The pharmaceutical supply chain continues to face significant challenges, including the circulation of counterfeit medicines, limited traceability, and insufficient transparency among stakeholders. To address these issues, this study presents a blockchain- and IPFS-based traceability framework designed to improve the secure tracking and verification of pharmaceutical products. The proposed system combines role-based smart contracts with decentralised off-chain storage to maintain product history and enable integrity validation using unique digital identifiers. The prototype was implemented in Ethereum-compatible environments. Repeated workflow measurements were obtained in the Remix VM environment, while Sepolia was used for deployment-level validation and public testnet verification. The system demonstrated the complete workflow, including participant registration, product enrollment, manufacturing, multi-stage transfer of ownership, and authenticity verification. Analysis of the execution outputs indicates that the core operations exhibit consistent gas consumption across repeated runs, offering clear insight into computational cost and system behaviour. The findings suggest that the proposed framework can serve as a viable foundation for improving traceability in pharmaceutical supply chains at the prototype level. However, further investigation is required to assess system performance under real-world deployment conditions, particularly with respect to scalability, latency, and large-scale operational constraints.

Pharmaceutical companies increasingly face difficulties in tracking products across the supply chain, enabling counterfeiters to introduce fake medicines that cause substantial economic losses and serious health risks. A mechanism capable of tracing and monitoring drug movement at every stage is therefore essential. Blockchain offers a promising foundation for secure and transparent supply chain tracking. This paper introduces a two-module framework. It integrates a blockchain-based drug supply chain management (DSCM) system with a multi-agent recommendation model driven by trust region policy optimization (TRPO). The first module employs a customized blockchain to continuously record, monitor, and verify drug movement within a simulated smart pharmaceutical environment. The second module is a sentiment analysis (SA) that operates with two TRPO agents in a blockchain-secured setting. To enhance policy performance, the TRPO agents incorporate entropy regularization. This setup specifically addresses key SA challenges, including handling unlabeled data, feature selection, and class imbalance mitigation. The first agent applies semi-supervised learning (SSL) with pseudo-labels on high-confidence unlabeled samples to expand the training set. The second agent performs SA, applies Shapley additive explanations (SHAP) for feature ranking, and uses reward mechanisms to improve performance on underrepresented classes. The framework was evaluated on two large real-world drug review datasets, Drugs.com and Druglib.com. For Drugs.com, the blockchain module achieved 3.015-second latency and 172.322 tps throughput, while the SA model reached 93.250% accuracy and 94.329% F-measure. For Druglib.com, latency was 2.930s, throughput was 189.538 tps, accuracy was 95.192%, and F-measure was 96.257%. These results demonstrate the effectiveness of the framework in analyzing patient reviews. It successfully provides secure supply chain recording and sentiment-based insights within controlled experimental conditions.

ABSTRACT The resilience of Regional Comprehensive Economic Partnership (RCEP) shipping network is crucial for supply chain security amidst growing geopolitical and operational risks. This study proposes a quantitative framework, integrating complex network theory and resilience triangle model, to assess and compare resilience of six key RCEP sub-networks. We simulate network dynamics across four phases—initial, disruption, recovery, and stabilisation—under four attack modes (targeting degree, betweenness, strength, and random) and recovery strategies. Resilience is quantified through the metrics of network efficiency, connectivity, and independent paths. Findings reveal significant regional heterogeneity. Bohai Rim, China–Australia–New Zealand, and China–Japan–Korea sub-networks exhibit superior resilience in efficiency and independent paths, while Bohai Rim, Guangdong–Hong Kong–Macao, and Yangtze River Delta excel in connectivity. Conversely, China–ASEAN network demonstrates relative vulnerability. Strength-based and Random Recovery strategies are identified as the most effective for most regions. Accordingly, we recommend enhancing hub functionality and path diversity for high-degree/strength ports (e.g. Shanghai , Hong Kong) in GHM and YRD regions, and strengthening transhipment capacity of high-betweenness ports (e.g. Busan, Melbourne) in CJK and CANZ networks. This study provides a robust analytical tool and strategic insights for building resilient maritime infrastructures within the RCEP framework.

