Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mathematical Model of Blood Flow in Merging Veins under a Magnetic Resonance Imaging Influence

Venous blood flow through the superior and inferior vena cavae and via a merger to the right atrium of the heart is investigated. Based on the Newtonian assumption for blood, the model is developed using Boussinesq’s approximation for the continuity, momentum, energy, and mass diffusion equations, which are non-linear partial differential equations. The governing equations are non-dimensionalized and solved by the regular perturbation technique. Expressions for the concentration, temperature, and velocity are obtained, analyzed, and presented graphically and quantitatively, and discussed. The results show that an increase in the magnetic field strength and conflux angle produce fluctuations in the flow velocity.
Propensity Models versus Weighting Class Approaches to Nonresponse Adjustment: A Methodological Comparison

Statistical adjustment of nonresponse is a deep and pervasive issue for sample surveys. Contemporary statistical methods offer two broad classes of approach to nonresponse adjustment. One is the use of a traditional weighting cell approach. More recently, response propensity modeling, using, typically, logistic regression, has been developed as a further approach to nonresponse adjustment. Additionally, RTI’s General Exponential Model (GEM) generalizes weight adjustments and includes poststratification and weight trimming. Data from the Education Longitudinal Study of 2002 (ELS:2002) are used to compare the results of the weighting class method, raking, a logistic regression propensity model, and GEM when used for nonresponse adjustment. For the one-dimensional case where each unit is in one unique cell, it can be shown that the four methods will produce similar results; the logistic propensity model approach produces slightly different results since it does not require calibration to weighted control totals for the selected sample. Expanding to many variables and multiple dimensions, marginal totals, variances, and weight distributions are compared for raking, logistic response propensity, and two GEM special cases. The weighting class method is limited to the cell model.
Optimization of antifungal activity by Bacillus subtilis isolate CCASU 2021-4 using Response Surface Methodology

Fungal infections represent an enormous load on the public especially with the development of resistance to the most currently used antifungal drugs in practice. In the present work, a bacterial isolate coded A3 was recovered from soil and was shown to express antagonistic activity against Candida (C.) albicans ATCC 10231. This isolate was identified as Bacillus (B.) subtilis subsp. spizizenii isolate CCASU 2021-4 using 16S ribosomal RNA sequencing. D-optimal design from response surface methodology (RSM) was used to optimize the environmental variables affecting the antifungal activity of the respective isolate. The optimum conditions were a temperature of 30 °C, a pH of 8, and an inoculum size of 5 x 10 7 cfu/mL, resulting in an enhancement in the antifungal activity by 1.2 fold. This is the first report, to the best of our knowledge, on an antifungal activity from B. subtilis subsp. spizizenii culture broth against human fungal pathogens along with its optimization through RSM.
Classification of authors, institutions, and countries, using productivity, centrality, and impact metrics: The field of tourism studies in Brazil (journals), 1990-2018

We investigated the set of Brazilian tourism journals, specifically 3,887 articles published in 16 jour- nals, from 1990 to 2018. The three main objectives of this study are: a) to rank authors, institutions, and countries in the field of tourism studies in Brazil, using productivity, centrality, and impact met- rics, for 1990-1999, 1990-2009, and 1990-2018; b) to characterize the leading elements – i.e. top-ranked ones; and c) to compare these rankings with h-index based rankings. A bibliometric and social network study was carried out, with manual data collection and revision and disambiguation. Productivity (full and fractional counting), centrality (degree, betweenness, and PageRank), and impact (based on actual citations) metrics were calculated. In summary, the leading authors are affiliated with institutions in the South and Southeast regions of Brazil, and they are linked to graduate pro- grams. For institutions, offering a graduate program in tourism or in a related field seems to be a key factor. The University of São Paulo (USP) and the University of Vale do Itajaí (UNIVALI) clearly stand out in all metrics. Brazil occupies the first position
Tracking motor activity in Virtual Reality to reveal cognitive functioning: A preliminary study

A challenge for cognitive research is the better understanding of how motor activity influences and is influenced by other cognitive domains. We developed a preliminary study to investigate whether tracking motor functioning in virtual reality provides useful insight on cognitive functioning. We chose the flankers task as an assessment measure and built a VR environment into which seven participants completed more than 1250 trials. In addition to classical results of the flankers task showing that incongruent stimuli induce slower responses than others. , we also identified how individuals are able to correct their initially incorrect motor response. This indicator may shed new light into the functioning of cognitive control in the future. We discuss the potential offered by virtual reality technology for cognitive assessment through embodied considerations of cognition.
POWER QUALITY CONSIDERATIONS FOR DISTRIBUTED GENERATION INTEGRATION IN THE NIGERIAN DISTRIBUTION NETWORK USING NEPLAN SOFTWARE

