Search Results (349)

Maritime transportation accounts for approximately 80 percent of global trade volume, with modern vessels increasingly reliant on Software-Defined Radio (SDR) technologies for communication and navigation. However, the very flexibility and reconfigurability that make SDRs advantageous also introduce complex radio frequency vulnerabilities exposing ships to threats that jeopardize vessel security, and this disrupts global supply chains. This survey paper systematically examines the security landscape of maritime SDR systems through a threat modeling lens. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we analyzed 84 peer-reviewed publications (from 2002 to 2025) and applied the STRIDE framework to identify and categorize maritime SDR threats. We identified 44 distinct threat types, with tampering attacks being most prevalent (36 instances), followed by Denial of Service (33 instances), Repudiation (30 instances), Spoofing (23 instances), Information Disclosure (24 instances), and Elevation of Privilege (28 instances). These threats exploit vulnerabilities across device, software, network, message, and user layers, targeting critical systems including Global Navigation Satellite Systems, Automatic Identification Systems, Very High Frequency or Digital Selective Calling systems, Electronic Chart Display and Information Systems, and National Marine Electronics Association 2000 networks. Our analysis reveals that maritime SDR threats are multidimensional and interdependent, with compromises at any layer potentially cascading through entire maritime operations. Significant gaps remain in authentication mechanisms for core protocols, supply chain assurance, regulatory frameworks, multi-layer security implementations, awareness training, and standardized forensic procedures. Further analysis highlights that securing maritime SDRs requires a proactive security engineering that integrates secured hardware architectural designs, cryptographic authentications, adaptive spectrum management, strengthened international regulations, awareness education, and standardized forensic procedures to ensure resilience and trustworthiness. Full article

Packaging plays a crucial role in product preservation and distribution but also constitutes a major source of environmental burden. In the beverage sector, where unit value is low, secondary and tertiary packaging significantly influence the environmental profile of the final product. This study quantifies the environmental trade-offs between conventional single-use and reusable packaging systems for aluminum cans, identifying the operational thresholds that justify a transition to circular models. A standardized Life Cycle Assessment (LCA) approach is applied to five packaging configurations: three current market scenarios and two alternative solutions based on reusable plastic crates (RPCs). System boundaries include production, distribution, end-of-life, and, where applicable, reverse logistics. A functional unit of one fully packaged 0.33 L aluminum can is adopted. Results reveal that while single-use cardboard solutions achieve favorable performance under certain impact categories, reusable systems outperform them when a sufficient number of reuse cycles is achieved and reverse logistics are efficiently managed. Sensitivity analyses highlight the critical influence of transport distances and reuse frequency on overall impacts, with performance deteriorating for reusable systems beyond 200 km or below 50 reuse cycles. These findings offer concrete, evidence-based guidance for supply-chain and logistics decision-makers to optimize packaging choices and distribution network design. The study also provides robust quantitative insights for policymakers and industry stakeholders by defining the precise operational conditions under which reusable systems deliver real environmental benefits. By presenting a comprehensive, system-level comparison of complete packaging systems, this research closes a critical gap in LCA studies and sets out a practical pathway for implementing circular, low-impact packaging strategies consistent with emerging EU regulations. Full article

Dual-channel retailing empowers the manufacturer to benefit from market opportunities by producing customized items that fulfill client requirements. The manufacturer and retailer sell customized products, which allow customers to express their chosen style to increase both the likelihood of customers making a purchase and their level of satisfaction with the product. This trend is demonstrated by the current study, in which customized consumer items are considered through online and offline channels. On the other hand, cybersecurity has become a crucial aspect of the digital era, ensuring the protection of sensitive data, networks, and systems from cyberattacks and unauthorized access. This study develops with a modern cybersecurity framework to protect against cyberattacks and increase customer trust. This model is based on customized product design, cybersecurity investment, advertisement investment, and increasing the green level of customized products. The model is solved using both centralized policy and vertical Nash policy. Numerical results indicate that centralized profit is 2.37% more than the decentralized profit. Without investing in customized products and cybersecurity, the profit of the supply chain decreases by 2.33% and 1.99% for the centralized method, 1.28% and 1.15% for the vertical Nash method for the retailer, and 1.85% and 1.38% for the vertical Nash method for the manufacturer. Full article

Based on a sample of Chinese listed firms from 2010 to 2024, this study employs a complex adaptive systems (CAS) lens to investigate the spillover effects of core-firm digital transformation (CDT) on innovation within supply chain networks. It reveals that CDT fosters supplier innovation while impeding customer innovation. Heterogeneity analysis shows that the spillover effect is more pronounced when suppliers (customers) are non-state-owned, larger, more competitive, and have more able managers. Mechanism analysis suggests that the spillover effect is realized through resource and competition mechanisms. This study contributes to the literature by integrating CAS theory with established supply chain management perspectives to provide a systemic understanding of digital transformation’s ripple effects, offering valuable insights for both managers navigating digital ecosystem evolution and policymakers designing industrial innovation strategies. Full article

