Search Results (15,927)

While most existing studies addressing greenwashing emphasize governmental regulation, they often overlook the role of public participation. This study examines how managers’ risk preferences affect enterprise decision-making under public scrutiny. In this study, an agent-based simulation model incorporating enterprises, media, and consumers is established on the basis of signaling theory and the wolf-sheep model. The conclusions are as follows: (1) Both positive and negative public opinion can curb greenwashing behavior in building material enterprises, but negative public opinion intensity is more effective in this context. (2) Regardless of whether building material enterprises operate in positive or negative public opinion scenarios, constraining managers’ risk preferences consistently serves as an effective internal mechanism for curbing greenwashing behavior. (3) The effectiveness of constraint managers’ risk preferences in suppressing greenwashing behavior is influenced by the external public opinion scenario, with its inhibitory effect being more pronounced under negative public opinion scenarios. This study incorporates public opinion as a key external factor and integrates dynamic managers’ risk preferences into a behavioral simulation framework. It expands the theoretical understanding of greenwashing decision-making mechanisms and offers practical implications for regulatory enforcement and enterprise governance. Full article

The increasing digitalization of electrical substations, enabled by IEC 61850-based architectures, has improved operational efficiency while expanding the cyber attack surface. This paper introduces a standards-aligned cybersecurity risk mitigation model specifically designed for digital substations and mapped to representative attack scenarios. The model integrates preventive, detective, and application-level controls derived from NIST SP 800-82r3, IEC 62443, and ISO/IEC 27019, and is validated in a laboratory process-bus environment. A baseline risk assessment identified four high-risk scenarios in the studied digital substation architecture. For validation, a selected subset of controls was experimentally evaluated against two representative attack vectors, namely false data injection (FDI) on GOOSE messages and denial-of-service (DoS) against PTP synchronization. For the remaining scenarios, the post-mitigation effects were reassessed analytically based on control coverage, architectural exposure, and standards-aligned cybersecurity reasoning. The experimental validation demonstrated that both empirically tested high-risk scenarios (FDI on GOOSE and DoS on PTP) were effectively mitigated, reducing their residual risk to moderate and low levels, respectively. For the remaining two scenarios, a post-mitigation analytical reassessment based on control coverage and architectural exposure suggested a consistent risk reduction trend, although without direct experimental confirmation. Under this combined empirical–analytical assessment, the number of high-risk scenarios decreased from four to one, corresponding to a 50% experimentally validated reduction in high-risk exposure, complemented by an analytical reassessment of the remaining scenarios. These results provide quantitative evidence about the effectiveness of the model, even with partial implementation. The scientific contribution of this study lies in integrating multistandard cybersecurity requirements into an operational mitigation model tailored to IEC 61850 substations, combined with experimental risk quantification in a realistic process-bus testbed. The proposed model offers practical guidance for utilities and establishes a scalable foundation for advancing cybersecurity in critical power infrastructure. Full article

This study analyzes the closure, decommissioning, and abandonment (CDA) of a fuel storage and distribution facility in southern Quito, Ecuador, conceptualizing the process as a socio-technical urban transition embedded within territorial governance dynamics. While infrastructure decommissioning is commonly addressed from a predominantly technical perspective, limited research integrates reverse logistics design, stakeholder influence structures, and territorial development into a unified analytical framework, particularly in Latin American metropolitan contexts. Using a mixed-methods case study approach, the research combines documentary analysis, operational data, and 34 semi-structured interviews with public authorities, engineers, fuel marketers, business owners, and community representatives. A thematic analysis was applied to reconstruct the decommissioning logistics chain and to develop a stakeholder mapping and influence matrix assessing actor positions, economic interdependencies, and legitimacy claims. The findings show that decommissioning operates as a structured reverse logistics system embedded within asymmetric governance configurations, where economic dependency, risk perception, and urban redevelopment expectations generate competing territorial imaginaries. Technical feasibility alone proves insufficient to guide decision-making; instead, legitimacy emerges through the alignment of engineering planning, institutional coordination, and community-level expectations. The study advances an integrated socio-technical framework that articulates Engineering Management, Social Management, and Territorial Development, positioning decommissioning as a governance-driven transition rather than a purely technical operation. The results contribute to sustainability and infrastructure transition scholarship while offering practical guidance for managing urban hydrocarbon infrastructure closure in socially vulnerable territories. Full article

