Search Results (54,841)

Introduction/Objective: Patient satisfaction with nursing care quality is an important patient-reported indicator of hospitalization experience. Previous studies have mainly examined sociodemographic, clinical, and organizational factors, while personality traits have rarely been included in explanatory models. This study examined the association of sociodemographic characteristics, hospitalization-related variables, and personality traits with patient satisfaction. Methods: A single-center cross-sectional study was conducted among hospitalized patients in a general hospital in Croatia. Data were collected at discharge using a demographic and hospitalization questionnaire, the NEO Five-Factor Inventory, and the Croatian version of the Patient Satisfaction with Nursing Care Quality Questionnaire. Group differences were analyzed using non-parametric tests, and hierarchical regression analysis was performed. Results: Younger age, employment, male gender, and better self-rated health were associated with higher satisfaction. Patients admitted on a scheduled basis and those staying alone or with one other person in the room were more satisfied. Sociodemographic variables explained 21.5% of the variance in satisfaction (R² = 0.215; adjusted R² = 0.168). After hospitalization-related variables were added, the explained variance increased to 30.1% (R² = 0.301; adjusted R² = 0.232). The addition of personality traits further increased the explained variance to 45.6% (R² = 0.456; adjusted R² = 0.385). In the final model, staying with two or more persons was negatively associated with satisfaction, whereas agreeableness and conscientiousness were positively associated with satisfaction. Conclusions: Patient satisfaction with nursing care quality was associated with patient characteristics, hospitalization conditions, and personality traits. Accommodation conditions and individual psychological differences should be considered when interpreting satisfaction as an indicator of nursing care quality. Full article

Background/Objectives: Blood transfusion is a crucial component in the treatment of individuals with sickle cell disease [SCD]; nonetheless, multiple transfusions can lead to considerable complications, notably alloimmunization. However, the prevalence of alloimmunization and its predictors remain incompletely explained. This review aimed to determine its global prevalence and identify associated risk factors. Method: Our protocol was registered in PROSPERO [ID: CRD420251167042] in accordance with the PRISMA 2020 criteria. A thorough literature search was conducted across PubMed, Embase, Web of Science, Scopus, and the Cochrane Library to identify studies reporting the prevalence of alloimmunization in adults with confirmed sickle cell disease who have received blood transfusions. This search included all publications up to 16 April 2026. Two reviewers independently screened and extracted data, and the Newcastle–Ottawa Scale was used to evaluate the study’s quality. After the Freeman–Tukey transformation, a random-effects model was used to estimate the pooled prevalence. We examined disparities among groups and geographies, study designs, and matching procedures to determine their differences. We additionally employed meta-regression to identify potential predictors. Results: Nine studies [n = 1711; 1978–2026] met the inclusion criteria. The overall rate of alloimmunization was 28.9% [95% CI 22.4–35.4; I² = 88.5%]. The most prevalent antibodies were those of the Rh and Kell systems, with anti-E antibodies being the most frequent, followed by anti-C and anti-K antibodies. A higher number of transfusions and the HbSβ⁰ genotype were both persistent risk factors, while older age at first transfusion appeared protective. Extended antigen matching dramatically reduced prevalence, though approximately 9% of individuals remained affected. Conclusions: Alloimmunization continues to challenge transfusion management in adults with SCD. Broader implementation of extended antigen matching and genotype-informed transfusion strategies may help mitigate this risk. Full article

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article

Source-free domain adaptation (SFDA) for cross-modality prostate MRI segmentation is challenging because source data are unavailable and pseudo-labels on target ADC images are often noisy. To address this problem, we propose Curriculum Anatomy-guided Learning Method with Population Template Priors (CALM), a source-free adaptation framework for this task. CALM constructs a population template prior from target predictions using top-k consensus aggregation and cross-round exponential moving average, then combines this prior with instance-level predictions through Soft-AND fusion. A high-confidence background constraint is further introduced to provide reliable negative supervision, and a coverage-driven curriculum is used to expand training from easy to hard cases based on pseudo-label/template agreement. This design forms an iterative process in which prior refinement and sample-reliability refinement reinforce each other during adaptation. Experiments on the PI-CAI dataset under the T2W-to-ADC setting show that CALM achieves an average Dice score of 73.63% and outperforms representative SFDA baselines in both segmentation accuracy and boundary quality. Ablation and model analyses support the contribution of each component. These results suggest that population-level anatomical priors can provide practical structural guidance for source-free cross-modality adaptation. Full article

