Against the backdrop of deepening global cultural integration, cross-cultural intimacy has emerged as a central narrative thread in North American English queer literature. For the Asian queer community, emotional expression embodies unique and intricate traits, shaped by the interplay of race, culture, and gender identity-each layer weaving into the next to forge a complex emotional tapestry. Taking contemporary North American English queer literature as its focus, this article centers on the authentic emotional experiences of Asian queer individuals in cross-cultural intimate bonds, systematically teasing out their emotional articulations, real-world predicaments, and viable paths toward reconciliation. Drawing upon the U.S. Census Bureau’s 2020 Asian Census, its 2023 official population projections, and empirical insights from the UCLA Williams Institute’s 2021 Special Survey on Asian Pacific Islander Queer Populations, this study delves into how racial, cultural, and gender dynamics exert a profound influence on the emotional formation of Asian queer people. Beyond mere analysis, it further offers a vivid portrayal of the diverse emotional landscape inhabited by Asian queer individuals in contemporary queer literature, illuminating the profound significance that literary narratives bear for their identity formation and emotional expression. In doing so, it seeks to serve as an empirical reference for related fields, including queer literary studies and cross-cultural research.

At present, generative AI technology is in rapid iteration and has exerted a broad and profound impact on the education industry. Experiential teaching emphasizes learners' experience and reflection in the learning process, so as to realize the internalization of knowledge, which is highly consistent with the core quality cultivation advocated by the new curriculum standard. However, the traditional experiential teaching is faced with such practical difficulties as static situation creation, superficial experience process, and formalization of reflection. Generative AI makes it possible to solve this dilemma by virtue of its capabilities of multimodal content generation, intelligent dialogue and interaction, and personalized adaptation. Drawing on Kolb's experiential learning cycle and taking the "VR Moot Court" course as an analytical case, this paper discusses four paths of generative AI enabled junior high school Morality and Rule of Law experiential teaching: situational infiltration, interactive infiltration, reflective infiltration and migration verification. These four paths together form a complete learning closed loop from experience to internalization, so that students can obtain embodied experience, deepen cognitive reflection in human-machine dialogue, and complete the meaning construction in the visual thinking. This study provides practical reference for the innovation of Morality and Rule of Law teaching in junior high school under the background of digital transformation of education.

In response to the core question of how to fairly integrate judges' professional ratings and audience voting in Dancing with the Stars (DWTS), this paper proposes a three-stage algorithm framework. In the first stage, a constrained Bayesian optimization model is constructed, mapping the weekly judge scores to Dirichlet priors, and using the elimination results as inequality constraints, the distribution of undisclosed audience votes is inversely estimated by sequential least squares programming (SLSQP). The model achieved a 87.2% agreement rate for elimination prediction over 185 weeks, and the estimated uncertainty decreased from 14.8% in the early stage to 6.2% in the final. In the second stage, the generalized weighted composite score (GWCS) framework and the Controversial Composite Index (CCI) are established, and the system compares the ranking method and the percentage method. Regression analysis (R²=0.996) showed that rank differences contributed 90% of the controversial variance, and 88.1% of the weeks had a balanced effective weight. In the third stage, structural equation model (SEM) and Shapley value decomposition were used to quantify the causal effects of celebrity characteristics and professional dancer quality on the score. The results showed that the number of weeks dominated the change in score (62.0% contribution), there was a significant negative bias in age (β=-0.318), and the partner quality contribution was 11.9%. This framework provides explainable algorithmic support for the scoring design of competitive variety shows.

As an important narrative space in digital media art, the construction of futuristic urban sci-fi scenes is undergoing a paradigm shift from static modeling to real-time interaction. This paper takes Unreal Engine as the research object to explore its technical logic and aesthetic mechanisms in constructing futuristic urban scenes. The study finds that Unreal Engine, through three major technical pathways-procedural generation, real-time rendering, and virtual production-resolves the long-standing "scale-detail-efficiency" impossible triangle in sci-fi city construction. Furthermore, the engine not only serves as a tool intermediary but also participates as a "technical subject" in the production of meaning within the scene. It enables the city to transform from a viewed landscape into an experienceable "world," redefining the ontological status of virtual cities at the intersection of technical operation and aesthetic perception. This provides a new theoretical perspective for understanding the practice of "world-building" in the digital age.

With the development of society and the progress of science and technology, CNC technology has been widely used in various industries. CNC skills practical training as an important part of training students' practical ability, not only need to focus on students' technical ability training, but also need to pay attention to students' ideological and moral education. This paper discusses how to cultivate students' ideological and moral cultivation and social responsibility through the education of course ideology and politics by studying the application of course ideology and politics in CNC skills practical training. It is found that the application of curriculum Civics and Politics in CNC skills practical training can effectively cultivate students' sense of social responsibility, professional ethics and innovation consciousness, so that they can become CNC skilled talents with comprehensive quality and good moral quality.

