Search Results (1,101)

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

The Textiles and Clothing sector is increasingly focused on transitioning towards circular production, with industrial companies striving to integrate sustainable practices. Achieving this goal can involve the rapid adoption of innovative raw fibers (e.g., biodegradable and biobased materials) and maximizing the use of recycled and recyclable fibers. This implicitly demands acting on the total transparency of information along the complex supply chains in this sector to guarantee the correct adoption of these innovative fibers. It is precisely this complexity that hinders efforts to track and accurately disclose material usage. To address this issue, this paper presents a systematic literature review to explore the main challenges in adopting technologies like digital product passports, which can help track materials information along supply chains to support sustainable transitions. The analyzed articles were selected by excluding student thesis works, non-retrievable articles, papers that had a different focus, and literature published before 2020 or in non-institutional journals. The 53 resulting contributions are analyzed through a thematic analysis and discussed, focusing on identifying key material-related data that should be monitored to ensure responsible material use and strengthen sustainable production practices in the Textiles and Clothing sector, thereby guaranteeing control over material use and preventing premature disposal. Full article

Low-altitude unmanned aerial vehicle (UAV) technology serves as a crucial pathway for developing a low-carbon circular economy and achieving the Sustainable Development Goals (SDGs). In order to achieve the diffusion of low-altitude UAV technology in sustainable development, a dynamic model of technology adoption decisions within enterprise clusters is constructed. This model is based on complex network evolutionary game theory. The present study investigates the mechanism through which government policies influence the diffusion of low-altitude UAV technology. The research findings indicate that government subsidy mechanisms and diffusion constraints play critical roles in the diffusion process of low-altitude UAV technology. Core Enterprises and Marginal Enterprises within clusters exhibit different responsiveness to subsidies, with Core Enterprises demonstrating higher sensitivity. The intensity of government subsidies is positively correlated with the diffusion rate of low-altitude UAV technology, while the penalty from constraints is negatively correlated with the diffusion rate. These findings establish a foundation for governments to devise pertinent subsidy mechanisms, establish and enhance the management system of the low-altitude economy, and cultivate a policy ecosystem conducive to the diffusion of low-altitude UAV technology, thereby propelling sustainable societal development. Full article

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance. Full article

This study evaluated the diffusion of Listeria monocytogenes (LM) in a beef steak tartare production chain, aiming to (1) evaluate Listeria spp. diffusion in finishing farms supplying beef cattle, (2) evaluate LM prevalence in carcasses, and (3) map LM diffusion in the production plant. A detection rate of 6/76 was observed in the farm, while carcasses after skinning and before refrigeration tested positive in 19/30 and 11/30, respectively. During tartare production, 57/154 meat and 35/191 environmental samples tested positive. A total of 114 LM isolates were characterized via a whole-genome sequencing approach. Five clonal complexes (CCs) and seven sequence types (STs) were identified, with CC9-ST580 being the most prevalent. Four clusters were identified from both the slaughtering and production phases. Genes related to resistance to fosfomycin, quinolones, sulfonamides, lincosamide, and tetracycline were detected. Two hypervirulent strains (CC6-ST6 and CC2-ST145), harboring a full-length inlA, several virulence genes, and stress islands, were detected. Stress Survival Islet 1 was found in almost all the isolates. The wide diffusion of LM in steak tartare requires the management of some critical phases of the production chain (mainly slaughtering); genomic methodologies could be useful in describing the circulation and virulence of LM strains. Full article

The humanitarian aviation supply chain (HASC) serves as a critical conduit for delivering essential aid to populations affected by disasters and conflicts, especially when ground routes are inaccessible. However, HASCs operate in high-risk environments marked by instability, infrastructure damage, and operational challenges. Existing risk assessment approaches often struggle to account for the complex interdependencies among the many factors influencing mission success and supply chain resilience. This study introduces a comprehensive risk analysis framework for HASCs using Bayesian networks (BNs). The BN model integrates data on factors such as political instability, infrastructure damage, adverse weather, crew fatigue, and aircraft maintenance. Through quantitative analysis, the framework identifies critical vulnerabilities and assesses the likelihood of mission failure. Full article

