Search Results (12,206)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Background: Moral harassment (mobbing) in healthcare, particularly among nurses, remains a persistent issue with detrimental effects on mental health, resilience, and quality of life. Aim: We examine the relationship between the resilience of nurses working in Emergency Departments (EDs) and how these factors influence experiences of workplace mobbing. Methods: This cross-sectional study included 90 nurses from four public hospitals in Greece’s 5th Health District. Data were collected between October 2023 and March 2024 using the WHOQOL-BREF, Workplace Psychologically Violent Behaviors (WPVB) scale and the Connor–Davidson Resilience Scale (CD-RISC). The sample consisted primarily of full-time nurses (84.3% female; mean age = 43.1 years), with 21.1% reporting chronic conditions. Most participants were married (80.0%) and had children (74.4%), typically two (56.1%). Statistical analyses—conducted using SPSS version 27.0—included descriptive statistics, Pearson and Spearman correlations, multiple linear regression, and mediation analysis, with significance set at p < 0.05. Results: Resilience was moderate (mean = 66.38%; Cronbach’s α = 0.93) and positively correlated with all WHOQOL-BREF domains—physical, psychological, social, and environmental (r = 0.30–0.40)—but not with the overall WHOQOL-BREF. The mean overall WHOQOL-BREF score was 68.4%, with the lowest scores observed in the environmental domain (mean = 53.76%). Workplace mobbing levels were low to moderate (mean WPVB score = 17.87), with subscale reliabilities ranging from α = 0.78 to 0.95. Mobbing was negatively associated with social relationships and the environmental WHOQOL-BREF (ρ = –0.23 to –0.33). Regression analysis showed that cohabitation and higher resilience significantly predicted better WHOQOL-BREF outcomes, whereas mobbing was not a significant predictor. Mediation analysis (bootstrap N = 5000) indicated no significant indirect effect of resilience in the relationship between mobbing and WHOQOL-BREF. Conclusions: Resilience was identified as a key protective factor for nurses’ quality of life in emergency care settings. Although workplace mobbing was present at low-to-moderate levels, it was negatively associated with specific WHOQOL-BREF domains. Enhancing mental resilience among nurses may serve as a valuable strategy to mitigate the psychological effects of moral harassment in healthcare environments. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

This study investigates the seasonal effectiveness of high-efficiency particulate air (HEPA) purifiers and window-opening behaviors in three London nurseries, using continuous indoor and outdoor PM_2.5 monitoring, window state and air purifier use, and occupant questionnaire data collected from March 2021 to February 2022. Of the approximately 40–50 nurseries contacted, only three agreed to participate. Results show that HEPA purifiers substantially reduced indoor particulate matter (PM_2.5), with the greatest effect observed during the heating season when windows remained closed for longer periods. Seasonal and behavioral analysis indicated more frequent and longer window opening in the non-heating season (windows were open 41.5% of the time on average, compared to 34.2% during the heating season) driven by both ventilation needs and heightened COVID-19 concerns. Predictive modeling identified indoor temperature as the main driver of window opening, while carbon dioxide (CO₂) had a limited effect. In addition, window opening often increased indoor PM_2.5 under prevailing outdoor air quality conditions, with mean concentrations rising from 2.73 µg/m³ (closed) to 3.45 µg/m³ (open), thus reducing the apparent benefit of air purifiers. These findings underscore the complex interplay between mechanical purification and occupant-controlled ventilation, highlighting the need to adapt indoor air quality (IAQ) strategies to both seasonal and behavioral factors in educational settings. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

Sulfur-containing fertilizers increase production costs, which leads to low utilization of this nutrient. Thus, evaluating how the absence of sulfur influences the early development of Urochloa brizantha is essential. Study was conducted in a greenhouse at the Federal University of Rondonópolis in a completely randomized design, with six treatments in a 3 × 2 factorial scheme, and eight replications. Three cultivars of U. brizantha (Marandu, Xaraés and Piatã) were evaluated under two fertilization strategies: with or without sulfur fertilization. Sufur presence increased the number of leaves and forage mass, in which cultivar Xaraés presented the greatest means. Piatã was the cultivar most sensitive to sulfur deficiency at establishment, which reduced forage mass, number of leaves and number of tillers by 42%, 32%, and 45%, respectively. Despite these differences between cultivars, sulfur efficiently increased the forage yield. Sulfur fertilization increased the concentrations of nutrients in the plants without significantly affecting the uptake of nitrogen, phosphorus, potassium, calcium and magnesium. Sulfur omission resulted in increased phosphorus uptake in all grass. In contrast, Marandu grass exhibited the greatest reduction in sulfur uptake. Therefore, the use of sulfur in the fertilization of grasses is recommended, it is important to evaluate the responses of each cultivar to better adjust the fertilization management. Full article

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article

Background/Objectives: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n = 644 images) was used for training and model selection, while a completely independent external dataset (n = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. Results: Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of global energy use, yet they are often operated without optimized control strategies. This study explores the application of deep learning to accurately model heating system behavior as a foundation for predictive control and energy-efficient HVAC operation. Experimental data were collected under controlled laboratory conditions, and 288 unique hyperparameter configurations were developed. Each configuration was tested three times, resulting in a total of 864 artificial neural network models. Five key hyperparameters were varied systematically: number of epochs, network size, network shape, learning rate, and optimizer. The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10⁻⁵, and Adam as the optimizer. The results highlighted the importance of hyperparameter tuning in improving model accuracy. Future research should extend the analysis to incorporate cooling operation and real-world building operation data for broader applicability. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

Background: The progression of Alzheimer’s Disease (AD) deeply affects not only the diagnosed person but also their close relatives, who are often called to take on the role of informal caregivers. This transition is frequently unplanned and emotionally complex, yet poorly understood in its deeper processual dimensions. This study aims to explore and theorize the transition experienced by a family member becoming the primary informal caregiver for a person with advanced AD. Methods: A qualitative study based on the Constructivist Grounded Theory according to Charmaz’s approach (2006) was conducted. In-depth interviews were carried out with 10 participants who had become informal caregivers for a loved one with advanced AD. Data were analyzed using initial coding, focused coding, the constant comparative method, and theoretical coding. Results: Ten caregivers (mean age 39 years, range 35–54; nine females) of patients with advanced AD participated in the study. The analysis revealed a complex, emotionally intense caregiving experience marked by sacrifice, feelings of powerlessness, identity loss, and the necessity of sharing caregiving responsibilities. A core category emerged: A Silent and Certain Willingness to Care, representing the caregivers’ deep, often unconscious commitment to prioritize the care of their loved ones above their own needs. Four interconnected phases characterized the caregiving process: (1) The Changing Daily Life—involving significant sacrifices in personal and social life; (2) Feeling Powerless—confronting the inevitable decline without means to alter the course; (3) Losing Oneself—experiencing physical and psychological exhaustion and a sense of identity loss; and (4) Sharing with Others—seeking external support to sustain caregiving. These findings highlight the evolving nature of becoming a caregiver and the enduring dedication that sustains this role despite the challenges. Conclusions: The progression of AD deeply transforms the lives of caregivers, who become co-sufferers and active participants in the disease’s management. The results underscore the urgency of designing integrative care strategies—including psychological, social, and potentially technological support—that can enhance both patient outcomes and caregiver resilience. Grounded in real-world experiences, this study contributes to the broader neurodegeneration discourse by emphasizing caregiving as a critical factor in long-term disease management and therapeutic success. Full article

Phage therapy, long overshadowed by antibiotics in Western medicine, has a well-established history in some Eastern European countries and is now being revitalized as a promising strategy against antimicrobial resistance (AMR). This resurgence of phage therapy is driven by the urgent need for innovative countermeasures to AMR, which will cause an estimated 10 million deaths annually by 2050. However, the emergence of phage-resistant variants presents challenges similar to AMR, thus necessitating a deeper understanding of phage resistance mechanisms and control strategies. The highest priority must be to prevent the emergence of phage resistance. Although phage cocktails targeting multiple receptors have demonstrated a certain level of phage resistance suppression, they cannot completely suppress resistance in clinical settings. This highlights the need for strategies beyond simple resistance suppression. Notably, recent studies examining fitness trade-offs associated with phage resistance have opened new avenues in phage therapy that offer the potential of restoring antibiotic susceptibility and attenuating pathogen virulence despite phage resistance. Thus, controlling phage resistance may rely on both its suppression and strategic redirection. This review summarizes key concepts in the control of phage resistance and explores evolutionary engineering as a means of optimizing phage therapy, with a particular focus on Pseudomonas infections. Harnessing evolutionary dynamics by intentionally breaking the spontaneous evolutionary trajectories of target bacterial pathogens could potentially reshape bacterial adaptation by acquisition of phage resistance, unlocking potential in the application of phage therapy. Full article

