Search Results (22,212)

Background/Objectives: This study aims to investigate whether rural community elderly care services can enhance older adults’ subjective well-being, with a specific focus on examining whether late-life stress mediates this association. Methods: Subjective well-being is operationalized through three dimensions: life satisfaction, positive life attitude, and depression levels. Data were collected from a sample of 796 rural Chinese adults aged 60 years and older. Regression analysis was used to assess the direct effects of rural community elderly care services on subjective well-being outcomes, while an instrumental variable two-stage least squares model was employed to test the robustness of these findings. A mediation analysis further explored the underlying mechanisms through which these services influence well-being. Results: Results indicate that rural community elderly care services exert significant direct and indirect effects on all three dimensions of subjective well-being. The indirect effects arise because these services provide social support that mitigates late-life stressors and buffers the negative impacts of adverse life events, thereby enhancing psychological well-being. Conclusions: These findings clarify the mediating role of stress in the relationship between community care services and subjective well-being among rural older adults, highlighting the importance of addressing age-related stressors in gerontological interventions. This study contributes to the literature by providing empirical evidence for the efficacy of rural community elderly care programs and offering actionable insights for developing contextually appropriate service models to meet the needs of aging rural populations. This study elucidates how rural older adults perceive community elderly care services, providing empirical evidence for government agencies to evaluate the effectiveness of policy-driven services. It further identifies key entry points for enhancing rural care service quality and promoting elderly well-being, bridging research insights with actionable strategies for policy improvement. Full article

Ongoing advancements in machine learning show great promise for automating medical data interpretation, potentially saving valuable time in life-threatening situations. One such area is the analysis of electrocardiograms (ECGs). In this study, we investigate the effectiveness of using a DenseNet121 encoder with three decoder architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and a Transformer-based approach. We utilize these models to generate automated ECG reports from the publicly available PTB-XL dataset. Our results show that the DenseNet121 encoder paired with a GRU decoder yields higher performance than previously achieved. It achieves a METEOR (Metric for Evaluation of Translation with Explicit Ordering) score of 72.19%, outperforming the previous best result of 55.53% from a ResNet34-based model that used LSTM and Transformer components. We also discuss several important design choices, such as how to initialize decoders, how to use attention mechanisms, and how to apply data augmentation. These findings offer valuable insights into creating more robust and reliable deep learning tools for ECG interpretation. Full article

Background: The role of vitamin D deficiency (VDD) in both mothers and neonates has been suggested as a possible factor in the development of allergic conditions in early infancy, however limited research has investigated this link in relation to allergic rhinitis (AR). This study investigates whether VDD in the mother–newborn dyad is associated with the onset of AR in neonates within the first three days after birth. The aim is to contribute to the understanding of neonatal allergic outcomes related to vitamin D status, which may inform future preventive strategies. This study investigates the role of vitamin D in the early onset of allergic rhinitis (AR) in neonates, specifically within the first three days of life. Although AR typically develops after years of allergen exposure and is rare in children under two, we aimed to explore its occurrence at this early stage. While no clear link was found between vitamin D and early AR onset, further research is needed to explore vitamin D levels at later ages and over longer time frames to clarify the relationship. Methods: A cross-sectional study was conducted between September 2019 and January 2022 in a single hospital. The study involved 248 infants born at ≥37 weeks of gestation and their mothers, who were of Greek nationality. The study included mother–infant pairs who met the inclusion criteria. Chi-square tests were applied to analyze the association between maternal or neonatal VDD and the presence of AR in neonates within the first three days after birth. In addition, multiple regression analysis was used to control other potential factors contributing to AR. Results: The results showed an unclear relationship between VDD and the onset of AR in neonates within the first three days of life. Although several factors were analyzed, the effect of VDD on the development of AR remained unclear. Conclusions: The findings highlight the lack of clarity regarding the effect of maternal and neonatal VDD on the incidence of AR in the immediate neonatal period. Few studies to date have specifically examined the role of VDD in neonatal AR. Further research with larger sample sizes is needed to verify these associations and to guide potential interventions aimed at reducing allergic outcomes in neonates. Full article

This study investigates, for the first time, the effects of polypropylene, steel, glass, and synthetic fibers on the mechanical and environmental properties of high-performance concrete (HPC) incorporating zeolite as a substitute for aggregates and cement. A series of tests, including compressive strength (load-displacement), slump, specific gravity, and water absorption percentage, were conducted to evaluate the performance of the composite materials. Additionally, the IMPACT2002+ method was employed to assess the environmental impacts of the different fiber types. Furthermore, a life cycle costing (LCC) analysis was performed to evaluate the economic feasibility of using these fibers in sustainable HPC applications. The findings reveal that the incorporation of steel fibers results in a notable improvement in compressive strength, achieving 92 MPa compared to 85 MPa for fiber-free samples. Additionally, modified synthetic macro fibers exhibited the second-highest compressive strength, at 83 MPa, while also demonstrating the lowest environmental impact among the tested fibers, characterized by the lowest cost index and minimal carbon dioxide emissions. Full article

