Search Results (2,581)

We analyzed six years (2019–2025) of Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to quantify how turbidity and water temperature relate to algal blooms in Utah Lake. We generated satellite-derived estimates of chlorophyll-a (chl-a), turbidity, and surface temperature at 600 randomly distributed sample points. Using generalized least squares models, we found that temperature and turbidity explain only a small fraction of the variance in chl-a (temperature coefficients 0.02–0.03; turbidity coefficients −0.18–0.42), and the strength and sign of correlations vary by location. Despite weak linear correlations, we identified a strong nonlinear pattern: 94% of intense bloom events (chl-a > 87 µg/L) occurred when turbidity was below 120 Nephelometric Turbidity Units (NTU), indicating that blooms more often form under low-turbidity conditions. We also found that the first mild blooms of the season (chl-a > 34 µg/L) typically occurred five days after the largest short-term temperature increase (3–12 °C/day) at a given location, but only when blooms first appeared in April. These results suggest that Utah Lake blooms may be light-limited, with turbidity constraining algal growth that would otherwise occur in response to high nutrient levels, while temperature spikes influence early-season bloom initiation. Our findings have direct implications for monitoring and management strategies that target algal blooms on Utah Lake. Full article

Background/Objectives: Volatile organic compounds (VOCs) have emerged as promising non-invasive biomarkers for assessing metabolic and reproductive health. In the context of assisted reproductive techniques (ARTs), the volatilomic composition of follicular fluid (FF) may reflect the biochemical environment surrounding the oocyte, influencing fertilization success and embryo development. This study aimed to characterize the volatilomic profile of FF in women undergoing ARTs and to explore associations between specific VOCs and female fertilization-related parameters (FFRPs). Methods: A total of 54 Caucasian women aged 19–39 years, enrolled between October 2015 and July 2019, were recruited at the Assisted Reproduction Laboratory of the Local Health Unit of Cova da Beira, Covilhã. FF samples were analyzed via gas chromatography–mass spectrometry (GC–MS) in scan mode, identifying 136 VOCs, of which 72 were selected based on prevalence. Sixteen FFRPs were evaluated, including age, body mass index (BMI), smoking habits, infertility factor, oocyte yield, embryo quality, β-hCG levels, country of birth, and reproductive history. Associations between VOCs and FFRPs were assessed using the Chi-square (χ²) test. Results: Significant correlations (p ≤ 0.05) were identified between 45 VOCs and 11 FFRPs. The detected compounds comprised alkanes, siloxanes, aromatics, alcohols, ketones, aldehydes, carboxylic acids and esters, fatty acid derivatives, epoxides, acrylates, nitriles, and sterols. Several VOCs were associated with more than one FFRP, indicating overlapping metabolic pathways that may influence reproductive performance. Conclusions: The volatilomic profile of FF demonstrates significant variability linked to individual reproductive and metabolic factors. VOC analysis may provide novel insights into follicular physiology, representing a promising approach for identifying potential biomarkers of infertility and ART outcomes. Full article

The electronic band structure of two-dimensional lithium is calculated using the Dirac equation. Lithium is modeled as a two-dimensional square lattice in which the two strongly bound inner electrons and the fixed nucleus are treated as a positively charged ion (+e), while the outer electron is assumed to be uniformly distributed within the cell. The electronic potential is obtained by considering Coulomb-type interactions between the charges inside the unit cell and those in the surrounding cells. A numerical method that divides the unit cell into small pieces is employed to calculate the potential and then the Fourier coefficients are obtained. The Bloch method is used to determine the energy bands, leading to an eigenvalue matrix equation (in momentum space) of infinite dimension, which is truncated and solved using standard matrix diagonalization techniques. Convergence is analyzed with respect to the key parameters influencing the calculation: the lattice period, the dimension of the eigenvalue matrix, the unit-cell partition used to compute the potential’s Fourier coefficients, and the number of neighboring cells that contribute to the electronic interaction. Full article

