This study explores the transformative role of global value chain (GVC) participation in driving industrial upgrading in China, Vietnam, and India between 2018 and 2023. Employing an explanatory, mixed-methods research design, it examines the causal relationships between GVC integration and structural economic transformations. This research thoroughly examines the smile curve, leapfrogging, and global production network theories, showing where they fit and their constraints regarding GVC relations in developing economies. The results provide insights to such stakeholders as policymakers, indicating that strategies aimed at GVC structural growth and competitiveness maximization should be implemented while advising against low-risk strategies such as being overly dependent, regionalized, or technology dependent. Offering the perspective of GVC participation, this study sheds light on the ongoing transformation of the world economy structure and industrial development in global competition.

With the increasing complexity of enterprise systems and the rise in cyber threats, managing security risks while optimizing resources has become a significant challenge. Traditional models often address security and resource management in isolation, making it difficult to adapt to evolving threats and dynamic workloads. This paper proposes the deep learning-based dynamic security assessment and optimization model, which integrates dynamic security assessment, anomaly detection, multi-modal data fusion, security investment optimization, and cloud resource optimization into a unified framework. By leveraging deep learning techniques such as convolutional neural networks for feature extraction and recurrent neural networks for temporal anomaly detection, alongside reinforcement learning for resource optimization, the deep learning-based dynamic security assessment and optimization model provides real-time risk evaluation and adapts resource allocation based on system needs.

The exponential growth of e-commerce platforms has generated vast amounts of user behavior data, making it increasingly important to predict consumer preferences and spending patterns. Traditional recommendation systems often struggle with challenges such as data sparsity, the cold-start problem, and the inability to capture the dynamic nature of user behavior. These limitations hinder the accurate prediction of consumer actions, especially in evolving markets where user preferences change over time. To address these challenges, the authors propose deep behavioral and sentiment-aware personalized recommendation model, a novel approach that integrates dynamic user behavior modeling and sentiment analysis within a hybrid recommendation framework. The model leverages both collaborative filtering and content-based filtering, enhanced by deep learning techniques, to continuously adapt to evolving user preferences and emotional context, improving both recommendation relevance and consumer spending prediction.

The purpose of this research is to evaluate the effect of artificial intelligence (AI)-facilitated leadership on team member productivity. To realize this goal, a quantitative methodological design based on positivism was used to investigate the systematic impact of AI-empowered leadership practices on teamwork dynamics and performance determinants. A survey consisting of different cadres, i.e., 308 lower, middle, and top-level managers, as well as team members from different fast-moving consumer goods (FMCG) firms, was conducted. The outcome indicated a strong and positive connection between AI-backed leadership and team efficiency through a good big data culture. The findings emphasize the transformative impact of AI on leadership positions within the FMCG industry, implying that companies that manage to successfully infuse AI while nurturing a culture based on data can increase their competitiveness, as well. This information is crucial for FMCG firms aiming at utilizing AI and big data to improve team performance and ultimately achieve organizational success.

Although many studies have examined social status in offline contexts, the combined influence of digital metrics—such as the number of followers a person has—and traditional cues, such as expertise on online status perceptions, remains underexplored. Drawing on the stereotype content model, this study investigated how expertise and number of followers jointly affect perceived social status, dominance, and warmth across both real-life and social media contexts. The findings show that high expertise and a large number of followers significantly boost perceptions of status and dominance yet have no meaningful impact on warmth. These results underscore the interplay between digital and traditional status cues and offer practical insights for personal branding, influencer marketing, and social media platform design.

This research unravels the strategic confluence of environmental leadership and cutting-edge Artificial Intelligence (AI) in the realm of Carbon Capture technology, and their combined effect on the financial fortitude of U.S. firms. It posits that a firm's environmental vision, when led by transformative green leadership, significantly propels the effective adoption of Carbon Capture solutions. Drawing on data from 145 publicly traded U.S. entities from the years 2017 to 2019, provided by the Carbon Disclosure Project and Compustat, this study meticulously explores the interrelation between a firm's environmental initiatives - including managerial focus, shared vision, proactive green strategies, and innovation—and its performance outcomes. The findings illuminate that while environmental commitments like management focus and a unified vision greatly encourage the embracement of Carbon Capture, the financial implications of these adoptions present a complex picture.

Driven by the global pandemic, the demand for online education has significantly increased, making it crucial to enhance the interactive experience of online teaching to improve student learning outcomes and engagement. In this context, we have designed a model based on Particle Swarm Optimization (PSO) that combines the strengths of LSTNet in handling long-term dependencies and the capabilities of the Prophet model in trend and seasonality modeling. Our PSO-optimized LSTNet-Prophet model outperformed baseline models by 10% in accuracy and 12% in F-1 score, as shown by experiments conducted on the CMU-MOSEI dataset. Additionally, we have implemented a real-time emotion monitoring system capable of analyzing students' emotional states in real-time and providing feedback to teachers, aiding them in promptly adjusting teaching strategies, thereby improving teaching quality and interaction effectiveness. Our method achieved an emotion recognition accuracy of 82.42% and an F-1 score of 82.31%, demonstrating its effectiveness and robustness.

Based on the legal system of enterprises, in the realm of intelligent financial management, accurately predicting the impact of retail promotional activities on revenue is crucial. However, traditional methods often struggle to address the complexity of promotional effects and the variability of consumer behavior, resulting in less accurate predictions. To tackle this issue, we propose a novel model, TCGNet, based on deep learning techniques. This model integrates TCN and GNN, optimized by the GWO, to provide more precise predictions of the impact of promotional activities on revenue. Experimental results demonstrate significant advantages of the TCGNet model across various metrics, offering more reliable support for retail financial management. This research aims to furnish businesses with more precise and timely financial planning and budget management support, with the potential to aid in the formulation of more effective marketing strategies and financial decisions.

The rapid process of urbanization and industrial development has raised significant concerns regarding the presence and management of hazardous substances. However, conventional methods employed for identifying hazardous substances and monitoring urban safety often suffer from low efficiency and accuracy. This paper proposes a novel approach that combines deep learning and genetic algorithms, which utilizes the Bidirectional Long Short-Term Memory model to capture temporal features in hazardous substance data and introduces the Attention Mechanism for weighted processing of crucial information, thereby improving recognition capability. Genetic Algorithms are employed to optimize the performance and generalization capacity of the deep learning model. Experimental validation demonstrates that the proposed approach achieves higher accuracy and faster processing speed, effectively enhancing urban safety monitoring. This research holds practical implications for urban safety management and accident prevention, offering an innovative solution to guarantee urban safety.

This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.