This paper introduces a novel framework for generating synthetic time-series data from turbine engine sensor readings using a text-conditioned diffusion model. The approach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized signals into three components, mapped to the RGB channels of an image. These components, combined with engine identifiers and cycle information, form compact 19 × 19 × 3 pixel images, later scaled to 512 × 512 × 3 pixels. A variational autoencoder (VAE)-based diffusion model, fine-tuned on these images, leverages text prompts describing engine characteristics to generate high-quality synthetic samples. A reverse transformation pipeline reconstructs synthetic images back into time-series signals, preserving the original engine-specific attributes while removing padding artifacts. The quality of the synthetic data is assessed by training Remaining Useful Life (RUL) estimation models and comparing performance across original, synthetic, and combined datasets. Results demonstrate that synthetic data can be beneficial for model training, particularly in the early epochs when working with limited datasets. Compared to existing approaches, which rely on generative adversarial networks (GANs) or deterministic transformations, the proposed framework offers enhanced data fidelity and adaptability. This study highlights the potential of text-conditioned diffusion models for augmenting time-series datasets in industrial Prognostics and Health Management (PHM) applications. Full article

Urban green spaces (UGS) play a crucial role in enhancing the quality of life in urban environments by offering a wide range of ecological, psychological, socio–cultural, and economic benefits. This research addresses the multidimensional impact of functional characteristics of UGS on user satisfaction in Novi Sad, focusing on the various ways in which these spaces contribute to well-being. By integrating multiple perspectives, including ecological benefits like stress reduction, social and cultural aspects such as fostering community connections, and economic factors like enhancing property values, this study presents a holistic analysis of UGS. Through a mixed-methods approach, combining surveys and regression analysis, the research explores how different UGS functions interact to shape users’ perceptions. The findings highlight the importance of socio–cultural and ecological functions in improving the urban experience, providing key insights for urban planning that aims to create sustainable, inclusive, and resilient green spaces. This study emphasises the significance of considering these diverse dimensions in the development and management of urban green spaces for long-term sustainability and user satisfaction. Full article

The objective of this research was to prepare robust edible films possessing antioxidant properties by utilizing konjac glucomannan (KGM), sodium alginate (SA), and epigallocatechin gallate (EGCG). This research also involved structural characterization and the assessment of functional attributes of the composite films with varying EGCG concentrations. It was found that the inclusion of EGCG reduced the viscosity of the edible film solutions while enhancing their mechanical strength. Fourier transform infrared spectroscopy demonstrated adequate compatibility among the film-forming materials, with EGCG forming hydrogen bond interactions with KGM and SA. SEM analysis revealed that increasing EGCG concentration led to the formation of discontinuous blocks and rough surfaces, with smooth and fine-grained particles observed at 0.2% (w/v) EGCG concentration. Furthermore, results from the application of the KGM-SA-based films in chilled mandarin fish showed that they could exert antioxidant function when incorporated with EGCG. The values of TVB-N and TBARS of fish pieces were obviously decreased in the 12-day storage period, indicating their potential to increase the shelf life of freshwater fish food. Full article

Background: Surgical patients often experience nutritional imbalances due to their underlying condition and the forthcoming surgical procedure. These imbalances can increase the risk of post-operative complications. To mitigate such risks, a comprehensive nutritional assessment—also known as morphofunctional assessment—should be conducted. This includes evaluating body composition (muscle and fat mass), muscle strength, and functional capacity. Methods: We conducted an observational, prospective, pre-post interventional study involving 138 patients undergoing major elective surgery. Each patient received a morphofunctional assessment and an individualized prehabilitation program, including nutritional supplementation, physical exercise, and comorbidity optimization for 21 days before surgery and one month afterward. Outcomes were assessed through bioimpedance (muscle mass), muscle ultrasound (QRF thickness), dynamometry (strength), and visceral fat ultrasound (fat reduction). Results: The patient’s morphofunctional assessment and subsequent nutritional and physical exercise optimization performed during the month before surgery in the prehabilitation consultation led to an increase in muscle mass (measured by bioimpedance analysis, p = 0.001), and muscle ultrasound, (QRF thickness: p < 0.001) and dinamometry (muscle strength: p = 0.014); a reduction in preperitoneal visceral fat thickness (reduction p < 0.001); and an improvement in the patients’ nutritional status, with a decrease in malnutrition rates (64.8% vs. 31.8%). As a result, post-operative complications were effectively prevented (p < 0.001). Conclusions: Pre-operative patient optimization by means of a prehabilitation program led to increased muscle strength, improved muscle mass, reduced complication rates, and shorter hospital stays. In addition, patients maintained their quality of life and functional capacity following surgery. Full article

