Search Results (2,877)

Digital government is widely regarded as a catalyst for sustainable development, yet the mechanisms by which e-government adoption translates into progress on the SDGs remain poorly understood, particularly in high-income contexts where governance is already mature. This study addresses that gap using a balanced panel of all 27 EU member states over 2015–2023. Applying two-way fixed-effects estimation with formal Baron–Kenny mediation and country-block bootstrap inference, we identify three findings that collectively reframe the relationship between digital government and sustainable development in the European context. First, the widely assumed governance reform pathway is not empirically supported in the EU27: e-government adoption is not associated with measurable improvement in institutional quality, consistent with structural saturation rather than policy failure. Second, the benefits of digital government are unevenly distributed across the EU: old member states (EU15) exhibit significant positive effects on SDG 9: Innovation and Infrastructure, whereas new member states (EU13) do not, challenging the assumption that digital strategies yield symmetric returns across the Union. Third, and most importantly, the EU15 effect appears to be fully channelled through household internet access, consistent with a digital co-investment mechanism in which e-government uptake and broadband infrastructure co-evolve as expressions of a shared national digital transformation strategy. These findings inform the policy debate: the question for EU15 is not whether to invest in e-government, but how to sustain the joint infrastructure investment that makes it effective; for EU13, the priority is to establish the digital and institutional foundations that enable the mechanism to be activated. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

To address the critical challenges of lithology acquisition and low blasting refinement under extreme low temperatures and varying thermal conditions in high-altitude environments, this study develops a real-time dynamic identification method for rock-like interfaces using Measurement While Drilling (MWD) technology. The scope of this research involves the use of a self-developed indoor digital drilling experimental platform to simulate both ambient and freezing (−20 °C) conditions. Procedures included conducting comprehensive comparative drilling experiments on various rock-like materials with distinct strength levels to evaluate their mechanical responses during penetration. The major findings reveal a significant influence of low-temperature hardening effects on MWD parameters; specifically, the frozen state notably increases drilling torque and feed pressure while simultaneously decreasing the stable rotational speed of the drill bit. To resolve the feature parameter drift induced by temperature variations, a novel interface recognition algorithm is proposed that integrates Z-score normalization, change-point detection, and multi-dimensional spatial clustering. Through a dual-detection mechanism involving both single-point and cumulative features, the algorithm effectively captures precise mutation information during rock layer transitions. It further incorporates multi-dimensional indicators, such as consistency, change intensity, and point density, to perform comprehensive weighted scoring. Experimental results demonstrate that the proposed algorithm effectively eliminates the systematic offset of parameters caused by temperature fluctuations. The prediction error at both “strong-weak” and “weak-strong” transition interfaces is maintained within 1.5 mm, which significantly improves the accuracy and robustness of interface recognition under complex and varying working conditions. These key conclusions provide essential technical support for the implementation of differentiated charging and green refined mining operations, ensuring greater energy efficiency and environmental protection in cold-region engineering. Full article

Background: Postprandial triglyceride (TG) metabolism represents a dynamic dimension of lipid physiology that complements conventional fasting lipid assessment. Although low-density lipoprotein cholesterol (LDL-C) remains the primary therapeutic target in cardiovascular prevention, residual cardiovascular risk persists in many individuals despite apparently adequate fasting lipid control. Because most individuals spend the majority of their waking hours in a fed state, postprandial TG responses may provide clinically relevant insight into metabolic flexibility, dietary exposure, and the efficiency of TG-rich lipoprotein clearance. Methods: This narrative review was conducted using a literature search guided by predefined themes, keywords, and databases, without following a formal systematic review protocol. Randomized controlled trials, observational studies, meta-analyses, and major reviews addressing postprandial lipid metabolism, dietary determinants, and cardiometabolic risk were included, with priority given to human studies. Results: Postprandial TG responses are strongly influenced by dietary composition, eating patterns, and metabolic health. Individuals with insulin resistance, type 2 diabetes, obesity, and metabolic-associated steatotic liver disease (MASLD) frequently demonstrate exaggerated or prolonged postprandial lipemia even when fasting TG concentrations appear acceptable. While circulating TGs serve as practical clinical markers of postprandial lipid handling, cholesterol-enriched remnant lipoproteins more closely reflect atherogenic burden. Nutritional interventions, weight management, and physical activity consistently improve postprandial TG dynamics, whereas pharmacologic therapy provides additional benefit in selected high-risk patients. Non-fasting TG measurements may provide additional insight into postprandial lipid metabolism and residual cardiovascular risk, although standardized protocols and validated clinical thresholds remain to be established. Conclusions: Postprandial TG metabolism provides clinically meaningful information beyond fasting lipid measurements and represents a useful adjunct for refining residual cardiovascular risk assessment. Although standardized protocols remain limited, integrating nutritional and clinical perspectives may support a more comprehensive and individualized approach to cardiometabolic prevention. Full article

