The rise of generative AI (GenAI) has prompted significant attention and discourse across academia and industry, as stakeholders grapple with its capabilities, potential applications, and associated risks. Driven by the aim to address the key question of whether and how GenAI can reshape the manufacturing industry, this paper explores the role, applications and prospects of GenAI for manufacturing. A traditional paradigm of AI implementation in manufacturing is initially outlined, followed by a review of GenAI applications in manufacturing through a proposed five-level framework characterizing the depth of GenAI integration. Building on this review and an analysis of the development trajectory of foundation models, it is argued that GenAI not only enhances each stage of the traditional paradigm but also has the potential to establish a new paradigm in smart manufacturing. In the envisioned paradigm, GenAI functions as a self-contained service provider, capable of directly addressing complex manufacturing needs with innovative solutions, while maintaining a balance between task efficiency, human well-being, environmental sustainability, and societal impacts. Aligned with the core principles of Industry 4.0 and Industry 5.0, this paradigm represents a highly desirable evolution for the manufacturing sector. Following this, a GenAI-driven product design-to-manufacturing framework is introduced to ground the paradigm in practical applications. This research provides a robust framework for understanding GenAI’s transformative trajectory in manufacturing and sets forth a research agenda for future exploration. Rather than offering definitive conclusions, this work aims to stimulate on-going discussions and encourage further exploration in this evolving field.
Recent advances in artificial intelligence (AI) have significantly enhanced quality management, enabling more effective handling of complex, high-dimensional, and multi-modal data. AI methods, including machine learning (ML) and deep learning (DL), have been pivotal in advancing key areas such as quality optimization, monitoring, and diagnosis. These methods have increased adaptability, efficiency, and scalability, making them particularly suitable for modern industrial applications. This review provides a comprehensive examination of AI methods in quality management, covering the integration of surrogate models, Bayesian optimization (BO), intelligent control charts, change-point detection (CPD), and interpretable quality diagnosis. The review concludes with proposed directions for future research aimed at overcoming existing challenges and enhancing the deployment of AI in real-world quality management implementation.
Efficient maintenance of battery energy storage systems (BESS) is critical for reliable operation under large-scale renewable integration. In modular architectures, heterogeneous degradation creates strong interactions between component health and system topology, meaning system capacity and risk depend not only on individual states but also on dynamic module configuration. Conventional maintenance policies often assume a fixed mapping from component states to system performance and thus fail to address topology-induced bottlenecks and imbalance-driven risks, which frequently leads to premature replacements or hidden safety hazards. This study proposes a novel predictive maintenance (PdM) framework that synergistically coordinates module replacement and structural reconfiguration. Unlike existing approaches, our method treats maintenance as a coupled decision process where replacement restores component health while reconfiguration reshapes the topology to mitigate degradation constraints. The problem is formulated as a nested mixed-integer nonlinear programming (MINLP) model. To solve this computationally challenging problem, we develop a tailored Bayesian Hierarchical Optimization with Adaptive Large Neighborhood Search (BHO-ALNS) algorithm that efficiently explores the joint decision space of replacement thresholds and reconfiguration actions. Numerical experiments based on data-driven degradation simulations demonstrate that the proposed strategy significantly outperforms various common conventional schemes. Specifically, compared to the fixed-threshold replacement-only strategy frequently employed in real-world maintenance practice, our approach increases net economic benefit by approximately 23%, while simultaneously enhancing capacity utilization and mitigating safety risks. These findings highlight the necessity of jointly managing component degradation and system configuration, offering a paradigm shift from passive component renewal to active structural adaptation in BESS.