Background/Objective: Besides the common risks associated with agriculture, recently, there has been growing concern about the impact of agriculture on farmers’ mental health, due to high stress levels, depression, anxiety, and increasing rates of suicide, especially complex considering that many of these farmers are older people. The potential of the practice of mindfulness to minimize mental health problems and improve people’s sense of well-being has been studied in recent decades, although there is a dearth of literature related to farmer populations. This study aimed to correlate the presence of mindfulness traits with general life quality and well-being and assess the levels of mindfulness and life satisfaction among family farmers, as well as to evaluate which characteristics might be associated with them. Method: The sample was composed of 30 farmers from the region of Viseu—Portugal, who were randomly selected for a survey consisting of an adaptation of the Mindful Attention Awareness Scale (MAAS) and the Satisfaction with Life Scale (SWLS), with some new items specific to the context of agriculture. A proposed Farmers’ Mindfulness and Life Satisfaction Scale (FMLSS) was validated through factor analysis and internal reliability analysis. Result: The results showed a relatively high average score for the 10 items of the mindfulness scale (4.23 ± 0.56) and the global sum of scores for the 5 items of the life satisfaction scale (26.67 ± 4.76). Factor analysis revealed six factors, globally explaining 77% of the variance, with values of alpha varying from 0.640 to 0.874. The FMLSS was validated with 19 items of the 20 initially considered (α = 0.672). Cluster analysis revealed two typologies of participants, “Pleased” and “Accommodated” family farmers. These two clusters had global values for the FMLSS of 5.19 ± 0.51 and 4.37 ± 0.59, with the higher value obtained for the “Pleased” family farmers, who were mostly of male gender and worked more hours per week and whose agricultural activities had higher significance for their family income. Conclusions: Overall, we observed a relatively high level of mindfulness and satisfaction with life among family farmers. This suggests the importance of future research on mental health among family farmers. Full article

New ways of ensuring sustainable development in various areas of life are being intensively researched. One of the key priorities is to maximize the use of invaluable natural ingredients in cosmetic products while minimizing the negative impact on the environment. In this study, a three-component natural composite based on amber, diatomite, and Phytokeratin^TM (hydrolyzed plant protein) was developed using mechanochemical synthesis. The goal was to maximize the release of biologically active substances, such as succinic acid and Phytokeratin^TM, in aqueous solution. The physicochemical properties of the materials were characterized using Scanning Electron Microscopy (SEM), thermogravimetric (TG) and differential thermogravimetric (DTG) analysis, Fourier Transform Infrared (FTIR) spectroscopy, and Ultraviolet–Visible (UV-Vis) spectrophotometry. Additionally, Density Functional Theory (DFT) was used to perform quantum chemical calculations and characterize molecular interactions in the composite. The optimized composite demonstrated favorable release characteristics and structural properties, confirming its suitability for cosmetic applications. DFT calculations revealed the potential molecular-level interactions between the organic components, indicating the stability and functional integration of the composite. The resulting innovative composite was successfully incorporated into eco-friendly cosmetic formulations, including a solid shampoo bar and a nail conditioner. Full article

Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation. Full article

Population growth has significantly increased energy and resource demands, driving research toward cost-effective technologies that repurpose waste into alternative energy forms such as biohydrogen. This review aims to comprehensively evaluate biohydrogen production via anaerobic digestion, addressing gaps in previous studies focusing on a single sustainable development goal or limited environmental benefits. The methodology used the Scopus database with specific keywords, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol to screen relevant articles, and bibliometric analysis to delineate research directions from 2002 to 2024. Findings indicate that research on biohydrogen production via anaerobic digestion has grown exponentially over the past two decades, with increasing emphasis on advanced techniques, innovative reactor configurations, and diverse microbial consortia. Emerging trends, including the integration of artificial intelligence for process optimization and comprehensive life cycle assessments, suggest promising avenues for large-scale implementation. Anaerobic digestion-based biohydrogen production supports several Sustainable Development Goals (SDGs), including the ones related to clean energy (SDG7), SDG 13 (climate action), and SDG 12 (waste management), among others. Recent advancements are synthesized to provide a clear roadmap for future research toward sustainable energy solutions. Full article