Introduction: Caffeine is the most widely consumed psychoactive stimulant worldwide and acts primarily through antagonism of adenosine A1 and A2A receptors, thereby reducing sleep pressure and promoting wakefulness. Although its alerting and performance-enhancing effects are well established, its influence on sleep-related electroencephalography (EEG) has been investigated across diverse paradigms with substantial methodological heterogeneity. This systematic and mechanistic review aimed to synthesize human evidence on how caffeine affects sleep architecture, quantitative sleep EEG, and neurophysiological markers of sleep homeostasis, and to interpret these findings within current models of adenosine-mediated sleep–wake regulation. Materials and methods: A systematic search of PubMed/MEDLINE, Web of Science, Scopus, Embase, PsycINFO, ResearchGate, and Google Scholar was conducted for studies published between January 1980 and January 2026, with the final search performed on 10 January 2026. Eligible studies were original human investigations examining caffeine exposure or administration and reporting sleep-related EEG outcomes, including polysomnographic sleep staging, spectral EEG analyses, or other EEG-derived sleep metrics. Two reviewers independently screened records and assessed eligibility, with disagreements resolved by consensus. Data on study design, participant characteristics, caffeine interventions, EEG methodology, and outcomes were extracted using a predefined form. Risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Owing to marked heterogeneity across studies, findings were synthesized narratively within a mechanistic interpretive framework. Results: Thirty-two studies were included. Across highly heterogeneous paradigms—including acute bedtime or evening dosing, daytime or repeated caffeine use before nocturnal sleep, administration during prolonged wakefulness followed by recovery sleep, withdrawal protocols, and ambulatory/home EEG monitoring—the most consistent finding was suppression of low-frequency NREM EEG activity, particularly slow-wave activity and the lowest delta frequencies. Caffeine frequently increased faster EEG activity, including sigma/spindle and beta ranges, producing a lighter, more aroused, and more wake-like sleep EEG profile. These effects were especially prominent during early-night NREM sleep and in recovery sleep after sleep deprivation, where caffeine attenuated the expected homeostatic rebound in low-frequency power. REM-related effects were less consistent, but some studies reported delayed REM timing and subtler alterations in REM EEG. Emerging evidence further suggests that caffeine increases EEG complexity and shifts sleep dynamics toward a more excitation-dominant state. Several studies indicated that quantitative EEG measures were more sensitive than conventional sleep-stage variables in detecting caffeine-related sleep disruption. Dose, timing, habitual caffeine use, withdrawal state, age, circadian context, and adenosinergic genetic variation, particularly involving ADORA2A, moderated the magnitude of effects. We also highlighted the connection between current results and sports and sports science. Conclusions: Caffeine reliably alters the neurophysiological architecture of human sleep in a direction consistent with reduced sleep depth and weakened homeostatic recovery. The overall evidence supports a mechanistic model centered on adenosine receptor antagonism, attenuation of sleep-pressure build-up and expression, and a shift toward greater cortical arousal during sleep. Sleep EEG appears to be a sensitive marker of these effects, often revealing physiological disruption even when conventional sleep architecture changes are modest. Future research should prioritize larger and more diverse samples, pharmacokinetic and pharmacogenetic characterization, and ecologically valid high-resolution sleep monitoring to clarify the real-world and functional consequences of caffeine-induced EEG changes. Full article

Endurance performance is regulated through dynamic interactions between physiological capacity, nutritional status, and psychological control processes. While traditional endurance models have emphasized metabolic and cardiorespiratory determinants, growing evidence indicates that energy availability also influences cognitive function, perceived effort, and decision-making during prolonged exercise. This narrative review synthesizes current literature on the interplay between nutritional strategies and psychological regulation in endurance sports, with particular emphasis on low energy availability, carbohydrate availability, mental fatigue, and pacing behavior. Acute and chronic reductions in energy availability are associated not only with endocrine and metabolic disturbances but also with amplified perceived exertion, impaired executive functioning, reduced effort tolerance, and altered risk-related decision-making, even in the absence of overt physiological failure. Carbohydrate availability emerges as a central modulator operating through both peripheral mechanisms (substrate supply and glycogen preservation) and central neurocognitive pathways influencing perception, motivation, and fatigue regulation. Hydration status, caffeine ingestion, and gastrointestinal tolerance further interact with perceptual and cognitive processes to shape real-time pacing and endurance sustainability. Integrating sport nutrition and sport psychology provides a unifying framework for understanding endurance regulation as a multilevel process linking metabolic state to perceptual experience and behavioral decision-making. From an applied perspective, optimizing endurance performance requires maintenance of adequate long-term energy availability, strategic carbohydrate periodization aligned with training demands, and systematic monitoring of perceived effort alongside physiological load. Future research should prioritize interdisciplinary, ecologically valid designs combining metabolic, perceptual, and cognitive measurements, supported by wearable and data-driven technologies capable of capturing real-time endurance regulation. Bridging nutritional and psychological mechanisms within a unified conceptual model offers a stronger scientific basis for improving performance sustainability while safeguarding athlete health in modern endurance sport. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