Background/Objectives: Dairy yogurts are a source of protein and micronutrients in the Canadian diet. However, Canada’s Food Guide emphasizes the consumption of plant-based foods, which is facilitated by a greater availability of dairy alternatives on the market. The nutritional composition of these products varies and can differ from dairy foods such as yogurt, which contain high-quality protein and micronutrients. The objective of this study was to examine the effect of dairy yogurt consumption as part of a diet on any given day on nutrient intakes in Canadians across ages. Methods: The 2015 Canadian Community Health Survey (CCHS)—Nutrition first day 24 h recalls of males and females > 1 years of age (n = 17,308) and of yogurt consumers (n = 3788) were examined to estimate nutrient intakes arising from yogurt consumption. Respondents were allocated into four groups defined by their daily yogurt intake in grams (i.e., Group I/non-yogurt consumers: <1 g; Group II: 1–90 g; Group III: 90–115 g; Group IV: >115 g). Results/Conclusions: The results of this study provide timely data on Canadian yogurt consumption across the ages and show that those consuming yogurt have higher intakes of essential nutrients, such as protein, calcium, potassium, vitamin D, and dietary fibre. The data from this study emphasize the importance of yogurt in the context of a healthy eating pattern and emphasize the need to encourage consumption of yogurt within Canada’s Healthy Eating Strategy. Full article

How AI can contribute to green sustainable development (GSD) in China is a critical yet underexplored question. Leveraging the staggered implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) as a quasi-natural experiment, this study employs a difference-in-differences approach with panel data from 285 prefecture-level cities (2017–2022). The main findings are threefold. First, AI directly promotes GSD and, more importantly, indirectly enhances GSD by upgrading new-quality productivity (NQP)—a novel mechanism that distinguishes this study from conventional environmental policy evaluations. Second, the facilitating effect is not uniform: significant positive effects are detected in the western, eastern, and central regions, but not in the northeastern region; among major urban agglomerations, the Pearl River Delta, Chengdu-Chongqing, and Yangtze River Deltaexhibit significant effects, whereas the Middle Reaches of the Yangtze River and Beijing-Tianjin-Hebei region does not. Third, spatial spillover analysis reveals that AI’s favorable effect on GSD spreads primarily through intercity similarity in economic development level. These findings provide actionable insights for policymakers aiming to harness AI for sustainable development, highlighting the importance of fostering NQP and designing regionally differentiated strategies. Full article

Full-waveform inversion (FWI) is a powerful interpretation method in geophysics for inferring high-resolution subsurface models by minimizing the difference between observed and simulated seismic data. In mineral exploration, FWI has shown particular promise for delineating complex ore bodies in hard-rock environments where conventional reflection seismic methods often fail. However, traditional FWI remains computationally expensive due to the iterative solution of forward and adjoint problems. The integration of deep learning, particularly the U-Net architecture, has recently emerged as a promising approach to address these computational challenges. Originally developed for biomedical image segmentation, U-Net employs a symmetric encoder–decoder structure with skip connections, enabling precise localization and efficient feature extraction from complex data. This paper proposes a modified dual-path architecture, termed DU-Net, specifically designed for the simultaneous detection and extraction of high-contrast velocity anomalies (representing potential ore bodies) and reconstruction of the background velocity model. The key innovation lies in parallel processing branches—one dedicated to anomaly segmentation and another to background reconstruction—combined with a specialized composite loss function, SeismoLoss, that independently supervises each component. This design allows the network to focus on the distinctive features of the anomaly while filtering out background complexity that typically degrades prediction quality in single-path approaches. We provide a detailed description of the DU-Net architecture and evaluate its performance on two synthetic datasets representing different styles of mineralization and host-rock complexity. Experimental results demonstrate that DU-Net achieves superior accuracy in localizing anomalous bodies and reconstructing background geology compared to the standard U-Net baseline, with a substantial reduction in boundary blurring artifacts. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

This study estimates daily nitrogen dioxide (NO₂) concentrations at ground level across the Marmara Region of Türkiye at 0.01° resolution. The framework integrates Sentinel-5P (S5P) TROPOspheric Monitoring Instrument (TROPOMI) and GEOS Composition Forecast (GEOS-CF) tropospheric NO₂ vertical column density (VCD) data with meteorological, topographic, land-use, socioeconomic, and temporal features through four tree-based ensemble algorithms trained on 74 ground station observations. Under a temporal split (2019–2022 training, 2023 validation, 2024 testing), S5P-Categorical Boosting (CatBoost) achieved the best performance (Pearson correlation coefficient (R) = 0.706, R² = 0.498, root mean square error (RMSE) = 14.31 µg/m³). Random splitting inflated R by +0.168 due to temporal autocorrelation, while leave-one-station-out and leave-one-province-out cross-validation reduced R to ~0.50 by removing spatial dependence, together revealing the combined effect of temporal and spatial autocorrelation. SHapley Additive exPlanations (SHAP) analysis identified TROPOMI NO₂ VCD, population density, road length, and nighttime light as dominant predictors; population density was the top predictor in the GEOS-CF model, followed by VCD. Concentration maps for 2024 showed that 95.9% of the region’s 26.74 million inhabitants were exposed above the WHO annual air quality guideline of 10 µg/m³, with a population-weighted mean of 21.08 µg/m³. Full article

