Search Results (72)

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

As universities increasingly work towards the adoption of their third mission—fostering entrepreneurship and innovation—the concept of the Entrepreneurial University (EntUni) emphasizes the need to cultivate a defined set of entrepreneurial competencies in students, such as opportunity recognition, risk-taking, perseverance, self-efficacy, and adaptability. The purpose of this study is to identify which entrepreneurial competencies are most critical for student readiness within the context of an Entrepreneurial University. However, limited consensus remains on which competencies are most essential. This study identifies the entrepreneurial competencies most critical for students within an Entrepreneurial University context through a mixed-methods approach. A student survey assesses self-perceived competencies; a stakeholder survey captures the perspectives of faculty, industry experts, and entrepreneurs; and qualitative interviews with industry professionals explore best practices for competency development. Findings reveal six core competencies that EntUnis should help students cultivate: proactiveness, perseverance, grit, risk propensity, self-efficacy, and entrepreneurial intention. Industry experts further highlight the importance of teamwork, ethical and sustainable thinking, and ambiguity tolerance—competencies often underdeveloped in academic environments. The study also identifies a disconnect between entrepreneurial education and practical application, with many students demonstrating high entrepreneurial intention but limited participation in start-up activities. These insights offer actionable implications for educators, policymakers, and university administrators. Overall, the study highlights the importance of experiential learning, academia-industry collaboration, and structured competency-building to enhance entrepreneurial readiness. By addressing these gaps, EntUnis can better equip students to drive innovation, economic growth, and societal impact. Full article

As China’s population ages and the sustainability of the public pension system is at risk, personal savings become crucial. As such, the quality of financial planning for retirement (FPR) has been recognized as a key to safeguarding financial well-being during retirement. This study examines the relationships of two predictors (future time perspective and risk tolerance) and a mediator (subjective financial literacy) in shaping financial planning for retirement, with particular attention to potential gender differences. Using survey data retrieved from respondents aged between 23 and 60 years old, overall sample and gender-based multigroup analysis were used to examine whether gender moderates these relationships. The results reveal that both future time perspective and subjective financial literacy positively influence financial planning for retirement across all gender groups. Notably, we found no significant gender gap in retirement planning behavior. Subjective financial literacy serves as a significant mediator linking both future time perspective and risk tolerance to retirement planning, though the indirect effect of risk tolerance through financial literacy differs significantly between genders. Academically, theoretical propositions related to retirement planning can be accounted for by both genders. Practically, standardized policy can be tailored to address retirement issues across genders. The study emphasizes that financial planning for retirement in China shows no gender gap, and this provides meaningful guidance to policymakers and financial institutions to develop measures to encourage individuals to take financial actions in retirement planning. Finally, the combined interpretation of a strong effect of subjective financial literacy and an insignificant effect of risk tolerance raises concern that adult income earners in China are affected by financial literacy bias when practicing financial retirement planning. Full article

Recent advances in wearable devices and physiological signal monitoring technologies have motivated research into non-invasive glucose estimation for diabetes management. However, the existing studies are often limited by sample constraints, in terms of relatively small numbers of subjects, and few address personalized differences. Physiological signals vary considerably for different individuals, affecting the reliability of accuracy measurements, and training data and test data are both used from the same subjects, which makes the test result more affirmative than the truth. This study aims to compare the two scenarios mentioned above, regardless of whether the testing/training involves the same individual, in order to determine whether the proposed training method has better generalization ability. The publicly available MIMIC-III dataset, which contains 700,000 data points for 10,000 subjects, is used to create a more generalized model. The model architecture uses a ResNet CNN + Transformer block, and data quality is graded during preprocessing to select signals with less interference for training to increase data quality. This preprocessing method allows the model to extract useful features without being adversely affected by noise and anomalous data that decreases performance; therefore, the model’s training results and generalization capability are increased. This study creates a model to predict blood glucose values from 70 to 250 for 180 classes, using mean absolute relative difference (MARD) as the evaluation metric and a Clarke error grid (CEG) to determine a reasonable error tolerance. For personalized cases (specific individual data during model training), the MARD is 11.69%, and the optimal Zone A (representing no clinical risk) in the Clarke error grid is 82.7%. Non-personalized cases (test subjects not included in the model training samples) using 60,000 unseen data yields MARD = 15.16%, and the optimal Zone A in the Clarke error grid is 75.4%. Across multiple testing runs, the proportion of predictions falling within Clarke error grid zones A and B consistently approached 100%. The small performance difference suggests that the proposed method has the potential to improve subject-independent estimation; however, further validation in broader populations is required. Therefore, the primary objective of this study is to improve subject-independent, non-personalized PPG-based glucose estimation and reduce the performance gap between personalized and non-personalized measurements. Full article

