Search Results (164,740)

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

Corydalus cornutus hellgrammites are known to inhabit riffles, but information is scarce regarding their habitat relationships at the plot scale and in the northern part of their range in the United States. We investigated the relationship of C. cornutus hellgrammite occurrence, density, and body size with environmental variables at the 1 m² plot scale within central Ohio riffles. We collected hellgrammites and measured hydrological, substrate, large instream wood, and canopy cover variables in nine riffles in 2023 and ten riffles in 2024. Occurrence and density were best predicted by water velocity and grain size score, while none of the measured variables were a good predictor of head capsule width. Occurrence and density increased with increasing water velocity, grain size score, and substrate richness. Density also increased with increases in edge-interior scores. Head capsule width increased with increasing distances to the nearest plot with hellgrammites. Our results suggest that C. cornutus hellgrammites can serve as an indicator species because their occurrence and density reflect the water velocity and substrate conditions within riffles in the Midwestern United States. Full article

Life evolved under a persistent 1 g field that is continuous, ubiquitous, and directionally structured. Here, we synthesize evidence across evolutionary biology, mechanobiology, and genome architecture to propose gravity as a mechanical boundary condition that helped canalize the emergence of complex multicellularity. Order-of-magnitude considerations indicate that gravity-derived hydrostatic loads can fall within force/pressure regimes relevant to nuclear and chromatin mechanosensitivity when transmitted through adhesion–cytoskeleton–LINC–lamina coupling. Comparative genomic and imaging frameworks suggest that complex animals increasingly rely on volumetric genome organization (packing domains and higher-order 3D architectures) that supports durable transcriptional memory and stable differentiated cell identities. Integrating these concepts with altered-gravity experiments, we argue that microgravity and hypergravity perturb chromatin topology and region-level transcription in rapid, largely reversible patterns consistent with a mechanically defined 1 g reference state. We advance a boundary-condition thesis: gravity is not a sole driver but a stable reference that likely contributed to the evolvability and long-term robustness of mechanogenomic architectures required for high-dimensional differentiation and tissue homeostasis. Full article

Background and Clinical Significance: Cirrhosis-associated immune dysfunction (CAID) is characterized by impaired innate and adaptive immune responses, gut dysbiosis, and increased bacterial translocation, predisposing patients to severe and atypical infections. While spontaneous bacterial peritonitis and other intra-abdominal infections are well-recognized complications of cirrhosis, extraintestinal infectious manifestations related to bacterial translocation are less commonly described. A tubo-ovarian abscess (TOA) typically arises from ascending pelvic infections associated with pelvic inflammatory disease and is rarely reported in patients with cirrhosis without gynecologic risk factors. Thus, recognizing unusual infectious presentations in cirrhotic patients is important given their functionally immunocompromised state. Case Presentation: We report a 46-year-old woman with previously undiagnosed alcohol-related cirrhosis who presented with sepsis and abdominal pain. She had no prior gynecologic history or known risk factors for pelvic inflammatory disease. Contrast-enhanced computed tomography (CT) demonstrated bilateral tubo-ovarian abscesses. Image-guided percutaneous drainage was performed, and cultures from both ascitic fluid and bilateral adnexal collections grew Enterococcus faecium, supporting a shared intra-abdominal source of infection and suggesting transperitoneal dissemination via infected ascitic fluid as a plausible mechanism, although an ascending genital tract source cannot be fully excluded. The patient was treated with targeted intravenous antibiotics and drainage with subsequent clinical improvement. Conclusions: This case highlights bilateral tubo-ovarian abscesses as a rare infectious complication of cirrhosis-associated immune dysfunction. In cirrhotic patients presenting with sepsis and intra-abdominal pathology, clinicians should consider atypical infection pathways related to bacterial translocation among the differential mechanisms of spread. Thus, recognizing cirrhosis as a functionally immunocompromised state is essential for the timely diagnosis and management of unusual infections. Full article

