abstract: To extend the lifetime of complementary metal-oxide-semiconductors (CMOS), emerging process techniques are being proposed to conquer the manufacturing difficulties. New structures and materials are proposed with superior electrical properties to traditional CMOS, such as strain technology and feedback field-effect transistor (FB-FET). To continue the design success and make an impact on leading products, advanced circuit design exploration must begin concurrently with early silicon development. Therefore, an accurate and scalable model is desired to correctly capture those effects and flexible to extend to alternative process choices. For example, strain technology has been successfully integrated into CMOS fabrication to improve transistor performance but the stress is non-uniformly distributed in the channel, leading to systematic performance variations. In this dissertation, a new layout-dependent stress model is proposed as a function of layout, temperature, and other device parameters. Furthermore, a method of layout decomposition is developed to partition the layout into a set of simple patterns for model extraction. These solutions significantly reduce the complexity in stress modeling and simulation. On the other hand, semiconductor devices with self-feedback mechanisms are emerging as promising alternatives to CMOS. Fe-FET was proposed to improve the switching by integrating a ferroelectric material as gate insulator in a MOSFET structure. Under particular circumstances, ferroelectric capacitance is effectively negative, due to the negative slope of its polarization-electrical field curve. This property makes the ferroelectric layer a voltage amplifier to boost surface potential, achieving fast transition. A new threshold voltage model for Fe-FET is developed, and is further revealed that the impact of random dopant fluctuation (RDF) can be suppressed. Furthermore, through silicon via (TSV), a key technology that enables the 3D integration of chips, is studied. TSV structure is usually a cylindrical metal-oxide-semiconductors (MOS) capacitor. A piecewise capacitance model is proposed for 3D interconnect simulation. Due to the mismatch in coefficients of thermal expansion (CTE) among materials, thermal stress is observed in TSV process and impacts neighboring devices. The stress impact is investigated to support the interaction between silicon process and IC design at the early stage. / Dissertation/Thesis / Ph.D. Electrical Engineering 2011

The Brazilian coast holds 21 species of crabs from the Leucosiidae family, being an interesting group for biogeographical studies. This work gathered 531 occurrence points from these species. This data survey was done to allow the usage of a predictive modeling technique implemented by Maxent software, in order to estimate the species geographical distribution. Those models were then subject of a Parsimony endemism analysis. Maps from the potential distribution of 15 species from this family were generated and used to determine biogeographical distributional patterns, that indicated an endemism area at the western Atlantic ocean, that was also considered a secondary dispersion center. Even though predictive modeling was never used before at continental shelf environments, it proved to show results that match the species distributional limits proposed in previous studies. / Os caranguejos da família Leucossidae possuem 21 espécies distribuídas na plataforma continental da costa brasileira, e representam um interessante grupo para estudos de biogeografia. Para a realização deste trabalho, foram avaliados 531 pontos de ocorrência de espécies desta família. Este levantamento foi feito com o intuito de estimar a distribuição geográfica das espécies, e para isto a técnica de modelagem preditiva foi utilizada, através do programa Maxent. A fim de maximizar os resultados, foram analisadas dentre as áreas de ocorrência quais seriam centro de endemismo utilizando a Análise de Parcimônia de Endemismo. Quinze espécies da família foram modeladas originando mapas de distribuição potencial, que por fim determinaram padrões de distribuição biogeográficos e apontaram uma área de endemismo no Atlântico ocidental, além de considerar esta porção do oceano, como um centro de dispersão secundário. Apesar da modelagem preditiva nunca ter sido utilizada em ambientes marinhos de plataforma continental, a mesma se mostrou coerente com estudos anteriores na determinação de limites de distribuição das espécies.q

O objetivo deste trabalho foi desenvolver e comparar modelos preditivos para detecção de diabetes não diagnosticado utilizando diferentes algoritmos de aprendizagem de máquina. Os dados utilizados foram do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil), um conjunto bastante completo com aproximadamente 15 mil participantes. As variáveis preditoras foram selecionadas de forma que sejam informações simples dos participantes, sem necessidade de exames laboratoriais. Os testes foram realizados em quatro etapas: ajuste dos parâmetros através de validação cruzada, seleção automática de variáveis, validação cruzada para estimativa de erros e teste de generalização em um conjunto independente dos dados. Os resultados demonstram a viabilidade de utilizar informações simples para detectar casos diabetes não diagnosticado na população. Além disso, os resultados comparam algoritmos de aprendizagem de máquina e mostram a possibilidade de utilizar outros algoritmos, alternativamente à Regressão Logística, para a construção de modelos preditivos. / The aim of this work was to develop and to compare predictive models to detect undiagnosed diabetes using different machine learning algorithms and data from the Longitudinal Study of Adult Health (ELSA-Brasil), which collected an extensive dataset from around 15 thousand participants. The predictor variables were selected from literature research. The tests were performed in four steps: parameter tuning with cross validation, automatic feature selection, cross validation to error evaluation and generalization test in an independent dataset. The results show the feasibility of extracting useful information from ELSA-Brasil as well as the potential to use other algorithms, in addition to logistic regression, to build predictive models from ELSA-Brasil dataset.

Submitted by Gustavo Reis de Brito null (gustavrbrito@hotmail.com) on 2018-03-26T12:32:43Z No. of bitstreams: 1 brito_gr_de_dissert_comp.pdf: 9722325 bytes, checksum: 531c28b91fdc904535dc79b2434e8422 (MD5) / Approved for entry into archive by Laura Akie Saito Inafuko (linafuko@assis.unesp.br) on 2018-03-26T17:07:47Z (GMT) No. of bitstreams: 1 brito_gr_me_assis.pdf: 9722325 bytes, checksum: 531c28b91fdc904535dc79b2434e8422 (MD5) / Made available in DSpace on 2018-03-26T17:07:47Z (GMT). No. of bitstreams: 1 brito_gr_me_assis.pdf: 9722325 bytes, checksum: 531c28b91fdc904535dc79b2434e8422 (MD5) Previous issue date: 2018-02-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A Biologia da Conservação é uma ciência multidisciplinar surgia em meados dos anos 80 através da necessidade da junção de diferentes áreas do conhecimento frente às mudanças ambientais que afetam a biota como um todo. De maneira concomitante, o avanço das tecnologias permitiu a integração de áreas como a Ecologia com a computação, permitindo estudos que fossem capazes de gerar predições não só atuais, mas futuras em relação às espécies e o ambiente em que estas estão inseridas. Conhecido como modelagem preditiva de distribuição de espécies, modelagem de nicho ecológico ou simplesmente modelagem preditiva, o processo de modelamento da relação entre espécies e ambiente se baseia em diferentes tipos de algoritmos computacionais visando atender não só a demanda por um conhecimento ecológico, mas atender principalmente estudos de conservação. O presente trabalho, demonstrou que a modelagem preditiva de distribuição de espécies é uma importante ferramenta aliada à Ecologia e à Biologia da Conservação e que, embora seja uma área em ascensão, ainda necessita de estudos quanto aos processos utilizados na produção dos modelos. Neste trabalho foi avaliada a interferência do tamanho da amostra no resultado final do modelo através da utilização de diferentes tamanhos amostrais para seis espécies de aves brasileiras, produzindo resultados que demonstram que o tamanho amostral é um dos principais pontos críticos para o processo de modelagem, requerendo atenção por parte do pesquisador para evitar modelos de baixa qualidade, ou ainda, que contenham informações que sub ou superestimam a real distribuição das espécies. / Emerged in the mid-80s as a multidisciplinary science, the Conservation Biology was the result of the need to bring together different areas of knowledge in the face of environmental changes that affect the biota as a whole. At the same time, the advance of technologies permitted the integration of Ecology with Computation, allowing studies capable of generating not only current but future predictions regarding the species and the environment in which they are inserted. Known as species distribution modeling, ecological niche modeling, or simply, predictive modeling, the process of modeling the relationship between species and the environment is based on different types of computational algorithms, aimed at meeting not only the demand for ecological knowledge, but to attend the studies of conservation. The present work showed that the predictive modeling of species distribution is an important tool for Ecology and Conservation Biology and that although it is a growing area, it still needs studies on the process used in the production of the models. This study evaluated the interference of sample size in the final result of the model through the use of different sample sizes for six species of Brazilian birds, producing results that demonstrate that sample size is one of the main critical points for the modeling process, requiring attention on the part of the researcher to avoid low quality models or that contain information that under or overestimates the real distribution of the species.

O objetivo deste trabalho foi desenvolver e comparar modelos preditivos para detecção de diabetes não diagnosticado utilizando diferentes algoritmos de aprendizagem de máquina. Os dados utilizados foram do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil), um conjunto bastante completo com aproximadamente 15 mil participantes. As variáveis preditoras foram selecionadas de forma que sejam informações simples dos participantes, sem necessidade de exames laboratoriais. Os testes foram realizados em quatro etapas: ajuste dos parâmetros através de validação cruzada, seleção automática de variáveis, validação cruzada para estimativa de erros e teste de generalização em um conjunto independente dos dados. Os resultados demonstram a viabilidade de utilizar informações simples para detectar casos diabetes não diagnosticado na população. Além disso, os resultados comparam algoritmos de aprendizagem de máquina e mostram a possibilidade de utilizar outros algoritmos, alternativamente à Regressão Logística, para a construção de modelos preditivos. / The aim of this work was to develop and to compare predictive models to detect undiagnosed diabetes using different machine learning algorithms and data from the Longitudinal Study of Adult Health (ELSA-Brasil), which collected an extensive dataset from around 15 thousand participants. The predictor variables were selected from literature research. The tests were performed in four steps: parameter tuning with cross validation, automatic feature selection, cross validation to error evaluation and generalization test in an independent dataset. The results show the feasibility of extracting useful information from ELSA-Brasil as well as the potential to use other algorithms, in addition to logistic regression, to build predictive models from ELSA-Brasil dataset.

