Urbanisation Effect on Hydrological Response: A Case Study of Asan River Watershed, India

Human being keeps on modifying the environment especially land use/land cover (LULC), in pursuance of excel, comfort and development. The subsequent impact of urbanization to the environment, especially land cover change, now occurs on scales that significantly affect hydrologic variations. The altering environment makes it necessary to understand and quantify various hydrological components for efficient water resource management. Therefore, in the present study, an attempt was  made to study the impact of LULC change on runoff generation potential. Asan River watershed, which lies in Dehradun, capital of newly created Uttarakhand State, India, is selected as study region. A huge industrialization is been taken place within this watershed immediately after declaration of state in year 2000. Initially, LULC change detection analysis was carried out by simple LULC class area difference between two years under consideration i.e. 2000 and 2010. The hydrological simulation using variable infiltration capacity macro-scale hydrological model depicted increase in runoff after urbanization took place. Keywords: Land use land cover change, Urbanization, Impact assessment, hydrological modeling, variable infiltration capacity model, runoff potentia

Student psychology-based optimization-tuned cascaded controller for frequency regulation of a microgrid

This paper presents a student psychology-based optimization (SPBO)-tuned cascaded control scheme for an interconnected microgrid scenario. Generally, the different distributed energy sources are assembled to form the microgrid architecture, and the majority of the sources are environment-dependent. Furthermore, the intermittent power output from these sources causes a generation–load power mismatch, resulting in power and frequency oscillations. In this regard, the proposed student psychology-based optimization-tuned cascaded controller tackles the power-frequency mismatch issues under an interconnected microgrid scenario. Additionally, an improved power tie-line model is introduced considering the effect of line resistance in the microgrid scenario, as line resistance plays a significant role in power flow between the control areas. In addition, numerous case studies are investigated to examine the effectiveness of the proposed design methodology under the suggested control scheme. Furthermore, a detailed performance analysis is carried out considering the proposed model operation under a 12-node radial distribution network in order to examine the system compatibility in a practical distribution network. The obtained results ensure superior performances in terms of the system’s overall peak over/undershoots, oscillations, and settling time utilizing the proposed controller under the improved microgrid scenario

Glutathione Transferase from Trichoderma virens Enhances Cadmium Tolerance without Enhancing Its Accumulation in Transgenic Nicotiana tabacum

BACKGROUND: Cadmium (Cd) is a major heavy metal pollutant which is highly toxic to plants and animals. Vast agricultural areas worldwide are contaminated with Cd. Plants take up Cd and through the food chain it reaches humans and causes toxicity. It is ideal to develop plants tolerant to Cd, without enhanced accumulation in the edible parts for human consumption. Glutathione transferases (GST) are a family of multifunctional enzymes known to have important roles in combating oxidative stresses induced by various heavy metals including Cd. Some GSTs are also known to function as glutathione peroxidases. Overexpression/heterologous expression of GSTs is expected to result in plants tolerant to heavy metals such as Cd. RESULTS: Here, we report cloning of a glutathione transferase gene from Trichoderma virens, a biocontrol fungus and introducing it into Nicotiana tabacum plants by Agrobacterium-mediated gene transfer. Transgenic nature of the plants was confirmed by Southern blot hybridization and expression by reverse transcription PCR. Transgene (TvGST) showed single gene Mendelian inheritance. When transgenic plants expressing TvGST gene were exposed to different concentrations of Cd, they were found to be more tolerant compared to wild type plants, with transgenic plants showing lower levels of lipid peroxidation. Levels of different antioxidant enzymes such as glutathione transferase, superoxide dismutase, ascorbate peroxidase, guiacol peroxidase and catalase showed enhanced levels in transgenic plants expressing TvGST compared to control plants, when exposed to Cd. Cadmium accumulation in the plant biomass in transgenic plants were similar or lower than wild-type plants. CONCLUSION: The results of the present study suggest that transgenic tobacco plants expressing a Trichoderma virens GST are more tolerant to Cd, without enhancing its accumulation in the plant biomass. It should be possible to extend the present results to crop plants for developing Cd tolerance and in limiting Cd availability in the food chain

Target highlights in CASP14 : Analysis of models by structure providers

Abstract The biological and functional significance of selected CASP14 targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modelled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins. This article is protected by copyright. All rights reserved.Peer reviewe

Inter-calibration of DMSP-OLS and SNPP-VIIRS-DNB annual nighttime light composites using machine learning

The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership spacecraft and National Oceanic and Atmospheric Administration – 20 (NOAA-20) spacecraft’s, are now available since April 2012. This study investigates the potential of machine-learning algorithms for inter-calibrating them (i.e., DMSP-OLS and VIIRS-DNB) to produce time-series annual VIIRS-DNB-like NTL datasets for the time when VIIRS-DNB data did not exist, for long-term studies. Uttar Pradesh, one of the most populous and largest States of India, is selected as the study area. Two machine-learning algorithms are utilized: (1) Multi-Layer Perceptron (MLP), having deep neural networks (DNN) architecture, and (2) Random Forest (RF), a widely used method. The DMSP-OLS and VIIRS-DNB data of 2013 (common year of data availability) and ancillary data pertaining to land cover, topography, and road network are used to train the models. The qualitative and quantitative analysis of annual VIIRS-DNB-like NTL images simulated from annual DMSP-OLS composites of 2004–2012 indicates that RF captures better spatial details at the local-scale and is able to efficiently handle the saturation problem at urban centers; while MLP is found to be superior at regional-scale. Both MLP and RF models significantly reduce the blooming effect around settlements, a common problem observed in DMSP-OLS data. It is inferred that depending on the research objectives, both RF and MLP algorithms can be appropriately utilized for producing VIIRS-DNB-like NTL images from DMSP-OLS annual NTL composites. The research can be further expanded by using other DNN architecture-based algorithms and improved spatio-temporal ancillary datasets over areas with different socio-economic, physiographic, and climatic settings

Assessing the effect of training sampling design on the performance of machine learning classifiers for land cover mapping using multi-temporal Remote Sensing Data and Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples

The Potential of Channel Specific Reflectance in Landsat 8 OLI Sensor for Retrieving Coal Fire Affected Pixels

Coal fire is a serious threat in major coal producing countries across the globe and poses significant constraints in mining operations, often leading to environmental degradation. The applications of thermal and shortwave infrared remote sensing play a substantial role in systematically detecting and monitoring the coal fire. Over the last few decades, researchers have extensively examined the importance of spectral radiance for retrieving reliable pixel-integrated temperature threshold to delineate coal fire from its background. However, such an assumption does not necessarily consider the local information, thereby leading to difficulty in isolating the actual coal fire affected pixels. Therefore, we propose to utilise the channel specific reflectance to retrieve the thermally anomalous pixels in coal fire related applications using Landsat 8 OLI data. This paper explores the practicability of incorporating the active fire detection technique using channel specific reflectances based on both fixed and contextual thresholds in the Jharia coalfield, India

shivareddyiirs/Trivim1.0: 3D Street View Generation from Handheld Camera Images

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