Mining Temporal Association Rules with Temporal Soft Sets

This work was partially supported by the National Natural Science Foundation of China (grant no. 11301415), the Shaanxi Provincial Key Research and Development Program (grant no. 2021SF-480), and the Natural Science Basic Research Plan in Shaanxi Province of China (grant no. 2018JM1054).Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q-clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.National Natural Science Foundation of China (NSFC) 11301415Shaanxi Provincial Key Research and Development Program 2021SF-480Natural Science Basic Research Plan in Shaanxi Province of China 2018JM105

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

© 2020 The Authors. It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data

Development of computational tools and resources for systems biology of bacterial pathogens

Bacterial pathogens are a major cause of diseases in human, agricultural plants and farm animals. Even after decades of research they remain a challenge to health care as they are known to rapidly evolve and develop resistance to the existing drugs. Systems biology is an emerging area of research where all of the components of the system, their interactions, and the dynamics can be studied in a comprehensive, quantitative, and integrative fashion to generate predictive models. When applied to bacterial pathogenesis, systems biology approaches will help identify potential novel molecular targets for drug discovery. A pre-requisite for conducting systems analysis is the identification of the building blocks of the system i.e. individual components of the system (structural annotation), identification of their functions (functional annotation) and identification of the interactions among the individual components (interaction prediction). In the context of bacterial pathogenesis, it is necessary to identify the host-pathogen interactions. This dissertation work describes computational resources that enable comprehensive systems level study of host pathogen system to enhance our understanding of bacterial pathogenesis. It specifically focuses on improving the structural and functional annotation of pathogen genomes as well as identifying host-pathogen interactions at a genome scale. The novel contributions of this dissertation towards systems biology of bacterial pathogens include three computational tools/resources. “TAAPP” (Tiling array analysis pipeline for prokaryotes) is a web based tool for the analysis of whole genome tiling array data for bacterial pathogens. TAAPP helps improve the structural annotation of bacterial genomes. “ISO-IEA” (Inferred from sequence orthology - Inferred from electronic annotation) is a tool that can be used for the functional annotation of any sequenced genome. “HPIDB” (Host pathogen interaction database) is developed with data a mining capability that includes host-pathogen interaction prediction. The new knowledge gained due to the implementation of these tools is the description of the non coding RNA as well as a computationally predicted host-pathogen interaction network for the human respiratory pathogen Streptococcus pneumoniae. In summary, the computation tools and resources developed in this dissertation study will enable building systems biology models of bacterial pathogens

Transcriptome landscape of the human placenta

<p>Abstract</p> <p>Background</p> <p>The placenta is a key component in understanding the physiological processes involved in pregnancy. Characterizing genes critical for placental function can serve as a basis for identifying mechanisms underlying both normal and pathologic pregnancies. Detailing the placental tissue transcriptome could provide a valuable resource for genomic studies related to placental disease.</p> <p>Results</p> <p>We have conducted a deep RNA sequencing (RNA-Seq) study on three tissue components (amnion, chorion, and decidua) of 5 human placentas from normal term pregnancies. We compared the placental RNA-Seq data to that of 16 other human tissues and observed a wide spectrum of transcriptome differences both between placenta and other human tissues and between distinct compartments of the placenta. Exon-level analysis of the RNA-Seq data revealed a large number of exons with differential splicing activities between placenta and other tissues, and 79% (27 out of 34) of the events selected for RT-PCR test were validated. The master splicing regulator <it>ESRP1 </it>is expressed at a proportionately higher level in amnion compared to all other analyzed human tissues, and there is a significant enrichment of ESRP1-regulated exons with tissue-specific splicing activities in amnion. This suggests an important role of alternative splicing in regulating gene function and activity in specific placental compartments. Importantly, genes with differential expression or splicing in the placenta are significantly enriched for genes implicated in placental abnormalities and preterm birth. In addition, we identified 604-1007 novel transcripts and 494-585 novel exons expressed in each of the three placental compartments.</p> <p>Conclusions</p> <p>Our data demonstrate unique aspects of gene expression and splicing in placental tissues that provide a basis for disease investigation related to disruption of these mechanisms. These data are publicly available providing the community with a rich resource for placental physiology and disease-related studies.</p