Power quality studies are increasingly becoming a toast of major researchers as it is now a major factor utilized in determining the overall efficiency of an electrical power system. Voltage profile and power loss are significant pointers to the quality of power and as such, distributed generation comes into play since it offers competitive advantages over the traditional grid interconnected system. In this study, therefore, an attempt is made in comparing the voltage profile of the traditional grid system of a typical distribution network and with that incorporated with the distributed generation, using real data of a practical system of the Nigeria distribution company. The conceptual framework as well as the mathematical formulations required for the study are presented. The data are, then, simulated using a NEPLAN software. The results obtained from the simulations for power losses in both scenarios are examined. The results obtained clearly showed that there is a significant reduction in power losses and a substantial improvement in the voltage profile of an embedded generation in-feed scheme in comparison with the conventional grid in-feed.
Representative democracy in Brazil: an analysis of the performance of congressional representatives in light of the development indices of their states and regions

Purpose – This study seeks to verify whether the performance of Brazilian Congressional Representatives is related to MHDI and GDP per capita indices and the regions they represent. Methodology – Regarding the performance of the parliamentarians, the data used were those made available by the Transparencia Brasil portal and analyzed using the multivariate exploratory non-hierarchical cluster analysis technique K-means. Federative Units were divided into clusters according to the similarities they presented with respect to the variables that made up the analysis. Findings – After the analyses, we were able to determine that there is no relation of equivalence between the performance of representatives in Congress and the observed research development indices (MHDI and GDP per capita) with states of different regions and quite distinct conditions of development making up the same group of analysis. Originality/value – The researched data, or even the identification of transparency actions demonstrates the wide variety of analysis possibilities that are available in terms of discussions of the performance of political agents and their respective returns for the population. We would suggest that future studies can use other data or other reports displayed in the transparency portals of various spheres of government. Other research possibilities can be developed based on qualitative analyses of the effective representativity of the projects proposed by these representatives of the people.
Kinetic Modeling of Essential Oil Extraction by Hydrodistillation of Xylopia aethiopica (Dunal) A. Rich Fruits from Congo-Brazzaville

The extraction kinetics of the essential oil of Xylopia aethiopica (Dunal) A. Rich by hydrodistillation was studied for modeling its process and optimizing its yield. The oils obtained, analyzed by GC/MS, consists mainly of pinenes, sabinene, myrenal, terpinene-4-ol, limonene. Experimental data were fitted into first and second order kinetics for a 2-steps extraction, washing and diffusion, of the phenomenological model, according to the hypothesis used. The essential oil which moves inner vegetable cells by diffusion and is extracted at the surface of the particle by washing with an extraction solvent. When the washing step is instantaneous compared to that of diffusion, the mechanism, which is under diffusion control, admits first order. Considering both washing and diffusion steps, kinetic order became 2, in agreement with the Peleg model. The Monod and Langmuir models also fitted experimental data. All these models validated by the experimental data with determination coefficients R> 0.96 can be used for optimizing the extraction of the essential oil of Xylopia aethiopica (Dunal) A. Rich.
Yield Stability of Different Elite Wheat Lines under Drought and Irrigated Environments using AMMI and GGE Biplots

This is an open access article & it is licensed under a Creative Commons Attribution Non-Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/) Wheat is the principal winter crop in Nepal. Drought affects 44% of the lands of the total wheat area in the country with a yield loss of 15–20%. This research focuses to minimize this loss through the identification of highyielding lines stable across the drought stress and irrigated environments. The experiment was conducted in Alpha Lattice Design with 20 genotypes replicated twice with five blocks per replication from November 2019 to April 2020. The findings showed that genotypes, environments, and genotype-environment interaction have a highly significant effect on grain yield and explained 28.95%, 52.57%, and 18.47% of variation on yield, respectively. The which-won-where model revealed elite line NL 1420 is the most responsive line in the drought environment, followed by BL 4407, while elite line NL 1179 is the most stable line in irrigated environment. The mean vs stability model with principal component 1 and 2 explaining 65.76% and 34.24% respectively, showed that elite line NL 1420, BL 4407, BL 4919, Bhrikuti are both high yielding and stable lines while line NL 1179, Gautam, and NL 1384 are less stable in both test environments. Similarly, the ranking genotypes model indicated lines close to the ideal line are NL 1420, BL 4407, BL 4919, Bhrikuti as the most representative line for genotype evaluation. Thus, elite wheat line NL 1420 and NL 1179 are recommended as specifically adapted to drought and irrigated environments, respectively, and elite line NL 1420, BL 4407, BL 4919, Bhrikuti are recommended for further evaluation for stability.
Robust Bayesian Analysis for Econometrics

