Search Results (4,606)

This study examines whether broader supply chain insurance coverage is associated with lower preventive resilience investment through perceived managerial moral hazard. Drawing on moral hazard theory and supply chain resilience research, it tests a moderated-mediation model using survey data from 241 managers in manufacturing-intensive firms. PLS-SEM is used as the main estimator, and covariance-based SEM is reported as an estimator-sensitivity check. Results show that insurance coverage breadth is positively associated with moral hazard perceptions, moral hazard perceptions are negatively associated with preventive resilience investment, and preventive investment is negatively associated with perceived disruption impact. Moral hazard perceptions significantly mediate the coverage breadth–preventive investment relationship, while the direct effect is not significant. The total effect of insurance coverage breadth on preventive resilience investment is negative and significant. Firm-perceived insurer verification stringency is associated with a weaker coverage–moral hazard perception relationship, whereas supply chain visibility provides a smaller attenuation effect. Exploratory risk-type moderation is directional but inconclusive. This study offers evidence from an emerging-market manufacturing context and suggests that contractual verification may help preserve prevention incentives, without estimating causal treatment effects. Full article

The construction sector continues to experience elevated levels of insolvency, driven by an interplay of structural vulnerabilities and macroeconomic pressures, including supply chain disruptions and cost inflation. These challenges have been particularly prominent in Australia, especially among micro and small construction firms, which account for over 90% of reported insolvency cases. In 2024, the Australian construction sector contributed nearly one-quarter of all company insolvencies nationally. This study undertakes a comprehensive review of construction insolvency research, synthesising key themes, causes, early warning indicators, and mitigation strategies, while contextualising global insights using empirical evidence from the Australian construction sector. The methodology integrated systematic literature screening, scientometric analysis, and critical thematic synthesis with a descriptive and selective statistical examination of the Australian Securities and Investments Commission (ASIC) data, complemented by practice-informed insights. The review identified dominant research trajectories, centred on financial risk management, insolvency prediction models, project-level cost and governance risks, and emerging data-driven approaches. Empirical analysis revealed that inadequate cash flow (~16–20%), poor strategic management (~12–18%), and weak financial controls (~11–15%) consistently rank among the leading causes of construction firm failure over the last decade. Indicators such as non-payment of statutory obligations and deteriorating working capital are observed in over half of insolvency cases, highlighting persistent structural fragility. Although global strategic focus areas emphasised financial monitoring and early warning systems, practice-informed findings indicated that effective mitigation requires their operationalisation through capability development, early intervention tools, regulatory oversight, and stakeholder-informed support mechanisms. The study shows how global insolvency risk concepts align with Australian regulatory evidence and highlights the need to translate early-warning approaches into accessible tools and support mechanisms for micro and small construction firms. Full article

Global supply chains are increasingly exposed to multi-source uncertainties, ranging from geopolitical tensions to climate extremes, making the accurate and interpretable prediction of disruptions an urgent operational priority. Existing predictive models often rely on either shallow statistical learners, which struggle with high-dimensional interactions, or deep neural networks, which trade off interpretability for marginal performance gains. To address this gap, we propose an interpretable machine learning framework that couples a feature-attention mechanism with a gradient-boosted decision tree ensemble for early warning of shipment-level disruption events. First, a dedicated attention module is trained to assign importance weights to 14 heterogeneous risk factors, generating an interpretable feature ranking that highlights pivotal signals such as lead-time volatility and geopolitical risk. The reweighted features are then fed into a gradient boosting classifier, which effectively captures non-linear patterns and interaction effects. Evaluated on a publicly available dataset of 5000 international freight records available on Kaggle, the proposed framework achieves an AUC of 0.8213 (±0.0002 over three independent runs), matching the best-performing baseline (standard gradient boosting, 0.8212 ± 0.0001) and surpassing logistic regression (0.777), random forest (0.806), and a standalone feature-attention network (0.805). The attention module preserves full predictive accuracy while adding an interpretability layer that conventional black-box implementations lack. Notably, the framework preserves the predictive accuracy of gradient boosting while enhancing interpretability through attention-based feature ranking and dual-perspective importance analysis, achieving a precision of 0.770 and a balanced F1-score of 0.781. The convergence of attention-based interpretability and ensemble learning efficiency provides supply chain managers with a transparent decision-support tool—distinguishing genuine “tipping points” from “false alarms” and enabling targeted risk mitigation under deep uncertainty. Full article

