Search Results (10,752)

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning. Full article

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm³, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d. Full article

Background: The risk of depression during pregnancy and postpartum is high and has intensified during the COVID-19 pandemic. The aim of this study was to estimate the risk of depressive disorders and self-harm thoughts in the third trimester of pregnancy and the first week postpartum in the pandemic period. Methods: This study involved a total of 317 pregnant and postpartum women. The risk and severity of depressive disorders and the prevalence of self-harm thoughts in women during the perinatal period were assessed using EPDS. Results: Pregnant women were significantly more likely to have higher EPDS scores compared to postpartum women. Mild depressive symptoms were reported by 22.08% of pregnant women and 17.18% of postpartum women, and severe symptoms were observed in 25.97% of pregnant women and 16.56% of postpartum women. Thoughts of self-harm were reported by 11.69% of pregnant women and 17.79% of postpartum women. Self-harm thoughts were significantly more common among pregnant women: multiparous women, pregnant women who received psychiatric treatment before pregnancy, those diagnosed with depressive disorders, those who suffered from prolonged periods of anxiety and sadness, and those lacking a supportive person. Among postpartum women, there were statistically significant differences in the prevalence of self-harm thoughts for place of residence, education, type of occupation, number of pregnancies, course of pregnancy, and presence of a supportive person. Conclusions: The increased prevalence of depressive symptoms and self-harm thoughts related to the COVID-19 pandemic highlights the urgent need for screening among pregnant women and the implementation of clinical interventions. Full article

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology. Full article

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. Full article

The reproductive biology of the invasive blue crab Callinectes sapidus was studied in the Thermaikos Gulf (Northwest Aegean Sea, Eastern Mediterranean). In the two-year survey, 5698 (2897♂/2801♀) crabs were caught with the use of fyke nets. Total sex ratio (♂/♀) reached equality (1.03:1). The female blue crab exhibited a protracted reproductive period. Mature and ovigerous females exhibit short migratory movements from estuarine and inshore waters, where the population mostly congregates (0–3 m), and move to slightly deeper waters (1–3 m) up to 9 m for spawning. A total of 340 ovigerous females were caught. Their number varied both spatially and temporally; they were observed for a 7-month period (April to October) with a clear peak in July–August and at a 3 m depth gradient corresponding to ≈60% of the total number of ovigerous females caught in both years. Size at first sexual maturity (CW₅₀) was estimated at 113.1 mm CW. Average fecundity was ≈790,000 eggs. Experimental trawling showed that inshore waters (<1 m) in the estuaries serve as nursery areas for juveniles. Defining the spatiotemporal and bathymetrical distribution of ovigerous females in any invaded coastal habitat could be considered key information for the implementation of a management policy for the species. Full article

Background/Objectives: To collect evidence from studies that explored the associations between serum vitamin D (25[OH]D) concentrations/status and the presence of depressive/anxiety symptoms in the ante- and/or postnatal periods (PROSPERO-CRD42023390895). Methods: Studies that assessed serum 25[OH]D concentrations in adult women during the ante/postnatal periods and those that used valid instruments to identify the experience/severity of depressive/anxiety symptoms were included. Independent researchers performed the identification/selection of studies, data extraction, risk of bias (RoB) assessment, and bibliometric analysis steps. Results: Of the total of 6769 eligible records, 15 cohort studies [high (n = 3), moderate (n = 7), and low (n = 5) RoB], nine cross-sectional studies [moderate (n = 3) and low (n = 6) RoB], and one case‒control study [moderate RoB] were included (n = 25). Depression (n = 24) and anxiety (n = 4) symptoms were assessed. A significant difference in antenatal serum 25[OH]D concentrations between the groups of women with and without depression was identified (mean difference: −4.63 ng/mL; 95% confidence interval [95% CI]: −8.88; −0.38). Postnatal serum 25[OH]D concentrations were found to be, on average, −2.36 ng/mL (95% CI: −4.59; −0.14) lower in women with postnatal depression than in those without. Maternal antenatal anxiety was associated with significantly lower concentrations/deficiency of 25[OH]D in only one included study. Conclusions: Based on very low/low-quality evidence, it was observed that reduced serum 25[OH]D concentrations in the ante- and postnatal period are associated with the presence of ante- and postnatal depressive symptoms, respectively. Low/deficient antenatal serum 25[OH]D concentrations may not be related to the presence of anxiety symptoms before childbirth. Well-designed longitudinal studies are needed to explore the estimated pooled effect of these associations. Full article

In this study, a grid-based precipitation quantile was estimated using long-term reanalysis precipitation data, considering the homogeneity of the annual maximum series (AMS) for the Korean Peninsula. For regions where significant changes in homogeneity were observed, the precipitation quantile was estimated using only the AMS from after the change point, and these results were compared with those from the original AMS. The examination of homogeneity revealed a significant increasing trend in homogeneity variability in the southeastern region of Korea. This change was particularly pronounced in the location parameter of the Gumbel distribution, resulting in an improved model fit. The change in precipitation quantile was most noticeable for a 2-year return period with a 36 h duration, with an average increase of approximately 11.5%. The results obtained from this study are anticipated to offer crucial foundational data for the design of hydraulic structures in regions with insufficient long-term ground observation data. Full article

Carbon dioxide (CO₂) concentrations play an important role in plant production, as they have a direct impact on both plant growth and yield. Therefore, the objectives of this study were to predict CO₂ concentrations in the greenhouse by applying time series models using five datasets. To estimate the CO₂ concentrations, this study was conducted over a four-month period from 1 December 2023 to 31 March 2024, in a strawberry-cultivating greenhouse. Fifteen sensors (MCH-383SD, Lutron, Taiwan) were installed inside the greenhouse to measure CO₂ concentration at 1-min intervals. Finally, the dataset was transformed into intervals of 1, 5, 10, 30, and 60 min. The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The evaluation indicated that the best model performance was achieved with data collected at 1-min intervals, while model performance declined with longer intervals, with the lowest performance observed at 60-min intervals. Specifically, the ARIMA model outperformed across all data collection intervals while comparing with the PFM. The ARIMA model, with data collected at 1-min intervals, achieved an R² of 0.928, RMSE of 7.359, and MAE of 2.832. However, both ARIMA and PFM exhibited poorer performances as the interval of data collection increased, with the lowest performance at 60-min intervals where ARIMA had an R² of 0.762, RMSE of 19.469, and MAE of 11.48. This research underscores the importance of frequent data collection for precise environmental control in greenhouse agriculture, emphasizing the critical role of short-interval data collection for accurate predictive modeling. Full article

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW. Full article

The roles of finance are well stipulated in the various indicators of the sustainable development goals (SDGS). However, the extant literature still finds conflicting outcomes of the finance-led growth. Hence, this study redirects empirical evidence by unbundling the effects of financial development on sustainable economic growth into aggregated and disaggregated, focusing on seven robust indicators (financial development index, financial institution index, depth, and access, and financial market index, depth, and access) in selected African countries from 1995 to 2021. Similarly, the intervening roles of government expenditure, digital economy, domestic investment, human capital, macroeconomic volatility, and trade openness are evaluated based on advanced estimators. Findings show that the seven indices of financial development drive sustainable economic growth in Africa both in the long and short runs. Similarly, government expenditure, digital economy, and human capital promote sustainable economic growth both in the short- and long-term periods. The driving effects of domestic investment are only noticeable in the long run. Conversely, trade openness and macroeconomic instability are noted to be growth-deterring. Policy insights that support sustainable economic growth in Africa emanate from the outcomes. Full article

At the end of the dry season, from early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass-burning period, smoke aerosols blanketed the whole region and were transported and dispersed by predominant westerly and southwesterly winds to southern China, Taiwan, and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperatures, making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burning period on the exposed population and their health impact. The Weather Research Forecast-Chemistry (WRF-Chem) model was used to predict the PM_2.5 concentration over the simulating domain, and health impacts were then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia, and Vietnam. Using the health impact based on log-linear concentration-response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 315 deaths, respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact of agricultural waste burnings in Southeast Asia at the end of the dry season. Hence, policymakers should take these into account to design measures to reduce the negative impact of widespread burnings on the exposed population in the region. Full article

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case. Full article

Children and adolescents face a wide variety of developmental changes and environmental challenges, and it is estimated that at least one in five children aged 3–17 will experience behavioral or mental health issues. This period of life coincides with major changes in brain structure and function that have profound long-term consequences for learning, decision-making (including risk taking), and emotional processing. For example, continued development of the prefrontal cortex in adolescence is a sensitive period during which individuals are particularly susceptible to risky behaviors, environmental stressors, and substance use. While recent advances in mental health literacy programs have paved the way for increased awareness of the benefits of mental health curricula in schools, these efforts could be greatly bolstered with support in basic neuroscience education in developmentally appropriate and area-specific content. Here, we provide a discussion on the basic structural and functional changes occurring in the brain throughout childhood, how this contributes to changes in cognitive function, and the risk factors posed by early life adversity, stress, and drug use. Finally, we provide a perspective on the benefits of integrating findings from the field of neuroscience and suggestions for tools to better equip students, teachers, administrators, and school mental health staff to provide new directions for addressing the mental health crises faced by millions of children and youth each year. Full article

Estimating and controlling the horizontal deformation of eccentric core-tube structure is challenging due to the time-varying characteristics of concrete materials and structural construction. In order to solve the construction uncertainty, an analysis method of horizontal deformation control theory based on construction error uncertainty is proposed in this paper, which is used to predict the overall deflection in the project design stage. At the same time, considering the construction complexity, the relationship between deviation correction value, structural initial deformation, structural positional posture, and deformation increment data is established. And the “prediction-measurement-construction-adjustment” stage transformation control method is established, which is used to check and adjust the predicted pre-arch target curve in the construction period. The engineering implementation method of the deviation correction scheme of wall line control is put forward based on the construction stringing habit. The proposed method was evaluated on a 390-m high-rise building with numerical simulation and measure verifications. The results show that when the control method is adopted, the top displacement of the structure is only 8 mm, which is much smaller than 75 mm without considering the horizontal deformation control strategy. The proposed control method can effectively control the horizontal deflection of the structure under construction, and the predicted value is in good agreement with the measured value during the observation period. Full article