In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.

With the rapid growth of online entertainment live streaming, how to continuously innovate and achieve long-term sustainability has become a major challenge for the industry. Augmented reality (AR) technology offers users immersive interactive experiences and potentially addresses this challenge. The aim of this study is to explore how AR technology influences key components of user online experience—immersion and intention to pay—using survey data. Building upon the Hedonic Motivation System Adoption Model (HMSAM), this research incorporates aesthetic variables to theoretically expand the model in order to gain a deeper understanding of the mechanisms influencing user behavior. A questionnaire survey was conducted to collect 450 valid samples. Detailed analysis was conducted using structural equation modeling. The findings confirm that aesthetic design significantly impacts users’ judgments of content value and perceived ease of use, generating positive effects at the perceptual level. Additionally, AR applications enhance the quality of user experience, thereby stimulating intrinsic motivations such as curiosity and joy. Further analysis indicates that users’ curiosity and perceived behavioral control directly influence the level of immersion and intention to pay. Overall, the research results offer important insights into industry applications. This study successfully expands the HMSAM theoretically by incorporating aesthetic variables to enhance the explanatory power of user judgment mechanisms. The analytical framework proposed aids in understanding the potential mechanisms of new technologies on customer experience and commercial value creation. The research findings provide guidelines for technological design and marketing strategies of streaming platforms.

With the development and popularization of the concept of a sustainable environment, the green energy industry represented by electric scooters has entered a vigorous development period. However, consumers are reluctant to buy electric scooters, resulting in low sales, which is limiting the development of the electric scooter industry. Therefore, from the perspective of different consumer life styles, this study explored the impact on the purchasing decisions and user demands, so as to inform design strategy for the development of a sustainable electric scooter industry. This study adopted the methods of expert interview and questionnaire survey, used the self-made consumer life style and electric scooter product attribute scale as the research tool, and carried out quantitative analysis with the help of statistical software. It showed that compared with consumers’ awareness of environmental protection, product performance is the key factor that attracts consumers to buy electric scooters. More than consumers’ awareness of environmental protection, our study showed that product performance is the primary determinate of a consumers’s decision to purchase electric scooters. The consumer of the electric scooter can be categorized into three life style groups: “brand-conscious group”, “environmental and cost saving group” and “trendy adventurers group”. Outside of the “environmental and cost savings” group, the least important attribute that drive purchasing decisions was environmental sustainability. This can also inform more effective governmental policies and education to promote greater environmental protection. Each life style consumer group prioritizes different product attributes; therefore, electric scooter manufacturers should view each group as distinct market segments and draw up different product design and marketing strategies.

人工智能与元宇宙的深度融合正在重构人机情感交互范式，本研究针对情感计算中微观响应与伦理适配的关键挑战，提出以“情感精微度”为核心的新型评估体系。与传统基于单一模态的情感识别模型不同，本研究构建的“情感精微度定律”通过动态平衡响应精度、认知负荷阈值和情境适配度三维指标，率先实现多模态生物信号（微表情/脑波/语音）的协同解析。创新性地开发了神经适应性接口技术，结合深度强化学习构建具有认知补偿机制的量子混合模型，在脑机接口实验中使情感交互的双通道验证框架有效解决了传统模型中的语义-情感解耦难题。技术突破体现于两方面：其一，基于量子计算的机器学习模型突破了传统算法的维度限制，通过构建情感状态纠缠矩阵，在心理治疗场景中实现共情准确度；其二，建立首个情感计算的伦理评估矩阵，提出“响应精度-隐私保护”的动态平衡机制，当系统延迟控制时用户信任度可有效提升。研究证实，遵循ISO/IEC 23894标准的情感精微度优化策略，可使教育领域的人机协作效率提高，同时将认知过载风险可以降低。这些进展标志着情感计算正从技术工具向具备伦理自觉的认知生态系统演进，为构建负责任的元宇宙情感交互范式提供了理论框架与技术路径。

Anti-food-waste apps increasingly use gamified “eco-points” to nudge sustainable choices, yet when rewards align—or inadvertently fail—remains unclear, particularly under high price salience. This study examines how gamification, cost salience, and platform-trust cues shape consumer intentions toward sustainable food platforms. This study used an explanatory sequential mixed-methods design. First, three semi-structured expert interviews (interaction design, food co-op operations, and secondary education) were open-axial-selectively coded to surface design levers and pain points. Next, a cross-sectional survey of Taiwanese consumers ( N = 256; 5-point Likert) assessed cognition, platform demand, reward perceptions, trust/traceability cues, assortment, and price acceptance. Reliability and construct adequacy were strong (Cronbach’s α = 0.949; KMO = 0.938; Bartlett’s χ ² = 5353.75, df = 435, p < 0.001). Exploratory factor analysis extracted four components (63.09% variance). Consumers endorse platform potential ( M = 3.94), rate educational content ( M = 4.10), and third-party product certificates/traceability highest ( M = 4.27). Willingness to pay a price premium is modest ( M = 3.32), indicating cost salience as a key barrier. Eco-points are viewed favorably ( M = 3.92), but, in importance–performance map analysis (IPMA), rank below reliability information; transparency gaps (e.g., missing test reports or real-time verification) erode trust. The four-factor structure captures (1) sustainability cognition, (2) platform enablement, (3) reliability/traceability, and (4) cost/education salience. Occupation moderates platform demand, F (10,245) = 2.281, p = 0.014, with finance/insurance respondents showing higher demand than public-sector and retired groups. Rewards align when foundational trust-and-perceived value are strong, and costs are not front-of-mind; when price salience is high, transparent reliability cues and educational framing dominate behavior, and eco-points function best as supportive—not primary—motivators. Design implications for anti-food-waste apps include: (a) prioritize real-time verification, third-party certification, and end-to-end traceability; (b) pair eco-points with cost-offsets (e.g., bundle discounts) to blunt price salience; (c) expand assortment to reduce search costs; and (d) ensure fast confirmations and in-app customer support. Limitations include a Taiwan-focused, cross-sectional sample; future work should test experimentally and across cultures the causal interactions between rewards and cost salience.

Geologic CO ₂ storage in saline sandstone aquifers is controlled in part by injectivity, because the ability to sustain a target storage rate affects both pressure management and the number of injection wells required. Here we examine the Shenhua CCS Demonstration Project in the Ordos Basin, China, targeting the Triassic Liujiagou Formation at approximately 1.7–1.9 km depth, using an integrated workflow that combines field injection monitoring and pressure‐transient analysis, laboratory characterization of preinjection and postinjection reservoir cores, and coupled flow and reactive‐transport simulation. Pressure‐rate monitoring and pressure‐transient interpretation indicate that the injectivity index estimated from monitored pressure‐rate data increased by about one order of magnitude during the test program, whereas falloff‐derived apparent near‐well permeability increased from ~0.06 to ~0.18 mD. Laboratory measurements of core samples show little change in bulk porosity (~10%), but a clear redistribution of pore‐throat sizes, with more large throats and some additional fine pores after injection. XRD/XRF, thin‐section, and SEM observations are consistent with partial dissolution of calcite and chlorite, limited secondary illite precipitation, and improved near‐well pore connectivity. Reactive‐transport model predictions indicate spatially differentiated reactions, with dissolution concentrated in the acidic near‐well zone and model‐predicted calcite/clay precipitation farther from the injector as a possible, unvalidated distal low‐permeability halo. Improved injectivity could reduce the number of injectors needed to achieve a target storage rate and thus improve project economics. However, the core comparison is quasi‐paired rather than strictly paired, and the far‐field precipitation/fines‐migration behavior remains incompletely validated because it is inferred mainly from a homogeneous radial model rather than direct far‐field observations.