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.

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.

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.

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.

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.

At present, China has carried out a series of policy and institutional arrangements for the custody of material evidence records. However, in practice, due to manual operation error prone, lack of information technology and low efficiency, there is a problem of incomplete connection between institutional arrangements and specific practice, which leads to the damage and loss of key material evidence in criminal cases, and has a great impact on the final litigation and trial of the case. In this regard, based on specific project research, Radio Frequency Identification (RFID) technology is successfully applied to the intelligent traceability management system of public security material evidence. Through technology enabling, the traceability management of material evidence is realized, and the standardization and transparency of material evidence management are improved.

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.

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.

Propose a method for sudden leakage warning of hazardous chemical storage tanks based on Internet of Things technology. Based on Internet of Things technology to monitor hazardous chemical storage tanks, design anomaly detection and correction algorithms based on pattern matching, preprocess monitoring data, and apply extension theory to provide safety warnings for sudden tank leakage accidents. The experimental results show that the design method can effectively achieve monitoring and early warning of sudden leaks in hazardous chemical storage tanks.

The edge computing architecture provides a low-latency and high-efficiency implementation path for multimodal artificial intelligence perception. By deploying lightweight neural networks at edge nodes and applying compression techniques such as model pruning, quantization, and knowledge distillation, the model size is reduced to 15 MB, and the inference latency is controlled within 95 ms. Combined with the attention fusion mechanism and distributed collaborative inference strategy, the multimodal perception accuracy is increased to 91.3%, effectively reducing network bandwidth occupation and system response latency in scenarios such as intelligent manufacturing and autonomous driving, demonstrating significant practical value.

Aiming at the problems of trust deficiency and permission management in cross-domain Internet of Things data sharing, a trusted data sharing method is proposed. A hierarchical trust measurement algorithm, an adaptive permission control framework, and a lightweight traceability mechanism are designed. Simulation tests show that the method achieves a response delay of 85 ms, a throughput of 265 transactions per second, and a security detection rate of 94.3%, effectively balancing efficiency and security, providing technical support for cross-domain data sharing.

In order to improve the accuracy and efficiency of fault detection of Internet of Things devices, this paper designs a fault detection system of Internet of Things devices based on intelligent optimization algorithm. The data acquisition layer is responsible for the efficient collection of heterogeneous data from multiple sources, the edge computing layer uses Particle Swarm Optimization (PSO) algorithm for feature selection and fault detection, and the cloud computing layer performs global fault decision fusion. Cooperate with the management to schedule tasks, and realize dynamic collaboration and resource optimization between edge and cloud. The experiment verifies that the system is superior to the traditional method in different working conditions, and provides a feasible and effective solution for the fault detection of Internet of Things equipment.

To address the issues of high computational complexity, low efficiency, and limited ability to capture temporal dependencies and global information in existing deep learning-based action recognition models, a new model adopting a pure Transformer architecture has been designed. This model is designed to better capture spatiotemporal information by inverting the temporal-spatial sequence and introduces a quiet attention mechanism to reduce computational complexity, avoid gradient vanishing, and enhance efficiency. Experiments have shown that, compared with classic Transformer models, the new model exhibits significant improvements in recognition accuracy and efficiency, particularly demonstrating stronger feature extraction and temporal modeling capabilities when dealing with complex actions and long video sequences.

The application of intelligent routing algorithms in data center networks. Firstly, the basic theory and methods of intelligent routing algorithms are elaborated, and the network architecture and routing requirements of data centers are analyzed. Subsequently, the practical application of intelligent routing algorithms in data centers was focused on, including how to implement intelligent routing algorithms, optimize traffic management in data centers, enhance network fault recovery capabilities, achieve efficient network fault tolerance, and how to perform real-time routing adjustments and dynamic optimization. Through experimental analysis, this article verifies the effectiveness and advantages of intelligent routing algorithms in practical applications, providing theoretical support and practical guidance for the intelligent upgrading of data center networks in the future.

This paper designs and implements a wireless radio frequency communication system based on the Internet of Things. The system is constructed with an STM32F407ZGT6 microcontroller, CC1101 wireless communication module, and BME280 environmental sensor to build a prototype. By changing the node spacing and the number of obstacles, the performance of the system under different scenarios is evaluated. The experimental results show that the system has good anti-interference capabilities and stability, with a high data transmission success rate, low transmission latency, and reasonable system power consumption. These characteristics make the system an efficient and reliable wireless communication solution for Internet of Things applications, which has important theoretical significance and practical value.

In this study, a real-time monitoring system is constructed, which combines multi-source sensor network and digital twin model to dynamically evaluate and intelligently warn the seismic performance of building structures. The system collects structural response data through multi-source sensor networks, establishes a virtual model with digital twin technology, combines multi-modal data fusion and feature extraction methods, and then uses deep learning algorithm to identify structural parameter changes and potential damage. From the perspective of practical engineering application, this technology has greatly improved the ability of early warning of seismic safety of building structures, and provided strong technical support for safety management of building structures and urban earthquake resistance and disaster reduction.

The application of reinforcement learning algorithms in locomotion control for quadrupedal sprawling robots is relatively scarce, and the impact of robot joint configurations on the training of control policies remains unknown. This paper proposes a motion control method for quadruped sprawling robots based on reinforcement learning algorithms and research the influence of different initial joint angles on the convergence performance of locomotion control policies for quadrupedal sprawling robots. Experimental results indicate that when the robot’s joint initial angles approximate their limit values, the convergence effect of the control policies is poorer. By implementing the control policy, which has been trained with appropriate joint initial angles, onto a robot prototype, adaptive locomotion of the quadrupedal sprawling robot on grass, gravel, and hard ground is successfully achieved.

Addressing the issues of complex energy coupling, multi-objective conflicts, and dynamic load fluctuations in the operation of combined cooling and heating supply networks, a distributed collaborative optimization method based on Internet of Things is proposed. This method collects and processes real-time data from the network through sensors, constructs a multi-objective optimization model using an improved consensus algorithm, and dynamically adapts the load by incorporating a rolling horizon strategy. Experiments show that compared to traditional centralized optimization, the designed method significantly reduces daily average energy consumption costs, narrows temperature fluctuations at the user side, shortens single calculation time, compresses communication data volume, and effectively improves system economy, robustness, and computational timeliness.

This paper introduces the definition of remote operation and maintenance technology of computer terminal, lists the current mainstream remote operation and maintenance technology, analyzes the principle and key technology of remote operation and maintenance technology of computer terminal, summarizes the problems that need to be solved by remote operation and maintenance of computer terminal and gives corresponding solutions.

In the field of urban rail transit signal and monitoring system, the real-time data processing mechanism is the key factor affecting the data processing performance of the real-time system. This paper discusses the insufficiencies of periodic scanning in the process of real-time data processing, and proposes a notification concurrent processing method. This paper focuses on the design scheme based on notification concurrent processing mode and the problem analysis of concurrent process, and discusses the corresponding solution. The notification based concurrent processing method mentioned in this paper has been used in the real-time data processing system dealing with large data capacity to ensure the real-time performance of the system.