The first-principles methods such as density functional theory (DFT) often produce quantitative predictions for physics and chemistry of materials with explicit descriptions of electron’s behavior. We were able to provide information of electronic structures with chemical doping and metal-insulator transition of rare-earth nickelates that cannot be easily accessible with experimental characterizations. Moreover, combining with mean-field microkinetic modeling, we utilized the DFT energetics to model water gas shift reactions catalyzed by Fe3O4at steady-state and determined favorable reaction mechanism. However, the high computational costs of DFT calculations make it impossible to investigate complex chemical processes with hundreds of elementary steps with more than thousands of atoms for realistic systems. The study of molecular high energy (HE) materials using the reactive force field (ReaxFF) has contributed to understand chemically induced detonation process with nanoscale defects as well as defect-free systems. However, the reduced accuracy of the force fields canalso lead to a different conclusion compared to DFT calculations and experimental results. Machine learning force field is a promising alternative to work with comparable simulation size and speed of ReaxFF while maintaining accuracy of DFT. In this respect, we developed a neural network reactive force field (NNRF) that was iteratively parameterized with DFT calculations to solve problems of ReaxFF. We built an efficient and accurate NNRF for complex decomposition reaction of HE materials such as high energy nitramine 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX)and predicted consistent results for experimental findings. This work aims to demonstrate the approaches to clarify the reaction details of materials using the first-principles methods and machine learning force fields to guide quantitative predictions of complex chemical process.

Stimuli caused by our own voluntary actions receive a special treatment in the brain. In auditory processing, the N1 and/or P2 components of the auditory event-related brain potential (ERP) to self-initiated sounds are attenuated compared to passive sound exposure, which has been interpreted as an indicator of a predictive internal forward mechanism. Such a predictive mechanism enables differentiating the sensory consequences of one´s own actions from other sensory input and allows the mind to attribute actions to agents and particularly to the self, usually called the “sense of agency”. However, the notion that N1 and/or P2 attenuation effects to self-initiated sounds reflect internal forward model predictions is still controversial. Furthermore, little is known about the relationship between N1 and/or P2 attenuation effects and the sense of agency. Thus, the aim of the present thesis was to further investigate the nature of the N1 and/or P2 attenuation effect to self-initiated sounds and to examine its specific relationship to the sense of agency. The present thesis provides evidence that N1 and/or P2 attenuation effects to self-initiated sounds are mainly determined by movement intention and predictive internal motor signals involved in movement planning and rules out non-predictive explanations of these effects. Importantly, it is shown that sensory attenuation effects in audition are directly related to the feeling of agency, but occur independent of agency judgments. Taken together, the present thesis supports the assumptions of internal forward model theories.

<div> <div> <div> <p>Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanisms of diseases. This dissertation introduces the computational method to predict PPIs. In the first chapter, the history of identifying protein interactions and some experimental methods are introduced. Because interacting proteins share similar functions, protein function similarity can be used as a feature to predict PPIs. NaviGO server is developed for biologists and bioinformaticians to visualize the gene ontology relationship and quantify their similarity scores. Furthermore, the computational features used to predict PPIs are summarized. This will help researchers from the computational field to understand the rationale of extracting biological features and also benefit the researcher with a biology background to understand the computational work. After understanding various computational features, the computational prediction method to identify large-scale PPIs was developed and applied to Arabidopsis, maize, and soybean in a whole-genomic scale. Novel predicted PPIs were provided and were grouped based on prediction confidence level, which can be used as a testable hypothesis to guide biologists’ experiments. Since affinity chromatography combined with mass spectrometry technique introduces high false PPIs, the computational method was combined with mass spectrometry data to aid the identification of high confident PPIs in large-scale. Lastly, some remaining challenges of the computational PPI prediction methods and future works are discussed. </p> </div> </div> </div>

No description available.

The ability to rapidly assess the condition of a structure in a manner which enables the accurate prediction of its remaining capacity has long been viewed as a crucial step in allowing communities to make safe and efficient use of their public infrastructure. This objective has become even more relevant in recent years as both the interdependency and state of deterioration in infrastructure systems throughout the world have increased. Current practice for structural condition assessment emphasizes visual inspection, in which trained professionals will routinely survey a structure to estimate its remaining capacity. Though these methods have the ability to monitor gross structural changes, their ability to rapidly and cost-effectively assess the detailed condition of the structure with respect to its future behavior is limited.<div>Vibration-based monitoring techniques offer a promising alternative to this approach. As opposed to visually observing the surface of the structure, these methods judge its condition and infer its future performance by generating and updating models calibrated to its dynamic behavior. Bayesian inference approaches are particularly well suited to this model updating problem as they are able to identify the structure using sparse observations while simultaneously assessing the uncertainty in the identified parameters. However, a lack of consensus on efficient methods for their implementation to full-scale structural systems has led to a diverse set of Bayesian approaches, from which no clear method can be selected for full-scale implementation. The objective of this work is therefore to assess and enhance those techniques currently used for structural identification and make strides toward developing unified strategies for robustly implementing them on full-scale structures. This is accomplished by addressing several key research questions regarding the ability of these methods to overcome issues in identifiability, sensitivity to uncertain experimental conditions, and scalability. These questions are investigated by applying novel adaptations of several prominent Bayesian identification strategies to small-scale experimental systems equipped with nonlinear devices. Through these illustrative examples I explore the robustness and practicality of these algorithms, while also considering their extensibility to higher-dimensional systems. Addressing these core concerns underlying full-scale structural identification will enable the practical application of Bayesian inference techniques and thereby enhance the ability of communities to detect and respond to the condition of their infrastructure.<br></div>