Background: Non-thyroidal illness syndrome is frequent in critically ill patients, but the prognostic value of dynamic changes in thyroid function tests remains unclear. This study evaluated whether serial measurements of thyroid-stimulating hormone (TSH) and free triiodothyronine (FT3) provide additional predictive value for 30-day mortality beyond conventional severity scores in ICU patients. Methods: This single-center retrospective observational study included 74 adult patients treated for ≥72 h in a general ICU who had TSH and FT3 measured within 24 h of admission and repeated at 48–72 h. Patients aged 18 years or above admitted to the intensive care unit were included in the study. Demographic characteristics, comorbidities, APACHE II, SOFA, modified NUTRIC (mNUTRIC) scores, and routine laboratory data (including albumin, CRP, and lactate) were recorded. The primary outcome was 30-day mortality. Between-group comparisons were performed using t-tests, Mann–Whitney U, and Chi-square tests. Variables significant in univariate analyses were entered into binary logistic regression models, and predictive performance was assessed using receiver operating characteristic (ROC) curves and the Youden index. Results: The mean age was 68.7 ± 14.7 years, and 41.9% (n = 31) of the patients died within 30 days. Non-survivors had higher APACHE II, SOFA, and mNUTRIC scores and lower albumin, lymphocyte count, and second FT3 levels compared with survivors (all p ≤ 0.003). Baseline FT3 and TSH were not associated with mortality, whereas both the subsequent FT3 measurements and the ΔT3 (variance in former to latter FT3) were remarkably predictive. The latter FT3 < 1.63 pg/mL produced an AUC of 0.835 (sensitivity: 77%, specificity: 74%), and a ΔT3 log ratio threshold of −0.09 (≈20% early FT3 decline) produced an AUC of 0.835 (sensitivity: 71%, specificity: 81%). The APACHE II + ΔT3 (numeric) model showed the best discrimination (AUC: 0.921; sensitivity: 87.1%, specificity: 81.4%), outperforming APACHE II alone (AUC: 0.861). Conclusions: In critically ill adult patients, dynamic T3 kinetics—particularly premature decline in FT3 within the first 72 h—provide incremental prognostic value for 30-day mortality beyond APACHE II. Serial FT3 monitoring may help identify high-risk patients whose endocrine adaptation to critical illness is failing. Full article

Background and Objectives: Given the high rates of anterior cruciate ligament graft rupture in the pediatric population, lateral extra-articular tenodesis (LET) is increasingly used in combination with anterior cruciate ligament reconstruction (ACLR) to mitigate the risk of re-injury. This study aimed to compare postoperative postural stability between patients undergoing ACLR with and without LET. It was hypothesized that postural stability would be comparable between patients with and without LET. Materials and Methods: This retrospective, single-center, double-surgeon case–control study included patients who underwent primary ACLR using hamstring tendon autografts between January 2022 and May 2025. Postoperative postural stability was assessed using the Biodex Stability System (BSS) global stability index (GSI), which was the primary outcome of interest. Demographic and surgical data were collected as well as all postoperative GSIs. GSI comparisons between the LET and no-LET groups were made at ≤6 months and >6 months postoperatively. Secondary analysis compared GSI differences between the healthy and operated legs. Results: Among 229 patients screened, 100 met the inclusion criteria (median age, 16 years [IQR, 15–17]); 65 underwent LET and 35 did not, and 54 were female (54%). The groups were comparable on demographic and surgical data (p-value: n.s.). No operated leg GSI difference was observed between the LET and no-LET groups at ≤6 months (p = 0.372) and >6 months postoperatively (p = 0.424). Patients with LET had significantly better (lower) healthy leg GSIs (Mean ± SD; 2.7 ± 0.9) than no-LET patients (3.9 ± 1.8) at >6 months postoperatively (p = 0.004). At ≤6 months, patients showed better GSIs on their operated limb (Median [IQR]; 2.6 [2.1–3.9]) compared to the healthy limb (3.5 [2.3–4.6]) (p = 0.003). This difference disappeared at the latest follow-up. Conclusions: The addition of LET concomitant with ACLR was not associated with a significant difference in postural stability, as assessed by the GSI from the BSS. However, given the sample size and study limitations, these findings should be interpreted with caution. Increased attention to the healthy limb during ACLR rehabilitation may be warranted, particularly in the early postoperative period (<6 months). Further studies with larger cohorts are needed to confirm these observations. Full article

