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A study of pulsation & rotation in a sample of A-K type stars in the Kepler field
We present the results of time-series photometric analysis of 15106 A-K type stars observed by the Kepler space mission. We identified 513 new rotational variables and measured their starspot rotation periods as a function of spectral type and discuss the distribution of their amplitudes. We examined the well-established period-color relationship that applies to stars of spectral types F5-K for all of these rotational variables and, interestingly, found that a similar period-color relationship appears to extend to stars of spectral types A7 to early-F too. This result is not consistent with the very foundation of the period-color relationship. We have characterized 350 new non-radial pulsating variables such as A- and F-type candidate ? Scuti, ? Doradus and hybrid stars, which increases the known candidate non-radial pulsators in the Kepler field significantly, by ?20%. The relationship between two recently constructed observables, Energy and Efficiency, was also studied for the large sample of non-radial pulsators, which shows that the distribution in the logarithm of Energy (log (En)) can be used as a potential tool to distinguish between the non-radial pulsators, to some extent. Through visual inspection of the light curves and their corresponding frequency spectra, we found 23 new candidate red giant solar-like oscillators not previously reported in the literature. The basic physical parameters such as masses, radii and luminosities of these solar-like oscillators were also derived using asteroseismic relations. 2018, The Author(s). -
Psychological distress among college students of coastal district of Karnataka: A community-based cross-sectional survey
Background: Common Mental Disorders (CMDs) are frequent among adolescents and early adults. National Mental Health Survey of India 20152016 shows alarming results, especially for depression. This study explored the prevalence of psychological distress among college students. Additionally, the relationship between gender, living arrangement with psychological distress in various educational streams were explored. Materials and Methods: Through a cross-sectional community-based survey a total of 4839 college going students of various educational streams in Mangalore, Karnataka were assessed for psychological distress with Self-Reporting Questionnaire (SRQ-20). Results: Participants had median age of 19 (range = 9) years and the majority (59.5%) were females. The median SRQ score was 4 (range 20) and about 28.5% of students were found to be psychologically stressed. The suicidal ideation was reported by 13.6% of the students. Engineering and Arts/science/commerce students had significantly higher psychological distress scores as compared to health and allied sciences (k = 47.7; p < 0.001) and those who were staying with families (U = 2,687,648.5; p = 0.004) reported higher levels of psychological distress in comparison to those who were staying away from their families. Conclusion: Prevalence of psychological distress was high among students, especially non-medical students, students who were staying with their families, and those who were younger in age. A significant proportion of students had suicidal ideation, which needs early mental health interventions at the college level. 2018 -
Challenges of Indian girls with maternal schizophrenia
Schizophrenia, earlier known as dementia praecox, is considered to be one of the most devastating mental illnesses due to its impact on the individual as well as family members. The Indian context characterized by ones rootedness to family, warrant enquiry about difficulties and burnouts faced by girl children.When it is the mother who is suffering from the illness, there tends to be a huge lag in terms of primary care giving. A disturbed home environment along with inadequate parenting have shown to adversely affect the girl children. The present qualitative research study aimed to explore challenges faced by the girl children with maternal schizophrenia with the help of 43 Mental Health Professionals (MHPs) across India. Interpretative Phenomenological Approach (IPA)was adopted and interviews were conducted using a validated interview guide. Thematic analysis revealed that girl children whose mothers are diagnosed with schizophrenia faced challenges in self, family and social sphere of life. Neglect, self blame and the question why me were recurrent themes.They experienced difficulties in cognitive, behavioral and social domains. The added burden of family responsibilities and social stigma made the surroundings challenging.Exploring the world of girls with maternal schizophrenia would deepen our understanding about impact of schizophrenia on family members and aid us develop interventions to support the care givers. 2019 Oriental Scientific Publishing Company. All rights reserved. -
Exploratory analysis of legal case citation data using node embedding
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019. -
Photophysical and Electrochemical Studies of Anchored Chromium (III) Complex on Reduced Graphene Oxide via Diazonium Chemistry
Covalently anchored chromium complex on reduced graphene oxide (rGO-Cr) is successfully synthesised through trimethoxy silyl propanamine (TMSPA) and phenyl azo salicylaldehyde (PAS) coupling. The rGO-Cr is characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron dispersive analysis of X-rays (EDAX), Raman spectroscopy, scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM). Absorption and emission properties of rGO-TMSPA-PAS are studied by excitation dependent photoluminescence emissions at room temperature. Electrochemical sensing activity of rGO-Cr is monitored for paracetamol using modified glassy carbon electrode. Cyclic voltammetry measurements indicated that rGO-Cr substantially enhance the eletrochemical response of paracetamol. The experimental factors are investigated and optimized. 2019 John Wiley & Sons, Ltd. -
Emotional needs of women post-rescue from sex trafficking in India
Sex trafficking has persisted a social crime that maintains its status despite being unlawful. Since it prevails, there is a need to investigate it to understand the effects and consequences of the same on the survivors. The current study aims to understand the emotional needs of survivors post-rescue from sex trafficking living in aftercare homes in India and to look into survivors suggestions post-rescue to NGOs, society, family, government and police. It included ten survivors from sex trafficking, ages between 18 to 24years old. They are emerging adults who have experienced sex trafficking for at least one year, regardless of whether trafficking happened in childhood, adolescence or early adulthood, rescued one to five years ago. The researcher used a phenomenological approach. Thematic analysis was employed to identify themes within the data collected from the participants. Findings revealed that survivors had got a better life after the rescue, and they need acceptance, respect, understanding, and they need to develop trust on people around them. They still have many challenges post-rescue such as lack of education and job opportunities. They need guidance to start a new life. Mostly, sex trafficking survivors need safety and protection. 2019, 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Job Search Methods in the Software Industry in Bangalore: Does Social Capital Matter?
Heterogeneity of workers and jobs results in imperfections such as information asymmetry in the labour market. Acquisition and evaluation of information by buyers and sellers to arrive at a decision is, therefore, both difficult and costly. Referrals in the labour market act as a conduit through which necessary information flows between prospective employers and job seekers, thereby reducing problems associated with information asymmetry. The information technology (IT) industry hires about a quarter of their workforce through referrals. We look at realised job search outcomes of IT workers some of whom have found jobs through informal methods of search deploying their social networks while others through formalised channels of recruitment. We examine social capital, human capital and firm-specific differences among those who found jobs through formal vis-a-vis informal methods of search. The empirical analysis is primarily descriptive and is based on a survey conducted among software workers in the IT industry in Bangalore. The results suggest that there are no significant differences in the human capital characteristics of workers between the two methods of job finding. However, certain social capital and firm-specific characteristics significantly differ among the methods of job finding. 2019, Indian Society of Labour Economics. -
Human behavior analysis of BBC-news comments posted on facebook using lexicon-rule based approach
Today people spend a considerable part of their time on online platforms say, social media than with the real world. Social media, particularly Facebook is the platform for the users to post, share, like, tag and comment any photos and videos. This paper deals with the Facebook platform to study the human behavior based on the comments of five posts from BBC-news Facebook page. For every post in Facebook we can get different opinion or emotional behavior by different users. The behavior of people to the same event need not be similar, they can be different. A response through comments and smileys for a post portrays behaviors of people. Here the behavior analysis is performed on comments of the BBC news Facebook posts. The comments of the post are fetched by the online extractor named Socialfy [12]. This paper considered five news from unique from BBC-news Facebook page. The human behavior analysis performed using Python VADER (Valence Aware Dictionary and Sentiment Reasoner) package. This work uses the Lexicon approach to assign scores for the words and rule-based approach used to find the polarity type of words. The polarity of a post is the sentimental behavior of the people towards the post. The total polarity of this work tends towards neutral so, we could conclude that for each situation behavior of man can take positive or negative poles. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Gold jewellery making and migrant labour force in Kerala
The extent of the gold jewellery market in Kerala has widened and consumption patterns have drastically altered. The increasing presence of migrant workers in the industry is a direct consequence of the deregulation of the gold industry in the early 1990s. While resorting to a labour process framework, this paper elucidates the process of recruitment and the composition of workforce. The empirical data is based on the findings and observations gathered through intensive fieldwork conducted during the course of three years, from 2010 to 2013, in the gold jewellery making industry in Thrissur and Kozhikode districts. This work also relies on a larger data set, the Inter-State Migrant Survey conducted by the Centre for Development Studies in 2012, which collected data on migrant workers from four districts of Kerala. 2019 Economic and Political Weekly. All rights reserved. -
