Search Results (1,467)

This study examines the relationship between manufacturing-specific foreign direct investment (FDI) and manufacturing output in ASEAN-6 countries over the period 2012–2022. While existing empirical studies largely rely on aggregate FDI measures, such evidence may obscure sector-specific mechanisms through which foreign investment affects production capacity and industrial performance. Focusing on manufacturing-oriented FDI allows for a more direct assessment of how sector-targeted investment is associated with industrial resilience and value-added stability, which represent the economic dimension of sustainability considered in this study. Sustained industrial output performance is proxied by manufacturing value added (GDPm) and interpreted as the manufacturing sector’s ability to maintain and expand value added over time amid macroeconomic volatility and external shocks. Using a balanced panel dataset of six ASEAN economies (ASEAN-6) with 66 country-year observations and a fixed-effects specification selected through standard model-selection tests, the results indicate that manufacturing-specific FDI is positively and statistically significantly associated with manufacturing output at the panel-average level. Manufacturing contribution to GDP also exhibits a strong positive association, while exchange rate movements are negatively associated with manufacturing output. Inflation is positively associated with output during the study period and is interpreted as a context-specific co-movement rather than a normative implication for long-run sustainability. To provide additional insight into shock-period dynamics, the analysis compares pre-COVID (2012–2019) and COVID/post-COVID (2020–2022) sub-period estimates. The positive association between manufacturing-oriented FDI and output is more pronounced before the pandemic. It weakens during the pandemic and early recovery years, consistent with supply-chain disruptions and temporarily reduced absorption capacity. The findings highlight the importance of sector-specific FDI, industrial structure, and macroeconomic stability in supporting manufacturing resilience in ASEAN-6 economies. Full article

Megaprojects involve complex, multi-tier supply chains characterised by high interdependence, diverse stakeholders, and significant uncertainty. Despite their strategic and economic importance, many megaprojects continue to experience persistent cost overruns and schedule delays, suggesting that performance challenges stem largely from difficulties in managing supply chain complexity rather than technical issues alone. This study adopts a systematic literature review of peer-reviewed publications from 2015 to 2025, sourced from Scopus and Web of Science. Using PRISMA-guided screening and eligibility procedures, 94 relevant articles were analysed to examine the drivers of supply chain complexity, their performance implications, and resilience strategies applicable to megaproject contexts. The review identifies various supply chain complexity drivers that intensify coordination challenges, reduce supply chain visibility, and increase disruption risks, contributing to inefficiencies, delays, and cost escalation. Proactive and reactive resilience strategies, such as multi-sourcing, collaboration, flexibility, redundancy, and contingency planning, are found to strengthen adaptive capacity and recovery. The study concludes that integrating complexity management with resilience-oriented practices provides a critical pathway for improving megaproject supply chain performance and offers a conceptual foundation for future empirical validation. Full article

Diabetic chronic wounds (particularly diabetic foot ulcers) are difficult to heal due to factors such as high glucose levels, infection, and inflammatory imbalance. In severe cases, they can lead to tissue necrosis and amputation. Hydrogel materials, as moist wound dressings, possess high water content, biocompatibility, and tunability, making them an important platform for promoting diabetic wound healing. In recent years, novel smart hydrogels have been developed to integrate multiple functions. They respond to abnormal stimuli in the wound microenvironment—such as acidic pH, high glucose levels, or excessive reactive oxygen species—to trigger the release of drugs, delivering on-demand antimicrobial, antioxidant, and anti-inflammatory effects. Simultaneously, they modulate immune responses (promoting macrophage polarization toward the M2 type) and stimulate angiogenesis, creating a microenvironment conducive to tissue regeneration. Some hydrogels incorporate antimicrobial agents, anti-biofilm components, or photothermal/photodynamic agents to effectively eliminate drug-resistant pathogens and control infections. Others serve as carriers for delivering stem cells and their exosomes, enhancing cell survival rates and releasing growth factors to accelerate wound healing. This review systematically summarizes recent advances in hydrogel strategies for diabetic wound treatment, focusing on stimulus-responsive hydrogels, antimicrobial and immune modulation mechanisms, pro-angiogenic and oxygen-supplying therapies, smart dressings and monitoring technologies, integration of stem cells and exosomes, as well as hydrogel injection, self-healing, and adhesion properties. Based on this, we analyze challenges and prospects for clinical translation of these strategies. Collectively, functionalized hydrogels hold promise as multifunctional therapeutic platforms for diabetic non-healing wounds. They offer a multi-pronged approach to disrupt the vicious cycle of “infection–inflammation–tissue destruction” thereby achieving more efficient wound healing. Full article

