Sufficient Conditions for Starlike Functions Associated with the Lemniscate of Bernoulli

Let -1\leq B<A\leq 1. Condition on \beta, is determined so that 1+\beta zp'(z)/p^k(z)\prec(1+Az)/(1+Bz)\;(-1<k\leq3) implies p(z)\prec \sqrt{1+z}. Similarly, condition on \beta is determined so that 1+\beta zp'(z)/p^n(z) or p(z)+\beta zp'(z)/p^n(z)\prec\sqrt{1+z}\;(n=0, 1, 2) implies p(z)\prec(1+Az)/(1+Bz) or \sqrt{1+z}. In addition to that condition on \beta is derived so that p(z)\prec(1+Az)/(1+Bz) when p(z)+\beta zp'(z)/p(z)\prec\sqrt{1+z}. Few more problems of the similar flavor are also considered

Composition of primary cosmic rays near the knee

The size dependence of high energy muons and the size spectrum obtained in the air shower experiment suggest that the mean mass of cosmic rays remains nearly constant at approx 15 up to 5 x 1000,000 GeV and becomes one beyond. The composition model in which nuclei are removed spectrum steepens at 6.7 x 10 power GeV due to leakage from the galaxy, which explains the data which are consistent with data from other experiments

Evaluating the Performance of the Indian Diabetes Risk Score in Different Ethnic Groups

Aim To evaluate the performance of Madras Diabetes Research Foundation -Indian Diabetes Risk Score (MDRF-IDRS score) in different ethnic groups including Indians, Hispanic, Non-Hispanic Whites, Non-Hispanic Blacks and other American. Methods The MDRF-IDRS score is calculated based on a risk equation that includes age, waist circumference, family history of diabetes and physical activity. The National Health and Nutrition Examination Survey (NHANES) data on American and Chennai Urban Rural Epidemiology Study data on Indians were used in this study. Study participants aged ≥ 20 years with and without type 2 diabetes were included. Performance of the MDRF-IDRS score was assessed using sensitivity, specificity, positive predictive value, negative predictive value and the area under the receiver operating characteristic curve measures within each ethnic group. IDRS scores' performance was then compared with existing non-invasive American diabetes risk scores. Results Total number of participants included was 11,035 (2292 Indians and 8743 American). MDRF-IDRS score (cut off≥ 60) performed well in Indians with an AUC, sensitivity and specificity of 0.73, 80.2% and 57.3% respectively. MDRF-IDRS score cut off ≥ 70 had the highest discriminative performance among Hispanic, Non-Hispanic Whites and Non-Hispanic Blacks with sensitivity and specificity of between 70.1-86.9% and 61.2-72.2% respectively. The AUC for American was between 0.77-0.81 with the highest and lowest AUC in Non-Hispanic Black and Non-Hispanic White respectively. With a smaller number of variables, IDRS score showed almost the same performance in predicting diabetes among American compared with the existing non-invasive American diabetes risk score. Conclusion The MDRF-IDRS score performs well among Indians and American including Hispanic, Non-Hispanic White, Non-Hispanic Black and other American. It can be used as a screening tool to help in early diagnosis, management and optimal control of diabetes mainly in mass screening programmes in India and America

Artificial neural network modeling of the tensile properties of indigeneously developed 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel

The severe and hostile operating conditions of fast breeder reactors demand the development of new austenitic stainless steels that possess higher resistance to void swelling and irradiation embrittlement. This paper discusses the efforts made in the laboratory and industrial scale development of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel and the evaluation of tensile properties. Melting and casting were carried out in a vacuum induction furnace and the data on recovery of various alloying elements was obtained for charge calculations. Based on the recovery data and decarburisation behavicur under different vacuum levels, a series of alloys with close chemistry variations were prepared. Heat treatment was optimised for these special steels to control the grain size at required level. The ingots were thermo-mechanically processed and tensile properties were evaluated. This experimental data has been used to train and test an artificial neural network. The input parameters of the neural network are chemical compositions and test temperature while the yield strength, ultimate tensile strength and uniform elongation were obtained as output. A multilayer perceptron (MLP) based feed-forward network with back-propagation learning algorithm has been employed. A very good performance of the developed network is obtained. The model can be used as a guideline for new alloy development