Hybrid approach: Naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments
Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Author profiling: Age prediction of blog authors and identifying blog sentiment
Authorship profiling is about finding out different characteristic of an author like age, gender, native languages, education background etc., by finding out the patterns in their writing. Blog authors write about a lot of topics like purchase decisions, digital advertising, personality development, fitness, technology updates etc., and these authors play an influential role on its readers. In this paper, we are categorizing the blog authors in three different age groups based on the content available from the blog. Natural Language Toolkit (NLTK) is a set of libraries used for natural language processing to distinguish among the different writing pattern of the author based on the different age groups. NLTK helps to make analysis on the words of the blogs which is an important feature in our research. We also wanted to conduct sentiment analysis on the blog in order to understand the insight on how the author feels about the blog topic. Thus, we have used Nae Bayes Classifier for doing the analysis and considered two sentiments for the same: positive and negative. An average accuracy of 66.78% was achieved in predicting the age of authors. From the sentiment analysis we figured out that elder authors tend to have more positivity in their blogs as compared to younger authors. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
An advanced variable temperature refrigerator for preservation and management of food items
All food items will have shelf life period. The main aim of food preservation is to maximize the shelf life period and preservation of nutrients for a long period. One of the preservation methods is refrigeration. Each food item will have its own optimum storage temperature to maximize the shelf life period. Normal refrigerators have fixed temperature. The work proposes a refrigerator with six compartments which is equipped with temperature sensors to maintain the fixed temperature for that compartment and with weighing sensors to monitor the depleting food items with the help of a controller. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Long run relationship between macroeconomic indicators and Indian sectoral indices
Investors and fund managers continuously strive to find new ways to diversify their portfolio and minimise risk exposure. The study aims to find out whether the macroeconomic indicators exert the same influence on stock prices across the entire stock market or varies across different sectors. The impact of macroeconomic indicators would not be the same on all the sectors. This paper provides empirical evidence of macroeconomic indicators such as crude oil prices, interest rates, foreign currency rates, money supply and inflation rates having a varied impact on Nifty50 index and each of the select sectoral stock indices namely, Nifty Bank, Nifty IT and Nifty financial services. The sample period runs from Jan 2009 to Jan 2019. The study employs the Error Correction Mechanism to study whether the macroeconomic indicators have the same impact across sectoral stock indices in the long run. The findings show that variations in macroeconomic variables do not trigger the same response from all the sectoral stock indices. While most of the variables chosen have a significant influence on Nifty50 index and NiftyIT; Nifty financial services and Nifty Bank remain unaffected by changes in few major macroeconomic variables or show opposite reaction than the other sectors. The findings of the study have significant implications for long term investors and investment managers for building a diversified portfolio and thereby protecting themselves from financial losses during adverse market conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Performance evaluation of machinelearning techniques indiabetes prediction
Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. -
Non-enzymatic electrochemical determination of progesterone using carbon nanospheres from onion peels coated on carbon fiber paper
A simple electrochemical sensor was developed by coating Onion peel wastes derived carbon nanospheres on carbon fiber paper (CFP) electrode. Carbon nanospheres (CNS) were prepared from Onion peels utilizing an environmentally benign and cost-effective strategy. In the present investigation, the obtained carbon nanospheres were coated on carbon fiber paper and the modified electrodes were physicochemically characterized by Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) spectroscopy and X-ray photoelectron spectroscopy (XPS) techniques. Electrochemical characterizations of the modified electrodes were done by Cyclic voltammetry (CV) and Electrochemical impedance spectroscopy (EIS). CNS modified CFP electrode was successfully used in the determination of Progesterone, an important steroid hormone at an ultra-nanomolar level with superior detection limit of 0.012 nM. The developed electrochemical sensor was effectively utilized for the determination of Progesterone in pharmaceutical Progesterone injections, human blood serum samples and cow milk samples. 2019 The Electrochemical Society. -
Mobile banking technology adoption model: Revisiting the tam approach