As the population ages, policymakers struggle to cope with the increasing demands for home care and institutional long-term care. This thesis project focuses on factors associated with the transition from home to institutional care for people with dementia. Using health administrative data at a population level, we construct a multivariable model that estimates the time between home care initiation after dementia diagnosis and placement in a long-term care home. From the model, we identify protective factors that allow people with dementia to remain at home for longer, with a particular emphasis on the health workforce and the contribution of formal and informal caregivers to delaying the transition from home to institutional care. Together, these results inform policymakers in capacity planning and in determining where investments should be targeted to maintain people with dementia at home, along with the associated health workforce implications.

La prise de décision médicale se définit par le choix du traitement de la maladie, dans l’attente d’un résultat probable tentant de maximiser les bénéfices sur la santé du patient. Ce choix de traitement doit donc reposer sur les preuves scientifiques de son efficacité, ce qui renvoie à une problématique d’estimation de l’effet-traitement. Dans une première partie, nous présentons, proposons et discutons des méthodes d’inférence causale, permettant d’estimer cet effet-traitement par des approches expérimentales ou observationnelles. Toutefois, les preuves obtenues par ces méthodes fournissent une information sur l’effet-traitement uniquement à l’échelle de la population globale, et non à l’échelle de l’individu. Connaître le devenir probable du patient est essentiel pour adapter une décision clinique. Nous présentons donc, dans une deuxième partie, l’approche par modélisation prédictive, qui a permis une avancée en médecine personnalisée. Les modèles prédictifs fournissent au clinicien une information pronostique pour son patient, lui permettant ensuite le choix d’adapter le traitement. Cependant, cette approche a ses limites, puisque ce choix de traitement repose encore une fois sur des preuves établies en population globale. Dans une troisième partie, nous proposons donc une méthode originale d’estimation de l’effet-traitement individuel, en combinant inférence causale et modélisation prédictive. Dans le cas où un traitement est envisagé, notre approche permettra au clinicien de connaître et de comparer d’emblée le pronostic de son patient « avant traitement » et son pronostic « après traitement ». Huit articles étayent ces approches. / Medical decision-making is defined by the choice of treatment of illness, which attempts to maximize the healthcare benefit, given a probable outcome. The choice of a treatment must be therefore based on a scientific evidence. It refers to a problem of estimating the treatment effect. In a first part, we present, discuss and propose causal inference methods for estimating the treatment effect using experimental or observational designs. However, the evidences provided by these approaches are established at the population level, not at the individual level. Foreknowing the patient’s probability of outcome is essential for adapting a clinical decision. In a second part, we present the approach of predictive modeling, which provided a leap forward in personalized medicine. Predictive models give the patient’s prognosis at baseline and then let the clinician decide on treatment. This approach is therefore limited, as the choice of treatment is still based on evidences stated at the overall population level. In a third part, we propose an original method for estimating the individual treatment effect, by combining causal inference and predictive modeling. Whether a treatment is foreseen, our approach allows the clinician to foreknow and compare both the patient’s prognosis without treatment and the patient’s prognosis with treatment. Within this thesis, we present a series of eight articles.

The high expenditure of healthcare in the United States (U.S.) does not translate into better quality of care. Indeed, the U.S. healthcare system is recognized by its lack of efficiency and waste (which represents about 20% of the country’s healthcare expenses). Lack of coordination is one of the most referenced causes of waste in the U.S. healthcare system, and preventable hospital readmissions have been acknowledged to be evidence of poor coordination of care. In fiscal year 2013, the Centers for Medicare and Medicaid Services (CMS) established financial penalties for inpatient care reimbursements in hospitals with excessive readmissions. All the same, the preliminary results of this effort have yet to result in a consistent reduction of readmission rates. Research in healthcare policy is usually reported through case studies, which makes it difficult to apply that research to different spatiotemporal contexts. Additionally, relevant research can remain overlooked due to the challenge of translating it from other fields. Therefore, in order to create effective healthcare policies, a system that can provide the most accurate information to stakeholders about their decisions and the future impact of those decisions should be developed. This dissertation proposes a decision-based support system that could aid hospital administrators in the design of disease-specific interventions that target specific groups of patients who are at risk for readmission. First, the use of disease-specific interventions that were designed to reduce readmissions will be explored. Second, a variety of predictive tools for readmissions will be developed and compared to complete the search for the best tool. Finally, an optimization model bringing together the two ideas will be formulated so that hospitals can use it to design interventions. This model will target specific patients depending on their risk for readmission and minimize the cost of intervention while ensuring quality hospital performance. In sum, this work will help hospital administrators to better plan in the reduction of readmissions and in the implementation of interventions. In addition, it will deepen knowledge about the impacts of economic penalties on hospitals and facilitate the construction of stronger arguments for decisions about healthcare policy.

