Search Results (136)

Shock-aware aerodynamic shape optimization of transonic airfoils requires surrogate models that capture both integral aerodynamic trends and shock-relevant pressure distribution features. This study addresses drag-oriented optimization of the RAE2822 transonic airfoil under a lift-targeted condition with baseline relative thickness feasibility, rather than strict target pressure inverse design. Each airfoil is parameterized by a 16-dimensional CST vector and mapped to a two-channel vertical signed distance field representation of the upper- and lower-surface $C_p$ curves, from which shock descriptors, including the shock location indicator $x_s$ and the pressure jump magnitude $\Delta C_p$, are extracted in a deterministic, implementation-consistent manner. To quantify the reliability of surrogate-derived shock metrics, a held-out uncertainty analysis is performed on 500 samples. The surrogate achieves MAE/RMSE values of 0.00474/0.00602 for $C_L$ and $4.66\times {10}^{-4}/6.33\times {10}^{-4}$ for $C_D$, while the recovered shock-related quantities yield 0.00201/0.01598 for $x_s$ and 0.00200/0.00336 for $\Delta C_p$. Scatter plots and error histograms show tight one-to-one trends for most samples, with limited outliers mainly associated with locally ambiguous pressure gradient patterns. Overall, the surrogate is more reliable for capturing shock intensity trends than for prescribing an exact shock location; accordingly, $x_s$ is interpreted as a trend-level descriptor, whereas $\Delta C_p$ is treated as the more stable engineering indicator inside the optimization loop. The trained surrogate is embedded in a differential evolution optimizer with soft penalties on lift deviation and thickness feasibility violation, and selected designs are re-evaluated through closed-loop SU2 RANS simulations. CFD verification shows that the optimized design reduces drag from $C_D=0.01463$ to $C_D=0.01229$ (a 16.0% reduction) and reduces the shock jump from $\Delta C_p=0.239$ to $\Delta C_p=0.046$ (an 80.7% reduction). For the optimized design, the prediction-to-CFD differences are $\Delta C_L=+0.0042$ and $\Delta C_D=+0.00012$. These results support an engineering-oriented and auditable shock-aware closed-loop optimization workflow, with final design conclusions established by CFD verification rather than surrogate-predicted shock location alone. Full article

The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a framework that automates the conversion of clinical narratives into an auditable, semantically structured knowledge base. The framework combines BioClinicalBERT embeddings, c-TF-IDF statistical grounding, and semantic clustering, enabling high-fidelity classification (Macro F1 = 0.877 ± 0.012), traceable topic labeling, and temporal trend analysis. By demonstrating that semantic representation methods, reinforced with statistical grounding, are essential for large-scale medical text processing, this work establishes a foundation for privacy-preserving data governance and real-time intelligence within modern healthcare infrastructures. Full article

We present a Retrieval-Augmented Generation (RAG)-based question-answering system for nuclear energy science communication, characterizing retrieval quality in generated responses. The system introduces a dual-similarity analysis that jointly measures (i) question-to-context (Q→C) and (ii) answer-to-context (A→C) semantic consistency, serving as “retrieval-side semantic alignment signal” and “post-generation semantic alignment indicator” respectively. Built with LangChain, FAISS retrieval, and a large language model, our pipeline separates offline indexing from online inference and is grounded on authoritative Taiwanese Nuclear Safety Commission documents. We evaluate two settings: (a) in-domain prompts derived from the corpus and (b) out-of-domain, randomly generated nuclear energy questions. Results show that generated answers are, on average, more semantically similar to retrieved contexts than the original questions under the present setup, while the overall association between retrieval-side and answer-side signals remains stronger in the in-domain setting. Out-of-domain questions show weaker but still observable answer-to-context alignment patterns, contingent on corpus overlap. These findings suggest that combining RAG with dual-similarity analysis offers a practical and audit-oriented approach for educational Q&A, and we discuss potential improvements in versioned regulations, re-ranking, and abstention strategies. In this study, the RAG technique and dual-similarity analysis are combined together to promote nuclear energy knowledge. The research flow chat of this study can be applied to many other fields of scientific knowledge. Full article