The 2025 update of the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27701 standard offers a major advantage by enabling organizations to implement a Privacy Information Management System (PIMS) autonomously while maintaining alignment with the General Data Protection Regulation (GDPR). However, it remains unclear to what extent privacy legal frameworks in developing jurisdictions, particularly in Latin American countries, align with this new standard. At the same time, the traditional method for assessing the alignment between privacy legal frameworks and ISO/IEC 27701 continues to rely on manual mapping between the standard’s subclauses and privacy regulatory articles, a process that is time-consuming, costly, and error-prone. More critically, no method exists to quantitatively assess the reliability of such mappings, leaving alignment assessments largely subjective. To address these limitations, this paper proposes a novel method based on an OpenAI Generative Pre-trained Transformer (GPT) combined with a Chain-of-Thought (CoT) reasoning strategy to quantitatively assess the alignment between privacy legal frameworks and ISO/IEC 27701:2025. By leveraging GPT’s logarithmic probabilities (logprobs) and the standard’s subclause definitions as classification categories, the method enables confidence-based evaluation of legal–standard alignment. The proposed method is then applied to analyze the privacy legal frameworks of Paraguay, Chile, Ecuador, México, Colombia, and Perú, examining how effectively they promote the standard’s guidelines. A suitable confidence threshold is then selected by assessing the GDPR and comparing the results with the reference mappings reported in Annex D of the standard. Finally, the method identifies the number of compliant subclauses per clause, the regulatory articles influencing the resulting logprobs, and the underlying privacy gaps for reduced alignment across the analyzed privacy legal frameworks. Overall, our results indicate that while Latin American privacy legal frameworks mandate protective measures by promoting a suitable operation and continuous improvement of a PIMS, they do not explicitly demand adequate risk management and sufficient preventive safeguards for citizens’ Personally Identifiable Information (PII) in dynamic contexts. Full article

Recent progress in text-to-music generation has enabled high-quality audio synthesis from natural language prompts. However, such models are at risk of unintended replication, raising concerns regarding originality and intellectual property. While training-time mitigation strategies can address this issue, they typically require retraining or curated datasets, limiting their practicality for large-scale systems. Inference-time methods provide a lightweight alternative but often involve a trade-off between fidelity and memorization risk. This work introduces repulsive guidance (RG), a systematic inference-time mitigation strategy that reduces memorization without disrupting the intended conditional guidance from the text prompt. RG operates by enforcing divergence between dual diffusion trajectories through a repulsive term applied only during early denoising steps, without reversing the conditional guidance from the prompt. Experiments on MusicBench with the Tango model demonstrate that RG offers a complementary mitigation strategy, providing new insights into balancing fidelity and memorization risk. Full article

