Search Results (20,788)

Coconut coir, a key substrate in soilless cultivation, presents challenges for accurate moisture detection because of its complex internal structure, which limits the understanding of water infiltration and redistribution. This study employed RGB image recognition techniques combined with machine learning algorithms to systematically investigate the effects of initial moisture content (10%, 20%, and 30%), coarse-to-fine coir volume ratio (1:0, 1:1, and 0:1), and emitter discharge rate (1.0, 1.5, and 2.0 L h⁻¹) on wetting front morphology, water transport dynamics, and moisture variation within coir substrates. Morphological features of the wetting front were extracted from images and incorporated into three machine learning models—Support Vector Regression (SVR), Random Forest (RF), and Polynomial Regression—to construct a predictive framework for coir moisture estimation. The results showed that the SVR model achieved the best predictive performance in coarse coir substrates (R² = 0.89, RMSE = 3.37%), whereas Polynomial Regression performed best in mixed substrates (R² = 0.861, RMSE = 4.34%). All models exhibited lower accuracy in fine coir, particularly at high moisture levels. Under the same irrigation volume, increasing the initial moisture content enhanced both the water transport rate and the wetting front extent, with the aspect ratio (AR) decreasing from approximately 2.0 to 1.3, indicating a morphological transition of the wetting front from a “thumb-shaped” to a “hemispherical” pattern. Coarse particles facilitated vertical infiltration, while fine particles exhibited stronger water retention. By integrating RGB image recognition with machine learning approaches, this study achieved reliable prediction of coir moisture content and proposed an optimal management strategy using mixed substrates with an initial moisture content of 20–30% to balance infiltration efficiency and water-holding capacity while minimizing percolation risk. These findings provide a robust technical pathway for precise water management in coir-based cultivation systems. Full article

The Body Mass Index (BMI), integrating body weight and length, is a widely used metric for obesity assessment in humans. As pigs serve as crucial biomedical models, the application of BMI in swine and its genetic basis remain poorly explored. This study aimed to investigate the genetic architecture of pig BMI and compare two carcass-based BMI metrics (BMI-S and BMI-O) for breeding applicability. A total of 439 Landrace × Yorkshire crossbred pigs were genotyped with a 50 K SNP chip; heritability was estimated via a mixed linear model, and genome-wide association study (GWAS) was performed using the BLINK model. BMI-S and BMI-O exhibited moderate-to-high heritability of 0.55 and 0.47, respectively, with 17 genome-wide significant SNPs detected—including the top associated SNP rs81382440 on chromosome 4 and rs80898583 on chromosome 7. Key candidate genes (GPHN, ADAM33, KCNH8, PDCD4) and 5 SNP-trait associations validated in PigQTLdb were linked to lipid/energy metabolism and muscle development. Carcass-based BMI improved phenotypic accuracy, and our findings provide core genetic markers and a theoretical basis for molecular breeding of pig body conformation and lipid deposition traits. Full article

The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article

As municipalities increasingly adopt artificial intelligence (AI) and generative AI (GenAI) to automate self-service technologies (SSTs), concerns related to fairness, transparency, accountability, and citizen trust have become central to sustainable public-sector governance. While existing studies emphasize either AI adoption or high-level ethical principles, limited empirical research explains how governance mechanisms translate into user-level outcomes in municipal services, particularly in the context of emerging GenAI capabilities. This study addresses this gap by examining how governance antecedents and system design attributes shape user satisfaction, trust, and perceived fairness in AI-enabled municipal SSTs in the United Arab Emirates (UAE). A mixed-methods research design was employed, combining a comparative analysis of international and UAE AI governance frameworks with semi-structured interviews (n = 16) and a survey of municipal employees and service users (n = 272). Qualitative findings reveal persistent concerns regarding data privacy, fairness, explainability, and the absence of standardized municipal-level accountability instruments. Quantitative analysis shows that perceived helpfulness significantly increases user satisfaction, while perceived fairness strongly predicts continued usage intentions. In contrast, system responsiveness exhibits a negative association with satisfaction, highlighting an expectation–performance gap in automated service delivery. Based on these findings, the study proposes a governance–implementation–outcomes model that operationalizes ethical AI principles into measurable governance and service-design mechanisms. Unlike prior adoption-focused or purely normative frameworks, this model empirically links governance instrumentation to citizen-centered outcomes, offering practical guidance for inclusive and sustainable AI and GenAI deployment in municipal self-service systems. The findings contribute to debates on sustainable digital governance by demonstrating how ethically governed AI systems can reinforce public trust, service equity, and long-term institutional resilience. Full article

Phosphorus is an essential resource for numerous industrial applications. However, its uneven global distribution makes Europe heavily dependent on imports. Recovering phosphorus from waste streams is therefore crucial for improving resource security. The FlashPhos project addresses this challenge by developing a process to recover phosphorus from sewage sludge, in which phosphorus-rich slag is produced in a flash reactor and subsequently reduced in a Submerged Arc Furnace (SAF). In this process, approximately 250 kg/h of sewage sludge is converted into slag, which is further processed in the SAF to recover about 8 kg/h of white phosphorus. This work focuses on the development of a computational model of the SAF, with particular emphasis on slag behaviour. Due to the extreme operating conditions, which severely limit experimental access, a numerically efficient three-dimensional CFD model was developed to investigate the internal flow of the three-phase, AC-powered SAF. The model accounts for multiphase interactions, dynamic bubble generation and energy sinks associated with the reduction reaction, and Joule heating. A temperature control loop adjusts electrode currents to reach and maintain a prescribed target temperature. To further reduce computational cost, a novel simulation approach is introduced, achieving a reduction in simulation time of up to 300%. This approach replaces the solution of the electric potential equation with time-averaged Joule-heating values obtained from a preceding simulation. The system requires transient simulation and reaches a pseudo-steady state after approximately 337 s. The results demonstrate effective slag mixing, with gas bubbles significantly enhancing flow velocities compared to natural convection alone, leading to maximum slag velocities of 0.9–1.0 m/s. The temperature field is largely uniform and closely matches the target temperature within ±2 K, indicating efficient mixing and control. A parameter study reveals a strong sensitivity of the flow behaviour to the slag viscosity, while electrode spacing shows no clear influence. Overall, the model provides a robust basis for further development and future coupling with the gas phase. Full article

Many denoising methods model noise as Gaussian noise. However, the realistic noise captured by camera devices does not satisfy Gaussian distribution. Hence, those methods do not perform well when being applied to real-world image denoising tasks. In this work, we indicate that the spatial correlation in noise and the variation of noise intensity are the main factors that impact the performance of Gaussian noise-based methods, and accordingly propose an extension of the method based on the weighted, mixed non-convex ${\mathsf{\ell }}_p$ norm. The proposed method first strengthens the intensity of the noise pattern in the original denoising result through the Guided Filter, then removes the over-amplified frequency in the local area by the proposed regularization term. We prove that the optimal solution can be achieved through the sub-gradient-based iterative optimization scheme, and further reduce the computational cost by optimizing the initial values. Numerical experiments show that the proposed extending method can balance well texture preservation and noise removal, and the PSNR of the extending method’s results are greatly improved, even outperforming the recently proposed realistic noise removal methods which also include deep learning based methods. Full article

This study investigates the dispersion and condensation behavior of tritiated water vapor released into the atmosphere using moist air as a carrier, with an emphasis on safety optimization for nuclear power plant effluent discharge. A coupled heat and mass transfer model was developed and implemented in CFD simulations to analyze the evolution of temperature and relative humidity during the mixing of exhaust moist air with ambient air. The effects of key atmospheric and operational parameters—including the ambient wind speed, turbulence intensity, ambient temperature, relative humidity, and exhaust velocity—were systematically examined. The results indicate that the temperature difference between the exhaust gas and ambient air is the primary factor governing condensation risk. Low wind speeds and weak turbulence favor near-field humidity accumulation, while higher wind speeds and turbulence intensities enhance mixing and dilution, thereby reducing local humidity peaks but extending the downwind impact range. Increasing exhaust velocity strengthens plume rise and long-range transport due to enhanced momentum and latent heat release, mitigating accumulation near the chimney outlet. Furthermore, high ambient temperatures significantly increase the air’s moisture-holding capacity, allowing higher exhaust humidity without inducing condensation. Full article

Wildfire is a dominant ecological force in Mediterranean pine forests, and post-fire silvicultural practices can substantially alter their recovery trajectories. In this study, we examined how natural regeneration and artificial plantations influence the composition, structure, and functional roles of medium and large-sized mammal communities in burned Pinus brutia forests of southwestern Türkiye. Camera trap data were combined with linear mixed-effects models, functional diversity metrics, and indicator species analysis to assess community responses. Mammalian assemblages showed marked shifts across treatments: generalist carnivores such as Vulpes vulpes and Canis aureus dominated burned areas, whereas higher-trophic specialists like Caracal caracal were restricted to unburned forests. Functional richness was consistently higher in unburned stands, while artificial plantations reduced both richness and evenness. Natural regeneration partly mitigated these declines by sustaining more balanced community structures. Indicator species analysis confirmed these patterns, with Lepus europaeus strongly associated with burned sites and C. caracal with unburned forests. Overall, findings demonstrate that post-fire silvicultural practices strongly shape mammalian community assembly and functional diversity. Natural regeneration preserves structural heterogeneity and supports functionally diverse assemblages, whereas artificial plantations promote homogenization. Effective restoration strategies should therefore integrate wildlife responses with vegetation recovery to strengthen ecosystem resilience and maintain the ecological roles of mammals. Full article

