Indiana University-Purdue University Indianapolis (IUPUI) / Purpose: This in vitro study aims to analyze the effect of combining two ADA-recommended professionally applied 1:1 Chlorhexidine/Thymol varnish ((Cervitec Plus)) and professionally prescribed 5,000 ppm fluoride toothpaste ((PreviDent 5000 Plus)) on reducing lesion depth and increasing mineral content Materials & Methods: Forty-eight dentin specimens were randomly distributed into four treatment groups (n=12 per treatment). Biofilms of Streptococcus mutans and Candida albicans were created on the polished surfaces of bovine root dentin specimens (n=12 per treatment). 1:1 Chlorhexidine/Thymol varnish was applied once then the tested 5,000 ppm fluoride toothpaste was applied for 120 seconds twice daily over the course of 2 days. Tested groups were: (1) 1:1 Chlorhexidine/Thymol varnish ((Cervitec Plus)) (C/T). (2) 5,000 ppm F toothpaste ((PreviDent 5000 Plus)) (F). (3) Combination of 1:1 Chlorhexidine/Thymol varnish ((Cervitec Plus)) & 5000 ppm F toothpaste ((PreviDent 5000 Plus)) (C/T+F). (4) Deionized water (DIW) as control group. Biofilms were analyzed for biofilm dry weight. Dentin specimens were analyzed using transversal microradiography (TMR) for mineral content change and lesion depth. PH data was analyzed using two-way ANOVA. Total biofilm dry weight data was analyzed using one-way ANOVA. Integrated mineral loss and lesion depth data was analyzed using two-way ANOVA All pair-wise comparisons from ANOVA analysis were made using Fisher’s Protected Least Significant Differences to control the overall significance level at 5%. Results: Treatment with (C/T+F) resulted in higher mean pH values compared to the control group (DIW) and (F) group. The average pH values of group (C/T) were not statistically different than group (C/T+F). the biomass of the combined S. mutans & C. albicans biofilm among all the groups were not significantly different. (DIW) presented significantly deeper lesions for both surfaces (sound &demineralized) when compared to (F) (P=0.0118), (C/T) (P=0.0002), and (C/T+F) (P<.0001). The sound surfaces for the specimens for group (C/T) and Group (F) showed superficial lesion depth. However, the sound surfaces of specimens treated with (C/T+F) showed the most superficial depth. Due to mineral gain, the demineralized surfaces of the specimens of both (C/T) & (C/T+F) showed a decrease in the lesion depth. Conclusion: Within the limitations of our study. The combination of 5,000 ppm fluoride toothpaste and CHX/Thymol had no significant effect on mineral content. However, the combination had a considerable effect on lesion depth reduction.

Constraint Satisfaction Problems (CSPs) are a class of decision problems where one usually fixes a structure A and seeks to decide whether or not a given conjunction of atomic formulas is satisfiable in A or not. It has been shown by Bodirsky and Grohe that every computational decision problem is equivalent to some CSP via a polynomial-time Turing reduction. For structures A with finite domain Zhuk and Bulatov both proved an algebraic criterion classifying in which cases the CSP of A is in P and when it is NP-hard. For some classes of structures with infinite domain, there are similar P vs NP-hard dichotomies. This thesis continues the latter line of research for CSPs of first-order theories. In this version of CSPs, a theory is fixed and one seeks to decide whether or not a given conjunction of atomic formulas is satisfiable in some model of T. Assuming that the CSPs of theories T1 and T2 are polynomial-time tractable, we prove necessary and sufficient conditions for polynomial-time tractability of the union of T1 and T2. For some classes of theories, P vs NP-hard dichotomies are proven. To achieve this, various 'combinations' of structures are examined, a technique called 'sampling' is generalized to theories and clones of polymorphisms of temporal structures are examined in detail.

