The integrated development of urban and rural areas is the main line and focus of the three rural issues'' work in the new era, which is of great significance for solving the problems of unbalanced and inadequate development in ethnic regions and promoting the comprehensive revitalization of rural areas. Based on the review of the evolution of urban-rural relations and related theories of urban-rural integrated development, this paper analyzes the prominent problems in ethnic regions, such as the imbalance of urban-rural development, rural hollowing, lagging infrastructure and public services, and ecological environment protection. Centering on the main line of digital empowerment for rural revitalization in ethnic regions, systematic countermeasures and suggestions are put forward from the aspects of constructing a digital rural development strategy'', promoting the system integration of `people-industry-land' '', taking classified measures to promote the revitalization of different types of rural areas'', promoting the free flow of urban and rural production factors'', and promoting rural space reconstruction''. At the same time, based on typical case practices, the replicable and extendable experience of digital rural development is summarized, the main challenges faced are analyzed, and policy suggestions are put forward from the aspects of top-level design, financial support, resource integration, pilot demonstration, and talent cultivation, so as to provide theoretical support and practical guidance for the integrated development of urban and rural areas and rural revitalization in ethnic regions.

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

Achieving common prosperity is the essential requirement of socialism with Chinese characteristics and requires promoting social fairness and justice by improving the health level of the entire population. Health capital plays a crucial role in personal development, social mobility, and economic growth. However, health disparities among different groups also constrain income distribution and social mobility. In the process of building a healthy China, emerging technologies have provided new paths for narrowing health inequalities and improving health capital. This paper first analyzes how inequality in health capability affects social mobility and then explores the application of emerging technologies such as internet healthcare, artificial intelligence, wearable devices, and genetic technology in promoting public health. It also presents practical cases to demonstrate the positive role of these technologies in improving medical efficiency and quality. In terms of theoretical foundation, the paper comprehensively applies theories of human capital, social stratification, and public economics, striving to deepen the understanding of the relationship between health and common prosperity from an interdisciplinary perspective. Finally, policy suggestions are put forward in terms of optimizing fiscal expenditure structure, improving health insurance, and incentivizing health promotion. The challenges and countermeasures that may be faced in policy implementation are also discussed. Narrowing the health gap requires leveraging the empowering role of emerging technologies, optimizing health investment structure, and improving policy systems while adhering to the concept of co-construction and sharing. This will ultimately enable the entire population to share the fruits of healthy development and provide a health foundation for promoting common prosperity.

This paper underscores the importance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in data mining. In this research, data mining clustering methods were applied to investigate consummated felonies related to the "Anti-Violence Against Women and Their Children Act of 2004 - RA 9262" from 2018 to 2023. The criminal data processed from the Police Regional Office 6 of the Philippine National Police encompassed 248 attributes reflecting cases over the specified period. The significance of this study lies in its utilization of ArcGIS Pro software to process the provided data through clustering techniques, presenting a robust approach for detecting criminal activities and recognizing patterns to aid law enforcement in crime reduction efforts. Spatial data mining proves practical when dealing with geographic crime datasets, facilitating the analysis of large volumes of crime data. The DBSCAN algorithm was employed to cluster crime incidents centered on predefined events, with the resultant clusters used to identify hotspots. These clustering outcomes are then visualized using GIS, enabling real-time mapping of crime distribution for law enforcement agencies to comprehend and engage with effectively. The outcomes empower stakeholders to devise interventions tailored to specific locations, thereby contributing to a safer environment for women and children. The study illuminates the localized analysis of crime distribution, offering insights into the interconnected factors influencing criminal incidents and providing a framework for crafting targeted and efficient strategies for crime prevention, thereby enriching the broader dialogue on crime management and public safety.

With the rapid development of information technology, warehouse management information systems are playing an increasingly important role in modern logistics management. This paper first elaborates on the importance of warehouse management information systems and the impact of technological development on warehouse management, and then reviews the development process of warehouse management information systems. Next, it focuses on introducing the application of key technologies such as barcode technology, RFID technology, UWB high-precision positioning technology, wireless network communication technology, cloud computing technology, and big data analysis technology in warehouse management. In addition, this paper also analyzes the current problems faced by warehouse management information systems and discusses the application prospects of emerging technologies such as artificial intelligence, Internet of Things, blockchain, automation, and robotics in warehouse management. Finally, it summarizes the main findings of the review and proposes insights and suggestions for the development of warehouse management information systems and future research directions.

Blockchain technology, with its decentralized and tamper-proof features, provides new solutions for optimizing enterprise financial processes. This study takes T Group, a leading Chinese hotel group, as an example to explore the application value of blockchain in the hotel finance field. Through a mixed-methods approach combining case analysis and questionnaire survey, we deeply analyze the current state and problems of T Group's financial processes, design blockchain-based solutions, and evaluate their cost-effectiveness and implementation challenges.The study finds that blockchain solutions can significantly improve financial efficiency, reduce costs, and streamline processes for T Group, but still face obstacles in technological maturity and organizational awareness. The survey shows that management has higher acceptance of blockchain, while poor process status quo is the main driving force. This research aims to provide theoretical guidance and practical references for financial transformation in hotel enterprises, promoting the innovative application of blockchain in traditional service industries.

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Aiming at the problems of poor convergence and local optimization in path planning using the basic ant colony algorithm, this paper studies an improved ant colony algorithm to enhance the effect of mobile robot path planning. Firstly, the state transition probability of the ant colony algorithm is modified, and the influence of the angle on node selection is increased by adding a new angle index heuristic function. Then, the sorting and elite ant colony algorithm strategies are fused to research a path length difference pheromone update method, improving the efficiency of the ant colony algorithm in planning the optimal path. Finally, through a comparison with the basic ant colony algorithm simulation on MATLAB, the feasibility and effectiveness of the improved ant colony algorithm are verified.

In the digital era, the widespread use of video content has led to the rapid development of video editing technologies. However, it has also raised concerns about the authenticity and integrity of multimedia content. Video splicing forgery has emerged as a challenging and deceptive technique used to create fake video objects, potentially for malicious purposes such as deception, defamation, and fraud. Therefore, the detection of video splicing forgery has become critically important. Nevertheless, due to the complexity of video data and a lack of relevant datasets, research on video splicing forgery detection remains relatively limited. This paper introduces a novel method for detecting video object splicing forgery, which enhances detection performance by deeply exploring inconsistent features between different source videos. We incorporate various feature types, including edge luminance, texture, and video quality information, and utilize a joint learning approach with Convolutional Neural Network (CNN) and Vision Transformer (ViT) models. Experimental results demonstrate that our method excels in detecting video object splicing forgery, offering promising prospects for further advancements in this field.