Search Results (21,586)

Background/Objectives: Abnormalities in the partial pressure of carbon dioxide (PCO₂) can occur during respiratory support and may contribute to adverse neonatal outcomes. This study aimed to assess the incidence of early hypocapnia and hypercapnia in mechanically ventilated preterm infants and their major associated outcomes. Methods: A single-center retrospective cohort study (2017–2024) was conducted in preterm infants < 32 weeks’ gestation who required > 24 h of invasive ventilation within the first 3 days of life. Perinatal–neonatal data were retrieved from the medical database. Admission blood gas values (arterial and capillary–venous) and the maximum and minimum PCO₂ in the first 72 h were evaluated. Normocapnia was defined as PCO₂ 35–45 mmHg, hypocapnia as < 35 mmHg, and hypercapnia as > 45 mmHg. Primary outcomes were the incidence of PCO₂ abnormalities; secondary outcomes included death or severe brain injury (SBI), SBI alone, and bronchopulmonary dysplasia (BPD) among survivors. Logistic regression identified independent predictors of the secondary outcomes. Results: Among the 134 infants evaluated, most experienced both hypercapnia and hypocapnia. Hypercapnia occurred in 81.3% of infants, and hypocapnia in 93.2%. Death or SBI was observed in 51.5%, and SBI alone in 42.5%. Gestational age < 28 weeks, air-leak syndromes, and pulmonary hemorrhage were independent predictors of death or SBI. Among survivors, hypercapnia and gestational age < 28 weeks independently predicted BPD. Infants with adverse outcomes had higher maximum PCO₂ values and greater PCO₂ variability, although these were not independent predictors of SBI or death. Conclusions: PCO₂ instability is highly prevalent in ventilated preterm infants, underscoring the need for individualized ventilation strategies. Extreme prematurity emerged as the primary risk factor for adverse outcomes, while hypercapnia was independently associated with BPD. Full article

This study benchmarks eight Python-based machine learning models for predicting nitrogen content across four sequential stages of BOF steelmaking. A dataset of 291 metallic samples from 76 heats was employed, covering pig iron desulfurization (PHASE #1), crude steel before BOF tapping (PHASE #2), and secondary metallurgy start (PHASE #3) and completion (PHASE #4). Linear regression, polynomial regression, ridge regression, decision tree, random forest, feedforward neural networks (FNNs), Gaussian Process Regression (GPR), and Support Vector Regression (SVR) were implemented in Python 3 with Z-score normalization and an 80/20 train–test split, and evaluated via MAE, MSE, MAPE, and R². Ridge regression achieved the highest accuracy in PHASE #1 (84.59%) and PHASE #4 (84.04%); FNNs excelled in PHASE #2 (78.27%) with consistent cross-phase performance; linear regression was optimal for PHASE #3 (79.06%). The advanced kernel-based methods demonstrated competitive performance, with GPR achieving 84.73% in PHASE #1 and SVR attaining 77.10% in PHASE #3 and 83.40% in PHASE #4, confirming their suitability for limited industrial datasets with a nonlinear structure. A hybrid strategy remains recommended: ridge regression for PHASES #1 and #4, FNNs for PHASES #2 and #4, and linear regression for PHASE #3, with SVR as a robust alternative in phases with moderate nonlinearity. Full article

