Comparative analysis of domestic psychologists and ergonomists’ publication activity using citation indices

The advantages and disadvantages of indices for assessing scientists’ activities using the scientometric databases Web of Science (USA), Scopus (EU) and RSCI (RF) are considered. It is proposed to use such indicators as the citation index and the publication relevance index to objectify the data in addition to the known indicators, namely the number of publications, the number of links, the average number of citations per publication, the Hirsch index. It is shown that the main disadvantage of the h-index proposed by the American physicist Jorge Hirsch for assessing ergonomists’ scientific activities is not taking into account the relevance of breakthrough scientific results and inventions. The rating of 25 leading domestic psychologists and 25 domestic ergonomists is given, which is obtained from the RSCI database and it includes such indicators as the number of publications, the total number of citations, the average number of citations, the average number of citations per publication, and the Hirsch index. It is concluded that using relevance and citation indices is, to a certain extent, evidence of this scholar’ official recognition by the scientific community and the formal confirmation of his authority. It is shown that applying scientometric citation indices and their correct use in assessing scientists’ activities should be carried out by the qualified experts in the relevant field of knowledge.

Manifold reduction techniques for the comparison of crank angle-resolved particle image velocimetry (PIV) data and Reynolds-averaged Navier-Stokes (RANS) simulations in a spark ignition direct injection (SIDI) engine

In this article, different manifold reduction techniques are implemented for the post-processing of Particle Image Velocimetry (PIV) images from a Spark Ignition Direct Injection (SIDI) engine. The methods are proposed to help make a more objective comparison between Reynolds-averaged Navier-Stokes (RANS) simulations and PIV experiments when Cycle-to-Cycle Variations (CCV) are present in the flow field. The two different methods used here are based on Singular Value Decomposition (SVD) principles where Proper Orthogonal Decomposition (POD) and Kernel Principal Component Analysis (KPCA) are used for representing linear and non-linear manifold reduction techniques. To the authors’ best knowledge, this is the first time a non-linear manifold reduction technique, such as KPCA, has ever been used in the study of in-cylinder flow fields. Both qualitative and quantitative studies are given to show the capability of each method in validating the simulation and incorporating CCV for each engine cycle. Traditional Relevance Index (RI) and two other previously developed novel indexes: the Weighted Relevance Index (WRI) and the Weighted Magnitude Index (WMI), are used for the quantitative study. The results indicate that both POD and KPCA show improvements in capturing the main flow field features compared to ensemble-averaged PIV experimental data and single cycle experimental flow fields while capturing CCV. Both methods present similar quantitative accuracy when using the three indexes. However, challenges were highlighted in the POD method for the selection of the number of POD modes needed for a representative reconstruction. When the flow field region presents a Gaussian distribution, the KPCA method is seen to provide a more objective numerical process as the reconstructed flow field will see convergence with an increasing number of modes due to its usage of Gaussian properties. No additional criterion is needed to determine how to reconstruct the main flow field feature. Using KPCA can, therefore, reduce the amount of analysis needed in the process of extracting the main flow field while incorporating CCV.

Instrument Context Relevance Evaluation, Translation, and Psychometric Testing of the Diabetes Eating Problem Survey-Revised (DEPS-R) among People with Type 1 Diabetes in China

Background: People with type 1 diabetes are susceptible to disordered eating behaviors. The American Diabetes Association recommends using the Diabetes Eating Problem Survey-Revised (DEPS-R) to screen them. There is no validated diabetes-specific screening measure in China. The objectives were to adapt DEPS-R into Mandarin Chinese and to test its psychometric properties among youths and adults with type 1 diabetes in China, respectively. Methods: This study was conducted in two phases. Phase 1 included context relevance evaluation and instrument translation. Phase 2 was psychometric testing of reliability and construct validity among 89 youths (8~17 years old) and 61 adults with type 1 diabetes. Result: The Context Relevance Index and Translation Validity Index of this instrument were good. Strong internal consistency reliability correlations and convergent validity were demonstrated among youths and adults. Discussion: The Chinese version of the DEPS-R is a valid and reliable tool for screening disordered eating behaviors in Chinese youths and adults with type 1 diabetes. The Context Relevance Index is advocated to evaluate the difference between the context in which an instrument was originally developed and the target context.

Development of a relevance index for the subjects in a study plan - Case study: Systems and Computer Engineering, Universidad Tecnológica de Pereira

This document presents the development of an index that aims to quantify, according to some criteria known in graph theory, how relevant a subject is, taking into account its location in the curriculum, its number of credits, its prerequisites and the subjects dependents. The first thing was to model the academic plan using a graph, which considers only two things: the assigned credits and the prerequisites that must be met before taking the subjects. After having this model, graph theory algorithms were applied that allow to measure the importance of a subject with respect to the location in its curricular mesh (Centrality) and allow to give a measure of the importance of the subjects based on academic credits, its prerequisites and subjects depending on it (Neighborhood). It is important to note that the analysis presented is not intended to indicate that one subject is more important than another for the student's professional development, but rather to analyze, in an estimative way, which subjects contribute more to the connectivity of the program and academic flow by this network only taking into account the information found in the curriculum.The result obtained is a composite index, which allows visualizing the relevance degree of the subjects in the study plan.

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.