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BackgroundPrevious studies have found an association between early entry to childcare and risk for overweight and obesity at 3 years of age. These studies, however, have been conducted primarily with higher income White populations or have found increased risk in the children of educated mothers.ObjectiveTo assess the relationship between timing of entry to childcare and duration of childcare and pediatric overweight and obesity in a high risk population.MethodsUsing data from a longitudinal cohort of low-income Latino children in San Francisco, we evaluated the association between time of entry to childcare, hours in childcare, and risk for overweight and obesity at age 4. Similarly, we evaluated the relationship between these same childcare parameters and body mass index Z score and risk of having a waist circumference (WC) percentile ≥90th at 4 years of age.ResultsIn contrast with previous studies, we found no association between being in childcare at 4 years of age or number of hours per week in childcare and risk for childhood overweight, obesity or WC ≥90th percentile at age 4. Additionally, we found no association between age of entry to childcare (≤6 months or ≤12 months of age) with risk for overweight or obesity at age 4. Future studies need to further evaluate the differential impact of childcare on early childhood obesity in relation to race/ethnicity and lower socioeconomic status.ConclusionLow-income children may not be at increased risk for obesity in relation to early childcare exposure.

Abstract We introduce an impact‐based framework to evaluate seasonal forecast model skill in capturing extreme weather and climate events over regions prone to natural disasters such as floods and wildfires. Forecasting hydroclimatic extremes holds significant importance in an era of increasing hazards such as wildfires, floods, and droughts. We evaluate the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (CMCC, DWD, ECCC, UK‐Met, and Météo‐France) in predicting extreme precipitation events from 1993 to 2016 using 14 indices reflecting timing and intensity (using absolute and locally defined thresholds) of precipitation at a seasonal timescale. Performance metrics, including Percent Bias, Kendall Tau Rank Correlation Score, and models' discrimination capacity, are used for skill evaluation. Our findings indicate that the performance of models varies markedly across regions and seasons. While models generally show good skill in the tropical regions, their skill in extra‐tropical regions is markedly lower. Elevated precipitation thresholds (i.e., higher intensity indices) correlate with heightened model biases, indicating deficiencies in modeling severe precipitation events. Our analysis using an impact‐based framework highlights the superior predictive capabilities of the UK‐Met and Météo‐France models in capturing the underlying processes that drive precipitation events, or lack thereof, across many regions and seasons. Other models exhibit strong performance in specific regions and/or seasons, but not globally. These results advance our understanding of an impact‐based framework in capturing a broad spectrum of extreme weather and climatic events, and inform strategic amalgamation of diverse models across different regions and seasons, thereby offering valuable insights for disaster management and risk analysis.

The Islamic heritage constitutes a very precious treasure in Algeria, especially the places of worship (mosques), which reflect the symbol of this heritage. They are considered important landmarks in cities through architectural elements such as the dome and the minaret. Minarets are used by the muezzin to invite people to pray; they were previously built near the mosque or inside it, next to the prayer room. Later, they appeared as angular towers of mausoleums. This research is part of an analytical study of the minarets in Algeria during the medieval period (Bani Hamad, Moravid, Zayyanid, Marinid, and Ottoman) which were selected and then classified according to their various typologies (dimensions, forms, designs, etc.), and evolution over time. The main objectives of this work are to search for the forms that determine the typology of the minarets of each period. It also examines the correlation between dimensions and geometric parameters employed in the design conception of these minarets. The research followed a monographic method that relies on field observation and documentation, while including a thorough reading of history. The results show that the constant form of the vertical section of the minaret, with the same dimensions between the top and bottom, represents two-thirds of the main tower of the minarets. We also conclude that the square shape of the horizontal section of the minaret represents two-thirds of the main tower of the minarets, which expresses the most earthquake-resistant form. These dimensions play a pivotal role in enhancing the minaret's visual allure and architectural equilibrium, in addition to their earthquake resistance virtues.

Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9.