The user acceptance of Mobile banking technology is limited in terms of appropriate measurement variables. In M-banking practice, the influencing factors and the relationship with adoption is unrevealed. The data was collected through crowd sourcing method which is considered to be most relevant method. The results of hypotheses testing and SEM analysis showing that the relationship between the perceived usefulness and behavioral intention is significant direct relationship is existed in the same way perceived trust on the m-bankers has direct effect on m-banking user behavioral intention. The Perceived Ease of Use and Perceived Social Influence is not significantly influencing the behavioral intention but the important missed constructs, Perceived Trust along with Perceived Usefulness is highly influencing the M-banking users. The M-banking App developers should emphasis on need based apps and must incorporate strong security aspects for eliminating model risk associated with the M-banking application. The present study developed a new measurement variable called Perceived Social Influence and Perceived Trust along with Perceived Usefulness and Perceived Ease of Use of original TAM which are hypothesized to adopt M-banking technology. For M-Banking technology services, the original TAM did not hold good as there was an absence of a crucial factor for M-banking, Perceived Trust and Social Influence. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Changes in Wage Trends and Earnings Differences in Kerala
In this article, the weekly earnings gap between men and women in Kerala is examined by a number of inequality indices such as the percentile ratio and the Gini coefficient. The entropy measures of inequality are used to decompose wage inequality into within-group and between-group inequalities. The earnings inequality between men and women has been increasing, even though their wage grows faster than mens wage. The indices of inequality suggest the growing wage disparity in the regular and casual labour market. The result reveals that the levels of education and earnings are positively correlated, but women with the same level of education earn much less than men in regular salaried work. The rising wage inequality of men and women during 20042009 were associated with the growth rate of wages in the same period. That is, the wage rates of both regular and casual workers have increased more than four per cent during the period that experienced the highest inequality. 2019, Indian Society of Labour Economics. -
Enhancing the performance in education by implementing gamification
The gaming industry is growing rapidly in the present generation along with the advancement of technology. Gaming has captured all the young minds with its high and realistic graphics. What makes the gaming industry so attractive is that the players have complete freedom in the game. Freedom to fail, they can try until they succeed another feature is that game is user-centric. Consequently, a lot of research is been in the field of education to increase student's engagement towards studies. The main aim of this paper is to combine these game elements with learning to see if it yields better results. A quantitative approach is used to analyze the student's performance and interest in learning. Using these game elements in education will encourage the students to learn as well as have the flexibility to complete the course at their own pace. Copyright 2019 American Scientific Publishers All rights reserved. -
Sentiment analysis on social media data using intelligent techniques
Social media gives a simple method of communication technology for people to share their opinion, attraction and feeling. The aim of the paper is to extract various sentiment behaviour and will be used to make a strategic decision and also aids to categorize sentiment and affections of people as clear, contradictory or neutral. The data was preprocessed with the help of noise removal for removing the noise. The research work applied various techniques. After the noise removal, the popular classification methods were applied to extract the sentiment. The data were classified with the help of Multi-layer Perceptron (MLP), Convolutional Neural Networks (CNN). These two classification results were checked against the others classified such as Support Vector Machine (SVM), Random Forest, Decision tree, Nae Bayes, etc., based on the sentiment classification from twitter data and consumer affairs website. The proposed work found that Multi-layer Perceptron and Convolutional Neural Networks performs better than another Machine Learning Classifier. International Research Publication House. -
A metal-A nd base-free domino protocol for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines and related scaffolds
Efficient protocols have been described for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines, 2-aryl thiazin-4-ones and diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. These transformations were successfully driven towards the product formation under mild acid catalyzed reaction conditions at room temperature using 2-amino aryl/hetero-aryl alkyl alcohols and amides as substrates. The merits of the present methods also rely on the easy access of rarely explored bioactive scaffolds like 1,3-benzoselenazine derivatives, for which well-documented methods are rarely known in the literature. A broad range of substrates with both electron-rich and electron-deficient groups were well-tolerated under the developed conditions to furnish the desired products in yields up to 98%. The scope of the devised method is not only restricted to the synthesis of 1,3-benzoselenazines, but it was also further extended towards the synthesis of 1,3-benzothiazines, 1,3-benzothiazinones and the corresponding eight membered N-heterocycles such as diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. 2018 The Royal Society of Chemistry.