Nile tilapia (Oreochromis niloticus) is a major aquaculture species worldwide, yet bacterial infections remain a critical constraint to production sustainability. Although pathogen-associated dysbiosis has been widely described, most studies have focused exclusively on bacterial communities, overlooking the multi-kingdom nature of the intestinal microbiota. This study evaluated the impact of experimental Providencia vermicola infection on both prokaryotic and microeukaryotic intestinal communities under controlled conditions. Using 16S (V3–V4) and 18S (V9) rRNA amplicon sequencing, we compared healthy and infected fish and assessed taxonomic, structural, and predicted functional changes. Infection was associated with significant compositional shifts, including increased relative abundances of Bacteroidota and Proteobacteria and decreased relative abundances of Fusobacteriota and Patescibacteria. Concomitantly, microeukaryotic groups such as Protalveolata, Nematozoa, and Phragmoplastophyta were significantly reduced. Functional prediction revealed metabolic pathway reconfiguration consistent with infection-associated ecological disturbance. Together, these results suggest that the pathogen challenge is associated with coordinated changes in the intestinal microbiota as an integrated system across multiple microbial kingdoms rather than as isolated bacterial shifts. This study supports ecosystem-level interpretations of dysbiosis and highlights the importance of incorporating cross-domain analyses into health assessment strategies in aquaculture species. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer defined by the absence of estrogen and progesterone receptors, as well as the lack of human epidermal growth factor 2 receptor overexpression. TNBC is associated with early onset, high metastatic potential, therapeutic resistance, and poor clinical outcomes exacerbated by the limited availability of effective targeted therapies. Advances in multi-omics profiling have further stratified TNBC into distinct molecular subtypes, each exhibiting unique genomic, epigenomic, and immune-related features that influence therapeutic responsiveness. This review explores the interplay between TNBC molecular heterogeneity, immune evasion mechanisms, and epigenetic regulation. TNBC demonstrates variable immunogenicity, with tumor-infiltrating lymphocytes serving as important prognostic and predictive biomarkers. However, immune escape commonly occurs through tumor microenvironment remodeling, T-cell exhaustion, cancer stem cell enrichment, and immune checkpoint pathways activation. Although immune checkpoint inhibitors have improved outcomes in selected patients, particularly in combination with chemotherapy, primary and acquired therapeutic resistance remain a significant challenge. Emerging evidence highlights the central role of epigenetic mechanisms in regulating immune-related gene expression and shaping the tumor immune microenvironment. Epigenetic silencing of antigen presentation machinery, interferon signaling pathways, and chemokine expression contributes to immune evasion and immunotherapy resistance. Importantly, pharmacological modulation of epigenetic regulators can restore immune recognition and induce “viral mimicry” through reactivation of endogenous retroelements, thereby enhancing antitumor immunity. Collectively, this review underscores the therapeutic potential of integrating epigenetic therapies with immunotherapy and chemotherapy to overcome immune resistance in TNBC. A deeper understanding of epigenetic-immune interactions may facilitate the development of more precise and effective treatment strategies tailored to TNBC molecular subtypes. Full article

Background/Objectives: Minor neurocognitive disorder (minor NCD) represents a heterogeneous and potentially modifiable stage along the continuum from normal aging to dementia, offering a critical window for targeted health promotion interventions. Early identification of individuals at increased risk of progression is essential for implementing preventive strategies that may delay functional decline. This study developed a transparent machine learning (ML) framework to predict diagnostic change from minor to major NCD at 12 and 24 months using baseline demographic, clinical, and multidomain neuropsychological data. Methods: A retrospective cohort of 162 memory clinic patients was analyzed using a rigorously controlled pipeline incorporating nested stratified cross-validation, SMOTE-based imbalance correction, and sequential forward feature selection. Logistic regression, support vector machines (SVMs), and XGBoost were evaluated, with SHapley Additive exPlanations (SHAPs) applied to ensure interpretability. Results: SVM achieved the most balanced predictive performance at both 12 months (accuracy = 0.90) and 24 months (accuracy = 0.81). Short-term progression was primarily driven by subtle multidomain cognitive inefficiencies, while longer-term risk reflected continued cognitive vulnerability modulated by metabolic factors such as diabetes. Conclusions: These findings highlight the potential of explainable ML as a health promotion tool and suggest that explainable ML can uncover clinically meaningful cognitive risk signatures at the earliest stages of NCD. By identifying modifiable systemic contributors alongside cognitive risk profiles, this framework supports precision-oriented preventive strategies and proactive longitudinal monitoring in minor NCD. Full article

As reservoir development enters the middle and late stages, variations in formation pressure and water cut lead to significant changes in liquid supply capacity. Under conventional fixed stroke-per-minute (SPM) operation, the production capacity of beam pumping wells often fails to match the dynamically varying inflow, resulting in severe dynamic fluid level fluctuations and subsequent pump-off, gas locking, and abnormal rod string loading. To address these issues, this paper develops a dynamic fluid level model based on the operating mechanism of beam pumping wells, explicitly incorporating system uncertainties and reservoir disturbances. On this basis, a tube-based robust model predictive control (Tube-RMPC) strategy is proposed, in which nominal predictions are combined with local feedback compensation to effectively mitigate model uncertainties and external disturbances. Simulation results demonstrate that, compared with conventional PID control and traditional MPC methods, the proposed approach achieves superior performance in dynamic fluid level tracking accuracy, disturbance rejection, and closed-loop stability. Full article

