Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context, large language models (LLMs), with their ability to use human language and continuously interact with learners, not only provide personalized feedback but also offer a data-driven approach to sustainable development in education. By leveraging real-time data, LLMs have the potential to deliver more effective and interactive feedback that enhances both individual learning experiences and scalable, long-term educational strategies. Therefore, this study utilized a quasi-experimental design to examine the effects of LLM-based feedback on learners’ SRL, aiming to explore how this data-driven application could support learners’ sustainable development in computer programming learning. The findings indicate that LLM-based feedback significantly improves learners’ SRL by providing tailored, interactive support that enhances motivation and metacognitive strategies. Additionally, learners receiving LLM-based feedback demonstrated better academic performance, suggesting that these models can effectively support learners’ sustainable development in computer programming learning. However, the study acknowledges limitations, including the short experimental period and the initial unfamiliarity with LLM tools, which may have influenced the results. Future research should focus on refining LLM integration, exploring the impact of different feedback types, and extending the application of these tools to other educational contexts.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

With the rapid development of digital technology, the role of digitalisation in urban governance continues to emerge. Building a theoretical analysis framework and evaluation system of digitally driven urban governance has important theoretical and practical significance for stimulating the efficiency of digital technology tools and improving the energy level of urban digital governance. This paper aims to explore the mechanism of urban governance enabled by digital technology, innovatively change the previous thinking mode that only attaches importance to facility construction and e-government platforms, adopt ecological thinking, and comprehensively consider the role of “soft elements” such as strategic support, industrial support, the security environment, talent support, and the market environment. Then, the extreme value variance method and the coefficient of variation method are used to calculate the overall capacity and secondary index scores of each city, and the standard deviation of secondary index scores is used to represent the sub-environmental balance of the cross-sectional data of China’s provinces. In order to further explore which indicators restrict the improvement of China’s urban digital governance capacity, this study also constructs an obstacle degree model. The results show the following: (1) The overall capability of China’s digitally driven urban governance is low, with a total score of 27.25, indicating that China’s digitally driven urban governance is in its infancy. (2) There is a significant development imbalance among Chinese provinces, with Beijing ranking first with a score of 81.16, and Tibet, Qinghai, Xinjiang, Heilongjiang, and Ningxia scoring less than 13.30 points, ranking as the bottom 5 among the 31 provinces. (3) The shortcomings of talent support, industrial support, and the security environment restrict the improvement of the entire digital ecological governance ability.

Faculty mobility is one of the most important research issues in the field of higher education. Reasonable faculty mobility can actively promote the fair, coordinated, balanced, healthy, and sustainable development of higher education. Scientific impact is the best proof of faculty members’ research abilities and is often represented by the quality of their articles. In particular, the gradual increase in citations of high-quality papers is undoubtedly an important reflection of healthy development in the academic field. This paper aims to explore the influence of faculty mobility on scientific impact, while comparative analysis is used to investigate whether there are disciplinary differences in the relationship between faculty mobility and scientific impact. Four major disciplines—sociology, mathematics, mechanical engineering, and philosophy—are selected as the scope of this study. Articles in these four major disciplines from 2000 to 2020 are obtained from the Web of Science, and Spearman’s rank correlation coefficient and the Wilcoxon signed-rank test are used to analyze the collected data. The results indicate the following: (1) faculty mobility has increased, with differences across disciplines; (2) mobility leads to a decrease in the number of citations, which decreases significantly with increased mobility frequency; and (3) the impact of mobility has disciplinary differences, with a relatively obvious decrease in mechanical engineering.

Since the 21st century, Russia has pursued the construction of world-class universities as a core reform goal in higher education, aiming to address the inadequacies of its post-Soviet higher education system in adapting to international competition. This study systematically examines the historical evolution, strategic framework, key characteristics, and practical challenges of Russia’s efforts over more than two decades. Russia’s practice has gone through three distinct stages: foundational construction (2003–2012), marked by joining the Bologna Process and establishing federal and national research universities; in-depth advancement (2012–2020), driven by initiatives such as the “5–100 Plan,” Global Education Program, and regional flagship universities; and adjustment and optimization (2020–present), represented by the “Priority-2030” plan. These efforts have formed a strategic framework centered on system construction, internationalization, balanced development, and operational support. Key characteristics of Russia’s practice include the integration of strategic stability and tactical flexibility, the complementarity of tiered and classified development, and the coordination of government leadership and university autonomy. However, challenges persist, such as underperformance in global rankings, insufficient funding, uneven resource distribution, and severe talent drain.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly $\mathcal{NP}$-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.