Search Results (221)

Background: Sleep is a fundamental component of health, and deprivation has been linked to numerous adverse outcomes, including reduced academic and occupational performance, greater risk of accidents, and increased susceptibility to chronic diseases and premature mortality. Dietary and lifestyle behaviors are increasingly recognized as key determinants of sleep quality. Women are particularly susceptible to sleep disturbances due to hormonal fluctuations and psychosocial factors. However, women remain underrepresented in sleep research. This study aims to examine the associations between sleep quality, nutrition, and lifestyle in a large cohort of Spanish women. Methods: A cross-sectional study was conducted with 785 women aged 18–64. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and the NutSo-HH questionnaire on dietary and lifestyle behaviors. Descriptive analyses, correlation matrices, Gaussian Graphical Models, and Principal Component Analyses were used to assess relationships between variables. Results: More than half of the participants rated their sleep quality as good or very good, although over 30% experienced frequent nighttime awakenings. Poor sleep quality was significantly associated with higher alcohol consumption, lower vegetable and white fish intake, and lower levels of physical activity. Diets rich in ultra-processed foods correlated moderately with subjective poor sleep and daytime dysfunction. However, no strong associations were found between stimulant consumption, late meals, or dietary patterns (e.g., Mediterranean diet) and sleep. Self-perceived health emerged as a protective factor, while nocturnal lifestyles were linked to longer sleep latency and fragmented sleep. Conclusions: In adult women, better sleep quality is linked to healthy dietary choices, regular physical activity, and a positive perception of general health. In contrast, alcohol use and irregular lifestyles are associated with poor sleep. Individual variability and cultural adaptation may moderate the impact of some traditionally harmful behaviors. Personalized, multidimensional interventions are recommended for promoting sleep health in women. Full article

Geographic routing is an appealing method for wireless sensor networks, as it routes data packets solely based on nodes’ location information rather than global network topology. A fundamental requirement for geographic routing is that source nodes must know the locations of sink nodes to deliver their data. To efficiently provide sink location information, quorum-based sink location service schemes have been introduced, using crossing points between sink location announcement (SLA) and sink location query (SLQ) quorums. However, existing quorum-based schemes typically construct quorums along fixed paths, causing rapid energy depletion of particular sensor nodes and resulting in shorter network lifetimes, especially in irregular sensor fields. To overcome this limitation, we propose an energy-efficient quorum-based sink location service scheme that extends network lifetime by reducing and balancing sensor nodes’ energy consumption. Specifically, our scheme constructs a quadrilateral-shaped SLA quorum using four randomly selected points, and a line-shaped SLQ quorum defined by two randomly chosen points located inside and outside the SLA quorum, respectively. We also address key issues of the proposed scheme, including network holes, irregular boundaries, multiple sources and sinks, and Base Zone sizing, and present methods to handle them. Simulation results demonstrate that the proposed scheme outperforms existing approaches, achieving approximately 29% lower total energy consumption and 27% higher energy balancing across sensor nodes on average. Full article

The functional performance of magnesium hydroxide (Mg(OH)₂) is intrinsically governed by its crystallographic morphology. Herein, we demonstrate an electrochemical deposition strategy to synthesize Mg(OH)₂ from abandoned MgCl₂ resources in salt lakes, achieving simultaneous waste valorization and morphology control. Systematic investigations were conducted on the effects of polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG) as surfactants on electrochemical parameters (cell voltage, pH, current efficiency, and energy consumption) and morphological evolution (XRD, SEM, and laser particle size analysis). Results show that the cell voltage and pH increased proportionally with surfactant concentration, with a current efficiency of 93.86% and an optimal energy consumption of 4.15 kW h·t⁻¹ at an optimal PVP concentration of 6 g·L⁻¹. PEG addition exhibited a similar trend in process parameter modulation. Morphological evolution analysis revealed that appropriate PEG dosage promoted the transformation of irregular Mg(OH)₂ flakes into near-spherical platelets, accompanied by a measurable increase in particle size. This work establishes structure–property relationships between surfactant molecular design and Mg(OH)₂ crystallization, providing theoretical support for the controllable electrochemical preparation of magnesium hydroxide with different morphologies. Furthermore, it opens up a novel and innovative technical pathway to promote the high-value utilization of abandoned magnesium resources in salt lakes. Full article