The proposed human intestinal absorption prediction model is applied to over 900 pharmaceuticals and has about 82.5% true prediction power. This study will provide a screening tool that can differentiate well absorbed and poorly absorbed drugs in the early stage of drug discovery and development. This model is based on fundamental physicochemical properties and can be applied to virtual compounds. The maximum well-absorbed dose (i.e., the maximum dose that will be more than 50 percent absorbed) calculated using this model can be utilized as a guideline for drug design, synthesis, and pre-clinical studies.

Ready to eat (RTE) meat products are subject to recontamination after industrial processing, mainly by Listeria monocytogenes, a pathogenic microorganism that can persist for a long time in the environment. A RTE meat product that is contaminated with L. monocytogenes due to cross contamination during some stage after industrial processing, such as weighing, slicing or wrapping, can be an important causer of disease, due to absence of a kill step before consumption. The objective of this project was to measure the transfer of L. monocytogenes during slicing of cooked ham (cross contamination) at retail, simulating in the laboratory the practices in commercial slicing, and to develop a predictive model capable of describing this transfer. It was observed that in the first slices obtained after the experimental contamination of the slicer, the counts and the transfer rates of L. monocytogenes were higher than in the subsequent slices, and the counting curves presented a long tail as the slices were obtained. The data demonstrate that the slicer may be a relevant source of cross contamination of L. monocytogenes for RTE meat products, regardless of the level of contamination of the slicer. With the data obtained, a new transfer model was proposed called 4p-2se, as it contained four parameters (4p) and two environments (2se), and was independent of the quantification of the pathogen transferred to the slicer. The proposed model was compared to two pathogen transfer models previously described, and the predicted data presented lower RMSE (Root Mean Sum of squared errors) values than the other models. The 4p-2se model was able to satisfactorily predict the pathogen transfer data during slicing of cooked ham, which could assist the food retail establishments and regulatory agencies in the evaluation and control of cross contamination of RTE foods and in the design of proper risk management strategies. / Os produtos derivados da carne que são prontos para consumo estão sujeitos à recontaminação após o processamento industrial, principalmente por Listeria monocytogenes, um microrganismo patogênico capaz de persistir por longo tempo no ambiente. Um produto cárneo pronto para consumo que se contamina com L. monocytogenes devido à contaminação cruzada durante alguma etapa após o processamento industrial, tal como pesagem, fatiamento ou acondicionamento, pode ser um importante causador de enfermidade, pois não há uma etapa de eliminação do patógeno antes do consumo. Este projeto teve por objetivo mensurar a transferência de L. monocytogenes durante o fatiamento de presunto cozido (contaminação cruzada), simulando em laboratório práticas adotadas nos estabelecimentos comerciais de fatiamento de produtos prontos para o consumo, e desenvolver um modelo preditivo capaz de descrever esta transferência. Foi observado que nas primeiras fatias obtidas após a contaminação experimental do fatiador, as contagens e as taxas de transferência de L. monocytogenes eram mais altas que nas subsequentes, observando-se que as curvas de contagem apresentavam uma longa cauda ao longo do fatiamento. Os dados demonstram que o fatiador pode ser uma fonte importante de contaminação cruzada de L. monocytogenes para produtos cárneos prontos para o consumo fatiados, independentemente do nível de contaminação do fatiador. Com os dados obtidos, foi possível sugerir um novo modelo de transferência, denominado 4p-2se, formado por uma equação com apenas quatro parâmetros (4p) e dois ambientes (2se,) sendo esse modelo independente da quantificação do patógeno transferido para o fatiador. O modelo sugerido foi comparado a outros dois modelos de transferência previamente descritos, observando os dados preditos no modelo 4p-2se apresentavam valores de RMSE (Root Mean Sum of squared erros) mais baixos que os demais modelos. O modelo proposto mostrou-se capaz de predizer satisfatoriamente os dados de transferência de patógeno durante o fatiamento de presunto cozido, podendo auxiliar os estabelecimentos comerciais de alimentos e as agências reguladoras na avaliação e controle da contaminação cruzada de alimento prontos para consumo e na concepção de estratégias adequadas de gestão de risco.