ABSTRACT Despite the maritime supply chain being the backbone of global trade, it faces persistent challenges in transparency, fraud prevention, shipment tracking and data privacy. Blockchain technology has emerged as a transformative solution, enhancing trust and traceability within supply chain networks. However, its limitations in data privacy and scalability necessitate advanced privacy-preserving mechanisms. Zero-Knowledge Proofs (ZKP) offers a cryptographic approach to validate data without exposing sensitive information, addressing blockchain’s privacy constraints. This paper reviews the state of the art on current applications of blockchain in maritime supply chain management and explores the integration of ZKP for secure trade document verification, fraud detection, privacy-preserving traceability and regulatory compliance. Additionally, it examines computational overhead, scalability and adoption barriers while proposing future research directions. Implementing ZKP within blockchain-based port operations enables robust governance models, ensuring data verification without revealing confidential details. This approach fosters a secure and privacy-compliant trade environment, enhancing trust and collaboration among stakeholders. By optimising resource allocation and mitigating risks, integrating ZKP can significantly improve maritime supply chain efficiency. Integrating Zero-Knowledge Proofs with blockchain, maritime logistics can achieve a balance between transparency, security and operational efficiency, addressing existing challenges in data privacy and regulatory compliance, improving the sustainability of port operations.

The smart manufacturing revolution: how industrial robotics reshape supplier networks

Many hands make light work: An LLM-based multi-agent system for detecting malicious PyPI packages

Policy-mediated resilience memory in strategic mineral supply chains: How China\u2019s rare earth sector learns from disruption

ABSTRACT Amid intensifying global technological competition and sharply rising geopolitical uncertainty, semiconductor supply chain security has become a critical issue shaping national industrial security and macroeconomic stability. Focusing on the structural risks faced by China's semiconductor supply chain under the dual pressures of external shocks and domestic transformation, this study adopts a multi‐actor collaborative governance perspective. It systematically analyses the behavioural logic and strategic interactions among government agencies, core semiconductor firms and foreign suppliers in supply chain security governance. Grounded in the prevailing institutional environment and industrial practice, the study characterizes how these actors weigh interests and evolve strategies under varying levels of regulatory stringency, firm‐level resilience building and cooperation choices, with particular attention to the hub role of core semiconductor firms in the security architecture. The findings indicate that a stable and credible policy environment is a necessary condition for aligning actors around shared security objectives. Core semiconductor firms are pivotal in mitigating systemic risk and enhancing supply chain security by advancing resilience strategies and reducing dependence on single‐source supply channels and narrow technological trajectories. Semiconductor supply chain security is not the product of isolated decisions by any single actor. Instead, it emerges through an evolutionary process shaped by government guidance, firm leadership and supplier coordination. These findings offer practical implications for strengthening China's governance framework for semiconductor supply chain security.

The spread of fake goods poses serious problems for industries like luxury goods, electronics, fashion, and pharmaceuticals, leading to monetary losses, harm to brand reputation, and a decline in consumer confidence. Conventional authentication techniques, like QR codes, RFID tags, and centralized databases, are insufficient for effective counterfeit deterrence because they are susceptible to duplication, tampering, and security flaws. To address these issues, this paper suggests a blockchain-based authentication system that uses a decentralized, transparent, and impenetrable ledger to confirm the legitimacy of products. The proposed framework makes it simple to verify product information by using MetaMask for secure user interactions and Ganache to simulate a local blockchain network. By automating procedures like product registration, ownership tracking, and authentication, smart contracts make sure that data is indestructible and verifiable throughout the supply chain. Blockchain technology greatly increases transparency and cuts the risk of fraud through doing away with the need for centralized systems. By scanning a QR code connected to blockchain records, customers can instantly access the product's history and confirm the authenticity of the product. This strategy strengthens consumer confidence and improves supply chain security. To improve counterfeit prevention and authentication techniques even more, future advancements could involve AI-driven fraud detection, predictive analytics, and IoT-enabled real-time tracking. Index Terms: Blockchain, Product Authentication, Smart Contracts, Fraud Prevention, MetaMask, Ganache, QR Code