Crack sealant is commonly used to fill pavement cracks and improve the Pavement Condition Index (PCI). However, during asphalt pavement hot in-place recycling (HIR), irregular shapes and random distribution of crack sealants can cause issues like agglomeration and ignition. To address these problems, it is necessary to mill large areas containing crack sealant or pre-mark locations for removal after heating. Currently, detecting and recording crack sealant locations, types, and distributions is conducted manually, which significantly reduces efficiency. While deep learning-based object detection has been widely applied to distress detection, crack sealants present unique challenges. They often appear as wide black patches that overlap with cracks and potholes, and complex background noise further complicates detection. Additionally, no dataset specifically for crack sealant detection currently exists. To overcome these challenges, this paper presents a specialized dataset created from 1983 pavement images. A deep learning detection algorithm named YOLO-CS (You Only Look Once Crack Sealant) is proposed. This algorithm integrates the RepViT (Representation Learning with Visual Tokens) network to reduce computational complexity while capturing the global context of images. Furthermore, the DRBNCSPELAN (Dilated Reparam Block with Cross-Stage Partial and Efficient Layer Aggregation Networks) module is introduced to ensure efficient information flow, and a lightweight shared convolution (LSC) detection head is developed. The results demonstrate that YOLO-CS outperforms other algorithms, achieving a precision of 88.4%, a recall of 84.2%, and an mAP (mean average precision) of 92.1%. Moreover, YOLO-CS significantly reduces parameters and memory consumption. Integrating Artificial Intelligence-based algorithms into HIR significantly enhances construction efficiency. Full article

The textile industry is under increasing pressure to adopt sustainable practices due to the significant environmental impacts associated with fiber production, including high energy consumption, water usage, and substantial greenhouse gas emissions. The recycling of textile waste, particularly cotton, is a promising solution that has the potential to reduce landfill waste and decrease the demand for virgin fibers. However, mechanically recycled cotton fibers frequently demonstrate diminished mechanical properties compared to virgin fibers, which limits their potential for high-quality textile applications. This study explores the use of cross-linking agents (citric acid (CA) and sodium hypophosphite (SHP)), polymers (polyethylene glycol (PEG), chitosan (CH), carboxymethyl cellulose (CMC) and starch (ST)), and silicas (anionic (SA) and cationic (SC)) to enhance the mechanical properties of recycled cotton fibers. The treatments were then subjected to a hierarchical ranking, with the effectiveness of each treatment determined by its impact on enhancing fiber tenacity. The findings of this research indicate that the most effective treatment was starck (ST_50), which resulted in an enhancement of tenacity from 14.63 cN/tex to 15.34 cN/tex (+4.9%), closely followed by CA-SHP_110/110, which also reached 15.34 cN/tex (+4.6%). Other notable improvements were observed with CMC_50 (15.23 cN/tex), PEG_50 (14.91 cN/tex), and CA_50 (14.89 cN/tex), all in comparison to the control. In terms of yarn quality, the CA-SHP_110/110 treatment yielded the most substantial reductions in yarn irregularities, including thin places, thick places, and neps with decreases of 36%, 10%, and 7%, respectively. Furthermore, CA_50 exhibited moderate enhancements in yarn regularity, thin places (−12%), thick places (−6.1%), and neps (−8.9%). The results of this study demonstrate that combining CA with SHP, particularly when preceded by the heating of the solution before the addition of the fibers, results in a substantial enhancement of the structural integrity, strength, and overall quality of recycled cotton fibers. This approach offers a viable pathway for the improvement of the performance of recycled cotton, thereby facilitating its wider utilization in high-quality textile products. Full article

