Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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 Journal of the Indian Institute of ScienceJournal Prestige (SJR): 0.212 Citation Impact (citeScore): 1Number of Followers: 3      Hybrid journal (It can contain Open Access articles) ISSN (Print) 0970-4140 - ISSN (Online) 0019-4964 Published by Springer-Verlag  [2469 journals]
• Towards a Sustainable Future with Materials

PubDate: 2022-05-26

• The Development of Bayesian Statistics

Abstract: The incorporation of Bayesian inference into practical statistics has seen many changes over the past century, including hierarchical and nonparametric models, general computing tools that have allowed the routine use of nonconjugate distributions, and the incorporation of model checking and validation in an iterative process of data analysis. We discuss these and other technical advances along with parallel developments in philosophy, moving beyond traditional subjectivist and objectivist frameworks to ideas based on prediction and falsification. Bayesian statistics is a flexible and powerful approach to applied statistics and an imperfect but valuable way of understanding statistics more generally.
PubDate: 2022-05-21

• IntroSurvey of Representation Theory

Abstract: There could be thousands of Introductions/Surveys of representation theory, given that it is an enormous field. This is just one of them, quite personal and informal. It has an increasing level of difficulty; the first part is intended for final year undergrads. We explain some basics of representation theory, notably Schur–Weyl duality and representations of the symmetric group. We then do the quantum version, introduce Kazhdan–Lusztig theory, quantum groups and their categorical versions. We then proceed to a survey of some recent advances in modular representation theory. We finish with 20 open problems and a song of despair.
PubDate: 2022-05-17

• Guest Editorial: Materials for a Sustainable Future

PubDate: 2022-05-10

• Clinician Scientists in the Indian Context

Abstract: Advances in healthcare are fueled by discovery and innovation in science and technology. However, such discovery and innovation cannot occur without vital inputs from a clinician. Here, we make a case for the training of clinicians as scientists and engineers to help integrate over disciplines and accelerate translational research in India. We identify and suggest possible solutions to the challenges of creating an Indian clinician-scientist. We further take stock of programs across the globe, initiatives in India, including at our institute, and highlight possible paths that could be taken to train such individuals. With an adequate investment of effort and time, we believe that the clinician-scientist will have the ability to transform healthcare in India.
PubDate: 2022-05-04

• Editor’s Desk

PubDate: 2022-04-28

• Likelihood Ratio Tests for Elaborate Covariance Structures and for MANOVA
Models with Elaborate Covariance Structures—A Review

Abstract: In this paper a review is made from the primordia of the history of likelihood ratio tests for covariance structures and equality of mean vectors through the development of likelihood ratio tests that refer to elaborate covariance structures. Relations are established among several covariance structures, taking more elaborate ones as umbrella structures and examining then their particular cases of interest. References are made to bibliography where the corresponding likelihood ratio tests are developed and the distributions of the corresponding statistics addressed. Most of the likelihood ratio test statistics for one-way manova models where the covariance matrices have elaborate structures were developed quite recently. Also for these likelihood ratio tests a similar approach is taken. Although we start with the common test that uses unstructured covariance matrices, then we go on to consider tests with more elaborate covariance structures, and subsequently we specify them to their particular cases of interest. Some special attention is also given to the so-called Wilks $$\Lambda$$ statistics.
PubDate: 2022-04-17

• Review: Fatigue of Fiber-Reinforced Composites, Damage and Failure

Abstract: A concise review of fatigue of fiber-reinforced composites, covering fatigue life and damage development and how the properties of constituents, orientations and other parameters affect fatigue life, is presented. The subject broadly covers polymer, metal, and ceramic matrix composites, by including specific examples of fatigue data from literature. Studies of composite fatigue have mostly evolved over the last 60 years, largely driven by aerospace applications of composites. The field is very vast in terms of accumulated technical literature and fatigue data. Therefore, only some iconic examples, each with good experimental data, have been considered in this review to illustrate the behavior and the trends as clearly as possible. First, the general nature of tensile deformation of fiber composites under various combinations of fiber and matrix failure strains are reviewed to provide a background with which the more complicated fatigue behavior can be easily understood. Second, examples of S–N fatigue data of glass (GFRP) and carbon (CFRP) fiber-reinforced plastics are provided, illustrating the effects of reinforcement, constituent properties, temperature, and orientation effects on fatigue failure. These analyses are also modeled by S–N curve calculations using exponential S–N fatigue constitutive equations proposed by author. These calculations helped to easily rationalize the trends in S–N data, as influenced by strength and failure strains of fiber and matrix. Next, stiffness degradation behavior in fiber composites are reviewed, with specific examples including CFRP (polymer matrix) and SiC/SiC (ceramic matrix) composites. The nature of stiffness degradation is also modeled using a semi-empirical equation that relates the fractional remaining stiffness to fractional remaining fatigue life in the composite. Finally, a few examples of fatigue behavior of laminated composites that are typically used in real-world applications are reviewed.
PubDate: 2022-03-25
DOI: 10.1007/s41745-021-00280-y

• Bayesian Modeling of Discrete-Time Point-Referenced Spatio-Temporal Data

Abstract: Discrete-time point-referenced spatio-temporal data are obtained by collecting observations at arbitrary but fixed spatial locations $$\varvec{s}_{1},\varvec{s}_{2},\ldots ,\varvec{s}_{n}$$ at regular intervals of time $$t := 1,2,\ldots ,T$$ . They are encountered routinely in meteorological and environmental studies. Gaussian linear dynamic spatio-temporal models (LDSTMs) are the most widely used models for fitting and prediction with them. While Gaussian LDSTMs demonstrate good predictive performance at a wide range of scenarios, discrete-time point-referenced spatio-temporal data, often being the end product of complex interactions among environmental processes, are better modeled by nonlinear dynamic spatio-temporal models (NLDSTMs). Several such nonlinear models have been proposed in the context of precipitation, deposition, and sea-surface temperature modeling. Some of the above-mentioned models, although are fitted classically, dynamic spatio-temporal models with their complex dependence structure, are more naturally accommodated within the fully Bayesian framework. In this article, we review many such linear and nonlinear Bayesian models for discrete-time point-referenced spatio-temporal data. As we go along, we also review some nonparametric spatio-temporal models as well as some recently proposed Bayesian models for massive spatio-temporal data.
PubDate: 2022-03-25
DOI: 10.1007/s41745-022-00298-w

• Mutations and the Distribution of Lifetime Reproductive Success

Abstract: Evolution proceeds in large part by the establishment of mutations in the genome of organisms, but even an advantageous mutation may be lost by chance. The probability of such loss is the extinction probability of an individual with a random lifetime reproductive success (LRS). We show here that the traditional approximation of extinction probability in terms of the mean and variance of LRS does not always apply, because the LRS has a skewed, often multimodal, distribution. To exemplify distinct life history patters, we use the Hadza and Pacific Chinook salmon. The traditional approximation overestimates the exact extinction probability from complete LRS distribution. An accurate analysis of the distribution of LRS strengthens our ability to successfully analyze evolution.
PubDate: 2022-03-25
DOI: 10.1007/s41745-022-00297-x

• Phase-field Modeling of Phase Transformations in Multicomponent Alloys: A
Review

Abstract: Almost all alloys of engineering importance are multicomponent in character. Multicomponent alloys are subject to complex interplay of thermodynamic and kinetic parameters and display a rich variety of microstructural features which are not seen in binary alloys. Achieving microstructural control of multicomponent alloys is central to their efficacy in specific applications. Unraveling the chemistry-thermomechanical processing-microstructure relationships in multicomponent alloys only through experiments have been proven to be a resource intensive approach. Quantitative simulations of microstructural evolution in multicomponent alloys using the technique of phase-field modeling can significantly offset the experimental burden and provide an energy efficient and sustainable framework for alloy design. In this review, we focus on those phase-field models which can consider the evolution of multiple phases simultaneously in a multicomponent system and attempt to understand the history of their emergence as tools of predictive value. We briefly review the studies conducted with such multiphase, multicomponent phase-field models and conclude with a commentary on the future role of phase-field modeling towards the sustainable development of novel multicomponent alloys.
PubDate: 2022-03-20
DOI: 10.1007/s41745-022-00288-y

