Search Results (21,316)

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

Biallelic pathogenic variants in DHCR7 result in decreased activity of 7-dehydrocholesterol (7-DHC) reductase, which converts 7-DHC to cholesterol, and causes Smith–Lemli–Opitz syndrome (SLOS). Elevated serum 7-DHC levels are indicative of SLOS as are intellectual disability (ID), growth retardation, microcephaly, craniofacial anomalies, and 2–3 toe syndactyly. Additional congenital malformations may be present in SLOS, and broad clinical variability has been recognized in SLOS. Rarely, biallelic pathogenic DHCR7 variants were reported with low-normal and normal intelligence quotient (IQ) and development. We report here a pair of siblings with mild global developmental delay, infrequent epileptic seizures, and elevated serum 7-DHC levels, associated with the homozygous DHCR7 variant c.988G>A (p.Val330Met). Remarkably, neither sibling displayed congenital anomalies nor dysmorphisms. Quattro-exome sequencing performed for global delay and mild ID in both siblings did not identify other ID causes. c.988G>A affects a highly conserved amino acid and displays a relatively high global population allele frequency of 0.04%, with absence of homozygotes from the population database gnomADv4.1.0. Our observation leads us to suggest that DHCR7 variant c.988G>A and other DHCR7 variants might be generally considered as underlying non-syndromic ID. Full article

Body composition is associated with multiple factors. The main objective of this study is to determine the association between diet and sociodemographic factors on the body structure and composition of university students at a public university in Ecuador. This cross-sectional study allowed for the collection of detailed body composition and dietary data from 204 students (41.7% men and 58.3% women, with an average age of 23.3 ± 4.4 years). The study was conducted using validated questionnaires and bioimpedance techniques. Statistical analysis included ANOVA tests, complemented by a PCA-Biplot, to examine the relationships between study variables. Statistical analysis revealed that men’s birthplace had a significant impact on several body measurements, such as hip circumference and weight, but no significant differences were observed in body structure and composition based on nutrient intake. Furthermore, larger upper-arm circumference in women was correlated with higher fat intake. The results of the multivariate analysis indicated a differential influence of dietary components on body composition. The study highlights the need for nutritional intervention strategies and educational programs that consider the diversity of students’ backgrounds to promote healthy habits and mitigate the negative effects of eating habits and irregular physical activity patterns on their health and body composition. Full article

Introduction: Gynoid lipodystrophy (GL) affects most women, manifesting itself from puberty to adulthood. Its multifactorial etiology generates controversy in the literature about the most suitable treatment. Several methods are used, from the smallest to the most invasive, in the search for an effective fight against the severity of GL. The positive effect of ultrasound therapy (US) in decreasing subcutaneous adipose tissue is in increasing the skin permeability of pharmacological molecules, and it has aroused interest in the effect of a combination of the two techniques on the severity of GL. However, the results of this technique associated with an exercise program are unknown. Objective(s): To analyze the effect of three sessions of US + 5% caffeine in association with the realization of an exercise program, in females, on the level of severity of GL in the gluteal region and on the posterior proximal third part of the thigh. Methods: A total of 36 healthy women, aged between 18 and 55, who were considered to have GL, were randomly allocated in two experimental groups and one placebo group. The placebo group (PG) performed only physical exercise during the study. Experimental group 1 (EP1) performed US with 5% caffeine alongside a physical exercise protocol and experimental group 2 (EP2) performed US with a conventional US gel alongside a physical exercise protocol. The three groups completed three intervention sessions over 3 weeks, with one session per week. In addition to the level of severity assessed by the Cellulite Several Scale (CSS), anthropometric measures, body composition, and lipid profile of the participants were evaluated. The first assessment was carried out before the intervention (M0) and the last assessment after the three interventions (M1). The results were analyzed using the ANOVA test. The Tukey test was used for multiple comparisons of the groups in all variables, except for those related to the CSS, where the Kruskal–Wallis test was used with a significance level of 0.05. Results: A total of 29 women completed the study. There was a significant decrease inside the PG related to triglycerides (p = 0.012). In M1, all groups started to present median values below 200 mg of triglycerides. In cholesterol, a significant reduction was observed in all groups (p = 0.05). On the gluteal level at 5 cm, there was a decrease in EP1 and EP2 between M0 and M1 with p = 0.006 and p = 0.002, respectively. On the CSS there were no significant differences between groups or between moments. Conclusions: Three sessions of 5% caffeine and US in association with a physical exercise protocol have no effect on reducing the level of severity of GL. Full article

A reliable estimation of protein requirements in lactating dairy cows is necessary for formulating nutritionally adequate diets, improving feed efficiency, and minimizing nitrogen excretion. This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). A total of 1779 observations were assembled from 436 peer-reviewed publications and open-access databases. Predictor variables included farm-ready variables such as milk yield, dry matter intake, days in milk, body weight, and dietary crude protein content. NPm was estimated based on the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) equations, while NPl was derived from milk true protein yield. The model adequacy was evaluated using 10-fold cross-validation. The RFR model demonstrated higher predictive performance than SVR for both NPm (R² = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R² = 0.82, RMSEP = 95.17 g/d, CCC = 0.89), reflecting its capacity to model the rule-based nature of the NASEM equations. These findings suggest that RFR may provide a valuable approach for estimating protein requirements with fewer input variables. Further research should focus on validating these models under field conditions and exploring hybrid modeling frameworks that integrate mechanistic and machine learning approaches. Full article

The preparedness of healthcare workers for disaster situations depends on their technical skills, disaster knowledge, and psychosocial strength, including teamwork and emotional regulation. This study aims to assess disaster preparedness among healthcare professionals in primary healthcare centers (PHCs) in Alqurayat, Saudi Arabia, with a specific focus on evaluating technical competencies, psychosocial readiness, and predictive modeling of preparedness levels. A mixed-methods approach was employed, incorporating structured questionnaires, semi-structured interviews, and observational data from disaster drills to evaluate the preparedness levels of 400 healthcare workers, including doctors, nurses, and administrative staff. The results showed that while knowledge (mean: 3.9) and skills (mean: 4.0) were generally moderate to high, notable gaps in overall preparedness remained. Importantly, 69.5% of participants reported enhanced readiness following simulation drills. Machine learning models, including Random Forest and Artificial Neural Networks, were used to predict preparedness outcomes based on psychosocial variables such as emotional intelligence, teamwork, and stress management. Sentiment analysis and topic modeling of qualitative responses revealed key themes including communication barriers, psychological safety, and the need for ongoing training. The findings highlight the importance of integrating both technical competencies and psychosocial resilience into disaster management programs. This study contributes an innovative framework for evaluating preparedness and offers practical insights for policymakers, disaster planners, and health training institutions aiming to strengthen the sustainability and responsiveness of primary healthcare systems. Full article

Background: Stroke recovery is influenced not only by clinical but also sociodemographic factors (SDFs). However, data on how variables such as age, sex, marital status, education, and employment status affect rehabilitation outcomes remain limited, particularly in structured inpatient settings. This study aimed to analyze the impact of key SDFs on functional recovery after stroke. Methods: A retrospective cohort of 289 stroke patients undergoing structured inpatient rehabilitation was analyzed. Functional status was assessed at admission, after three weeks, and at discharge using five standardized outcomes: gait speed (primary outcome), Barthel Index, Berg Balance Scale, Action Research Arm Test, and Ashworth scale. Repeated measures ANOVA and multivariable logistic regression were used to evaluate within-subject changes and associations with SDFs. Results: The cohort consisted predominantly of middle-aged to older adults (58% female, 62% married, 60% retired, 60% with primary education or less). Most patients (88%) had ischemic strokes of moderate severity. Significant improvements were observed across all functional measures. Employed, married, younger, and male patients achieved better outcomes. Interaction models indicated that older and female patients with moderate stroke severity demonstrated greater improvement than younger and male counterparts with milder strokes. Mean gait speed increased by +0.32 m/s, exceeding the minimal clinically important difference (MCID) of 0.16 m/s. Conclusions: Age, sex, marital status, education, and employment status are relevant predictors of stroke rehabilitation outcomes. These findings emphasize the importance of incorporating sociodemographic profiles into individualized rehabilitation planning to optimize functional recovery and reduce disparities among stroke survivors. Full article

The micro-scale interplay between the built environment and innovation has attracted increasing scholarly attention. However, discussions on how such microdynamics operate and vary across high-density cities remain insufficient. This study focuses on nine high-density urban centres along the G60 S&T Innovation Valley and employs a fine-grained grid unit, viz. 1 km × 1 km, combined with the gradient boosting decision tree (GBDT) model to address these issues. Results show that urban construction density-related variables, including the building density, floor area ratio, and transportation network density, generally rank higher than the amenity density and proximity-related variables. The former contributes 50.90% of the total relative importance in predicting invention patent application density (IPAD), while the latter two contribute 13.64% and 35.46%, respectively. Threshold effect analysis identifies optimal levels for enhancing IPAD. Specifically, the optimal building density is approximately 20%, floor area ratio is 5, and transportation network density is 8 km/km². Optimal distances to universities, city centres, and transportation hubs are around 1 km, 17 km, and 9 km, respectively. Furthermore, significant city-level heterogeneity was observed: most density-related variables consistently have an overall positive association with IPAD, with metropolitan cities (e.g., Hangzhou and Suzhou) exhibiting notably higher optimal values compared to medium and small cities (e.g., Xuancheng and Huzhou). In contrast, the threshold effects of proximity-related variables on IPAD are more complex and diverse. These findings offer empirical support for enhancing innovation in high-density urban environments. Full article

