Search Results (712)

The primary objective of this document is to provide a structured reference for performance evaluation of the different sensor technologies that can be found in counter-drone systems. Standardised parametric models for the sensor(s), drone(s) and the external environment are proposed, with the aim of predicting sensor performance under real-world conditions in representative protective scenarios against hostile drones. In addition to defining these models, the document introduces a set of standardised reference parameter values to ensure consistency, comparability, and practical usability in cases where complete system data are unavailable. A set of clearly stated assumptions will therefore be formulated, dictating the boundary conditions for which the selected model can or cannot be used. The advantages of the parametric and standardised approach are numerous. For example, it makes it straightforward to compare the performance of different systems during a tender process, given that a set of common parameters must be known and provided (supplier data or standardised default values) for each system. This modelling approach also reduces the costs linked to practical field tests/deployment. Note that the focus is placed on developing simpler models to ensure ease of use and practical applicability, while maintaining an acceptable level of reliability and accuracy. The framework presented in this work is strictly oriented towards the defensive sensing function of C-sUAS systems and does not address effector design, kinetic engagement or offensive sUAS capabilities. Full article

Global value chains (GVCs) are undergoing significant reconfiguration in a context of slower trade growth, rising geopolitical tensions and repeated supply chain disruptions. This article examines how these pressures have shaped the sourcing geography of Spanish apparel imports between 1999 and 2023. Drawing on a panel of the 25 main supplier countries (625 country-year observations), it analyses the changing structure of sourcing through three restructuring dynamics widely discussed in the recent literature: nearshoring, diversification and friendshoring. The results show that diversification, rather than regionalization, has been the main response to recent disruptions. While Spain’s apparel sourcing has become less concentrated, this shift has not led to a sustained shortening of supply chains or to a clear reduction in dependence on Asia. Geopolitical alignment has limited explanatory power at the aggregate level, although it becomes more relevant among semi-proximity suppliers competing on the basis of speed, flexibility and political reliability. Overall, the findings suggest that post-pandemic restructuring in Spanish apparel is better understood as a selective form of risk management within an existing buyer-driven GVC than as a broad move toward nearshoring. Full article

The efficiency of scientific and technological achievement transformation is constrained by supply–demand matching challenges. Concurrently, Artificial Intelligence (AI) offers novel pathways for digital-intelligence service platforms to mitigate this challenge. To resolve AI investment decision problems of such platforms, this study constructs a bilateral matching model involving high-quality/low-quality technology providers and high-capability/low-capability technology seekers. Based on expected value theory and Stackelberg games, it derives optimal AI investment strategies for the Commercial Platform (platform’s expected revenue maximisation objective) and the Public Welfare Platform (social expected revenue maximisation objective). Findings indicate that higher AI investment contributes to a rise in the matching probability between high-quality providers and high-capability demanders. Owing to incomplete benefit internalization, platforms of different types show divergent willingness for AI investment. The AI investment level of the Commercial Platform is lower than that of the Public Welfare Platform, which results in losses of expected matching value. Furthermore, declines in AI technology costs and reduced external selection value of suppliers will drive platforms to raise their AI investment intensity. This research provides theoretical foundations for optimising AI strategies in online technology transfer service platforms and informing targeted government interventions. Full article

Micro-structured SAE H13 tool steel inserts for polymer injection molding require accurate replication of sub-millimeter features while retaining adequate densification and heat-treatment response. This study compared two powder-based routes on the same hemispherical insert containing pyramidal features of approximately 0.145 mm base width: hot embossing (HE) of water-atomized SAE H13 powder (supplier d50 = 5.7 µm, irregular morphology) compounded with a commercial M1 binder, and material extrusion (MEX) of a commercial gas-atomized SAE H13 filament processed on a Markforged Metal X. Rheological screening selected a 57:43 vol% powder-to-binder ratio for the in-house HE feedstock, and DSC/TGA measurements defined two-step debinding windows. The best HE conditions were 220 °C, 8 MPa, and 45 min for the in-house mixture, and 210 °C, 8 MPa, and 30 min for the granulated commercial filament; the latter showed a 0.15% linear deviation from the silicone replica diameter among the best-rated samples. Under the tested commercial MEX configuration, the pyramidal features were not resolved because the 0.40 mm deposition line width exceeded the target feature base width, causing the slicer to omit the sub-line-width geometry. The defect populations differed qualitatively: HE specimens showed porosity and local cracking associated with powder morphology and pressureless sintering, whereas MEX specimens showed build-direction-aligned inter-raster voids associated with the toolpath. Microhardness and tensile data are therefore interpreted as process-history-specific results rather than as a direct route ranking, because sintering conditions were not uniform across all specimens. The study defines an experimentally bound process-selection limit for SAE H13 micro-tooling: HE remains preferable for sub-nozzle surface features, whereas MEX remains attractive for macro-scale geometric freedom, if resolution, densification, and post-sintering consolidation are addressed. Full article

This study examines how digitalization discourse is mobilized in public carbon reporting under institutional complexity and how it varies across different carbon-accountability structures in an emerging-market context within the Global South. A longitudinal comparative discourse analysis was conducted on 70 annual and sustainability reports (2015–2024) from seven Vietnamese listed firms, contrasting firms with internal carbon accountability against those with supply-chain-mediated accountability. The 2015–2024 timeframe was deliberately selected to capture a critical decade of regulatory evolution, marked by the aftermath of the Paris Agreement and the escalating enforcement of net-zero and environmental, social, and governance (ESG) disclosure mandates. Findings reveal that digitalization functions as an ambivalent rhetorical resource rather than a uniformly substantive sustainability enabler. Firms with operationally visible emissions utilize digitalization for “temporal buffering,” deferring immediate physical abatement by framing technology as a future transition pathway. Conversely, firms with supply-chain-mediated emissions employ “boundary displacement,” framing accountability as contingent on fragmented supplier data. These patterned responses constitute “digital institutional camouflage”. We conclude that digital reporting sophistication should not be conflated with substantive decarbonization; effective oversight requires cross-validating digital infrastructures with concrete emission-reduction measures. Ultimately, this study empirically specifies institutional decoupling theory by demonstrating how emissions visibility and organizational control shape distinct pathways of discursive decoupling. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Background: Circular logistics translates circular economy principles into practical supply chain processes, but its adoption varies across firms because organizations differ in sustainability commitment, circular supply chain motivation, shared circular identity, and digital traceability capability. This study examines how supplier sustainability commitment and circular supply chain motivation influence circular logistics adoption through circular supply chain identity, while also testing the moderating role of digital traceability capability. Methods: Data were collected from 350 supply chain professionals in Saudi Arabia and analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Supplier sustainability commitment and circular supply chain motivation positively influenced both circular logistics adoption and circular supply chain identity. Circular supply chain identity also positively affected circular logistics adoption and partially mediated the effects of both antecedents. Digital traceability capability acted as a selective moderator: it weakened the circular supply chain motivation–identity relationship, did not significantly moderate the supplier sustainability commitment–adoption relationship, but strengthened the circular supply chain identity–adoption relationship. It also moderated the indirect effect of circular supply chain motivation on circular logistics adoption through circular supply chain identity. Conclusions: Circular logistics adoption is driven not only by commitment and motivation, but also by shared circular identity and digitally enabled traceability. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

Background: Circular supply chains (CSC) have emerged as a strategic response to sustainability challenges, while adoption remains uneven. Supplier selection is a key driver of effectiveness, shaped by organizational capabilities, institutional support, and leadership. This study develops a structured framework for circular supplier selection (CSS) using a hybrid multi-criteria decision-making approach, addressing fragmented research and strengthening the link between methodological innovation and practice. Methods: The proposed framework integrates fuzzy DEMATEL, the Best-Worst Method (BWM), and the Analytic Hierarchy Process (AHP) within MCDM. Fuzzy DEMATEL identifies cause-and-effect relationships among criteria, distinguishing net causes from net effects. The most influential and dependent criteria serve as anchors for the BWM weighting, followed by AHP to evaluate sub-criteria and alternatives. Results: Environmental governance emerged as the most influential driver in the causal analysis, while circular performance received the highest weight in BWM. The final AHP evaluation ranked Alternative 5 as the most suitable, followed by A9 and A3, confirming the framework’s ability to deliver consistent, actionable insights for circular supplier selection. Conclusions: This integration enables a more granular and robust evaluation of supplier strategies within CSC, reinforcing their role in accelerating sustainability transitions. It establishes a structured framework for CSS, highlighting CSS performance and upstream supply chain decision-making. Full article

Large inland waterway infrastructure projects are increasingly exposed to supply disruptions, logistics uncertainty, carbon-control pressure, and dredged-material management challenges. Although resilient supplier selection, closed-loop supply chains, and ESG-oriented optimization have been widely studied, existing models rarely integrate resilient sourcing, hub configuration, forward material supply, reverse dredged-material resourceization, and social externality penalties within a unified maritime infrastructure decision framework. To fill this gap, this study proposes an ESG-endogenous closed-loop supply-chain optimization model for construction of an inland waterway navigation hub. The model jointly optimizes resilient supplier selection, transshipment/resourceization hub activation, equipment deployment, forward material flows, and reverse dredged-material flows. Three objectives are considered: minimizing economic cost, minimizing carbon emissions, and maximizing net social benefit. In particular, a social benefit and ecological-debt penalty function is introduced to quantify the transition from beneficial reuse to disposal-related negative externalities. NSGA-II is adopted as a multi-objective solver, with parameter calibration, convergence analysis, and benchmark comparison used to evaluate computational performance. The Pinglu Canal project is used as a case study. The results produce 14 Pareto-optimal solutions and show that the lowest-cost and lowest-emission configurations may still generate negative social benefits. A low-cost ESG transition region around 197.3–197.8 million CNY is identified, where limited additional investment can activate resourceization pathways and shift the system from ecological debt to near-saturated social benefit. These findings suggest that sustainable infrastructure planning should move beyond isolated cost or carbon minimization and instead identify balanced supplier–hub–equipment–flow configurations that jointly support resilience, circularity, and ESG performance. Full article