As digital technology reshapes the labor market, real-time insights into employment dynamics have become key to the smooth operation of the economy. Based on 5,000 anonymized sampling data in Yichang City, this study constructs a multi-dimensional employment status analysis and prediction framework. The first step is to use data cleaning and visualization to evaluate the influence of age, gender, education and other characteristics to reveal structural characteristics such as high youth unemployment rate and weak female employment stability. In the second step, the chi-square test was used to screen significant variables, and a logistic regression model was constructed to predict the employment status of 20 test samples, with an accuracy of 81.93% and a recall rate of 97.7%. The third step is to introduce macroeconomic indicators such as GDP growth rate, urban registered unemployment rate, and policy support level, and use the random forest model to optimize the prediction after integrating with individual data, with an accuracy of 81.2% and an F1 value of 0.90, confirming the moderating effect of macro factors on employment. This study provides a data-driven decision-making basis for regional employment policy formulation and targeted assistance.

This study is grounded in the practice of educational digital transformation, and was designed in strict accordance with the methodological norms of meta-analysis. By systematically sorting out the inconsistent research conclusions on artificial intelligence teaching feedback, we systematically searched and screened the China National Knowledge Infrastructure (CNKI) and Wanfang Database, and formulated strict literature inclusion and exclusion criteria, we ultimately selected 32 eligible empirical studies published in China from 2016 to 2024, involving a total effective sample of 12,486 participants. The random-effects model was adopted to calculate the pooled effect size, and we simultaneously conducted heterogeneity tests, publication bias assessments, and multidimensional moderating effect analyses. The results revealed that artificial intelligence teaching feedback had a pooled effect size of Hedges' g=0.51 (95% CI [0.42, 0.60], Z=11.24, p<0.001) on learning outcomes, representing a significant moderate positive facilitative effect; Academic stage, feedback type, and subject category all exerted significant moderating effects on the effect size. The research conclusions are consistent with the actual situation of classroom teaching, and the analytical process is rigorous and credible. This study thus provides robust empirical evidence for the implementation and application of artificial intelligence teaching feedback, its program optimization, and the construction of a localized precision teaching feedback system.

The “Timeline of the Central Axis: Solar Terms in the Capital Study Tour” project is grounded in the cultural heritage of the Twenty-Four Solar Terms and the intangible cultural heritage resources of Beijing’s Central Axis, aiming to construct an educational system for study-based learning centered on traditional culture. Drawing on the project’s practice and the author’s professional experience in a ski winter camp, this paper explores how the transmission of solar term culture and the transformation of educational value can be achieved in outdoor education settings. By analyzing the intersection of solar term culture and ice and snow sports, the relationship between children’s cognitive development and the design of study-based curricula, and the role of educators in informal learning environments, this paper proposes the educational philosophy of “teaching in accordance with the seasons, educating in accordance with the place,” offering practical pathways and theoretical support for the integration of traditional culture and modern study-based education.

In the context of digital education, the sluggish update and poor adaptability of English as a Foreign Language (EFL) teaching materials have become prominent problems, which have become key factors impeding the improvement of foreign language teaching quality. Artificial intelligence, relying on technologies such as natural language processing and learning analytics, can intelligently reconstruct traditional teaching materials and optimize and upgrade their content and form simultaneously. Based on teaching practice, this paper analyzes the existing problems in EFL teaching material development, elaborates on the technical support and application paths of AI tools, quantitatively analyzes the application effects with authoritative pilot data, and constructs a human-machine collaborative application system. The findings of this study indicate that the proper utilization of AI tools can significantly improve the efficiency and accuracy of teaching material development and enhance classroom teaching effectiveness. Adhering to the teacher-led and AI-assisted development model in EFL teaching material development can facilitate the transformation of teaching resources toward personalization and dynamism, and provide solid resource support for the high-quality development of foreign language teaching.

The comprehensive integration of artificial intelligence technology is facilitating a paradigm shift in higher education from “digital assistance” to “intelligent reconstruction”. Management information system courses represent a critical intersection of management science and information technology. However, the traditional hybrid teaching model encounters structural challenges, including outdated content, superficial practical applications, limited evaluation metrics, and a lack of ethical education. This research draws upon disciplinary insights, integrating social cultural theory and distributed cognition theory to develop a theoretical framework centered on “cognitive spiral development”, with AI embedded within the four-dimensional integration of “teaching, learning, evaluation, and research”. Based on this framework, four primary optimization strategies are proposed: dynamic content generation and knowledge graph construction render course content dynamic; human-machine collaborative exploratory learning design deepens the practical process; multi-dimensional data-driven precise evaluation enhances learning feedback accuracy; and the integration of technology and ethics establishes a clear educational orientation. This research aims to offer a systematic solution that combines theoretical depth with practical applicability for the intelligent transformation of management information system courses empowered by AI, thereby providing an operational reference paradigm for cultivating interdisciplinary talent in the context of emerging business and engineering fields.