Cruise ships and superyachts have experienced significant global expansion throughout the 21st century. Although the growth in cruise passenger numbers was temporarily disrupted by the COVID-19 pandemic, occupancy rates have since rebounded and even exceeded pre-pandemic levels. This study highlights the significant environmental impact of cruise ships and luxury yachts, particularly in terms of air emissions and marine pollution. Emission levels associated with different fuel types and marine engines are analysed, including the average emissions generated by the Norwegian Cruise Line fleet while docked in ports, as well as the estimated emission reductions achievable through the implementation of onshore power supply systems. To identify environmentally preferable fuel options, a hybrid ANN/MCDM framework is applied. The weighting coefficients of eight evaluation criteria are determined using the Artificial Neural Network/Extreme Learning Machine (ANN/ELM) model, ensuring an objective and data-driven assessment of their relative importance. The ANN/ELM model was trained using emission and fuel-related data collected from the literature and industry reports, and its performance was validated using standard validation procedures, achieving satisfactory predictive accuracy for determining the weighting coefficients. The final ranking of eight fuel alternatives is subsequently performed using the Ranking Alternatives by Weighting of Evaluated Criteria (RAWEC) method. The considered alternatives include conventional and emerging marine fuels currently used in practice or under technological development (A₁–A₈), while the optimization criteria (C₁–C₈) encompass major air pollutants (CO₂, NOx, SOx, CO, PM, CH₄), the fuel cost-to-consumption ratio, and the potential impact on water pollution. The water pollution criterion is assessed qualitatively using the Saaty scale. The integrated ANN/ELM–RAWEC approach enables a systematic comparison of marine fuels and supports the identification of options with the lowest overall environmental impact. Full article

Supply chains are increasingly exposed to compounding disruptions, volatile demand, and sustainability constraints, which challenge optimization approaches designed for stable operating conditions. This review synthesizes recent advances in supply chain optimization with a focus on the integration of artificial intelligence and operations research in decision-making. The paper examines three major capability layers: prescriptive optimization for planning and resource allocation, predictive modeling for demand and risk anticipation, and digitalized execution through simulation and digital twin environments. Across these layers, the analysis shows that hybrid AI-OR architectures tend to outperform isolated methods in settings characterized by high demand volatility, multi-echelon complexity, and disruption exposure, by combining predictive adaptability with constraint-aware decision quality. The review also highlights a strategic shift from single-objective efficiency toward multi-objective performance that jointly manages cost, service, resilience, and environmental impact. From an implementation perspective, the evidence indicates that measurable industrial gains depend less on algorithm novelty alone and more on system-level integration, data governance, and cross-functional deployment. Key research gaps remain in benchmark standardization, explainability, uncertainty-aware optimization, and long-horizon validation under disruption. The paper concludes that the next generation of supply chain optimization will be defined by continuously learning, human-supervised decision ecosystems that remain robust under uncertainty while delivering operational and sustainability outcomes. Full article

Hydropower stations, as critical infrastructure for basic energy supply, play a pivotal role in ensuring the reliability of power systems through their safe and stable operation. Grounding grids operating long-term in complex soil environments are prone to corrosion and degradation, disrupting current distribution balance and causing spatial asymmetry in the voltage field, thereby compromising system safety. Corrosion branch resistance increment identification based on the electrical network method is typically modeled as a parameter inversion optimization problem. However, this problem exhibits underdetermination and other characteristics, making it difficult for traditional analytical methods to obtain stable solutions. To address this, this paper proposes a quantum perturbation scheduling candidate pool-guided sine–cosine algorithm (QSPSCA). Building upon the classical sine–cosine algorithm framework, it incorporates a dynamic candidate pool with multi-source attractor points and a quantum-inspired long-tail scheduling local refinement operator. This achieves an enhanced and smooth transition between global exploration and local refinement. Comparative experiments based on the CEC2017 benchmark and a hydropower station grounding grid corrosion diagnosis case demonstrate that QSPSCA outperforms multiple comparison algorithms in terms of average optimality and result stability. Furthermore, QSPSCA is applied to three typical engineering-constrained optimization problems. Results demonstrate that, whilst satisfying engineering constraints, this method consistently yields higher-quality feasible solutions with superior convergence accuracy and stability compared to alternative algorithms. Therefore, QSPSCA is not only applicable to underdetermined inversion diagnostics but also provides a solution framework with broad applicability for complex engineering optimization problems under structural symmetry perturbations. Full article