In recent years, the energy policies of both Spain and the European Union have pursued the development of renewable energies, including solar power. One way these installations will appear in the Region of Murcia is on bodies of water, which do not alter existing land uses, but ground-mounted solar energy installations do bring about such changes. The Region of Murcia is located in the south-eastern quadrant of the Iberian Peninsula. Positioned on the leeward side of the westerly zonal circulation, characteristic of mid-latitudes, and influenced by the layout of the Betic mountain ranges that cross it from north-west to south-east, it experiences significant scarcity and irregularity of rainfall. In contrast, it benefits from an abundance of sunlight, with more than 3400 h of sunshine per year. This makes it one of the most productive locations for capturing solar energy and converting it into electricity. As a result, the land occupied by photovoltaic parks has increased at the expense of dry farming areas, irrigated land, and woodland. High energy prices have also led to self-consumption measures, with solar panels being installed on the roofs of industrial buildings, floating panels in irrigation reservoirs, photovoltaic solar farms associated with desalination and lift irrigation pumps, and pressure required by localized irrigation, etc. Full article

Objective: Intraoral scanners (IOS) provide high-precision 3D data of teeth and gingiva, critical for personalized orthodontic diagnosis and treatment planning. However, traditional segmentation methods exhibit reduced performance with complex dental structures, such as crowded, missing, or irregular teeth, constraining their clinical applicability. This study aims to develop an advanced 3D point cloud segmentation model to enhance the automated processing of IOS data in intricate orthodontic scenarios. Methods: A 3D point cloud segmentation model was developed, incorporating relative coordinate encoding, Transformer-based self-attention, and attention pooling mechanisms. This design optimizes the extraction of local geometric features and long-range dependencies while maintaining a balance between segmentation accuracy and computational efficiency. Training and evaluation were conducted using internal and external orthodontic datasets. Results: The model achieved a mean Intersection over Union (IoU) of 92.14% on the internal dataset and 91.73% on the external dataset, with Dice coefficients consistently surpassing those of established models, including PointNet++, TSGCN, and PointTransformer, demonstrating superior segmentation accuracy and robust generalization. Conclusions: The model significantly enhances tooth segmentation accuracy in complex orthodontic scenarios, such as crowded or irregular dentitions, enabling orthodontists to formulate treatment plans and simulate outcomes with greater precision—for example, optimizing clear aligner design or improving tooth arrangement efficiency. Its computational efficiency supports clinical applicability without excessive resource consumption. However, due to the limited sample size and potential influences from advancements in IOS technology, the model’s generalizability requires further clinical testing and optimization in real-world orthodontic settings. Full article

Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO–ICEEMDAN–iTransformer model outperformed the other comparison models, with an R² of 0.9873, RMSE of 48.0014, and MAE of 66.2221. Full article

The issue of energy transfer is extremely important. In order to achieve the lowest possible energy consumption and the required thermal efficiency in energy-efficient buildings, it is necessary, among other things, to minimize the heat-transfer coefficient, which depends on the properties of the insulating material. Analyses of the relationship between the structure of a material and its thermal conductivity coefficient have shown that lower values of this coefficient can be achieved with a more complex structure that mimics natural forms. This paper presents a design method based on the Voronoi diagram to obtain a three-dimensional structure of a porous composite material. The method was found to be effective in producing structures with predefined and functionally graded porosity. The porous specimens were fabricated from a biodegradable soybean oil-based resin using mSLA additive technology. Analyses were performed to determine the thermal parameters of the anisotropic composites. Experimental results showed that both porosity and irregularity affect the thermal properties. The lowest thermal conductivity coefficients were obtained for a 100 mm-thick prototype composite with the following parameters: wall thickness D = 0.2 mm, cell size S = 4 mm, number of structural layers n = 2, and degree of irregularity R = 4. The lowest possible thermal conductivity of the insulation was 0.026 W/(m·K), and the highest possible thermal resistance was 3.92 (m²·K)/W. The method presented in this study provides an effective solution for nature-inspired design and topological optimization of porous structures. Full article