Soil salinization, characterized by complex ionic compositions, threatens global wheat production. Current research often focuses on single salts, leaving a gap in systematic comparisons of specific salt effects. This study comprehensively evaluated six prevalent salts (NaCl, Na₂SO₄, KCl, NaHCO₃, MgSO₄, and MgCl₂) across concentrations (10–200 mmol/L) during wheat (Triticum aestivum L.) germination. By integrating ten physiological indicators with principal component analysis (PCA), membership function evaluation, and median lethal concentration (LC₅₀) calculation, we identified distinct salt-specific toxicities. Results established a clear toxicity hierarchy: MgCl₂ was consistently most toxic (LC₅₀ = 32.92 mmol/L), indicating Mg²⁺/Cl⁻ synergy, while KCl was least inhibitory (LC₅₀ = 159.66 mmol/L). PCA simplified the 10-trait dataset, extracting 1 principal component (PC, 89.29–92.35% contribution) for most salts (fresh weight as key loading, reflecting growth) and 2 PCs (95.65% cumulative contribution) for MgSO₄ (separating root-growth and germination-vigor responses), thus identifying salt-specific key evaluation traits. Building on this PCA-derived trait screening, this analysis further revealed fresh weight (FW), germination rate (GR), shoot length (SL), and simple vigor index (SVI) as core evaluation indicators, and identified distinct mechanistic pathways: while most salts caused a generalized growth inhibition reflected in biomass reduction, MgCl₂ exerted a more specific and severe inhibitory effect on shoot elongation. MgSO₄ uniquely employed dual pathways, separately affecting root and germination traits. An innovative aspect of this work is the synergistic application of three synergistic evaluation methodologies with multi-physiological parameters, which allows for the rigorous quantitative characterization of distinct salt-specific effects on both early germination and seedling growth in wheat. This laboratory-based study provides a theoretical framework and practical indicators for salt damage risk assessment and preliminary screening of salt-tolerant wheat germplasm and lays a foundation for field validation and targeted management strategies for specific saline–alkali soils. Full article

Conventional climate-risk frameworks, ranging from ESG ratings to Integrated Assessment Models (IAMs), systematically underestimate physical risks by overlooking the non-linear physics that govern infrastructure failure. These top-down models perceive climate change as a manageable operational expense, thereby obscuring the substantial capital requirements necessary to sustain system reliability as global temperatures escalate. This study proposes a physics-first framework to quantify the “Adaptation Gap”—a measurable, unaccounted-for capital liability representing the additional cost needed to upgrade assets to maintain fault tolerance. Within this specific geographic and asset context, it has been determined that restoring fault tolerance for new equipment necessitates a 19.7% (95% CI: 16.5–22.9%) increase in capital expenditure, which increases the Adaptation Gap to 28.7% for typical in-service assets, potentially increasing the true cost for aging assets to between 25% and 30%. Although the quantitative findings are specific to the case study, the methodological framework—assessed as superior to traditional risk metrics—is designed for global application in pricing the Adaptation Gap across all infrastructure sectors with thermal constraints. Our methodology provides a blueprint for establishing a new standard of climate-adjusted valuation, transforming abstract physical risks into a tangible, auditable capital liability. Full article

Introduction: During pharmaceutical care, community pharmacists play a crucial role by carrying out interventions aimed at preventing, detecting, and resolving drug-related problems (DRPs) and negative outcomes associated with medication (NOM), simultaneously enhancing patients’ knowledge about their treatments. The chronic use of Benzodiazepines (BZDs) is known to be associated with risks such as tolerance, dependence, and cognitive impairment. Furthermore, the combined use of BZDs with other medications or alcohol may expose patients to significant drug interactions. Objectives: This study aimed to characterize and describe the clinical profile of patients using BZDs, to evaluate the extent of polypharmacy and potential drug interactions, to investigate their level of knowledge regarding BZD treatment, and ultimately, to propose evidence-based interventions from the community pharmacy to contribute to improving patient safety and minimizing risks associated with BZD use. Method: A cross-sectional, descriptive study was conducted in a single community pharmacy in Gran Canaria (Canary Islands, Spain). The study population comprised 125 adult patients with active BZD prescriptions. Data collection was performed through pharmacist–patient structured interviews using a questionnaire that included sociodemographic, clinical, and BZD knowledge variables. Results: Lormetazepam and alprazolam were the BZDs most frequently prescribed and dispensed. Potential drug interactions with other medications were detected in 38.4% of BZD users. Notably, 61.5% of patients using BZDs also reported the concurrent use of opioid analgesics, with tramadol being the most common opioid (48.1% of BZD users were also treated with tramadol). Statistically significant differences were observed between patients with and without BZD and other drug interactions in several adverse outcome variables, including the risk of falls (p = 0.003), cognitive impairment (p = 0.047), and urinary incontinence (p = 0.016). Existing BZD dependence is detected in 25% and 22.1% of cases, respectively. Patients’ knowledge of their BZD treatment revealed critical gaps, which are identified as a challenge and a clear opportunity for intervention through pharmaceutical care services. Conclusions: The findings underscore the essential and proactive role of community pharmacists in identifying and managing drug interactions, as well as in supporting deprescribing strategies through collaborative and interprofessional care models. Full article

The reliability of embedded processors in safety- and mission-critical domains is increasingly threatened by radiation-induced soft errors, particularly multiple-cell upsets (MCUs) that simultaneously corrupt adjacent cells in external SDRAM. While prior studies on the LEON3 processor have largely focused on single-event upsets (SEUs) in internal SRAM structures, they overlook MCU effects in off-chip SDRAM, a critical gap that limits fault coverage and compromises system-level reliability assessment in modern high-density embedded systems. This paper presents an SDRAM-based fault injection framework using FPGA emulation to evaluate the impact of MCUs on the LEON3 soft-core processor, with faults directly injected into the external memory subsystem where data corruptions can rapidly propagate into system-level failures. The methodology injects spatially correlated two-bit MCUs directly into SDRAM during realistic workload execution. Three architecturally diverse benchmarks were analyzed, each representing a distinct computational workload: a numerical (matrix multiplication), signal-processing (FFT), and a cryptographic (AES-128 encryption) application, chosen to capture arithmetic-intensive, iterative, and control-intensive execution profiles, respectively. The results reveal a distinct workload-dependent vulnerability profile. Matrix multiplication exhibited >99.99% fault activation, with outcomes overwhelmingly dominated by data store errors. FFT showed >97% activation in steady-state execution, following an initial phase sensitive to alignment and data access exceptions. AES displayed 88.12% non-propagating faults, primarily due to injections in inactive memory regions, but remained exposed to critical memory access violations and control-flow exceptions that enable fault-based cryptanalysis. These findings demonstrate that SEU-only models severely underestimate real-world MCU risks and underscore the necessity of selective, workload-aware fault-tolerance strategies: lightweight ECC for cryptographic data structures, alignment monitoring for signal processing, and algorithm-based fault tolerance (ABFT) for numerical kernels. This work provides actionable insights for hardening LEON3-based systems against emerging multi-bit threats in radiation-rich and adversarial environments. Full article

Acute inflammatory visceral pain (AIVP) is a prevalent yet challenging clinical condition associated with inflammatory diseases, characterized by diffuse pain that often escalates into nausea, vomiting, and systemic autonomic disturbances. The absence of effective and patient-centered therapies remains a significant clinical challenge. While dexmedetomidine (Dex) has demonstrated promising analgesic effects, its conventional intravenous administration involves slow infusion, heightening risks of infection and compromising patient comfort and compliance. Here, we present a breakthrough strategy using a hyaluronic acid (HA) hydrogel and microneedle-based transdermal system for Dex delivery to enhance clinical practicality. We successfully fabricated Dex-loaded HA hydrogel microneedles (MN/Dex), enabling efficient skin penetration and controlled drug release. Comprehensive biosafety evaluations, including skin irritation, cytotoxicity, and hemolysis assays, confirmed the excellent biocompatibility of the HA hydrogel microneedle system (HA-MN). In the acetic-acid-induced AIVP model, MN/Dex not only produced significant and sustained reduction in visceral and somatic hyperalgesia but also maintained normal physiological activity, avoiding sedation burden, preserving feeding behavior, and supporting natural mobility. MN/Dex offers a minimally invasive, easy-to-administer, and well-tolerated alternative to intravenous therapy, with the potential to transform outpatient management and improve quality of life for patients suffering from AIVP. This advanced delivery platform bridges a critical translational gap in pain management, combining efficacy with outstanding clinical adaptability. Full article

As an emerging production factor, data assets are gaining strategic prominence, yet their application in collateralized financing faces persistent challenges, including illiquidity and risk evaluation complexities. This study introduces an innovative $P_{max}$ model to enhance the Collateral Value of data assets through insurance mechanisms, systematically demonstrating the feasibility conditions under which risk transfer optimizes asset valuation and delineating implementation pathways to integrate data insurance with asset-backed financing. Building on the theoretical framework of Value-at-Risk (VaR), this study develops a dynamic valuation model to assess the value of the collateral before and after insurance. Our analysis shows that insurance coverage for potential losses significantly enhances financing viability when premiums satisfy $P_{max}$. Empirical analysis employing Monte Carlo simulations reveals a nonlinear positive correlation between pledgees’ risk tolerance thresholds and the maximum acceptable premium $P_{max}$. This study bridges theoretical gaps in understanding insurance-value relationships for data assets while providing conceptual foundations and operational blueprints to standardize data markets and foster financial innovation. Full article

Aquatic ecosystems are increasingly threatened by multiple anthropogenic stressors, notably climate change and pollution by pharmaceuticals. Global warming is predicted to raise water temperatures by 2–5 °C by the end of the century. As ectotherms, fish are particularly vulnerable due to limited thermal tolerance and temperature-dependent physiology. Pharmaceuticals are introduced into aquatic systems at concentrations ranging from ng·L⁻¹ to µg·L⁻¹, including widely prescribed classes such as antibiotics, hormones, analgesics, antifungals, and neuropsychiatric drugs. This narrative review synthesizes experimental evidence on the interactive effects of warming and pharmaceutical exposure in fish. Thirty-nine peer-reviewed studies published since 2005 were analyzed. The findings indicate that higher temperatures often exacerbate pharmaceutical-induced toxicity, altering oxidative stress, metabolism, reproduction, and behavior. Antibiotic-focused studies showed temperature-dependent acceleration of absorption, distribution, metabolism, and excretion, with shorter half-lives and reduced tissue persistence at higher temperatures. Estrogenic hormones and antifungals have been shown to interact with thermal regimes, disrupting reproductive physiology and skewing sex ratios, particularly in species exhibiting temperature-dependent sex determination. Neuropsychiatric drugs exhibited altered uptake and metabolism under warming conditions, resulting in increased brain bioaccumulation and behavioral alterations affecting ecological fitness. Analgesics and anti-inflammatories remain understudied despite their widespread use, with evidence suggesting synergistic effects on oxidative stress at elevated temperatures. Significant research gaps persist regarding chronic exposures, early developmental stages, ecologically relevant temperature scenarios, and underrepresented or absent drug classes, such as hypolipidemic drugs. Ultimately, broader and integrated approaches are needed to better understand and predict the ecological risks of pharmaceutical pollution in a warming world. Full article

Supply chain financing offers advantages over traditional channels such as bank loans and equity financing, including greater flexibility, lower transaction costs, and simplified approval procedures. However, when a firm’s sustainability faces uncertainty, access to supply chain financing may become constrained by multiple factors, including the risk tolerance of supply chain partners, market transparency, and corporate reputation. ESG, representing Environmental, Social, and Governance standards, is a critical framework for assessing corporate sustainability performance. Given that divergent ESG evaluations reflect disparate market assessments of a firm’s sustainable development capabilities, such divergence may affect supply chain financing by altering stakeholder trust dynamics. This research examines A-share listed firms in China (2016–2022) and reveals that divergence in ESG evaluations significantly inhibits firms’ access to supply chain financing. Mechanism validation suggests that divergent ESG evaluations amplify informational opacity, operational risks, and negative reputation, thereby influencing supply chain partners’ risk perceptions and trust levels. Heterogeneity analysis shows that corporate governance quality, regional trust levels, and ESG awareness modulate the negative impact of divergent ESG evaluations on supply chain financing. The asymmetric effects of divergent ESG evaluations on supply chain financing are further confirmed, with distinct manifestations between upstream suppliers and downstream customers. By bridging gaps in existing research on divergent ESG evaluations and supply chain finance, this work offers regulatory guidelines, operational recommendations for firms, and investment decision frameworks. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

Interstitial lung diseases (ILDs) are chronic respiratory disorders often leading to dyspnoea, reduced exercise tolerance, and poor quality of life. Pulmonary rehabilitation (PR) improves symptoms and function but remains underused in Portugal, with only ~1% of eligible patients enrolled. This study retrospectively analysed 61 ILD patients at Hospital Garcia de Orta (July–December 2024) to identify PR candidates. Most had idiopathic pulmonary fibrosis (44%), exertional dyspnoea (67.2%), and moderate lung impairment (49%). Comorbidities and risk factors were common. Findings highlight a significant gap between clinical need and access, reinforcing the urgency of structured referral strategies to expand PR availability. Full article

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring. Full article