Non Contact Mechanical Testing at High Temperature Using Electromagnetic Forces.

Ultra high temperature ceramics (UHTCs) recently captured interest as potential materials for reusable thermal protection systems and other components in future generation supersonic and hypersonic vehicles where temperatures can reach > 2000 °C. A novel method for mechanical testing of UHTCs at such ultra high temperatures is developed utilizing electromagnetic force. Resistively heated and self-supported specimens in thin ribbon geometry under application of a transverse magnetic field undergo flexural stress from the electromagnetic Lorentz forces, which act as a distributed mechanical load and deform the specimen. This non-contact technique, termed Electro-Magnetic Mechanical Apparatus (EMMA), allows performing rapid tests in a low cost table-top apparatus at temperatures, as high as 2200 °C, otherwise impossible to achieve. The flexibility of this method offers ample opportunity to explore a wide range of mechanical properties. For example utilizing a DC current for resistive heating with a DC magnetic field creates constant loads for Creep testing; replacing with a AC current generates cyclic loads for Fatigue testing; larger magnetic fields can be used for Fast – Fracture experiments; and impulse excitation of the magnetic field vibrates the specimens and enables the determination of the material’s Elastic and Loss Modulus. Zirconium Diboride and Silicon Carbide (ZrB2-SiC) is a prominent member of UHTCs. The creep properties of this composite are explored using this technique in the temperature range 1600 – 2200 °C under stress ranging from 20 – 50 MPa in ambient air as well as non-reactive Nitrogen atmosphere. The kinetic parameters of creep, activation energy and stress exponent are established in the testing range. The creep response from the two environments is compared to understand the effect of the concomitant oxidation during high temperature testing in air. Comparison of creep data from conventional 3-pt, 4-pt flexure tests corroborate the results obtained from EMMA and validate the use of the technique to obtain comparable creep rates.Ph.D.Materials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91586/1/sindhu_1.pd

A study of the effects of cycling frequency on lithium-ion and lithium polymer batteries' degradation

Conventional energy sources are depleting rapidly and might last for another 50-150 years depending on our current usage. Environmental issues like global warming are also rising quickly. Renewable energy sources can address both these issues and offer various other advantages like stabilizing the load, lower maintenance requirements etc. However, due to their intermittent supply, they are not always reliable. The energy harnessed from renewable sources needs to be stored and readily available for our use. Hence, the power system network is shifting towards energy storage technologies. There are various energy storage technologies available, out of which the battery energy storage systems (BESS) for large-scale energy storage are widely used. Examples of BESS are in the modern electronics [and] electrical devices like laptops, smartphones, iPad etc. This thesis focuses on various types of batteries used in BESS, their degradation processes and the factors contributing to their degradation. Two types of batteries, lithium-ion and lithium-polymer were tested under different conditions to observe the degradation process and the results obtained were analyzed.Includes bibliographical references (pages 43-50

Consumer Complaints and Protection: Stable Analysis and Design Patterns

The concept of consumer complaints and protection has numerous applications across various domains. Using traditional methods of modeling design patterns is a tedious and costly task. The Software Stability Mode (SSM) is a more efficient and effective modeling method. In this thesis, the differences between the traditional method and the SSM is addressed. Then, several patterns are developed using the SSM to deal with consumer complaints. Each area, Advice, Appraisal, Commitment, Complaint, Compliance, Deed, Guideline, Gratification, Judgment, Model, Need, Ownership, Promotion, Rate, Review, Selling, Support, View, and Violation, is explored and the core knowledge of the concept of consumer complaints and protection is developed visually as well as in detail. Useful SAP and SDP templates are included for each concept. The main contribution of this thesis is the creation of stable, reusable templates that build an unlimited number of applications for the consumer complaints and protection concept

Supervised Learning for Multi-Domain Text Classification

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In this project, focus has been laid on Sentiment Analysis, which identifies the opinions expressed in a piece of text. It involves categorizing opinions in text into categories like \u27positive\u27 or \u27negative\u27. Existing works in Sentiment Analysis focused on determining the polarity (Positive or negative) of a sentence. This comes under binary classification, which means classifying the given set of elements into two groups. The purpose of this research is to address a different approach for Sentiment Analysis called Multi Class Sentiment Classification. In this approach the sentences are classified under multiple sentiment classes like positive, negative, neutral and so on. Classifiers are built on the Predictive Model, that consists of multiple phases. Analysis of different sets of features on the data set, like stemmers, n-grams, tf-idf and so on, will be considered for classification of the data. Different classification models like Bayesian Classifier, Random Forest and SGD classifier are taken into consideration for classifying the data and their results are compared. Frameworks like Weka, Apache Mahout and Scikit are used for building the classifiers

Neuroprotective Effect of Diosmin on Arsenic Trioxide Induced Neurotoxicity in Albino Wistar Rats.

