Emotional and Cognitive Aptitudes and Successful Academic Performance: Using the ECCT

Understanding factors that influence academic performances is vital. The aim of this study is to longitudinally test, with three timepoints, the unique contribution of several predictors to academic performance. In a sample of 796 Ecuadorian students, dominance analyses were performed with the R program to test the relative and unique importance of the seven variables under study (verbal aptitude, numerical aptitude, abstract reasoning, emotional regulation scenarios, emotional regulation self-questionnaire, and academic performance measured in timepoint one and two) for academic performance, measured in timepoint three in the entire sample and separately in each of the ten degrees in the academic center. Results show that the strongest predictors are past academic performance, followed by gender, numerical aptitude, scenarios, verbal aptitude, abstract reasoning, and, finally, the emotional regulation self-questionnaire. This study contributes to explaining the complex topic of academic performance. More studies are needed in order to better understand the role played by emotional intelligence, as well as differences between different degrees or areas of study.

Investigation of hesitancy to donate plasma among COVID-19 survivors in Goa: an online survey

Background: The ongoing COVID-19 pandemic led to surge in mortality. In the absence of definitive treatment, convalescent plasma therapy was accepted as a modality to treat COVID-19 patients. There exists hesitancy with regards to COVID-19 convalescent plasma donation. To find the deterrents to CCP donation.Methods: An online survey was conducted by snowball technique. The study participants were COVID-19 survivors. They were asked to express their willingness to donate CCP. The reasons for not donating CCP were recorded. The data was analyzed using R-program. The adjusted and unadjusted Odds ratios were calculated to find the predictors of willingness to donate CCP.Results: 110 study participants responded to the survey. 49.1% of the responders were willing to donate plasma. The top three deterrents of CCP donation were ill health (27.6%; 95% CI, 18.28%-39.27%), ineligibility (10.5%; 95% CI, 4.98%-20.21%), not recovered completely (6.58%; 95% CI, 2.45%-15.34%). The female gender, older age, being symptomatic, unaware of government incentives, tested by RAT, and unaware of CPT were associated with lower odds of donating CCP.Conclusions: Ill health, ineligibility, and perceived incomplete recovery were the major deterrents of CCP donation. Being female, older age, being symptomatic, unaware of government incentives, tested by RAT, and unaware of CPT were associated with lower willingness to donate CCP. There is a need to develop interventions to target these factors to improve CCP donation whenever it is indicated.

Increased Accuracy to c-Fos-Positive Neuron Counting

There is not a described method to count the core label of c-Fos-positive neurons, avoiding false-positive and false-negative results. The aim of this manuscript is to provide guidelines for a secure and accurate method to calculate a threshold to select which core of c-Fos-positive neurons marked by immunofluorescence has to be scored. A background percentage was calculated by dividing the intensity value (0 to 255) of the core of c-Fos-positive neurons by its surrounding background from the 8-bit images obtained in a previous study. Using the background percentage from 20% up to 98%, raising 2% once for each score, as threshold to choose which core has to be counted, a script was written for the R program to count the number of the c-Fos-positive neurons and the comparison between control and experimental groups. The differences of the average number of the core counted c-Fos-positive neurons between control and experimental groups, at all thresholds studied, showed a rising value related to an increase of the background percentage threshold as well as a decrease of its p value related to an increase of the threshold of background percentage. For the smallest thresholds (high intensity of label), the differences between groups are suppressed (false negative). However, for the biggest thresholds (nonspecific label), these differences are always the same (false positive). Therefore, to avoid the false-negative and the false-positive values, it was chosen as the threshold of 62% the inflection point of the linear regression, which is equally different from the biggest and smallest values of the differences between groups.

Trends and Directions of Financial Technology (Fintech) in Society and Environment: A Bibliometric Study

The contemporary innovations in financial technology (fintech) serve society with an environmentally friendly atmosphere. Fintech covers an enormous range of activities from data security to financial service deliverables that enable the companies to automate their existing business structure and introduce innovative products and services. Therefore, there is an increasing demand for scholars and professionals to identify the future trends and directions of the topic. This is why the present study conducted a bibliometric analysis in social, environmental, and computer sciences fields to analyse the implementation of environment-friendly computer applications to benefit societal growth and well-being. We have used the ‘bibliometrix 3.0’ package of the r-program to analyse the core aspects of fintech systematically. The study suggests that ‘ACM International Conference Proceedings’ is the core source of published fintech literature. China leads in both multiple and single country production of fintech publications. Bina Nusantara University is the most relevant affiliation. Arner and Buckley provide impactful fintech literature. In the conceptual framework, we analyse relationships between different topics of fintech and address dynamic research streams and themes. These research streams and themes highlight the future directions and core topics of fintech. The study deploys a co-occurrence network to differentiate the entire fintech literature into three research streams. These research streams are related to ‘cryptocurrencies, smart contracts, financial technology’, ‘financial industry stability, service, innovation, regulatory technology (regtech)’, and ‘machine learning and deep learning innovations’. The study deploys a thematic map to identify basic, emerging, dropping, isolated, and motor themes based on centrality and density. These various themes and streams are designed to lead the researchers, academicians, policymakers, and practitioners to narrow, distinctive, and significant topics.