Background: Dentinal hypersensitivity (DH) significantly impacts oral health-related quality of life. While global prevalence estimates range from 10–15%, region-specific data from Saudi Arabia remain limited. This study also aligns with Saudi Vision 2030’s mental health initiatives, as DH-associated anxiety impacts overall well-being. This study assessed DH prevalence and quality of life impact among Saudi adults. Methods: A cross-sectional online survey was conducted among 748 Saudi adults aged ≥ 18 years between April and May. Data were collected using a validated Arabic Dentinal Hypersensitivity Experience Questionnaire (DHEQ) alongside socio-demographic variables. Participants reporting DH symptoms within 12 months were included in impact analyses. Descriptive statistics and one-way ANOVA examined associations between DHEQ scores and participant characteristics. Results: Self-reported DH prevalence was 54.3% (n = 406), substantially exceeding global estimates. Among affected individuals, mean DHEQ score was 0.56 ± 0.19, indicating moderate-to-substantial quality-of-life impact. Functional limitations were most affected, particularly enjoyment of eating and drinking (0.72 ± 0.21). Significant associations were identified between higher DHEQ scores and age extremes (<18 and >35 years; p < 0.001), higher income levels (p = 0.032), fewer teeth (p = 0.040), and dental pain presence (p = 0.009). Sex, residence, education, and employment showed no significant associations. Conclusions: More than half of Saudi adults reported DH symptoms, representing a significant public health concern with substantial quality of life implications. Prevalence substantially exceeds global estimates, highlighting the need for targeted interventions. Age, income, tooth count, and pain presence emerged as key factors. These findings support developing population-specific prevention strategies, particularly targeting younger and older adults with tooth loss. Full article

Purpose: Our understanding of the influence of preterm birth and related perinatal exposures on early brain development is limited, hampering personalized optimization of neuroprotective strategies. This study assesses the effect of gestational age (GA) at birth on brain volumes at term-equivalent age (TEA) in infants without overt brain injury born across the GA spectrum. Methods: A cohort of infants born across the GA spectrum (25–40 weeks’ gestation) underwent 3T brain MRI around TEA (40–46 weeks postmenstrual age). Eight brain regions, intracranial and total tissue volumes were segmented using MANTiS (morphologically adaptive neonatal tissue segmentation toolbox). Segmentations were visually quality-checked and excluded if segmentation failed. Absolute TEA volume in relation to GA was assessed using univariate and multivariate (correction for postmenstrual age) linear regression analysis. Statistical significance was set at p < 0.05. Post hoc scatter plots of brain volumes relative to intracranial volumes were created. Results: Fifty infants were included (mean GA = 35.0 [SD = 3.3, range = 25.7–40.1] weeks). A higher GA at birth was significantly related to lower cerebrospinal fluid (p = 0.004) and amygdala (p = 0.02) volumes; no significant relation was found between GA and other volumes. Post hoc analyses showed positive trends between GA and several brain structures, including total brain tissue, cortical gray matter, deep gray matter, hippocampus, cerebellum and brainstem volumes. Conclusions: Our results suggest that GA has an effect on TEA brain volumes that is independent of brain lesions, with lower GA being associated with smaller brain tissue volumes and significantly larger cerebrospinal fluid volume. Preterm birth and related exposures may thus affect early brain growth and contribute to neurodevelopmental challenges encountered by preterm-born children. Full article

Background/Objectives: Direct oral anticoagulants (DOACs) are now the guideline-recommended alternative to vitamin K antagonists (VKAs) for long-term anticoagulation in patients with non-valvular atrial fibrillation. However, accurately assessing their impact on ischemic stroke outcomes remains challenging, primarily due to uncertainty regarding anticoagulation status at the time of hospital admission. This preliminary study addresses this gap by using point-of-care testing (POCT) to confirm DOAC activity at bedside, allowing for a more accurate comparison of 90-day functional outcomes between anticoagulated and non-anticoagulated stroke patients. Methods: We conducted a retrospective cohort study of 786 ischemic stroke patients admitted to the University of Pécs between February 2023 and February 2025. Active DOAC therapy was confirmed using the ClotPro^® viscoelastic testing platform, with ecarin Clotting Time (ECT) employed for thrombin inhibitors and Russell’s Viper Venom (RVV) assays for factor Xa inhibitors. Patients were categorized as non-anticoagulated (n = 767) or DOAC-treated with confirmed activity (n = 19). Mahalanobis distance-based matching was applied to account for confounding variables including age, sex, pre-stroke modified Rankin Scale (mRS), and National Institutes of Health Stroke Scale (NIHSS) scores at admission and 72 h post-stroke. The primary outcome was the change in mRS from baseline to 90 days. Statistical analysis included ordinary least squares (OLS) regression and principal component analysis (PCA). Results: After matching, 90-day functional outcomes were comparable between groups (mean mRS-shift: 2.00 in DOAC-treated vs. 1.78 in non-anticoagulated; p = 0.745). OLS regression showed no significant association between DOAC status and recovery (p = 0.599). In contrast, NIHSS score at 72 h (p = 0.004) and age (p = 0.015) were significant predictors of outcome. PCA supported these findings, identifying stroke severity as the primary driver of outcome. Conclusions: This preliminary analysis suggests that ischemic stroke patients with confirmed active DOAC therapy at admission may achieve 90-day functional outcomes comparable to those of non-anticoagulated patients. The integration of bedside POCT enhances the reliability of anticoagulation assessment and underscores its clinical value for real-time management in acute stroke care. Larger prospective studies are needed to validate these findings and to further refine treatment strategies. Full article