Search Results (1,810)

Lithium iron phosphate (LiFePO₄) (LFP) has emerged as the most popular cathode material in the current lithium battery market because of its stable charge–discharge cycle performance, low cost, and high safety. Moreover, this material does not require scarce resources such as nickel and cobalt, which alleviates supply chain conflicts and reduces the environmental and health impacts associated with Ni and Co. In this study, a cost-effective preparation method is implemented to synthesize a series of all-element integrated LiFePO₄ precursors using precursor solutions with varying concentrations of oxalic acid. The final LFP materials are subsequently obtained through a one-step heat treatment. To evaluate the advantages of this method, we compare the structural and electrochemical properties of the obtained LFP materials with those synthesized via the traditional solid-phase method. The experimental results reveal that the LFP material synthesized using an oxalic acid solution with a concentration of 0.125 mol L⁻¹ exhibits optimal performance. This material has a grain size in the range of 300–500 nm, which is smaller and more uniform than those of the other samples. This initial specific discharge capacity of the designed LFP is 150.3 mAh·g⁻¹, with an initial coulombic efficiency of 88%. Notably, the material maintains a high capacity of 98 mAh·g⁻¹ even at −20 °C and achieves a discharge capacity of 98.7 mAh·g⁻¹ at a high discharge rate of 5 C. The lithium-ion diffusion coefficient was determined to be 7.1 × 10⁻¹² cm² s⁻¹, which is approximately 2.5 times greater than that of the material synthesized via the solid-phase ball-milling method. These results highlight the significant improvements in both the structural and electrochemical properties of LFP materials synthesized through this novel liquid-phase method. 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

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Background/Objectives: The COVID-19 pandemic imposed logistical challenges on health systems, particularly for mass vaccination campaigns under emergency conditions. In decentralized health systems, the absence of a structured preparedness phase may compromise coordination, allocation, and operational performance. This study analyzes the vaccination campaign in a municipality in southern Brazil, examining how the overlap of the preparedness and response phases affected outcomes and how alternative logistical scenarios could have altered campaign performance. Methods: An empirical analysis was conducted using scenario-based simulation with stock and flow structures. The model represents vaccine procurement, distribution across national, state, regional, and municipal levels, and municipal vaccination capacity. Real data from the 2021 vaccination campaign in the municipality were used to build a Business-as-Usual scenario, compared with alternative scenarios involving changes in procurement predictability, allocation rules, and operational capacity. Results: Vaccination outcomes were strongly conditioned by upstream allocation decisions, particularly at the national state level. Isolated adjustments at intermediate supply chain levels produced limited improvements when upstream constraints persisted. Scenarios combining improved alignment between forecasted and acquired doses with operational capacity showed higher vaccination potential, revealing a gap between observed performance and system capacity. Conclusions: The findings reinforce that preparedness is a critical determinant of vaccination performance and must precede response in emergency contexts. Supply predictability alone is insufficient without coordinated allocation mechanisms and operational readiness across governance levels. This study provides empirical evidence on how preparation-related decisions shape vaccination outcomes in decentralized health systems and inform logistical coordination in future emergencies. Full article

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. 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

This study investigates the influence of blockchain-enabled supply chain traceability (BESCT) on sustainable supply chain practices (SSCP) in the context of small and medium-sized enterprises (SMEs) in the Turkish manufacturing sector. Grounded in the Resource-Based View (RBV), the research further examines the mediating roles of perceived information transparency (PIT) and supply chain collaboration (SCC) and the moderating effect of environmental orientation (EO). The study employs a quantitative research design using data collected from 652 managers representing various manufacturing SMEs. Structural equation modeling via SmartPLS 4.0 is applied to test a moderated mediation model and assess the relationships among the constructs. The results indicate that BESCT is positively associated with SSCP both directly and through PIT and SCC as mediating mechanisms. PIT is linked to improved visibility and information integrity, while SCC is associated with joint sustainability efforts across supply chain partners. Moreover, EO strengthens the positive associations between BESCT and PIT with SSCP, while its effect on collaboration is more nuanced. Given the cross-sectional design, these findings should be interpreted as associative rather than causal. In addition, the use of a non-probability convenience sampling approach may limit generalizability, and the results should be interpreted with caution. This study contributes to the RBV literature by conceptualizing blockchain as a traceability-enabled dynamic capability that supports sustainability-oriented practices in SMEs. Full article