The present study the term ‘neurotoxic’ is used to describe a substance, condition or state that damages the nervous system and/or brain, usually by killing neurons. Arsenic is a semi-metalloid and exposure to As is a worldwide health problem causing various disorders and diseases in millions of people around the world. Arsenic causes various diseases such as numerous organ cancers and also patients show severe effects on their nervous system. However, the mechanisms of As neurotoxicity remain somewhat obscure while the instance of As exposure remains a prevalent human health concern.114 Diosmin is a semisynthetic flavone derivative of hesperidin occur naturally in citrus fruits. The drug is widely used in treatment of varicose veins and venous ulcers, lymphatic insuffi ciency and hemorrhoids. In these conditions, Diosmin exerts a venotonic action, decreasing venous reflux, and thereby alleviating edema and providing effective venous drainage.115 Moreover, the drug has been shown to provide better outcomes for patients with impaired cardiac function before undergoing cardiac operations that require cardiopulmonary bypass. These effects of Diosmin can be ascribed to the antiinflammatory, microcirculatory, and antioxidant effects of its flavonoid substances. In this context, Diosmin has been shown to decrease the levels of granulocyte and macrophage infi ltration into the infl amed tissues as well as leucocyte adhesion to the vascular endothelium. The decrease in release of oxygen free radicals, cytokines, and proteolytic matrix metalloproteinases from activated infl ammatory and endothelial cells, results in lower levels of inflammation, decreased microvascular permeability and decreased leukocyte-dependent endothelial damage. Diosmin decreases vascular permeability more than any of its single constituents, suggesting that the flavonoids present in its formulation have a synergistic action. The drug possesses an antioxidant effect, significantly decreasing the level of hydroxyl free radicals, increasing free SH-group concentration, and natural scavenger capacity.115 The effects of arsenic on nervous system have received considerably less attention. In this study we planned to investigate the effects of Arsenic trioxide on the oxidative stress, contents of lipids, proteins, antioxidant defence systems in various regions of the rat brain to seek contribution of arsenic, if any, in peroxidative damage and other neurochemical perturbations. Furthermore, to unravel the effects of recovery on arsenic induced neurotoxicity in various regions of the rat brain. The present study attempts to screen the arsenic poisoning particularly the role of oxidative stress in the toxic manifestation, an attempt for the treatment and a possible beneficial role of antioxidants supplementation to achieve the optimum effects in wistar rats. CONCLUSION: In summary, the results of the present study suggest that arsenic neurotoxicity in rats initiated peroxidative reactions in membrane lipids of the brain. The present study demonstrates the inhibitory effect of Diosmin pretreatment on the brain oxidative stress in an in vivo model. Histopathological evaluation revealed the neuroprotective effect of Diosmin mainly at a dose of Diosmin 100 mg/kg. Treatment with Diosmin revealed a significant amelioration in arsenic induced neurotoxicity showing its protective effect by improving the Biogenic amines in various regions of brain and the AchE levels in serum and brain. These findings derive the significance of Diosmin in ameliorating the oxidative stress and neuroinflammation which are important contributors in the pathogenesis of several neurodegenerative disorders. Furthermore, it is implicated that arsenic is a de-myelinating agent. And may alter neuronal functions followed by CNS dysfunctions. Much remains to be learned about this ancient neurotoxicant. It is planned in future to study the effects of arsenic in brain with respect to myelin structure and functions, DNA and RNA levels and to seek a correlation with oxidant stress, and to estimate the levels of antioxidant defence system enzymes

<em>Lecanicillium</em> spp. for the Management of Aphids, Whiteflies, Thrips, Scales and Mealy Bugs: Review

Lecanicillium spp. are potential microbial bio-control agent mainly used for the management of sucking insect pests such as aphids, whiteflies, scales, mealy bugs etc. and gaining much importance at present for management of pests. Due to indiscriminate use of chemical pesticides which results in development of resistance, resurgence, outbreak of pests and residue problem, the farmers/growers are forced to use bio-pesticides for sustainable agriculture. Lecanicillium spp. is promising biocontrol agent against sucking insect pests and can be used as one of the components in integrated pest management (IPM). However, optimum temperature and relative humidity are the major environmental factors, for the performance of Lecanicillium spp. under protected/field conditions. The present review is mainly focused on nomenclature of Lecanicillium spp., mode of infection, natural occurrence, influence of temperature and humidity on the growth, factors influencing the efficacy, virulence/pathogenicity to target pests, substrates used for mass production, safety to non-target organisms, compatibility with agrochemicals and commercially available products. This review is mainly useful for the researchers/students to plan their future work on Lecanicillium spp

COOD:Combined out-of-distribution detection using multiple measures for anomaly &amp; novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities

COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities