Cure Behavior Study and Elastic Modulus Characterization of Resin System of a Quasi Poloidal Stellarator Modular Coil

Composites materials are increasingly becoming choice materials because of their tremendous strength-to-weight properties and impressive design flexibility. A more recent application of composites is in nuclear fusion reactors. One such reactor is the Quasi-Poloidal Stellarator (QPS) being developed by Oak Ridge National laboratory. QPS, with a non-axisymmetric, near-poloidally symmetric magnetic configuration, has stranded copper/epoxy composite coils, used for magnetic confinement of plasma. CTD- 404 and CTD-101K are the resins under consideration for the modular coils with copper fiber as reinforcement. Structural integrity of the modular coils over wide range of temperatures, including liquid nitrogen temperature, is of vital importance and appropriate resin with optimal cure cycle has to be used for this purpose. In this regard, a study of the stresses induced on the fibers during cure of CTD-404 and CTD-101K was performed using the Cure Induced Stress Test (CIST) setup at UT composites laboratory. Carbon fiber was used for comparison purposes. It was observed that both CTD-404 and CTD-101K induced low cure stresses and high cool down stresses. Later in this study a new method was developed to calculate the elastic modulus of a resin during cure. The knowledge of elastic modulus development of a resin during cure is vital in minimizing the residual stresses by appropriately changing the parameters of cure cycle. The method was developed based on difference in the displacements of the resin sample during cure, with fiber and without fiber. The method was developed for 3501-6 as the volume change data for CTD-101K and CTD-404 were not available. The volume change data for 3501-6, obtained by using volumetric dilatometer previously, was used and the load data of the reinforced fiber was obtained from cure-induced stress test. The curve for elastic modulus was developed for two isothermal cure cycles. Results obtained were compared with available experimental data and the data available in literature from three-point bend tests of cured samples at different cure times. The values of modulus obtained with this approach compared well with the available data. Also, a study of the effect of liquid nitrogen temperature on the elastic modulus of the modular coil composite was performed. A fixture was designed to perform a cantilever bend test in liquid nitrogen on a MTS machine. It was observed that the liquid nitrogen temperature did not affect the modulus

Technology, Leisure and Growth

This thesis develops models and methods to investigate leisure, technology and growth. Models in chapters two, three and four study the macroeconomic impacts of technology on the consumer side. The models allow for consumer habit formation for a technology good purchased for leisure. However, for the consumption good, habits are irrelevant. A method is introduced to determine the steady state of the technology good sector and consumption good sector in- dependently. These chapters show that the models can contribute to the theoretical and empirical understanding of changes in consumption growth, interest rates, labour income share and wages. Models are constructed in chapters five and six to analyse technology on the production side in the form of job replacement by robots. Chapter five shows that the impact on welfare is ambiguous because leisure in the utility function can mitigate against wage decreases. In chapter six, policy to mitigate job losses from technology/robots is discussed

Monumental Matters

Built in the sixteenth and seventeenth centuries, India’s Mughal monuments—including majestic forts, mosques, palaces, and tombs, such as the Taj Mahal—are world renowned for their grandeur and association with the Mughals, the powerful Islamic empire that once ruled most of the subcontinent. In Monumental Matters, Santhi Kavuri-Bauer focuses on the prominent role of Mughal architecture in the construction and contestation of the Indian national landscape. She examines the representation and eventual preservation of the monuments, from their disrepair in the colonial past to their present status as protected heritage sites. Drawing on theories of power, subjectivity, and space, Kavuri-Bauer’s interdisciplinary analysis encompasses Urdu poetry, British landscape painting, imperial archaeological surveys, Indian Muslim identity, and British tourism, as well as postcolonial nation building, World Heritage designations, and conservation mandates

Climate Change Litigation: Chronicles from the Global South. A Comparative Study

The battles against climate change are being fought at the international level; on the domestic front; on the streets and in the courts. Climate change litigation is one such effort. The global expansion in climate litigation gives substance to claims of a transnational climate justice movement that casts courts as important players in shaping multilevel climate governance. Climate change litigants, lawyers, and judges of one country are taking their cue from their counterparts in other countries. However, only the efforts of the Global North have received prominence. The rest of the world is slated to be sleeping silently. The authors aim to de-bunk this myth. In doing so, the authors endeavour to highlight important contributions by the Courts in the Global South in furthering the jurisprudence of climate change litigation

Source Apportionment and Forecasting of Aerosol in a Steel City - Case Study of Rourkela