This study introduces the LESG index, a composite analytical framework designed to assess the structural coherence of national development systems by integrating logistics capability, governance quality, and sustainability performance. Traditional development metrics evaluate these dimensions separately, limiting their ability to capture systemic interactions. Using cross-country data for 123 countries, the LESG index is constructed through normalization procedures and Principal Component Analysis (PCA) to derive a composite indicator reflecting the multivariate structure of the selected dimensions. Cluster analysis is subsequently applied to identify distinct structural development regimes. The results indicate a consistent empirical association between the LESG index and broader development outcomes, while also highlighting heterogeneous configurations of logistics capability, institutional quality, and sustainability performance across countries. These findings suggest that composite indicators can provide useful diagnostic tools for examining the structural alignment of development conditions beyond single-dimension metrics. The LESG framework contributes an integrated perspective for analyzing national development systems and offers a basis for future research on the structural conditions supporting sustainable economic transformation. Full article

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

Power battery enterprises are a key link in the new energy vehicle (NEV) industry chain. However, studies analyzing the investment layout of power battery enterprises from a micro perspective are relatively scarce. This study takes Contemporary Amperex Technology Co. Limited (CATL) as a case and employs various spatial analysis methods and an optimal parameter-based geographical detector (OPGD) to analyze the spatiotemporal evolution and driving mechanisms of its investment layout from 2020 to 2024. The results indicate that CATL’s investment center has shifted from Jiangxi to Hubei, and the spatial expansion axis has changed from a northwest–southeast to a southwest–northeast direction. The investment layout has evolved from a “one core with two secondary cores” structure to a “provincial dual core, multi-core outside the province” structure and, ultimately, to a nationwide networked pattern. By 2024, CATL’s investment network covered the southeastern coast, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), central China, and southwestern regions. County-level spatial autocorrelation analysis shows that the investment agglomeration effect has continuously strengthened (with the global Moran’s I increasing from 0.006 to 0.025). High–high agglomeration areas gradually expanded from the southeastern coast to Xiamen and several provinces in central and western China, while high–low agglomeration areas, as early signals of investment diffusion, initially expanded and then contracted. The driving mechanism analysis reveals that fiscal support (q = 0.668), industrial structure upgrading (q = 0.585), tax burden (q = 0.543), and economic development (q = 0.536) are the primary factors driving investment layout, with significant synergistic effects between these factors. The synergy between industrial structure upgrading and clean energy supply stands out as particularly prominent. These findings contribute to optimizing the spatial layout of the NEV industry and promoting regional economic development. 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

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing Agri-Food 4.0 supply chain resilience. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R² of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support. Full article

Background: The growing emphasis on sustainability has increased interest in understanding how environmentally oriented supply chain practices translate into organizational outcomes. However, empirical research examining how circular economy principles, technological integration, and sustainable supply chain management (SSCM) practices jointly influence green supply chain performance remains limited, particularly in developing economies. Methods: A quantitative research design was employed using survey data collected from 333 professionals in the Lebanese consumer goods industry through structured Likert-scale questionnaires. The proposed conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and test the relationships among circular economy practices, technological integration, SSCM practices, green supply chain performance, and organizational performance. Results: The findings indicate that technological integration, circular economy practices, and SSCM practices collectively enhance green supply chain performance. The results further show that improved green supply chain performance supports stronger organizational outcomes. Conclusions: This study contributes to sustainable supply chain literature by integrating circular economy principles, technological capabilities, and SSCM practices within a unified framework. It highlights the strategic role of green supply chain performance in linking sustainability initiatives to organizational outcomes and provides insights for managers seeking to implement integrated sustainability strategies. Full article