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society. Full article

Food supply chains play a critical role in advancing sustainability within today’s food systems. In this work, we construct a differential equation-based model with a four-layer supply chain framework that captures the intricate relationships among producers, manufacturers, distributors, and retailers while considering resource optimization, waste minimization, and supply–demand equilibrium. To better understand and predict supply chain behavior, we perform a series of model analyses. By applying chaos theory, we analyze the system’s equilibrium states and evaluate their local stability. Our findings reveal that manufacturers and retailers encounter significant difficulties when the system shifts into chaotic behavior. This can be made worse by future uncertainties. This entails formulating tailored strategies to mitigate risks. Hence, we design a set of nonlinear feedback control strategies to synchronize two chaotic supply chain networks. Theoretical validity is established using Lyapunov theory. Our simulation results confirm that the proposed strategy can eliminate synchronization errors. Furthermore, it allows for swift alignment and coordination between the networks. Overall, this synchronization method is both effective and easy to implement for managing risks and enhancing sustainability in food supply chains affected by chaotic dynamics. Full article

Palm oil (Elaeis guineensis), one of the most widely used vegetable oils globally, originates from West Africa and has played a significant role in food, health care, and historical trade networks. It holds cultural, historical, ecological and symbolic significance in Bahia, Brazil. Unlike industrial monocultures, Bahia’s dendê economy is rooted in biodiverse landscapes, maintained through artisanal methods and generational knowledge. Yet, this traditional system faces mounting pressures from deforestation, declining artisanal production, and the industrialization of palm oil supply chains. Parallel to these ecological and economic threats is the abandonment of Bahia’s historical processing infrastructure: many traditional mills and industrial heritage sites have been lost, eroding both tangible and intangible cultural landscapes. These shifts have profound implications for the Baianas do Acarajé, the iconic street vendors who embody the matriarchal cultural lineage and rely on high-quality, traditionally produced dendê for their Afro-Brazilian cuisine. The increasing cost and reduced availability of artisanal oil compromise not only their livelihoods but also the authenticity of comidas de azeite, diminishing a sensory and symbolic culinary tradition. This study adopts a rigorous interdisciplinary methodology, synthesizing ethnography, heritage science, and engineering principles to explore how these artisanal practices can help us solve modern industrial sustainability problems. This article argues that Bahia’s palm oil heritage embodies a multifaceted heritage, spanning religious, economic, ecological, and cultural dimensions, that remains under-recognized and vulnerable. Drawing from UNESCO’s framework of intangible cultural heritage, the study not only details how these practices are transmitted across generations through the matriarchal culinary lineage, but ultimately advances three core contributions, analyzing artisanal performance, proposing a transferable sustainability framework, and outlining actionable pathways, to demonstrate that local communities are co-designers of solutions whose heritage offers a proven blueprint to address contemporary industrial sustainability challenges, calling for informed recognition and support to safeguard this essential component of Brazil’s Afro-descendant cultural identity. Full article

Accurate and efficient tomato ripeness estimation is crucial for robotic harvesting and supply chain grading in smart agriculture. However, manual visual inspection is subjective, slow and difficult to scale, while existing vision models often struggle with cluttered field backgrounds, small targets and limited throughput. To overcome these limitations, we introduce TSE-YOLO, an improved real-time detector tailored for tomato ripeness estimation with joint detection and segmentation. In the TSE-YOLO model, three key enhancements are introduced. The C2PSA module is improved with ConvGLU, adapted from TransNeXt, to strengthen feature extraction within tomato regions. A novel segmentation head is designed to accelerate ripeness-aware segmentation and improve recall. Additionally, the C3k2 module is augmented with partial and frequency-dynamic convolutions, enhancing feature representation under complex planting conditions. These components enable precise instance-level localization and pixel-wise segmentation of tomatoes at three ripeness stages: verde, semi-ripe (semi-maduro), and ripe. Experiments on a self-constructed tomato ripeness dataset demonstrate that TSE-YOLO achieves 92.5% mAP@0.5 for detection and 92.2% mAP@0.5 for segmentation with only 9.8 GFLOPs. Deployed on Android via Ncnn Convolutional Neural Network (NCNN), the model runs at 30 fps on Dimensity 9300, offering a practical solution for automated tomato harvesting and grading that accelerates smart agriculture’s industrial adoption. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a growing global health challenge, driven in part by how antibiotics are accessed, distributed, and used within complex value chains. In peri-urban India, these supply chains involve a range of formal and informal actors and practices, making them a critical yet underexamined focus for antimicrobial stewardship efforts. While much research has focused on the manufacturing and regulatory end, less is known about how antibiotics reach consumers in rural and peri-urban settings. This study aimed to map the human antibiotic value chain in West Bengal, India, and to analyse how formal and informal governance structures influence antibiotic use and stewardship. Methods: This qualitative study was conducted in two Gram Panchayats in South 24 Parganas district, West Bengal, India. Semi-structured interviews were carried out with 31 key informants, including informal providers, medical representatives, wholesalers, pharmacists, and regulators. Interviews explored the structure of the antibiotic value chain, actor relationships, and regulatory mechanisms. Data were analysed thematically using a value chain governance framework and NVivo 12 for coding. Results: The antibiotic value chain in rural West Bengal is highly fragmented and governed by overlapping formal and informal rules. Multiple actors—many holding dual or unofficial roles—operate across four to five tiers of distribution. Informal providers play a central role in both prescription and dispensing, often without legal licences but with strong community trust. Informal norms, credit systems, and market incentives shape prescribing behaviour, while formal regulatory enforcement is inconsistent or absent. Conclusions: Efforts to promote antibiotic stewardship must move beyond binary formal–informal distinctions and target governance structures across the entire value chain. Greater attention should be paid to actors higher up the chain, including wholesalers and pharmaceutical marketing networks, to improve stewardship and access simultaneously. This study highlights how fragmented governance structures, overlapping actor roles, and uneven regulation within antibiotic value chains create critical gaps that must be addressed to design effective antimicrobial stewardship strategies. Full article

