A STUDY ON EMOTIONAL MATURITY AMONG INDIAN HOCKEY PLAYERS

The purpose of this study was to compare Emotional Maturity among sub-junior level, junior level and senior level Hockey Players. To obtain data, the investigators had selected Ninety Nine (N=99), Female subjects between the age group of 12-28 years (Mean ± SD: Age 16.90 ± 3.80 (yrs), Body Height 161.41 ± 4.97 (cm), Body Mass 52.36 ± 5.35 (kg)). For evaluating the levels of Emotional Maturity among subjects, Singh and Bhargava’s (1988) Emotional Maturity Scale (EMS) was used. This scale consists of five parameters namely: (Emotional Unstability, Emotional Regression, Social Maladjustment, Personality Disintegration and Lack of Independence). The Statistical Package for the Social Sciences (SPSS) was used for all analyses. The differences in the mean of each group for selected variables were tested for the significance of difference by One-way Analysis of Variance (ANOVA). For testing the hypotheses, the level of significance was set at 0.05. To conclude, it is significant to mention in relation to Emotional Unstability, Emotional Regression and Social Maladjustment that results of Analysis of Variance (ANOVA) among Hockey Players were found statistically insignificant (P > .05). Furthermore, in relation to Personality Disintegration and Lack of Independence that results of Analysis of Variance (ANOVA) among Hockey Players were found statistically significant (P < .05).  Article visualizations

The erosion behaviour of pure tungsten electrodes in Gas Tungsten Arc Welding (GTAW)

A cross-time study has been made on the erosion behaviour of Gas-Tungsten Arc Welding (GTAW) for pure tungsten electrode. Its behaviour during arcing was analyzed and compared from the points of view of metallurgical changes in electrode due to long-term operation. Metallographic studies of the electrodes indicate that the crack formation and grain growth during periodic temperature variations. These observations are discussed theoretically based on the experimental results and the thermal expansion parameters of Tungsten

An Efficient Multistage Fusion Approach for Smartphone Security Analysis

Android smartphone ecosystem is inundated with innumerable applications mainly developed by third party contenders leading to high vulnerability of these devices. In addition, proliferation of smartphone usage along with their potential applications in diverse field entice malware community to develop new malwares to attack these devices. In order to overcome these issues, an android malware detection framework is proposed wherein an efficient multistage fusion approach is introduced. For this, a robust unified feature vector is created by fusion of transformed feature matrices corresponding to multi-cue using non-linear graph based cross-diffusion. Unified feature is further subjected to multiple classifiers to obtain their classification scores. Classifier scores are further optimally fused employing Dezert-Smarandache Theory (DSmT). Strength of suggested model is assessed both qualitatively and quantitatively by ten-fold cross-validation on the benchmarked datasets. On an average of outcome, we achieved detection accuracy of 98.97% and F-measure of 0.9936.&nbsp

A Multistage High Capacity Reversible Data Hiding Technique Without Overhead Communication

Reversible Data Hiding(RDH) has been extensively investigated, recently, due to its numerous applications in the field of defence, medical, law enforcement and image authentication. However, most of RDH techniques suffer from low secret data hiding capacity and communication overhead. For this, multistage high-capacity reversible data hiding technique without overhead is proposed in this manuscript. Proposed reversible data hiding approach exploits histogram peaks for embedding the secret data along with overhead bits both in plain and encrypted domain. First, marked image is obtained by embedding secret data in the plain domain which is further processed using affine cipher maintaining correlation among the pixels. In second stage, overhead bits are embedded in the encrypted marked image. High embedding capacity is achieved through exploiting histogram peak for embedding multiple bits of secret data. Proposed approach is experimentally validated on different datasets and results are compared with the state-of-the-art techniques over different images

Machine Learning with Digital Signal Processing for Rapid and Accurate Alignment-Free Genome Analysis: From Methodological Design to a Covid-19 Case Study

