A combat system-of-systems (CSoS) is a network of independent entities that interact to provide overall operational capabilities. Enhancing the resilience of CSoS is garnering increasing attention due to its practical value in optimizing network architectures, improving network security and refining operational planning. Accordingly, we present a unified framework called CSoS space-time resilience enhancement (CSoS-STRE) to enhance the resilience of CSoS. Specifically, we develop a spatial combat network model and a space-time resilience optimization model that captures the complex spatial relationships between entities and reformulates the resilience enhancement problem as a linear optimization model with spatial features. Moreover, we extend the model to include obstacles. Next, a resilience-oriented recovery optimization method based on the improved non-dominated sorting genetic algorithm II (R-INSGA) is proposed to determine the optimal recovery sequence for the damaged entities. This method incorporates spatial features while providing the optimal travel paths for multiple recovery teams. Finally, the feasibility, effectiveness, and superiority of the CSoS-STRE are demonstrated through a case study, providing valuable insights for guiding recovery and developing more resilient CSoS.

In recent years, improvements in energy storage technology, cost reduction, and the increasing imbalance between power grid supply and demand, along with new incentive policies, have highlighted the benefits of battery energy storage systems. These systems offer long life, low cost, and high energy conversion efficiency. While energy storage is gradually transitioning from demonstration projects to commercial operations, its technical and economic performance is still limited, and it lacks economies of scale. Research on the design and operational optimization of energy storage systems is crucial for advancing project demonstrations and commercial applications. Therefore, this paper aims to provide insights into system configuration and operational optimization. It first summarizes the optimal configuration of energy storage technology for the grid side, user side, and renewable energy generation. It then analyzes and reviews the economic optimization and cybersecurity challenges in power system operations. Finally, this paper discusses unresolved issues in energy storage applications and highlights important considerations for future implementation and expansion.

With the rapid expansion of unmanned system capabilities, integrating and sharing computing resources has become essential. In addition to enhancing resource utilization efficiency, this architecture may also introduce conflicts related to resource competition. Therefore, effective resource-sharing configurations are crucial to ensure the Safety of the Intended Functionality (SOTIF). This paper proposes a computing resource configuration analysis and optimization methods for SOTIF. First, four SOTIF requirements are explored using the computing resource-sharing architecture for unmanned systems, encompassing computing time, computing power, energy consumption restrictions, and mutual exclusion and correlation. Secondly, the computing resource configuration model and its SOTIF constraints are formalized based on the graph and set theories. Subsequently, this study divides the design process of computing resource configuration schemes into resource selection and allocation. It introduces a resource selection optimization method based on Forward Checking and a resource allocation optimization method based on NSGA-II. Finally, a typical unmanned driving scenario is considered as an example, and the optimal resource selection and allocation schemes are sequentially determined using the proposed method on the computing platform.

Consistently threatened by climate change, cities need to adapt to emerging hazards and risks. One such risk relates to extreme heat, which is a particular problem in urban areas and is also linked to air pollution. Together, these risks can have a substantial impact on human health. Our analysis of air quality, ambient temperatures, and climate change adaptation plans in 30 UK cities found strong evidence that London and Cambridge exhibit the highest risk of both extreme temperature and air pollution. Furthermore, although a heatwave in London led to lower levels of PM₁₀ and NO₂, it was highly correlated with increased levels of O₃, a low-level pollutant that exacerbates respiratory problems. We also found a lack of data availability (e.g., O₃, PM₁₀) in some local authorities and inconsistencies in their climate change adaptation strategies. We therefore identify a clear need for standardised assessment of hazards at the city level, and their incorporation into local adaptation plans. Further assessment of climate hazards and risks at the city level are required for effectively adapting to a changing climate in the UK and other cities worldwide.

The acceleration of digitalization and networking in the global landscape has been prompting organizations to connect into innovation communities beyond geographic boundaries within innovation ecosystems. These communities, consisting of firms that collaborate frequently, serve as a vital sub-environment for co-innovation and value creation. Despite the significant role played by these innovation communities, the impact of a firm’s embeddedness within these communities on its innovation performance remains underexplored. This paper addresses this gap by examining the effects of both within-community and cross-community embeddedness on firm innovation, with a specific focus on the contingency of collaboration complementarity. We introduce a conceptual model analyzing the effects of both relational and structural embeddedness within and across communities. An empirical study is conducted using 22 years of panel data from the global 3D printing industry. We construct patent collaboration networks among 6,109 relevant organizations over 5-year windows and identify innovation communities in each network through topological clustering algorithms. A negative binomial regression model is employed to test our hypotheses. Our findings reveal that firms benefit from both within-community and cross-community embeddedness. Notably, firms with higher collaboration complementarity experience greater benefits from within-community relational embeddedness and cross-community structural embeddedness, while those with lower complementarity gain more from cross-community relational embeddedness. This research enriches the innovation ecosystem literature by introducing an innovation community perspective and highlighting how embeddedness, coupled with collaboration orientation, drives firm-level innovation. Additionally, it offers insights into how firms can leverage collaborations and optimize their positions within innovation ecosystems to enhance their innovation performance.