Background/Objectives: Spirituality is a key component of coping and well-being in chronic and advanced illness, yet its assessment remains inconsistent across clinical settings. To identify, classify, and critically analyze the most commonly used and validated instruments for measuring spirituality in clinical contexts, focusing on their ability to assess the current spiritual state from a multidimensional perspective (cognitive, behavioral, and affective expressions). Methods: A systematic literature review was conducted using PubMed, Scopus, and Web of Science (2011–2024). Inclusion criteria targeted validation studies of instruments assessing spirituality in adults with chronic or advanced illnesses or in palliative care. A dual conceptual–functional classification was applied, and a custom scoring system was developed to evaluate psychometric quality. Contamination and tautological aspects were also examined. Results: Forty-three instruments were identified across 42 studies. Of these, 93.02% included cognitive, affective, and behavioral dimensions. Most were validated in oncology or chronic disease populations. Content validity and internal consistency were the most reported psychometric properties; responsiveness was rarely evaluated. Conclusions: The available instruments reflect several conceptual and functional approaches. The classification proposed in this review provides practical guidance for selecting scales according to specific clinical goals and settings, supporting the evaluation of the current spiritual state and the integration of spirituality into healthcare practice. Further research is recommended to develop culturally sensitive and responsive instruments suitable for diverse clinical contexts. Full article

Objective: Clinical remission is a major therapeutic goal in systemic lupus erythematosus (SLE) because of its association with improved long-term outcomes. However, its relationship with patient-reported burden, quality of life, and disease perception remains incompletely understood. This study aimed to evaluate patient-reported outcomes (PROs) in patients with SLE in clinical remission, identify factors associated with impaired health-related quality of life (HRQoL), and assess physician–patient discordance in disease activity perception. Methods: A total of 106 adult patients with SLE in clinical remission according to the definition proposed by Zen et al. were enrolled at a single rheumatology center. Patients were classified into complete remission, clinical remission off corticosteroids, or clinical remission on corticosteroids. Demographic, clinical, and treatment-related data were collected, including organ damage (SLICC-SDI) and disease activity (SLEDAI-2K). Patients completed PRO measures including SF-36, Global Health (GH), pain VAS, STAI-Y1 and STAI-Y2, Zung Depression Scale, Insomnia Severity Index, and HAQ. Disease activity was assessed by both the patient (PGA) and the physician (PhGA); a PGA–PhGA difference >25 mm was considered clinically relevant discordance. Results: Among patients in clinical remission, mild anxiety was observed in 17.1% according to STAI-Y1 and in 27.9% according to STAI-Y2, mild-to-moderate depressive symptoms in 47.1%, and mild insomnia in 25.5%. Of the 106 patients, 24 (22.6%) were in complete remission, 27 (25.5%) in clinical remission off corticosteroids, and 55 (51.9%) in clinical remission on corticosteroids. Patients in clinical remission on corticosteroids showed worse patient-reported outcomes than those in complete remission or clinical remission off corticosteroids. In multivariable analyses, poorer physical HRQoL was independently associated with functional disability, pain intensity, and depressive symptoms, whereas poorer mental HRQoL was independently associated with trait and state anxiety. Clinically relevant physician–patient discordance was observed in 22.6% of the cohort and was almost exclusively driven by higher patient than physician scores. Pain intensity emerged as the most robust independent correlate of discordance. Conclusions: A substantial patient-reported burden may persist in patients with SLE despite clinical remission. Pain, psychological distress, insomnia, and functional disability contribute to impaired HRQoL, while physician–patient discordance appears to reflect a broader mismatch between inflammatory disease control and the patient’s lived experience of illness. These findings support a more comprehensive and patient-centered approach to remission assessment in SLE. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

