Search Results (1,418)

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

Habitat and climate changes driven by human activities are altering the distribution of organisms globally. In South Korea, recent temperature increases have exceeded twice the global average, and habitats have markedly changed and shrunk due to urban development driven by population growth and economic expansion. Despite its high biodiversity and over 500 years of preservation, Gwangneung Forest in South Korea has experienced habitat alterations due to the urbanization of surrounding rural areas since the 1990s. In this study, we aimed to evaluate how butterfly communities respond to urbanization and climate change using long-term monitoring data (1998–2015) from the conserved Gwangneung Forest. We considered the thermal adaptation types (cold-, warm-, and moderately adapted species), habitat types (forest edge, forest inside, and grassland), diet breadth (monophagous, oligophagous, and polyphagous), and wingspan of butterflies. Linear regression analysis of the abundance trends for each species revealed that cold-adapted species experienced population declines, while warm-adapted species showed increases. Changes in butterfly abundance were associated with both thermal adaptation type and wingspan, with larger, more mobile species showing greater resistance to habitat loss in surrounding areas. To preserve butterfly diversity in Gwangneung Forest and across South Korea, it is crucial to conserve open green habitats—such as gardens, small arable lands, and grasslands—within urban areas, especially considering the impacts of climate change and habitat loss, which disproportionately affect smaller species with limited mobility. Full article

This study analyzes ten-year trends in the incidence of iron-deficiency anemia (IDA), diabetes mellitus (DM), and endocrine, nutritional, and metabolic disorders (ENMDs) among children and adolescents (0–17 years) in urban areas of Kazakhstan, considering socio-economic influences. A retrospective analysis of national data from 2013 to 2023 was conducted using linear regression to assess temporal trends and associations with health and economic indicators. Nationally, IDA incidence declined significantly: –278.4 cases per 100,000 among children aged 0–14 and –305.4 among adolescents aged 15–17 (both p < 0.001). ENMD incidence also decreased, particularly among adolescents (–154.0 per 100,000; p < 0.001). A 1000 KZT increase in household food expenditures was associated with a reduction in IDA incidence by 203–216 cases per 100,000 (p < 0.001), emphasizing the importance of accessible, nutritious diets. In contrast, DM incidence among adolescents rose by 1.7 cases annually per 100,000 (p < 0.05), possibly reflecting urbanization, lifestyle changes, and increasing obesity. DM and ENMD rates were significantly linked to consumption expenditures, pediatric bed availability, and endocrinologist density. These findings underscore the need for integrated, equity-focused prevention and improved healthcare access for children and adolescents amid ongoing demographic and nutritional transitions. Full article

Urbanization is a dominant force reshaping human settlements, driving socio-economic development while also causing significant environmental challenges. With over 56% of the world’s population now residing in urban areas—a figure expected to rise to two-thirds by 2050—land use changes are accelerating rapidly. The conversion of natural landscapes into impervious surfaces such as concrete and asphalt intensifies the Urban Heat Island (UHI) effect, raises urban temperatures, and strains local ecosystems. This study investigates land use and landscape changes in Ceredigion County, UK, utilizing remote sensing and GIS techniques to analyze urbanization impacts over two decades (2003–2023). Results indicate significant urban expansion of approximately 122 km², predominantly at the expense of agricultural and forested areas, leading to vegetation loss and changes in water availability. County-wide mean land surface temperature (LST) increased from 21.4 °C in 2003 to 23.65 °C in 2023, with urban areas recording higher values around 27.1 °C, reflecting a strong UHI effect. Spectral indices (NDVI, NDWI, NDBI, and NDBaI) reveal that urban sprawl adversely affects vegetation health, water resources, and land surfaces. The Urban Thermal Field Variance Index (UTFVI) further highlights areas experiencing thermal discomfort. Additionally, machine learning models, including Linear Regression and Random Forest, were employed to forecast future LST trends, projecting urban LST values to potentially reach approximately 27.4 °C by 2030. These findings underscore the urgent need for sustainable urban planning, reforestation, and climate adaptation strategies to mitigate the environmental impacts of rapid urban growth and ensure the resilience of both human and ecological systems. Full article

Growing interest in organic food, along with European regulations limiting chemical usage, and the declining effectiveness of herbicides due to weed resistance, are all contributing to the growing trend towards mechanical weeding. For mechanical weeding to be effective, tools must pass near the crops in both the inter- and intra-row areas. The use of AI-based computer vision can assist in detecting crop lines and accurately guiding weeding tools. Additionally, AI-driven image analysis can be used for selective intra-row weeding with mechanized blades, distinguishing crops from weeds. However, until now, there have been two separate systems for these tasks. To enable simultaneous in-row weeding and row alignment, YOLOv8n and YOLO11n were trained and compared in a lettuce field (Lactuca sativa L.). The models were evaluated based on different metrics and inference time for three different image sizes. Crop lines were generated through linear regression on the bounding box centers of detected plants and compared against manually drawn ground truth lines, generated during the annotation process, using different deviation metrics. As more than one line appeared per image, the proposed methodology for classifying points in their corresponding crop line was tested for three different approaches with different empirical factor values. The best-performing approach achieved a mean horizontal error of 45 pixels, demonstrating the feasibility of a dual-functioning system using a single vision model. Full article

This study presents a semi-empirical approach to generalizing the acoustic radiation generated by serrated airfoil configurations, based on small-scale aerodynamic/acoustic experiments and functional regression techniques. In the context of passive noise reduction strategies, such as leading-edge and trailing-edge serrations, acoustic measurements are performed in a controlled subsonic wind tunnel environment. Sound pressure level (SPL) spectra and acoustic power metrics are acquired for various geometric configurations and flow conditions. These spectral data are then analyzed using regression-based modeling techniques—linear, quadratic, logarithmic, and exponential forms—to capture the dependence of acoustic emission on key geometric and flow-related variables (e.g., serration amplitude, wavelength, angle of attack), without relying explicitly on predefined nondimensional numbers. The resulting predictive models aim to describe SPL behavior across relevant frequency bands (e.g., broadband or 1/3 octave) and to extrapolate acoustic trends for configurations beyond those tested. The proposed methodology allows for the identification of compact functional relationships between configuration parameters and acoustic output, offering a practical tool for the preliminary design and optimization of low-noise serrated profiles. The findings are intended to support both physical understanding and engineering application, bridging experimental data and parametric acoustic modeling in aerodynamic noise control. Full article

Plastic pollution is one of the leading global problems of modern society. The growing demand for and production of plastic polymers has caused bisphenol A (BPA) and its emergent substitute molecules bisphenol S and F (BPS and BPF) to be present in water, food, and soil worldwide, exposing humans to endocrine disruptors. Exposure to these compounds has been associated with pathologies such as diabetes, obesity, hypertension, and psychiatric disorders. Interestingly, hypovitaminosis D (or low 25(OH)D) is also associated with this class of diseases. Therefore, the present work, for the first time, explores the relationship patterns between urinary bisphenols (BPs) and low 25(OH)D in a large general cohort (NHANES 13–16). Descriptive statistical analyses, comparative analyses, linear regressions, and binomial and multinomial logistic regressions were performed. Descriptive and comparative analysis, and simple linear regressions, showed different trends between BPs, and binomial logistic regressions showed that only BPS is a risk factor of low 25(OH)D, independently of age, BMI, gender, diabetes, dyslipidemia, smoking, and vitamin supplements consumption; odds ratio (95% confidence interval) of 1.10 (1.04–1.17). The different trend patterns observed in urinary bisphenols show that, despite being structurally similar molecules and potential analogs, they may affect the body in different ways. From an integrated perspective, this could represent an even greater potential threat than that posed by BPA alone. Future integrated studies will be required to further explore and clarify this emerging paradigm. Full article

Despite the known relevance of punch impact in boxing, limited evidence exists regarding how anthropometric and muscle performance variables contribute to it. This study investigated the relationship between anthropometric characteristics, muscle power and strength performance, and punch impact power in 69 boxing practitioners (mean ± SD age: 27.0 ± 6.1 years). Anthropometric variables (body height (BH), armspan (AS), body mass (BM)) and muscle power and strength tests (countermovement jump (CMJ), one repetition maximum in bench press (1RM BP), and handgrip strength (HS)) were assessed. Punch impact power was assessed with PowerKube (PK), a specific device designed to measure punch impact power. Punch impact power was positively correlated with BH, AS, and BM. Linear regression indicated that BH and AS explained about 36% of the variance in Straight punch impact power and 30–34% in Hook punch impact power. BM showed weaker predictive capacity, explaining 10% of the variance in Straight punch impact power and 11% in Hook punch impact power. When comparing punch impact power differences across groups with varying BH, AS, and BM, it was found that groups with High BH exhibited higher punch impact power than the groups with Low and Medium BH for both Straight and Hook punches. For AS, the High AS group also demonstrated higher punch impact power, with similar trends for BM, where significant differences were observed only between the High and Low BM groups. Additionally, our findings confirm significant relationships between anthropometric characteristics, muscle power, and strength performance. These findings highlight the importance of a comprehensive assessment of anthropometric profiles, alongside muscle power and strength evaluations, to better predict punch impact power. This approach provides valuable insights for boxing training and may also inform exercise programming for the general population. Full article

This study utilized the North American PhenoCam network to evaluate phenological characteristics and their relationships with geographic and climatic factors across deciduous broadleaf (n = 39) and evergreen needleleaf (n = 13) forests over the past decade. Using high temporal resolution near-surface imagery, key phenological indicators including the start, end, and length of growing season were derived and analyzed using linear regression and structural equation modeling. The results revealed substantial spatial variation; the evergreen needleleaf sites exhibited earlier starts to the growing season (112 vs. 130 Julian date), later ends to the growing season (286 vs. 264 Julian date), and longer lengths for the growing season (172 vs. 131 days) compared with the deciduous broadleaf sites. Latitude was significantly related to the start of the growing season and the length of the growing season at the deciduous broadleaf sites (R² = 0.28–0.41, p < 0.01), while these relationships were weaker at the evergreen needleleaf sites, and elevation had mixed effects. The mean annual temperature strongly influenced the phenology for both forest types (R² = 0.18–0.76, p < 0.01), whereas longitude, distance to the coast, and precipitation had negligible effects. Temporal trends in the phenological indicators were sporadic across both the deciduous broadleaf and evergreen needleleaf sites. Structural equation modeling revealed distinct causal pathways for each forest type, highlighting complex interactions among the geographical and climatic variables. At the deciduous broadleaf sites, geographical factors (latitude, elevation, and distance to the nearest coast) predominated the mean annual temperature, which in turn significantly affected phenological development (χ² = 2.171, p = 0.975). At the evergreen needleleaf sites, geographical variables had more complex effects on the climatic factors, start of the growing season, and end of the growing season, with the end of the growing season emerging as the primary determinant of growing season length (χ² = 0.486, p = 0.784). The PhenoCam network provides valuable fine-scale phenological dynamics, offering great insights for forest management, biodiversity conservation, and understanding carbon cycling under climate change. Full article

Background: Armed conflicts impose complex logistical and behavioral challenges on healthcare systems, particularly in managing acute conditions such as ST-elevation myocardial infarction (STEMI) and ischemic stroke. Although both diagnoses require timely intervention, their clinical pathways differ significantly. Few studies have systematically compared their management during active warfare, particularly within the warzone. Methods: This retrospective cohort study was conducted at Soroka University Medical Center (SUMC), the sole tertiary hospital in southern Israel and the main referral center for cardiovascular and neurological emergencies in the region. We included all adult patients (≥18 years) admitted with new-onset STEMI or ischemic stroke during three-month periods of wartime (October–December 2023) and matched routine periods in 2021 and 2022. Patients with in-hospital events, inter-hospital transfers, or foreign citizenship were excluded. Data on demographics, comorbidities, arrival characteristics, treatment timelines, and outcomes were extracted from electronic medical records. Categorical variables were compared using Chi-squared or Fisher’s exact test, and continuous variables using t-tests or Mann–Whitney U tests, as appropriate. Multivariable logistic and linear regression models were adjusted for age, sex, Charlson Comorbidity Index (CCI), and mode of arrival. Interaction terms assessed whether wartime modified the associations differently for STEMI and stroke. Results: A total of 410 patients were included (193 with STEMI and 217 with stroke). Patients with STEMI were significantly more likely to arrive by self-transport during the war (38, 57.6% vs. 32, 25.2%, p < 0.001) and had higher rates of late arrival beyond 12 h (19, 28.8% vs. 13, 10.2%, p = 0.002). These findings support the conclusion that patients were more prone to delayed and unstructured presentations during a crisis. In contrast, patients with stroke showed a reduction of 354 min in symptom-to-door times during the war [median 246 (30–4320 range) vs. 600 min (12–2329 range), p = 0.026]. Regression models revealed longer delays for stroke vs. STEMI in routine settings [β = 543.07 min (239.68–846.47 95% CI), p < 0.001], along with significantly lower in-hospital (OR = 0.39, 95% CI= 0.15–0.97, p = 0.05) and 30-day mortality (OR = 0.43, 95% CI= 0.19–0.94, p = 0.04). However, these differences were no longer significant during wartime. Patients with STEMI showed a trend toward lower 180-day mortality during the war (OR = 0.33, 95% CI = 0.09–0.99; p = 0.07), although this difference did not reach statistical significance. Conclusions: During wartime, patients with stroke arrived earlier and in greater numbers, while patients with STEMI showed reduced admissions and delayed, self-initiated transport. Despite these shifts, treatment timelines and short-term outcomes were maintained. These diagnosis-specific patterns highlight the importance of reinforcing EMS access for STEMI and preserving centralized protocol-based coordination for stroke during crises. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article