Voluntary turnover represents a critical challenge in essential public services, where workforce attrition affects both employee well-being and service quality. The primary objective of this study was to identify the psychosocial predictors of well-being profiles and turnover intention among public transport workers, using the Job Demands–Resources model as a theoretical framework. A cross-sectional study design was employed, with 131 employees of an Italian public transport company completing a questionnaire assessing turnover intention and key psychosocial factors (job satisfaction, perceived work-related stress, work engagement, meaning of work, and perceived workplace safety). The analytical strategy integrated Latent Profile Analysis (LPA), logistic regression, and path analysis. LPA identified two distinct well-being profiles: a “low well-being profile,” with high perceived stress and low engagement and meaning of work; and a “high well-being profile,” with low stress and high engagement and work meaning. Logistic regression analyses showed that satisfaction with pay and the intrinsic nature of work tasks predicted membership in the high well-being profile. Path analysis indicated that profile membership significantly predicted turnover intention, with employees in the high well-being profile reporting lower turnover intention. Additionally, satisfaction with supervision, perceived workplace safety, and age showed direct effects on turnover intention. These findings highlight the organizational and psychological resources that can increase employee well-being and retention in the public transport sector, offering insights for preventive interventions and for promoting safer and more sustainable public transport systems. Full article

Raphidiopsis raciborskii is one of the most widely reported cyanobacteria worldwide, responsible for dense blooms and cyanotoxin production. Classified as invasive, it has been documented across all continents except Antarctica. While its distribution has been extensively studied, abiotic factors have consistently emerged as the main determinants of its success, which are therefore the focus of the present study. The objective of the present review is to synthesize findings from both experimental and field-based studies to identify which are the key drivers of its dominance. In total, 30 abiotic factors were reported, reflecting the broad strategies of the species. Results show the temperature as a consistent universal factor (11–35 °C), while differences were found regarding nutrient dynamics. Particularly, nitrogen forms and N/P ratios predominated in field-based evidence, whereas photosynthetically active radiation was disproportionately emphasized within experimental studies under controlled conditions. Factors such as salinity and micronutrients, and synergistic interactions remain critically understudied, limiting predictive capacity under global change scenarios. Understanding which combinations of these drivers create favorable conditions is essential for anticipating bloom dynamics in order to establish management strategies for avoiding or mitigating the negative impact of them. Full article

Amid the global transition to carbon neutrality, tidal current energy has become a strategic sustainable energy resource due to its high predictability, power density, and environmental compatibility. Horizontal-axis turbines show great potential for marine energy harvesting, yet the large-scale commercialization of tidal turbines is severely hindered by complex wake dynamics and the lack of reliable, efficient prediction tools for out-of-distribution (OOD) operating conditions. Traditional high-fidelity CFD methods are computationally prohibitive for engineering optimization, while conventional data-driven surrogate models suffer from poor extrapolation performance, extrapolation collapse near training parameter boundaries, and the absence of uncertainty quantification. To address these bottlenecks, this study focuses on the OOD extrapolation of wake flow prediction across tip speed ratio (TSR) distributions for a single horizontal-axis tidal turbine. A CFD-generated spatiotemporal benchmark dataset is constructed for comparative OOD evaluation across various TSR conditions with 9504 total samples. A novel physics-constrained Fourier neural operator framework named TSR-FNO is proposed to improve OOD generalization. The model integrates TSR–Lipschitz regularization to suppress extrapolation collapse and Monte Carlo Dropout to provide reliable uncertainty estimation. Extensive experiments demonstrate that the proposed method effectively reduces prediction error in unseen TSR regimes, mitigates performance degradation in far-field extrapolation, and produces well-calibrated uncertainty estimates consistent with actual prediction confidence. This work provides a data-driven surrogate modeling strategy for fast and reliable wake prediction on a common CFD-generated benchmark, supporting the efficient design, array layout optimization, and engineering deployment of tidal current energy systems. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