Background and Objectives: Integrated health services are health services that are managed and delivered in a way that ensures patients receive a continuum of health promotion, disease prevention, diagnosis, treatment, disease management, rehabilitation, and palliative care services at different levels and sites of care within the health system, and according to their needs, throughout their life course. Assessing the effectiveness of their implementation, the perspective of the process participant—the patient—is examined. There are three main types of patient-reported measures: PROM, PREM and HLS. PREM (patient-reported experience measure) is a tool that allows the objective measurement of the patient’s experience related to healthcare services, for instance, the timeliness of visits or receiving recommendations. The aim of this study was to evaluate the coordinated care experienced by patients (PREMs) before and after the introduction of coordinated care, using the JOP-POP tool as a key measure. Materials and Methods: This longitudinal study was conducted in two stages. The first stage concerned the joining of the coordinated care program by the entity in which the study was conducted; the study was repeated six months after joining coordinated care (CC). At each stage of the study, the study group included 40 patients. The Shapiro–Wilk test was used to verify the normality of the distribution of quantitative variables. For statistical analysis, the Wilcoxon test for paired samples was used to compare two ordinal dependent variables. For independent variables, the Mann–Whitney and the Kruskal–Wallis ANOVA by ranks tests were used, with a post hoc test of multiple comparisons of mean ranks. Results: A statistically significant relationship (p = 0.00157) was observed between the number of chronic diseases and health status assessment before inclusion in coordinated care. The patients’ responses showed statistically significant improvement 6 months after the introduction of coordinated care (CC). The improvement in assessment was related to the time physicians spent with patients. The greatest improvement over 6 months was achieved in coordination of care and the smallest improvement was noted in the approach to the patient. Conclusions: The JOP-POP tool may be useful in future studies to assess patients’ experiences with implementing coordinated care. Full article

Objective: Immune checkpoint inhibitors (ICIs) are front-line treatment options for NSCLC. ICI therapy is associated with a risk of immune-related adverse events (irAEs). Checkpoint inhibitor pneumonitis (CIP) is a potentially life-threatening irAE. Previous studies have demonstrated that asthma and interstitial lung disease are associated with an increased risk of CIP. We sought to determine whether chronic obstructive pulmonary disease (COPD) is associated with CIP. Methods: This retrospective study examines a cohort of ICI-treated NSCLC patients either with or without chemotherapy at the Anna and Peter Brojde Lung Cancer Centre, Jewish General Hospital in Montreal, Canada between 2014 and 2023. We explored associations between risk factors and CIP using the Mann–Whitney U test or Fisher’s exact test. Analysis of prognostic factors was performed using a logistic regression model. All statistical analyses were carried out using SPSS software, version 24.0 (SPSS, Chicago, IL, USA). p-values of 0.05 or less were considered significant. Results: Of the 327 selected patients on ICIs, 23 experienced an acute respiratory deterioration that was attributed to CIP, 87/327(26.6%) patients had a pre-existing diagnosis of COPD, and 11/87 (12.6%) COPD patients experienced CIP compared to 13/240 (5.5%) non-COPD patients (p = 0.061). There was no statistical or clinically meaningful correlation between COPD severity and CIP. The only variable significantly associated with CIP was a poor ECOG performance status. Among ECOG 1 patients, 18/91 (19.8%) experienced CIP compared to 5/226 (2.2%) of those with an ECOG of 0. A multivariate assessment involving all 327 patients revealed no significant factors affecting CIP development. Conclusions: Our single-institution study revealed that although there was a trend, the presence of COPD was not statistically associated with an increased risk of CIP. Additionally, neither FEV1 nor DLCO had a meaningful impact on the development of CIP in COPD patients. Given these findings, we emphasize the need for larger prospective studies to confirm these observations before drawing definitive clinical recommendations. Full article

Background/Objectives: There is a significant gap in accessibility to mental healthcare in Canada. This study aims to examine the population of Canadian crisis hotline responders and investigate the variables that contributed to burnout and professional quality of life during COVID-19. Crisis hotline responders are hypothesized to be affected by burnout and poor professional quality of life, due to the inherent nature of the job and the widespread negative mental health effects of COVID-19, which are expected to continue even after the pandemic. Methods: An online, cross-sectional, mixed-methods survey assessed sociodemographic information, shift-related variables, burnout and related factors, and current support methods utilized by crisis hotline responders across Canada. The open-ended questions helped to more personally reflect participants’ experiences. Data were analyzed using chi-square tests, an analysis of variance, and a regression analysis. Results: The survey was completed by 136 participants (78.7% female) with an average age of 39.68. Participants reported relatively high levels of burnout/stress on both the Copenhagen Burnout Inventory and professional quality of life survey. Younger age, less work experience, and working overnight shifts emerged as possible predictors of worse mental wellbeing. Conclusions: Findings suggest that Canadian crisis hotline responders require greater support to manage workplace burnout/stress. Nevertheless, conducting comprehensive studies during times when there are no public health emergencies are warranted to understand the full scope of burnout in this population. We offer five recommendations to support the mental wellbeing of responders and improve access to this important public health resource. Full article