Understanding Alzheimer’s Disease (AD) progression using resting-state functional Magnetic Resonance Imaging (rs-fMRI) remains an open challenge. Variational Autoencoders (VAEs) provide compact representations of high-dimensional neuroimaging data but lack mechanisms to highlight disease-relevant features. We propose KS-VAE, a novel framework that integrates the Kolmogorov–Smirnov test into the latent space of VAEs to identify statistically significant variables discriminating healthy from pathological brain states. This integration enables measurement of latent space shifts associated with cognitive decline, offering a quantitative approach to neurodegenerative processes. By modifying the most relevant variables, KS-VAE generates synthetic samples that simulate transitions between clinical conditions while preserving anatomical plausibility. The method enhances the modeling of temporal and distributional dynamics underlying disease progression and provides interpretable analysis of class-relevant features. Applied to rs-fMRI scans of 220 subjects from the ADNI cohort, KS-VAE demonstrated robust class separation between cognitively normal and Alzheimer’s disease subjects, achieving a classification accuracy of 84.5% and an F1-score of 84.5%, and clinically consistent synthetic transitions. KS-VAE thus offers a statistically grounded and clinically interpretable framework for understanding Alzheimer’s disease progression. Full article

Demographic decline in many Organization for Economic Co-operation and Development (OECD) countries is widely considered the principal source of hurling public pension disbursements, whilst trade unions are often blamed for staunch antagonism towards any transformations that might alleviate the fiscal encumbrance. If financialization is state-acquiesced, with the state being considered fundamental for market integration and social regulation of markets to protect against market failures, how then should inter-generational equity be addressed? This work tests the hypothesis that deindustrialization (measured as the declining proportion of employment in manufacturing) and lower trade-union density are quintessential channels through which demographic change translates into ascending pension outlays. Using OECD data from 1960 to 2013, we utilize longitudinal and panel quantile statistical methods to dissect these links across assorted pension system clusters (total, mandatory private, mandatory public, mandatory public & voluntary, and mandatory public & private). This study highlights the mediating role of labor market structure in pension financing. Full article

Artificial intelligence (AI) is increasingly transforming talent acquisition and workforce analytics, raising both efficiency opportunities and ethical concerns. This study aims to map the intellectual structure and evolution of AI-enabled recruitment research with a focus on ethical and data-driven approaches. A bibliometric analysis was conducted on 1893 Scopus-indexed journal articles published between 2014 and 2025 using VOSviewer. The results reveal rapid growth in the field, dominant thematic clusters spanning machine learning applications, HR analytics, and ethical governance, and strong international collaboration led by the United States, China, and the United Kingdom. Findings also highlight the increasing prominence of fairness, transparency, and explainability within AI recruitment research. The study concludes by identifying research gaps and proposing future directions for integrating ethical AI frameworks with workforce analytics to support responsible talent acquisition. Full article

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes. Full article

A diverse range of 4-dimethylaminopyridinium (DMAP) bis(trifluoromethylsulfonyl)-amide ionic liquids with specific functionalities (alkyl, alkoxy, hydroxyalkyl and benzyl) were designed, characterized and compared with their pyridinium analogs in terms of their physical and radiolytic properties. The influence of the dimethylamino group on ionic liquid structure was investigated by X-ray diffraction and molecular dynamics simulations. The influence of the electron-donating ability of the dimethylamino-substituted cation is evident in the differences in the electronic density of states between the DMAP and pyridinium ILs. This leads to substantial changes in the radical transients observed in pulse radiolysis of the neat ILs. It was found that the DMAP salts were higher melting, more viscous and less conducting than their pyridinium analogs. However, the DMAP salts exhibited higher thermal stabilities and could therefore be useful for high-temperature applications. Full article

Accurate multivariate long-term time series forecasting (LTSF) is critical for smart grid operations. However, non-stationary distribution shifts frequently induce compounding error accumulation in conventional architectures. This study proposes the Mamba-LSTM-Attention (MLA) framework, a distribution-aware architecture engineered for forecasting stability. The pipeline integrates Reversible Instance Normalization (RevIN) to neutralize statistical drift. To address computational bottlenecks, the architecture utilizes a linear-time Selective State Space Model (Mamba) to capture global trend dynamics, cascaded with a single-layer gated Long Short-Term Memory (LSTM) unit to model localized non-linear residuals. A terminal information bottleneck structurally bounds cross-step error propagation. Empirical results across standard ETT and Electricity benchmarks reveal a precision–stability trade-off. By prioritizing structural resilience, the MLA framework limits error accumulation on highly volatile datasets, yielding MSEs of 0.210 and 0.128 on ETTh2 and ETTm2 at the T = 96 horizon. This structural bottleneck inherently smooths high-frequency periodic patterns, yielding lower absolute accuracy on stationary benchmarks such as ETTh1 and ETTm1. Ultimately, the architecture establishes a computationally efficient, structurally stable baseline tailored for non-stationary anomaly tracking in smart grids. Full article