The increasing complexity of global supply chains has presented critical challenges for businesses in coordinating resources, forecasting demand, and dynamically optimizing processes. Traditional supply chain management (SCM) methods are often inflexible, reactive, and prone to inefficiencies, which can result in missed opportunities and lost revenue. Technological advancements have played a pivotal role in addressing these challenges, with Generative Artificial Intelligence (GAI) emerging as a transformative force that offers numerous advantages for SCM. Despite the abundance of literature on the role of GAI in enhancing supply chain performance, it remains insufficient in providing a comprehensive theoretical framework for the construction of GAI applications and their empowerment mechanisms within SCM. This study first outlines the core GAI capabilities necessary for constructing the SCM framework. We then examine the empowerment mechanisms and challenges of GAI in SCM and propose corresponding solutions. Afterward, we discuss notable gaps and propose a comprehensive research agenda, focusing on the SCM framework empowered by GAI.

The extensive integration of AI with renewable energy systems is a major trend in technological advancement, but its energy consumption and carbon emissions are also a major challenge. Generative AI can quickly generate human-like content responding to cues, with excellent reasoning and generative capabilities. Generative AI-based renewable energy systems can cope with dynamic system changes and have great potential for resilience optimization and green low-carbon transition. In this paper, we first explore the role that generative AI can play in renewable energy systems and explain shock incidents. Secondly, intelligent maintenance strategies of renewable energy systems under different failure modes are developed based on generative AI. Then spatiotemporal resilience is introduced and a spatiotemporal resilience optimization model is proposed. A green and low-carbon transformation strategy for smart renewable energy systems has also been proposed. Finally, a case study is used to illustrate the utilization of the proposed method by using a wind power system as an example of a renewable energy system.

This study investigates a truck scheduling problem in open-pit mines, which focuses on optimizing truck transportation and commercial coal production. Autonomous dump trucks are essential transportation tools in the mines; they transport the raw coals and rocks excavated by electric shovels to the unloading stations. Raw coals with different calorific values are processed to produce commercial coals for sale. This process requires maintaining a calorific balance between the excavated raw coals and the blended commercial coals. We formulate a mixed-integer linear programming model for the truck scheduling problem in open-pit mines. The objective of this decision model is to minimize the total working time of all trucks. To solve the proposed model efficiently in large-scale instances, a branch-and-price based exact algorithm is devised. Based on real data of an open-pit mine in Holingol, Inner Mongolia, China, numerical experiments are performed to validate the efficiency of the proposed algorithm. The experiment results show that the optimality gap of the proposed algorithm by comparing with CPLEX is zero; and the solution time of CPLEX is 2.46 times that of the proposed algorithm. Moreover, sensitivity analyses are conducted to derive some managerial insights. For example, open-pit mine managers should carefully consider the truck fleet deployment, including the number of trucks and the capacity of trucks. Additionally, the spatial distribution of unloading stations and electric shovels is crucial for enhancing transportation efficiency in open-pit mines.

The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.

Participants in oil markets are increasingly aware of the climate risks posed by frequent extreme weather. This paper examines the role of extremely high-temperature weather information in predicting oil futures prices on the China International Energy Exchange (INE). An extreme high-temperature weather index (HTI) is developed on the basis of meteorological data at INE’s crude oil production and storage sites. The local interpretable model-agnostic explanations (LIME) and accumulated local effects (ALE) methods are used to compare the predictive contribution of the HTI with that of 15 common predictors. The results indicate that the HTI enhances the out-of-sample accuracy of five classical prediction models for INE oil prices. The recurrent neural network (RNN) model exhibits superior out-of-sample forecast performance, with an MAE of 14.379, an RMSE of 19.624, and a DS of 66.67%. The predictive importance of the HTI in the best RNN model ranks third in most test instances, surpassing conventional oil price predictors such as stock market indicators. The ALE analysis reveals a positive correlation between extremely high-temperature weather and INE oil prices. These findings can help investors and oil market regulators improve oil price forecast accuracy while also providing new evidence about the relationship between climate risk and oil prices.