(1) Background. Food sovereignty and local sustainability are ensured by large agro-industrial holdings and small-scale farms; this synergy forms a complementary model of the agrifood system. Maintaining this model’s balance requires the creation of a unified digital ecosystem that integrates all suppliers and consumers into production chains, thereby eliminating unnecessary intermediaries. (2) Methods. This study employs a comprehensive methodological framework, including systems analysis and mathematical modeling, to develop service algorithms. Object-oriented design and software engineering methods facilitated the development and implementation of a service-oriented architecture for the digital system. (3) Results. The study presents a multi-tier architecture featuring an integration bus based on a service-oriented approach. To implement direct supply-and-demand coupling strategies, the system integrates both internal services (microeconomic indicators) and external services (macroeconomic indicators). Additionally, a recommender system based on neural networks and mathematical models was developed to personalize consumer requests and manage product sales. (4) Conclusions. The software solution is consistent with the AgTech 4.0 concept and enables the creation of a seamless environment for interstate trade. The implementation of the system enhances the transparency of the “product footprint”, facilitates the redistribution of surpluses, and, consequently, contributes to price stabilization. Full article

The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain. Full article

A sustainable cost optimization framework for the bioethanol supply chain is developed, using household food waste as a renewable feedstock. Unlike previous studies that mainly focus on agricultural residues, algae, or industrial biomass, this research addresses the specific logistical and economic challenges associated with household food waste, including decentralized generation, perishability, transportation complexity, and variable supply patterns. The proposed mathematical model incorporates key supply chain costs, including waste purchasing, handling, transportation, storage, processing, and facility installation, to minimize the total operational cost of the supply chain network. A genetic algorithm-based optimization approach is applied to determine the optimal configuration of collection centres, processing facilities, and distribution hubs subject to operational and capacity constraints. The numerical results indicate that the proposed framework improves supply chain efficiency while reducing overall system cost. The findings suggest that household food waste can serve as a sustainable and economically viable resource for decentralized bioethanol production and environmentally sustainable urban waste management. Full article

Background/Objectives: Exposure to high and sustained levels of non-esterified fatty acids (NEFA) in the peripartal period is the main cause of fatty liver disease in dairy cows. Rumen-protected choline is often fed as part of the nutritional management of peripartal cows, with in vivo and in vitro data indicating positive effects of this nutrient on alleviating liver lipid accumulation. Although hepatic molecular mechanisms associated with choline supply have been studied using a target gene, protein, or metabolite approach, application of high-throughput technologies could vastly enhance fundamental knowledge on the functional role of choline. The main objective was to challenge isolated hepatocytes with a mixture of NEFA and determine proteome- and metabolome-wide effects in response to choline supply. Methods: Three healthy female calves (1 d old, 30–45 kg) were sacrificed to harvest hepatocytes. During a 12 h incubation, isolated hepatocytes were challenged without NEFA (control), 1.2 mM NEFA (c9-18:1, 18:2, 16:0, 18:0, and c9-16:1 at 43.5%, 4.9%, 31.9%, 14.4%, and 5.3% of total NEFA, respectively), or NEFA for 6 h followed by 10 μM choline chloride for another 6 h (NEFA + Chol). iTRAQ labeling-based protein profiling and GC/MS-based metabolomics profiling were used to determine changes in proteins and metabolites. Differentially abundant proteins for each group comparison were determined at a threshold of 1.4-fold change. Differences in metabolite profiles were assessed via pairwise comparisons. A subset of differentially abundant proteins was validated via qRT-PCR and Western blotting. Results: Compared with the control, there were 90 proteins and 22 metabolites in the NEFA group, and 83 proteins and 29 metabolites in the NEFA + Chol. Compared with NEFA, there were 49 proteins and 17 metabolites in the NEFA + Chol group. Greater abundance of hexokinase-1 (HK1), fructose-bisphosphate aldolase (ALDOA), mitochondrial pyruvate carrier 1 (MPC1), and increased concentrations of lactate with high NEFA treatment alone suggested greater glycolytic and TCA cycle activity. Accumulation of triacylglycerol in the NEFA group was associated with lipotoxicity and markers of inflammation, such as greater abundance of prostaglandin reductase 1 (PTGR1), serious cell autophagy processes, such as greater abundance of cell division cycle 42 (CDC42), and NFκB-related proteins. Choline supplementation reduced TAG partly due to greater VLDL secretion driven by greater abundance of diacylglycerol acyltransferase (DGAT1), perilipin 3 (PLIN3), and apolipoprotein C-III (APOC3). In addition, a greater abundance of carnitine O-palmitoyltransferase 1b (CPT1B) with choline suggested enhanced mitochondrial β-oxidation. Activation of the CDC42/JNK pathway and ROS/NFκB axis-related proteins, along with depressed PI3K/AKT/RAC-related proteins, indicated enhanced mitochondrial autophagy in response to NEFA. Conclusions: Overall, data confirmed published effects of choline on TAG accumulation, VLDL secretion, and fatty acid oxidation, while highlighting negative effects of NEFA on the respiratory electron transport chain, autophagy, and inflammatory processes. Full article

