Against the background of green transformation, enhancing regional economic resilience has become a critical requirement for achieving sustainable development and maintaining long-term competitiveness. Taking the textile industry as a representative case, this study systematically examines the formation mechanism of regional economic resilience from the perspectives of industrial transformation, innovation capability, resource allocation, and regional collaboration. The analysis identifies major constraints, including path dependence in traditional industries, insufficient green technology innovation, inefficient resource allocation, and regional development disparities. Based on these challenges, a resilience-enhancement framework is proposed that integrates industrial structure optimization, innovation-driven development, factor resource integration, and institutional coordination. The study highlights the role of interconnected innovation networks, information-sharing mechanisms, and cross-regional collaboration in strengthening adaptive capacity and improving risk response performance during green transformation. The findings indicate that resilient regional development depends on the coordinated evolution of industrial systems, innovation ecosystems, and resource circulation mechanisms. This work provides a theoretical and engineering-oriented perspective for understanding resilience formation in complex socioeconomic systems and offers strategic guidance for sustainable regional development under long-term environmental and structural transition pressures.
J. Han, K. Duan· Advanced Electromagnetics· 0 citations
Accurate forecasting of electricity spot market supply and demand and timely anomaly detection are essential for intelligent energy management and communication-assisted monitoring systems operating over distributed electromagnetic information networks. To improve prediction accuracy under high-frequency non-stationary fluctuations a nd a ccelerate h yperparameter c onvergence i n multi-source feature spaces, this study proposes a power market forecasting and indicator anomaly detection framework integrating Autoformer with particle swarm optimization and Bayesian optimization (PSO-BO). The Autoformer model exploits trend–seasonal decomposition to capture long- and short-term temporal dependencies, while PSO performs global exploration and Bayesian optimization refines local hyperparameters for efficient model tuning. A Bayesian probabilistic anomaly detection model based on a normal-inverse-gamma prior is further established to quantify residual uncertainty and achieve adaptive online warning. Experimental results on a 24-hour rolling forecasting task demonstrate MAE/RMSE values of 1.23/1.75 GW for load prediction and 1.85/2.53 GW for generation prediction, with anomaly detection accuracy reaching 93.87%. The proposed framework exhibits strong sensitivity to rapid market fluctuations and provides an effective solution for realtime intelligent scheduling and secure monitoring. Moreover, its distributed forecasting and adaptive decision mechanisms offer valuable support for communication-enabled energy systems and electromagnetic sensing infrastructures requiring reliable information transmission and operational awareness.
Y. Ma, J. Cui, M. Zhang et al.· Advanced Electromagnetics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
