In the era of the digital and intelligent society, artificial intelligence technology has developed rapidly. Against this backdrop, deepfake technology has also undergone iterative upgrades, achieving a phased leap from early encoding-decoding networks to text-to-video large models. Deepfake has officially entered the 3.0 era, characterized by multi-modality, low thresholds, and ultra-realism, and it has already posed a severe threat to national security, public social order, the legitimate rights and interests of citizens, and even the security of judicial procedures. Therefore, the risk regulation targeting the abuse of deepfake technology is already urgent.Based on this, this paper will, on the basis of conducting an exploration and analysis of the crime types and harmful effects of artificial intelligence deepfake, combine relevant domestic and international literature and practical cases to explore the construction of a multidimensional risk governance framework integrating technology, ethics, and law into one, with a view to achieving effective regulation of deepfake risks in the new era.

As an important carrier of internet of things technology, intelligent wearable devices are profoundly changing people’s health management methods. Design an intelligent wearable embedded system, with the STM32F103C8T6 microcontroller as the core, integrating detection modules such as the MAX30102 heart rate sensor, and construct a physiological parameter monitoring and motion recognition system. The system achieves multi-sensor data fusion through Kalman filtering algorithm, completes motion pattern recognition using support vector machine classifier, and optimizes power consumption control through dynamic voltage frequency scaling technology. Experimental results show that heart rate detection accuracy reaches 96.8%, motion state recognition accuracy is 90.6%, system average power consumption is 18.5 mA with continuous operation exceeding 74 h, laying a technical foundation for the industrialization of intelligent wearable devices.

Amid the new wave of technological transformation driven by Artificial Intelligence (AI), data centers have emerged as critical strategic infrastructure underpinning algorithm training, large-scale data processing, and the provision of global digital services. This article offers a systematic analysis of the structural challenges confronting U.S. data centers under mounting pressures, including rapidly growing demand for computing power, constraints on energy and water resources, vulnerabilities in the supply of critical minerals, labor shortages, and regulatory and permitting bottlenecks. By examining these interrelated pressures, the study provides forward-looking insights into the systemic risks and governance requirements faced by data centers as essential infrastructure in the era of AI, thereby contributing policy-relevant perspectives for future infrastructure planning and regulation.

This article analyzes how the EU Artificial Intelligence Act achieves a balance between the two key objectives of the EU's AI strategy: independent innovation and effective regulation. The AI Act promotes innovation through measures such as risk-based classification, regulatory sandboxes, and the unification of EU AI market. It builds a legal framework centered on EU values to strengthen regulatory coordination, achieving effective oversight while bolstering its extraterritorial influence. The AI Act also enables mutual reinforcement between innovation and regulation. The Act offers insights for China’s AI governance, and its future development merits attention.

ASEAN Guide on AI Governance and Ethics, released in early 2024, aims to regulate the responsible design and deployment of artificial intelligence models by ASEAN member states. The guideline seeks to foster a consensus on AI governance within the region, establish international cooperation platforms both within and outside ASEAN, and enhance the region’s global influence in AI governance. The guideline emphasizes the “ASEAN-centered” approach to AI development and governance, primarily due to differences in the readiness of member states, varying governance of related issues within each country, and the disparities in societal understanding of AI. Due to the inconvenience of data flow within the region and the lack of synchronization in AI development as well as digital transformation among member states, ASEAN faces challenges in further developing a regional AI strategy. In the future, ASEAN will focus on areas such as the integration of artificial intelligence with education, the establishment of mechanisms for sharing best practices among member states, and conducting international cooperation with countries outside the region.

Aiming at the problem of high energy consumption caused by using fixed schedule control equipment in high-rise office buildings, the research on energy-saving control methods under the environment of Internet of Things is carried out. A four-layer Internet of Things architecture is constructed, and various types of sensors are deployed to collect energy consumption data in real time. A Bayesian network combined with attention mechanism is used to build an energy consumption prediction model. Based on the prediction results and multidimensional data, the hierarchical intelligent control strategy is designed, and the closed-loop feedback of abnormal energy consumption monitoring is established to realize the intelligent energy-saving control of high-rise office buildings. Experiments show that this method has high prediction accuracy and low energy consumption, and significantly improves the level of building energy saving.

After 2023, Japan suspended the updating of a new artificial intelligence strategy, and the fundamental cause lay in the disparity between technological development and strategic cognition. The technological breakthrough represented by “Generative AI”, not only surpassed the original expectations of Japan’s strategic planners but also profoundly transformed their strategic perception of artificial intelligence, thereby inducing significant alterations in their strategic organizational model and governance thinking. Nevertheless, the implementation of a strategy demands an organic integration with market practice. Currently, the most significant challenge for Japan’s artificial intelligence strategy is not how to understand artificial intelligence but the difficulty in aligning the Japanese strategy with the market.

With the rapid development of information technology, the application of intelligent internet of things technology in grass-roots governance has gradually become a research hotspot. Intelligent internet of things technology connects various physical devices and sensors to achieve real-time data collection, transmission and analysis, providing a new solution for grassroots governance. This paper introduces intelligent internet of things technology, discusses the specific application of intelligent internet of things in grassroots governance and the technical path of realization, and analyzes the challenges and countermeasures in the application process, so as to promote the continuous deepening of the application of intelligent internet of things technology in grassroots governance and promote the intelligent and refined development of grassroots governance.

Real-time cloud rendering is a new rendering method based on cloud computing, which fully integrates the three key concepts of “real-time”, “cloud” and “rendering”, which can make full use of cloud computing resources, improve rendering efficiency, reduce hardware threshold, and promote technological innovation and industrial upgrading. Based on this, this paper describes the development background of real-time cloud rendering, introduces the principle and key technical points of real-time cloud rendering, and deeply analyzes the development trend of real-time cloud rendering technology.

As one of the key technologies of intelligent manufacturing, machine vision positioning technology has been widely used in the fields of assembly, welding, grinding and spraying. A machine vision positioning technology for intelligent assembly units is studied which is used for the positioning and grasping of materials to be assembled in intelligent assembly unit, mainly including design and layout, hand-eye calibration of robots, large-field coarse positioning detection and small-field fine positioning detection and grasping. A vision-based positioning and grasping solution was designed to meet the needs of intelligent assembly units, facilitating automated production and being widely applied in assembly scenarios.

In recent years, the global artificial intelligence industry has continuously achieved breakthroughs in technological innovation, market expansion, and strategic computing power development. At the global level, a tripartite pattern dominated by the United States, China, and the United Kingdom has emerged. The United States has established a formidable industrial scale through supportive policies and suppression of China; China emphasizes security, with vibrant market competition but a shortage of computing power; and the United Kingdom focuses on its role in international governance, boasting strong capabilities in AI research. Geopolitics, algorithms and data, as well as talent shortages, pose major challenges. In the future, geopolitical influences will persist, while regulatory and labor issues are expected to improve.

Artificial intelligence has become a key focus for the GCC countries. In response, these nations have actively formulated policies that include establishing “Vision” strategies, strengthening technology regulation, and engaging in external cooperation. The motivations behind AI development in the GCC are driven by the need for economic diversification, inherent advantages, and the pursuit of data sovereignty. However, challenges remain, including geopolitical tensions, technology regulation, private sector application, and talent shortages. In the future, GCC countries should aim to address these challenges by minimizing the impact of geopolitical factors, enhancing regional cooperation, promoting private sector adoption, and strengthening talent development to ensure sustained progress in the field of artificial intelligence.

The unmanned aerial vehicle traffic monitoring system uses the unmanned aerial vehicle for real-time traffic monitoring, which provides a new perspective and data support for traffic management. In this process, the fusion application of air pressure sensor and radar technology greatly enhances the operation efficiency and data accuracy of the unmanned aerial vehicle by providing accurate altitude and position information. Air pressure sensors can compensate for radar positioning errors in complex environments, while radar technology can track the dynamics of vehicles on the ground in real time. The combination of the two allows unmanned aerial vehicle to work steadily in various weather conditions and optimize their flight strategy. This paper explores the application of pneumatic sensor and radar fusion technology in unmanned aerial vehicle traffic monitoring, demonstrating the significant advantages of this technology combination in improving the efficiency and accuracy of unmanned aerial vehicle traffic monitoring.

Open-source Artificial Intelligence (AI) has emerged as a pivotal force in the development of AI. Breakthroughs such as DeepSeek have prompted significant industry reflection and offer valuable reference points. This article the progression of open source, delves into the technical merits and possible hazards of open-source AI, and examines the interplay between open and closed paradigms in AI development. In the context where technology and policy are intertwined, this research offers valuable perspectives for comprehending and steering the direction of AI progress while striking a balance between innovation and security.

To address signaling congestion and protocol heterogeneity in building weak current systems, this study proposes a distributed architecture based on “Device-Edge-Cloud” collaboration. The architecture utilizes an edge Deterministic Finite Automaton (DFA) to achieve normalized parsing of heterogeneous physical protocols. It employs a computation offloading mechanism based on the M/M/1 queuing model and a Lagrange optimization algorithm with a latency penalty term to control system long-tail latency. Additionally, a Kalman filter algorithm is applied to smooth network jitter and resolve audio-visual synchronization drift. Experimental results demonstrate that under 500 QPS high concurrency, the proposed architecture keeps P99 latency within 210 ms. Under weak network conditions, lip-sync error is limited to the 
±40 ms range. Furthermore, during WAN interruptions, the system ensures the deterministic execution of critical commands via an edge software-hardware dual redundancy mechanism. This work provides a reference engineering paradigm for constructing highly stable smart building systems.

Aiming at the problems of low recognition accuracy and poor scene adaptability of wildlife in forest and grass informatization monitoring, a two-stage “binary classification detection - species characteristic matching” architecture is proposed. The first stage adopts improved YOLOv8 for binary classification of wildlife and non-wildlife, enhancing the detection capability of small and occluded targets in complex scenes by introducing Convolutional Block Attention Module (CBAM) mechanism and optimizing Complete Intersection over Union (CIoU) loss function. The second stage constructs a species feature database based on ArcFace, achieving accurate recognition of crested ibis, siberian tiger and other species through cosine similarity calculation. Experiments on a forest and grass area dataset with more than 80 species and 
120 000 images show that the overall accuracy rate reaches 93.1%, which can adapt to complex backgrounds, light changes and occluded scenes, providing technical support for wildlife monitoring and protection.

Zero-shot robotic grasping focuses on realizing high-dimensional perception-to-control mapping under unsupervised semantic input. This paper analyzes a specific production line case of an intelligent sorting enterprise for automotive parts, and investigates the hierarchical Vision-Language-Action (VLA) architecture DexGraspVLA+, including its high-level semantic planning and low-level control mechanisms. A cross-modal alignment method and contact force embedding optimization strategy are proposed to construct a closed-loop system for grasp execution path generation and contact force regulation driven by semantic mapping.