ABSTRACTCounterfeiting is a growing global challenge with significant economic and social implications. Physical Unclonable Functions (PUFs), exploiting manufacturing randomness to generate unique and unclonable identifiers, have emerged as a promising solution for secure authentication. This study presents a novel, scalable method for fabricating inkjet‐printed PUFs by exploiting the randomness of ink droplet deposition on substrates such as paper. By optimizing geometric features, the proposed system ensures high uniqueness, reliability, and bit uniformity. The PUFs also exhibits excellent durability, maintaining performance under mechanical stress and chemical exposure. Furthermore, the system incorporates a low‐cost imaging setup and advanced positional markers, enabling fast and accurate database validation. This work establishes a robust and low‐cost route to PUFs that can be interrogated with consumer‐grade devices, making them suitable for various anticounterfeiting applications, including supply chain security and luxury goods authentication.

ABSTRACT During public health emergencies, vaccine supply chains (VSCs) face systemic challenges including information silos, slow risk responses, and poor traceability. Generative AI offers a new approach to rebuilding VSC security governance with its strong multi-source data processing and knowledge generation capabilities. This paper develops the 3D-DeGAI model. Using a dual knowledge-driven framework and CAS theory, 32 core risk factors are identified. With LLM-guided causal inference, a causal network with 432 directed edges is built to reveal whole-chain risk transmission, including radiation, convergence, and bridging effects, and 30 key risk paths are recognized. Simulation results verify the model's adaptability and robustness. It effectively captures implicit risk correlations, supports risk fuse design, and enhances supply chain resilience. This interdisciplinary method can assist the WHO vaccine implementation plan and inform intelligent VSC safety management.

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

Specialized, Refined, Unique and Novel (SRUN) listed firms are pivotal to the high-quality development of China’s real economy, and their growth quality underpins the security of industrial and supply chains. This study empirically examines the relationship between R&D investment persistence and growth quality of Chinese A-share SRUN listed firms from 2006 to 2024, with technology conversion efficiency as the mediating variable. R&D investment persistence is measured from the dual dimensions of investment intensity and stability, and firm growth quality is a comprehensive indicator constructed via principal component analysis (PCA) from revenue growth, profitability and risk resilience. Panel data regression models, combined with mechanism, endogeneity, robustness and heterogeneity tests, are adopted for empirical analysis. The results show a significantly positive correlation between R&D investment persistence and SRUN firms’ growth quality, with the regression coefficient of R&D investment persistence on growth quality reaching 0.189 (p < 0.01); both investment intensity and stability exert significant positive effects on all dimensions of growth quality, with their regression coefficients on growth quality being 0.156 and 0.132 (both p < 0.01) respectively. Technology conversion efficiency plays a partial mediating role in this relationship, with the mediating effect ratio of R&D investment persistence on growth quality through technology conversion efficiency at 34.2%, as R&D investment persistence indirectly improves growth quality by enhancing patent output and new product conversion efficiency. Heterogeneity analysis indicates that this positive correlation is more pronounced in high-tech industries, small and medium-sized enterprises (SMEs) and eastern China-based firms, driven by differences in industrial R&D dependence, resource endowments and financing frictions. Though endogeneity is mitigated by instrumental variables, propensity score matching (PSM) and difference-in-differences (DID), strict causal identification is constrained by data availability. This study enriches the theories of R&D investment and firm growth, and provides empirical insights for SRUN firms to optimize their R&D strategies and for the government to formulate targeted support policies, so as to promote the high-quality development of SRUN firms and the transformation of China’s manufacturing industry.

