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Increased urbanisation influences the morphometric traits of various species, often resulting in urban individuals being smaller than their non-urban counterparts. Urbanisation can affect fundamental eco-evolutionary patterns and impact species' ability to adapt to and occupy rapidly changing environments through morphological changes. We investigated the morphometric responses of two passerine species, the non-native house sparrow (Passer domesticus) and its native congener, the Cape sparrow (Passer melanurus), along gradients of spatial and temporal urbanisation in South Africa over a 52-year period. The house sparrow was significantly heavier, larger and in better condition with increasing urban infrastructure and lower urban vegetation cover, while the Cape sparrow showed opposing trends along these gradients. Temporally, the house sparrow's body mass increased consistently over the 52-year study period, suggesting changes in morphology were concomitant with increasing urbanisation over time. This study demonstrates distinct differences in the morphological responses of the non-native house sparrow and the native Cape sparrow to increasing urban development. These morphological responses may also underpin community-level changes caused by urbanisation, enhancing the capabilities of non-native species to thrive over their native counterparts in these environments. [ABSTRACT FROM AUTHOR]

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11–19, 2019 and March 01–06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 m 3 / m 3 ), and bias = 0.004 m 3 / m 3 . Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture. [ABSTRACT FROM AUTHOR]

In this work the chemical characterization (elemental analysis and the content of phospholipids) and surface-active properties of two biosurfactants (BS) extracted with both chloroform or ethyl acetate from corn steep liquor were compared. The phospholipids content for the BS extracted with chloroform (BS1) was considerably higher (8.15%) than that obtained for the BS extracted with ethyl acetate (BS2), 0.11%. When comparing the FTIR spectra of the two BS studied in this work with the spectrum of the commercial surfactant lecithin, a greater similarity (75%) was observed with the spectrum of the BS1. The biosurfactant extract (BS2) provided the most favorable conditions for the solubilization of cuprous oxide (Cu-Ox) in water (12.54% of copper dissolved), in comparison with BS1. The results achieved were considerably better than those obtained with chemical surfactants (Tween 80, SDS and CTAB) on solubilizing Cu-Ox, resulting in the latter cases on percentages of Cu dissolved always lower than 0.21%. In addition, a factorial design was performed obtaining the optimum conditions to solubilize Cu-Ox, where the maximum water solubility of Cu-Ox (13.17%) was achieved using 3.93 g/L of BS2 with a contact time of 19.86 min and using a concentration of Cu-Ox of 1.96 g/L. Thus, the BS2 extract could have a promising future as solubilizing agent in the formulation of more sustainable Cu-Ox based pesticides. Moreover, it was confirmed that the presence of phospholipids prevents the solubilization of copper-based pesticides in water. [ABSTRACT FROM AUTHOR]

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively. [ABSTRACT FROM AUTHOR]

Pathogen populations can evolve in response to selective pressure from vaccine-induced immune responses. For HIV, models predict that viral adaptation, either via strain replacement or selection on de novo mutation, may rapidly reduce the effectiveness of an HIV vaccine. We hypothesized that behavioral risk compensation after vaccination may accelerate the transmission of vaccine resistant strains, increasing the rate of viral adaptation and leading to a more rapid decline in vaccine effectiveness. To test our hypothesis, we modeled: (a) the impact of risk compensation on rates of HIV adaptation via strain replacement in response to a partially effective vaccine; and (b) the combined impact of risk compensation and viral adaptation on vaccine-mediated epidemic control. We used an agent-based epidemic model that was calibrated to HIV-1 trends in South Africa, and includes demographics, sexual network structure and behavior, and within-host disease dynamics. Our model predicts that risk compensation can increase the rate of HIV viral adaptation in response to a vaccine. In combination, risk compensation and viral adaptation can, under certain scenarios, reverse initial declines in prevalence due to vaccination, and result in HIV prevalence at 15 years equal to or greater than prevalence without a vaccine. [ABSTRACT FROM AUTHOR]

We investigate the fundamental limit of radiative cooling of objects on the Earth's surfaces under general conditions including nonradiative heat transfer. We deduce the lowest steady-state temperature attainable and highest net radiative cooling power density available as a function of temperature. We present the exact spectral emissivity that can reach such limiting values, and show that the previously used 8–13 μm atmospheric window is highly inappropriate in low-temperature cases. The critical need for materials with simultaneously optimized optical and thermal properties is also identified. These results provide a reference against which radiative coolers can be benchmarked. [ABSTRACT FROM AUTHOR]