Multiaxial fatigue studies on carbon steel piping material of Indian PHWRs

The tests studies and analyses have been carried out in the area of “Multiaxial Fatigue” with an objective to improve the damage assessment methodologies and design rules. Nearly 50 numbers of fatigue tests were conducted on solid and tubular specimens of SA333Gr.6 material under pure axial, pure shear and combined axial-torsion in-phase/ out-of-phase loading combinations. A software has been developed for the evaluation of multiaxial fatigue damage for the analyses of tests data using different invariant fatigue models such as ASME Sec.III code procedures, von-Mises etc. The fatigue crack initiation life was predicted using the best fit axial fatigue life curve (without use of safety factors). These tests and their analyses have helped in understanding the fatigue failure behavior of piping material under complex cyclic loadings where the principal directions rotate during a loading cycle. The crack initiation angles have also been measured by analyzing the image of the tested specimens. The measured crack angles will help in validation of the critical plane based models

Impact on health and provision of healthcare services during the COVID-19 lockdown in India: A multicentre cross-sectional study

Introduction The COVID-19 pandemic resulted in a national lockdown in India from midnight on 25 March 2020, with conditional relaxation by phases and zones from 20 April. We evaluated the impact of the lockdown in terms of healthcare provisions, physical health, mental health and social well-being within a multicentre cross-sectional study in India. Methods The SMART India study is an ongoing house-to-house survey conducted across 20 regions including 11 states and 1 union territory in India to study diabetes and its complications in the community. During the lockdown, we developed an online questionnaire and delivered it in English and seven popular Indian languages (Hindi, Tamil, Marathi, Telegu, Kannada, Bengali, Malayalam) to random samples of SMART-India participants in two rounds from 5 May 2020 to 24 May 2020. We used multivariable logistic regression to evaluate the overall impact on health and healthcare provision in phases 3 and 4 of lockdown in red and non-red zones and their interactions. Results A total of 2003 participants completed this multicentre survey. The bivariate relationships between the outcomes and lockdown showed significant negative associations. In the multivariable analyses, the interactions between the red zones and lockdown showed that all five dimensions of healthcare provision were negatively affected (non-affordability: OR 1.917 (95% CI 1.126 to 3.264), non-accessibility: OR 2.458 (95% CI 1.549 to 3.902), inadequacy: OR 3.015 (95% CI 1.616 to 5.625), inappropriateness: OR 2.225 (95% CI 1.200 to 4.126) and discontinuity of care: OR 6.756 (95% CI 3.79 to 12.042)) and associated depression and social loneliness. Conclusion The impact of COVID-19 pandemic and lockdown on health and healthcare was negative. The exaggeration of income inequality during lockdown can be expected to extend the negative impacts beyond the lockdown

Low cycle fatigue and cyclic plasticity bahaviour of Indian PHWR / AHWR primary piping materials

The integrity assessment of the primary piping components needs to be demonstrated under normal operation cyclic loadings as well as under complex cycling loadings of extreme magnitude as may come during a severe earthquake event. In order to understand material's cyclic plasticity and fatigue ratcheting behaviour, systematic experimental and analytical investigations have been carried out on specimens of SA333Gr.6 carbon steel and SS304LN stainless steel. The materials specification of SA333Gr.6 is same as used in Primary Heat transport (PHT) piping of Pressurized Heavy Water Reactors (PHWRs) and materials specification of SS304LN steel is same as proposed for Indian Advanced Heavy Water Recactor (AHWRs) Main Heat Transport (MHT) piping. The test program included the properties and cyclic plasticity behaviour. The results of these tests have been investigated in detals using few popular finite element cyclic plasticity models to understand and quantify the materials' cyclic plasticity behaviour. The studies revealed the need to modify the Chaboche model to simulate the LCF/cyclic plasticity and ratcheting under different stress/strain amplitude loading conditions. On accounting for modification, the Chaboche model nicely predicted the LCF and ratcheting response for all the tests. The tests, finite element analyses results and their interpretations have been presented in this paper

Association of Climatic Factors on Population Dynamics of Leaf Roller, Diaphania pulverulentalis

The production of quality mulberry leaf and subsequent production of quality silk is hampered due to the incidence of various insect pests. The present study analyses the population dynamics of Diaphania pulverulentalis (leaf roller), a serious pest of mulberry in a sericulture seed farm. The results indicated that maximum population buildup of the pest was recorded during rainy season. High humidity coinciding with low temperature because of southwest and northeast monsoon was conducive for breeding and multiplication of the pest. Correlation studies revealed that there was a significant negative correlation between increase in temperature and pest infestation. All other weather factors recorded from the study location have a positive correlation with incidence of the pest. The regression model developed also supported the relationship between the pest population buildup and weather factors