Inland Waterway Transport (IWT) is widely recognised as an energy-efficient freight mode, yet its decarbonisation is increasingly constrained not by propulsion technology, but by the absence of infrastructure capable of delivering clean energy where and when it is needed. This paper presents a structured review of over a decade of academic, policy and technical literature, identifying systemic gaps in current decarbonisation strategies. The analysis shows that most pilot projects are vessel-specific, and poorly scalable, with infrastructure planning rarely based on vessel-level energy demand data, leaving energy provision as an afterthought. Current approaches overemphasise technology readiness while neglecting the complexity of aligning supply chains, operational diversity, and infrastructure deployment. This review reframes IWT decarbonisation as a problem of provision, not propulsion. It calls for demand-led, demand driven, fuel agnostic infrastructure models and proposes a roadmap that integrates technical, operational, and policy considerations. Without rethinking energy access as a core design challenge—on par with vessel systems and regulatory standards—the sector risks investing in stranded assets and missing climate and modal shift targets. Aligning vessel operations with dynamic, scalable energy delivery systems is essential to achieve a commercially viable, fully decarbonised IWT sector. Full article

Chicken meat production is a critical component of the global protein supply, significantly influenced by rearing advancements, including the use of antimicrobial agents. However, the pervasive use of antibiotics has raised concerns regarding the occurrence of antimicrobial resistance (AMR). This study examined the prevalence and AMR profiles of Salmonella spp., Escherichia coli, and Enterococcus spp. in chicken meat from conventional and antibiotic-free (ABF) production chains. A total of 284 samples were analyzed for Salmonella spp. and E. coli, while 164 samples were tested for Enterococcus spp. From that, 143 were from conventional production chains and 141 were from ABF chains. The results indicated a 10.9% prevalence of Salmonella spp., 22.1% for E. coli, and 93.9% for Enterococcus spp. Regarding production chains, the conventional chain had 18.2% of the isolates for Salmonella spp., 20.3% for E. coli, and 91.6% for Enterococcus spp., while the ABF chain had 3.5% of the isolates for Salmonella spp., 24.1% for E. coli, and 96.3% for Enterococcus spp. In terms of AMR, 86.1% of the Salmonella spp. isolates that underwent the disk diffusion test were resistant to at least one antibiotic tested, 95.1% of E. coli, and 88.4% of Enterococcus spp. Notably, carbapenem resistance was detected in Salmonella spp., with 2.3% of isolates being resistant to imipenem, while resistance to vancomycin and linezolid was detected in Enterococcus spp., all of which are critically important antimicrobials. Comparisons between these production chains revealed significant differences in antibiotic resistance patterns in Salmonella spp. for two antibiotics, amoxicillin/clavulanic acid and nitrofurantoin, while no differences were observed in E. coli. For Enterococcus spp., resistance varied for three antibiotics: streptomycin, penicillin, and tetracycline. For all other antibiotics tested, the resistance profiles were consistent across both conventional and ABF production chains. Multidrug resistance (MDR) was observed in 90.7% of Salmonella spp. isolates, 42.9% of E. coli isolates, and 12.0% of Enterococcus spp. isolates. Statistically significant differences were noted in MDR prevalence between production chains, with conventional production systems exhibiting higher levels of MDR isolates compared to ABF systems. These findings underscore the need for targeted AMR control strategies that consider the complexity of resistance dynamics across production systems. Full article

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R² = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions. Full article

Agri-food supply chains have experienced notable changes in recent decades, with tomatoes (Solanum lycopersicum) maintaining their status as a key global crop in terms of both production and consumption. These supply chains comprise a complex network of stakeholders—including producers, processors, distributors, and retailers—who collectively ensure the delivery of tomatoes from farms to consumers. This study develops mathematical models of agri-food tomato supply chains (AFTSCs) and examines their behavior through stability analysis and dynamic simulations based on a compartmental approach. Furthermore, the environmental impact is evaluated using a sustainability index, to which the waste diversion rate is introduced. This metric is defined as the proportion of diverted waste (i.e., materials recycled, reused, or composted) relative to the total waste generated, thereby enabling the quantification of sustainability performance within the system. Finally, a sensitivity analysis is conducted on the proposed dynamical models to validate and reinforce the findings. Full article