The rapidly changing world is challenging established logistics routes. Coronavirus, the escalating situation in the Red Sea, a special military operation, and much more are changing cargo delivery routes from China to Europe. Northeast China borders Russia, Mongolia, and North Korea. Several international railway lines pass through the region, but they are unable to cope with the rapidly growing freight volume. Local Chinese authorities are trying to stimulate the development of new multimodal international transport routes. In this article, the author attempted to analyze the development features of the New Northeast Land-Sea Corridor, which connects the coast of the Bohai Gulf, the interior regions of northeast China, Mongolia and Russia. Despite the efforts of the governments of the regions in northeast China, the construction of the New Corridor is facing problems. The stumbling block is the presence of several unfinished sections of railway lines in Inner Mongolia, China, a long new section of railway in Mongolia. At the same time, the author emphasizes the obvious advantages of the new transport corridor: Connecting the coast of Liaoning Province with the interior regions of Northern China, Mongolia, and Russia will reduce transportation costs, mitigate potential transport risks, and increase regional trade exchanges, optimize the structure of commodity flows, create new jobs and new opportunities for economic development in areas along the new transport artery. China’s New Northeast Transport Corridor is an "engine" for sustainable development, security and stability of supply chains. The construction of the corridor is based on a new development concept and complies with the principles of environmental friendliness, openness, convenience, efficiency and sharing.

Purpose Blockchain technology is increasingly viewed as a key enabler of transparency, traceability and resilience in automotive supply chains; however, adoption priorities differ markedly between electric vehicle (EV) and traditional vehicle (TV) manufacturers. This study aims to benchmark and compare blockchain adoption enablers across EV and TV supply chains and to develop a structured, sector-specific decision framework to support managerial and strategic adoption decisions. Design/methodology/approach The study proposes a hybrid multi-method framework integrating supervised machine learning techniques (random forest, permutation importance and BORUTA) with total interpretive structural modeling and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis. Expert evaluations from 100 professionals across the automotive and digital transformation domains were analyzed to shortlist and structurally model high-impact blockchain adoption enablers. Findings From an initial set of 30 enablers, 12 critical enablers were identified and hierarchically structured. Results indicate that EV manufacturers prioritize sustainability, traceability and innovation, whereas TV manufacturers emphasize cost efficiency, cybersecurity and regulatory compliance. Robust cybersecurity infrastructure, regulatory governance and risk management emerge as foundational enablers across both sectors. Research limitations/implications The study relies on expert judgment within the automotive sector, which may limit generalizability to other industries. Future research could extend the framework by incorporating Internet of Things and artificial intelligence enablers and validating the model across additional industrial contexts. Practical implications The proposed framework provides managers and policymakers with a structured roadmap for prioritizing blockchain investments, aligning adoption strategies with sector-specific objectives and targeting high-leverage enablers to accelerate digital transformation in automotive supply chains. Social implications By supporting transparent, traceable and secure supply chain operations, blockchain adoption can enhance sustainability performance, regulatory accountability and stakeholder trust across automotive ecosystems. Originality/value This study offers a novel integration of machine learning-based feature selection with interpretive structural modeling to benchmark blockchain adoption enablers across EV and TV manufacturers, delivering a decision-oriented, sector-comparative modeling framework for digital supply chain transformation.

ABSTRACT Post-harvest fish losses remain a persistent barrier to food security, profitability, and sustainability in tropical aquaculture supply chains. This study develops a novel fuzzy-integrated decision-support framework that incorporates both efficiency and stakeholder influence into the House of Risk 2 model, extending it into a multidimensional prioritization tool. The framework combines the Fuzzy Delphi Method to validate expert-driven mitigation options, Fuzzy Data Envelopment Analysis to measure strategy efficiency, and Fuzzy Interpretive Structural Modeling to quantify actor interdependence and influence, integrated through a fuzzy-modified House of Risk 2 model under a triangular fuzzy set environment. Eight mitigation strategies were validated, with capacity building, ice-use training, and market-facility improvement identified as the most effective and feasible. The model offers a data-driven approach for designing resource-efficient and institutionally coordinated mitigation strategies, highlighting the need for synergistic technical, infrastructural, and governance actions to achieve Sustainable Development Goal targets 12.3 and 14.7.