Ongoing health system reforms in Kazakhstan have transformed the working environment of primary health care (PHC) staff and may increase workload and psychosocial stress. This study aimed to assess perceived stress among PHC workers in Almaty and its associations with socio-demographic characteristics and health-related behaviors. A cross-sectional survey was conducted in October–November 2023 across all 36 state-funded PHC facilities in Almaty. General practitioners (GPs) and family nurses employed in these facilities were invited to participate. In total, 1484 respondents completed a standardized questionnaire in Kazakh or Russian administered electronically via Google Forms. Perceived stress was assessed using PSS-10, physical activity using IPAQ-SF, alcohol consumption using AUDIT-C, and tobacco use through items aligned with STEPS/GATS. Statistical analyses were performed using SAS. Associations between variables were evaluated using χ² and Fisher’s exact tests, and multivariable logistic regression models were applied. Statistical significance was set at p < 0.05. Higher stress levels were more common among GPs than nurses (OR = 2.58; p < 0.0001) and less common in younger workers (18–29 vs. 50–59: OR = 0.504; p = 0.017) and alcohol abstainers (OR = 0.587; p = 0.0004). Kazakh ethnicity showed a borderline protective association (OR = 0.472; p = 0.057), while physical activity was not a significant predictor. Perceived stress is highly prevalent in Almaty PHC and disproportionately affects GPs; younger age and alcohol abstinence are protective. The findings support prioritizing organizational measures to reduce role-related burden and maladaptive coping behaviors. Full article

Background: The COVID-19 pandemic significantly altered the daily routines of young adults. This study investigated alcohol consumption patterns and associated factors among young adults in Romania during this period. Method: A cross-sectional study was conducted using an online survey. Participants were asked to retrospectively report their alcohol consumption patterns before and during the COVID-19 pandemic, including the period of university campus closures. A cohort of 249 young adults (68.6% female) participated in an online survey focused on their alcohol consumption patterns, utilizing the standardized AUDIT-C questionnaire and some modified questions to better establish the habit of drinking. Results: In total, 41.7% of the included subjects were in medical school, 10% in IT, and 44% in various areas of work. Most respondents were female, between 20 and 25 years old (65%) and living in urban areas, with wine being the most favorable drink. Regarding AUDIT scores, approximately 90% fall into low-risk drinking or even abstinence, 10% belong to the high-risk group of alcohol consumption, and 3 people have a high score, which suggests drinking abuse and the likelihood of developing alcohol dependence. A comparison of pre- to post-closure drinking among medical students showed statistically significant changes in the typical number of drinks per week (from 11.5 to 9.9) and maximum drinks per day (from 4.9 to 3.3) and a slight increase in typical drinking days per week (from 3 to 3.2), p < 0.05, outlining a decrease in alcohol consumption. Conclusions: The study highlights specific drinking patterns during the pandemic. While some individuals decreased consumption, a significant portion remained at risk for alcohol-related complications, emphasizing the need for targeted screening and prevention programs. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

Metabolic and bariatric surgery (MBS) has evolved into a highly effective neurohormonal intervention for severe obesity; however, it introduces unique long-term vulnerabilities, particularly regarding alcohol (AUD) and substance use disorders (SUD). This review synthesizes the epidemiological, pharmacokinetic, and neurobiological drivers of postoperative substance misuse. Procedures like Roux-en-Y gastric bypass (RYGB) radically alter ethanol metabolism, eliminating first-pass metabolism and accelerating gastric emptying, while simultaneously recalibrating reward pathways, creating a “reward gap” that facilitates addiction transfer. These physiological shifts exacerbate critical micronutrient deficiencies (thiamine, B12, iron), increase the risk of post-bariatric hypoglycemia, and correlate with higher rates of liver cirrhosis and suicide. Furthermore, substance use is a primary driver of suboptimal weight loss trajectories and weight regain. Mitigation requires a lifelong, multidisciplinary framework involving preoperative risk stratification, validated screening (e.g., AUDIT-C), and targeted nutritional supplementation to safeguard the long-term metabolic and psychological benefits of MBS. Full article

Central business district (CBD) hospitals must sustain reliable pressure relationships in critical rooms while reducing whole-facility carbon under tight space and disruption constraints. We developed an ontology-grounded semantic digital twin that normalizes building automation system (BAS) and building management system (BMS) telemetry into a unified semantic store consistent with Brick Schema, enabling portable asset discovery via query and thereby supporting forecasting, anomaly detection, and multi-objective optimization without dependence on vendor point naming conventions. Whole-facility impacts were verified using International Performance Measurement and Verification Protocol Option C–style measurement and verification with an S0-calibrated baseline model and residual-based savings attribution. Relative to the baseline (S0), the intervention (S3) produced a step increase in the critical-room pressure-compliance pass rate, tighter room-to-corridor differential-pressure (ΔP) control across airborne infection isolation and open room strata, and intent-aligned ventilation delivery (air changes per hour ratio distribution concentrated near unity; p < 0.05 where letter groups differ). Operational-state discrimination improved (AUC 0.649→0.696) and issue-resolution times shortened (left-shifted cumulative distribution function), indicating reduced service burden. Option C verification showed energy residuals shifting negative under S3, consistent with net savings versus baseline expectations. Across progressive maturity (S0→S3), time-to-value and burden fractions decreased, carbon intensity (tCO₂e m⁻²) decreased, long-tail exposure compressed (log-scale horizon), and composite performance indices increased (p < 0.05). These results demonstrate a verifiable pathway to pressure-reliable, decarbonized hospital operations at the whole-facility boundary while making the semantic layer’s utility explicit through query-driven, ontology-grounded asset discovery. We present an IPMVP Option-C–verifiable semantic digital-twin governance framework that links audited operational evidence (telemetry → actions → verification) to whole-facility energy and carbon outcomes while maintaining critical-room pressure-relationship reliability. Optimization benchmarking (including quantum annealing) is used as supporting decision-support evaluation, rather than as the central contribution. Full article

Current research in consent management techniques focuses on isolated aspects of data security, privacy, or auditability, but important issues like (i) dynamically integrating regulatory updates into form generation, (ii) support in content generation with verifiable audit trails, and (iii) tools that make compliance reasoning transparent for non-legal users are not yet addressed. This paper introduces CONSENT, an architecture that integrates AI-based consent reasoning using Large Language Models (LLMs) for automated consent-form drafting and compliance evaluation, alongside blockchain technology for secure and auditable storage. The architecture builds on prior work to address the aforementioned issues by introducing three supporting mechanisms: (a) Specialized AI models coordinated through expert routing which coordinate subtasks such as automation in form generation and regulatory compliance, (b) Retrieval-Augmented Generation (RAG) that supports the integration of regulatory updates into forms, and (c) Explainable AI (XAI) for the reasoning behind form content and compliance assessments. CONSENT architecture is evaluated through 250 test cases and a pilot case study for clinical trial consent management involving 20 engineers and attorneys, who evaluated the prototype on form quality (i.e., coherence, conciseness, factuality, fluency, and relevance) as well as time and effort efficiency. Results show that CONSENT substantially reduces the manual effort in consent-form creation while providing transparent, audit-ready compliance assessments, highlighting its potential for dynamic, user-centric consent management. Full article

Background and Objectives: Pregnancy is associated with profound physical and psychological changes in a woman’s life. Psychological distress and medical comorbidities during pregnancy remain under recognized despite their potential impact on maternal well-being. This study aimed to examine the associations between psychological distress, physical and mental components of health-related quality of life (HRQoL), lifestyle factors (alcohol and tobacco use), and the presence of medical comorbidities in pregnant women. Materials and Methods: A cross-sectional study was conducted among pregnant women in the second and third trimesters admitted to a tertiary obstetrics and gynecology center in Romania. Psychological distress was assessed using the Symptom Checklist-90-Revised (SCL-90-R) Global Severity Index (GSI), while health-related quality of life (HRQoL) was evaluated with the Short Form Health Survey-36 items (SF-36) physical (PCS) and mental (MCS) Component Summary scores. Alcohol and tobacco use were assessed using Alcohol Use Disorders Identification Test—Consumption (AUDIT-C) and the Fagerström Test for Nicotine Dependence (FTND). Nonparametric tests were used for correlation and group-comparison analyses. Results: Among the 337 valid answers, higher psychological distress was significantly associated with lower physical (R = −0.16, p < 0.01) and mental (R = −0.26, p < 0.01) HRQoL. Pregnant women with medical comorbidities reported higher psychological distress and poorer physical HRQoL compared with those without comorbidities, while mental HRQoL did not differ significantly. Alcohol and tobacco use were not significantly associated with HRQoL or psychological distress. Conclusions: Psychological distress is a central factor associated with both physical and mental quality of life during pregnancy. Integrating routine mental health screening into antenatal care, particularly for women with medical comorbidities, may improve maternal well-being and support better pregnancy outcomes. Full article

University–employer alliances have expanded as a strategy to foster innovation, employability, and knowledge transfer; however, their growth often results in instrumental arrangements oriented toward short-term metrics (agreements, hours, deliverables) that weaken curricular transformation and Social Responsibility. This article proposes a governance architecture to design and audit sustainable Collaborative Education, understood as a technologically mediated multi-actor network organized by a shared principle of Social Responsibility. The method operates in two moves: (i) a conceptual ordering that uses the substance–accidents distinction and a formative telos to subordinate organizational and technological means to the educational purpose; and (ii) the translation of concepts into decision domains (who decides, with what evidence, under what risks, and with what safeguards), positioning Technological Mediation as governance infrastructure rather than a neutral support. The proposal delivers three managerial outputs: (a) a hierarchy of seven support entities (metaphysical question, Social Responsibility, projects and strategies, institutional management, institutional development, stakeholders, and benefits); (b) governance principles (primacy of purpose, multi-actor accountability, justifiable distribution of benefits and risks, and deliberative traceability); and (c) a compact matrix and checklist applicable through document auditing and platform design review, without requiring field data collection. Taken together, the framework shows how employer-side corporate governance can align incentives, rules of evidence, and data use to enable co-responsibility and avoid capture, strengthening the sustainability of collaboration over time across organizational contexts. Full article