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks. Full article

Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration. Full article

Addressing two types of supply chain disruptions—frequent short-duration disruptions (e.g., minor natural disasters) and infrequent long-duration disruptions (e.g., geopolitical conflicts, public health crises)—while considering their impact on logistics capacity, this paper proposes a multi-echelon inventory management optimization framework based on the Proximal Policy Optimization (PPO) algorithm. Unlike traditional inventory control models with simplistic assumptions, this study integrates factors such as the frequency, duration, and impact of disruptions into the inventory optimization process. It is designed to coordinate replenishment decisions at the warehouse while reacting to local retailer states. Since retailers share the same cost parameters and demand dynamics, their decision problems are structurally symmetric, which allows us to use a shared policy across retailers and thus keep the learning model compact and scalable. Numerical experiments compare the PPO policy with classical inventory heuristics under various network sizes and disruption types. The results show that PPO consistently achieves lower total costs than the benchmarks, and its relative advantage becomes more pronounced under severe or longer disruptions. These findings suggest that modern policy-gradient methods, combined with simple forms of structural symmetry, can provide an effective and scalable tool for managing disrupted multi-echelon supply chains. Full article

Integration of renewable energy sources into existing residential and communal district heating systems requires technical adjustments and corrections. Measures aimed at reducing heat consumption at the points of delivery have a similar impact. This study aims, through simplified partial models (in heating mode), to present the relationships between these modifications and their potential effects on operational problems and deficiencies. The main parameters assessed in the design and correction of systems are temperature differentials, derived flow rates, pumping work, and control methods. Within the chain of heat source–primary distribution–secondary distribution–consumers, the analysis focuses on secondary circuits with consumers. A simplified multi-building network model was used to compare static and dynamic control strategies under temperature regimes of 70/50 °C, 60/40 °C, and 40/30 °C. The results show that dynamic control based on variable-frequency pumps, weather-compensated supply regulation, and optimized temperature differences between supply and return lines (ΔT) reduces pumping energy by 30–40% and increases heat delivery efficiency by up to 10%. A significant reduction in CO₂ emissions is also observed due to decreased pumping work, reduced heat losses in the distribution network, and the integration of renewable energy sources. The savings depend on the type and extent of RES utilization. The implementation of dynamic control in these systems significantly improves exergy efficiency, operational stability, and the potential for low-temperature operation, thus providing a practical framework for the modernization of district heating networks. Full article

The increasing frequency and intensity of extreme disasters, exacerbated by climate change, pose significant challenges to sustainable development by disrupting critical infrastructure and hampering relief efforts. Enhancing disaster resilience—a core objective of sustainable development—requires integrated approaches that simultaneously address infrastructure restoration and efficient resource allocation. This study proposes a sustainable optimization framework for post-disaster response, integrating road rehabilitation decisions with emergency logistics planning within a three-tier supply chain network. We develop a mathematical model that synergistically optimizes repair crew scheduling, depot location, and vehicle routing, with the objective of maximizing a comprehensive satisfaction index that balances timely delivery (time satisfaction) and fulfillment of material needs (demand satisfaction). This integrated approach directly contributes to sustainable disaster management by ensuring more reliable and equitable access to vital resources in affected communities. A tailored variable neighborhood search algorithm is designed to solve the model efficiently, as demonstrated through large-scale numerical experiments. Our findings highlight several policy-relevant insights for sustainable emergency planning: adequate budgeting is crucial for uninterrupted relief operations; strategic investments in rapid road repair capabilities or vehicle fleets significantly enhance system efficiency; and prioritizing time satisfaction (rapid response) yields greater overall benefits than merely increasing delivered quantities. Furthermore, restoring critical road infrastructure is shown to mitigate transportation uncertainties, thereby strengthening the resilience of the entire relief system. This work provides a quantifiable methodology and practical decision support tools for building more sustainable and resilient communities in the face of disasters. Full article