Background/Objectives: The transition to university life brings significant social, psychological, and environmental changes, making it a critical period for establishing long-term dietary habits. However, many Mexican university students fail to meet national dietary guidelines, increasing their risk of non-communicable diseases. This study examines the determinants of healthy eating among university students in Oaxaca using a holistic, multi-level approach grounded in the Social Ecological Model (SEM) and Social Cognitive Theory (SCT). Methods: A mixed-methods approach was employed, integrating ethnography with a validated self-report questionnaire completed by 411 students at the Universidad Autónoma Benito Juárez de Oaxaca (UABJO). The ethnographic data included observations, field notes, photographs, informal conversations, and 13 semi-structured interviews. Data triangulation provided a comprehensive understanding of dietary behaviors by capturing both self-reported patterns and real-world eating practices and the food environment, as captured through ethnographic methods. The analysis included descriptive statistics, normality tests, and parametric tests to assess significant differences. Results: The findings revealed a decline in diet quality, characterized by low fruit and vegetable intake, high snack consumption, and irregular meal patterns, particularly among students living independently. Key barriers included academic stress, time constraints, low cooking self-efficacy, limited nutritional knowledge, peer pressure, and negative social norms, which contributed to reliance on convenient, processed foods. The lack of healthy food options on campus and the high perceived cost of nutritious food further led students to prioritize cheap, calorie-dense foods over healthier choices. Conversely, enablers included structured university schedules; peer support; hands-on culinary interventions; and improved access to affordable, healthy food. Conclusions: Addressing these barriers requires multi-level interventions that enhance nutrition literacy, cooking self-efficacy, and peer-led strategies while improving the campus food environment. Future research should explore SCT-based initiatives leveraging social networks and culinary education to foster sustainable dietary behavior change in university settings and assess how these findings can be applied in other socioeconomic and cultural contexts. Full article

Chert gravel, a byproduct of sand quarrying, remains an underutilized material in construction due to its low microwave (MW) absorption and high mechanical strength. The present study deals with the potential of MW irradiation as a novel, energy-efficient method for processing chert gravel into high-quality aggregates, reducing reliance on virgin materials. The research systematically examines MW exposure duration (1–2.5 min), rock size (150–800 g), moisture conditions, and cooling methods (air vs. water quenching) to optimize fragmentation. Experimental results indicate that larger rock sizes (600–800 g) yield coarser, less uniform aggregates, while prolonged MW exposure (>2 min) induces extensive micro-fracturing, producing finer, well-graded particles. Water quenching significantly intensifies fragmentation, generating irregular but highly fragmented aggregates, whereas pre-wetted samples exhibit finer and more uniform breakage than dry samples. The findings introduce a novel approach for optimizing chert gravel fragmentation, a material previously considered unsuitable for MW treatment. The study proposed a customizable methodology for tailoring aggregate properties through precise control of MW parameters, offering a sustainable alternative to conventional crushing. The results contribute to resource conservation, reduced energy consumption, and climate change mitigation, paving the way for more sustainable construction practices. Full article

Ice accumulation on heating, ventilation, air conditioning, and refrigeration (HVACR) structures presents significant operational challenges. These challenges include reduced efficiency, increased energy consumption, and potential damage to equipment. Traditional de-icing methods, such as chemical treatments, mechanical scraping, or heating-based techniques, are often labor-intensive, costly, and environmentally harmful. This study uniquely investigates ultrasonic de-icing as an energy-efficient alternative for HVACR applications, focusing on the specific structural geometries found in these systems. A comprehensive numerical simulation framework was developed using finite element analysis to explore ultrasonic wave propagation across four distinct HVACR structures. Key parameters such as ultrasonic frequency, power levels, and the number and placement of actuators were examined for their impact on ice detachment efficiency. Results from simulations on a plate structure reveal that ultrasonic excitation can propagate effectively across large areas (at least 150 × 150 mm), enhancing the de-icing coverage. Lower frequency (e.g., 30 to 45 kHz) excitation results in greater displacement, improving de-icing performance, while increased actuator numbers with the same total power input also enhance effectiveness. Two actuators seem sufficient for the de-icing of a 300 × 300 mm plate. For tube-and-fin structures, specific high-power ultrasonic frequencies selectively excite the fin plates, demonstrating efficient ice removal when actuated on the tube. However, optimal performance requires careful design of actuator placement and vibration modes to accommodate the irregular shapes of these structures. Full article