This study aims to explore characteristics and clinical predictors of Lymphogranuloma venereum (LGV) and non-LGV Chlamydia trachomatis (Ct) serovars. We conducted a retrospective study on men who have sex with men (MSM) diagnosed with rectal or urethral Ct between 2015 and 2022 at the Infectious Diseases Unit of San Raffaele Scientific Institute, Milan, Italy. Nucleic acid amplification test with sequencing was used for Ct serovar determination. Individuals’ characteristics were described by median (interquartile, IQR) or frequency (%) and compared using Kruskal–Wallis or Chi-Square tests, as appropriate. Logistic regression model was used to identify predictors of LGV; multinomial logistic regression model, with LGV group as reference category, investigated factors associated with the LGV group (serovars L1, L2B, L2C), specific highly prevalent non-LGV serovars (D, E, G) or the non-amplifiable group. Overall, 211 MSM were included: 29.8% with LGV, 50.2% non-LGV and 19.9% non-amplifiable. Symptomatic cases were 46% of which 48% LGV; rectal infection was the most common (86%), followed by urethral (10%) and both sites (4%). People living with HIV were 91.5%; 31.3% had ≥1 concomitant STI and 65.4% ≥1 previous one. According to logistic regression analysis, after adjustment for the diagnosis of ≥1 concomitant and previous STI, LGV serovars were significantly associated with symptomatic infections (adjusted odds ratio, aOR = 6.05; 95%CI = 2.92, 13.13; p < 0.001) and anorectal site (aOR = 17.12; 95%CI = 3.17–319.17, p = 0.007) compared to non-LGV. Among MSM, almost 30% of Ct infections were due to LGV serovars. Presence of symptoms and anorectal site involvement, identified as clinical predictors of LGV, should guide clinicians during diagnosis. Full article

This study analyzes the relationship between macroeconomic distress and financial dollarization in Türkiye using annual regional panel data for 26 Nomenclature of Territorial Units for Statistics 2 regions over the period 2005–2021. Macroeconomic distress is captured using the misery index, computed as the compound of inflation and unemployment rates, while the share of foreign-currency-denominated deposits in total deposits measures financial dollarization. Applying second-generation panel econometric models that account for regional heterogeneity, we investigate both long-run equilibrium relationships and short-run interactions. Panel cointegration tests show a long-run connection between macroeconomic distress and dollarization. Short-run effects estimated using a Panel Vector Error Correction Model and a Cross-Sectionally Augmented ARDL framework point to bidirectional causality. Long-run coefficient estimates obtained via Dynamic Ordinary Least Squares indicate an apparent asymmetry. Increases in dollarization exert a substantial and economically significant effect on macroeconomic distress, whereas the long-run impact of distress on dollarization is comparatively modest. The findings suggest that dollarization functions not only as a response to macroeconomic instability but also as a structural element that intensifies inflationary pressures and labor market distortions over time. Focusing on regional patterns rather than national aggregates, the paper provides new evidence on the spatial dimension of the dollarization–instability link. Full article

In this analysis, the dynamic nexus between green governance, energy transition, and carbon emissions in the period spanning 1990 and 2022 for the twenty-one member economies of the Organization for Economic Cooperation and Development (OECD) and partner economies is examined. Employing Feasible Generalized Least Squares (FGLS), Driscoll–Kraay Standard Errors (DKSE), and Quantile-on-Quantile Regression (QQR), this analysis encompasses the effects of the use of renewable energy sources, economic growth, and changes in the population on carbon emissions. Results for the analysis show that the adoption of renewable energy sources, tough environmental regulations, and green innovation play a significant role in offsetting carbon emissions since the results are more pronounced at the tail ends of the distribution of carbon emissions. Conversely, changes in the level of population and economic growth are identified as potential exacerbators of environmental concerns. In offering implications for policymakers, this analysis argues that environmental laws and taxation and green innovation are potential means of improving environmental governance in achieving the United Nations’ Sustainable Development Goals and climate change commitments. By addressing the issue of differential environmental effects based on varying levels of carbon emissions, this analysis makes contributions to the expanding literature on sustainable environmental governance in the twenty-first-century energy economy. Full article