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

This study explores key factors influencing Aging-in-Place Attachment (AiPA) among older adults in Macau’s high-density community spaces, emphasizing interactions between the built environment, behavior, and psychology. A multidimensional framework evaluates environmental, behavioral, human-factor, and psychological contributions. A mixed-methods, multisource approach was employed. This study measured spatial characteristics of nine public spaces, conducted systematic behavioral observations, and collected questionnaire data on place attachment and aging intentions. Eye-tracking and galvanic skin response (GSR) captured visual attention and emotional arousal. Hierarchical regression analysis tested the explanatory power of each variable group, supplemented by semi-structured interviews for qualitative depth. The results showed that the physical environment had a limited direct impact but served as a critical foundation. Behavioral variables increased explanatory power (~15%), emphasizing community engagement. Human-factor data added ~4%, indicating that sensory and habitual interactions strengthen bonds. Psychological factors contributed most (~59%), confirming AiPA as a multidimensional construct shaped primarily by emotional and social connections, supported by physical and behavioral contexts. In Macau’s dense urban context, older adults’ desire to age in place is mainly driven by emotional connection and social participation, with spatial design serving as an enabler. Effective age-friendly strategies must extend beyond infrastructure upgrades to cultivate belonging and interaction. This study advances environmental gerontology and architecture theory by explaining the mechanisms of attachment in later life. Future work should explore how physical spaces foster psychological well-being and examine emerging factors such as digital and intergenerational engagement. Full article

The physiological and biomechanical characteristics of inline speed skating have not been systematically mapped nor research evidence synthesized. The aim was to identify and synthesize novel elements across studies, including participant characteristics, outcomes measures, experimental protocol, main outcomes and other relevant information, to inform evidence-based guidelines and recommendations. Following the PRISMA 2020 guidelines, a systematic search of databases was conducted to identify relevant studies. The extracted data were charted and synthesized to summarize the physiological and biomechanical aspects of inline speed skating. From 272 records, 22 studies met the defined criteria. Studies related to inline speed skating focused primarily on physiological variables (n = 14) and lower limb muscles function, with limited evidence on biomechanics of inline speed skating (n = 5) and the combination of biomechanics and physiology (n = 3). An overall unclear risk of bias was observed (59% of studies). Although studies have examined physiological and biomechanical variables, continuous physiological and biomechanical assessments of skaters performing different skills on both straight and curved tracks have not been conducted. Therefore, well-planned physiological and biomechanics studies are required to uncover underexplored areas in research and support the development of sport-specific studies. Full article

Background: Gender dysphoria refers to the psychological distress arising from a mismatch between an individual’s physical embodiment and their internal sense of gender. Gender-affirming mastectomies can be a pivotal component of gender affirmation for transgender, non-binary, and gender expansive individuals assigned female at birth. The impact of obesity on the outcomes of gender-affirming mastectomies has yet to be fully defined. Methods: A retrospective review of 205 gender-affirming mastectomies performed by the senior author was conducted. Patients were categorized into obese (BMI ≥ 30) and non-obese groups. Baseline characteristics, intraoperative variables, and complication rates were compared. Univariate and multivariate models were performed to evaluate the association between obesity and postoperative complications. Results: Obese patients had higher mean resection weights and liposuction volumes (p < 0.001). Significant differences were observed in the prevalence of fibromyalgia, prior chest surgeries, and hormone therapy usage (p = 0.002, 0.002, and 0.03, respectively). However, no statistically significant differences were found in overall complication rates between obese and non-obese groups in the univariate or multivariate analyses. Conclusions: Our study suggests that obesity is not a significant risk factor for complications in gender-affirming mastectomies patients. The varying impact of high BMI and obesity on surgical outcomes in different surgical fields highlights the importance of patient-centered care and a holistic and individual approach for each patient. Full article

This study focused on the carbonate system dynamics and air–sea CO₂ fluxes in the open-ocean waters of the Madeira–Tore Seamount Complex during August 2024. Surface water properties revealed pronounced latitudinal gradients in sea surface temperature (21.9–23.1 °C), salinity (36.2–36.7), and dissolved oxygen (228–251 µmol Kg⁻¹), influenced by mesoscale eddies and topographically driven upwelling. Despite oligotrophic conditions, distinct phytoplankton assemblages were observed, with coccolithophores dominating southern seamounts and open-ocean stations, and green algae and diatoms indicating episodic nutrient input. Surface total alkalinity (TA: 2236–2467 µmol Kg⁻¹), dissolved inorganic carbon (DIC: 2006–2183 µmol Kg⁻¹), and pCO₂ (467–515 µatm) showed spatial variability aligned with water mass characteristics and biological activity. All stations exhibited positive air–sea CO₂ fluxes (2.8–11.5 mmol m⁻² d⁻¹), indicating the region is a CO₂ source during summer. Calcite and aragonite saturation states were highest in stratified, warmer waters. Principal Component Analysis highlighted the role of physical mixing, carbonate chemistry, and biological uptake in structuring regional variability. Our findings emphasize and contribute to the complex interplay of physical and biogeochemical drivers in modulating carbon cycling and ecosystem structure across Atlantic seamounts. Full article