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.

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.

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.

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.

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.

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.

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.

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 severe heat dissipation challenge faced by high-density Micro Light Emitting Diode (MicroLED) Augmented Reality (AR) optical machine in a limited space, a multi-dimensional collaborative heat dissipation design method of "material-structure-algorithm" is proposed. Magnesium alloy AZ91D is used to replace the traditional aluminum alloy bracket, and a graphene composite interface layer with three-dimensional Interpenetrating Network (IPN) is introduced on the back of the MicroLED chip. At the same time, the thermal path topology is optimized, and the IPN thermal conductive film is attached to the back of the MicroLED and led out from the cavity to the outside along the Flexible Printed Circuit (FPC) flat cable, which significantly improves the thermal diffusion efficiency; A dynamic temperature control model based on Back Propagation (BP) neural network is constructed. The ambient temperature, power consumption and time step are input to predict the temperature distribution and adjust the driving current in real time. The steady-state thermal analysis based on ANSYS Workbench shows that the collaborative design scheme reduces the peak temperature of the system from the initial 141.85℃ to below 50℃, with a decrease of 64.8%, and at the same time improves the uniformity of temperature distribution, which effectively solves the heat dissipation problem of high-integration MicroLED AR optical machine and provides feasible schemes and design theoretical basis for thermal management.

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.

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.

With the development of Internet of Things technology and the maturity of artificial intelligence algorithms, the deep integration of the two has ushered in a new era of intelligent applications. Internet of Things provides artificial intelligence with massive real-time data, while artificial intelligence endows Internet of Things systems with intelligent perception, autonomous decision-making, and optimized control capabilities. This paper systematically analyzes artificial intelligence-related technologies for Internet of Things applications, focuses on exploring the application principles and implementation methods of core technologies such as machine learning, deep learning, edge intelligence, and Natural Language Processing ( NLP) in Internet of Things, and reviews typical application scenarios such as smart cities, smart manufacturing, smart agriculture, and smart homes. The research indicates that the integration of artificial intelligence and Internet of Things will continue to deepen, and technologies such as edge intelligence, federated learning, and lightweight models will become key development directions, providing crucial technical support for building an intelligent society where everything is intelligently connected.

In order to ensure the safety and stability of the home environment, a smart home safety monitoring system based on Internet of Things technology is designed. First, build a hierarchical system framework. Secondly, the software design is completed, the mixed encryption scheme is adopted to ensure the security of data transmission, and the intrusion detection mechanism is designed to prevent network intrusion. Finally, with the help of real-time monitoring and abnormal alarm strategy, potential safety hazards can be found in time, and convenient control of household equipment can be realized by remote control technology, and a three-level authority management system is designed to ensure control safety. The experimental results show that the system can realize the safe transmission and processing of home environment data, accurately identify network intrusion and environmental anomalies, and improve the security protection ability and management convenience of smart home.

With the increasing intensity and depth of mining, the difficulty of mining and the risk of disasters are increasing. Especially in mines with complex geological environments, once the risk monitoring is not improper, it will often directly induce various mine disasters. Limited by the problems of limited range, insufficient sensitivity and high degree of data discretization in traditional monitoring technology, it is often difficult to accurately and real-time monitor the disaster risk and its causative effects in the whole process of mining. In order to solve the above problems, this manuscript proposes a dual-drive mine disaster risk monitoring method that integrates optical fiber sensing and data retrieval. Firstly, the vertical and horizontal combined optical fiber neural perception network is constructed to perceive the disaster risk of mining engineering in real time. Secondly, based on data retrieval technology, the real-time information of disasters and accidents is visualized and analyzed, which provide data support for emergency rescue and command decision-making. Finally, the above dual driving effects are combined to achieve accurate monitoring and reliable prediction of various mine disaster risks. The research results provide important technical support for improving the intelligent monitoring, safety assessment and emergency decision-making capabilities of mine disaster risks.

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.

Electricity demand response is a strategy to balance supply and demand by adjusting or shifting electricity use periods, aiming to optimize the operational efficiency of the grid and reduce energy costs. With the development of big data technology, its application in the power system provides a new perspective and method. By analyzing a large amount of consumption data to predict power demand and adjust power supply in real time, the real-time and accuracy of response strategies can be improved. The integration of this technology provides a more efficient tool for power demand response management, making the power system more intelligent and adaptive. Based on this, this paper expounds the theoretical basis of power demand response, introduces the collection, processing and analysis methods of power data, optimizes management efficiency by simulating demand response strategies, and demonstrates how to optimize power demand response management by using big data analysis through empirical analysis, so as to improve the response efficiency and reliability of the entire power system.