Gendered Portrayals of Domestic Work in Indian Television

Despite the entry of women into the labour force in India, women still participate in paid employment at a low rate; the rate even fell recently from 31% to 24% (ILO, 2013). This represents an alarming need for change in gender roles. The present article focuses on how electronic media content portrays men’s and women’s roles in performing housework. In a study of 30 TV serials that aired from 1990-2016 and 14 old and new TV advertisements, the findings show that mostly women are depicted doing domestic work. Media demonstrates gender disparities between men and women in performing domestic work, which is keeping our society from achieving gender equality. There is a need to reconsider men’s and women’s roles in the family. So, this study suggests that media content should not show biased gender roles. To avoid gender inequality at the micro level, we need to disseminate idealistic content to our nation’s audience. To ensure women’s participation in the work force, men’s involvement in domestic chores needs to be increased; this can also improve the gross domestic productivity of the country

A Robust Dynamic Data Masking Transformation approach To Safeguard Sensitive Data

Large amount of digital data is generated rapidly all around the corners. Providing security to digital data is the crucial issue in almost all types of organizations. According to the Identity Theft Resource Center, there were 8,069 data breaches between January 2005 and November 2017, and in recent years the number of data breaches and compromised records has skyrocketed [1]. To provide protection to the digital sensitive data, from data breaches in the need of hour. Almost all domains like insurance, banking, health care, and educational and many more are concern about security of sensitive data. Data masking is one of the vital discussions everywhere as data breach leads to threats. Masking is a philosophy or new way of thinking about safeguarding sensitive data in such a way that accessible and usable data is still available for non- production environment. In this research paper authors proposed a dynamic data masking model to protect sensitive data using random deterministic masking algorithm with shift left approach. This paper describes methodology & experimental design and results

The Influence Work-Life Policies Can Have on Part-Time Employees in Contrast to Full-Time Workers and The Consequence It Can Have on Their Job Satisfaction, Organizational Commitment and Motivation

This proposal will discuss previous literature reviews concerning Work-Life balance as a vast matter and topic through the Human Resource Management realms scope. Several readings have observed the benefits of work-life policies in countless firms across the world. The consequences designated that part-time workers practiced lesser work-life balance levels in this specific example compared to full-time workers in the same industry. Other closes were made, such as how work-life persuaders changed with gender, wedded status, personal position, and educational grade. This study displays that organizational culture and organization provision are subsidizing factors in how work-life balance practices are supposed and qualified. On the other hand, the purpose of this study is to better understand work-family decision making by investigating episodes of time-based work-life conflict in which individuals indicate that they made clear decisions to choose work over family or family overwork. Therefore, this study seeks to survey the organizational work plan approaches and schedules that can be planned in encouraging and achieving work-life balance

RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent

Evaluation of Candidate Reference Genes for Gene Expression Normalization in Brassica juncea Using Real Time Quantitative RT-PCR

The real time quantitative reverse transcription PCR (qRT-PCR) is becoming increasingly important to gain insight into function of genes. Given the increased sensitivity, ease and reproducibility of qRT-PCR, the requirement of suitable reference genes for normalization has become important and stringent. It is now known that the expression of internal control genes in living organism vary considerably during developmental stages and under different experimental conditions. For economically important Brassica crops, only a couple of reference genes are reported till date. In this study, expression stability of 12 candidate reference genes including ACT2, ELFA, GAPDH, TUA, UBQ9 (traditional housekeeping genes), ACP, CAC, SNF, TIPS-41, TMD, TSB and ZNF (new candidate reference genes), in a diverse set of 49 tissue samples representing different developmental stages, stress and hormone treated conditions and cultivars of Brassica juncea has been validated. For the normalization of vegetative stages the ELFA, ACT2, CAC and TIPS-41 combination would be appropriate whereas TIPS-41 along with CAC would be suitable for normalization of reproductive stages. A combination of GAPDH, TUA, TIPS-41 and CAC were identified as the most suitable reference genes for total developmental stages. In various stress and hormone treated samples, UBQ9 and TIPS-41 had the most stable expression. Across five cultivars of B. juncea, the expression of CAC and TIPS-41 did not vary significantly and were identified as the most stably expressed reference genes. This study provides comprehensive information that the new reference genes selected herein performed better than the traditional housekeeping genes. The selection of most suitable reference genes depends on the experimental conditions, and is tissue and cultivar-specific. Further, to attain accuracy in the results more than one reference genes are necessary for normalization

