Search Results (1,000)

Globalization refers to the increasing density of economic, social, and technological interconnections on a global scale. In the healthcare industry, it simultaneously accelerates innovation and increases systemic vulnerabilities. This study aims to review and conceptually synthesise the main channels of impact: (1) pharmaceuticals, clinical development, and regulation; (2) supply chains and resilience; (3) service mobility (health tourism); (4) human resources and competencies; (5) digitalization, artificial intelligence (AI), and data governance; (6) ethics, law, and public policy; and (7) sustainability and climate change. The COVID-19 pandemic highlighted the risks associated with global interdependencies, particularly in supply chains, while also demonstrating the innovation-accelerating effects of knowledge sharing and international cooperation. Particular attention is given to artificial intelligence and digital health, which open up new potential for efficiency and quality improvement from research and development through diagnostics to healthcare organization, while simultaneously intensifying concerns related to data protection, cyber security, and liability. Telemedicine, platform-based systems, and real-world data may contribute to addressing the care needs of ageing societies, but only when supported by appropriate competencies and sound data governance. As global data flows intensify, the importance of data protection, bias mitigation, transparency, and accountability correspondingly increases. Through the cultural channels of globalization, health-conscious lifestyles and complementary approaches are also spreading, which we address in a brief, separate subsection. The guidelines of international organizations foster standardization; however, due to differences in local capacities and institutional environments, the effects are not homogeneous. In conclusion, the study emphasises the dual nature of globalization; it expands access and accelerates innovation, while at the same time creating new vulnerabilities—in supply chains, labour mobility, and data security—and, together with climate-related risks, generating complex adaptive pressures for the healthcare industry. Full article

The effective closure of the Strait of Hormuz on 28 February 2026 disrupted approximately 20 million barrels (bbl) per day of crude oil transit, constituting the largest supply shock in global oil market history. This study quantifies the resulting economic losses under three blockade-duration scenarios and evaluates the Northern Sea Route (NSR) as a partial mitigation mechanism through a novel framework integrating sustainable supply chain resilience (SSCR), the Triple Bottom Line (TBL), and the United Nations Sustainable Development Goals (SDGs). A 3 × 3 scenario matrix crossing three blockade durations with three NSR utilization levels estimates global and country-level impacts using data from the International Energy Agency (IEA), the International Monetary Fund (IMF), and the Centre for High North Logistics (CHNL). Even under maximum feasible NSR utilization, net environmentally adjustment mitigation offsets only 1.1–3.6% of total global losses, demonstrating that the Northern Sea Route functions as marginal insurance rather than a viable substitute for Hormuz-dependent supply chains. Global Gross Domestic Product (GDP) losses range from USD 330 billion to USD 2.2 trillion, with South Korea (68–70% Middle East crude dependency) and Japan (approximately 95%) disproportionately affected. After TBL environmentally adjustment monetizing CO₂, black-carbon, and icebreaker costs, the NSR mitigates 1.1–3.6% of total losses, functioning as insurance rather than substitution. The SDG assessment reveals a fundamental trade-off: the NSR offsets energy-security losses (SDGs 7, 9) but worsens climate and marine outcomes (SDGs 13, 14). Theoretically, this study proposes “alternative maritime route availability” as a conceptual extension of supply chain resilience (SCRes) capabilities and outlines a sustainability-adjusted resilience score (SARS) framework that, pending further validation, could serve as a replicable assessment tool. These findings underscore that accelerating the energy transition remains the most effective long-term response to chokepoint vulnerability. Full article

Against the backdrop of global energy landscape restructuring, the advancement of the “dual-carbon” goals, and escalating external uncertainties, coal, as the “ballast stone” of China’s new energy system, faces new challenges in terms of supply chain stability and security. Therefore, scientifically assessing China’s coal supply chain resilience (CSCR) is of significant theoretical and practical importance for systematically identifying its supply vulnerabilities and ensuring energy supply security under extreme conditions. In the paper, we construct a composite evaluation indicator system using national statistical data from 2010 to 2024. We operationalize resilience across the following four capacities: resistance, absorption, recovery, and adaptive capacity. Annual resilience levels are measured using an integrated framework. This framework combines an improved entropy weight method, TOPSIS, and gray relational analysis (GRA). We then use the indicator contribution degree and obstacle degree models to identify the most influential factors. The results indicate that China’s CSCR followed a fluctuating upward, W-shaped trajectory during 2010–2024, with a marked acceleration after 2020. Resistance and absorption capacities display pronounced volatility. Recovery and adaptation capacities improve steadily. The dominant obstacle factors include the share of intelligent coal production capacity, labor productivity per employee, the scale of workforce security, and the working-capital turnover ratio. These findings provide empirical evidence and policy-relevant insights for strengthening China’s CSCR and reinforcing national energy security. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