Cybersecurity has become a critical concern in the automotive sector, where the increasing connectivity and complexity of modern vehicles—particularly in the context of autonomous driving—have significantly expanded the attack surface. In response to these challenges, this paper presents the Welcome To The Machine (WTTM) framework, developed to support proactive and structured cyber risk management throughout the entire vehicle lifecycle. Specifically tailored to the automotive domain, the framework encompasses four core actions: detection, analysis, response, and remediation. A central element of WTTM is the WTTM Questionnaire, designed to assess the organizational cybersecurity maturity of automotive manufacturers and suppliers. The questionnaire addresses six key areas: Governance, Risk Management, Concept and Design, Security Requirements, Validation and Testing, and Supply Chain. This paper focuses on the development and validation of WTTM-Q. Statistical validation was performed using responses from 43 participants, demonstrating high internal consistency (Cronbach’s alpha > 0.70) and strong construct validity (CFI = 0.94, RMSEA = 0.061). A supervised classifier (XGBoost), trained on 115 hypothetical response configurations, was employed to predict a priori risk classes, achieving 78% accuracy and a ROC AUC of 0.84. The WTTM framework, supported by a Vehicle Security Operations Center, provides a scalable, standards-aligned solution for enhancing cybersecurity in the automotive industry. Full article

Offsite construction (OSC) has been increasingly promoted as a solution for a more sustainable construction industry. This method enhances the performance and efficiency of the construction industry by improving time and cost planning, health and safety, enhanced construction quality, and fostering a more environmentally sustainable built environment. China’s Fourteenth Five-Year Plan (2021–2025) mandates that at least 30% of new homes be constructed using OSC techniques by 2025, with the target of achieving 100% by 2035. With such a scalable challenge, this systematic research aims to identify and classify OSC adoption barriers, whether modular, volumetric, or panelised construction, by synthesising existing research studies. Through the analysis of 48 research articles published from 2013 to 2023, the review identifies key barriers hindering OSC adoption in China. The five most frequent barriers are as follows: lack of skills and expertise in OSC within organisations, absence of design codes and national standards for prefabrication, poor cooperation and integration among stakeholders in the supply chain, immature regulatory systems, and complexity in OSC project management. Trends in barrier prevalence by publication year are also discussed to highlight changes in research focus and to inform recommendations for future work that could support greater uptake of OSC in China. Full article

In low-carbon transition policy management, rationally determining the energy-related carbon emission responsibilities (ERCERs) across multiple regions is a fundamental issue. Reasonable allocation must take into account regional heterogeneities, such as energy endowments, economic development levels, industrial structures, and complex interconnections within the multi-regional energy supply chain. Previous studies mostly analyzed it via the multi-regional input–output (MRIO) model on the energy-consumption side, often neglecting the regional distribution of energy production and inter-regional energy transport on the energy-production side. This limitation risks a mismatch between energy policies and economic policies in practical policy governance. To address this gap, this study develops an energy allocation-induced MRIO (EA-MRIO) method integrating energy allocation analysis and an MRIO model to trace ERCER transmissions holistically across the entire energy supply chain. The framework covers seven stages including energy supply, inter-regional energy transport, direct energy consumption of end-use sectors, inter-regional intermediate products input and output, final products supply, inter-regional final products transport, and final demand, applied to a case study of China’s 31 provinces in 2017. Results show that ERCERs mainly transfer from western and northern regions to eastern and southern coastal areas: ERCERs embodied by energy production in western and northern provinces first flow to northern coastal provinces (main intermediate products producers), then to eastern and southern coastal provinces (main final products producers), with 23% ultimately attributed to exports. These findings call for allocating ERCERs based on different subregions’ roles within the national energy–economic system to facilitate more equitable and effective carbon reduction policymaking. Full article

In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a novel heuristic-based scheduling method that compresses and decompresses activity durations dynamically within the context of multi-project scheduling under uncertainty and resource flexibility—while preserving resource and precedence feasibility. The technique integrates Critical Path Method (CPM) calculations with heuristic rules to identify candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities and sustainability goals. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. For practitioners, the method offers a responsive and sustainable planning tool that supports real-time adjustments in project portfolios, enhancing resource visibility and execution resilience. For researchers, the study contributes a reproducible, Python-based implementation grounded in Design Science Research (DSR), addressing gaps in stochastic multi-project scheduling and sustainability-aware planning. Full article