We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for setidentified structural vector autoregressions, including details about numerical implementation and an empirical illustration.
NMR Investigations on Structure, Dynamics and Function of VAT-N and DOTATOC

Residual Dipolar Couplings (RDCs) have been recently established as a structural restraint for bio-molecular structure calculation. Here the physical concept of the RDC constant is demonstrated using an easy mathematical approach. RDCs were used for structure refinement and to calculate the subdomain orientation of VAT-N (20.5 kDa). VAT-N is the substrate recognition domain of the hexameric VAT complex formed by a tripartite domain structure, N-D1-D2. The inter-domain dynamics of VAT-N has been studied by 15N backbone relaxation parameters in the model-free framework. In conclusion, the relative orientation of VAT-N subdomains is parallel (kidney shape) and no large amplitude motion exists between them. Results of titration of VAT-N with SsrA, Ubiquitin, casein and Barstar are presented. The differences in the potency of Ga-III and Y-III chelated somatostatin analogue (DOTATOC) are studied by NMR.
A Mobile Health App for Premarital Related Word Advice (AmeeSehat): Usability and Usefulness Evaluation Study

Background:Premarital counseling demonstrates promising approach for enhancing interaction between couples and to prevent deterioration in the well-being of relationships. Mobile health (mHealth) apps may provide an appropriate solution to this issue, as many potential brides and grooms are frequent users of internet or web-based health info. Aim:This research aimed to measure the usability and usefulness of the AmeeSehat App as interpreted by potential end users. Method: This study employed a mixed method carried out in one of the public-health centers in East Jakarta, Indonesia with the future bride and groom. Think Aloud (TA) sessions with participants (n=10 final users and 3 experts) and quantitative assessment with 60 participants were held.A think aloud meetings have been captured, transcripts, and analyzed. The usability issues were evaluated using Nielsen severity scale and the Device Usability Scale (SUS). The usefulness of the AmeeSehat App was assessed using a tool to evaluate an improvement in healthy family knowledge, attitude, and intention, and readiness to marry. Results: The SUS score obtained (mean=78.4, SD=6.83), and none of the participants experienced (severe) problems with usability. Only 1 usability problems have been reported with a severity of 3 (major), namely terminology issues. The change after using AmeeSehat was statistically significant different as McNemar test revealed p <0.05. Conclusion:This researcher revealed that, in terms of its usability and usefulness, the AmeeSehat app is feasible to future brides and grooms. Participants considered AmeeSehat beneficial because they had the opportunity to learn more about a stable home life in preparation for marriage. Further assessment should be carried out in the form of a randomized controlled trial to test AmeeSehat's effectiveness in enhancing awareness, attitude, intention and readiness.
Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment. Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.
Research and Optimization of Gas Extraction by Crossing-Seam Boreholes from Floor Roadway

Deep coal seams are characterized by large stress, high gas pressure, and low permeability. The gas disaster threatens the safe production of coal mine seriously. Gas extraction by crossing-seam boreholes from floor roadway (GECMBFR) can reduce the pressure and content of coal seam gas, which is the main measure to prevent gas disaster. Considering the Klinkenberg effect, governing equations of gas adsorption/desorption-diffusion, gas seepage, and stress fields within the coal seam are established to form the seepage-stress coupling model. The governing equations are embodied into a finite element driven software to numerically simulate gas migration and fluid-solid coupling law in coal seam. On this basis, the process of gas extraction under different borehole spacings and diameters is simulated. The effects of these two key parameters on coal seam gas pressure, gas content, and gas permeability were analyzed. The borehole spacing and diameter were determined to be 5 m and 0.09 m, respectively. Combined with the actual situation of a mine, the process of gas extraction from floor roadway with different cross-sectional schemes, ordinary drilling boreholes and punching combined drilling boreholes, is comparatively analyzed. The results show that the gas extraction effect by ordinary drilling boreholes is lower than that of the punching combined drilling boreholes, and the extraction is uneven and makes it difficult to meet the standard. Hydraulic punching was carried out, and coal was washed out of the borehole, which expanded the contact area between the borehole wall and coal seam. The coal seam around the punching borehole is unloaded, which improves coal permeability and accelerates gas migration towards the borehole, thus promoting the efficiency of gas extraction. It is more reasonable to use punching combined drilling borehole scheme when implementing the GECMBFR technology.
Expanding a Lattice-based HVE Scheme