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media

Effect of exercise therapy on lipid profile and oxidative stress indicators in patients with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Yoga has been shown to be a simple and economical therapeutic modality that may be considered as a beneficial adjuvant for type 2 diabetes mellitus. This study investigated the impact of Hatha yoga and conventional physical training (PT) exercise regimens on biochemical, oxidative stress indicators and oxidant status in patients with type 2 diabetes.</p> <p>Methods</p> <p>This prospective randomized study consisted of 77 type 2 diabetic patients in the Hatha yoga exercise group that were matched with a similar number of type 2 diabetic patients in the conventional PT exercise and control groups. Biochemical parameters such as fasting blood glucose (FBG), serum total cholesterol (TC), triglycerides, low-density lipoprotein (LDL), very low-density lipoproteins (VLDL) and high-density lipoprotein (HDL) were determined at baseline and at two consecutive three monthly intervals. The oxidative stress indicators (malondialdehyde – MDA, protein oxidation – POX, phospholipase A2 – PLA2 activity) and oxidative status [superoxide dismutase (SOD) and catalase activities] were measured.</p> <p>Results</p> <p>The concentrations of FBG in the Hatha yoga and conventional PT exercise groups after six months decreased by 29.48% and 27.43% respectively (P < 0.0001) and there was a significant reduction in serum TC in both groups (P < 0.0001). The concentrations of VLDL in the managed groups after six months differed significantly from baseline values (P = 0.036). Lipid peroxidation as indicated by MDA significantly decreased by 19.9% and 18.1% in the Hatha yoga and conventional PT exercise groups respectively (P < 0.0001); whilst the activity of SOD significantly increased by 24.08% and 20.18% respectively (P = 0.031). There was no significant difference in the baseline and 6 months activities of PLA2 and catalase after six months although the latter increased by 13.68% and 13.19% in the Hatha yoga and conventional PT exercise groups respectively (P = 0.144).</p> <p>Conclusion</p> <p>The study demonstrate the efficacy of Hatha yoga exercise on fasting blood glucose, lipid profile, oxidative stress markers and antioxidant status in patients with type 2 diabetes and suggest that Hatha yoga exercise and conventional PT exercise may have therapeutic preventative and protective effects on diabetes mellitus by decreasing oxidative stress and improving antioxidant status.</p> <p>Trial Registration</p> <p>Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12608000217303</p

Measurement and interpretation of same-sign W boson pair production in association with two jets in pp collisions at s = 13 TeV with the ATLAS detector

This paper presents the measurement of fducial and diferential cross sections for both the inclusive and electroweak production of a same-sign W-boson pair in association with two jets (W±W±jj) using 139 fb−1 of proton-proton collision data recorded at a centre-of-mass energy of √s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed by selecting two same-charge leptons, electron or muon, and at least two jets with large invariant mass and a large rapidity diference. The measured fducial cross sections for electroweak and inclusive W±W±jj production are 2.92 ± 0.22 (stat.) ± 0.19 (syst.)fb and 3.38±0.22 (stat.)±0.19 (syst.)fb, respectively, in agreement with Standard Model predictions. The measurements are used to constrain anomalous quartic gauge couplings by extracting 95% confdence level intervals on dimension-8 operators. A search for doubly charged Higgs bosons H±± that are produced in vector-boson fusion processes and decay into a same-sign W boson pair is performed. The largest deviation from the Standard Model occurs for an H±± mass near 450 GeV, with a global signifcance of 2.5 standard deviations

Studies of new Higgs boson interactions through nonresonant HH production in the b¯bγγ fnal state in pp collisions at √s = 13 TeV with the ATLAS detector

A search for nonresonant Higgs boson pair production in the b ¯bγγ fnal state is performed using 140 fb−1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. This analysis supersedes and expands upon the previous nonresonant ATLAS results in this fnal state based on the same data sample. The analysis strategy is optimised to probe anomalous values not only of the Higgs (H) boson self-coupling modifer κλ but also of the quartic HHV V (V = W, Z) coupling modifer κ2V . No signifcant excess above the expected background from Standard Model processes is observed. An observed upper limit µHH &lt; 4.0 is set at 95% confdence level on the Higgs boson pair production cross-section normalised to its Standard Model prediction. The 95% confdence intervals for the coupling modifers are −1.4 &lt; κλ &lt; 6.9 and −0.5 &lt; κ2V &lt; 2.7, assuming all other Higgs boson couplings except the one under study are fxed to the Standard Model predictions. The results are interpreted in the Standard Model efective feld theory and Higgs efective feld theory frameworks in terms of constraints on the couplings of anomalous Higgs boson (self-)interactions

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion