A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Current Issues Related to the Assessment of Sexual Deviance in Special Sex Offender Populations

The assessment of sexual deviance among sex offenders represents a mature and robust field of study, and yet there are particular offender populations that have received relatively little empirical attention and that were the focus of the current project. The present studies were archival in nature and utilized offender data from the Regional Treatment Centre (RTC) in Kingston, Ontario. Participants were adult male federal sex offenders who had been referred to the RTC for intensive sex offender treatment. As a requisite component of the program, participants completed a standardized assessment battery which included interviews, phallometric testing, and the administration of various psychometric instruments. Study 1 examined potential differences in phallometric responding based on participant ethnicity and phallometric stimulus type (i.e., visual or auditory). It was found that both White and visible minority offenders demonstrated greater deviant responding to auditory stimuli relative to visual stimuli, with no other significant differences in responding based on stimulus type between the two groups of offenders. These results suggested that both White and visible minority offenders were likely able to imagine their ideal victim when being exposed to auditory stimuli, which may have been influenced by a variety of victim characteristics including, but not limited to, victim ethnicity. Study 2 examined potential correlations between social desirability, IQ, and phallometric responding. The majority of the study hypotheses were not supported, although there was some evidence for the influence of social desirability on phallometric responding. Overall, the results of the study demonstrated the effectiveness of using differential and/or ratio transformations of penile plethysmography (PPG) data in order to accommodate the influence of extraneous variables on phallometric responding. Finally, Study 3 examined the influence of age on phallometric responding and the utility of an alternative measure of sexual deviance, the Multiphasic Sex Inventory (MSI). Age was generally found to be negatively correlated with phallometric responding, and as with Study 2, the results illustrated the importance of using PPG data transformations in order to control for the effects of variables such as age. The study also offered promising findings for the utility of the MSI as a measure of sexual deviance. Strengths, limitations, and implications are discussed

Economic evaluation of seawater desalination : a case study analysis of cost of water production from seawater desalination in Saudi Arabia

As a result of the increasing scarcity of freshwater resources worldwide, many countries have resorted to the use of unconventional sources, of which seawater desalination is the most significant, for meeting the supply-demand gap. However, despite the recorded advances in desalination technologies of recent decades, desalination remains a very expensive operation and operators will be greatly assisted if reliable means of predicting the costs are available to aid effective decision making during planning of new plants or the operation of existing plants. To achieve this, it is important to fully understand the factors that contribute to desalination costs, which could then be used to develop appropriate models for predicting costs that can support budgeting and/or cost reductions decision making. Consequently, this project has investigated the development of such models for predicting monthly production costs using data from 16 operational plants in Saudi Arabia. Monthly and annual data spanning 2001 – 2010 were collected on total water production, type of desalination technique, sea water salinity, product water salinity, energy consumption, and total (capital and operational) unit cost of water production. Because of the way in which the data were archived, some of the variables only had the annual totals for some of the years, which made them unsuitable for the monthly scale adopted for the analyses. Consequently, disaggregation schemes based on several variants of the method of fragments widely used in hydrological studies were used to obtain monthly data from the annual data. Exploratory analysis showed that the monthly costs correlated most with the total water production, which then formed the lone independent variable for various tested regression model formulations. In general, an inverse regression model performed best during both calibration and validation. To enhance the usefulness of the predictive model for decision making, uncertainty limits of the predictions were constructed using a Monte Carlo simulation approach involving the seasonal, lag-1 autoregressive generation of equally likely realisations of the available historic records that have been transformed to remove the skewness. Extensive testing of the data generation technique showed that the assumed lag-1 auto-regressive dependence structure was adequate. This study thus provides for the first time a predictive model for costs of desalination in Saudi Arabia and its uncertainty range for effective budgeting and operational management. Although the models were developed using Saudi Arabia data, the fact that only one independent variable was used means that the replication of the methodology in other desalination-intensive countries can be readily carried out

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

The molecular epidemiology of Renal cell carcinoma: subtypes and prognosis

Ph.DDOCTOR OF PHILOSOPH

Genetic And Phenotypic Evolution In The Ornate Chorus Frog (pseudacris Ornata): Testing The Relative Roles Of Natural Selection,

Understanding how migration, genetic drift, and natural selection interact to maintain the genetic and phenotypic variation we observe in natural populations is a central goal of population genetics. Amphibians provide excellent model organisms for investigating the interplay between these evolutionary forces because amphibians are generally characterized by limited dispersal abilities, high philopatry, and are obligately associated with the areas around suitable habitats (e.g. breeding ponds). Thus, on relatively small geographic scales, the relative effects of all of these evolutionary forces can be studied together. Here, we study the interaction of migration, genetic drift, natural selection, and historical process in the ornate chorus frog (Pseudacris ornata). We report the development and characterization of 10 polymorphic microsatellite genetic markers. Number of alleles per locus ranged from 2 to 21 averaging 9.2 and expected heterozygosities ranged from 0.10 to 0.97 averaging 0.52. However, in an analysis of two populations, three locus-by-population comparisons exhibited significant heterozygote deficiencies and indicated that null alleles may be present some loci. Furthermore, we characterized genetic structure and historical biogeographic patterns in P. ornata using these microsatellite markers along with mitochondrial DNA sequence data. Our data indicate that in these frogs, migration may play a large role in determining population structure as pairwise estimates of FST were relatively small ranging from 0.04 to 0.12 (global FST = 0.083). Additionally, we observed an overall pattern of isolation-by-distance in neutral genetic markers across the species range. Moreover, our data suggest that the Apalachicola River basin does not impede gene flow in P. ornata as it does in many vertebrate taxa. Interestingly, we identified significant genetic structure between populations separated by only 6 km. However, this fine scale genetic structure was only present in the more urbanized of two widespread sampling localities. Finally, in this study, we demonstrated that there was a significant correlation between the frequency of green frogs and latitude. There was a higher frequency of green frogs in southern samples and a lower frequency of green frogs in northern samples. However, when we interpreted this phenotypic cline in light of the overall pattern of isolation-by-distance, it was apparent that the neutral evolutionary forces of genetic drift and migration could explain the cline, and the invocation of natural selection was not necessary