Background: Healthcare-associated infections (HAIs) remain a significant challenge in intensive care units (ICUs), especially in trauma settings where invasive interventions are frequent. This study aimed to assess the impact of a structured quality improvement project (QIP) on nosocomial infection rates and patient outcomes in a polytrauma ICU. Methods: We conducted a retrospective observational study at the “Pius Brînzeu” County Emergency Clinical Hospital, Timișoara. A total of 78 ICU trauma patients were included: 35 in the Pre-QIP group and 43 in the Post-QIP group. The QIP integrated evidence-based interventions, including hand hygiene reinforcement, individualized protective equipment, improved nurse staffing, and antimicrobial stewardship. Outcomes analyzed included nosocomial infection rate, ICU length of stay, antibiotic use, mechanical ventilation days, and mortality. Multivariable logistic, linear, and Poisson regression models were applied to control for confounding variables. Results: The Post-QIP group showed a significantly lower number of infections per patient (0.60 ± 0.95 vs. 1.41 ± 1.97, p = 0.03) and a trend toward lower mortality (0.19 vs. 0.34, p = 0.18). While ICU stay, antibiotic use, and ventilation days decreased post-QIP, these changes were not statistically significant. ISS and Charlson scores were consistent predictors of worse outcomes. Conclusions: Implementation of a targeted, multidisciplinary QIP was associated with improved infection control and patient outcomes. These results support the feasibility and value of structured infection prevention strategies in resource-constrained ICU settings. Full article

This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student performance, their limited coverage across countries and years constrains broader analyses. To address this limitation, we harmonized observed test scores across assessments and imputed missing values using both linear interpolation and machine learning (Least Absolute Shrinkage and Selection Operator (LASSO) regression). The dataset included (i) harmonized test scores for 15 year olds, (ii) annual educational quality indicators for the 15–19 age group, and (iii) educational quality indexes for the working-age population (15–64). These measures are provided in machine-readable formats and support empirical research on human capital, economic development, and global education inequalities across economies. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

As the laying cycle is prolonged, the egg albumen quality exhibits a declining trend. A Haugh unit (HU) is a standard measure of the albumen quality, which reflects viscosity and freshness. During the late laying period, the HU not only decreased significantly, but also exhibited greater variability among individuals. The magnum, as the primary site of albumen synthesis, plays a central role in this process; however, the mechanisms by which it regulates the albumen quality remain unclear. To address this, we obtained genomic and transcriptomic data from 254 individuals, along with single-cell RNA sequencing (scRNA-seq) data of the magnum tissue. Genome-wide association studies (GWAS) across five laying stages (66, 72, 80, 90, and 100 weeks of age) identified 77 HU-associated single-nucleotide polymorphisms (SNPs). Expression quantitative trait locus (eQTL) mapping linked these variants to the expression of 12 genes in magnum tissue. In addition, transcriptomic analysis using linear regression and random forest models identified 259 genes that significantly correlated with the HU. Single-cell RNA sequencing further revealed two key cell types, plasma cells and a subset of epithelial cells, marked by ADAMTSL1 and OVAL, which are functionally relevant to the HU. Through integrated Transcriptome-Wide Association Study (TWAS) and Summary-data-based Mendelian Randomization (SMR) analyses, we identified four robust regulators of the albumen quality: CISD1, NQO2, SLC22A23, and CMTM6. These genes are functionally involved in mitochondrial function, antioxidant defense, and membrane transport. Overall, our findings uncovered the genetic and cellular mechanisms underlying age-related decline in the albumen quality and identified potential targets for improving the egg quality in aging flocks. Full article