Search Results (167)

Listeria monocytogenes poses a significant challenge in ready-to-eat (RTE) foods due to its persistence in processing environments and severe impact on vulnerable populations. Regulatory approaches differ internationally, reflecting distinct conceptual frameworks and tolerance thresholds. These differences arise from the adoption of zero-tolerance or risk-based regulatory models, which define qualitative or quantitative microbiological limits (absence in 25 g or up to 100 cfu/g) based on a product’s growth potential, and vary in the extent of environmental monitoring, sampling plans, and verification intensity across jurisdictions. In 2024, the European Union updated its regulatory framework governing the microbiological criteria for L. monocytogenes. Previous requirements were strengthened, responsibility was extended across the supply chain, and a strategic role was assigned to challenge testing carried out by manufacturers. This review examines how the European Union and the United States apply risk assessment principles, challenge testing, predictive modelling, and environmental monitoring to control L. monocytogenes in RTE foods. By integrating epidemiological trends, regulatory criteria, and experimental evidence, key differences in safety objectives, validation procedures, and risk management strategies are highlighted. This review also identifies gaps and opportunities for harmonisation, providing guidance for improved evidence-based decision-making and regulatory compliance. Full article

The Netherlands is widely recognised as the global leader in water management, with its flood risk management (FRM) infrastructure lauded as being of the best in the world. This status notwithstanding, Dutch FRM primarily maintains established infrastructural practices and experimental applications of NBSs remain less frequent than established structural projects. This paper details and examines the challenges associated with the prevailing ‘business-as-usual’ approach to FRM in The Netherlands, in which traditional ‘grey’ infrastructural techniques are prioritised over innovative ‘green’ nature-based solutions (NBSs). In line with emerging international trends, such as the EU Water Resilience Strategy, NBSs are increasingly advocated as a strategic, complementary layer to enhance the resilience of existing safety frameworks rather than a self-evident replacement for them. Contrary to grey infrastructure, NBSs provide a number of environmental and social co-benefits extending beyond their flood and drought protection utility. The literature on NBSs details the design, effectiveness, and positive socio-economic impact of the operationalisation of such projects for FRM. This notwithstanding, the uptake and practical implementation of NBSs have been slow in The Netherlands. From a legal and policy perspective, this has been attributed to a lack of political will and the corresponding failure to include NBSs in long term FRM planning. Given the long planning horizons associated with FRM (50–100 years), the failure to incorporate NBSs can lead to policy lock-in that blocks future adaptations. Against this backdrop, this paper employs a semi-systematic literature review to clarify the obstacles to implementing NBSs in Dutch FRM and sets a research agenda that charts a course to mainstreaming NBSs in Dutch FRM. Seven core focus areas for future research are identified. The paper concludes by drawing on these identified focus areas to construct a research agenda aimed at systematically addressing each barrier to the practical operationalisation of NBSs in Dutch FRM, emphasising a hybrid green–grey approach which may serve to inspire similar research in other jurisdictions. Full article

Crypto asset markets are often described as globally integrated. However, empirical evidence suggests that they remain segmented across exchanges and jurisdictions. One notable example is the Korean premium (i.e., Kimchi premium), which refers to persistent price gaps between Korean exchanges and offshore venues. The Korean market accounts for a substantial share of global crypto trading activity. Therefore, this segmentation can affect price discovery and create opportunities for systematic trading. Motivated by the Korean premium, this study introduces the Korean Venue Share Indicator (KVSI). Based on the price discovery literature, KVSI is an interpretable venue-level indicator that uses the relative trading volume share between Korean and global exchanges. This study integrates KVSI into the state space of multiple reinforcement learning algorithms to evaluate whether venue-level information improves trading decisions. The results show that the proposed model with KVSI achieves statistically significant improvements in cumulative return (CR), Sharpe ratio (SR), and maximum drawdown (MDD) compared to the baseline model without KVSI. It also achieves higher CR and mixed effects on risk metrics (SR, MDD) relative to benchmark strategies. Additional analyses indicate that the performance gains from KVSI are market-regime-dependent. Overall, the findings have practical implications for developing cross-market systematic trading strategies by leveraging a venue-level indicator as a proxy for market segmentation. Full article

Fragmented, sector-by-sector governance is poorly suited to cascading risks that couple climate, food, water, health, biodiversity, soils, energy, and environmental quality. This paper addresses the translation gap between integrative security–sustainability paradigms and the routine machinery of government, including planning, budgeting, procurement, and accountability. We develop the Spheres of Security (SOS) model as a conceptual–operational method organised around four overlapping spheres (biophysical, economic, social, and governance) and a repeatable cycle—diagnose → co-design → deliver → demonstrate → adapt—illustrated through two stylised vignettes (urban heat and health; watershed food–water–energy). SOS introduces an auditable overlap rule and an Overlap Score, supported by lean assurance, to make verified multi-sphere co-benefits commissionable and to surface trade-offs transparently within normal, accountable institutions (consistent with weak securitisation). We provide implementation guidance, including minimum institutional preconditions and staged entry-point options for jurisdictions where pooled budgets and full administrative integration are not immediately feasible. Full article

With the rapid development of artificial-intelligence technologies in the field of automated driving, many jurisdictions have successively adopted legislation and policy guidance to regulate the safe use of such technologies and to promote their orderly development. This article takes as its objects of study a set of jurisdictions that are particularly representative in terms of legislation and practice across different legal systems. The study finds that liability regimes for traffic accidents involving automated driving fall mainly into four types: the driver liability regime, the system liability regime, the manufacturer or operator liability regime, and the composite liability regime. In application, each of these regimes reveals different types of institutional dilemmas, including blurred boundaries of liability, underdeveloped mechanisms for evidence production and fact-finding, imbalanced allocation of liability, and fragmentation of the rules governing liability determination. In response to these dilemmas, this article proposes corresponding optimisation pathways, including clarifying the boundaries of driver liability and improving supplementary liability mechanisms; specifying in greater detail the obligations of system providers and strengthening data-related fact-finding rules; developing a reasonable allocation of liability between manufacturers and operators together with supporting insurance arrangements; and enhancing institutional coordination under the composite liability regime. These optimisation pathways not only provide institutional reference for jurisdictions seeking to maintain risk controllability while fostering innovation amid rapid technological evolution, but also lay the groundwork for the systematic improvement of future governance of automated driving. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. We conducted an empirical analysis of 132 jurisdictions in 2024 using the Basel AML Index (AMLI) and the WJP Rule of Law Index (RLI). The Random Forest method was employed to model the relationship between rule-of-law indicators and AML risk levels. Findings reveal a significant inverse relationship between rule-of-law indicators and AML risk levels, with an overall classification accuracy of 69.6%. The model performed best for low-risk countries (precision 75%, recall 92.31%), moderately for medium-risk countries (precision 65.22%, recall 78.95%), but failed to identify high-risk jurisdictions, suggesting a legal institutional “threshold” necessary for effective AML functioning. Key predictors included protection of fundamental rights and mechanisms for civil oversight, with strong negative correlations between AML risk and criminal justice impartiality (−0.35), civil justice fairness (−0.35), and equality before the law (−0.41). These results show that legal factors strongly affect AML risk and can guide regulators in improving risk-based standards, enhancing regulatory certainty, and managing financial risk. Full article

In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures. Full article

The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, the use of narrative clinical data raises distinct ethical challenges that are not fully addressed by conventional frameworks for structured data. We conducted a narrative review synthesizing conceptual and empirical literature on ethical issues in free-text EMR research, focusing on privacy, fairness, autonomy, interpretability, and governance. We examined technical methods, including de-identification, differential privacy, bias mitigation, and explainable AI, alongside normative approaches, such as participatory design, dynamic consent models, and multi-stakeholder governance. Our analysis highlights persistent risks, including re-identification, algorithmic bias, and inequitable access, as well as limitations in current regulatory guidance across jurisdictions. We propose ethics-by-design principles that integrate ethical reflection into all stages of machine learning research, emphasize relational accountability to patients and stakeholders, and support global harmonization in governance and stewardship. Implementing these principles can enhance transparency, trust, and social value while maintaining scientific rigor. Ethical integration is therefore not optional but essential to ensure that machine learning research using free-text EMRs aligns with both clinical relevance and societal expectations. Full article

Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems. Full article

Privacy harms have expanded alongside rapid technological change, challenging the adequacy of existing regulatory frameworks. This systematic review (1990–2025) systematically maps documented privacy harms to specific legal mechanisms and observed enforcement outcomes across jurisdictions, using PRISMA-guided methods and ROBIS risk-of-bias assessment. We synthesize evidence on major regimes (e.g., GDPR, COPPA, CCPA, HIPAA, GLBA) and conduct comparative legal analysis across the U.S., E.U., and underexplored regions in Asia, Latin America, and Africa. Key findings indicate increased recognition of data subject rights, persistent gaps in cross-border data governance, and emerging risks from AI/ML/LLMs, IoT, and blockchain, including data breaches, algorithmic discrimination, and surveillance. While regulations have advanced, enforcement variability and fragmented standards limit effectiveness. We propose strategies for harmonization and risk-based, technology-neutral safeguards. While focusing on the U.S. sectoral and E.U. comprehensive models, we include targeted comparisons with Canada (PIPEDA), Australia (Privacy Act/APPs), Japan (APPI), India (DPDPA), Africa (POPIA/NDPR/Kenya DPA), and ASEAN interoperability instruments. This review presents an evidence-based framework for understanding the interplay between evolving harms, emerging technologies, and legal protections, and identifies priorities for strengthening global privacy governance. Full article

Open-sea fishing vessel accidents are difficult to assess systematically because no state holds exclusive jurisdiction, and reporting and investigative duties are not applied consistently. This study analyzed 67 officially reported accidents from the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS) using a bow-tie framework combining fault tree analysis (FTA), Firth logistic regression, event tree analysis (ETA), and quantitative risk assessment (QRA). COLREG violations and watchkeeping failures dominated collisions; overload and stability issues caused capsizes; pump capacity, hull leakage, and vessel aging (≥30 years) caused sinkings. Firth regression confirmed older vessels and high beam-to-length ratios (≥0.30) significantly increased sinking likelihood. ETA and QRA estimated probabilities of 0.522 for collisions, 0.090 for capsizes, and 0.388 for sinkings, with risks of R = 0.155, 0.048, and 0.036. Because open-sea accident data rely on limited and voluntary reporting, results are preliminary. However, the bow-tie framework effectively identifies dominant causal factors and high-severity event pathways in open-sea fishing operations. Full article

Marine sediments are a key component of aquatic ecosystems, linking diverse water uses, functions, and services. Chemical contamination of sediments is a global concern, with many jurisdictions striving to prevent future pollution and manage existing contamination. This study evaluates the contamination status of Rovinj’s coastal waters using an integrated approach that combines sediment and biota chemical analyses. Sediments were analyzed to assess long-term contaminant accumulation (D8.C1), while the Mediterranean mussel (Mytilus galloprovincialis) served as a bioindicator of bioavailable contaminants and their cumulative effects on marine habitats (D8.C2). Sediment samples were collected from five sites (S1–S5), and mussels were caged using Mussel Watch installations for approximately 120 days at a control site (Lim Bay) and within Rovinj harbor. Both matrices were analyzed for heavy metals (As, Cd, Cu, Cr, Hg, Ni, Pb, and Zn), polycyclic aromatic hydrocarbons (16 PAHs), and polychlorinated biphenyls (PCBs), following the EU Water Framework Directive. All sampled locations showed a reduction in sediment contamination relative to 2011 data, with most concentrations below ecotoxicological thresholds. Exceptions included elevated ΣPAH and PCB concentrations in the harbor (S1 = 3.18 mg/kg DW; 0.33 mg/kg DW) and marina (S2 = 3.64 mg/kg DW; 0.89 mg/kg DW), as well as Ni levels (S3 = 30 mg/kg DW; S4 = 34 mg/kg DW). Despite higher contaminant loads at some locations, mussel contaminant bioaccumulation remained limited, and their vitality and survival were only moderately affected in the harbor. Although localized increases in some contaminants were detected, all calculated Q_PECm values remained below 1.0, indicating no significant ecological risk. However, a moderate-to-high probability of toxic effects (P) may occur with long-term exposure for biota inhabiting harbor and marina areas. The results of this study demonstrate continued improvement in the environmental quality of Rovinj’s coastal waters compared to the previous decade. Full article

The integration of Bitcoin into corporate treasuries constitutes a critical strategic choice, motivated by its capacity to bolster liquidity and serve as an inflation hedge, while simultaneously being encumbered by pronounced financial volatility and regulatory ambiguity. This investigation examines sectoral variations in Bitcoin adoption, with particular attention to the manner in which financial risks, regulatory structures, and decentralized governance mechanisms shape corporate conduct across the technology, cryptocurrency mining, retail, healthcare, and e-commerce sectors. Drawing on a cross-sectional dataset encompassing 102 publicly traded firms collectively holding 1,001,861 BTC, the analysis employs MAD-based volatility, Firth logistic regression incorporating a U.S. regulatory dummy to account for the BITCOIN Act of 2025, and heatmap visualization to evaluate risk profiles and adoption patterns. Results demonstrate marked sectoral disparities: the technology and mining sectors command predominant holdings yet confront heightened risk exposure, whereas retail and healthcare sectors proceed with greater caution, guided by considerations of cost-value efficiency and regulatory adherence. The U.S. regulatory dummy is significant, indicating the BITCOIN Act facilitates high Bitcoin adoption, while recent transactional activity is marginally significant. The heatmap accentuates the technology sector’s pre-eminence in aggregate Bitcoin reserves and illuminates the differential influence of regulatory frameworks in non-U.S. jurisdictions. Anchored in Institutional Theory, the Technology Acceptance Model, and Transaction Cost Economics, the study advances the field by quantifying sector-specific risks and visually representing regulatory impacts, thereby furnishing actionable insights for treasury risk management and regulatory policy formulation within a decentralized financial ecosystem. Full article

Contractual agreements often contain clauses that are unfair, creating unjust suffering in one party to the agreement. ContractNerd leverages advanced Large Language Models (LLMs) to analyze contractual agreements and identify issues across four categories: missing clauses, unenforceable clauses, legally sound clauses, and legal but risky clauses. By using a structured methodology that integrates LLM-based clause comparison, enforceability checks against jurisdiction-specific regulations, and assessments of risk-inducing traits, ContractNerd provides a comprehensive analysis of contractual terms. To evaluate the tool’s effectiveness, we compare its analyses with those from existing platforms on rental clauses that have led to court litigation. ContractNerd’s interface helps users (both drafters and signing parties) to navigate complex contracts, offering actionable insights to flag legal risks and disputes. Full article