Background/Objectives: Persistent and long-standing persistent atrial fibrillation (AF) presents a therapeutic clinical challenge balancing complex rhythm management with a heightened stroke risk. The left atrial appendage (LAA) is the primary source of thromboembolisms in these patients. This study evaluated the safety and efficacy of combining LAA exclusion with Convergent Hybrid Ablation for stroke prevention and rhythm control in a refractory patient cohort. Methods: A single-center observational cohort study was conducted including 28 patients with symptomatic persistent or long-standing persistent AF. The cohort was highly refractory, with 82.1% having failed at least one endocardial catheter ablation. The hybrid procedure consisted of sub-xiphoid epicardial ablation, thoracoscopic LAA exclusion (AtriClip), and endocardial catheter ablation. Safety and efficacy were assessed at 3 months and 12 months. Results: LAA exclusion was successfully performed in 96.4% of patients. The peri-operative safety profile was acceptable, with zero procedure-related strokes or deaths. At the 12-month follow-up, the rate of stroke or any other major adverse events was at 0.0%. Freedom from AF was 75.0%, shown by a 12-lead electrocardiography (ECG). Freedom from any atrial arrhythmia off anti-arrhythmic drugs (AADs) was achieved in 50.0% of patients. A total of 32.1% of the cohort required catheter ablation within 12 months to maintain sinus rhythm as part of the hybrid treatment. Conclusions: Concomitant LAA exclusion during Convergent Hybrid Ablation is a safe procedure with a high clinical success rate in maintaining sinus rhythm in a highly complex AF patient group. While no thromboembolic events were observed at 12 months, larger studies with longer follow-up are needed to confirm the potential for long-term stroke risk reduction. The findings suggest that for many patients, the hybrid procedure should be viewed as part of a multi-step strategy often requiring endocardial “touch-up” ablation. Full article

Background/Objectives: Restenosis following coronary artery bypass grafting (CABG) remains a major long-term complication that adversely affects patient prognosis. Although prior studies have investigated clinical features, imaging parameters, and circulating biomarkers for restenosis risk stratification, the metabolic mechanisms underlying long-term restenosis—particularly those reflecting both the local cardiac microenvironment and systemic circulation—remain poorly defined. Therefore, this study aims to identify restenosis-associated metabolic alterations and develop a risk prediction model based on integrated targeted metabolomic profiling of pericardial fluid (PF) and serum in patients undergoing isolated CABG. Methods: Patients undergoing isolated CABG were prospectively enrolled. Paired PF and serum samples were collected during surgery or the perioperative period for targeted metabolomic analysis. Differential metabolite (DM) analysis was performed between patients with and without restenosis. Key metabolites were selected to construct a restenosis risk prediction model, which was subsequently evaluated in training and validation cohorts. Results: Compared with patients without restenosis, those who developed restenosis exhibited two key differential metabolites identified in PF and serum: 7α-Hydroxy-4-cholesten-3-one and Phenoxyacetic acid (PAA). A logistic regression-based prediction model incorporating these metabolites was developed and evaluated using receiver operating characteristic (ROC) analysis, integrated discrimination improvement (IDI), and decision curve analysis (DCA). The model demonstrated robust predictive performance in both training and validation cohorts. Kaplan–Meier survival analysis further revealed that higher model scores were significantly associated with an increased risk of long-term restenosis in the training cohort (HR = 1.44, p = 0.047) and validation cohort (HR = 1.83, p = 0.012). Conclusions: This study provides the first evidence that integrated metabolomic signatures derived from PF and serum are associated with long-term restenosis after CABG. By capturing complementary metabolic information from the local cardiac microenvironment and systemic circulation, this integrated approach enhances current understanding of restenosis biology and supports the potential clinical utility of targeted metabolomics for long-term restenosis risk prediction following CABG. Full article