[pt] Esta tese consiste em três ensaios sobre previsão de inflação, com foco na inflação brasileira. No primeiro ensaio, examinamos a eficácia de vários métodos de previsão para prever a inflação, com foco na agregação de previsões desagregadas. Consideramos diferentes níveis de desagregação para a inflação e empregamos uma variedade de técnicas tradicionais de séries temporais, bem como modelos lineares e não lineares de aprendizado de máquina que lidam com um número grande de preditores. Para muitos horizontes de previsão, a agregação de previsões desagregadas performa tão bem quanto expectativas baseadas em coleta e modelos que geram previsões a partir do agregado. No geral, os métodos de aprendizado de máquina superam os modelos de séries temporais tradicionais em precisão preditiva, com excelente desempenho para os desagregados da inflação. Em nosso segundo ensaio, investigamos os potenciais benefícios de combinar previsões de inflação individuais ao propor uma correção para viés variável no tempo da média de previsões. Nossa análise inclui estimações empregando janelas rolantes e modelos em espaço de estados que usam a recursividade do filtro de Kalman. Obtivemos um bom desempenho de previsão para modelos baseados em janelas rolantes pequenas em horizontes de previsão curtos e intermediários, enquanto um modelo em espaço de estados obtem um desempenho um pouco pior do que os procedimentos baseados em janelas rolantes. No terceiro ensaio, usamos aprendizado supervisionado para gerar índices prospectivos baseados em tweets e notícias para inflação acumulada e investigamos se esses índices podem melhorar o desempenho da previsão de inflação. Nossos resultados indicam que os índices baseados em notícias fornecem ganhos preditivos significativos, principalmente para os horizontes de 3 e 12 meses à frente. Esses achados sugerem que a incorporação de mais fontes de informação do que apenas expectativas baseadas em opiniões de especialistas pode levar a previsões mais precisas. / [en] This dissertation consists of three essays concerning inflation forecasting, taking the Brazilian case as an application. In the first essay, we examine the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts. We consider different disaggregation levels for inflation and employ a range of traditional time series techniques, as well as linear and nonlinear machine learning (ML) models that deal with a larger number of predictors. For many forecast horizons, aggregation of disaggregated forecasts performs just as well as survey-based expectations and models generating forecasts directly from the aggregate. Overall, ML methods outperform traditional time series models in predictive accuracy, with outstanding performance in forecasting disaggregates. In our second essay, we investigate the potential benefits of combining individual inflation forecasts by proposing a time-varying bias correction for the average forecast. Our analysis includes estimations using both rolling windows and state-space models that use the recursiveness of the Kalman filter. We achieve good forecast performance for models based on small rolling windows for shorter and intermediate forecast horizons, while a state-space model performs slightly worse than procedures based on rolling windows. In the third essay, we use supervised learning to generate forward-looking indexes based on tweets and news articles for accumulated inflation and investigate whether these indexes can improve inflation forecasting performance. Our results indicate that news-based indexes provide significant predictive gains, particularly for 3- and 12-month-ahead horizons. These findings suggest that incorporating more information sources than just expectations based on experts opinions can lead to more accurate forecasts.

Le « problème difficile de la conscience » consiste à comprendre pourquoi certains processus physiques, comme ceux ayant lieu dans le cerveau humain, donnent lieu à de la conscience, c’est-à-dire à de l’expérience subjective. Ce mémoire se penche sur une théorie cherchant à répondre au problème difficile de la conscience : le monisme russellien. Cette théorie affirme en bref que la conscience est la nature intrinsèque de la matière. Le monisme russellien espère pouvoir expliquer l’existence de la conscience humaine en posant la conscience au niveau fondamental de la réalité. Toutefois, des critiques affirment que cette théorie est incapable d’expliquer comment la conscience présente dans la matière pourrait se combiner pour former la conscience humaine. Une telle objection se nomme le « problème de la combinaison ». Après une présentation du monisme russellien et du problème du la combinaison, la plausibilité de quatre différentes réponses au problème de la combinaison sera évaluée. Ces quatre réponses tâchent de rendre intelligible la manière dont la conscience présente dans la matière pourrait se combiner pour constituer la conscience humaine. / The "hard problem of consciousness" is the problem of understanding why certain physical processes, such as those taking place in the human brain, give rise to consciousness, or subjective experience. This thesis examines a theory that seeks to answer the hard problem of consciousness: Russellian monism. In short, this theory asserts that consciousness is the intrinsic nature of matter. Russellian monism hopes to explain the existence of human consciousness by positing consciousness at the fundamental level of reality. However, critics claim that this theory is unable to explain how consciousness present in matter could combine to form human consciousness. Such an objection is called the "problem of combination". After a presentation of Russellian monism and the combination problem, I critically evaluate the plausibility of four different answers to the combination problem. These four different answers try to make intelligible the way in which consciousness present in matter could combine to constitute human consciousness.

