The effect of combined use of chitosan and PIPS on push-out bond strength of root canal filling materials

Adhesion of root canal filling materials to root dentin is important for the long-term success of the treatment. Push-out bond strength test is used to evaluate the adhesion capacity of root canal filling materials to root canal walls. The aim of the present study is to compare the bond strength of root canal filling materials to root dentin after irrigation with EDTA, chitosan and the combination of chitosan and PIPS irridation using push-out bond strength test. Forty-eight extracted teeth were resected until 13-mm long roots were obtained. Root canals were prepared with a size-25 OneShape instrument. Samples were divided into three groups each including 15 roots. Group 1: Canals were rinsed with 0.2% chitosan and subjected to laser irridation with PIPS at the same time. Group 2: Canals were rinsed with 0.2% chitosan. Group 3: Canals were rinsed with EDTA. All canals were filled with .06 tapered gutta-percha and AH-plus sealer. One-mm thick slices were taken from coronal, middle and apical one-thirds of the roots. Push-out bond strength was determined using a Universal Testing Machine. One root from each group was observed under SEM to evaluate the degree of smear removal. Statistical analysis was performed with Kruskall-Wallis test. Results showed that bond strength values were statistically similar in overall evaluation for all groups (p > .05). In segmental evaluation, group 1 revealed the highest bond strength in apical one-third compared to other groups (p < .05).     

Experimental and theoretical study for the determination of thermal conductivity of porous building material made with pumice and tragacanth

In this study, porous solid materials were produced with mixture combinations of materials such as tragacanth, cement, and pumice aggregate. Thermal conductivities of the produced materials were determined using hot wire methodology. The thermal conductivities of the produced samples ranged from 0.433 to 0.177 W/mK, depending on pumice diameter, pumice, tragacanth, and cement rates. A new model was developed to determine the effective thermal conductivities of the produced samples. The thermal conductivities obtained by the measurements were compared to the ones predicted by the model. The predicted values deviated as 1–31% from the measured values. It was finally shown that the experimental results were in good agreement to the predicted results.     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Element-by-Element Post-Processing of Discontinuous Galerkin Methods for Timoshenko Beams

In this paper, we consider discontinuous Galerkin approximations to the solution of Timoshenko beam problems and show how to post-process them in an element-by-element fashion to obtain a far better approximation. Indeed, we show numerically that, if polynomials of degree p≥1 are used, the post-processed approximation converges with order 2p+1 in the L ^∞-norm throughout the domain. This has to be contrasted with the fact that before post-processing, the approximation converges with order p+1 only. Moreover, we show that this superconvergence property does not deteriorate as the the thickness of the beam becomes extremely small.Supported in part by NSF Grant DMS-0411254 and by the University of Minnesota Supercomputing Institute.     

Multi‐level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.     

Achieving real-time object detection and tracking under extreme conditions

In this survey, we present a brief analysis of single camera object detection and tracking methods. We also give a comparison of their computational complexities. These methods are designed to accurately perform under difficult conditions such as erratic motion, drastic illumination change, and noise contamination.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Nodal Superconvergence of SDFEM for Singularly Perturbed Problems

In this paper, we analyze the streamline diffusion finite element method for one dimensional singularly perturbed convection-diffusion-reaction problems. Local error estimates on a subdomain where the solution is smooth are established. We prove that for a special group of exact solutions, the nodal error converges at a superconvergence rate of order (ln ε ⁻¹/N)^2k (or (ln N/N)^2k ) on a Shishkin mesh. Here ε is the singular perturbation parameter and 2N denotes the number of mesh elements. Numerical results illustrating the sharpness of our theoretical findings are displayed.