Database Clustering after Automatic Feature Analysis of Nonmetallic Inclusions in Steel

Non-metallic inclusions (NMIs) in steel have a negative impact on the properties of steel, so the problem of producing clean steels is actual. The existing metallographic methods for evaluating and analyzing nonmetallic inclusions make it possible to determine the composition and type of NMIs, but do not determine their real composition. The analysis of single NMIs using scanning electron microscope (SEM), fractional gas analysis (FGA), or electrolytic extraction (EE) of NMIs is too complicated. Therefore, in this work, a technique based on the automatic feature analysis (AFA) of a large number of particles by SEM was used. This method allows to obtain statistically reliable information about the amount, composition, and size of NMIs. To analyze the obtained databases of compositions and sizes of NMIs, clustering was carried out by the hierarchical method by constructing tree diagrams, as well as by the k-means method. This made it possible to identify the groups of NMIs of similar chemical composition (clusters) in the steel and to compare them with specific stages of the steelmaking process. Using this method, samples of steels produced at different steel plants and using different technologies were studied. The analysis of the features of melting of each steel is carried out and the features of the formation of NMIs in each considered case are revealed. It is shown that in all the studied samples of different steels, produced at different steel plants, similar clusters of NMIs were found. Due to this, the proposed method can become the basis for creating a modern universal classification of NMIs, which adequately describes the current state of steelmaking.

Genotypic diversity of multi- and pre-extremely drug-resistant Mycobacterium tuberculosis isolates from Morocco

In Morocco, the prevalence of multidrug resistant tuberculosis (MDR-TB) continues to increase especially within previously treated cases; these MDR cases may evolve to extensively drug resistant tuberculosis (XDR-TB) raising major concern to TB control programs. From an epidemiological window, scarce informations are available about the genetic diversity of Mycobacterium tuberculosis (MTB) strains fueling these forms of resistance. The aim of this study was to assess to genetic diversity of MDR-MTB strains. Hence, this prospective study was conducted on patients diagnosed with MDR-TB at Pasteur Institute of Casablanca from 2010 to 2013. A total of 70 MDR-MTB isolates were genotyped by spoligotyping and 15-loci MIRU-VNTR methods. Spoligotyping generated four orphan patterns, five unique profiles whereas 61 strains were grouped in nine clusters (2 to 25 strains per cluster), the clustering rates being 87.1%. Subtyping by 15 loci MIRU-VNTR splitted all clusters already established by spoligotyping and generated 70 unique profiles not recognized in SITVIT2 database; clustering rate was equal to zero. HGDI analysis of 15 loci MIRU demonstrated that eight out of 15 loci were highly discriminant. Of note, all pre-XDR strains belongs to many clades, meaning that there no association between gyrA mutants and particular clade. Overall, the data generated by this study (i) describe the population structure of MDR MTBC in Morocco which is highly homogenous, (ii) confirm that TB in Morocco is almost exclusively transmitted by modern and evolutionary lineages with high level of biodiversity seen by MIRU, and (iii) validate the use of optimized 15-loci MIRU-VNTR format for future investigations in Morocco.

Basic principles of TikTok algorithms

Social network TikTok has strong competitive differentiator in comparing with other platforms. ByteDance exploits machine learning algorithms to generate a recommendation feed (for you page). The algorithm bases on two main mechanisms. The first mechanism provides content database clustering depending on the type, audio track, video captions, and hashtags. The second mechanism analyzes the user’s behavioral patterns based on their actions in the application. The next step is the formation of user interaction scenarios. The difference between the predicted behavior and the real one is the object of analysis. If it equals zero, then the recommendations feed is formed correctly. The user is watching more and more interesting videos, just scrolling through video after video.

Studying the heterogeneity of European higher education institutions

The heterogeneity of the Higher Education (HE) Institutions is one of the main critical issues in the assessment of their performance. This paper adopts a multi-level and multi-dimensional perspective, combining national (macro) and institution (micro) level data, and measuring both research and teaching activity, using performance indicators derived from the European Tertiary Education Register, CWTS Leiden Ranking, and PATSTAT patent database. Clustering and efficiency analysis are combined to characterize the heterogeneity of national HE systems in European countries, and reveal the potential of using micro level data to characterize national level performance. Large differences are observed between the European countries, partially due to the fact that they are in different phases of their scientific (and economic) development and of the re-structuring of their HE systems. Evidence is found that universities specializing either in teaching or in research tend to have a higher efficiency than those institutions balancing research and teaching. Tradeoffs are observed between undergraduate and post-graduate activities, and a “Matthew cumulative effect” seems in place on the European institutions analysed: high quality research is able to attract external funds that stimulate innovative and patenting activities that in turn are self-reinforcing to the scientific activities. The results reveal once more the limits and dangers of one-dimensional approaches to the performance of HEIs.

New business models in supply chains: a bibliometric study

Purpose The purpose of this paper is to find and classify the most relevant works in the literature on the latest technologies applied in global supply chains. To help future researchers find the most relevant the authors according to the authors’ research interest quickly and to provide insights into the most promising areas. Design/methodology/approach The authors provide a bibliometric analysis of 292 documents referenced in the Scopus® database clustering by relatedness of works and keywords. Findings The authors present insights and deduce new perspectives in the potential search for new business models. The authors show that in specific fields, some works and authors have a much greater influence than others. Research limitations/implications Some documents published on the web or in paper form may be missing. The analyses largely depend on the choice of keywords. Another selection might have shown different results. Practical implications This paper provides the basis for new research in applications of the latest technologies in supply chains and corresponding new business models. Originality/value This work is a first effort to help researchers make sense of the mass of published scientific results on new technologies and their impact on new supply chain business models.

Association Rules-Based Analysis in Multidimensional Clusters

Many approaches for identifying potentially interesting items exploiting commonly used techniques of multidimensional data analysis. There is a great need for designing association-rule mining algorithms that will be scalable not only with the number of records (number of rows) in a cluster but also among domain's size (number of dimensions) in a cluster to focus on the domains. Where the items belong to domain is correlated with each other in a way that the domain is clustered into classes with a maximum intra-class similarity and a minimum inter-class similarity. This property can help to significantly used to prune the search space to perform efficient association-rule mining. For finding the hidden correlation in the obtained clusters effectively without losing the important relationship in the large database clustering techniques can be followed by association rule mining to provide better evaluated clusters.