A Contemporary Overview about Status and Challenging Issues of Tribal Education in India

India is the second-largest tribal population in the world. However, after the seven decades of independence, the tribal groups are disadvantaged and socially backward from the cycle of growth in many areas such as health, education, employment, and empowerment, and more. Among these, for tribal society, education is an essential requirement. The state and central governments have initiated several programs to educate tribal groups. Many of these programs have achieved only 10 percent of the targets. The vast numbers of tribal peoples are missing their education at various levels. They lag in education due to the high illiteracy rates among the tribal population relative to Scheduled Castes (S.C.s). Hence, the time has come to consider tribal education and inclusive growth seriously. In this context, the comprehensive literature review seeks to provide a contemporary overview of India's current status and challenging issues of tribal education. The paper is purely based on second-hand information from various research studies conducted in India and collected from different government sources. The outcomes are more helpful in implementing schemes that can improve tribal literacy and inclusive growth perspectives

Constraints in Production and Marketing of Arecanut in Salem District of Tamil Nadu, India

Arecanut is an important cash crop in our country. The study was carried out to ascertain the constraints faced by arecanut farmers in Salem district of Tamil Nadu with a sample size of 120, by employing proportionate random sampling technique. Majority of the respondents expressed lack of specific grading of nuts in marketing as a constraint. More than three-fourths of the respondents suggested that there should be a mechanism to regulate import of nuts from other countries and to create market potential for nuts in the local markets

Analysis on knowledge level of recommended plant protection technologies in areca nut (Areca catechu) cultivation in Salem district of Tamil Nadu

Areca nut (Areca catechu) is one of the important cash crops in India. India ranks first in terms of area and production of areca nut and accounts of 54.07 per cent of its world production. The harvesting of nuts commence on the Tamil month of ‘Thai’ (Mid-January to Mid-February) and spread over six months in carrying out the post-harvest practices and marketing of nuts. This study was purposively conducted in Salem district 2018-2019 and occupies first position in area (2,421 hectares) of areca nut in Tamil Nadu. The Peddanackenpalyam, Valapddy, Gengavalli and Attur blocks were selected based on the 87.28 per cent of the area under areca nut in this district with a sample size of 120 areca nut farmers selected by using a proportionate random sampling technique. Most of the respondents (80.00 per cent) had knowledge level of medium to high level of knowledge on the recommended plant protection technologies in areca nut cultivation. It was mainly due to the medium to the high level of information seeking behaviour and social participation. The study revealed that the areca nut growers differed widely in their social characteristics. Most of the respondents had a medium to a high level of knowledge on recommended technologies in areca nut cultivation. This finding stressed the importance of formulating different extension strategies for different audiences by the change agency system.

Group-level Emotion Recognition using Transfer Learning from Face Identification

In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand Challenge

The Pulsar Wind Nebula Around PSR B1853+01 in the Supernova Remnant W44

We present radio observations of a region in the vicinity of the young pulsar PSR B1853+01 in the supernova remnant W44. The pulsar is located at the apex of an extended feature with cometary morphology. We argue on the basis of its morphology and its spectral index and polarization properties that this is a synchrotron nebula produced by the spin down energy of the pulsar. The geometry and physical parameters of this pulsar-powered nebula and W44 are used to derive three different measures of the pulsar's transverse velocity. A range of estimates between 315 and 470 km/s are derived, resulting in a typical value of 375 km/s. The observed synchrotron spectrum from radio to X-ray wavelengths is used to put constraints on the energetics of the nebula and to derive the parameters of the pulsar wind.Comment: ApJ Let (in press

Deep convolutional neural network classifier for travel patterns using binary sensors

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Smart wearable biosensor for non-invasive real time detection of sweat lactate using compression garments.