Urban air pollution is one of the biggest problems ascending due to rapid urbanization and industrialization. The improvement of air quality in an urban area in general, constitutes of three phases, monitoring, modeling and control measures. The present research work addresses the requirements of the urban air quality management programme (UAQMP) in Rourkela steel city. A typical UAQMP contains three aspects: monitoring of air pollution, modeling of air pollution and taking control measures. The present study aims to conduct the modeling of particulate air pollution for a steel city. Modeling of particulate matter (PM) pollution is nothing but the application of different mathematical models in source apportionment and forecasting of PM. PM (PM10 and TSP) was collected twice a week for two years (2011-2012) during working hours in Rourkela. The seasonal variations study of PM showed that the aerosol concentration was high during summer and low during monsoon. A detailed chemical characterization of both PM10 and TSP was carried out to find out the concentrations of different metal ions, anions and carbon content. The Spearman rank correlation analysis between different chemical species of PM depicted the presence of both crustal and anthropogenic origins in particulate matter. The enrichment factor analysis highlighted the presence of anthropogenic sources. Three major receptor models were used for the source apportionment of PM, namely chemical mass balance model (CMB), principal component analysis (PCA) and positive matrix factorization (PMF). In selecting source profiles for CMB, an effort has been put to select the profiles which represent the local conditions. Two of the profiles, namely soil dust and road dust, were developed in the present study for better accuracy. All three receptor models have shown that industrial (40-45%) and combustion sources (30-35%) were major contributors to particulate pollution in Rourkela. Artificial neural networks (ANN) were used for the prediction of particulate pollution using meteorological parameters as inputs. The emphasis is to compare the performances of MLP and RBF algorithms in forecasting and provide a rigorous inter-comparison as a first step toward operational PM forecasting models. The training, testing and validation errors of MLP networks are significantly lower than that of RBF networks. The results indicate that both MLP and RBF have shown good prediction capabilities while MLP networks were better than that of RBF networks. There is no profound bias that can be seen in the models which may also suggest that there are very few or zero external factors that may influence the dispersion and distribution of particulate matter in the study area

A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP

Artificial Intelligence (AI) and Business Innovation in Insurance: A Comparison of Incumbent Firms versus New Entrants

Artificial Intelligence (AI) systems evolve in response to new data by using adaptive algorithms. The insurance industry is data intensive, and dynamic. It is therefore particularly suitable for AI implementation. An innovation triangle framework is proposed that consists of product, process and value chain innovation. A comparison of leading incumbent insurance firms with new entrants illustrates significant competitive differences. The incumbents apply AI to defend their market positions by enhancing existing strengths and capabilities across the three innovation types. The new entrants exploit AI technology to build new products with innovative features that emphasise customer value and user experience. The innovation triangle is a useful managerial tool to analyse the nature and extent of innovation in insurance and can be used to evaluate and plan AI strategies by mapping existing AI initiatives to specific types of innovation and identifying innovation objectives and opportunities. Future trends and research opportunities are outlined

Information Centric Strategies for Scalable Data Transport in Cyber Physical Systems (CPSs)

Cyber-Physical Systems (CPSs) represent the next generation of computing that is ubiquitous, wireless and intelligent. These networked sens- ing systems are at the intersection of sensing, communication, control, and computing [16]. Such systems will have applications in numerous elds such as vehicular systems and transportation, medical and health care systems, smart homes and buildings, etc. The proliferation of such sensing systems will trigger an exponential increase in the computational devices that exchange data over existing network infrastructure.;Transporting data at scale in such systems is a challenge [21] mainly due to the underlying network infrastructure which is still resource con- strained and bandwidth-limited. Eorts have been made to improve the network infrastructure [5] [2] [15]. The focus of this thesis is to put forward information-centric strategies that optimize the data transport over existing network infrastructure.;This thesis proposes four dierent information-centric strategies: (1) Strategy to minimize network congestion in a generic sensing system by estimating data with adaptive updates, (2) An adaptive information exchange strategy based on rate of change of state for static and mobile networks, (3) Spatio-temporal strategy that maintains spatial resolution by reducing redundant transmissions, (4) Proximity-dependent data transfer strategy to ensure most updated information in high-density regions. Each of these strategies is experimentally veried to optimize the data transport in their respective setting

Fintech and the future of financial services: What are the research gaps?

New financial technologies (FinTech) have erupted around the world. Consequently, there has been a considerable increase in academic literature on FinTech over the last five years. Research tends to be scantily connected with no coherent research agenda. Signi - cant research gaps and important questions remain. There is much work to be done before this area becomes an established academic discipline. This paper offers coherent research themes formulated through focus group meetings with policymakers and academics, and also based on a critical assessment of the literature. We outline seven key research gaps with questions that could form the basis of academic study. If these are addressed it would help this area become an established academic discipline

Energy-biased technical change in the Chinese industrial sector with CES production functions

We develop a theoretical framework to study energy-biased technical change considering capital, labor and energy as inputs. The framework involves a first order condition estimation of elasticity and technical change parameters for a three factor-nested Constant Elasticity of Substitution (CES) function. Technical change parameters, elasticities and time derivatives of marginal products are combined to compute technical change bias. Conceptually, we introduce total bias in order to estimate the direction without requiring a direct comparison with another factor. For Chinese industries from 1990 to 2012, the optimal structure is capital and energy to be combined at the composite level and then with labor to form total output. Technical change is found to be unambiguously energy biased, it increases in every year, and the bias is predominately away from labor. The results show that Chinese industrialization was fuelled by fossil fuels and energy-intensive technologies. Nonetheless, the growth rate of energy-biased technical change decreased during the 2000s that may result from more energy efficient development.financial support provided by the China Natural Science Funding No. 71673134, Qing Lan Project