McMaster University MASTER OF PUBLIC HEALTH (2020) Hamilton, Ontario (Health Research Methods, Evidence, and Impact) TITLE: Applying Machine Learning to Determine Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data AUTHOR: Jason D. Morgenstern, B.Sc. (University of Guelph), M.D. (Western University) SUPERVISOR: Professor L.N. Anderson, NUMBER OF PAGES: xv, 121 / The use of big data and machine learning may help to address some challenges in nutritional epidemiology. The first objective of this thesis was to explore the use of machine learning prediction models in a hypothesis-generating approach to evaluate how detailed dietary features contribute to CVD risk prediction. The second objective was to assess the predictive performance of the models. A population-based retrospective cohort study was conducted using linked Canadian data from 2004 – 2018. Study participants were adults age 20 and older (n=12 130 ) who completed the 2004 Canadian Community Health Survey, Cycle 2.2, Nutrition (CCHS 2.2). Statistics Canada has linked the CCHS 2.2 data to the Discharge Abstracts Database and the Canadian Vital Statistics Death database, which were used to determine cardiovascular outcomes (stroke or ischemic heart disease events or deaths). Conditional inference forests were used to develop models. Then, permutation feature importance (PFI) and accumulated local effects (ALEs) were calculated to explore contributions of nutrients to predicted disease. Supplement-use (median PFI (M)=4.09 x 10-4, IQR=8.25 x 10-7 – 1.11 x 10-3) and caffeine (M=2.79 x 10-4, IQR= -9.11 x 10-5 – 5.86 x 10-4) had the highest median PFIs for nutrition-related features. Supplement-use was associated with decreased predicted risk of CVD (accumulated local effects range (ALER)= -3.02 x 10-4 – 2.76 x 10-4) and caffeine was associated with increased predicted risk (ALER= -9.96 x 10-4 – 0.035). The best-performing model had a logarithmic loss of 0.248. Overall, many non-linear relationships were observed, including threshold, j-shaped, and u-shaped. The results of this exploratory study suggest that applying machine learning to the nutritional epidemiology of CVD, particularly using big datasets, may help elucidate risks and improve predictive models. Given the limited application thus far, work such as this could lead to improvements in public health recommendations and policy related to dietary behaviours. / Thesis / Master of Public Health (MPH) / This work explores the potential for machine learning to improve the study of diet and disease. In chapter 2, opportunities are identified for big data to make diet easier to measure. Also, we highlight how machine learning could find new, complex relationships between diet and disease. In chapter 3, we apply a machine learning algorithm, called conditional inference forests, to a unique Canadian dataset to predict whether people developed strokes or heart attacks. This dataset included responses to a health survey conducted in 2004, where participants’ responses have been linked to administrative databases that record when people go to hospital or die up until 2017. Using these techniques, we identified aspects of nutrition that predicted disease, including caffeine, alcohol, and supplement-use. This work suggests that machine learning may be helpful in our attempts to understand the relationships between diet and health.

Tropical forests provide important habitat for a tremendous diversity of plant and animal species. However, limitations in measuring and monitoring the structure and function of tropical forests has caused these systems to remain poorly understood. Remote-sensing technology has provided a powerful tool for quantification of structural patterns and associating these with resource use. Satellite and aerial platforms can be used to collect remotely sensed images of tropical forests that can be applied to ecological research and management. Chapter 1 of this article highlights the resources available for tropical forest remote sensing and presents a case-study that demonstrates its application to a neotropical forest located in the Petén region of northern Guatemala. The ancient polity of Tikal has been extensively studied by archaeologists and soil scientists, but little is known about the subsistence and ancient farming techniques that sustained its inhabitants. The objective of chapter 2 was to create predictive models for ancient maize (Zea mays L.) agriculture in the Tikal National Park, Petén, Guatemala, improving our understanding of settlement patterns and the ecological potentials surrounding the site in a cost effective manner. Ancient maize agriculture was described in this study as carbon (C) isotopic signatures left in the soil humin fraction. Probability models predicting C isotopic enrichment and carbonate C were used to outline areas of potential long term maize agriculture. It was found that the Tikal area not only supports a great variety of potential food production systems but the models suggest multiple maize agricultural practices were used.

This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.

No description available.

No description available.