• Empirical Bayes and Selective Inference

Abstract: We review the empirical Bayes approach to large-scale inference. In the context of the problem of inference for a high-dimensional normal mean, empirical Bayes methods are advocated as they exhibit risk-reducing shrinkage, while establishing appropriate control of frequentist properties of the inference. We elucidate these frequentist properties and evaluate the protection that empirical Bayes provides against selection bias.
PubDate: 2022-03-07
DOI: 10.1007/s41745-022-00286-0

• Fractional Processes and Their Statistical Inference: An Overview

Abstract: We give an overview of properties of fractional processes such as fractional Brownian motion, mixed fractional Brownian motion, sub-fractional Brownian motion, fractional Lévy process , fractional Poisson process and present a short review of problems of statistical inference for processes driven by fractional processes.
PubDate: 2022-02-24

• Statistical Thermal Efficiency and Quantum Interactions

Abstract: We statistically deal with two distinct quantum interactions embedded within an exactly solvable model that mimics some well known nuclear effects. By recourse to finite temperature statistical quantifiers we describe how these two fermion–fermion interactions compete and strongly influence each other. Statistically scrutinizing such competition leads to interesting insights on many body dynamics’ features. We discuss, in particular, the thermal efficiency of the fermion–fermion interactions
PubDate: 2022-02-23

• Neutralizing Antibodies and Antibody-Dependent Enhancement in COVID-19: A
Perspective

Abstract: Antibody-dependent enhancement (ADE) is an alternative route of viral entry in the susceptible host cell. In this process, antiviral antibodies enhance the entry access of virus in the cells via interaction with the complement or Fc receptors leading to the worsening of infection. SARS-CoV-2 variants pose a general concern for the efficacy of neutralizing antibodies that may fail to neutralize infection, raising the possibility of a more severe form of COVID-19. Data from various studies on respiratory viruses raise the speculation that antibodies elicited against SARS-CoV-2 and during COVID-19 recovery could potentially exacerbate the infection through ADE at sub-neutralizing concentrations; this may contribute to disease pathogenesis. It is, therefore, of utmost importance to study the effectiveness of the anti-SARS-CoV-2 antibodies in COVID-19-infected subjects. Theoretically, ADE remains a general concern for the efficacy of antibodies elicited during infection, most notably in convalescent plasma therapy and in response to vaccines where it could be counterproductive.
PubDate: 2022-02-04

• Fifty Years with the Cox Proportional Hazards Regression Model

Abstract: The 1972 paper introducing the Cox proportional hazards regression model is one of the most widely cited statistical articles. In the present article, we give an account of the model, with a detailed description of its properties, and discuss the marked influence that the model has had on both statistical and medical research. We will also review points of criticism that have been raised against the model.
PubDate: 2022-01-23

• The Kullback–Leibler Divergence Between Lattice Gaussian
Distributions

Abstract: A lattice Gaussian distribution of given mean and covariance matrix is a discrete distribution supported on a lattice maximizing Shannon’s entropy under these mean and covariance constraints. Lattice Gaussian distributions find applications in cryptography and in machine learning. The set of Gaussian distributions on a given lattice can be handled as a discrete exponential family whose partition function is related to the Riemann theta function. In this paper, we first report a formula for the Kullback–Leibler divergence between two lattice Gaussian distributions and then show how to efficiently approximate it numerically either via Rényi’s $$\alpha$$ -divergences or via the projective $$\gamma$$ -divergences. We illustrate how to use the Kullback-Leibler divergence to calculate the Chernoff information on the dually flat structure of the manifold of lattice Gaussian distributions.
PubDate: 2022-01-23

• A Review of Lamellar Eutectic Morphologies for Enhancing Thermoelectric
and Mechanical Performance of Thermoelectric Materials

Abstract: We present in this review how the existence of lamellar eutectic morphologies in different classes of thermoelectric systems has been explored to enhance the thermoelectric and mechanical performance of such systems. Following a brief discussion on the physics of thermoelectricity, the case for using eutectic morphologies to achieve similar thermoelectric performance compared to those reported in multilayer thin-film and superlattice, was presented. This was followed by the presentation of eutectic morphologies in different classes of thermoelectric systems from traditional chalcogenide to half-Heusler and high-entropy alloys. Eutectic lamellar can be quickly produced in large quantities via traditional metallurgy routes that are cost-effective and can be scaled compared to other synthesis routes. As this review shows, eutectic morphologies could play a double role in simultaneously improving a thermoelectric device's thermoelectric and mechanical performance. These devices are of macroscale dimensions and require some measure of good energy conversion efficiencies and mechanical stability simultaneously.
PubDate: 2022-01-01

• A Review on Micro-mechanical Testing of NiTi-Based Shape Memory Alloys

Abstract: NiTi-based shape memory alloys are considered as potential candidates for various structural, functional and biomedical applications. This is particularly related to their unique characteristics such as pseudoelastic and shape memory effects. Considering the increasing demand of NiTi alloys in miniaturized devices, the small-scale deformation micro-mechanisms of such a material system is being thoroughly reviewed in this article. At the first hand, the fundamental characteristics of NiTi system is discussed briefly. The influence of different factors such as chemical composition, crystallographic phases and precipitates on the phase transformation and mechanical behavior of the material are emphasized next. Subsequently, an extensive overview is provided regarding the assessment of small-scale deformation behavior of NiTi alloys using two prime techniques: micro-pillar compression and instrumented nanoindentation. Pros and cons for both the characterization techniques are analyzed as well. Interestingly, uniaxial compression of the micro-pillar reveals the pseudoelastic behavior in the alloy with remarkable enhancement in martensitic transformation stress and plateau strain with respect to those observed from macro-scale testing. It is also evident from the studies that mechanical behavior of the material strongly depends on different crystallographic orientation. Contrary to the uniaxial compression, nanoindentation generates triaxial state of stress beneath the tip of indenter, which is likely to influence the deformation micro-mechanism for the NiTi system, as well. Variation in hardness, elastic modulus and recoverability of NiTi system are primarily assessed through nanoindentation-based studies. In addition, recent studies highlight the importance of optimizing the nanoindentation parameters such as tip configuration, tip radius and load level for precise estimation of pseudoelastic activity in the alloy. Moreover, a simplified approach is generated for estimating the indentation stress–indentation strain curve. Overall, this paper generates a thorough and comprehensive insight about the micro-mechanical testing of NiTi-based shape memory alloys.
PubDate: 2022-01-01

Gas Turbine/Aerospace Applications: A Review

Abstract: A dozen or so alloys have been occupying the alloy landscape in metal additive manufacturing (MAM) in terms of addressing all aspects of research, aiding the maturity of development of these alloys towards various qualified applications for the aerospace/gas turbine sector, in the last decade or so. These include, AlSi10Mg, Al7Si0.5 Mg (F357), AlMgSc, Ti6Al4V, γ-TiAl, CoCrMo, Stellite12, IN718, IN625, CM247LC, HastelloyX, SS316L, CuCrNb, CuCrZr, to name a few. There has been a tremendous interest in the design and development of novel high temperature materials for MAM MAM: Metal Additive Manufacturing is a layer-by-layer manufacturing process making use of digital manufacturing, using lasers, electron beam, plasma, to melt powders and wires to enable direct manufacturing of metallic parts. , with isotropic microstructure and high defect tolerance, to facilitate accelerated adoption of this technology to the aerospace sector. These include developing difficult to weld chemistries, alloys prone to cracking, alloys with improved high temperature properties, composite materials and creating novel alloys that are otherwise not achievable via conventional manufacturing. This article comprises a review of some of the innovations in alloy development that have been explored in recent times, comprising Aluminum, Titanium, Nickel, Cobalt, Copper, and others, with relevance to the gas turbine/aerospace arena. This includes over 100 different alloys that have been studied via laser powder bed fusion, direct energy deposition, binder jet technology, along with some novel methods of manufacturing new materials via MAM, to give a flavour for the importance that this subject has garnished in the scientific community in recent times.
PubDate: 2022-01-01

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