The selection of transportation service providers (TSPs) is a strategically critical decision in sustainable supply chain management. However, existing decision-making frameworks exhibit three recurring limitations: the absence of formally validated, sector-specific sustainability criteria; reliance on weighting methods that inadequately handle expert judgment uncertainty; and limited application to emerging market contexts, particularly export-oriented garment and textile industries facing growing environmental, social, and traceability pressures from global buyers. To address these gaps, this study develops and validates an integrated multi-criteria decision-making framework combining Content Validity Ratio $\left(CVR\right)$ analysis, the Fuzzy Best–Worst Method (FBWM), and Fuzzy Additive Ratio Assessment (FARAS). $CVR$ analysis was applied to an initial pool of $28$ candidate criteria, retaining $22$ validated criteria spanning economic, environmental, social, and operational dimensions. FBWM was subsequently used to derive criterion weights from nine decision-makers (DMs) representing garment manufacturers, transportation providers, and academia in Vietnam, while FARAS ranked five candidate TSPs. Results indicate that operational and economic criteria are the most influential dimensions, while cost for the service, financial performance, industry experience, environmental awareness, and environmental legal and policy framework emerge as the five highest-weighted sub-criteria. The final ranking order, TSP2 > TSP4 > TSP5 > TSP1 > TSP3, remained stable across benchmarking with FTOPSIS, FVIKOR, and FMOORA, as well as underweight perturbation and equal-weighting scenarios, confirming the robustness of the ranking results. Full article

This study developed a hybrid fuzzy decision-making algorithm based on the Fuzzy Best-Worst Method (FBWM) and the Fuzzy Square-Root-based Evaluation Method (FSREM). Despite the widespread application of the 8D methodology in engineering practice, the importance of its disciplines has not been sufficiently investigated; therefore, the aim of this study is to determine their significance and priority. The proposed fuzzy algorithm was applied to three companies operating within the automotive supply chain. FBWM was used to determine the criteria weights, while FSREM was applied to rank the 8D disciplines. Sensitivity analysis showed that the expert teams from the three considered companies perceived the problem in a very similar manner. The results of applying the proposed algorithm in all three companies showed that the discipline Identify and Verify Root Cause (D4) has the greatest influence on problem-solving effectiveness. In two of the three companies, Prevent Recurrence (D7) was ranked as the second most influential discipline, while in one company Define Permanent Corrective Actions (D5) was identified as the second most influential discipline. It can be concluded that the results demonstrated a high degree of consistency, while minor ranking deviations can be attributed to different quality management system approaches within each company. Full article

Antimicrobial resistance and persistent biofilm-associated infections have renewed interest in bacteriophages as alternatives or complements to conventional antibiotics. However, broader therapeutic adoption remains constrained by slow phage discovery, incomplete genome characterization, narrow host range, complex therapeutic matching, and manufacturing variability. Artificial intelligence (AI) offers computational approaches that may help address several of these limitations. This comprehensive narrative review discusses current AI applications across the bacteriophage pipeline, including metagenomic phage discovery, genome annotation, phage–host interaction prediction, personalized phage selection, cocktail optimization, and phage–antibiotic combination design. The review also examines AI-assisted synthetic biology approaches, including receptor-binding protein redesign, CRISPR-enabled engineering, generative genome design, and biosafety screening, as well as emerging applications in bioprocess optimization, yield prediction, purification analytics, quality assurance, and supply-chain management. Current evidence suggests that AI may accelerate phage identification, improve host-range prediction, support therapeutic optimization, and strengthen manufacturing consistency, potentially facilitating the transition of phage therapy from individualized rescue interventions toward more scalable antimicrobial platforms. Nevertheless, major limitations remain, including fragmented, taxonomically biased datasets; limited external validation; restricted interpretability; privacy concerns; biosafety oversight; and evolving regulatory frameworks. Future progress will depend on standardized datasets, multimodal validation, scalable manufacturing systems, experimental and clinical verification, and coordinated regulatory development. Full article