Background: Non-specific low back pain (NSLBP) is associated with altered neuromuscular control of the lumbopelvic–hip complex (LPHC). However, the functional behavior of the transversus abdominis (TrA) and gluteus medius (GM) during upright postural tasks, with and without the abdominal drawing-in maneuver (ADIM), remains unclear. This study aimed to compare TrA and GM activation between individuals with NSLBP and asymptomatic controls during standing and single-leg stance using rehabilitation ultrasound imaging (RUSI). Methods: Thirty-two participants (16 with NSLBP and 16 asymptomatic controls) underwent RUSI assessment under four task conditions: standing and single-leg stance, with and without ADIM. Muscle function was quantified using thickness change derived from ultrasound measurements. A two-way mixed-model analysis of variance with Bonferroni-adjusted post hoc comparisons was performed. Results: Significant group × condition interactions were identified for TrA activation (p < 0.05). Individuals with NSLBP demonstrated reduced TrA activation during standing with ADIM and reduced GM activation during single-leg stance compared with asymptomatic controls. The effect sizes were moderate to large for TrA activation and small to moderate for GM activation. Conclusions: These findings suggest task-specific differences in neuromuscular activation patterns in individuals with NSLBP. Ultrasound-derived thickness change measures obtained during functional, weight-bearing tasks may provide clinically relevant information to support motor control rehabilitation strategies. Full article

Hypoglycemia is associated with cardiovascular events reflected by platelet abnormalities. We hypothesized that sequential endothelial changes may occur during hypoglycemia that may enhance cardiovascular risk. In type 2 diabetes (T2D) (n = 23) and controls (n = 23), blood SOMAscan proteomic analysis of endothelial proteins at baseline, insulin-induced hypoglycemia and post hypoglycemia to 24 h were examined using repeated-measures linear mixed modeling with a prospective parallel study design. Most endothelial proteins that changed over time did not differ between groups. Baseline levels of P-selectin, plasminogen activator inhibitor-1 (PAI-1; serpine-1), E-selectin and angiopoietin-1 (ANGPT1) were significantly higher, whilst cadherin-5 was lower in T2D. Several proteins exhibited changes versus baseline in both T2D and controls. Under hypoglycemia, decreases in cadherin-5 and soluble angiopoietin-1 receptor (sTie-2) were observed, with increased P-selectin, intercellular adhesion molecule-3 (ICAM3), ANGPT1 and PAI-1. Post hypoglycemia, decreased cadherin-5 and ICAM5 were observed at 2 h and PAI-1 at 4 h, as well as increases in P-selectin at 30 min, 1 h and 24 h and ICAM3 at 24 h. Post hypoglycemia, E-selectin, P-selectin and ICAM3 were significantly lower in T2D patients at 2 h, while PAI-1 was significantly lower at 4 h and ICAM3 was significantly lower at 24 h. Baseline endothelial proteins differed between T2D and controls, which may suggest local endothelial inflammatory activation leading to a pro-thrombotic, destabilized vascular phenotype characteristic of diabetic vasculopathy. Hypoglycemia may exacerbate this towards a pro-adhesive and pro-thrombotic phenotype, worsening endothelial dysfunction. Full article

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

Write amplification factor (WAF) is a critical performance and endurance bottleneck in flash-based solid-state drives (SSDs). Multi-streamed SSDs mitigate WAF by enabling logical data streams to be written separately, thereby improving the efficiency of garbage collection. However, despite the architectural potential of multi-streaming, prior research has largely overlooked the design of write buffer management schemes tailored to this model. In this paper, we propose a stream-aware block-level write buffer management technique that leverages both spatial and temporal locality to further reduce WAF. Although the write buffer operates at the granularity of pages, eviction is performed at the block level, where each block is composed exclusively of pages from the same stream. All pages and blocks are tracked using least recently used (LRU) lists at both global and per-stream levels. To avoid mixing data with disparate hotness and update frequencies, pages from the same stream are dynamically grouped into logical blocks based on their recency order. When space is exhausted, eviction is triggered by selecting a full block of pages from the cold region of the global LRU list. This strategy prevents premature eviction of hot pages and aligns physical block composition with logical stream boundaries. The proposed approach enhances WAF and garbage collection efficiency without requiring hardware modification or device-specific extensions. Experimental results confirm that our design delivers consistent performance and endurance improvements across diverse multi-streamed I/O workloads. Full article

