The effectiveness of film cooling

Imperial Users onl

Estimation and Clustering in Network and Indirect Data

The first part of the dissertation studies a density deconvolution problem with small Berkson errors. In this setting, the data is not available directly but rather in the form of convolution and one needs to estimate the convolution of the unknown density with Berkson errors. While it is known that the Berkson errors improve the precision of the reconstruction, it does not necessarily happen when Berkson errors are small. Furthermore, the choice of bandwidth in density estimation has been an open problem so far. In this dissertation, we provide an in-depth study of the choice of the bandwidth which leads to the optimal error rates. The second part of the dissertation studies a generative network model, the so-called Popularity Adjusted Block Model (PABM) introduced by Sengupta and Chen (2018). The PABM generalizes popular graph generative models such as the Stochastic Block Model (SBM) and the Degree Corrected Block Model (DCBM). The advantages of the PABM is that, unlike mixed membership models or the DCBM, it does not rely on any identifiability conditions, and leads to more flexible spectral properties. We expand the theory of PABM to the case of an arbitrary number of communities which possibly grows with a number of nodes in the network and is not assumed to be known. We produce the estimators of the probability matrix and the community structure and provide non-asymptotic upper bounds for the estimation and the clustering errors. Majority of real-life networks are sparse, in the sense that they have few high degree nodes while the rest of the nodes have low degrees. Since the SBM and DCBM do not allow to set any probabilities of connections to zero, they model sparsity by enforcing the maximum connection probability to be bounded above by a small quantity which precludes existence of high degree nodes. On the contrary, the PABM allows modeling some of the probabilities of connections between the nodes as identical zeros while maintaining the rest of the probabilities non-negligible. This leads to the Sparse Popularity Adjusted Block Model (SPABM). The SPABM reduces the size of parameter set and leads to improved precision of estimation and clustering. We produce the estimators of the probability matrix and the community structure in SPABM. Finally, we provide non-asymptotic upper bounds for the estimation and the clustering errors

Zebrafish: a novel model in neuropsychopharmacological research

The zebrafish (danio rerio) has recently become a powerful animal model for research purposes and drug discovery due to its ease of maintenance, genetic manipulability and ability for high-throughput screening. It has emerged as a model species for translational research in various neuroscience areas, including pharmacogenetics and neuropharmacology. Due to their physiological (neuroanatomical, neuroendocrine, neurochemical) and genetic homology to mammals, robust phenotypes, and value in high-throughput genetic and chemical genetic screens, zebrafish are ideal for developing valid experimental models of major neuropsychiatric disorders and discovering novel therapeutics. Both larval and adult zebrafish are presently used to enhance our understanding of brain function, dysfunction, and their genetic and pharmacological modulation. This article provides a review of the developing utility of zebrafish in the analysis of complex brain disorders (including, e.g., depression, autism, psychoses, drug abuse, and cognitive deficits), also covering zebrafish applications towards the goal of modelling major human neuropsychiatric and drug-induced syndromes

Understanding the product distribution from biomass fast pyrolysis

Fast pyrolysis of biomass is an attractive route to transform solid biomass into a liquid bio-oil, which has been envisioned as a renewable substitute for crude oil. However, lack of fundamental understanding of the pyrolysis process poses a significant challenge in developing cost-effective pyrolysis based technologies for producing transportation fuels. The fundamental knowledge of pyrolysis pathways, product distribution and underlying mechanisms will have a direct and significant impact on the reactor design, strategic operation and kinetic modeling of the pyrolysis process. However, this knowledge has remained obscure due to the complexity of the pyrolysis process and lack of well established analytical methodologies. The present work provides a systematic approach to study pyrolysis, where many factors that affect the pyrolysis process are decoupled and their effect is systematically studied. The study employs a combination of analytical techniques such as Gas Chromatography - Mass Spectrometry, Gas analysis, Liquid Chromatography - Mass Spectrometry, Capillary Electrophoresis, Ion Chromatography and Gel Permeation Chromatography to identify and quantify the pyrolysis products and establish the mass balance. Pyrolysis involves a complex scheme of reactions consisting of several primary and subsequent secondary reactions. Disassociating primary and secondary reactions is often challenging because of the typical residence time of pyrolysis vapors in the traditional pyrolysis reactors. However, mechanistic understanding of the pyrolysis pathways needs information of the primary pyrolysis products, prior to complex series of secondary reactions. This was achieved by employing a system consisting of a micro-pyrolyzer which had vapor residence time of only a few milliseconds, directly coupled with the analytical equipment. The problem was further simplified by considering the pyrolysis of each individual component of biomass (hemicellulose, cellulose and lignin) one at a time. Influence of minerals and reaction temperature on the primary pyrolysis products was also studied. Secondary reactions, which become important in industrial-scale pyrolysis systems were studied by comparing the cellulose pyrolysis product distribution from micro-pyrolyzer and a bench scale fluidized bed reactor system. The study provides fundamental insights on the pyrolysis pathways of hemicellulose, cellulose and lignin. It shows that the organic components of biomass are fragmented completely into monomeric compounds during pyrolysis. These monomeric compounds re-oligomerize to produce heavy oligomeric compounds and aerosols. It also provides the understanding of the effect of parameters such as presence of minerals and temperature on the resulting product distribution. This knowledge can help tailor the pyrolysis process in order to obtain bio-oil with desired composition. The pyrolysis product distribution data reported in this dissertation can also be used as a basis to build descriptive pyrolysis models that can predict yield of specific chemical compounds present in bio-oil. Further, it also serves as a basis for distinguishing secondary reactions from the primary ones, which are important consideration in the industrial-scale systems

Local Body Tax and its impact

Local Body Tax, popularly known by its abbreviation LBT. The tax imposed by the local civil bodies of India on the entry of goods into a local area for consumption, use or sale. The tax is imposed on 52 of the State List from the Schedule VII of the Constitution of India. which says; "Taxes on the entry of goods into a local area for consumption, use or sale therein." Trader pay taxes to the civic bodies and the rules and regulations of these change from state to state within India .LBT means Taxes on the entry goods into a local area for consumption, use or sale therein .Most of the state governments withdraw octroi about Six years ago when the (VAT) Value Added Tax was introduced. However, barring a same small cities, Octroi is collected in more cities of Maharashtra, such as Mumbai-Pune. The State Government has announced that octroi will be ended from most of the municipal corporations by April 2013 and LBT will be introduced

Improving Customer Relationship Management Using Data Mining Technique in Direct To Home (DTH) Television Sector

Customer Relationship Management (CRM) refers to the tools that help organization to maintain customer relationships in a structured way. Data Mining is the process that uses a variety of data analysis techniques to generate new rules & patterns and describe the relationships in data that may be used to make accurate forecast for future. It can help to select the right persons on who to be focus .Customer satisfaction plays an important role in any organization to improve the CRM. The purpose of this paper is to maintain personal and profitable relationship between DTH (Direct To Home) television providers and customers forever by using segmentation, classification and clustering technique of data mining .This study is going to find out valued DTH customers and offering them some extra packages as well as It focuses on some value added services, like to develop pack on hourly basis, to avoid unnecessary channels cost, to improve customer service and satisfaction, and to offer channels according to customer requirements in affordable price