Study of waves in the middle atmosphere and associated momentum flux using indian mst radar

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Development of a Method for Determining the Relative Manufacturing Complexity of Advanced Engineering Materials

The immediate adaptation of newly developed materials--with unique and highly desirable properties--is hampered by several factors, including: (1) high material cost and limited availability, (2) lack of information on them, including prior experience in their design and manufacture, immature manufacturing processes and general uncertainty in their behavior patterns, (3) unique handling issues, such as excessive manual labor, high process temperatures, toxicity, disposal problems, limited working lives, and low damage tolerance Therefore, in spite of their significant benefits, potential users tend to shy away from the widespread use of new materials, instead preferring conventional and tested materials forms. This dissertation is on a methodology developed to compare manufacturing complexity of new materials with that of conventional ones. It entails development of a 5 level multi-attribute hierarchy of 18 factors and several processes that influence the manufacturing risk of new materials. A Manufacturing Complexity Factor (MCF) and a Delta Complexity Factor (DCF) are developed to compare new materials with older, traditional ones. The Analytic Hierarchy Process is used to judiciously assign weights to all factors and sub-factors. Materials are assigned ranks based on information available about their unique properties and requirements. From the rank and attribute priorities, values for MCF/DCF can be obtained. Since information available is often limited, the ranks assigned to materials are not highly accurate values. The Monte Carlo simulation technique is used to take away some of the uncertainty in the ranks of the newly developed materials and generate a more robust MCF/DCF value. Sensitivity of the method to varying inputs is examined. An attempt is made to compare this practical methodology with two popular approaches, one used for analyzing the complexity of composite materials and another that develops manufacturing complexity factors for given input parameters. It is shown that the methodology in this dissertation generates results not possible by either of the other two methods

Synthesis and characterization of Na03RhO206H2O - a semiconductor with a weak ferromagnetic component

We have prepared the oxyhydrate Na03RhO206H2O by extracting Na+ cations from NaRhO2 and intercalating water molecules using an aqueous solution of Na2S2O8. Synchrotron X-ray powder diffraction, thermogravimetric analysis (TGA), and energy-dispersive x-ray analysis (EDX) reveal that a non-stoichiometric Na03(H2O)06 network separates layers of edge-sharing RhO6 octahedra containing Rh3+(4d6, S=0) and Rh4+ (4d5, S=1/2). The resistivities of NaRhO2 and Na03RhO206H2O (T < 300) reveal insulating and semi-conducting behavior with activation gaps of 134 meV and 7.8 meV, respectively. Both Na03RhO206H2O and NaRhO2 show paramagnetism at room temperature, however, the sodium-deficient sample exhibits simultaneously a weak but experimentally reproducible ferromagnetic component. Both samples exhibit a temperature-independent Pauli paramagnetism, for NaRhO2 at T > 50 K and for Na03RhO206H2O at T > 25 K. The relative magnitudes of the temperature-independent magnetic susceptibilities, that of the oxide sample being half that of the oxyhydrate, is consistent with a higher density of thermally accessible electron states at the Fermi level in the hydrated sample. At low temperatures the magnetic moments rise sharply, providing evidence of localized and weakl -ordered electronic spins.Comment: 15 fages 5 figures Solid State Communications in prin

Measurements and ab initio Molecular Dynamics Simulations of the High Temperature Ferroelectric Transition in Hexagonal RMnO3

Measurements of the structure of hexagonal RMnO3 (R=rare earths (Ho) and Y) for temperatures significantly above the ferroelectric transition temperature (TFE) were conducted to determine the nature of the transition. The local and long range structural measurements were complemented by ab initio molecular dynamics simulations. With respect to the Mn sites in YMnO3 and HoMnO3, we find no large atomic (bond distances or thermal factors), electronic structure changes or rehybridization on crossing TFE from local structural methods. The local symmetry about the Mn sites is preserved. With respect to the local structure about the Ho sites, a reduction of the average Ho-O bond with increased temperature is found. Ab initio molecular dynamics calculations on HoMnO3 reveal the detailed motions of all ions. Above ~900 K there are large displacements of the Ho, O3 and O4 ions along the z-axis which reduce the buckling of the MnO3/O4 planes. The changes result in O3/O4 ions moving to towards central points between pairs of Ho ions on the z-axis. These structural changes make the coordination of Ho sites more symmetric thus extinguishing the electric polarization. At significantly higher temperatures, rotation of the MnO5 polyhedra occurs without a significant change in electric polarization. The born effective charge tensor is found to be highly anisotropic at the O sites but does not change appreciably at high temperatures

DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing

Existing deep learning based visual servoing approaches regress the relative camera pose between a pair of images. Therefore, they require a huge amount of training data and sometimes fine-tuning for adaptation to a novel scene. Furthermore, current approaches do not consider underlying geometry of the scene and rely on direct estimation of camera pose. Thus, inaccuracies in prediction of the camera pose, especially for distant goals, lead to a degradation in the servoing performance. In this paper, we propose a two-fold solution: (i) We consider optical flow as our visual features, which are predicted using a deep neural network. (ii) These flow features are then systematically integrated with depth estimates provided by another neural network using interaction matrix. We further present an extensive benchmark in a photo-realistic 3D simulation across diverse scenes to study the convergence and generalisation of visual servoing approaches. We show convergence for over 3m and 40 degrees while maintaining precise positioning of under 2cm and 1 degree on our challenging benchmark where the existing approaches that are unable to converge for majority of scenarios for over 1.5m and 20 degrees. Furthermore, we also evaluate our approach for a real scenario on an aerial robot. Our approach generalizes to novel scenarios producing precise and robust servoing performance for 6 degrees of freedom positioning tasks with even large camera transformations without any retraining or fine-tuning.Comment: Accepted in International Conference on Robotics and Automation (ICRA) 2020, IEE

Comprehensive Observations of the Bright and Energetic Type Iax SN 2012Z: Interpretation as a Chandrasekhar Mass White Dwarf Explosion

We present UV through NIR broad-band photometry, and optical and NIR spectroscopy of Type Iax supernova 2012Z. The data set consists of both early and late-time observations, including the first late phase NIR spectrum obtained for a spectroscopically classified SN Iax. Simple model calculations of its bolometric light curve suggest SN 2012Z produced ~0.3 M_sun of (56)Ni, ejected about a Chandrasekhar mass of material, and had an explosion energy of ~10^51 erg, making it one of the brightest and most energetic SN Iax yet observed. The late phase NIR spectrum of SN 2012Z is found to broadly resemble similar epoch spectra of normal SNe Ia; however, like other SNe Iax, corresponding visual-wavelength spectra differ substantially compared to all supernova types. Constraints from the distribution of IMEs, e.g. silicon and magnesium, indicate that the outer ejecta did not experience significant mixing during or after burning, and the late phase NIR line profiles suggests most of the (56)Ni is produced during high density burning. The various observational properties of SN 2012Z are found to be consistent with the theoretical expectations of a Chandrasekhar mass white dwarf progenitor that experiences a pulsational delayed detonation, which produced several tenths of a solar mass of (56)Ni during the deflagration burning phase and little (or no) (56)Ni during the detonation phase. Within this scenario only a moderate amount of Rayleigh-Taylor mixing occurs both during the deflagration and fallback phase of the pulsation, and the layered structure of the IMEs is a product of the subsequent denotation phase. The fact that the SNe Iax population does not follow a tight brightness-decline relation similar to SNe Ia can then be understood in the framework of variable amounts of mixing during pulsational rebound and variable amounts of (56)Ni production during the early subsonic phase of expansion.Comment: Submitted to A&A, manuscript includes response to referee's comments. 39 pages, including 16 figures, 9 table

Predicting suicidal behavior among Indian adults using childhood trauma, mental health questionnaires and machine learning cascade ensembles

Among young adults, suicide is India's leading cause of death, accounting for an alarming national suicide rate of around 16%. In recent years, machine learning algorithms have emerged to predict suicidal behavior using various behavioral traits. But to date, the efficacy of machine learning algorithms in predicting suicidal behavior in the Indian context has not been explored in literature. In this study, different machine learning algorithms and ensembles were developed to predict suicide behavior based on childhood trauma, different mental health parameters, and other behavioral factors. The dataset was acquired from 391 individuals from a wellness center in India. Information regarding their childhood trauma, psychological wellness, and other mental health issues was acquired through standardized questionnaires. Results revealed that cascade ensemble learning methods using a support vector machine, decision trees, and random forest were able to classify suicidal behavior with an accuracy of 95.04% using data from childhood trauma and mental health questionnaires. The study highlights the potential of using these machine learning ensembles to identify individuals with suicidal tendencies so that targeted interinterventions could be provided efficiently.Comment: 11 pages, presnted at the 4th International Conference on Frontiers in Computing and Systems (COMSYS 2023), Himachal Pradesh, October 202

International multicentre study of candida auris infections

Background: Candida auris has emerged globally as a multi-drug resistant yeast and is commonly associated with nosocomial outbreaks in ICUs. Methods: We conducted a retrospective observational multicentre study to determine the epidemiology of C. auris infections, its management strategies, patient outcomes, and infection prevention and control practices across 10 centres from five countries. Results: Significant risk factors for C. auris infection include the age group of 61–70 years (39%), recent history of ICU admission (63%), diabetes (63%), renal failure (52%), presence of CVC (91%) and previous history of antibiotic treatment (96%). C. auris was commonly isolated from blood (76%). Echinocandins were the most sensitive drugs. Most common antifungals used for treatment were caspofungin (40%), anidulafungin (28%) and micafungin (15%). The median duration of treatment was 20 days. Source removal was conductedin 74% patients. All-cause crude mortality rate after 30 days was 37%. Antifungal therapy was associated with a reduction in mortality (OR:0.27) and so was source removal (OR:0.74). Contact isolation precautions were followed in 87% patients. Conclusions: C. auris infection carries a high risk for associated mortality. The organism is mainly resistant to most azoles and even amphotericin-B. Targeted antifungal therapy, mainly an echinocandin, and source control are the prominent therapeutic approaches