The Leibstadt Nuclear Power Plant (KKL) recently completed a comprehensive full-scope Fire Probabilistic Safety Assessment (Fire PSA) to fulfill the updated Swiss regulatory requirements (ENSI-A05) and align with international standards. The study was conducted using the NUREG/CR-6850 framework, incorporating state-of-the-art methodologies across different areas of the study, advanced fire modeling tools (CFAST and FDS), and the latest plant-specific data. As part of detailed fire modeling, a bespoke methodology was developed, tailored to KKL’s plant-specific characteristics, to ensure a systematic and standardized approach to fire scenario analysis while maintaining quality, consistency, and traceability. The analysis focused on evaluating fire risks in critical plant areas, such as the drywell, containment, main control room, remote shutdown areas, and cable spreading room. For each scenario, the fire-generated conditions, such as the extent of fire propagation and the time to damage targets, were analyzed using plant-specific heat release rate (HRR) and calorific potential (CALPOT) values. The study also addressed aspects such as multi-compartment analysis, fire-induced cable impacts, and treatment of multiple spurious operations. This paper highlights the methodological enhancements achieved by integrating international best practices and KKL-specific adaptations into a unified fire modeling framework. The results provide critical insights into fire propagation dynamics, validate the effectiveness of safety features, and support risk-informed decision-making for enhanced fire safety and regulatory compliance. The outcomes of fire modeling were utilized to develop fire event trees and refine the consequences of fire scenarios, thereby enabling a more realistic estimation of fire risk in the KKL Fire PSA study. Overall, the KKL PSA aims to serve as a benchmark for future fire risk assessments in the nuclear industry. Full article

False data injection (FDI) cyberattacks pose a growing threat to modern power distribution systems in smart cities by manipulating state-estimation processes and provoking covert voltage violations that traditional defense mechanisms fail to detect. Recent industry data indicate that coordinated FDI attacks can distort measurement sets by as little as 3–7%, yet trigger voltage deviations exceeding 10% in vulnerable feeders, resulting in operational instability, unnecessary load curtailments, and elevated outage risk. To address these challenges, this paper proposes a quantum-accelerated digital twin (QDT) framework that integrates quantum optimization algorithms with a high-fidelity digital twin (DT) of the distribution system to detect, localize, and remediate FDI-induced cyberattacks in real time. The rationale behind the approach lies in the superior combinatorial search capability of quantum solvers, which accelerates the identification of falsified measurement vectors and optimal corrective control actions compared with classical methods. In addition, the framework introduces an innovative Bitcoin-mining-oriented virtual energy storage (BMOVES) mechanism that treats mining facilities as dynamically controllable, fast-response electrical loads within smart city demand–response programs. By modulating mining power consumption with sub-second granularity, the proposed BMOVES resource provides up to 18–45% flexible capacity during attack scenarios, enabling voltage restoration without relying on conventional energy storage assets. The unified QDT + BMOVES architecture is validated using the 136-bus Brazilian distribution system, a realistic benchmark for cyber–physical resilience studies. Simulation results demonstrate over 99% FDI detection accuracy, up to an 82% reduction in peak voltage violations, and restoration of operational limits 11 times faster than state-of-the-art classical methods. These findings highlight the transformative potential of integrating quantum computing, digital twins, and nontraditional flexible assets to enhance cyber-resilient power infrastructure in future smart cities. Full article

Household rainwater tanks (HRWTs) have re-emerged globally as a decentralised strategy to address water scarcity, climate variability, and increasing urban water demand. In several jurisdictions, including New South Wales, Australia, rainwater tanks have been chosen to meet the mandatory potable water reduction target in new residential developments for nearly two decades; however, growing evidence indicates persistent underutilisation and variable performance in practice. Despite their recognised benefits in reducing potable water demand, mitigating stormwater runoff, and enhancing urban resilience, the global HRWT research landscape remains fragmented across disciplinary and thematic boundaries. This paper presents a multifaceted review, defined here as an approach that synthesises multiple perspectives on the topic. It integrates systematic mapping of peer-reviewed literature with a critical thematic analysis across four dominant research domains: technological and design innovation, policy and governance frameworks, environmental performance, and social–behavioural dimensions. The findings reveal a strong research focus on technical optimisation, while policy effectiveness, environmental trade-offs, and household-level behavioural factors receive comparatively uneven attention. Regulatory and incentive-based instruments are shown to produce inconsistent outcomes, shaped by local institutional capacity to design, implement, enforce, and sustain programs, as well as by climatic context and household acceptance. Environmental assessments identify both benefits and burdens, including energy use, treatment requirements, and operational complexity. Social and behavioural studies indicate growing acceptance of household rainwater tank (HRWT) systems. However, financial constraints, local conditions, and ongoing maintenance demands continue to influence adoption and performance. A key insight from this review is the limited attention given to households’ lived experiences, particularly how users adopt, adapt, operate, and maintain HRWT systems over time. This gap constrains progress across technical, policy, environmental, and social dimensions and risks cycles of early policy uptake followed by stagnation. The review highlights the need to integrate household perspectives into future research, policy design, and industry practice to improve system performance, user experience, and the long-term contribution of HRWTs to sustainable urban water management. Full article