Functional encryption systems provide finer access to encrypted data by allowing users to learn functions of encrypted data. A Hidden-Vector Encryption Scheme (HVE) is a functional encryption primitive in which the ciphertext is associated with a binary vector w and the secret key is associated with a special binary vector v that allows "don't care" entries. The decryption is only possible if the vectors v and w are the same for all elements, except the "don't care" entries in v. HVE schemes are used to construct more sophisticated schemes that support conjunctive and range searches. In this work we show how to expand the basic fuzzy IBE scheme of Agrawal et al. (PKC 2012) to a hierarchical HVE scheme. We also show how the version using ideal lattices affects the security proof.
Good Volatility, Bad Volatility, and the Cross Section of Cryptocurrency Returns

This paper examines the distributional properties of cryptocurrency realized variation measures (RVM) and the predictability of RVM on future returns. We show the cryptocurrency volatility persistence and the importance of the asymmetry on volatility forecasting. Signed jumps variations contribute around 18% of the cryptocurrency return quadratic variations. The realized signed jump (RSJ) strongly predicts the cross-sectional future excess returns. Sorting the cryptocurrencies into portfolios sorted by RSJ yields statistically and economically significant differences in future excess returns. This jump risk premium remains significant after controlling for cryptocurrency market characteristics and existing risk factors. The standard cross-sectional regression convinces the cryptocurrency return predictability from RSJ by controlling multiple cryptocurrency characteristics. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.
Financial Analysis and Performance Evaluation of Moderna

The report analyses the financial performance of Moderna Inc for the past four years. Moderna Inc is a biotechnological company that focuses on the development of vaccines using messenger ribonucleic acid (mRNA) technology. The study collected data about the company from secondary sources which are Yahoo Finance and Nasdaq websites. The report analyzed the financial performance of the company by computing profitability ratios, cash flow ratios, leverage ratios, and liquidity ratios. The study concluded by giving recommendations on what the company can do to improve its financial performance in the future.
Identification of gene expression location of angiotensin‐converting enzyme‐2 SNPs as a receptor for SARS‐CoV‐2 in different populations by using various databases

The World Health Organization (WHO) has announced that Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2) and Coronavirus disease (COVID‐19) is considered a worldwide pandemic. Rapidly rising numbers of patients have been reported in almost every country, along with the growing mortality rates. Uncontrolled growth in patient numbers may be due to reasons such as treatment options and vaccine availabilities and unidentified targets of SARS‐CoV‐2. Previous study has revealed that the molecular target of SARS‐CoV‐2 is analogous to SARS (2003), i.e. angiotensin‐converting enzyme‐2 (ACE‐2). Therefore, the determination of ACE‐2 may enrich existing information and facilitate development of drugs targeted toward SARS‐CoV‐2. This study aims to screen the expression of ACE‐2 genes and their relationship to the types of SNP variants in SARS‐CoV‐2. We explored a series of observations using powerful databases, e.g. GTEx portal, HaploReg, 1000 Genome and Ensembl, to identify the gene variant of ACE‐2. We showed that ACE‐2 is highly expressed in the testes and small intestine, while its lowest level is observed in lymphocytes. Subsequently, we observed 17 gene variants containing a missense mutation potentially damaging protein level. Among these genes, single nucleotide polymorphism (SNP) rs370187012 shows the highest damage‐level score, while the lowest effect is in SNP rs4646116. The highest frequency of the C allele was observed in European populations (1%). In addition to showing that ACE‐2 is expressed in several organs, we concluded that the ACE‐2 gene variation can be found in African, American, Southeast and East Asian, and European populations. The polymorphisms of ACE‐2 impact on the ACE2 protein structure and the binding capacity of the ACE‐2 receptor with the S‐Protein of SARS‐CoV‐2.