The big five personality traits as predictors of financial wellbeing: a big data approach

Research has posited credit card transactions as highly probable to be grounded on the personality of the card holder. In this research, we investigate whether the big five personality traits of customers derived from credit card transactions predict their financial wellbeing. Our approach uses real data from a private Turkish bank, which contain both the demographic and financial records of 10,172 consumers located in Istanbul with 911,280 transactions. We filter purchasing categories related to the big five personality traits from Matz, Gladstone, and Stillwell’s study (2016). First, we link spending categories to the big five personality traits by considering Matz et al.’s study (2016). Then we calculate the big five factor scores of customers by monthly aggregating the individual big five scores of their transactions. Next, we investigate the relationship between the monthly big five personality scores and payment behavior of their credit card statements. In our main model, we estimated customers’ on-time payment behavior of the full amount due 8.8 % better than a random prediction (with 54.4 % AUROC value) by using their monthly big five personality scores and yearly and six-month based trends as independent variables

Analysis of Signature Generation Schemes for Multiterm Queries In Linear Hashing with Superimposed Signatures

Signature files provide efficient retrieval of data by reflecting the essence of the data objects into bit patterns. Our analysis explores the performance of three superimposed signature generation schemes as they are applied to a dynamic signature file organization based on linear hashing: Linear Hashing with Superimposed Signatures (LHSS). The first scheme (SM) allows all terms set the same number of bits whereas the second and third schemes (MMS aid MMM) emphasize the terms with high discriminatory power. In addition, MMM considers the probability distribution of the number of query terms. The main contribution of the study is a detailed analysis of LHSS in multiterm query environments by incorporating the term discrimination values based on document and query frequencies. The approach of the study can also be extended to other signature file access methods based on partitioning. The derivation of the performance evaluation formulas, the simulation results based on these formulas for various experimental settings, and the implementation results based on INSPEC and NPL text databases are provided. Results indicate that MMM and MMS outperform SM in all cases in terms of access savings, especially when terms become more distinctive. MMM slightly outperforms MMS in high weight and low weight query cases. The performance gap among all three schemes decreases as the database size increases, and as the signature size increases the performances of MMM and MMS decrease and converge to that of the SM scheme when the hashing level is fixed

Hypervariable Regions in 16S rRNA Genes for the Taxonomic Classification

16S ribosomal RNA (rRNA) gene sequences are reliable markers for the taxonomic classification of microbes and widely used in environmental microbiology. Production of 16S rRNA gene amplicons in large amounts, encompassing the full length of genes is not yet feasible, because of the limitations of the current sequencing techniques. They are mostly in short reads of length less than 300 base pairs. Hence, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is a current research area. It is found that nine hypervariable regions (V1–V9), resides in bacterial 16S ribosomal RNA (rRNA) genes. Family, genus, and species-specific sequences within a given hypervariable region constitute useful targets for diagnostic assays and other scientific investigations. In this study systematic studies that compare the relative advantage of hypervariable regions grouped as V1–V2–V3, V4–V5–V6, and V7–V8–V9 for specific diagnostic goals are done. In the present research, the built in function Longest–Common–Subsequence in computer algebra package MATHEMATICA is used to create an in silico pipeline to evaluate the taxonomic classification sensitivity of the hypervariable regions compared with the corresponding full-length sequences. Conclusions: Our results suggest that V4–V5–V6 region might be an optimal sub-region for the design of universal primers with superior phylogenetic resolution for bacterial phyla

Clustering 16S rRNA for OTU prediction: A similarity based method

To study the phylogeny and taxonomy of samples from complex environments Next-generation sequencing (NGS)-based 16S rRNA sequencing , which has been successfully used  jointly with the PCR amplification and NGS technology. First step for many downstream analyses is clustering 16S rRNA sequences into operational taxonomic units (OTUs). Heuristic clustering is one of the most widely employed approaches for generating OTUs in which one or more seed sequences to represent each cluster are selected. In this work we chose five random seeds for each cluster from a genes library, and  we present a novel distance measure to cluster bacteria in the sample. Artificially created sets of 16S rRNA genes selected from databases are successfully clustered with more than %98 accuracy, sensitivity, and specificity

Genomic Signal Processing Techniques for Taxonomy Prediction

To analyze complex biodiversity in microbial communities, 16S rRNA marker gene sequences are often assigned to operational taxonomic units (OTUs). The abundance of methods that have been used to assign 16S rRNA marker gene sequences into OTUs brings discussions in which one is better. Suggestions on having clustering methods should be stable in which generated OTU assignments do not change as additional sequences are added to the dataset is contradicting some other researches contend that the methods should properly present the distances of sequences is more important. We add one more de novo clustering algorithm, Rolling Snowball to existing ones including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. We use GreenGenes, RDP, and SILVA 16S rRNA gene databases to show the success of the method. The highest accuracy is obtained with SILVA library

A De Novo Clustering Method: Snowball for Assigning 16S Operational Taxonomic Units

To analyze complex biodiversity in microbial communities, 16S rRNA marker gene sequences are often assigned to operational taxonomic units (OTUs). The abundance of methods that have been used to assign 16S rRNA marker gene sequences into OTUs brings discussions in which one is better. Suggestions on having clustering methods should be stable in which generated OTU assignments do not change as additional sequences are added to the dataset is contradicting some other researches contend that the methods should properly present the distances of sequences is more important. We add one more de novo clustering algorithm, Rolling Snowball to existing ones including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. We use GreenGenes, RDP, and SILVA 16S rRNA gene databases to show the success of the method. The highest accuracy is obtained with SILVA library

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP

Performance Evaluation of Nature-Inspired Algorithms in constrained Optimization

In almost all scientific contributions to the field of Nature-Inspired Algorithms (NIAs), the researchers select some benchmark test suites, which makes possible to draw conclusions on the merit of the proposed algorithm. Hence, it is a vital task to compose comprehensive test suites with the aim of covering variety of different scenarios. Furthermore, while conducting comparative analysis of results obtained with NIAs, selection of the proper performance indicators are of paramount importance. This paper intends to address these two topics with a special stress on NIAs designed for constrained optimization

An Overview of Rare and Unusual Clinical Features of Bietti’s Crystalline Dystrophy

Bietti’s crystalline dystrophy (BCD) is a rare disease presenting with the appearance of intraretinal crystalline deposits and varying degrees of chorioretinal atrophy commencing at the posterior pole. Within time, intraretinal crystals gradually disappear and chorioretinal atrophy extends beyond the macula even resulting in complete chorioretinal atrophy. Concomitant corneal crystals can be noted in 1/2 - 1/3 of the patients, and the presence of corneal crystals is not a must for establishing the diagnosis. For the past decade, genetic evaluations and newer imaging modalities expand our knowledge about the disease. CYP4V2 gene is found to be the gene responsible for the disease process and new mutations are still being described. Modern imaging modalities, such as a spectral domain optical coherence tomography (SD-OCT) shed light on the anatomic features of the disease. By this, we reiterate the rare and unusual clinical features of BCD

Der Antrag auf das Verbot der prokurdischen HDP beim türkischen Verfassungsgericht: Beispiel für die Verschränkung von Politik und Justiz und böses Omen für eine friedliche Lösung der Kurdenfrage

Am 2. März 2021 hat die türkische Generalstaatsanwaltschaft Ermittlungen gegen die Demokratische Partei der Völker (HDP) eingeleitet, am 17. März die Klageschrift auf deren Verbot beim Verfassungsgericht eingereicht. Der Generalstaatsanwalt hat ferner beantragt, 687 Funktionären der Partei zu verbieten, sich in den nächsten fünf Jahren poli­tisch zu betätigen. Das würde auf den Ausschluss fast aller HDP-Politiker von der Politik hinauslaufen und so die politischen Kanäle für die Diskussion und Lösung der Kurden­frage auf Jahre verschließen. Zwar hat das Verfassungsgericht am 31. März die Klageschrift wegen verfahrensrechtlicher Mängel zurückgewiesen. Doch am 6. Juni teilte die Generalstaatsanwaltschaft mit, dass sie einen weiteren Vorstoß zum Verbot der Partei unternommen hat. Damit besteht die Gefahr, dass die Verhinderung ziviler und gewaltfreier kurdischer Politik Wasser auf die Mühlen der illegalen Arbeiter­partei Kurdistans (PKK) ist und sich der Kurdenkonflikt erneut per­petuiert. Der Vor­gang wirft ein Schlaglicht auf die Verschränkung von Politik und Justiz in der Türkei und macht strukturelle Mängel der türkischen Verfassungsordnung deutlich. (Autorenreferat