Background: Childhood obesity represents the most common nutritional and metabolic disorder in industrialized countries and constitutes a major public health concern. In Italy, 20–25% of school-aged children are overweight and 10–14% are obese, with marked regional variability. Excess adiposity in childhood is frequently associated with hypertension, dyslipidemia, insulin resistance, and non-alcoholic fatty liver disease (NAFLD), predisposing to future cardiovascular disease (CVD). Objective: To investigate anthropometric indicators of cardiometabolic risk in 810 children and adolescents aged 7–17 years who underwent assessment for competitive sports eligibility at the Sports Medicine Unit of Modena, evaluate baseline knowledge of cardiovascular health aligned with ESC, AAP (2023), and EASO guidelines. Methods: 810 children and adolescents aged 7–17 years undergoing competitive sports eligibility assessment at the Sports Medicine Unit of Modena underwent evaluation of BMI percentile, waist circumference (WC), waist-to-height ratio (WHtR), and blood pressure. Cardiovascular knowledge and lifestyle habits were assessed via a previously used questionnaire. Anthropometric parameters, blood pressure (BP), and lifestyle-related knowledge and behaviors were assessed using standardized procedures. Overweight and obesity were defined according to WHO BMI-for-age percentiles. Elevated BP was classified based on the 2017 American Academy of Pediatrics age-, sex-, and height-specific percentiles. Statistical analyses included descriptive statistics, group comparisons, chi-square tests with effect size estimation, correlation analyses, and multivariable logistic regression models. Results: Overall, 22% of participants were overweight and 14% obese. WHtR > 0.5 was observed in 28% of the sample and was more frequent among overweight/obese children (p < 0.001). Elevated BP was detected in 12% of participants with available measurements (n = 769) and was significantly associated with excess adiposity (χ² = 7.21, p < 0.01; Cramér’s V = 0.27). In multivariable logistic regression analyses adjusted for age and sex, WHtR > 0.5 (OR 2.14, 95% CI 1.32–3.47, p = 0.002) and higher sedentary time (OR 1.41 per additional daily hour, 95% CI 1.10–1.82, p = 0.006) were independently associated with elevated BP, whereas BMI percentile lost significance when WHtR was included in the model. Lifestyle knowledge scores were significantly lower among overweight and obese participants compared with normal-weight peers (p < 0.01). Conclusions: WHtR is a sensitive early marker of cardiometabolic risk, often identifying at-risk children missed by BMI alone. Baseline cardiovascular knowledge was suboptimal. The observed gaps in cardiovascular knowledge underscore the importance of integrating anthropometric screening with structured educational interventions to promote healthy lifestyles and long-term cardiovascular prevention. Full article

This study is an economic evaluation of a composting facility in southeastern Spain (applying Life Cycle Costing), a key region in European horticulture with a significant availability of agricultural biomass. Composting helps reduce dependence on inorganic fertilizers, aligning with European policies that promote the transition toward organic fertilization practices. In addition, compost enhances soil health, increases soil organic carbon, and supports climate change mitigation. Despite its agronomic and environmental benefits, and the large availability of biomass in this region, there is a notable lack of literature addressing the economic costs of composting, which is the first step in assessing the sustainability of a production process. The proposed facility (production: 9000 tonnes of compost per year) utilizes pruning residues and manure to produce high-quality organic amendments. The analysis includes infrastructure, equipment, and every operational input. Likewise, the analysis also provides socio-economic indicators such as employment generation and contribution to the regional economy. Three scenarios were evaluated based on the pruning–shredding location: at the plant, at the farm with mobile equipment, and at the farm with conventional machinery. The most cost-effective option was shredding at the farm using mobile equipment, reducing the unit cost to EUR 65.19 per tonne due to the transport of a smaller volume of prunings and, therefore, lower fuel consumption. The plant also demonstrates high productivity per square metre and generates stable employment in rural areas. Overall, the findings highlight composting as a viable and competitive strategy within circular and low-carbon agricultural systems. Full article