Streamflow predictions are vital for detecting flood and drought events. Such predictions are even more critical to Sub-Saharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gaged, with few available gaging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. This work presents a novel workflow for predicting streamflow in the presence of missing gage observations. We leverage bias correction of the Group on Earth Observations Global Water and Sustainability Initiative ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art temporal fusion transformers (TFTs) at 10 river gaging stations in the Benin Republic. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the 10 Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods. We also show that the TFT yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and the state-of-the-art machine learning models into operational early-warning decision-making systems in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.

Several drought indices have been developed based on various processes (e.g., precipitation, soil moisture, vegetation health) that respond differently to modes of climate variability, shadowing their relatability to teleconnections, which in turn, limits drought forecasting. In this study, we advanced the multivariate analysis of droughts by using long-term Terrestrial Water Storage estimates, soil moisture and precipitation data along with normalized difference vegetation index. To this end, we employed a Vine copula approach using Archimedean and Elliptical copula families to generate two novel multivariate drought indices called Combined Standardized Drought Index (CSDI), based on agricultural, meteorological, hydrological and ecological univariate indices (i.e., the Eco-meteo-hydrologic index and the Agro-meteo-hydrologic index) for 33 major river basins across the globe between 1982 and 2015. To overcome the challenges associated with vine copula building blocks, we exhausted the possible choices of vine trees and selected the superior model based on a variety of performance metrics. CSDIs showed an integrated representation of univariate drought indices and revealed a more comprehensive and improved picture of intensity, duration and frequency of droughts. Our composite analysis showed that El Niño and La Niña have a significant impact on the regional drought occurrences across the globe, with highest impacts observed for fall. Results also showed that CSDIs can extract more conclusive anomalies in response to ENSO signals than univariate indices, as they better represent the ecosystem response to teleconnections.

Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites in Baghdad city were used. 70% of these results were used to train the prediction ANN models and the rest were equally divided to test and validate the ANN models. The performance of the developed models was examined using the correlation coefficient R. The final models have demonstrated that the ANN has capability for acceptable prediction of compression index and compression ratio. Two equations were proposed to estimate compression index using the connecting weights algorithm, and good agreements with test results were achieved.

Two types of polymers (plastomer (functionalized polyethylene (PE)) and elastomer (styrene-butadiene-styrene (SBS))) were used for shifting up asphalt binder performance grade (PG) and tensile strength resistance (moisture damage) of the asphalt concrete mixture. It is found that adding 3.5% functionalized polyethylene (PE) polymer to the binder is more effective than adding 4% styrene-butadiene-styrene (SBS) to shift up asphalt PG by two grades to PG 76-16. Furthermore, the viscosity of the binder increased about 200% when using 4% SBS, while there is no significant effect on viscosity when 3.5% PE is used. Therefore, there is no need to increase the temperature of mixing and compaction which may affect polymers. The indirect tensile test was used for measuring tensile strength ratio of dry and wet samples (conditioned according to ASTM D4867) and found that this ratio increased by 10 to 18% when using 4% SBS and 3.5% PE, respectively. Fracture energy (area under the strength-strain curve) and elasticity were estimated for neat and modified mixture samples.

This paper studies the effect of different design spectral response acceleration parameters as suggested by the uniform building code (UBC), unified facilities criteria (UFC), and Iraqi seismic code(1997) (ISC 97) on the seismic response of reinforced concrete multi-story framed building located in Baghdad city, Iraq. These parameters are: (a) spectral response accelerations Ss, at short periods, and S1 at a 1-second period in accordance with international building code (IBC), (b) seismic zone factor (Z) according to UBC, (c) Seismic hazard zoning coefficient (Z) according to ISC 97. In this paper, first, the elastic seismic responses for significant modes of vibration for chosen building under design response spectrum that obtained from the above mentioned codes are calculated, and then a comparison was made among different design spectral response acceleration parameters. The intent of this study is to review the seismic provisions of the current edition of Iraqi seismic code (1997) to determine whether it provides an equivalent level of safety to that contained in other international codes. Design base shears, lateral seismic forces, inter story drifts, response spectrum modal, effective seismic modification, floors acceleration and story shears are comparatively presented.