Digital stylization of Dunhuang murals can support cultural heritage revitalization by transferring their distinctive aesthetics to modern images, but existing methods face practical limitations. Transformer-based models can yield high visual quality, but often at a prohibitive computational cost. In contrast, standard state space models (SSMs) are more efficient but tend to incur issues such as semantic loss, inconsistent stylization, and an undesired coupling between color and structure when processing the complex textures of historical murals. To address these issues, we propose Dh-Mamba, a hierarchical visual Mamba framework tailored for high-fidelity Dunhuang mural style transfer. Dh-Mamba introduces a CrossMamba in-context style injection mechanism. This mechanism prefixes the style token sequence to the content sequence, which enables globally consistent style propagation as a persistent memory and retains linear-time efficiency. We also designed two additional components: a Modulated Style Perception Module (Δt) and an Orthogonal Decoupled HSV Modulator. The former adaptively regulates texture injection based on style complexity. The latter models mineral pigment palettes and mitigates oxidation-related artifacts by disentangling hue, saturation, and value. Experiments on a custom Dunhuang dataset show that Dh-Mamba improves content preservation and produces more natural mural textures than recent state-of-the-art methods; multiple quantitative metrics corroborate these gains. With 20.04 million parameters, Dh-Mamba provides a resource-efficient solution suitable for deployment in resource-constrained terminal applications for cultural heritage preservation. Full article

Effective software engineering education today requires tools that adapt to individual learner proficiency and progress, while ensuring positive student engagement. Gamified platforms represent an effective approach to learning and maintaining motivation, but their efficacy depends on a robust underlying architecture. This systematic literature review analyzes state-of-the-art artificial intelligence (AI)-based adaptive architectures designed to support gamified learning tools, highlighting their architectural models (such as intelligent tutoring systems, multi-agent systems, and immersive virtual reality/augmented reality environments), adaptation mechanisms (including Generative AI and chatbots), and personalization strategies. A significant focus is placed on Process Mining and Learning Analytics as methodological approaches to organize learning paths and guide dynamic adaptation based on student behavior. The results of the selected studies demonstrate advantages such as increased engagement, longer-term participation, and personalized learning pace. However, challenges remain, such as common assessment criteria, integrating different technologies, and system scalability. The findings offer concrete insights for designing the next generation of effective gamified learning tools, based on data and software engineering processes. Full article

Polyamide (PA) is widely used as a high-performance engineering thermoplastic in automotive components and textiles, due to its superior mechanical strength and chemical resistance. However, the increase in PA waste has posed significant challenges to resource sustainability and environmental protection. Despite breakthrough development achieved in PA recycling, key barriers remain in process scale-up and high-value recovery. This review examines the current state of PA recycling, analyzing the research prospects of mechanical and chemical recycling from economic feasibility and environmental impact. We present discussions on innovative recycling approaches for PA, including upcycling, molecular design of novel PA derivatives, chemo-biological coupling and solvent-based recovery, offering potential solutions to the sustainable circular economy and green cycles. Finally, by presenting case studies, we highlight pathways toward future innovation that inform industrial-scale implementation. Full article

Cronobacter sakazakii is an opportunistic pathogen commonly associated with powdered infant formula and causes severe neonatal infections. While whole-genome sequencing (WGS)-based single nucleotide polymorphism (SNP) analysis has revolutionized surveillance and outbreak investigations, comprehensive population-level analyses remain limited, and establishing proper thresholds for detecting epidemiologically related C. sakazakii isolates requires assessment using large-scale genomic datasets. We analyzed 1870 C. sakazakii genomes from the United States (1970–2025) to examine pan- and core-genomic structure, analyze SNP distance matrices encompassing 1,747,515 unique pairwise comparisons, and reconstruct population phylogeny. Our analyses revealed exceptional genomic diversity with a large pan-genome of 24,035 gene families and an average of 29,442 ± 13,097 SNPs between genome pairs. Phylogenetic reconstruction identified 22 major clusters encompassing 89.3% of genomes, including environmental complexes demonstrating persistent contamination spanning multiple years. Using 209 monophyletic genome pairs with concordant metadata, we propose a tiered SNP threshold framework (≤234 to 506 SNPs) for detecting potentially epidemiologically-related genomes with improved sensitivity. As genomes from Michigan comprised 39.3% of the dataset, these thresholds should be interpreted with caution when applied to other US regions. This study provides population genomics infrastructure to enhance C. sakazakii surveillance and traceback studies for improving powdered infant formula safety. Full article