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy. Full article

Local governance has been found to be an important determinant of individuals’ subjective well-being (SWB) in cross-municipality studies in Europe and Asia. In addition, previous literature suggests that increasing access to determinants of SWB provides lesser SWB benefit to racial minorities compared to white people in the United States (U.S.). Given this context, we ask the following: (1) does good local governance improve SWB in the U.S.? and (2) does good local governance improve SWB for Black and Hispanic people equally compared to white people? To answer these questions, we examine Chicago, Illinois, the third-largest city in the U.S. with substantial Black and Hispanic populations. We model local governance, our independent variable, as the number of weeks for the municipality to respond to pothole service requests reported to the city’s non-emergency services system. Our dependent variable was life satisfaction, measured by the Cantril Ladder. Covariates included self-reported health problems, lack of money for food, sex, age, age-squared, and marital status. Neighborhood race/ethnicity was tested as a moderator of the primary relationships. We estimated linear regression models with and without race × governance interactions. Our findings demonstrate that local governance is an important determinant of SWB, but that it benefits SWB in white neighborhoods more than in Black/Hispanic neighborhoods. Full article

Electrical curing is a viable alternative to traditional thermal curing for geopolymer materials due to its capability for rapid and internal geopolymerization. In this research, reinforced geopolymer-based composite beams were successfully fabricated at a macroscale using a binary system of fly ash (FA) and granulated blast furnace slag (GBFS). The mixture was activated with a solution of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH) with a fixed molar ratio of 2:1 for both, and aggregate-to-binder and activator-to-binder (A/B) ratios of 2.5 and 0.7, respectively. To ensure electrical conductivity, individual fiber systems were employed, including carbon fiber (CF), steel fiber (SF), and waste wire erosion (WWE), each incorporated at a dosage of 0.5 vol.% of the total mix volume. In addition, carbon black (CB) was introduced as a conductive filler at a constant dosage of 2.0 vol.% of the binder content in selected specimens. Each beam specimen contained only one type of conductive reinforcement or filler. A total of twelve reinforced geopolymer-based composite beams with a 150 mm square section and a span of 1300 mm, with a clear span of 1200 mm, were successfully cast and reinforced based on reinforced concrete beam designs and standards, with a dominant goal of enhancing beam behavior under flexure. The beams were cured in ambient curing conditions, or using thermal curing at 80 °C for 24 h, and using electrical curing from the fresh states with a fixed voltage of 25 V. Notwithstanding a common beam size and reinforcement pattern, distinct curing methods significantly influenced beam structure properties. Peak loads were between 20.8 and 31.5 kN, initial stiffness between 1.75 and 6.09 kN/mm, and total energy absorption between 690 and 1550 kN/mm, with a post-peak energy component of between 0.12 and 0.55. Displacement-based ductility measures spanned from 3.2 to 8.1 units with a distinct improvement in electrical curing regimes, especially in the SF-reinforced specimens; this indicates that electrical curing in reinforced geopolymer composite materials works as a governing mechanism in performance rather than simply a method for enhancing the strength of materials. Full article

Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original wind speed series into multiple Intrinsic Mode Functions (IMFs) with distinct frequency features, thereby reducing the non-stationarity of the original sequence. Second, SSA is utilized to adaptively optimize key parameters of the LSTM network, including the number of hidden units, learning rate, and dropout rate, to enhance the model’s capability in capturing complex temporal patterns. Finally, the predictions from all modal components are integrated to generate the final wind speed forecast. Experimental results based on 10-min resolution measured data from a coastal wind farm in southeastern China in June 2020 show that the model achieves a Root Mean Square Error (RMSE) of 0.208, a Mean Absolute Error (MAE) of 0.161, and a Mean Absolute Percentage Error (MAPE) of 3.284% on the test set, with its comprehensive performance significantly surpassing benchmark models such as LSTM, VMD-LSTM, MLP, XGBoost, and ARIMA. The limitations of this study mainly include the use of only one month of data for validation, the lack of segmented performance analysis across different wind speed regimes, and a fixed prediction horizon of 10 min. The results indicate that the proposed hybrid forecasting framework provides an effective approach with practical engineering potential for ultra-short-term wind power prediction. Full article

Robo-advisors are evolving fintech solutions that ask potential clients about their investment purpose and time horizon and then offer investment strategies to reach different goals. This study aims to build on prior research and gain insights into the influence of innovation attributes (relative advantage, complexity, compatibility, and observability), perceived trust, and image regarding robo-advisor adoption by applying and extending the Diffusion of Innovation (DOI) theory. Data were collected using a cross-sectional survey approach. A total of 187 valid responses were obtained from an online participant recruitment website based in the United States and analysed using the partial least squares approach. The findings indicate that relative advantage and attitude influence an individual’s intention to adopt a robo-advisor, while all innovation attributes, perceived trust, and image of a robo-advisor influence an individual’s attitude towards it. By extending the DOI framework, this research advances understanding of its applicability to robo-advisor adoption. This study contributes to the literature by clarifying the influences on robo-advisor adoption and their relationships. From a practical standpoint, the findings and measures could help wealth management companies improve their promotional campaigns and technical design. Full article

Background: Type B Aortic Dissection (TBAD) management relies on risk stratification, yet evidence-based tool adoption remains inconsistent in National Health Services (NHSs). Bridging the gap between Emergency Medicine (EM) and Vascular Surgery remains essential for timely diagnosis, optimal risk stratification, and appropriate intervention to improve outcomes and reduce mortality. Methods: A cross-sectional survey of EM consultants yielded n = 173 valid responses from n = 33 units across the UK. Subgroup analyses were conducted using a Chi-square test (p < 0.05) alongside descriptive analysis. A pooled prevalence analysis of the literature, utilizing a random-effects model at a 95% confidence interval (CI), served as a benchmark for perception analysis. Agreement was evaluated using Bland–Altman analysis, incorporating upper, lower, and overall bias of agreeability. Results: Access to a rapid Computed Tomography Angiogram (CTA) was 70% (95% CI: 63.3–76.8%, p < 0.001), while 32% had standard operating procedures (SOPs) for TBAD (95% CI: 25.3–39.1%), and 26% were aware of any decision tool (95% CI: 20.6–33.6%). Labetalol as a first-line antihypertensive was more common amongst least experience (p < 0.05). TBAD diagnosis increased 1.6-fold with every 4 years of additional experience (p < 0.05). Perception analysis showed strong agreement for pain (characteristics and location), hypertension, gender, and age with moderate-to-low agreement for other factors with a reported bias of 23.58% (−38.20% to 85.36%) (p = 0.02). Conclusions: The survey suggests a degree of misperception and inconsistency in recognition of most and least prevalence factors for TBAD suspicion and management. This outcome advocates targeted strategies to enhance diagnostic accuracy using tools aligned with NHS resources and QALY frameworks. Furthermore, upon recognition of the most prevalent factors, CTA and specialist referral is advocated. Full article

Aiming to address insufficient feature extraction, vanishing gradients, and low prediction accuracy in tool wear prediction, this paper proposes a hybrid deep neural network based on a Convolutional Neural Network (CNN), Residual Network (ResNet) residual connections, the Convolutional Block Attention Module (CBAM), and a Bidirectional Gated Recurrent Unit (BiGRU). First, a 34-dimensional multi-domain feature set covering the time domain, frequency domain, and time–frequency domain is constructed, and multi-sensor signals are standardized using z-score normalization. A CNN–BiGRU backbone is then established, where ResNet-style residual connections are introduced to alleviate training degradation and mitigate vanishing-gradient issues in deep networks. Meanwhile, CBAM is integrated into the feature extraction module to adaptively reweight informative features in both channel and spatial dimensions. In addition, a BiGRU layer is embedded for temporal modeling to capture bidirectional dependencies throughout the wear evolution process. Finally, a fully connected layer is used as a regressor to map high-dimensional representations to tool wear values. Experiments on the PHM2010 dataset demonstrate that the proposed hybrid architecture is more stable and achieves better predictive performance than several mainstream deep learning baselines. Systematic ablation studies further quantify the contribution of each component: compared with the baseline CNN model, the mean absolute error (MAE) is reduced by 47.5%, the root mean square error (RMSE) is reduced by 68.5%, and the coefficient of determination (R²) increases by 14.5%, enabling accurate tool wear prediction. Full article