This study investigates the role of data structure in multicriteria sorting by integrating supervised and unsupervised approaches. Specifically, a hybrid framework combining TOPSIS-Sort-B and cluster analysis is proposed to define class boundaries and evaluate sorting quality. Unlike traditional studies that focus primarily on methodological performance, this work emphasizes the impact of criteria conflict and trade-offs on class formation and stability. A unified performance-based labeling scheme is introduced, and a Criterion-Driven Consistency Indicator (CDCI) is used to quantify intra-class similarity. This indicator assesses the extent to which alternatives within the same class exhibit similar performance across criteria, offering a complementary perspective to conventional distance-based metrics. The proposed framework is validated through multiple case studies with distinct structural characteristics, including a highly structured dataset, a trade-off-intensive electric vehicle dataset, and an intermediate supplier selection problem. The results show that sorting outcomes are largely driven by the intrinsic structure of the data rather than by the choice of method. Datasets with low criteria conflict yield high class consistency and clear separation, whereas strong trade-offs lead to reduced cohesion and overlapping class boundaries, especially for intermediate alternatives. Overall, the study demonstrates that incorporating criteria-level information is essential for the robust evaluation of multicriteria sorting. The proposed approach enhances interpretability, reduces subjectivity in class definition, and provides new insights into the relationship between data structure and sorting consistency. Full article

Background: Nutrient-enriched sports foods can support efficient nutrient intake in specific circumstances in athletic nutrition management, such as during competition, when training away from the usual environment, or during periods of weight management. Despite their widespread availability, sports foods are not always used appropriately, necessitating tools to support informed product selection. Objective: This study aimed to characterize sports foods consumed by Japanese athletes and to develop a Japanese sports food exchange list to facilitate product selection based on target nutrient requirements. Methods: Seven sports food categories commonly used in Japanese sports settings were examined: sports drinks, energy jellies, energy bars, energy gels, protein drinks, protein bars, and protein powders. Following the methodology of Spain’s sports food exchange list, development proceeded in two stages. First, suppliers were selected based on INFORMED CHOICE certification or listing on the Japan Anti-Doping Agency’s product information website, with input from experienced sports dietitians. Subsequently, 523 products were classified into subcategories based on nutrient content per unit using established statistical criteria, including the mean, standard deviation, coefficient of variation, and z-values. Results: After excluding products with z-values outside ±2 or compositions deemed unsuitable for carbohydrate or protein supplementation, 498 products from 36 suppliers were classified into 24 subcategories. Japanese sports foods exhibited broad distributions in nutrient composition, variability derived from ingredient differences, and a high proportion of plant-based protein powders. Conclusions: This study developed a Japanese sports food exchange list comprising 498 products across 24 subcategories, enabling evidence-based product selection aligned with the nutrient intake goals of Japanese athletes. Full article

Amid intensifying geopolitical tensions, the security of critical mineral supply chains has drawn growing global concern. Rare earth elements have become a strategic battleground in U.S.–China competition over technology and trade. Using product-level trade data (2017–2023), this study employs a counterfactual approach to identify the impact of trade shocks on U.S. import patterns and further evaluates macroeconomic consequences through a multi-regional input–output (MRIO) model. Empirical findings indicate the following: First, the trade conflict has significantly reshaped the U.S. rare earth supply chain, with notable heterogeneity across product types. Second, our evidence suggests that geopolitical proximity is not the decisive factor in the selection of new rare earth suppliers. Instead, the technological capabilities of substitute countries and their existing integration within the China-centered global rare earth supply chain play a more critical role. Third, the input–output simulation quantifies the macroeconomic consequences of supply chain restructuring. The results show that reducing reliance on China leads to a systematic increase in U.S. domestic production costs, welfare losses, and negative spillovers to global trade partners through price shocks and efficiency declines. These findings reveal the inherent tension between market logic and geopolitical objectives in supply chain governance. They underscore the central importance of supply-side capacity building and technological self-sufficiency in securing critical minerals, while highlighting the sustainability implications of geopolitically driven supply chain restructuring, especially the need to balance resource security, economic efficiency, and long-term resilience. Full article