I inquire about second graders' capabilities of developing solution strategies and the original variety of strategies they bring forth while solving a combination problem. Based on analysis of the data presented in this paper, students developed five different general strategies. After analyzing what the second grade students were capable of developing, we can conclude that young children are capable of developing powerful systematic strategies grounded in their personal experiences. This research shows that even when the teacher does not foster personal agency, children will still exercise agency. The social interactions in the classroom helped students learn to propose mathematical ideas, make conjectures, evaluate their own and others' thinking, and develop mathematical reasoning skills.

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights. In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into a hierarchy made up of different temporal scales, produces forecasts at each level of the hierarchy, then reconciles those forecasts using optimized weighted forecast combination. While THieF's unique approach has shown substantial accuracy improvements in a diverse range of applications, such as operations management and emergency room admission predictions, this technique had not previously been applied to outbreak forecasting. We generated candidate models formulated using the THieF methodology, which differed by their hierarchy schemes and data transformations, and ensembles of the THieF models, computed as a mean of predictive quantiles. The models were evaluated using weighted interval score (WIS) as a measure of forecast skill, and the top-performing subset was compared to several benchmark models. These models included simple ARIMA and seasonal ARIMA models, a naive baseline model, and an ensemble of operational incident hospitalization models from the US COVID-19 Forecast Hub. The THieF models and THieF ensembles demonstrated improvements in WIS and MAE, as well as competitive prediction interval coverage, over many benchmark models for both the validation and testing phases. The best THieF model generally ranked second out of nine total models during the testing evaluation. These accuracy improvements suggest the THieF methodology may serve as a useful addition to the infectious disease forecasting toolkit.