Analysis of tar removal in a partial oxidation burner

+108hlm.;24c

Identification and Attenuation of Losses in Thermoacoustics: Issues Arising in the Miniaturization of Thermoacoustic Devices

Thermoacoustic energy conversion is based on the Stirling cycle and uses sound waves to displace and compress the working gas. When this process occurs inside a porous medium that is subject to a temperature gradient, a thermoacoustic engine creates intense sound. Conversely, when strong sound waves interact with a porous medium, a temperature gradient can be imposed through the attenuation of the pressure amplitude, creating a thermoacoustic refrigerator. The device size is a limiting factor to widespread use. This work investigates issues arising in their miniaturization in three separate ways. To date, the thermal properties of the driving components are largely ignored during the design phase, partially because the traditional design ``works,' and partially because of a lack of understanding of the thermal energy fluxes that occur during operation. First, a direct quantification of the influence of the thermal conductivity of the driving components on the performance of a thermoacoustic engine and refrigerator is performed. It is shown that materials with low thermal conductivity yield the highest sound output and cooling performance, respectively. As a second approach to decreasing the footprint of a thermoacoustic system, the introduction of curvature to the resonator tube was investigated. A CFD analysis of a whole thermoacoustic engine was performed, and the influence of the stack assembly on the flow behavior was investigated. Nonlinearities in the temperature behavior and vortices in the flow close to the stack ends were identified. Resonator curvature prompts a decrease in the amplitude of the pressure, velocity, and temperature oscillations. Furthermore, the total energy transfer from the stack to the fluid is also reduced. Finally, through combining the aforementioned investigations, an optimization scheme is applied to a standing wave engine. A black box solver was used to find the optimal combination of the design parameters subject to four objectives. When focusing solely on acoustic power, for example, the device should be designed to be as large as possible. On the other hand, when attempting to minimize thermal losses, the stack should be designed as small as possible

Doctor of Philosophy

dissertationThe proliferation of nonconventional subsurface hydrocarbon production methods has turned some attention toward production from deep coal seams. There exists little research into coal pyrolysis under conditions relevant to subsurface processing (large coal domains, very slow heating rates, high hydrostatic pressure, volumetric confinement). Basic studies into the phenomena of mass transfer and devolatilization in a high-volatile Utah bituminous coal are described for very large particles (>1 cm) at very slow heating rates (< 10K/min) at atmospheric pressure. Studied systems included large coal blocks heated via immersion heaters and 2 cm-diameter coal cores heated in a tube furnace apparatus. Changes in char porosity during pyrolysis as a function of heating rate are described in large coal blocks. Coal core data show char porosity evolution as a function of temperature and heating rate and demonstrate a distinct threshold for plastic deformation. Volumetric confinement of core swelling was shown to dramatically affect char morphology. Devolatilization data from coal cores are presented, showing little impact of heating rate upon total volatile yield, but a substantial impact upon the yield of tars. A Knudsen flow analysis is also presented to argue that the driving force for mass transfer at very slow heating rates is pressure-driven flow. Several novel pyrolysis phenomena are described, including a pore plugging effect at very slow heating rates. The presented experimental work suggests that many common assumptions for conventional coal pyrolysis would not apply in a subsurface processing environment

Tar production in coal pyrolysis - the effect of catalysts, pressure and extraction

Imperial Users onl

Modeling and Control of Energy Conversion during Underground Coal Gasification Process

The present book contains nine articles that were accepted and published in the Special Issue “Modeling and Control of Energy Conversion during Underground Coal Gasification Process” of the MDPI Energies journal. This book focuses on the energy conversion processes in underground coal gasification (UCG), as well as on the modeling and control of this process. The articles published in this book can be divided into three thematic parts of research in the field of underground coal gasification technology: the first part is the impact of technology on the environment, the second is research (studies) on the coal areas and coal properties of UCG technology, and the third is the monitoring, modeling, and control processes within UCG. We hope that this book will be interesting and useful for workers and researchers in the field of underground coal gasification technology, as well as for those who are interested in the mathematical modeling and control of this process