In recent years, assistive robots for the visually impaired have begun to enter peoples lives as an innovative tool. This study aims to explore the factors influencing the purchase intention of intelligent quadruped robots for the visually impaired, enhance public acceptance of these robots, and provide practical suggestions for their design. The study integrates the Technology Acceptance Model (TAM) with the Social Technology System (STS) theory to develop a STS-UTAUT integrated model for the purchase intention of intelligent quadruped robots for the visually impaired. Based on this model, 11 research hypotheses are proposed, and data are collected through in-depth interviews and other research methods, with empirical analysis conducted using SPSS. The study also analyzes the impact weights of each variable on the purchase intention of intelligent quadruped robots for the visually impaired. The findings indicate that both social environment and technology application significantly influence the purchase intention of these robots. The STS-UTAUT integrated model enables designers to better understand the needs of the visually impaired, leading to the development of more suitable intelligent quadruped robots, thereby improving their quality of life and social participation.

This study focuses on the utility dilemma of police robotic dogs in real-world applications, and systematically analyzes the synergistic imbalance between their technical and social subsystems based on the Science, Technology and Society (STS) theory. By integrating the feedback from police users and the STS theoretical framework, it reveals that there are core contradictions between the functional design of the current police robotic dog and the police demand, weak deterrence and arrest ability, and lagging system adaptation. The study proposes a two-way optimization scheme for technology and society: at the technical level, the modular hardware design, multimodal sensing system upgrade and deep reinforcement learning decision engine development improve the robotic arm’s arrest accuracy (by 37%) and the stability of complex terrain movement (by 42%); at the social level, it builds a hierarchical specification for the use of force, and a human-machine collaborative tactical system, which reduces the risk of law enforcement disputes by 58%. Collaborative validation shows that the two-way optimization mechanism effectively bridges the gap between technical expectations and real-world effectiveness, providing an interdisciplinary solution for the deep police application of police robot dogs.

This study examines how social and digital technologies shape gender representations, using Amazon’s biased AI recruitment tool as a case study. Through analysis of Amazon’s algorithmic discrimination against female candidates, this research demonstrates how society and digital technology assign different workplace values to men and women based on biological differences, social roles, and business interests, creating gender representations where women are perceived as less socially competent than men. The AI recruitment tool, which penalized resumes containing female-related terms, reflects entrenched social biases that become embedded in digital algorithms, challenging the notion of “technology neutrality.” However, the study argues that these gender representations are not irreversible. With the rise of feminist consciousness, legal protections for gender equality, and the development of social media platforms that provide new avenues for women’s voices and entrepreneurship, society and digital technology are beginning to reshape traditional gender stereotypes. The findings reveal the dual nature of technology’s role in gender representation: while it can perpetuate existing social biases, it also offers tools for challenging and transforming discriminatory practices.

In view of the current research situation of quantum information technology in China, this paper examines the quantum information technology related literature in China National Knowledge Infrastructure (CNKI), and derives 874 documents in Chinese. Through CiteSpace, we draw a visual knowledge map of quantum information technology, analyze the number of publications, high-frequency keywords, and the evolution of keyword time sequence, etc., analyze the current status and trend of China’s research, and provide quantitative data support for the future research of quantum information technology. The study shows that the number of Chinese literature in the field of quantum information has been increasing in recent years. Chinese quantum information research can be broadly categorized into two areas: quantum communication and quantum computing. In addition, new research hotspots in the field of quantum information continue to emerge, such as quantum entanglement, quantum error correction, and quantum network construction, etc. These indicate that the research in the field of quantum information in China is deepening and developing, and that there is a huge potential for research in quantum information. In order to promote the further development of quantum information, this paper puts forward strategies and suggestions such as strengthening basic research, promoting technological breakthroughs, cultivating human resources, promoting international cooperation and improving policy support. The research in this paper provides a reference for China’s strategic layout and scientific research innovation in the field of quantum information, aiming to promote the rapid development of quantum information technology.

The new energy vehicle (NEV) supply chain faces significant challenges stemming from highly uncertain end-user demand and sharp fluctuations in key raw material prices. These factors make procurement costs and inventory levels difficult to control, directly impacting supply chain stability and profitability. Traditional methods, such as stochastic dynamic programming (DP) and standard reinforcement learning (RL) models, which primarily respond only to historical and current state information, often prove insufficient for effectively addressing these complexities. To address these limitations, this paper proposes a Proactive Reinforcement Learning (Pro-RL) framework for joint procurement and inventory decision-making. By integrating a predictive information module into the sequential decision-making process of a Soft Actor-Critic (SAC) agent, the framework constructs an enhanced state space that incorporates predicted future information. This allows the agent to move beyond traditional passive response patterns, enabling proactive utilization of market information to achieve a better balance between immediate costs and long-term risks through iterative learning. To validate its effectiveness, this study develops a supply chain simulation platform aligned with NEV industry characteristics, and comparisons with multiple benchmarks were conducted. Experimental results demonstrate that this end-to-end decision-making policy, which integrates predictive information with deep reinforcement learning, offers advantages in responding to market volatility and achieving coordinated optimization of cost and service levels. This provides NEV enterprises with a theoretical model and practical approach for building flexible and efficient smart supply chains.