Prefabricated buildings are crucial for the transformation of the construction industry, while the Prefabricated Building Supply Chain Network (PBSCN) that supports their implementation is subject to uncertainties in production, transportation, and installation. These uncertainties lead to schedule delays and cost increases, which significantly hinder the widespread adoption of prefabricated buildings. To address these issues, this paper develops a three-tier optimization model that integrates component factories, logistics providers, and contractors to improve resource allocation and reduce total costs. This model explicitly accounts for uncertainty-induced delay propagation across stages and incorporates its impacts into the decision-making process through work stoppage cost at the construction site. A Scenario-Based Stochastic Programming (SBSP) approach is employed to determine optimal decisions, while Monte Carlo Simulation (MCS) is utilized to generate representative scenarios. Furthermore, the proposed model is extended to incorporate a carbon trading mechanism to examine the interaction between environmental regulation and supply chain decisions. The model’s effectiveness is validated through a hypothetical case adapted from a real-world project, in which the optimal solutions involved concentrating approximately 6% of orders in the baseline case and 33.0%–35.5% in the large-scale experiment. Results show that proactively accounting for uncertainties not only reduced costs but also strengthened coordination among entities to improve resource utilization. This paper provides practical decision support for PBSCN stakeholders, helping them mitigate risks, optimize order allocation, and improve overall supply chain performance in an uncertain environment.
The Difference-in-Differences (DID) method relying on observational data has become a well-established tool for causal inference in the evaluation of energy and environmental policies. Despite its popularity and rapid methodological advances, there is still a lack of practical guidelines for the full DID research design, which may in turn lead to limited credibility and misleading policy implications. This study presents a comprehensive, up-to-date, critical, and practical guide to the DID research design, which is intended to help early-career researchers improve the credibility, transparency, and replicability of policy evaluation studies. This guide offers a step-by-step framework covering real-world questions, clean identification strategies, appropriate controls, proper standard errors, transparent data, robust checks, and insightful analyses of heterogeneity and mechanism. By focusing on research logic and design principles rather than complex methodological details, this study helps researchers and policymakers obtain more credible evidence for policy learning and optimization.
In response to the pressing challenge of global climate change, advancing a low-carbon energy transition has emerged as a key international priority. As an integral policy instrument to guide this transition, carbon pricing is increasingly adopted by countries and regions worldwide. Drawing on a spatial panel model and covering 115 countries, this study investigates the effects of carbon pricing on carbon emission reduction and compares the outcomes between single and composite carbon pricing instruments. The spatial spillover effects of carbon pricing policies exhibit multidimensional heterogeneity. Hybrid carbon pricing policies form a cross-regional emission reduction network through regional synergistic governance mechanisms. In contrast, carbon tax and emissions trading systems (ETS) are associated with the ‘pollution paradise hypothesis’ and the ‘race to the bottom effect’, respectively. Further, it elucidates how different carbon pricing policies leverage unique economic and energy-related mechanisms to facilitate emission abatement. The findings offer important insights for policymakers aiming to optimize carbon pricing schemes that effectively support the global energy transition.
Solar power towers (SPTs) are a leading concentrated solar power (CSP) technology, utilizing heliostat fields to direct solar radiation onto a central receiver. Accurate simulation tools are vital for optimizing these systems. This review offers a structured and comparative analysis of simulation platforms used in optical, thermal, and system-level modeling. Tools such as SolTrace, Tonatiuh, and DELSOL are reviewed for their ray-tracing accuracy and computational trade-offs. Thermal and CFD tools, including OpenFOAM, ANSYS Fluent, and COMSOL, are evaluated for heat transfer modeling. System-level tools, such as SAM, TRNSYS, and Dymola, are assessed for techno-economic analysis and control strategy development. Rather than relying on standardized test cases, the comparison adopts consistent evaluation metrics such as modeling scope, integration ability, computational efficiency, and validation status, derived from field-tested case studies and empirical benchmarks. The review highlights that while several tools demonstrate high accuracy and real-world validation, persistent gaps remain in multi-domain interoperability and real-time modeling. Key contributions include a hybrid simulation framework integrating ray tracing and CFD for receiver modeling, a performance-based classification of tools grounded in validated case studies, and a decision-making roadmap for tool selection based on design context. Future directions include AI-based heliostat aiming using IUPSO, surrogate models for heat transfer prediction, and object-oriented digital twin architectures built on SolarTherm with real-time sensor integration to improve predictive accuracy, scalability, and deployment readiness.
