Bacteriological Evaluation of Gingival Crevicular Fluid in Teeth Restored Using Fixed Dental Prostheses: An In Vivo Study

The present in vivo study determined the microbiological counts of the gingival crevicular fluid (GCF) among patients with fixed dental prostheses fabricated using three different techniques. A total of 129 subjects were divided into three study groups: first, cobalt-chrome-based, metal-ceramic prostheses fabricated by the conventional method (MC, n = 35); the second group consisted of cobalt-chrome-based, metal-ceramic prostheses fabricated by the computer-aided design and computer-aided manufacturing (CAD/CAM) technique (CC-MC, n = 35); the third group comprised zirconia-based ceramic prostheses fabricated using the CAD/CAM technique (CC-Zr, n = 35). The control consisted of 24 patients using prostheses fabricated with either MC, CC-MC, or CC-Zr. The GCF was obtained from the subjects before treatment, and 6 and 12 months after the prosthetic treatment. Bacteriological and bacterioscopic analysis of the GCF was performed to analyze the patients’ GCF. The data were analyzed using SPSS V20 (IBM Company, Chicago, IL, USA). The number of microorganisms of the gingival crevicular fluid in all groups at 12 months of prosthetic treatment reduced dramatically compared with the data obtained before prosthetic treatment. Inflammatory processes in the periodontium occurred slowly in the case of zirconium oxide-based ceramic constructions due to their biocompatibility with the mucous membranes and tissues of the oral cavity as well as a reduced risk of dental biofilm formation. This should be considered by dentists and prosthodontists when choosing restoration materials for subjects with periodontal pathology.     

Modelling strategies for the incorporation and correction of optical effects in EUVL

Extreme ultraviolet lithography (EUVL) is a leading candidate for the 22 nm node lithography and beyond. However, there are still some critical problems before EUVL may be deployed in high-volume manufacturing. One of the critical problems is to estimate the EUVL aerial image formation for optical proximity correction (OPC) in order to compensate for EUVL effects such as shadowing and flare. This study discusses aerial image formation through modeling of optical transfer function to assimilate optical diffraction, long range layout dependent flare effects, and shadowing effects due to non-telecentric imaging optics in the EUV case. Hence, after optimizing optical process parameters to model the EUV aerial image, this study will investigate OPC modeling methods employed to compensate these optical effects in the mask design flow.     

Staged Versions of Liquid Membrane Extraction Processes

The application of the liquid membrane technique to the system consisting of serially connected mixer-settler extractors is suggested and evaluated. A comparative theoretical analysis of various flow schemes of contacting liquids in series of mixer-settler extractors has been performed. It is shown that the scheme of a staged version of supported liquid membrane provides the highest extraction efficiency. One laboratory plant consisting of eight mixer-settler was examined with the standard extraction system water-acetone-toluene using two schemes: with internal (the extractant recycles between each pair of extraction and stripping stages) and external (the extractant first passes through all the stages of extraction, then through all stripping stages) circulation of the extractant. The effectiveness of the first variant (supported liquid membrane scheme) was considerably higher than that of the second scheme. Good agreement between theory and experiment was established.     

Practical protocol for Yao’s millionaires problem enables secure multi-party computation of metrics and efficient privacy-preserving k-NN for large data sets

Finding the nearest k objects to a query object is a fundamental operation for many data mining algorithms. With the recent interest in privacy, it is not surprising that there is strong interest in k-NN queries to enable clustering, classification and outlier-detection tasks. However, previous approaches to privacy-preserving k-NN have been costly and can only be realistically applied to small data sets. In this paper, we provide efficient solutions for k-NN queries for vertically partitioned data. We provide the first solution for the L _∞ (or Chessboard) metric as well as detailed privacy-preserving computation of all other Minkowski metrics. We enable privacy-preserving L _∞ by providing a practical approach to the Yao’s millionaires problem with more than two parties. This is based on a pragmatic and implementable solution to Yao’s millionaires problem with shares. We also provide privacy-preserving algorithms for combinations of local metrics into a global metric that handles the large dimensionality and diversity of attributes common in vertically partitioned data. To manage very large data sets, we provide a privacy-preserving SASH (a very successful data structure for associative queries in high dimensions). Besides providing a theoretical analysis, we illustrate the efficiency of our approach with an empirical evaluation.