The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies. Full article

Background and Objectives: Early risk stratification remains challenging in patients undergoing left ventricular assist device (LVAD) implantation. Red cell distribution width (RDW) and serum albumin reflect systemic stress and nutritional reserve; their ratio (RDW-to-albumin ratio, RAR) may provide a simple preoperative index. We evaluated whether preoperative RAR is associated with early mortality after LVAD implantation. Materials and Methods: We conducted a retrospective cohort study of LVAD recipients (2019–2025). RAR was calculated as RDW (%) divided by albumin (g/dL) from preoperative blood tests obtained 24–48 h before surgery. The primary endpoint was in-hospital mortality. The secondary endpoint was 90-day survival. In-hospital mortality was analyzed using logistic regression with parsimonious adjustment for INTERMACS high-risk status (profiles 1–2 vs. 3–7); penalized regression was used to reduce small-sample bias. Discrimination was assessed using receiver operating characteristic (ROC) analysis. Ninety-day survival was evaluated using Cox proportional hazards models. Results: Forty-seven patients were included (37 survivors; 10 in-hospital deaths). Higher RAR was associated with increased odds of in-hospital mortality and remained significant after adjustment for INTERMACS high-risk status (OR 1.68, 95% CI 1.04–2.90). INTERMACS high-risk status was strongly associated with in-hospital mortality (OR 17.89, 95% CI 3.19–138.07). RAR demonstrated good discrimination for in-hospital mortality (AUC 0.801, 95% CI 0.648–0.955). For 90-day survival, RAR showed a borderline association in unadjusted analysis (HR 1.28, 95% CI 0.98–1.68) and was not significant after adjustment (HR 1.20, 95% CI 0.89–1.63). Conclusions: In this small single-center cohort, preoperative RAR was independently associated with in-hospital mortality after LVAD implantation. These findings should be considered hypothesis-generating and require external validation. Full article

Background: Reintervention after mitral valve repair represents a relevant clinical challenge, yet the mechanisms and timing of repair failure remain incompletely defined. Understanding how the interval between index repair and reoperation affects failure mechanisms and the feasibility of repeat repair may help refine surgical strategies. Methods: We retrospectively analyzed 194 patients undergoing repeat mitral valve surgery between 2010 and 2025 after prior repair. Median age was 70 years and 61.3% were male. Patients were stratified by time to reoperation: 0–5 years (n = 91), 6–10 years (n = 42), and >10 years (n = 61). Median left ventricular ejection fraction was 58%, atrial fibrillation prevalence 32.5%, minimally invasive approach 21.6%, and EuroSCORE II 4.8%. Results: Baseline characteristics and operative risk were comparable across groups. However, mechanisms of repair failure differed significantly. Early failures were more commonly due to recurrent leaflet prolapse (47.8%), whereas late failures showed a higher incidence of mitral stenosis (63.9%). The rate of repeat mitral repair decreased over time, being higher in early failures compared with intermediate and late failures (17.6% vs. 14.3% vs. 8.2%). Conclusions: Timing of mitral repair failure is associated with distinct mechanisms and influences surgical management. Early failures are more frequently related to prolapse recurrence and are more amenable to re-repair, whereas late failures are characterized by structural degeneration and more often require valve replacement. Full article