Shandong Management University

To address the noise challenges and complex heterogeneous environments experienced by new energy charging facilities, a real-time monitoring and fault diagnosis platform based on Power Line Communication (PLC) was developed. The approach includes: implementing PLC coupling and safety protection, protocol mapping; a basic data point management system; implementing a composite diagnostic technique based on edge computing principles, combining Temporal Convolutional Network (TCN) with attention mechanisms and graph theory; and integrating multi-source data and belief correction. A hardware closed-loop test platform, including a programmable power supply, electronic load, line impedance/contact resistance, noise and harmonic injection, and an adjustable attenuation link, was established to achieve experimental reproducibility. Even in harsh environments such as high electromagnetic interference, long-distance cables, and humidity and cold conditions, this solution maintains link stability and rapidly identifies critical faults such as contactor and connector overheating and insulation degradation. Experimental results indicate that the proposed system has stable carrier communication with signal-to-noise ratios greater than 12 dB and bit error rates less than 10^-4, and a fault diagnosis performance of F1-scores larger than 0.90 for major fault types. In addition, edge-side inference latency is kept below 20 ms, which shows the system's real-time capability as well as robustness under the complex working conditions. Explanatory outputs and safety procedures are provided, highlighting the implementation's high adaptability and strong scalability.

The advent of Generative AI (Gen-AI) has precipitated an epistemological crisis in strategic management, challenging the foundational assumptions of enterprise digital transformation. Traditional frameworks, operating under a “Paradigm of Rational Mastery”, view technology as a predictable, manageable tool. This paper argues that the unique technical characteristics of Gen-AI—specifically its “Emergent Abilities” and “Black-Box Nature”—have given rise to a novel strategic condition termed “Foundational Uncertainty”, rendering these established models obsolete. This study critiques the prevalent “Two-Layer Model” of digital transformation, which is predicated on predictable technological advancement and planned “enablement”. In its place, this paper develops and proposes a “Dynamic-Adaptive Model” designed for the Gen-AI era. This new model reconceptualizes the firm-technology relationship as a co-evolutionary process. It posits that firms must shift from planned implementation to continuous “Adaptive Sensemaking”, an action-driven process of experimentation and learning to navigate the technology’s unpredictability. At the enterprise level, strategy must become an “Evolutionary Process” that emerges from this learning. The model’s central mechanism is a cyclical interplay of “Contention & Adaptation”—a generative tension between top-down managerial control (exploitation) and bottom-up, technology-driven learning (exploration) that propels organizational evolution. Consequently, the source of sustained competitive advantage shifts from possessing static, inimitable (VRIN) resources to cultivating a higher-order “Dynamic Adaptive Capability”—the organizational meta-capability for reconfiguring its own learning processes to thrive amidst unknowable change. The paper concludes by outlining the model’s significant theoretical contributions to the literature on digital transformation, Dynamic Capabilities, and the Resource-Based View, providing a new framework for navigating the Gen-AI frontier.

This research assessed the carbon productivity within the textile industry and utilised spatial Durbin and threshold regression models to empirically examine the relationship between environmental regulation and carbon productivity in this sector. The findings demonstrate that, at the national level, environmental regulation has generally enhanced the carbon productivity of China’s textile industry. However, the local impact and spatial spillover effects of environmental regulation exhibit significant differences. At the regional level, environmental regulation has had a more substantial positive effect on the carbon productivity of the textile industry in the eastern and central regions, while it has hindered the improvement of carbon productivity in the western region. Additionally, the local and spatial spillover effects of environmental regulation vary greatly across different regions. The threshold effect analysis indicates that the impact of environmental regulation on the carbon productivity of the textile industry is contingent upon the level of textile technology. In provinces where the threshold value of textile technology has not been met, strengthening environmental regulation may impede the improvement of carbon productivity in the textile industry. Environmental regulation can boost the carbon productivity of the textile industry through two pathways: attracting foreign direct investment and optimising the energy structure.

This manuscript primarily focuses on the design of stealthy attacks based on innovations in cyber-physical systems. For multi-sensor systems, an optimal stealthy false data injection attack strategy is proposed, combining the attacker’s modified historical innovations with the system’s current innovations, thereby enabling the evasion of detection mechanisms across arbitrary measurement windows. Subject to the constraint of maintaining stealthiness, the attack maximizes the system state estimation error covariance, reformulating the optimal attack design as a constrained optimization problem that significantly degrades system performance. Unlike existing research on multi-sensor systems, an attack strategy is introduced that leverages the attacker’s modified historical innovations and the system’s current innovations, requiring less prior knowledge while achieving superior attack effectiveness. Finally, two simulation examples are presented to validate the effectiveness of the proposed method and confirm the theoretical results.