Facing increased societal demands for corporate sustainability, the challenges faced by companies in engineering procurement risk management (EPRM) expand from economic to environmental, social and governance aspects. Based on a systematic review, this study finds that current research focuses on a single Environmental, Social, and Governance (ESG) dimension or procurement risk management (PRM) business orientation. Companies urgently need to address multiple pressures of ESG compliance, transparent supplier ecology, and real-time risk management. However, fragmented ESG assessment, siloed data systems and reactive strategies have led to operational inefficiencies and missed technology dividends, and there is an urgent need to build a procurement risk solution that integrates ESG and digital technologies (DTs). Therefore, this paper proposes a framework, namely DEEP-RM, for ESG-oriented circulated PRM that leverages DTs such as the Large Language Model, Multi-Agent System, and Blockchain. The DEEP-RM aims to facilitate enterprises in PRM through ESG transformations and DTs to enhance supply chain resilience and achieve sustainability in supply chain management. This paper enriches the theory of EPRM by revealing, for the first time, the evolutionary path of research in PRM through literature analysis. Breaking the traditional linear management model, the research proposes a closed-loop framework that integrates DTs and ESG to achieve global visualisation of risk management and a trusted collaboration ecology. The framework innovatively uses various emerging technologies to overcome the problems in traditional EPRM, and promotes the transformation of risk management from experience-driven to machine intelligence-driven. The new energy company S has successfully implemented the framework and achieved remarkable results.

Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critical nodes is a central theme in contemporary research, serving as a vital bridge between theoretical foundations and practical applications. Nevertheless, the intrinsic complexity and structural heterogeneity characterizing real-world networks, with particular emphasis on dynamic and higher-order networks, present substantial obstacles to the development of universal frameworks for critical node identification. This paper provides a comprehensive review of critical node identification techniques, categorizing them into seven main classes: centrality, critical nodes deletion problem, influence maximization, network control, artificial intelligence, higher-order and dynamic methods. Our review bridges the gaps in existing surveys by systematically classifying methods based on their methodological foundations and practical implications, and by highlighting their strengths, limitations, and applicability across different network types. Our work enhances the understanding of critical node research by identifying key challenges, such as algorithmic universality, real-time evaluation in dynamic networks, analysis of higher-order structures, and computational efficiency in large-scale networks. The structured synthesis consolidates current progress and highlights open questions, particularly in modeling temporal dynamics, advancing efficient algorithms, integrating machine learning approaches, and developing scalable and interpretable metrics for complex systems.

This paper proposes a robust multivariable tracking control (RMTC) strategy to address tracking control of the biological wastewater treatment process (BWWTP) with external disturbances and uncertainties. Primarily, the system of BWWTP, attributed to an ill-defined nonaffine nonlinear system, is directly expanded with Taylor series expansion in the neighborhood of the nominal control law (NCL). To achieve NCL, the RMTC is drawn with a direct controller and an approximate controller. The direct controller is introduced with sliding-mode theory while the structure of the system is unknown and no prior knowledge about external disturbances/uncertainties is available. The approximate controller is employed to compensate for the uncertainties of BWWTW with a suitable-sized fuzzy neural network that is tuned by a self-organizing mechanism. Then, an adaptive strategy is established to optimize the control gain and parameters of fuzzy neural network to hold the steady-state control performance under different operational conditions by suppressing the negative impacts of external disturbances and approximation errors. Finally, the stability of RMTC is established to ensure successful applications. The RMTC is evaluated in an actual BWWTP. The results including tracking performance, controlinputs, and adaptive parameters have shown that RMTC can provide effective control performance with the external disturbances and uncertainties under different operational conditions.

Under different time and space conditions, the rutting evolution of asphalt pavement has a complex nonlinear relationship with axle load, temperature, humidity and material. How to express the rutting evolution of asphalt pavement systematically and explicitly has always been a difficult problem in the industry. In this paper, the I-R-F model is proposed for the first time, which can express the rutting evolution of all semi-rigid asphalt pavement forms explicitly. Compared with other mature explicit model frameworks, the structure is more standard, parameters are simplified and the fitting accuracy is higher. The proposed model has improved the rutting fitting accuracy on semi-rigid asphalt roads by an average of 0.018. It is obtained by improving the R-F model framework in the previous research results of the author, introducing the influence factors of ultraviolet aging to correct the model, and further performing Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet packet threshold denoising on the RIOHTrack data, ultimately achieving a good fitting effect for rutting evolution.

The low-altitude economy is transforming unmanned aerial vehicles into intelligent, networked agents. This commentary synthesizes the contributions of the Special Issue into four thematic areas. Health-management frameworks employ adaptive memory matrices, probabilistic diagnostics, and simulation to enable in-flight predictive maintenance. Perception and navigation systems fuse light detection and ranging data, intensity cues, vision, and inertial inputs to achieve Global Positioning System (GPS)-resilient localization and mapping in feature-sparse or degraded environments. Swarm communication combines decentralized multi-agent reinforcement learning with lightweight blockchain protocols to secure real-time policy exchange while balancing throughput, fairness, and energy efficiency. Resilient air-ground connectivity couples scenario-transfer neural channel models with joint trajectory and power optimization to sustain millimeter-wave links across diverse urban geometries. These advances outline an emerging unmanned aerial vehicle ecosystem, yet interoperable standards, trustworthy Artificial Intelligence (AI), renewable power integration, and large-scale field validation remain critical challenges to achieving truly scalable, autonomous, and sustainable operations.