Chronic obstructive pulmonary disease (COPD) and tuberculosis (TB) increasingly co-occur in low- and middle-income countries and aging populations. Prior pulmonary TB is a robust, smoking-independent determinant of COPD and is linked to persistent systemic inflammation, endothelial dysfunction, dyslipidemia, and hypercoagulability axes that also amplify cardiovascular disease (CVD) risk. We conducted a targeted narrative non-systematic review (2005–2025) of PubMed/MEDLINE, Embase, Scopus, and Web of Science, selecting studies for clinical relevance across epidemiology, clinical phenotypes, pathobiology, biomarkers, risk scores, sleep-disordered breathing, and management. No quantitative synthesis or formal risk-of-bias assessment was performed. Accordingly, findings should be interpreted as a qualitative synthesis rather than pooled estimates. Prior TB is associated with a distinctive COPD phenotype characterized by mixed obstructive–restrictive defects, reduced diffusing capacity (DLCO), radiographic sequelae, and higher exacerbation/hospitalization burden. Mechanistic insights: Convergent mechanisms chronic immune activation, endothelial injury, prothrombotic remodeling, molecular mimicry, and epigenetic reprogramming provide biologic plausibility for excess CVD, venous thromboembolism, and pulmonary hypertension. Multimarker panels spanning inflammation, endothelial injury, myocardial strain/fibrosis, and coagulation offer incremental prognostic value beyond clinical variables. While QRISK4 now includes COPD, it does not explicitly model prior TB or COPD-TB outcomes, but data specific to post-TB cohorts remain limited. Clinical implications: In resource-constrained settings, pragmatic screening, prioritized PAP access, guideline-concordant pharmacotherapy, and task-shifting are feasible adaptations. A history of TB is a clinically meaningful modifier of cardiopulmonary risk in COPD. An integrated, multimodal assessment history, targeted biomarkers, spirometry/lung volumes, DLCO, 6 min walk test, and focused imaging should guide individualized care while TB-aware prediction models and implementation studies are developed and validated in high-burden settings. Full article

Vaccination has been a cornerstone of the public health response to the COVID-19 pandemic, particularly in protecting older and frail populations. A detailed characterization of antibody titer dynamics and their determinants represents a crucial step toward optimizing vaccination strategies. However, antibody titers are bounded within assay-specific limited intervals and often display skewness and intra-subject correlation, which limit the suitability of conventional modeling approaches. We analyzed longitudinal antibody titer data from 608 residents and staff members of five nursing homes in Calabria (southern Italy) using beta-generalized linear mixed models (β-GLMMs). This framework enabled simultaneous modeling of the mean humoral response (μ), precision parameter (ϕ), and probability of achieving the maximum immune response (α), thereby providing a comprehensive assessment of factors influencing immune dynamics. Two distinct patterns of antibody titer evolution were identified. Among nursing home residents, stroke was associated with higher antibody concentrations, whereas atrial fibrillation, lower body mass index, non-Alzheimer’s dementia, and chronic obstructive pulmonary disease were linked to reduced responses. The β-GLMM approach allowed for a more accurate identification of demographic and clinical determinants compared with traditional methods. These findings underscore the utility of β-GLMMs for analyzing bounded longitudinal immunological data and highlight key factors shaping vaccine-induced immunity. Such insights may lead to more tailored immunization strategies in vulnerable older populations. Full article

The ubiquity of microplastics (MPs) in the environment has raised significant concerns, yet their potential impacts on human health are not fully elucidated. This study aimed to quantify human exposure to MPs in feces and evaluate their associations with oxidative stress and cardiopulmonary function. A panel study was conducted in 16 male college students with three-round visits. Fecal MPs were quantified using infrared micro-spectroscopy, and health effects were assessed through urinary biomarkers of oxidative damage (MDA and 8-OHdG) and cardiopulmonary function tests. Associations between MP exposure and health outcomes were analyzed using linear mixed-effect models. We found that fecal MP amount across 48 samples from 16 participants showed high intra-individual variation and poor reproducibility (ICCs < 0.4). MPs in feces were predominantly identified as sheets and fragments in the 100–200 μm size range, with polyamide (PA), polyester, polyethylene (PE), and polypropylene as the primary polymer types. Significant relationships were observed between fecal MP amount and oxidative damage biomarkers. Each one-unit increase in MPs corresponded to a 0.827 increase in MDA (95% CI: 0.116, 1.54) and a 1.11 increase in 8-OHdG (95% CI: 0.235, 1.98), with fibrous shapes and specific polymers (PE and PA) being the primary drivers. No significant associations were found between MP exposure and lung function or blood pressure. These findings indicated that MP exposure was significantly linked to increased oxidative damage, highlighting a pressing public health concern regarding their subclinical biological effects. Full article