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Climate and land-use changes can alter the dynamics of hydro-climatic extremes by modifying the flow regimes. Here, we have attempted to disentangle the relationship between changing environmental conditions and hydro-climatic extremes considering associated uncertainties for the Subarnarekha, a flood prone-basin of India. A comprehensive, integrated modelling system was developed that incorporates a spatially explicit land-use model, a hydrological model, and an ensemble of regional climate models (RCMs). MIKE SHE/MIKE HYDRO RIVER was used to simulate the hydrological processes. The uncertainties associated with model parameters, model inputs, and model structures are analysed collectively using ‘quantile regression.’ A transferable framework was developed for the analysis of hydro-climatic extremes that deal with numerous aspects like sensitivity, occurrences, severity, and persistence for four-time horizons: baseline (1976–2005) and early (2020s), mid (2050s), end-centuries (2080s). ANOVA is used for partitioning uncertainty due to different sources. The results obtained from numerous analysis of the developed framework suggests that low, high, and medium flows will probably increase in the future (20%-85% increase), indicating a higher risk of floods, especially in the 2050s and 2080s. Partitioning of uncertainty suggests RCMs contribute 40%-62% to the uncertainty in streamflow projections. The developed modelling systems incorporates a flexible framework so update any other water sustainability issue in the future. These findings will help better meet the challenges associated with the possible risk of increasing high flows in the future by ceding references to the decision-makers for framing better prevention measures associated with land-use and climate changes.

This study presents climate change impacts on streamflow for the Subarnarekha basin at two gauging locations, Jamshedpur and Ghatshila, using the Soil and Water Assessment Tool (SWAT) model driven by an ensemble of four regional climate models (RCMs). The basin's hydrological responses to climate forcing in the projected period are analysed under two representative concentration pathways (RCPs). Trends in the projected period relative to the reference period are determined for medium, high and low flows. Flood characteristics are estimated using the threshold level approach. The analysis of variance technique (ANOVA) is used to segregate the contribution from RCMs, RCPs, and internal variability (IV) to the total uncertainty in streamflow projections. Results show a robust positive trend for streamflows. Flood volumes may increase by 11.7% in RCP4.5 (2006–2030), 76.4% in RCP4.5 (2025–2049), 20.3% in RCP8.5 (2006–2030), and 342.4% in RCP8.5 (2025–2049), respectively, for Jamshedpur. For Ghatshila, increment in flow volume is estimated as 15.7% in RCP4.5 (2006–2025), 24.2% in RCP4.5 (2025–2049), 35.9% in RCP8.5 (2006–2030), and 224.6% in RCP8.5 (2025–2049), respectively. Segregation results suggests that the uncertainty in climate prediction is dominated by RCMs followed by IV. These findings will serve as an early warning for the alarming extreme weather events India is currently facing.

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet’s randomized t -test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land).

Abstract Understanding rainfall patterns is crucial for basin-wide risk management. The present study assesses rainfall patterns by smoothing their daily mean through Fourier fitting for the Subarnarekha basin of India. The adequate selection of the ensemble technique and corresponding best-performing regional climate models forced by global climate models (GCMs-RCMs) (sub-ensembles) has been carried out for projection of future rainfall patterns. The spatial performance metrics are used to select the GCMs-RCMs based on their ability to mimic the spatial patterns of the observed rainfall. The multi-model ensemble (MME) rainfall is generated by assimilating the simulated rainfall of selected GCMs-RCMs by employing statistical and machine learning (ML) techniques. Quantification of uncertainty in rainfall projections is performed through analysis of variance. Simple composite mean (SCM) statistical technique outperforms ML techniques. Optimum MME is obtained by combining 6-best performing sub-ensembles obtained from four spatial performance metrics (Fraction skill score, Goodman–Kruskal’s lambda Kling-Gupta efficiency, and spatial efficiency). The significant changes in rainfall patterns are obtained during 2010–2039, 2040–2069, and 2070–2099 with respect to the baseline period (1976–2005) as per Wilcoxon signed-rank test. An increase of 20–45% for RCP4.5 and 26–55% for RCP8.5 is obtained in peak mean rainfall per rainy day during future periods at both sub-basins. On the contrary, a decrease of 21–57% for RCP4.5 and 45–55% for RCP8.5 is obtained for trough (minimum) mean rainfall per rainy day during future periods. Our finding shows the possibility of early monsoon occurrences (8–30 days ahead) during future periods. Differences in projection between different choices of GCM-RCM models in the multi-model ensemble are the largest source of uncertainty (larger than differences between emission scenario or the effect of decadal variability). The overall finding of the study indicates that basin needs better preparedness to mitigate more erratic rainfall in future.

