North Eastern Regional Institute of Science and Technology

About: North Eastern Regional Institute of Science and Technology is a education organization based out in Itanagar, India. It is known for research contribution in the topics: Population & Raman spectroscopy. The organization has 813 authors who have published 1429 publications receiving 16122 citations.

Corrigendum: Application of Nanotechnology in Food Science: Perception and Overview.

Abstract: [This corrects the article on p. 1501 in vol. 8, PMID: 28824605.].

Simulating hydrological response of a monsoon dominated reservoir catchment and command with heterogeneous cropping pattern using VIC model

Abstract: Present study assesses the effect of finer land-use classification in simulating the rainfall-runoff response of Kangsabati reservoir catchment (3,627 km2) and command (7,112 km2) by considering cropland heterogeneity in variable infiltration capacity (VIC) model. High resolution LISS-IV satellite imageries were used for the land-use classification. Global sensitivity analysis was performed using VIC-ASSIST to identify the most and least influential parameters based on the sensitivity index of elementary effects. A fully distributed calibration approach was employed using 16 (detailed) and 8 (lumped) vegetation classes. Low flows during lean periods were over-estimated and peak flows were under-estimated by both the model setups at Kangsabati reservoir site. Detailed land-use classification resulted in the reduction in streamflow over-estimation (Percent Bias (PBIAS) from −20.99 to −14.41 during calibration and from –22.83 to –7.17 during validation) at daily time step. It further demonstrates the improvement in simulating the peak flows; hence, highlighting the importance of detailed land-use classification for vegetation parameterization in VIC model setup. River discharge regulation at Kangsabati reservoir resulted in poor model performance at Mohanpur, downstream site of Kangsabati reservoir. Therefore, calibration for Mohanpur was performed after updating the VIC simulated streamflow with routed reservoir spillage using Hydrologic Engineering Center-River Analysis System (HEC-RAS) model. Streamflow updation employing HEC-RAS at Mohanpur improved the modelling efficiency (Nash–Sutcliffe efficiency (NSE) from 0.50 to 0.65 during calibration and from 0.55 to 0.67 during validation) and reduced bias (PBIAS from 6.25 to –2.23 during calibration and from 15.06 to 7.40 during validation) considerably for daily flows. Model performance with reasonable accuracy was achieved at both the calibration locations which demonstrates the potential applicability of VIC model to predict streamflow in the monsoon dominated Kangsabati reservoir catchment and command.

A Survey of Antenna and User Scheduling Techniques for Massive MIMO-5G Wireless System

Abstract: Massive MIMO is a promising candidate for 5G future wireless communications system having ability to drastically scale-up the ergodic sum-rate and simultaneously cut off total radiated power by thousand times. Different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling algorithms that have been employed with massive MIMO systems has reported in this paper. A few number of research concept has been discussed in literature section related to performance analysis of massive MIMO regime associated with large number of antenna scheduling, user scheduling, and combined antenna and user scheduling algorithm. The prime goal of this survey paper is to provide a comprehensive overview of current and emerging research topics e.g., scheduling of antennas and users in massive MIMO. Last 5 years of research contributions has presented in this survey report with a key concentration on implementation of antenna and user scheduling algorithm in massive MIMO systems. Specifically, antenna selection and scheduling, user scheduling, and combined antenna and user scheduling algorithm have addressed. Moreover, different selection algorithms that deal with efficient utilization of power and the most scarcity thing i.e., bandwidth in massive MIMO system has reported. The discussed scheduling algorithms play a pivotal role for selecting the antennas and users in such a fashion that system sum rate has maximized. Furthermore, the list of references serves as a good roadmap for start-up and consequently motivates upcoming researchers to work in this emerging field.

Rice Lesion Mimic Mutants (LMM): The Current Understanding of Genetic Mutations in the Failure of ROS Scavenging during Lesion Formation

Abstract: Rice lesion mimic mutants (LMMs) form spontaneous lesions on the leaves during vegetative growth without pathogenic infections. The rice LMM group includes various mutants, including spotted leaf mutants, brown leaf mutants, white-stripe leaf mutants, and other lesion-phenotypic mutants. These LMM mutants exhibit a common phenotype of lesions on the leaves linked to chloroplast destruction caused by the eruption of reactive oxygen species (ROS) in the photosynthesis process. This process instigates the hypersensitive response (HR) and programmed cell death (PCD), resulting in lesion formation. The reasons for lesion formation have been studied extensively in terms of genetics and molecular biology to understand the pathogen and stress responses. In rice, the lesion phenotypes of most rice LMMs are inherited according to the Mendelian principles of inheritance, which remain in the subsequent generations. These rice LMM genetic traits have highly developed innate self-defense mechanisms. Thus, although rice LMM plants have undesirable agronomic traits, the genetic principles of LMM phenotypes can be used to obtain high grain yields by deciphering the efficiency of photosynthesis, disease resistance, and environmental stress responses. From these ailing rice LMM plants, rice geneticists have discovered novel proteins and physiological causes of ROS in photosynthesis and defense mechanisms. This review discusses recent studies on rice LMMs for the Mendelian inheritances, molecular genetic mapping, and the genetic definition of each mutant gene.

A Novel Deep Learning Approach for Classification of COVID-19 Images

Abstract: Novel coronavirus 2019 (COVID-2019) initially started at Wuhan, China and spread all over the world and announced as pandemic by World Health Organization in March 2020. It makes use of all the available resources to reduce the disastrous effect of such Black Swan event. This virus causes pneumonia in human being and changes the respiratory pattern (different from common cold and flu). Compared to the reverse-transcription polymerase chain reaction (RT-PCR) chest X-ray and computed tomography imaging may be reliable and quick to diagnose the COVID-19 patients in the epidemic regions. Above mentioned imaging modalities along with the machine learning techniques can be helpful for accurate diagnosis of the disease and may be assistive in the absence of specialized physicians. Further, ML can improve throughput by accurate figuration of contagious X-ray and CT images for disease diagnosis, tracking and prognosis.In this research, potential of artificial intelligence has been investigated to develop a deep neural network model for rapid, accurate and effective COVID-19 detection from the CT and X-ray images. The proposed method provides robust deep learning technique for binary (COVID vs. NON-COVID) and multi-class (COVID vs. NON-COVID vs. Pneumonia) classification from X-ray and CT images. A 24-layer CNN network has been proposed for the classification. It attains an accuracy of 99.68% and 71.81% on X-ray and CT images, respectively. For both the datasets Sgdm optimizer has been used with a learning rate 0.001.