This article compares selected fire safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—building information modelling (BIM), digital twins (DTs), artificial intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and the scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of exit, and the increasing importance of documenting the operational status of protection measures. Then, it contrasts key differences, including the permissibility of performance-based design (PBD), the degree to which digital documentation is formally recognised, organisational enforcement models, and cybersecurity approaches for integrated fire alarm/voice alarm/building management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms. The USA relies on a decentralised code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continues to strengthen harmonised standards and digital building registers, reinforced by lessons after the Grenfell Tower fire. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements yet lagging behind in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire safety systems in Poland. The study contributes to the sustainability literature by framing regulatory readiness for digital fire safety as a lifecycle resilience strategy, directly relevant to safe, resource-efficient, and inclusive built environments. Full article

This study explores how residents of Vrnjačka Banja (Serbia) perceive the impacts of tourism and how these attitudes influence their support for future tourism development. Specifically, it examines positive and negative economic, socio-cultural, and environmental impacts, as well as the types of tourism residents favor. Data were collected from 420 local residents using a structured survey, and the reliability of the scales was confirmed using Cronbach’s alpha. Descriptive statistics provided an overview of participant characteristics, while MANOVA and follow-up ANOVA tests were used to examine differences in perceived tourism impacts across tourism types. Multiple linear regression was used to assess how attitudes toward positive and negative impacts predict residents’ support for future tourism development. The results indicate that attitudes toward positive impacts are relatively consistent across residents, whereas negative socio-cultural and environmental impacts differ depending on the type of tourism they support. Regression analysis shows that positive socio-cultural and environmental impacts are the strongest drivers of residents’ support, while negative socio-cultural and economic impacts reduce support. These findings highlight the importance of social and environmental considerations in shaping community attitudes and suggest that sustainable tourism planning should prioritize local well-being and responsible environmental management alongside economic objectives. This study contributes to the literature by addressing the heterogeneity in residents’ attitudes through tourism type preferences, while also highlighting the limited research on this topic in spa destinations. It further provides practical guidance for destination managers and policymakers in developing more targeted and sustainable tourism strategies. Full article

Background: Intensive care units (ICUs) are high-acuity environments that require respiratory therapists (RTs) to maintain vigilance, manage emotions, and make rapid clinical decisions. In such settings, performance stability is critical for patient safety. Although emotional intelligence (EI) and work–life balance (WLB) have been linked to professional outcomes in health care, their independent and direction-specific associations with job performance among ICU respiratory therapists remain underexamined. Methods: A national cross-sectional survey was conducted among respiratory therapists working in ICUs across Saudi Arabia (June 2025–January 2026). EI was measured using the Wong and Law Emotional Intelligence Scale. WLB was assessed using the work interference with personal life (WIPL), personal life interference with work (PLIW), and work–personal life enhancement (WPLE) scales. Job performance was evaluated using the Individual Work Performance Questionnaire. Correlation and multivariable linear regression analyses were performed to estimate independent associations. Results: A total of 392 RTs were included in the final analysis. Higher EI was independently associated with greater task performance (B = 0.21, p < 0.01) and contextual performance (B = 0.30, p < 0.001), and with lower counterproductive work behaviours (B = −0.24, p < 0.001). Among WLB dimensions, PLIW showed the strongest adverse association, predicting lower task performance (B = −0.20, p < 0.05) and higher counterproductive behaviours (B = 0.39, p < 0.001), but was not significantly associated with contextual performance in the fully adjusted model. WPLE demonstrated modest positive associations with performance, whereas WIPL was not significant in adjusted models. Conclusions: Job performance among ICU respiratory therapists is shaped by both emotional regulatory capacity and cross-domain strain. Personal life interference with work emerged as the most influential adverse predictor, whereas EI was associated with constructive performance patterns. Findings should be interpreted in light of the cross-sectional design and self-reported data. Sustaining performance in high-acuity settings requires attention to emotional competencies and structural sources of role conflict alongside clinical expertise. These findings inform workforce strategies to support performance and sustainability in critical care settings. Full article