The disruptive acceleration of Generative Artificial Intelligence (GAI) has amplified the phenomenon of Global Friction (Globofriction), where technological speed undermines informational stability and weakens democratic resilience. Within higher education, this scenario demands training models capable of preparing future communicators to act as guarantors of truth amid automated erosion of discourse. This research evaluates the digital competence of Communication students through an interdisciplinary STEM-SSH (Science, Technology, Engineering, Mathematics—Social Sciences and Humanities) nexus approach based on the Kirkpatrick model. A mixed-methods methodology was employed, analyzing self-perception and cybersecurity data (n = 59), technical performance in the production of interactive infographics (n = 25), and qualitative evidence from reflection forums on systemic risks. The results reveal a “showcase digital competence”: a functional dissonance where future communicators demonstrate technical excellence under academic supervision but maintain negligent habits in their autonomous praxis. The study concludes that, given risks such as data porridge and strategic disinformation, it is urgent to transition toward a model of ethical resilience. This shift is imperative to reclaim the sovereignty of human judgment and ensure the integrity of public debate amidst current technological friction. Full article

Skeleton-based gait sex classification can reduce reliance on appearance cues, yet its robustness under everyday walking disturbances remains under-quantified. Using PsyMo 2D pose sequences (90° side view), we render Common Objects in Context (COCO) keypoints into compact grayscale skeleton images, segment sequences into fixed-length 15-frame clips, and classify them with a 3D residual convolutional neural network (CNN) under a subject-wise split shared across four aggregated conditions: overall (A), occlusion/carrying disturbance (B), speed variation (C), and smartphone use (D). To avoid an arbitrary decision rule, we select a global operating threshold on the validation set by sweeping τ to maximize macro-F1, apply it unchanged to the held-out test set, and report a threshold-sensitivity check. Robustness is audited via condition-wise confusion matrices, subgroup precision/recall with 95% subject-level bootstrap confidence intervals, and subject-level probability overlap. To contextualize condition-dependent behavior, we quantify joint-group attribution shifts using Gradient-weighted Class Activation Mapping (Grad-CAM) and examine a coarse arm-swing proxy under smartphone use. Subject-level test accuracy ranged from 0.761 to 0.870 across conditions A–D, with uncertainty summarized by 95% subject-level bootstrap confidence intervals; performance was lowest in B, with increased male→female errors. Overall, these results provide a transparent audit-and-interpretation framework for assessing skeleton-based gait sex classification under realistic walking perturbations in practical evaluation scenarios. Full article

Battery energy storage systems (BESSs) are increasingly deployed by European Balance Responsible Parties (BRPs) to mitigate system-imbalance exposure; yet, techno-economic assessments often represent degradation using fixed-lifetime or equivalent-full-cycle assumptions that obscure the dependence of wear on operating policy and sizing. This study develops a data-driven, parameterised degradation framework for LiFePO₄ (LFP) BESS operating under imbalance duty. Using historical imbalance datasets from five European countries spanning eight transmission system operators (TSOs), annual cycle-induced capacity loss, calendar-induced capacity loss, and total annual capacity loss at 25 °C are mapped as explicit functions of energy-to-power ratio (duration), maximum power rating, depth of discharge, state-of-charge operating bounds, and daily cycling intensity. A degree-2 Ridge specification yields compact, auditable coefficients that transfer across entities (including an out-of-time full-year hold-out for Belgium, 2025). The fitted response surfaces reveal consistent EU-wide operating regimes: cycling-dominant ageing for durations $\le 3$ h, a mixed regime for durations $\approx 3$–6 h, and calendar-dominant ageing for durations $\ge 6$ h, indicating a practical compromise around ≈4–5.5 h. The resulting coefficientised outputs are Techno-Economic Assessment (TEA)-ready and enable risk-aware sizing and state-of-charge policy design for imbalance-focused BESS portfolios. Full article

Information security auditing plays an important role in information security management because it assesses the status of security mechanisms, risk management, and regulatory compliance. Most information security auditing methodologies have been based on binary assessments or checklists, an approach that is limited in the constant evolution of cyber threats. This paper presents a comparative analysis of the most recognized maturity level structures, such as the Capability Maturity Model Integration (CMMI), the Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Maturity Model Certification (CMMC), in order to identify the most suitable one for an innovative change in the auditing process to obtain a deeper and more detailed evaluation of security controls and, consequently, better decision-making. Full article