Xi'an International Studies University

Vegetation constitutes the structural backbone of terrestrial ecosystems, regulating carbon exchange, water cycling, and energy flux at regional scales. The Normalized Difference Vegetation Index (NDVI), derived from satellite remote sensing, provides a robust proxy for vegetation vitality and has been extensively adopted in long-term ecological monitoring. This study examines the spatio-temporal dynamics of vegetation coverage across Shaanxi Province, northwestern China, over the period 2000–2019, using MODIS MOD13C1 NDVI data. Growing-season (May–October) composites were generated via the Maximum Value Compositing (MVC) method, and pixel-wise temporal trends were quantified through ordinary least-squares regression. Results indicate: (1) spatially, vegetation coverage follows a pronounced south-to-north declining gradient, with high-coverage zones (NDVI > 0.6) concentrated along the southern Qinling foothills and northern Daba Mountains, representing approximately 32.85% of the provincial area; (2) temporally, provincial mean NDVI increased from 0.4488 in 2000 to 0.5405 in 2019, an overall gain of ~20.4%; (3) 98.07% of pixels exhibited positive trends, concentrated in central-southern Shaanbei where afforestation programs were intensively implemented, while declining trends (~1.93%) were confined to the Guanzhong urban agglomeration, attributable to cropland conversion and urban expansion. These findings offer a quantitative baseline for regional ecological assessment and policy evaluation in Shaanxi Province.

Against the backdrop of the deep integration of digital transformation in education and the Rural Revitalization Strategy, digital technologies are playing an increasingly important role in rural primary school education. By breaking geographical barriers to educational resources, adapting to students' individual differences, building professional development platforms for teachers, and innovating teaching evaluation mechanisms, digital technologies provide a new multi-dimensional and collaborative approach to Chinese language teaching in rural primary schools. This approach effectively responds to long-standing problems, including insufficient educational resources, a weak teaching faculty, and rigid teaching methods. Based on the current development status of rural education and the characteristics of Chinese language teaching in primary schools, and combined with the application practices of digital technologies in the field of education, this paper analyzes the practical value and core advantages of digital empowerment for Chinese language teaching in rural primary schools. It constructs optimization paths from four dimensions, namely resource supply, teaching implementation, teacher development and evaluation system, and puts forward targeted innovative strategies, aiming to provide practical references for promoting the high-quality development of Chinese language teaching in rural primary schools and advancing educational equity.

The artificial intelligence (AI)-assisted 3-D modelling has become central to modern healthcare, yet the field still lacks a repeatable and scalable way to evaluate engineering competence for clinically used digital models. Existing quality checks tend to focus on final mesh outputs without considering the modelling workflow, operator's behavior, and interactions with AI assistance. This gap holds back reproducibility, effective training, and limits regulatory compliance. This work proposes the first end-to-end visual analytics framework that is designed to measure and communicate engineering skill during health-centric 3-D modeling tasks. The proposed framework defines a domain-specific construct of skill, ranging from geometric accuracy to operational proficiency and cognitive adaptability, and quantifies these dimensions through a set of interpretable behavioral, geometric, and physiological indicators. It integrates, within a four-layer architecture, the capture of multimodal data, real-time feature extraction through lightweight deep-learning models, Bayesian evidence fusion for continuous competence estimation, and intuitive visual feedback modules. The system maintains calibration for new tools and modelling scenarios through an online active-learning mechanism that minimizes expert annotation requirements. The framework was tested with 60 participants performing two clinically realistic modeling tasks concerning the design of orthopedic implants and vascular reconstruction. Results showed strong agreement with expert assessments, clear discrimination between skill levels, and meaningful prediction of future modeling quality. Python was used for correlation, regression, validity testing, and visualization tasks, respectively. Usability testing indicated high acceptance; the participants valued highly the clarity of the visual feedback that supported self-directed improvement. Ultimately, this proposed framework reshapes 3-D modelling assessment from a static outcome in-section to a dynamic, process-centric review underpinned by multimodal evidence. The result is a pragmatic, interpretable, and scalable solution for training, certification, and regulatory oversight of health-critical 3-D modelling workflows, and the basis for future human-AI collaboration and competency analytics in medical design.

Abstract Rising lithium resource scarcity and cost volatility have intensified interest in sodium‐ion (Na‐ion) batteries as a promising alternative. Although they currently do not surpass lithium‐ion batteries in energy density, their potential advantages in cost, safety, and resource sustainability make them attractive for specific applications. This study presents a comprehensive analysis of the academic and industrial trajectories of Na‐ion battery technology through integrated bibliometric and patent analyses. Data from Web of Science and Derwent Innovation Index (1980–2023) were visualized using VOSviewer to examine output trends, geographic contributions, research hotspots, and patent classifications. The results reveal that sodium‐ion battery has evolved through three phases: initial exploration, steady development, and rapid expansion post‐2010. China, European countries, the United States, Korea, and Japan lead in both academic and industrial outputs. Research focus has shifted from material‐level investigations to system‐level optimization, reflecting advancing technological maturity. Patent analysis indicates targeted commercialization efforts in electric vehicles, small mobile devices, and energy storage systems. Overall, while current Na‐ion batteries lack comprehensive superiority in key performance areas, they serve as a strategic complement by excelling in specific applications where their differentiated advantages are critical. This work offers a holistic perspective to guide future research, policy‐making, and industrial deployment of Na‐ion batteries.

Type of the article: Research ArticleAbstractFirms worldwide are embracing ESG principles and strengthening their ESG performance to foster sustainable development. This study uses five years of data from China to examine the relationship between ESG performance and corporate financial performance (CFP), measured by ROE, and tests the moderating effects of analyst and media attention using both ordinary least squares (OLS) and the fixed-effects model (HD-FE). Regression analyses demonstrate that: (1) the ESG performance has a significantly positive effect on ROE (coefficient = 0.026, p < 0.01), and after lagged by one and two periods, the effect is sustained (coefficient = 0.019/0.018, p < 0.01). (2) Analyst attention negatively modulates the relationship between ESG and ROE (coefficient = –0.011, p < 0.01), and the relationships for CSR (coefficient = –0.002, p < 0.01) and CG (coefficient = –0.011/–0.012, p < 0.01), but can mitigate the negative effect of environmental protection (ENV) on ROE (coefficient = 0.002, p < 0.01). (3) Media attention shows no consistent moderating effect on ESG-ROE relationship (coefficient = –0.001, p > 0.10; coefficient = –0.001, p < 0.05), but after classifying by sentiment, positive and neutral media coverage significantly weakens the positive impact of ESG on ROE (coefficient = –0.004/–0.005, p < 0.01; coefficient = –0.003/–0.004, p < 0.05/0.01), while negative coverage strengthens it (coefficient = 0.003/0.002, p < 0.05/0.10). Therefore, to meet external regulatory or public expectations, firms should strive to disclose more detailed and reliable ESG information, while investors and other stakeholders should critically evaluate the information presented.

Octavia Butler’s short story Bloodchild is a classical work of Afrofuturist literature, depicting the living conditions of humans after they have fled to the another settlement, the Tlic’s. This paper examines the historical metaphors and ethical dilemmas through the relationship between humans and the Tlic species by using Afrofuturism as a theoretical framework. It is found that Butler’s speculative narrative reconstructs the possibilities to imagine the future for African-American people and challenges historical oppression. It also exposes moral and ethical dilemmas under power subversion, highlighting the author’s reimagining of African American subjectivity beyond mainstream narratives.

With the continuous improvement of the accuracy and efficiency requirements for robot path planning in intelligent warehousing systems, enhancing the ability of path planning in complex dynamic environments has become a key issue. Therefore, a two-level path planning model integrating an improved Ant Colony Optimization algorithm with a Dynamic Window Approach is proposed. At the global level, a multi-objective fitness function that considers path length, corner smoothness, and obstacle clearance is introduced to guide the search, and elite solutions are strengthened in each iteration by increasing the pheromone reinforcement on high-quality paths. At the local level, a velocity-based DWA module dynamically samples feasible trajectories in velocity space and evaluates them using weighted functions of heading, speed, and obstacle distance to generate optimal control actions. Experimental results show that the proposed model improves accuracy to 88.4%, reduces root mean square error to 0.21, achieves a smoothness score of 0.94, and completes tasks with a success rate of 97.3%. The model effectively enhances global optimality and real-time local obstacle avoidance, making it suitable for complex warehousing and logistics scenarios.

The data that support the findings of this study are available from the corresponding author upon reasonable request.