Against the backdrop of frequent global shocks and increasingly complex supply chain networks, supply chain disruption risk exposure has become a major challenge affecting firms’ operational stability and sustainable competitive advantage. Meanwhile, generative artificial intelligence is being increasingly embedded in business operations and has demonstrated strong application potential in information processing, risk identification, and decision support. Based on data from Chinese A-share listed firms from 2017 to 2024 and using text measures based on Management Discussion and Analysis (MD&A) disclosures of Generative AI application and supply chain disruption risk exposure, this study examines the relationship between Generative AI application and corporate supply chain disruption risk exposure, and further explores the channels through which this relationship may operate from the perspective of risk governance transformation. The results show that Generative AI application is significantly associated with lower corporate supply chain disruption risk exposure, and this relationship remains robust across a series of robustness checks and supplementary endogeneity analyses. Channel analyses suggest that this relationship may be related to firms’ risk governance transformation, mainly reflected in enhanced risk identification capability, improved resource allocation capability, and strengthened collaborative response capability. Heterogeneity analyses show that this association is more pronounced among firms facing higher environmental uncertainty, manufacturing firms, and firms located in cities with lower entrepreneurial vitality. This study provides text-based firm-level evidence for understanding the relationship between Generative AI application and supply chain risk governance, and offers managerial implications for firms seeking to promote scenario-based Generative AI application and enhance supply chain resilience and risk governance capability. Full article

As the Italian hop industry undergoes consolidation, assessing the environmental pressure of diverse cultivation and processing models is essential for sustainable growth. This study characterizes the Product Environmental Footprint (PEF) of Italian hop production through a multi-case analysis of eight representative farms. A primary data collection tool was utilized to quantify resource inputs, including water management, nutritional strategies, and phytosanitary defense. Following a rigorous thermodynamic consistency screening of the field data to eliminate unrepresentative parameters, the life cycle inventory focused on two validated regional anchor cases. The findings reveal a high degree of management heterogeneity, with dry cone yields ranging from 400 to 1673 kg of dry matter per hectare. Two functional units were defined: 1 kg of fresh hop cones (FU1) to assess cultivation impacts, and 1 kg of processed products (FU2) at the brewery gate to evaluate the full supply chain. Integrating deterministic life cycle impact outputs with a probabilistic Monte Carlo uncertainty analysis, the results indicate that the environmental impact varies significantly across commercial formats: Cryogenic Powder (2.33 ± 0.34 mPt/kg) represents the most resource-intensive format, while Raw Bales and T90 Pellets from high-yield models exhibit scores as low as 1.36 and 1.55 mPt/kg, respectively. The study identifies the agricultural phase as the primary environmental hotspot, driven predominantly by water deprivation. To address these burdens, a Sustainable Italian Hop (SIH) integrated scenario was developed. By combining precision irrigation, thermal decarbonization via biomass valorization, and a direct-to-pellet processing flow, this model achieved a 70% total reduction in the environmental footprint score (0.465 ± 0.076 mPt/kg) and an 86% reduction in water use impacts. Finally, the socio-technical and financial barriers to implementing the SIH framework are qualitatively evaluated. These results provide actionable benchmarks for aligning the emerging Italian hop supply chain with European Union climate neutrality objectives. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

The relationship between corporate digitalization and green management practices has received increasing scholarly attention, but existing empirical findings remain inconsistent. To clarify this relationship, this research conducts a meta-analysis based on 94 effect sizes from 82 empirical studies, adopting a multivariable research framework to integrate existing findings and explore the factors that contribute to the generation of heterogeneity. The findings indicate that corporate digitalization facilitates green management practices, a conclusion robust across three key dimensions: environmental performance, green innovation, and green supply chain management. Furthermore, the findings show that digitalization exerts a stronger positive effect in non-manufacturing firms, non-heavy-polluting firms, and high-tech firms, while measurement approaches emerge as a critical factor influencing empirical outcomes. These findings provide integrated evidence on the digitalization–green management relationship, clarify its key boundary conditions, and offer practical implications for firms seeking to advance low-carbon transformation through digital technologies. Full article