge computing. Experimental results demonstrate that the detection accuracy of the model on the commonly used pipeline leaking detection dataset is 92.3%.

As one of the most important parts of rotating machinery, the early prediction of rolling bearing is very important for the safety of operation and cost reduction of maintenance. To improve the intelligent level of bearing fault recognition, this paper proposes an intelligent bearing fault diagnosis frame work based on a hybrid CNN-LSTM-GRU network, which combines the advantages of CNN, LSTM, and GRU, and makes structural innovation and optimization design on the basis of the existing deep learning model. The proposed model follows a three-segment hybrid structure, in the first segment, 1DCNN is employed to extract local time-domain feature from original vibration signal. Also, We feed the features that have been extracted by the CNN network as the inputs to the both LSTM network and GRU network in parallel in order to take care of the multiple dependencies between the sequence of data items and make the model optimize more efficiently, respectively. 5.fusion multi-head attention module is introduced to perform feature weighing for the dual-channel outputs, thus further enhancing the capability of identifying complicated failure modes. To prevent overfitting and coalescence while enhancing the generalization capability of the model, the final classification module is built upon Dropout regularized fully connected design with residual connection. Experiments on the CWRU bearing fault dataset demonstrate superior classification performance of the proposed network under different load and fault severities, providing an average accuracy of more than 94.2%.

It is important to diagnose rolling bearing, as it plays an important role in the safe operation and reliability of industrial rotatory machinery and equipment. In this work, the fault diagnosis of rolling bearings was investigated, and a fault diagnosis method of rolling bearings based on an improved SE-CNN was presented. The proposed method performs structure innovation and function enhancement on the classic Convolutional Neural Network (CNN) and Squeeze-and-Excitation (SE) attention mechanism. In particular, the proposed model includes a multi-scale convolution module for capturing both local and global fault feature information. The SE modules of both are intended to achieve adaptive strengthening of important information channels and spatial positions. Furthermore, the model has carried out hierarchical feature fusion, regarding shallow feature and deeper one between layers fusion, facilitating the robustness and expressive power of the model in multi working conditions and multi noise environment. Compared with original SE-CNN, the proposed model is of much higher structure complexity, more powerful cross-scale feature interaction and channel-adaptive crowd feature weight adjustment, hence it can more accurately represent the fault mode of bearings. Numerical experiments on the CWRU bearing dataset demonstrate that our methods outperform existing CNN-based methods in both accuracy of fault identification and generalization ability.

Under the dual background of accelerated development of digital economy and in-depth advancement of “dual-carbon” goal, the Beijing-Tianjin-Hebei region faces the challenges of regional empowerment of digital technology, transformation and upgrading of energy structure, synergistic development of regional economy and sustainable development of ecological environment. Based on the theory of digital-energy-economic-environmental coupling and coordination, this study constructs a spatio-temporal evaluation system with 32 indicators, employs the entropy weighting method to objectively assign weights, introduces the revised coupling coordination model (RCCC), and analyzes it based on the spatial autocorrelation model (Moran's I) combined with the spatial analysis tools of ArcGis and GeoDa to reveal the spatial and temporal evolution patterns of the Jing-Jin-Ji region in the period of 2006-2022. region's spatio-temporal evolution pattern from 2006 to 2022. The results show that the coupling coordination level of digital-energy-economy-environment in the Beijing-Tianjin-Hebei region shows a significant upward trend during the observation period, and at the end of the study period, the coupling coordination level of Beijing reaches the good coordination level, and the intermediate coordination level of Tianjin and Hebei respectively, but only Shijiazhuang and Langfang among the prefectural-level cities in Hebei province reach the intermediate coordination stage, while the rest are still in the primary coordination stage, and the region as a whole shows the The region as a whole shows a gradient difference of “Beijing leading-Tianjin catching up-Hebei lagging behind”, and there is an obvious spatial clustering effect among some regions.

Polyetherimide (PEI) is a high-performance thermoplastic engineering plastic. With excellent thermal stability, mechanical strength and chemical stability, it shows wide application potential in many fields. However, its stability at high temperatures and relatively low dielectric constant limit its application. In recent years, researchers have modified PEI by adding conductive fillers, inorganic nano-fillers and developing all-organic composite materials, effectively improving its dielectric properties. However, the balance between the thermal stability and dielectric properties of PEI, as well as the selection and optimization of fillers, remain urgent problems to be solved. In the future, in-depth research can be conducted in the directions of new matrix selection, optimization of filler types and forms, and structural improvement, in order to prepare dielectric materials with high temperature resistance and high energy storage density.