Modified Mechanical Coating Technique for the Preparation of Nanohydroxyapatite Coated Ti–6Al–4V Dental Implants

Protection of Metals and Physical Chemistry of Surfaces - To improve the mechanical and biological properties and also to increase the lifetime and performance of Ti–6Al–4V dental...     

A note on the Consecutive Ones Submatrix problem

A binary matrix has the Consecutive Ones Property (C1P) for columns if there exists a permutation of its rows that leaves the 1's consecutive in every column. The problem of Consecutive Ones Property for a matrix is a special variant of Consecutive Ones Submatrix problem in which a positive integer K is given and we want to know if there exists a submatrix B of A consisting of K columns of A with C1P property. This paper presents an error in the proof of NP-completeness for this problem in the reference cited in text by Garey and Johnson [Computers and Intractability, A Guide to the Theory of NP-Completeness, 1979].     

Water-soluble Copolymers of N-vinylpyrrolidone and Vinyl Acetate: Synthesis, Characterization, and Monomer Reactivity at High Conversions

Homogeneous copolymers of N-vinylpyrrolidone (VP) and vinyl acetate (VA) which form clear aqueous solutions were prepared by free radical polymerization in a solution of isopropanol alcohol, using 2,2-azobisisobutyronitrile as an initiator. They were characterized by FTIR, ¹H-NMR, and element analysis studies. The reactivity ratios of the monomer were computed by the Extended Kelen–Tüdós method at high conversions, using data from both ¹H-NMR and elemental analysis studies. The reactivity ratios of VP and VA in a homogenous copolymer were observed to be very different from that of a heterogeneous copolymer. Additional information was obtained by finding out the sequence length distribution for copolymers.     

A comparative study of iterative and non-iterative feature selection techniques for software defect prediction

Two important problems which can affect the performance of classification models are high-dimensionality (an overabundance of independent features in the dataset) and imbalanced data (a skewed class distribution which creates at least one class with many fewer instances than other classes). To resolve these problems concurrently, we propose an iterative feature selection approach, which repeated applies data sampling (in order to address class imbalance) followed by feature selection (in order to address high-dimensionality), and finally we perform an aggregation step which combines the ranked feature lists from the separate iterations of sampling. This approach is designed to find a ranked feature list which is particularly effective on the more balanced dataset resulting from sampling while minimizing the risk of losing data through the sampling step and missing important features. To demonstrate this technique, we employ 18 different feature selection algorithms and Random Undersampling with two post-sampling class distributions. We also investigate the use of sampling and feature selection without the iterative step (e.g., using the ranked list from a single iteration, rather than combining the lists from multiple iterations), and compare these results from the version which uses iteration. Our study is carried out using three groups of datasets with different levels of class balance, all of which were collected from a real-world software system. All of our experiments use four different learners and one feature subset size. We find that our proposed iterative feature selection approach outperforms the non-iterative approach.     

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

Neural Processing Letters - Deep learning is an important subcategory of machine learning approaches in which there is a hope of replacing man-made features with fully automatic extracted features....     

Imputation techniques for multivariate missingness in software measurement data

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

Thermodynamic studies on the complete phase diagram of aqueous two phase system containing polyethylene glycol dimethyl ether 2000 and di-potassium hydrogen phosphate at different temperatures

The liquid-liquid equilibrium of polyethylene glycol dimethyl ether 2000 (PEGDME₂₀₀₀)+K₂HPO₄+H₂O system has been determined experimentally at T=(298.15,303.15,308.15 and 318.15) K. The liquid-solid and complete phase diagram of this system was also obtained at T=(298.15 and 308.15) K. A nonlinear temperature dependent equation was successfully used for the correlation of the experimental binodal data. Furthermore, a temperature dependent Setschenow-type equation was successfully used for the correlation of the tie-lines of the studied system. Moreover, the effect of temperature on the binodal curves and the tie-lines for the investigated aqueous two-phase system have been studied. Also, the free energies of cloud points for this system and some previously studied systems containing PEGDME₂₀₀₀ were calculated from which it was concluded that the increase of the entropy is the driving force for formation of aqueous two-phase systems. Additionally, the calculated free energies for phase separation of the studied systems were used to investigate the salting-out ability of the salts having different anions. Furthermore, the complete phase diagram of the investigated system was compared with the corresponding phase diagrams of previously studied systems, in which the PEGDME₂₀₀₀ has been used, in order to obtain some information regarding the phase behavior of these PEGDME₂₀₀₀+salt+water systems.     

Fuzzy logistic regression based on the least squares approach with application in clinical studies

To model fuzzy binary observations, a new model named “Fuzzy Logistic Regression” is proposed and discussed in this study. In fact, due to the vague nature of binary observations, no probability distribution can be considered for these data. Therefore, the ordinary logistic regression may not be appropriate. This study attempts to construct a fuzzy model based on possibility of success. These possibilities are defined by some linguistic terms such as …, low, medium, high…. Then, by use of the Extension principle, the logarithm transformation of “possibilistic odds” is modeled based on a set of crisp explanatory variables observations. Also, to estimate parameters in the proposed model, the least squares method in fuzzy linear regression is used. For evaluating the model, a criterion named the “capability index” is calculated. At the end, because of widespread applications of logistic regression in clinical studies and also, the abundance of vague observations in clinical diagnosis, the suspected cases to Systematic Lupus Erythematosus (SLE) disease is modeled based on some significant risk factors to detect the application of the model. The results showed that the proposed model could be a rational substituted model of an ordinary one in modeling the clinical vague status.