This study proposes a novel dual-role weighting framework for dry port location selection, bridging the gap between commercial logistics efficiency and strategic disaster resilience. Designed to establish a new theoretical evaluation paradigm, the research utilizes the Fuzzy Rough SWARA (FR-SWARA) method and a 12-person expert panel to weigh a comprehensive set of 31 criteria under high-dimensional uncertainty. The findings reveal a decisive hierarchical shift, where spatial and infrastructure-related dimensions significantly outweigh traditional cost considerations. This empirical evidence substantiates the transition from ‘just-in-time’ to ‘Just-in-Case’ logistics architectures. Ultimately, the study reconceptualizes the dry port as a ‘strategic stabilizer’—a critical infrastructure node that absorbs systemic shocks and maintains supply chain continuity during diverse crises, including natural disasters and the COVID-19 pandemic. The proposed weighting framework offers a robust theoretical roadmap for policy and managerial decision-making in volatile geographies. Full article

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions. Full article

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. Full article

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. Full article

To improve the decoding performance of multi-edge type low-density parity-check (MET-LDPC) codes, this paper proposes an edge-type differentiated parameter coupling offset min-sum (EDPC-OMS) decoding algorithm. The contributions are threefold. First, we replace the traditional uniform compensation with edge-type differentiated compensation, resolving the mismatch between the decoding model and code structure. Second, we introduce a parameter coupling mechanism that enables joint optimization of multiple edge types while maintaining differentiated configurations. Third, a practically feasible design combining precomputation and look-up tables enables dynamic parameter adjustment with moderate additional overhead, achieving a favorable performance–complexity trade-off. Simulation results over additive white Gaussian noise (AWGN) channels and Rayleigh fading channels demonstrate that the proposed algorithm adaptively selects offset factors according to channel conditions and edge types without introducing significant computational complexity, effectively lowering the bit error rate and enhancing decoding capability. Full article

Managed Service Providers (MSPs) have increasingly become prime targets for cyberattacks due to their privileged access across multiple client environments. Utilizing a qualitative thematic synthesis and an Open-Source Intelligence (OSINT) methodology, this study examines a purposive sample of major MSP-targeted cyber incidents from 2020 to 2025 to identify common attack patterns, exploited vulnerabilities, and operational impacts on downstream clients, particularly small and medium-sized businesses. Analysis of publicly reported incidents reveals a clear trend toward attacks leveraging centralized management platforms, remote access tools, and multi-tenant architectures, resulting in cascading disruptions from limited initial compromise. The synthesis highlights extortion-driven ransomware, supply chain compromises, and the exploitation of unpatched edge devices as dominant threats. To counter these systemic risks, this study outlines contextualized mitigation strategies such as zero trust principles, strict identity controls, tenant isolation, and continuous monitoring tailored to balance security requirements with MSP operational constraints. While these strategies are evidence-informed and grounded in observed trends, they remain proposed solutions that require further empirical validation. The findings emphasize the critical need for proactive, collaborative security practices among MSPs, clients, and regulators to manage evolving cyber threats effectively. Full article

The urban trade in non-timber forest products (NTFPs) plays a key role in sustaining livelihoods in the Global South, while also suggesting potential pressure on resource supply systems. This study provides an integrated analysis of NTFP diversity, market structure, economic importance, and perceived drivers of resource decline in Kolwezi, a rapidly expanding mining city where such dynamics remain poorly documented. Data were collected through surveys conducted with 35 sellers across two major urban markets and 384 consumers from different neighbourhoods and analysed using descriptive and inferential statistics to examine patterns, associations, and socio-demographic influences. A total of 65 NTFP species were recorded, including 49 plant, 14 animal, and 2 fungal species, reflecting strong dependence on Miombo ecosystems. Medicinal (59.3%) and food uses dominate, with multifunctional species such as Bobgunnia madagascariensis (Desv.) J.H.Kirkbr. & Wiersama, Canarium schweinfurthii Engl., Terminalia mollis M.A.Lawson, Gardenia ternifolia subsp. jovis-tonantis (Welw.) Verdc., and Albizia antunesiana Harms, playing a central role in both household use and market supply. The trade is largely female-dominated (79.1%) and constitutes a major component of the informal urban economy, with monthly incomes ranging from USD 9 to 429.3, primarily driven by sales volume rather than unit price. However, the sector is constrained by structural and logistical limitations, including remoteness of supply areas, seasonality, and limited value addition. The perceived declining availability of high-use-value species, attributed by respondents to deforestation, mining expansion, and overexploitation, highlights perceived sustainability concerns. These pressures are perceived differently across socio-demographic groups, indicating heterogeneous understandings of environmental change. Overall, the results indicate a perceived mismatch between rising urban demand and declining resource availability, which may reflect an emerging socio-ecological imbalance between urban demand and perceived resource availability. Addressing these challenges requires integrated strategies that combine the domestication of priority species, the development of processing chains, improved infrastructure, and strengthened governance mechanisms. Such approaches are essential to reconcile livelihood support with the sustainable management of NTFPs in rapidly transforming urban landscapes. Full article

As interest in sustainable food consumption grows, local food has emerged as a key strategy for enhancing environmental sustainability, regional economic vitality, and food system resilience. This study examines how consumers’ sustainable value perceptions influence willingness to pay (WTP) and future purchase intention for local food within an integrated analytical framework. Using survey data from 400 consumers in South Korea, sustainable value perceptions were measured across social, environmental, and economic dimensions. Exploratory factor analysis and cluster analysis were used to identify consumer segments, while one-way ANOVA and binomial logistic regression analyzed differences in WTP and purchase intention. The results identify four consumer clusters and show that social and environmental value perceptions are positively associated with higher WTP and stronger purchase intention, whereas economic value perception does not significantly affect purchase intention. Prior purchase experience further reinforces purchase intention. This study contributes by proposing and empirically validating an integrated framework linking value perceptions, consumer segmentation, WTP, and purchase intention. The findings highlight the importance of value-based communication, information transparency, and experience-oriented strategies in promoting sustainable local food systems and strengthening agri-food supply chain resilience. Full article

Understanding how geopolitical risk propagates through shipping networks to impact shipping supply chain resilience (SSCR) is essential for advancing global maritime governance reform. This study examines the systemic effects of geopolitical risk on SSCR using cross-border panel data derived from international shipping networks and identifies the transmission mechanisms operating through critical logistics nodes. The results indicate that geopolitical risk exerts a significant and persistent negative impact on SSCR, with significant multidimensional heterogeneity. Mechanism analysis shows that SSCR is undermined through three channels: logistics infrastructure disruption, increased freight rate volatility, and reduced customs clearance efficiency. Node-level evidence further reveals consistently negative effects across most critical logistics nodes. Logistics infrastructure disruption is particularly pronounced in ports. Logistics nodes along Indian Ocean routes exhibit more pervasive effects through the freight rate volatility channel, while reduced customs clearance efficiency represents a common transmission channel across most nodes. Full article

This study investigates the comminution behavior and beneficiation potential of lithium-bearing ores, zinnwaldite from Cínovec (Czech-Germany border) and lepidolite from Villasrubias (Spain) by integrating mineralogical analysis and mechanical characterization. The research is driven by Europe’s need for secure lithium supply chains. In particular, it focuses on the challenges associated with low-grade, fine-grained lithium micas found in hard-rock ores, which offer significant potential to supply in Europe but also pose substantial processing challenges. QMA (Quantitative Microstructural Analysis) revealed distinct differences in the textural and structural characteristics of the studied ores. Zinnwaldite-bearing rocks are coarser-grained with high interlocking and roughness, while lepidolite-bearing samples showed finer grains, lower roughness, and more disseminated mica distribution, indicated by their low clustering degree. In terms of mechanical characterization, zinnwaldite-rich ores have the lowest compressive strength, while lepidolite-rich samples showed the highest values, attributed to their finer grain size and more cohesive structure. This suggests that lepidolite may require higher energy input and finer crushing stages to achieve the target liberation size. These features influenced the breakage behavior observed during mechanical testing and comminution and are essential for enabling selective comminution, separating mica from gangue material. This study contributes to analyzing the potential of European hard-rock lithium resources from the perspective of upstream comminution, which is an essential step influencing downstream energy consumption, reagent use, and overall recovery efficiency. The results of this research emphasize that selective comminution should not rely solely on mineral hardness contrasts but must incorporate microstructural parameters such as clustering, grain size distribution, and orientation. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article