Energy use remains central to Colombia’s economic growth, yet its composition shapes the scale and direction of environmental outcomes. This study investigates how coal, oil, and hydroelectricity influence ecological degradation within the context of economic growth. The study applies cross-quantilogram and bootstrap Fourier Granger causality techniques to capture directional dependence and predictive causality across different quantiles, respectively. The findings show that the relationships are heterogeneous rather than uniform across the distribution. Economic growth exhibits a predominantly negative dependence on ecological footprint, suggesting that higher output is associated with lower ecological pressure under several environmental states. Hydroelectricity also shows a largely negative dependence, indicating its general contribution to environmental sustainability, although this effect weakens under extreme conditions. By contrast, the effects of coal and oil are more conditional and vary across quantiles, reflecting the complex role of fossil fuels in Colombia’s environmental dynamics. The bootstrap Fourier Granger causality results further reveal that causality is not constant across the distribution, but emerges only at specific quantiles. The central policy implication from this result lies in adopting an adaptive environmental strategy in which preventive measures dominate under low degradation, green-supportive policies are emphasized under moderate degradation, and stronger corrective interventions are implemented under high ecological stress. Full article

New quality productive forces are increasingly recognized as important drivers of coordinated regional development, with urban agglomerations acting as key vehicles for their spatial implementation. Based on the theory of new quality productive forces, this study takes the 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration as its research subjects, spanning the period from 2005 to 2023, and constructs a four-dimensional evaluation index system for new quality productive forces covering economic, social, ecological, and technological dimensions. It employs the entropy method to determine indicator weights and calculate development indices for each dimension and utilizes a coupling coordination model to measure the overall and subsystem-level coordination by analyzing their spatiotemporal evolution characteristics. The results indicate a steady upward trend in the overall coordination level, progressing from a low level to an intermediate level, with the state of coordination continuously improving; spatial differentiation is significant, forming a gradient development pattern centered on Beijing, with marked disparities in coordination levels among cities. Subsystem analysis reveals an imbalanced synergy structure: while economic and ecological synergy levels are relatively high, the coupling and synergy between science and technology and the economy and society remain prominent weaknesses. Most cities in Hebei Province lack sufficient scientific and technological innovation capabilities, resulting in a weak supportive role for economic and social development. Based on these findings, this study proposes policy recommendations such as establishing a regional innovation community, promoting the integration of factor markets, and strengthening collaborative governance of the ecological environment, with the aim of leveraging new quality productive forces to drive a qualitative leap in the coordinated development of the BTH urban agglomeration. Full article

Background: Vitamin D deficiency remains a widespread public health issue in Europe, despite the availability of sunlight, dietary sources, supplements, and food fortification. National fortification strategies differ substantially in their regulatory approaches, food vehicles, and fortification levels, influencing the population’s vitamin D intake and status. Objective: The primary objective of this study was to map vitamin D food fortification policies across European Union (EU) Member States, European Free Trade Association (EFTA) countries, and the United Kingdom (UK), focusing on regulatory frameworks, eligible food categories, and implementation models. Methods: A structured review of national legislation and official guidance on vitamin D food fortification was conducted between December 2025 and March 2026 across EU Member States (n = 27), EFTA countries (n = 4), and the UK. For EU Member States, the framework established by Regulation (EC) No 1925/2006 was examined alongside national implementation measures. For EFTA countries and the UK, corresponding national legislation and official regulatory guidance were reviewed. Data were extracted on fortification policy status, eligible food categories, legal basis, and fortification levels. Targeted searches of PubMed and Scopus were performed to identify modeling studies and policy analyses supporting the interpretation of the findings. Results: Fortification policies show marked heterogeneity. Mandatory fortification is limited to a few countries and specific foods: Finland (homogenized skim milk), Sweden (low-fat milk, fermented dairy, plant-based alternatives, and fat spreads), Belgium (margarine and selected fats), and Poland (margarine and fat spreads). In most other European countries, vitamin D fortification is voluntary under EU legislation or equivalent national legislation, depending on market uptake. Food vehicles vary regionally, with Northern Europe extending fortification beyond fats to include fluid milk and plant-based drinks, whereas other regions mainly fortify margarines, cereals, dairy products, and plant-based beverages. Fortification levels also differ, with some countries specifying maximal or exact levels, while others lack national standards. Data on fortified foods are limited in several Central and Southern European countries. Modeling indicates that multi-vehicle fortification is more effective than single-vehicle approaches, safely increasing population intakes while reducing deficiency prevalence. Conclusions: Vitamin D fortification policies across Europe are highly heterogeneous. Most countries rely on voluntary approaches, which provide limited coverage. Strengthening policy through mandatory and well-coordinated multi-vehicle strategies, informed by modeling and population-based studies, can improve vitamin D intake, reduce deficiency prevalence, and enhance health equity. Full article

Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation. Full article