The Fourth Axiom of Similarity Measures

In this research, the fourth axiom to improve the well-defined examination of similarity measures is studied, where the measures have a symmetric structure. We first provide a theoretic enhancement of three correlation coefficient similarity measures that were proposed by a source paper. Second, we use the same numerical example proposed by the source paper for pattern recognition problems to illustrate that the weighted correlation coefficient similarity measure is dependent on the set of weights. Finally, we demonstrate that the correlation coefficient similarity measure in the intuitionistic fuzzy set environment can address the issue of practical fault diagnosis when solving the turbine engine problems using similarity measures with symmetric characteristics.

Using the “Uniform Scale” to facilitate meta-analysis where exposure variables are qualitative and vary between studies – methodology, examples and software

Meta-analyses often combine covariate-adjusted effect estimates (odds ratios or relative risks) and confidence intervals relating a specified endpoint to a given exposure. Standard techniques are available to do this where the exposure is a simple presence/absence variable, or can be expressed in defined units. However, where the definition of exposure is qualitative and may vary between studies, meta-analysis is less straightforward. We introduce a new “Uniform Scale” approach allowing expression of effect estimates in a consistent manner, comparing individuals with the most and least possible exposure. In 2008, we presented methodology and made available software to obtain estimates for specific pairwise comparisons of exposure, such as any versus none, where the source paper provides estimates for multiple exposure categories, expressed relative to a common reference group. This methodology takes account of the correlation between the effect estimates for the different levels. We have now extended our software, available in Excel, SAS and R, to obtain effect estimates per unit of exposure, whether the exposure is defined or is to be expressed in the “Uniform Scale”. Examples of its use are presented.

Automatic Academic Paper Rating Based on Convolutional Neural Network

As an ever increasing number of academic papers are being submitted to journals and conferences, assessing every one of these papers by experts is tedious and can cause imbalance because of the personal factors of the reviewers. In this system, in order to help professionals in assessing academic papers, here propose a task: Automatic Academic Paper Rating (AAPR), which automatically determine whether to accept academic papers. We build a convolutional neural network (CNN) model to achieve automatic academic paper rating task. It has two phases, first phase is identifying abstract part of source paper and generate rating score using CNN model and second phase is taking decision based on the score to accept or decline papers. This model takes word embedding of the abstracts as the input and learns useful features. The word embedding used for training the model is a semantically enriched set of Word2Vec word embedding. After the training phase, the proposed model will be able to generate the score of a new abstract. And find that the title and abstract parts have the most influence on whether the source paper quality when setting aside the other part of source papers. The proposed system outperforms the state-of-art technique.

Effects of Preparation Parameters on Morphology of Cellulose Nanowhiskers

This research will thus try to broaden the properties of bioplastics to be used as smart materials. Cellulose nanowhiskers will be initially prepared from different cellulose source and its morphology will be investigated. Water-activated shape memories nanocomposites will be consequently prepared from the obtained cellulose nanowhiskers and three polymers those are PBS and PBAT. The mechanical, physical and morphological will be investigated. The influences of cellulose nanowhiskers and their morphology on water-activated shape memory behavior will be studied.Recently, natural fiber have extensively used as reinforcement materials in polymeric nanocomposite due to environment concerns. Cellulose nanowhisker (CNWs) have also attracted much attention as environmentally friendly nanofillers for polymer composites. Cellulose nanowhiskers were extracted from commercial source, paper filter and alpha cellulose, using acid hydrolysis by sulfuric acid with various hydrolysis times (20,45,70,95,120 minutes), temperature (35,45,60°C). The hydrolyzed cellulose was neutralized with two different techniques, dialysis and titration. The samples were then sonicated and dried with a freeze dryer. The effects of preparation conditions on the morphological of cellulose nanowhisker were investigated. Cellulose nanowhisker powders were characterized scanning electron microscopy (SEM) and transmission electron microscope (TEM). The results showed that it was possible to obtain ultrathin cellulose nanowhiskers with diameters as low as 9 nm. A possible correlation between preparation conditions and particle size was not observed but titration technique time process was reduced.

Research on the Pumping Unit Model Driven by Linear Motor

With superior performances, Linear motor driven pumping unit becomes the electrical control of the focus and emphasis of the recent oil field equipment. Against the beam pumping unit motor efficiency, low power factor and stoke that difficult to regulate, this paper proposes new research programs about energy-saving line pumping systems by linear motor driven source. Paper Studys the mechanical model of linear motor in detail, using MATLAB / SIMULINK, a simulation model is analyzed. At the base of vector control of revolving induction motor, we established the linear motor control system. Theoretical analysis and computer simulation results show that the speed control system has good performance, response characteristics of the electromagnetic force, and demonstrate the proposed mathematical model and control method.

A Formal Process for Programming Language Evaluation for Introductory Courses

The selection of a programming language for introductory courses has long been an informal process involving faculty evaluation, discussion, and consensus. As the number of faculty, students, and language options grows, this process becomes increasingly unwieldy. As it stands, the process currently lacks structure and replicability. Establishing a structured approach to the selection of a programming language would enable a more thorough evaluation of the available options and a more easily supportable selection. Developing and documenting an instrument and a methodology for language selection will allow the process to be more easily repeated in the future. The objectives of this research are to: i) identify criteria for faculty use when selecting a computer programming language for an introductory course in computer programming; ii) develop an instrument that facilitates the assignment of weights to each of those selection criterion to determine their relative importance in the selection process, and; iii) allow various computer programming languages to be scored according to those selection criteria. A set of criteria for the selection of a programming language for introductory courses proposed in a previous paper is briefly reviewed here, with each criterion accompanied with a definition and justification. Readers are referred to the source paper for a complete discussion and literature review.