Language use variation of L2 writers in weblog across different gender and genres

Despite the growing interest in investigating learners’ corpora, surprisingly little research has been conducted on the language use of L2 writers and its relation to the gender and genres in writing. Therefore, this study was aimed to find out the variation of language use in different genres or gender in weblogs, one of popular modes of computer-mediated communication (CMC). The study was done by conducting multivariate analysis using R program to weblog entries from a sample balanced of author gender (female or male) and weblog genre (diary or filter). Taking linguistic preferential features by Argamon et al (2003) and Pennebaker (2011) as dependent variables, the effect of genres or gender toward the use of the features was analyzed. The results showed that significant effects of several features can be considered as predictors. Personal pronouns and hedges (I think, and I believe) were found as predictors for diary; while the indefinite articles a/an and numbers were found as predictors for filter. As for the different language use by gender, female predictors were personal pronoun, verbs, negation, certainty words, and hedges. Meanwhile, the indefinite articles a/an, numbers, and preposition were the predictors of male writers.

Efficient Change-Points Detection For Genomic Sequences Via Cumulative Segmented Regression

Motivation Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences. Results In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations. Supplementary information The proposed algorithms are implemented in R program and are available at Bioinformatics online.

The influence of the CIELAB parameters on the perception of color after the use of whitening toothpastes

Aim: To evaluate the influence of the parameters L* a* b* on the variation of the color of bovine tooth enamel submitted to artificial darkening, after simulated brushing, with whitening toothpastes containing blue covarine. Methods: To undertake this study in vitro, 60 specimens (SP) were divided into 6 groups (n=10): control group (CGwater) and 5 test groups (GT1-Colgate Total 12, GT2-Oral-B 3D White Perfection, GT3- Colgate Luminous Instant White, GT4-CloseUp White Diamond Attraction, GT5-Sorriso Xtreme White). The specimens were darkened with coffee and submitted to simulated brushing for 6, 12, and 24 months. The alteration in the color was evaluated using CIELAB parameters and the ΔL, Δa, Δb and ΔE were calculated. The data was analyzed through generalized linear models using the R program and considering a level of significance of 5%. Results: The parameters L*, a* and the ΔL, Δa obtained better results in the test group than in the control group. There were no statistical differences between CG and the test groups for the evaluation of the b* parameter. In the evaluation of the Δb, the GT3 differed statistically from the CG. In relation to the ΔE, all the group tests showed a variation in color statistically greater than that of the CG and the GT4 showed the greatest variation, not differing from the GT3 during the periods studied. Conclusion: The mechanical and optical whitening agents positively influenced the values L*a* and b*, as well as in its variations and in the ΔE. It is important to emphasize, however, that to analyze tooth whitening it is necessary to evaluate their parameters together.

Suicide cases in developed and emerging countries: an analysis using wavelets

ABSTRACT Objective The aim of this study was to use a wavelet technique to determine whether the number of suicides is similar between developed and emerging countries. Methods Annual data were obtained from World Health Organization (WHO) reports from 1986 to 2015. Discrete nondecimated wavelet transform was used for the analysis, and the Daubechies wavelet function was applied with five-level decomposition. Regarding clustering, energy (variance) was used to analyze the clusters and visualize the clustering process. We constructed a dendrogram using the Mahalanobis distance. The number of groups was set using a specific function in the R program. Results The cluster analysis verified the formation of four groups as follows: Japan, the United States and Brazil were distinct and isolated groups, and other countries (Austria, Belgium, Chile, Israel, Mexico, Italy and the Netherlands) constituted a single group. Conclusion The methods utilized in this paper enabled a detailed verification of countries with similar behaviors despite very distinct socioeconomic, geographic and climate characteristics.

Workplace spirituality, self-compassion and mindfulness as antecedents of employee mental wellbeing

PurposeMental wellbeing brings in multiple benefits to employees and their organizations like better decision-making capacity, greater productivity, resilience and so on. The purpose of this paper is to examine the relationship of a few antecedents of mental wellbeing like workplace spirituality, mindfulness and self-compassion, using structural equation modeling (SEM).Design/methodology/approachUsing the convenience sampling method, data were collected from 333 employees of various organizations in India and SEM was performed using the R Program to test the hypotheses.FindingsResults suggest that mindfulness and self-compassion influenced the mental wellbeing of employees. It was also observed that workplace spirituality has a significant influence on both mindfulness and self-compassion.Originality/valueAn in-depth review of the literature revealed that no previous studies had examined the complex relationship between workplace spirituality, mindfulness, self-compassion and the mental wellbeing of employees. This research suggests that workplace spirituality, mindfulness and self-compassion are important factors that influence employees' mental wellbeing, and it empirically tests this in a developing country context. The present study enriches the literature studies on mental wellbeing, mindfulness, self-compassion and workplace spirituality by integrating “mindfulness to meaning theory”, “socio-emotional selectivity theory”, and “broaden and build theory”.