Global carbon reduction mandates drive the energy transition but expose manufacturing supply chains to carbon reduction risks. This study employs complex network theory to construct a multi-stage industrial chain model, integrating numerical simulations to examine the transmission of carbon reduction risk under random and targeted shocks. Carbon reduction risks raise costs, trigger price increases, and induce risk transmission. Network robustness exhibited structural heterogeneity: random networks outperformed scale-free and small-world networks under random shocks, while small-world networks showed the highest resilience against targeted attacks. Targeted attacks triggered a 50% efficiency loss in scale-free networks at an early attack round, far exceeding the impact of random shocks of the same intensity. Network failure probability was inversely correlated with the strategic resilience parameter and positively correlated with the external dependency parameter: elevating the strategic resilience parameter above 0.5 and maintaining the external dependency parameter below 0.5 significantly enhanced small-world network resilience. Adjusting either parameter alone failed to mitigate cascading failures in scale-free networks under targeted attacks. For random networks, a strategic resilience parameter above 0.3 prevented efficiency from falling below the 50% threshold under random shocks. This study innovates by establishing a benchmark model that links carbon reduction risk dynamics to supply chain network structure, providing a quantitative analytical framework for policymakers to design targeted resilience strategies and for managers to optimize network robustness amid decarbonization pressures.

The United States has become increasingly dependent on imported pharmaceutical products and active pharmaceutical ingredients (APIs), reflecting the globalization of drug manufacturing and supply chains. While this shift has improved cost efficiency and production scalability, it has simultaneously introduced significant vulnerabilities, particularly the growing risk of falsified and substandard medicines entering regulated markets. These risks pose serious threats to patient safety, treatment efficacy, and broader public health outcomes. Despite the existence of regulatory frameworks such as the Drug Supply Chain Security Act (DSCSA), there remains a critical gap in the integration of trade compliance mechanisms with public health protection systems. This study adopts an analytical and policy-oriented approach to evaluate the effectiveness of pharmaceutical import compliance systems in the United States, with particular emphasis on trade compliance components including product classification, documentation integrity, audit procedures, and cross-border regulatory coordination. The findings suggest that robust compliance frameworks significantly enhance supply chain traceability, reduce exposure to counterfeit and substandard drugs, and strengthen regulatory enforcement capabilities. The study contributes by proposing a compliance-driven model that systematically links trade compliance practices with public health safeguards, offering a practical pathway for mitigating risks associated with global pharmaceutical supply chains. Strengthening these systems is essential for ensuring drug safety, improving regulatory oversight, and protecting public health in an increasingly interconnected global market.

Distributed ledger technology (DLT) has emerged as a transformative solution across industries, offering decentralization, transparency, and security. Among DLT architectures, IOTA’s Tangle stands out as a scalable, feeless framework well-suited for supply chain applications, particularly in critical sectors like agriculture. This work leverages IOTA’s Tangle to design a secure and traceable wheat supply chain system, addressing challenges such as opacity, fraud, and inefficiency. The proposed solution is an IOTA-based framework designed for enhancing supply chain traceability of wheat, offering trustworthy, transparent, secure and scalable services. Based on smart contracts, a transaction processing system was developed to enhance transparency and traceability and automate tasks. Furthermore, we used various technologies for increased visibility and operational efficiency throughout the supply chain, including IPFS (InterPlanetary File System) for storing extensive data such as stakeholder certifications, AI (Artificial Intelligence) for predicting crop yields, and Self-Sovereign Identity (SSI) for enhanced security. In addition, a government-provided mobile app helps silo managers and police officers verify transportation credentials and prevent contraband. The implementation of this system is expected to enhance supply chain resilience and transparency, particularly in Tunisia, thereby supporting the country’s goal of food autonomy.

Lithium extraction from oil and gas produced water: resource characteristics, technological challenges and future perspectives.