The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. Uncertainty analysis quantifies classification confidence, revealing ambiguous periods linked to irregular appliance usage patterns. Additionally, time-series decomposition and appliance-level predictions are contextualised with seasonal and occupancy dynamics, enhancing interpretability. Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10. By capturing occupancy-driven energy behaviours and accounting for inherent uncertainties, this research provides actionable insights for adaptive energy management. The proposed SIT hybrid model can contribute to sustainable and resilient smart energy systems, paving the way for efficient building energy management strategies. Full article

Highly pathogenic avian influenza (HPAI) H5N1 has been traditionally linked to poultry and wild birds, which has recently become a serious concern for dairy cattle, causing outbreaks all over the United States. The need for improved surveillance, biosecurity protocols, and interagency collaboration is highlighted by the discovery of H5N1 in dairy herds in several states and its human transmission. The epidemiology, transmission dynamics, and wide-ranging effects of H5N1 in cattle are reviewed in this paper, with particular attention paid to the disease’s effects on agricultural systems, public health, and animal health. Nonspecific clinical symptoms, such as decreased milk production and irregular milk consistency, are indicative of infection in dairy cows. Alarmingly, significant virus loads have been discovered in raw milk, raising worries about potential zoonotic transmission. The dangers of viral spillover between species are further highlighted by cases of domestic cats experiencing severe neurological symptoms after ingesting raw colostrum and milk from infected cows. Even though human cases remain rare, and they are mostly related to occupational exposure, constant attention is required due to the possibility of viral adaptability. The necessity of a One Health approach that integrates environmental, animal, and human health efforts is further supported by the broad occurrence of H5N1 across multiple species. For early detection, containment, and mitigation, cooperation between veterinary clinics, public health organizations, and agricultural stakeholders is crucial. Controlling the outbreak requires stringent movement restrictions, regular testing of dairy cows in reference labs, and adherence to biosecurity procedures. This review highlights the importance of thorough and coordinated efforts to manage H5N1 in dairy cattle by combining existing knowledge and pointing out gaps in surveillance and response strategies. Additionally, it sheds light on the potential risk of consumption of cow’s milk contaminated with H5N1 virus by humans and other companion animals like cats. In the face of this changing threat, proactive monitoring, strict biosecurity protocols, and cross-sector cooperation are crucial for reducing financial losses and protecting human and animal health. Full article

Sleep health is a multidimensional construct encompassing sleep quality, duration, efficiency, regularity, and alignment with circadian rhythms, playing a crucial role in overall well-being. Sleep health remains inconsistently defined across research and clinical settings despite its importance, limiting the ability to standardize assessments and interventions. Recent studies have emphasized the significance of defining sleep health beyond the absence of sleep disorders, integrating subjective and objective measures to assess its impact on physical and mental health outcomes. Disparities in sleep health exist across gender, socioeconomic status, and geographic regions, particularly in low- and middle-income countries where inconsistent work schedules, economic stress, and healthcare access influence sleep patterns. Poor sleep health is associated with increased risks of cardiovascular disease, obesity, metabolic dysfunction, and mental health disorders, reinforcing its role as a modifiable risk factor in public health. Lifestyle factors such as caffeine consumption, physical activity, and irregular eating patterns also contribute to sleep disturbances, highlighting the need for behavioral interventions. This narrative review aims to synthesize the current knowledge on sleep health, focusing on its definitions, measurement tools, global disparities, and associations. Full article