Research has increasingly been conducted to improve the toughness and aesthetics of zirconium oxide (zirconia) used in prosthetic dentistry. However, the balance between better mechanical properties and greater translucency, to ensure aesthetic requirements, is still a challenge in the development of a novel zirconia for dentistry applications. This study evaluated the transmittance of visible light for four types of pre-sintered zirconia blocks used in dentistry (3Y-SBE, Zpex, Zpex-4, and Zpex-Smile). The objective is to analyze the simultaneous influence of sintering temperature, in the range of 1450–1560 °C, as well as the chemical composition (%Y2O3), density, and thickness (1.0, 1.3, 1.6, and 2.0 mm) in the zirconia’s transmittance. To evaluate the interactive influence of these variables, a statistical learning model based on gradient boosting is applied. The results showed that the effect of the sintering temperature has an optimal (maximum transmittance) point. Increasing the temperature beyond this point reduces the transmittance of the material for all types of zirconia. Moreover, the optimal transmittance point is affected by the chemical composition of each type of zirconia. In addition, the results showed that the transmittance of all types of zirconia had an inverse relationship with the density, zirconia Zpex-Smile being the most sensitive to this parameter. Furthermore, the transmittance of 3Y-SBE, Zpex, and Zpex-4 decreases approximately linearly with the specimen thickness, whereas zirconia Zpex-Smile has a sublinear decrease, which is expected due to the optical isotropy of the cubic phase.

In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and real datasets.

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RESUMO O titânio comercialmente puro (Ti cp) é o principal material usado na fabricação dos implantes dentários osseointegráveis. Para melhorar os índices de sucesso do tratamento odontológico e reduzir o tempo de osseointegração dos implantes, foram desenvolvidas várias metodologias de tratamento da superfície dos implantes. Para caracterizar as morfologias das superfícies dos implantes são usadas as análises no microscópio eletrônico de varredura (MEV), rugosidade, molhabilidade, identificação de possíveis contaminantes, ensaios in-vitro de culturas de células e in-vivo com animais. Entre as técnicas citadas, as mais usadas são as análises no MEV, as medidas dos parâmetros da rugosidade e da molhabilidade. Alguns trabalhos disponíveis na literatura sugerem a caracterização somente do valor de Ra sem apresentar uma explicação para a escolha deste parâmetro. No presente trabalho é feita a proposta de um modelo matemático para quantificar a relação entre os vários parâmetros da rugosidade 3D (Ra, Rq, Rz, Rms, Pico, Vale, PV, R3z e Smax) com a molhabilidade e a energia de superfície do Ti cp com dois tamanhos de grão (micrométricos e sub-micrométricos) e submetidos ao tratamento com ácido. Os resultados mostraram que para o nível de confiança de 95%, as propriedades das superfícies do Ti cp tratados com ácido são mais influenciadas pelos parâmetros de rugosidade Ra e Rms.

We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem that is logarithmic in the time horizon.