Over the past decade, there had been a surge in the use of wearable sensors to monitor health specially to determine the individual’s fitness level. It has been reported that lactic acid is a significant biomarker of anaerobic metabolism and higher concentrations of lactate in sweat can cause Ischemia and lead to hypoxia. Although, there had been an increase in the use of smart wearables such as heart rate, blood pressure, skin pH, and so forth, very little had been reported on the use of body fluids such as sweat. Therefore, a non-invasive monitoring of blood lactate becomes essential in determining individual’s health and fitness. In this research, the development, characterization and optimization of an electrochemical-based amperometric lactate biosensor screen-printed on to a knitted fabric is reported. The prototype screen-printed fabric lactate biosensor is composed of three electrodes that senses lactate concentration from the body sweat collected. A highly sensitive and stable lactate sensor based on PEDOT: PSS/PVA has been developed. The research will use wearer trials wearing prototype compression garments and measurements such as blood lactate, sweat rate, and garment performance in the subsequent stages of the research. The information obtained from this study will inform the design and development of compression garments that enhances blood flow, increases oxygen delivery to the muscles, and reduces the blood lactate concentration. The wearable device will also enable athletes to monitor their real time lactate concentration and pace their activity

A novel amperometric gallic acid sensor based on polymelamine entrapped graphene composite

© 2017 The Authors. The present work describes an amperometric determination of gallic acid (GA) using a glassy carbon electrode (GCE) modified with graphene (GR) and polymelamine (PM) composite. The GR/PM composite modified electrode was prepared by electropolymerization of melamine on GR modified GCE. The as-prepared GR/PM composite was characterized by scanning electron microscopy, elemental mapping and Fourier transform infrared spectroscopy. The GR/PM composite modified GCE was used as electrocatalyst for oxidation of GA, and the composite modified electrode shows an enhanced catalytic activity than electrodes modified with GR and PM. Under optimum conditions, amperometric i-t was used to determine the GA, and the amperometric response of GA was linear over the concentration ranging from 0.1 to 728.9 μM. The limit of detection and sensitivity of the sensor was estimated as 0.027 μM and 0.697 μAμM -1 cm -2 , respectively. The GR/PM composite modified electrode exhibits high selectivity in the presence of range of potentially interfering polyphenol compounds, dopamine, uric acid and ascorbic acid. As a proof of concept, the practicality of the sensors was examined in green tea samples, and shows acceptable practicality for the determination of GA

Enhanced reversible redox activity of hemin on cellulose microfiber integrated reduced graphene oxide for H2O2 biosensor applications

© 2018 Elsevier Ltd. In recent years, the carbohydrate polymers incorporated composite materials have shown significant interest in the bioanalytical chemistry due to their enhanced catalytic performances of various enzymes or mimics. This paper reports the fabrication of novel H2O2biosensor using a hemin immobilized reduced graphene oxide-cellulose microfiber composite (hemin/RGO-CMF). The RGO-CMF composite was prepared by the reduction of GO-CMF composite using vitamin C as a reducing agent. Various physio-chemical methods have applied for the characterization of RGO-CMF composite. Cyclic voltammetry results revealed that the hemin/RGO-CMF composite shows a better redox electrochemical behavior than hemin/RGO and hemin/GO-CMF. Under optimized conditions, the hemin/RGO-CMF composite exhibit a linear response to H2O2in the concentration range from 0.06 to 540.6 μM with the lower detection limit of 16 nM. The sensor also can able to detect the H2O2in commercial contact lens solution and milk samples with functional recovery, which authenticates the potential ability in practical sensors

Preparation of chitosan grafted graphite composite for sensitive detection of dopamine in biological samples

© 2016 Elsevier Ltd. All rights reserved.The accurate detection of dopamine (DA) levels in biological samples such as human serum and urine are essential indicators in medical diagnostics. In this work, we describe the preparation of chitosan (CS) biopolymer grafted graphite (GR) composite for the sensitive and lower potential detection of DA in its sub micromolar levels. The composite modified electrode has been used for the detection of DA in biological samples such as human serum and urine. The GR-CS composite modified electrode shows an enhanced oxidation peak current response and low oxidation potential for the detection of DA than that of electrodes modified with bare, GR and CS discretely. Under optimum conditions, the fabricated GR-CS composite modified electrode shows the DPV response of DA in the linear response ranging from 0.03 to 20.06 μM. The detection limit and sensitivity of the sensor were estimated as 0.0045 μM and 6.06 μA μM-1 cm-2, respectively