The build-up of arsenic in agricultural soil through contaminated irrigational groundwater is a concern. Metagenomic analysis of such contaminated sites may provide a remarkable opportunity to extensively investigate the responses and adaptation of microbial communities to different levels of arsenic. The study focuses on the comparative analysis of microbial community composition and associated functions in paddy soil samples with a gradient of arsenic contamination (4.88 to 43.67 mg kg−1). Actinobacteria was found to dominate the bacterial phyla in all four samples with abundance ranging from 39.77% to 49.39% followed by Proteobacteria (20.71–38.24%). Whereas the fungal phylum Ascomycota (92.42–95.29%) dominated all the samples studied. In the study, bacteria were found to be abundant in the mid-level (15.89 and 24.84 mg kg−1) of arsenic, which included genera Gaiella, Nocardioides, Solirubrobacter, Microvirga, and Nitrospira. Fungi Beauveria, Talaromyces, Aspergillus, Pyrenophora, and Valsa were higher in relative abundance corresponding to soil arsenic concentration. Verticillium, previously reported for Pb, Zn, and Cd removal, was found in the soil sample with the mid-arsenic concentration (15.89 mg kg−1). The relative abundance of the arsenic metabolizing/ transport genes of native microbial communities also varied with the soil arsenic gradient, the genes arsJ, arsM, aioR, arsH, and arsC being the most affected. The study is the first report of Gaiella, Solirubrobacter, Beauveria, and Verticillium present in arsenic-contaminated soil, and further studies are required to explore their potential role in arsenic bioremediation.

Artificial images and recordings are broad on the web via different media channels such as blogs, YouTube videos, etc. These manipulated and synthesized images tend to steal the identity of individuals and majorly contribute to establishing societal disruptions such as theft, political errors, social engineering, disinformation attacks and reputation fraud. These fake visual objects gradually came to be known as deep fakes. Different deep learning techniques are used to generate deepfake images which go unnoticed by human eyes. It is essential to develop a defense mechanism that can stop the common people from being manipulated and harnessed. The objective of this work is to develop an ensemble deep learning-based system that can differentiate between fake and real images. With the use of the recommended optical flow technique, a novel approach is proposed that extracts the apparent motion of image pixels which gives more accurate results compared to other state-of-the-art. FaceForensics +  + dataset is used to test the extraction algorithms and ensemble model which fetched an accuracy of 86.02% for the DeepFake subset and 85.7% for the FaceSwap subset of the dataset. To the best knowledge, no one has completely used the ensemble model- OptiFake on the optical flow derived frames, highlighting a research gap in the field of deepfake detection.

The aim of this study was to determine the effects of early-life bovine lactoferrin and host specific probiotic interventions on growth performance, mortality, and concentrations of immunoglobulin A and immunoglobulin G and transforming growth factor beta 1 (a marker of intestinal integrity) in serum of neonatal piglets. A total of eight piglet litters from parity matched sows were randomly divided into four groups and assigned to one of the four interventions: control (sterile normal saline), bovine lactoferrin (100 mg bovine lactoferrin), probiotic (1 × 10 9 colony forming unit (cfu) of swine origin Pediococcus acidilactici FT28 probiotic), and bovine lactoferrin + probiotic (100 mg bovine lactoferrin and 1 × 10 9  CFU of P. acidilactici FT28 probiotic). All the interventions were given once daily through oral route for first 7 days of life. The average daily gain ( p  = 0.0004) and weaning weight ( p  < 0.0001) were significantly improved in the probiotic group. The piglet survivability was significantly higher in bovine lactoferrin and probiotic groups than control group in Log-rank (Mantel-Cox) test. The concentrations of immunoglobulin A on day 21 in bovine lactoferrin, probiotic, and bovine lactoferrin + probiotic groups increased significantly ( p  < 0.05). Immunoglobulin G concentrations on day 7 and 15 in bovine lactoferrin and bovine lactoferrin + probiotic groups and on day 15 in probiotic group were significantly ( p  < 0.05) elevated, whereas, the concentration of transforming growth factor - β1 was significantly ( p  < 0.05) increased from day 7 to 21 in all the supplemented groups. In conclusion, the early-life bovine lactoferrin and P. acidilactici FT28 probiotic interventions reduced the mortality in the suckling piglets by promoting the systemic immunity and enhancing the intestinal integrity.

Parental Encouragement is essential for the children at all stages of life, it is extremely important especially during the adolescence phase. It is a period of stress as these children have to deal with physical, emotional, intellectual and social change. Parents through their encouragement and nurturance foster a sense of motivation in adolescents, which helps them to achieve and become well-adjusted members of the society. Since, parental encouragement plays a vital role throughout the educational journey of a child, a study was undertaken to assess the parental encouragement of girls studying in both Government (n=30) and Private (n=30) schools and a comparative analysis was drawn. Data was collected from both the groups by using Parental encouragement scale (Sharma, 1998). The t-value was calculated and the results of the present study revealed that there is a significant difference in the parental encouragement of government and private school girls.