Surface Roughness and Microhardness of Two Recent CAD/CAM-Materials After Storage

Objectives: To investigate surface roughness and microhardness of two recent resin-ceramic materials for computer-aided design/computer-aided manufacturing (CAD/CAM) after polishing with three polishing systems. Surface roughness and microhardness were measured immediately after polishing and after six months storage including monthly artificial toothbrushing. Methods: Sixty specimens of Lava Ultimate (3M ESPE) and 60 specimens of VITA ENAMIC (VITA Zahnfabrik) were roughened in a standardized manner and polished with one of three polishing systems (n=20/group): Sof-Lex XT discs (SOFLEX; three-step (medium-superfine); 3M ESPE), VITA Polishing Set Clinical (VITA; two-step; VITA Zahnfabrik), or KENDA Unicus (KENDA; one-step; KENDA Dental). Surface roughness (Ra; μm) was measured with a profilometer and microhardness (Vickers; VHN) with a surface hardness indentation device. Ra and VHN were measured immediately after polishing and after six months storage (tap water, 37°C) including monthly artificial toothbrushing (500 cycles/month, toothpaste RDA ~70). Ra- and VHN-values were analysed with nonparametric ANOVA followed by Wilcoxon rank sum tests (α=0.05). Results: For Lava Ultimate, Ra (mean [standard deviation] before/after storage) remained the same when polished with SOFLEX (0.18 [0.09]/0.19 [0.10]; p=0.18), increased significantly with VITA (1.10 [0.44]/1.27 [0.39]; p=0.0001), and decreased significantly with KENDA (0.35 [0.07]/0.33 [0.08]; p=0.03). VHN (mean [standard deviation] before/after storage) decreased significantly regardless of polishing system (SOFLEX: 134.1 [5.6]/116.4 [3.6], VITA: 138.2 [10.5]/115.4 [5.9], KENDA: 135.1 [6.2]/116.7 [6.3]; all p<0.0001). For VITA ENAMIC, Ra (mean [standard deviation] before/after storage) increased significantly when polished with SOFLEX (0.37 [0.18]/0.41 [0.14]; p=0.01) and remained the same with VITA (1.32 [0.37]/1.31 [0.40]; p=0.58) and with KENDA (0.81 [0.35]/0.78 [0.32]; p=0.21). VHN (mean [standard deviation] before/after storage) remained the same regardless of polishing system (SOFLEX: 284.9 [24.6]/282.4 [31.8], VITA: 284.6 [28.5]/276.4 [25.8], KENDA: 292.6 [26.9]/282.9 [24.3]; p=0.42-1.00). Conclusion: Surface roughness and microhardness of Lava Ultimate was more affected by storage and artificial toothbrushing than was VITA ENAMIC.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Different bacterial models for in vitro induction of non-cavitated enamel caries-like lesions: Microhardness and polarized light miscroscopy analyses

The aim of this study was to compare different bacterial models for in vitro induction of non-cavitated enamel caries-like lesions by microhardness and polarized light microscopy analyses. One hundred blocks of bovine enamel were randomly divided into four groups (n = 25) according to the bacterial model for caries induction: (A) Streptococcus mutans, (B) S. mutans and Lactobacillus acidophilus, (C) S. mutans and L. casei, and (D) S. mutans, L. acidophilus, and L. casei. Within each group, the blocks were randomly divided into five subgroups according to the duration of the period of caries induction (4-20 days). The enamel blocks were immersed in cariogenic solution containing the microorganisms, which was changed every 48 h. Groups C and D presented lower surface hardness values (SMH) and higher area of hardness loss (ΔS) after the cariogenic challenge than groups A and B (P < 0.05). As regards lesion depth, under polarized light microscopy, group A presented significantly lower values, and groups C and D the highest values. Group B showed a higher value than group A (P < 0.05). Groups A and B exhibited subsurface caries lesions after all treatment durations, while groups C and D presented erosion-type lesions with surface softening. The model using S. mutans, whether or not it was associated with L. acidophilus, was less aggressive and may be used for the induction of non-cavitated enamel caries-like lesions. The optimal period for inducing caries-like lesions was 8 days.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Technology network for machine tools in Vietnam

When Vietnam joined the WTO, it accepted foreign direct investment and started to grow. Technically, it was then greatly influenced by the enterprises that entered the country through direct investment. This report shows that the technology network for machine tools is formed via direct investment and subcontracting.

Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.