The effective closure of the Strait of Hormuz on 28 February 2026 disrupted approximately 20 million barrels (bbl) per day of crude oil transit, constituting the largest supply shock in global oil market history. This study quantifies the resulting economic losses under three blockade-duration scenarios and evaluates the Northern Sea Route (NSR) as a partial mitigation mechanism through a novel framework integrating sustainable supply chain resilience (SSCR), the Triple Bottom Line (TBL), and the United Nations Sustainable Development Goals (SDGs). A 3 × 3 scenario matrix crossing three blockade durations with three NSR utilization levels estimates global and country-level impacts using data from the International Energy Agency (IEA), the International Monetary Fund (IMF), and the Centre for High North Logistics (CHNL). Even under maximum feasible NSR utilization, net environmentally adjustment mitigation offsets only 1.1–3.6% of total global losses, demonstrating that the Northern Sea Route functions as marginal insurance rather than a viable substitute for Hormuz-dependent supply chains. Global Gross Domestic Product (GDP) losses range from USD 330 billion to USD 2.2 trillion, with South Korea (68–70% Middle East crude dependency) and Japan (approximately 95%) disproportionately affected. After TBL environmentally adjustment monetizing CO₂, black-carbon, and icebreaker costs, the NSR mitigates 1.1–3.6% of total losses, functioning as insurance rather than substitution. The SDG assessment reveals a fundamental trade-off: the NSR offsets energy-security losses (SDGs 7, 9) but worsens climate and marine outcomes (SDGs 13, 14). Theoretically, this study proposes “alternative maritime route availability” as a conceptual extension of supply chain resilience (SCRes) capabilities and outlines a sustainability-adjusted resilience score (SARS) framework that, pending further validation, could serve as a replicable assessment tool. These findings underscore that accelerating the energy transition remains the most effective long-term response to chokepoint vulnerability. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

Decentralised manufacturing is expanding as digitally controlled fabrication tools become accessible to SMEs, independent operators, and community workshops outside traditional factory settings, but the resulting heterogeneous, autonomously operated network introduces systemic uncertainty that no central authority governs. This paper proposes a systems-theoretic framework in which Free and Open-Source Software (FOSS) governance acts as the structural interoperability layer of a distributed cyber–physical manufacturing system (CPS), and node-local digital twins—each hosting a machine learning (ML) disturbance estimator—provide local adaptive compensation without centralised data aggregation. A defining property of the architecture is automatic improvement propagation: learned corrections distribute via federated learning to structurally similar nodes without operator intervention, and the open, observable FOSS ecosystem enables advances in one fabrication modality to transfer to others through shared interface standards. The framework is applied analytically to three disturbance classes: regulatory restriction, technical process variability, and supply chain disruption. Across cases, the analysis shows how open modular interfaces and local adaptation preserve functional continuity under perturbations that would more strongly affect centralised architectures. The contribution is a unified mathematical basis for robustness analysis in decentralised manufacturing CPS and a foundation for future simulation and empirical validation. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle–UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient α within 0.2–0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes. Full article

Cardiovascular and cerebrovascular diseases are serious threats to human health and impose a significant burden on individuals and society. As the two critical and complex organs with the highest metabolic demands, the brain and the heart form an interactive relationship through metabolic networks. As a core prerequisite for maintaining the normal physiological functions of the body, metabolic homeostasis is also a crucial foundation for ensuring the brain–heart synergy. When the human metabolism is in a stable state, the energy supply and material exchange of the brain and the heart can accurately match demand, the neural signal transmission is smooth, and the myocardial contraction is strong and regular—thus ensuring the coordinated and unified functions of these two vital organs. However, once metabolic homeostasis is disrupted, problems such as energy metabolism disorders will arise, which will then become a core inducing mechanism for cardiovascular and cerebrovascular comorbidities. This article presents a review of the research progress on the potential mechanisms of brain-heart interactions based on metabolic regulation from three aspects: neurometabolic, endocrino-metabolic and immune–metabolic regulation, the impact of cardiac function on brain metabolism, and the bidirectional regulation of brain-heart metabolism. Full article

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

Evidence from recent large-scale disruptions indicates that efficiency-centered supply chain designs struggle to sustain operation under persistent and systemic uncertainty. This study introduces the Response and Adaptive Immune-Inspired Supply Chain Immune System (RAIE–SCIS), a continuous-time dynamic framework that extends existing viability and resilience approaches by explicitly modeling inter-temporal adaptation and operational memory within a control-theoretic structure. The framework represents supply chains as multi-layer control systems where structural protection, adaptive regulation, and memory mechanisms jointly shape system response over time. Viability is assessed using time-dependent indicators, including performance trajectories, recovery time, and an adaptation-based viability index. The model is applied to a carbon capture, utilization, and storage (CCUS) supply chain under heterogeneous disruption scenarios. Results show that immune-enabled configurations increase minimum performance levels by 15–30% and reduce recovery times by up to 25% compared to non-adaptive configurations. These improvements are not uniform across scenarios and depend on disturbance structure and recurrence. The analysis reveals that adaptive regulation introduces a trade-off between recovery speed and variability, while memory mechanisms shape recovery dynamics under recurrent disruptions—effects not captured by static or purely reactive models. Their effects become more pronounced when disturbances accumulate or propagate. Full article