Addressing climate change has become a global priority, and satellites play an important role. This study provides a bibliometric analysis and review of satellite-based climate change research from the perspectives of mitigation and adaptation strategies from 1994 to 2025. The analysis reveals a shift from early emphases on climate change, atmospheric CO2, and remote sensing toward emerging topics involving vegetation, land use, air quality, variability, and land-cover dynamics. Existing satellite-based studies on climate change mitigation focus on identifying emission hotspots and quantifying CO2 and CH4 emissions from ecosystems and socioeconomic systems, while N2O and emissions from industrial processes and waste remain underexplored. Satellite-based climate change adaptation has been used to assess water resources, agricultural systems, forest cover, and sea level rise, yet challenges such as uneven water distribution, agricultural instability, forest degradation, and sea-level rise, as well as their impacts on ecosystems and biodiversity, remain insufficiently addressed. The study provides valuable future research directions for satellite-based climate change research.
A popular strategy for network vulnerability assessment is to identify critical nodes, whose deletion maximally degrades the connectivity of the original network. Decision makers usually have information about the network structure they intend to obtain, but do not know the number of nodes to target. In this context, we study a distance-based critical node problem with unknown budget, known as the -distance-based vertex disruptor problem. To solve it, we first derive three integer linear programming formulations, i.e., recursive, triangular connectivity-based, and reduced path-based ones. They are then solved using the CPLEX solver. To approximately solve large instances that an exact solver fails to solve, we propose a simple and effective integrated strategic oscillation search that combines hill climbing and strategic oscillation. It examines a large neighborhood by alternating between a tabu-enhanced destruction procedure and a random order-based improving construction procedure. Extensive experiments on both real-world and synthetic benchmark instances demonstrate the advantages of the proposed formulations and heuristic. In particular, the reduced path-based formulation outperforms both recursive and triangular connectivity-based ones. The proposed heuristic is significantly faster than exact and state-of-the-art algorithms. Finally, we perform a case study on air transportation networks to gain insights into the impacts of COVID-19.
Despite growing interest in hydrogen as a clean energy source, limited research has explored the long-term operational challenges facing Australia’s household hydrogen supply chain (HHSC), particularly under transportation disruptions. This study investigates transportation disruptions in vehicles and routes within the Australian HHSC planned over the period 2026 to 2090. It focuses on disruptions across three distribution tiers: national distribution centers (NDCs), regional distribution centers (RDCs), and local distribution centers (LDCs). A multi-period network optimisation model is developed using scenario-based analysis to simulate and evaluate the impacts of various disruptive events over time. Mitigation strategies, including rerouting, additional vehicle hiring, and safety stock positioning at RDCs, are assessed for their effectiveness. The results reveal that combined disruptions, affecting both vehicles and routes, have the most severe impact on the HHSC, particularly when multiple routes and vehicles across NDCs, RDCs, and LDCs are simultaneously affected. While individual disruptions, such as those impacting only routes or only vehicles, also influence performance, their effects are comparatively less critical than the impact of combined disruptions. Mitigation strategies targeting routes, vehicles, and combined disruptions lead to higher demand fulfilment and lower penalty costs, resulting in a significant increase in overall profit. These outcomes are achieved despite the added costs associated with rerouting, additional vehicle hiring, and maintaining safety stock. The findings highlight the importance of targeted, disruption-specific planning to improve demand fulfilment and reduce penalty costs and provide practical implications for managing transportation disruptions in the HHSC.
The efficient transfer of tacit knowledge is crucial for enhancing teamwork resilience and promoting collaborative innovation in construction projects. This study used functional near-infrared spectroscopy (fNIRS) hyperscanning technology to measure interpersonal brain synchronization (IBS) during the transfer of different classifications of tacit knowledge. This study explored the relationships among types of tacit knowledge, IBS during the transfer process, and the performance of tacit knowledge transfer. Finally, the role of knowledge behavioral characteristics in the process of tacit knowledge transfer was revealed. The results show that i) there is a significant IBS between the sender and receiver during the transfer task, with the IBS level of the cognitive tacit knowledge group being significantly lower than that of the technical tacit knowledge group. ii) There is a significant causal relationship between the IBS level of the transferring subjects and transfer performance, and the type of tacit knowledge influences transfer performance through IBS. iii) The tacit knowledge learning willingness of the receiver and the tacit knowledge sharing willingness of the sender moderate the relationship between the classification of tacit knowledge and the IBS level, and the absorptive capacity of the receiver moderates the relationship between the IBS level and tacit knowledge transfer performance. This study identifies the transfer mechanism of engineering tacit knowledge and provides a reliable predictor for the performance of tacit knowledge transfer with strong hysteresis.
Rapid diffusion of electric vehicles (EVs) is central to global decarbonization strategies. Yet, large-scale uncoordinated charging threatens the stability of the distribution grid, increases congestion costs, and can erode the environmental benefits of electrified mobility. Smart charging has therefore emerged as a critical paradigm for aligning mobility demand with power system constraints through adaptive, information-driven coordination. This commentary develops a comprehensive operations-management–oriented perspective on EV smart charging as a complex socio-techno-economic system shaped by interacting physical infrastructures, digital platforms, market mechanisms, and heterogeneous human behavior. We synthesize existing research into an integrated framework that distinguishes between a physical layer (vehicles, charging infrastructure, grids, and mobility needs) and a digital layer (data exchange, coordination, and service provision), and organize the landscape into five interdependent ecosystems: electricity, charging, mobility services, users, and regulation. Building on this framework, we articulate five key research opportunities: (i) coordination of heterogeneous stakeholders with conflicting objectives; (ii) scalable and flexible control strategies; (iii) strategic infrastructure and investment planning under uncertainty; (iv) effective governance and policy design, and (v) modeling EV user preferences and behavior. We highlight how addressing these opportunities can enhance system efficiency, reliability, sustainability, and user acceptance. By positioning smart charging at the intersection of energy and mobility systems and emphasizing the central role of digital-physical integration, this commentary provides a unifying lens and a forward-looking research agenda for scholars seeking to contribute to the design of smart, sustainable urban mobility and power systems.
Phenomenological methodologies are fruitful to illuminate the meaning of human experiences but are rarely applied in construction research. These methodologies could reveal insights into the tacit and contextual dimensions of construction practice that are often overlooked by more traditional, quantitative approaches. Learning from practitioners’ experiences is particularly appropriate for construction, where much expertise is embedded in everyday practice rather than explicit documentation. It is therefore argued that construction research can benefit from methodologies that attempt to more fully appreciate practitioners’ experiences. As the lived experiences of construction professionals remain understudied, there is surprisingly little guidance for selecting any appropriate phenomenological methodology to tackle questions of concern. The objective of this paper is to offer such methodological guidance. Three phenomenological methodologies are discussed, and guidelines are provided for helping to select and apply one of them.
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), widely recognized as the “King of Spectroscopy,” stands as a milestone in modern astronomical infrastructure. By overcoming the long-standing difficulty of integrating large aperture with wide field of view through its innovative active reflecting Schmidt configuration and 4,000-fiber system, LAMOST made large-scale spectroscopic surveys operationally feasible. Over 14 years of continuous observation, it has progressed from an engineering breakthrough to a globally influential scientific data platform. Supported by science-oriented governance, systematic operational management, automated data pipelines, and an open-access policy, LAMOST has released more than 28 million spectra, enabling major advances in Galactic structure and evolution, stellar astrophysics, and black hole studies. As it moves into the era of artificial intelligence and data-driven discovery, LAMOST demonstrates how technological innovation combined with effective management can sustain long-term scientific impact and leadership in major research facilities.