Acute kidney injury (AKI) lacks targeted pharmacological interventions. While the DanShen–DaHuang (DS-DH) herb pair shows clinical potential for AKI treatment, and our prior study has validated its nephroprotective efficacy in a cisplatin-induced murine model, its specific molecular targets within the renal microenvironment remain undefined. In this study, we integrated network pharmacology and weighted gene co-expression network analysis (WGCNA) to screen AKI-related targets of the DS-DH pair. A multi-algorithmic machine learning pipeline (including LASSO, Boruta, Random Forest, GBM, XGBoost, and Decision Trees) was utilized to calculate feature importance scores and rank core genes. Subsequently, single-cell RNA sequencing (scRNA-seq) data (GSE197266) were analyzed for transcriptomic mapping, pseudotime trajectory, and cell–cell communication. Finally, molecular docking evaluated theoretical binding affinities. After database screening, a total of 603 drug–disease intersecting targets were obtained. Subsequently, 917 module genes significantly associated with AKI were identified by WGCNA, and 62 core candidate genes were determined after intersecting with the above targets. Multi-algorithm machine learning ranked the importance of the 62 targets, with transketolase (TKT) ranking the highest. To elucidate the mechanism of TKT in AKI, scRNA-seq analysis was performed on 77,593 high-quality cells. The results showed that Tkt was specifically enriched in renal macrophages, with the highest expression in the M2-polarized subset. Pseudotime analysis further revealed that Tkt expression dynamics were highly synchronized with the differentiation trajectory of M2 macrophages and positively correlated with the repair markers Arg1 and Mrc1. Cell–cell communication analysis predicted that Tkt⁺ M2 macrophages act as active communication hubs via the Spp1 and Mif signaling axes. Molecular docking validated the favorable binding affinity between core DS-DH compounds and the TKT active pocket. This computational framework predicts that the DS-DH herb pair might mitigate AKI by potentially targeting TKT, a metabolic enzyme closely associated with macrophage M2 polarization. By prioritizing targets via multi-algorithmic scoring, we provide a data-driven rationale and candidate targets for future experimental validation. Full article

Background/Objectives: The increasing reliance on the Internet for health information has contributed to the emergence of cyberchondria, a phenomenon closely associated with health anxiety and potentially linked to sleep disturbances. Evidence remains limited in the Saudi context, particularly regarding differences between individuals with and without psychosomatic disorders. Methods: A cross-sectional observational study was conducted among 1224 Saudi adults (535 with psychosomatic disorders and 689 without). Data were collected using validated instruments, including the Cyberchondria Severity Scale (CSS-12), Short Health Anxiety Inventory (SHAI-18), and Pittsburgh Sleep Quality Index (PSQI). Statistical analyses included Pearson correlation coefficients and two-way ANOVA. Results: The prevalence of cyberchondria was 56.78%, health anxiety 38.76%, and poor sleep quality 56.9%. Significant positive correlations were observed between cyberchondria, health anxiety, and poor sleep quality across both groups, with stronger associations among individuals with psychosomatic disorders. Two-way ANOVA revealed a significant main effect of clinical status on all variables and a significant effect of sex on health anxiety, with higher levels among females. Conclusions: Findings highlight a significant interplay between cyberchondria, health anxiety, and sleep quality, particularly among individuals with psychosomatic disorders. These results underscore the need for targeted public health interventions addressing digital health behaviours and mental health. Full article

Against the backdrop of concurrent economic transformation, deepening digitalization, and green upgrading, this study aims to uncover the mechanisms through which the system of new quality productive forces drives high-quality economic development. Drawing on a six-element framework comprising new-quality labor, new means of production, new labor objects, new technology, production organization, and data elements, the study uses panel data from 30 Chinese provinces covering the period 2014–2023 and applies dynamic qualitative comparative analysis (dynamic QCA) to examine the relevant configurational pathways and their cross-temporal evolution. The results show that no single condition constitutes a temporally and regionally stable necessary condition for high-quality economic development. Instead, high-quality economic development is primarily realized through three pathways: a technology–organization–tool-dominated pathway, a talent–data–carrier-led pathway, and a technology–organization–carrier-compensation pathway. The explanatory power of these pathways also varies across stages. The findings indicate that high-quality economic development arises from the synergistic configuration and dynamic recombination of multiple elements of new quality productive forces. Full article

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article