In the field of bioinformatics, taxonomic classification is the scientific practice of identifying, naming, and grouping of organisms based on their similarities and differences. The problem of taxonomic classification is of immense importance considering that nearly 86% of existing species on Earth and 91% of marine species remain unclassified. Due to the magnitude of the datasets, the need exists for an approach and software tool that is scalable enough to handle large datasets and can be used for rapid sequence comparison and analysis. We propose ML-DSP, a stand-alone alignment-free software tool that uses Machine Learning and Digital Signal Processing to classify genomic sequences. ML-DSP uses numerical representations to map genomic sequences to discrete numerical series (genomic signals), Discrete Fourier Transform (DFT) to obtain magnitude spectra from the genomic signals, Pearson Correlation Coefficient (PCC) as a dissimilarity measure to compute pairwise distances between magnitude spectra of any two genomic signals, and supervised machine learning for the classification and prediction of the labels of new sequences. We first test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of \u3e 97%. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4710 bacterial genomes into phyla with 95.5% accuracy. Second, we propose another tool, MLDSP-GUI, where additional features include: a user-friendly Graphical User Interface, Chaos Game Representation (CGR) to numerically represent DNA sequences, Euclidean and Manhattan distances as additional distance measures, phylogenetic tree output, oligomer frequency information to study the under- and over-representation of any particular sub-sequence in a selected sequence, and inter-cluster distances analysis, among others. We test MLDSP-GUI by classifying 7881 complete genomes of Flavivirus genus into species with 100% classification accuracy. Third, we provide a proof of principle that MLDSP-GUI is able to classify newly discovered organisms by classifying the novel COVID-19 virus

A Novel Traffic Based Framework for Smartphone Security Analysis

Android Operating system (OS) has grown into the most predominant smartphone platform due to its flexibility and open source characteristics. Because of its openness, it has become prone to numerous attackers and malware designers who are constantly trying to elicit confidential information by articulating a plethora of attacks through these designed malwares. Detection of these malwares to protect the smartphone is the core function of the smartphone security analysis. This paper proposes a novel traffic-based framework that exploits the network traffic features to detect these malwares. Here, a unified feature (UF) is created by graph-based cross-diffusion of generated order and sparse matrices corresponding to the network traffic features. Generated unified feature is then given to three classifiers to get corresponding classifier scores. The robustness of the suggested framework when evaluated on the standard datasets outperforms contemporary techniques to achieve an average accuracy of 98.74 per cent

Trend and need of Application Virtualization In Cloud Computing

As the variety of applications increases so does the complexity of delivering and managing those applications also increases, many organizations tried to manage that complexity by standardizing on a fixed portfolio of applications in a locked-down configuration. This approach reduces the IT labour costs, but the restrictions involved lead to a frustrating user experience and constraints on flexibility and business agility. Thus This paper presents a better solution that would enable IT to deliver and manage applications at reduced cost while enabling flexibility and agility. Here the concept of application virtualization which is a part of virtualization and how application virtualization is used by cloud computing to deliver application with fast speed, reliability and flexibility shall be discussed

Bundle branch reentrant ventricular tachycardia after transcatheter aortic valve replacement

©2017, original version available at: http://dx.doi.org/10.1016/j.hrcr.2016.12.005 Creative Commons Attribution License 4.

GENETIC SUSCEPTIBILITY OF TRANSCRIPTION FACTOR 7-LIKE 2 GENE VARIANT AND RISK OF TYPE 2 DIABETES IN ASIAN INDIANS

Background: The variants of transcription factor 7-like 2 (TCF7L2) gene have been shown to be associated with type 2 diabetes mellitus (T2DM) and its related complications. Objectives: We aimed to explore the possible association of rs7903146 (C/T) variant in TCF7L2 with the risk of T2DM in the North Indian population. Methods: The present case–control study included a total of 638 human subjects (318 T2DM subjects and 320 healthy controls). Various anthropometric, biochemical, and genetic parameters were studies in all the subjects. Genotyping of TCF7L2 gene was carried out using allele-specific polymerase chain reaction method. Results: The results of this study indicate significantly higher values of body mass index, waist circumference, waist-to-hip ratio, and body fat (%) in T2DM subjects than controls (p≤0.001). Dyslipidemia represented by higher levels of triglycerides and reduced values of high-density lipoprotein was more predominant in diabetic subjects compared to healthy subjects. The frequency of risk genotype (TT) frequency was significantly higher in T2DM subjects (16.4%) compared to controls (11.6%). The “T” allele was more dominant in diabetic subjects than controls. Logistic regression analysis of the data revealed a significant association of TT genotype with 2-fold (odds ratio with 95% of confidence interval; 2.09 [1.29–3.42] p=0.003) and CT genotype with 1.7-fold (1.73 [1.23–2.44] p=0.002) increased risk of developing T2DM. Conclusions: The present study demonstrated a significant association of rs7903146 (C/T) variant in TCF7L2 with the augmented risk of T2DM in North Indian population