Ziel der vorliegenden Arbeit ist die Entwicklung eines neuartigen Ferti-gungsprozesses für hochfunktionalisierte Strukturbauteile in PFH-Technologie. Eigens für die Verarbeitung von FKV-Profilen wird das IHU/Spritzgießverfahren grundlegend analysiert und die einzelnen Pro-zessstufen durch den Einsatz geeigneter Prozessmedien und -parametrierungen modifiziert. Mit Hilfe umfangreicher analytischer und numerischer Berechnungen sowie erfolgreicher Umsetzung eines neuar-tigen Temperierungskonzeptes lassen sich erstmals FKV-Profile ohne den Einsatz von Barriereschichten, endkonturnah und reproduzierbar in seriellen Taktzeiten umformen. Zudem ist somit eine Funktionalisierung mittels Spritzgießen und ein gesamtheitliches Beherrschen des Hybrid-prozesses gegeben. Ein Vergleich der Verbindungsfestigkeiten mit hyb-riden Referenzbauteilen beweist, dass sich durch die stoffschlüssige Anbindung zeit- und kostenintensive Vorbehandlungen einsparen und gleichzeitig höhere Verbundqualitäten erzielen lassen. Um das PFH-Strukturbauteil mit montagegerechten Verbindungsschnittstellen zu werkstofflich andersgearteten Multimaterialstrukturen zu versehen, dient ein eigens entwickeltes prozesskomplementäres In-situ-Integrationsverfahren. Abschließend ist ein allgemeiner methodischer Ansatz für den technologischen Transfer und Adaption systematisch aufgeschlüsselt, sodass sich die einzelnen Entwicklungspfade nachvoll-ziehen lassen.:1 Einleitung 2 Zielsetzung 3 Stand der Wissenschaft und Technik 4 Anforderungen an einen faserverbund-gerechten IHU/Spritzgießprozess 5 Prozessentwicklung und Validierung 6 In-situ-Integrationsprozess für metallische Inserts 7 Konfiguration einer seriellen Fertigungstechnologie 8 Zusammenfassung und Ausblick Anhänge / The goal of this thesis is the development of a new production technol-ogy for highly functional lightweight structural parts. Therefor the hy-droforming supported overmolding process was chosen as a suiting manufacturing process. By usage of analytical and numerical calcula-tions as well as adapting a new kind of temperature distribution con-cept, the new Polymer-FRP-Hbyrid-Technology is enabled to manufac-ture frp-profile based structural components without the need for a barrier layer. The final product possesses its’s final shape already and has no need for further machining processes. A comparison with state-of-the-art metal-plastic-hybrid bonding systems shows a way higher value for the new technology due to the adhesive bond between the tape and injection molding polymer. Furthermore, the technology was enhanced with a process implemented insert joining process. Finally, a methodical approach for the technology transfer is presented.:1 Einleitung 2 Zielsetzung 3 Stand der Wissenschaft und Technik 4 Anforderungen an einen faserverbund-gerechten IHU/Spritzgießprozess 5 Prozessentwicklung und Validierung 6 In-situ-Integrationsprozess für metallische Inserts 7 Konfiguration einer seriellen Fertigungstechnologie 8 Zusammenfassung und Ausblick Anhänge

Combining chemotherapeutics to treat malignant tumors has been shown to be effectivein preventing drug resistance, tumor recurrence, and reducing tumor size. We modeledcombination drug therapy in PC-3 human prostate cancer cells using mixture design responsesurface methodology (MDRSM), a statistical technique designed to optimize compositions thatwe applied in a novel manner to design combinations of chemotherapeutics. Conventionalchemotherapeutics (mitoxantrone, cabazitaxel, and docetaxel) and natural bioactive compounds(resveratrol, piperlongumine, and flavopiridol) were used in twelve different combinationscontaining three drugs at varying concentrations. Cell viability and cell cycle data werecollected and used to plot response surfaces in MDRSM that identified the most effectiveconcentrations of each drug in combination. MDRSM allows for extrapolation of data fromthree or more compounds in variable ratio combinations, unlike the Chou-Talalay method.MDRSM combinations were compared with combination index data from the Chou-Talalaymethod and were found to coincide. We propose MDRSM as an effective tool in devisingcombination treatments that can improve treatment effectiveness, and increase treatmentpersonalization because MDRSM measures effectiveness rather than synergism, potentiation orantagonism.

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Social networking sites such as Twitter, Facebook and Flickr play an important role in disseminating breaking news about natural disasters, terrorist attacks and other events. They serve as sources of first-hand information to deliver instantaneous news to the masses, since millions of users visit these sites to post and read news items regularly. Hence, by exploring e fficient mathematical techniques like Dempster-Shafer theory and Modi ed Dempster's rule of combination, we can process large amounts of data from these sites to extract useful information in a timely manner. In surveillance related applications, the objective of processing voluminous social network data is to predict events like revolutions and terrorist attacks before they unfold. By fusing the soft and often unreliable data from these sites with hard and more reliable data from sensors like radar and the Automatic Identi cation System (AIS), we can improve our event prediction capability. In this paper, we present a class of algorithms to fuse hard sensor data with soft social network data (tweets) in an e ffective manner. Preliminary results using are also presented. / Thesis / Master of Applied Science (MASc)