Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of ewom characteristics on online repurchase intention mediating role of trust: case study of Zalora

This research was conducted in order to find out the relationship between EWOM quantity, EWOM credibility, and EWOM quality with Repurchase Intention and whether Trust can mediate the relationship between EWOM quantity, EWOM credibility, and EWOM quality with Repurchase Intention. This research replicate Matute, Polo-Redondo, and Utrillas (2015). There are a total of 6 hypotheses which were proposed in this research. (1) EWOM Quantity affects Online Repurchase Intention in Zalora directly and positively; (2) EWOM Credibility affects Online Repurchase Intention in Zalora directly and positively; (3) EWOM Quality affects Online Repurchase Intention in Zalora directly and positively; (4) Trust mediates the relationship between EWOM Quantity and Online Repurchase Intention in Zalora; (5) Trust mediates the relationship between EWOM Credibility and Online Repurchase Intention in Zalora; (6) Trust mediates the relationship between EWOM Quality and Online Repurchase Intention in Zalora. Survey method was used in this research with a total sample of 413, in which questionnaire was used as a tool to collect data needed from the respondents. Smartpls 3.2. 8 Program is used to analyze the data. After the data was analyzed, the three EWOM characteristics does not affect repurchase intention significantly. Whereas Trust only mediates the relationship between EWOM Quality, EWOM Credibility towards Repurchase Intention. Managerial implications and suggestions for future research were also provided in this research. / Penelitian ini dilakukan untuk mengetahui hubungan antara kuantitas EWOM, kredibilitas EWOM, dan kualitas EWOM dengan Repurchase Intention dan apakah Trust dapat memediasi hubungan antara kuantitas EWOM, kredibilitas EWOM, dan kualitas EWOM dengan Repurchase Intention. Penelitian ini mereplikasi Matute, Polo-Redondo, dan Utrillas (2015). Ada total 6 hipotesis yang diusulkan dalam penelitian ini. (1) Kuantitas EWOM mempengaruhi Niat Pembelian Kembali Online di Zalora secara langsung dan positif; (2) Kredibilitas EWOM mempengaruhi Niat Pembelian Kembali Online di Zalora secara langsung dan positif; (3) Kualitas EWOM mempengaruhi Niat Pembelian Kembali Online di Zalora secara langsung dan positif; (4) Trust memediasi hubungan antara Kuantitas EWOM dan Niat Pembelian Kembali Online di Zalora; (5) Trust memediasi hubungan antara Kredibilitas EWOM dan Niat Pembelian Kembali Online di Zalora; (6) Trust memediasi hubungan antara Kualitas EWOM dan Niat Pembelian Kembali Online di Zalora. Metode survei digunakan dalam penelitian ini dengan total sampel 413, di mana kuesioner digunakan sebagai alat untuk mengumpulkan data yang dibutuhkan dari responden. Program Smartpls 3.2. 8 digunakan untuk menganalisis data. Setelah data dianalisis, ketiga karakteristik EWOM tidak mempengaruhi niat pembelian kembali secara signifikan. Sedangkan Trust hanya memediasi hubungan antara Kualitas EWOM, Kredibilitas EWOM terhadap Niat Beli Ulang. Implikasi dan saran manajerial untuk penelitian masa depan juga disediakan dalam penelitian ini.
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information

As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and multi-parties data training and analyses are infeasible due to the privacy and data security concerns. In this paper, we propose an extendable and elastic learning framework to preserve privacy and security while enabling collaborative learning with efficient communication. The proposed framework is named distributed Asynchronized Discriminator Generative Adversarial Networks (AsynDGAN), which consists of a centralized generator and multiple distributed discriminators. The advantages of our proposed framework are five-fold: 1) the central generator could learn the real data distribution from multiple datasets implicitly without sharing the image data; 2) the framework is applicable for single-modality or multi-modality data; 3) the learned generator can be used to synthesize samples for down-stream learning tasks to achieve close-to-real performance as using actual samples collected from multiple data centers; 4) the synthetic samples can also be used to augment data or complete missing modalities for one single data center; 5) the learning process is more efficient and requires lower bandwidth than other distributed deep learning methods.
MMES: Mixture Model-Based Evolution Strategy for Large-Scale Optimization

This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors from a mixture model, which facilitates exploiting the rich variable correlations of the problem landscape within a limited time budget. We analyze the probability distribution of this mixture model and show that it approximates the Gaussian distribution of CMA-ES with a controllable accuracy. We use this sampling method, coupled with a novel method for mutation strength adaptation, to formulate the mixture model-based evolution strategy (MMES)—a CMA-ES variant for large-scale optimization. The numerical simulations show that, while significantly reducing the time complexity of CMA-ES, MMES preserves the rotational invariance, is scalable to high dimensional problems, and is competitive against the state-of-the-arts in performing global optimization.
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymized Spatial Adjacency Information

To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ride-hailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio-temporal dependencies. To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymized spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing one-dimensional convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real-world datasets of Didi Chuxing, which shows that our models based on the proposed architecture can outperform conventional time-series models (e.g., ARIMA) and machine learning models (e.g., gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction.
Online Non-Iterative Estimation of Transmission Line and Transformer Parameters by SCADA Data

Utilization of abundant measurements provided by supervisory control and data acquisition (SCADA) system has attracted increasing attention. Real-time estimation of transmission line parameters, utilizing voltage and power flow measurements provided by remote terminal units (RTUs) located at two substations across the line, has been investigated, recently. This paper improves this approach by introducing a novel exact linear reformulation of the problem, which can be solved in closed form. The distributed-parameter model of long transmission lines is considered and its parameters are estimated in a non-iterative manner using RTU measurements. The method is also extended to estimate transformer series impedance and tap position by SCADA measurements, linearly. As such, the disadvantages associated with the previous iterative approach, e.g. concern over convergence, for transmission line parameters are avoided. Moreover, the novel technique for estimating transformer parameters allows to determine the tap position as well as updated transformer series impedance. Furthermore, a thorough analysis is presented to take the measurement accuracy into account. Simulation results for different transmission lines and transformers in the IEEE 118-bus test system are reported, where the result indicate successful performance of the proposed algorithms.
Prevalence of Obesity Among University Students. A Cross-Sectional Study

©Copyright 2021 by Anatolian Journal of Family Medicine Available online at www.anatoljfm. org INTRODUCTION Body mass index (BMI) is calculated by dividing the weight (kg) by the height in meters squared (kg/m2). This simple measurement and calculation is a very practical method to determine obesity, and its value can be classified according to international standards. [1] Several studies use this simple measurement. [2,3] Conditions causing obesity and caused by obesity are not fully understood. [4] Also, obesity and obesity-related diseases create a burden of disease and can negatively affect the health systems of countries. [5] Since obesity negatively affects almost every age of life, it is considered as a major public health problem. [6] Several factors cause obesity and a significant number of these are not well understood. The factors related to the social life such as nutrition, physical activity, and sleep condition are well known. [7,8] Some situations related to the development age can cause obesity, university students are population group affected by the development age. [9] This is because university life can inculcate both positive and habits to the individual. These negative habits could be risks for obesity and can turn the university age into a risky period. [10] For this reason, it is required to conduct obesity research on individuals at the university age. Various studies have been conducted on individuals of university age to understand and classify obesity. [10,11] In these studies, conditions Objectives: Obesity is a global public health problem awaiting a solution. The body mass index (BMI) is one parameter out of the many measurements used to evaluate obesity. There are limited studies comparing the BMI of students studying and those not studying health protection and promotion. Therefore, this study aims to evaluate the BMI of students studying in the department of health, social science, and engineering in university with various independent variables.
Knowledge, practice and barriers of exclusive breastfeeding among mothers in East Khasi hills district, Meghalaya, India: A mixed method study

Introduction: The benefits of Exclusive Breastfeeding are widely recognized, yet worldwide a large number of infants are not exclusively breastfed. WHO recommends Exclusive Breastfeeding for the First 6 months? In India, as per NFHS 4 (2015-16) reports, Exclusive Breastfeeding rate is 54.9%. Meghalaya has a low Exclusive Breastfeeding rate of 35.8% only. Low rate of Exclusive Breastfeeding imply that mothers in this region are constantly facing multiple barriers which limit them to exclusively breastfeed their infants. Understanding these barriers is crucial. Aim of the study: To assess Knowledge, Practice and Barriers of Exclusive Breastfeeding. Methodology: A Cross sectional study was conducted in areas under Mawlai Nongkwar UHC which was chosen randomly. The data collection period was 1 month. 181 mothers having children aged less than two years were recruited out of which 15 mothers were chosen purposely for the in-depth interview which aimed at identifying the barriers. Data collected using structured and semi-structured questionnaires. Results: Out of 181 mothers, 175 mothers have knowledge about duration of Exclusive Breastfeeding (i.e. Six months). The Mean Knowledge score was found to be 13.425 ± 2.1085 and the Median score is 14. Based on the Median score, 126 (69.6%) mothers were found to have adequate knowledge about Exclusive Breastfeeding. The Exclusive Breastfeeding rate found in the study is 44.2%. Majority of the mothers i.e. 56.65% use bottle for feeding and Prelacteal feeding is practiced by 10.5%. Association was found between Exclusive Breastfeeding Practice and Mother’s Occupational status. The common issues that emerged from the qualitative survey regarding barriers to Exclusive Breastfeeding are- Breast milk alone not sufficient to satisfy baby’s hunger, Exclusive Breastfeeding affecting mother’s day to day life, problems encountered during breastfeeding, influenced decision by family members and others, mothers returning to work and baby’s health condition at birth. Conclusion: The present study shows that although mothers have knowledge about Exclusive Breastfeeding but still they fail to practice due to various barriers. Unless these barriers are addressed, Exclusive Breastfeeding will remain a distant dream.
Hidden Abodes in Plain Sight: the Social Reproduction of Households and Labor in the COVID-19 Pandemic

ABSTRACT This article deploys a feminist political economy approach centered on social reproduction to analyze the reconfiguration and regeneration of multiple inequalities in households and the labor markets during the COVID-19 pandemic. Based on this approach, the analysis unpacks the multiple trajectories of fragility the current crisis is intervening on and reshaping in the home and in the world of work, and their gendered and racialized features across the world. It shows how the pandemic and the measures to contain it have further deepened the centrality of households and reproductive work in the functioning of capitalism and argues that the transformative potential of the crisis can only be harnessed by framing policy and political responses around social reproduction and its essential contributions to work and life. HIGHLIGHTS A feminist social reproduction approach reveals the COVID-19 crisis as a crisis of work. The crisis is reshaping the organization of production and reproduction in households and global labor markets. This reorganization is exacerbating gender, class, and race inequalities. The pandemic has renewed the centrality of households in welfare provisioning and made social reproduction work visible. An internationalist feminist response would ensure access to services based on the centrality of social reproduction.
Triple-Radiation Pattern Monopulse Horn Feed With Compact Single-Layer Comparator Network

A monopulse horn feed with a triple-radiation pattern (sum, elevation difference, and azimuthal difference) operating at 35 GHz is presented. The device consists of three main parts: a spline profiled horn, a mode converter that generates the modal field combinations that produce each radiation pattern, and a very compact single-layer monopulse comparator network. Unlike other state-of-the-art designs, the novel feed is based on a triple-radiation pattern horn with a mode converter excited by a comparator network implemented in a single level for improving the compactness and easing quasi-2-D manufacturing. The kernel of this network is a novel 180° H-plane waveguide coupler with in-line ports, which avoids any additional phase compensation in the routing waveguide sections used in other works. The design process of the $Ka$ -band antenna feed is described, and the simulated results for the three radiation patterns and their associated reflection coefficients are presented. The prototyping process is outlined, and the obtained experimental results are compared with the simulated ones showing very good agreement and validating the design process.
Effect of plant oil treatments and containers on electrical conductivity, dehydrogenase activity and Mycoflora incidence of sorghum seed during storage

The present investigation was carried out at Department of Seed Science and Technology, CCS Haryana Agricultural University, Hisar to assess the effect of plant oils treatments and storage containers on seed quality and storability in sorghum. Seeds of forage sorghum variety HJ 541 were treated with plant oils and stored in three containers. The two factor experiment comprising of ten different treatments including plant oils were given to seeds (T0 – Untreated, T1 - Castor oil @ 2 ml/kg seed, T2 - Neem oil @ 2 ml/kg seed, T3 - Aonla oil @ 2 ml/kg seed, T4 - Til oil @ 2 ml/kg seed, T5 - Linseed oil @ 2 ml/kg seed, T6 - Karanj oil @ 2 ml/kg seed, T7 - Akhrot oil @ 2 ml/kg seed, T8 - Ajwain oil @ 2ml/kg seed, T9 - Carbendazim @ 2 g/kg seed) and kept in different containers (C1: Cloth bag, C2: Polythene bag and C3: Metal box) under ambient conditions in laboratory in three replicates. All the treatment combinations were evaluated for seed quality parameters viz. electrical conductivity, dehydrogenase activity and seed mycoflora. The neem oil (2 ml kg-1) performed better than others and, among containers polythene bag was proved better followed by metal box and cloth bag for storability at ambient condition. The dehydrogenase enzyme activity of seeds found decreased with the passage of time in all the containers and treatments. The electrical conductivity and the percentage frequency of seed mycoflora showed increased values with the progress of storage period. Carbendazim and neem oil were found most effective against the different fungal genera of sorghum seed in all the three containers. Among the containers less incidence of mycoflora was observed in seed stored in polythene bag followed by metal box in the all the treatments along with control.
COVID-19 Fear in Sports Sciences Students and Its Effect on Academic Procrastination Behavior

This study aims to examine the COVID-19 fear levels of the students of the faculty of sports sciences and their academic procrastination behavior in terms of various variables and to determine the correlation between them With a total of 342 participants, the sample of the study consists of 129 female and 213 male students studying at the Faculty of Sports Sciences Data collection tools used in the study are Fear of COVID-19 Scale (FCV-19S) and Academic Procrastination Scale (APS) The research was conducted following the relational screening model which is a subgroup of the general survey model The normal distribution, descriptive statistics, t-test, Anova test, Post-hoc test, Pearson correlation coefficient, and regression analysis were used in the statistical evaluation of the data Findings revealed that there was a weak positive correlation between COVID-19 fear and academic procrastination behavior Moreover, the results also revealed that students who worked out during the pandemic had less fear of COVID-19, however, this did not affect students' academic procrastination behavior
Creating students’ communities of Inquiry (COI) in online learning using the Moodle Learning Management System

The issue of promoting high levels of interactivity in online learning is important and topical. There is always a need to provide opportunities for online learners to work with others and feel a sense of belonging. This desktop review paper explores the possibility of creating communities of inquiry using the Moodle learning management system. In this discussion, we review the general use of a learning management system in an institution of higher learning. We discuss the advantages and disadvantages of online learning. The concept community of inquiry is unpacked, with emphasis on the three presences namely the cognitive, social and teaching presences. By drawing on the interactive features of the Moodle LMS, we discuss how the three presences could be promoted. Conclusions and recommendations are drawn from the discussion. Keywords: online learning,, interactivity, collaborative learning, digital learning platform, higher education DOI: 10.7176/JEP/11-30-18 Publication date: October 31 st 2020
CLUSTER KINETICS OF PHASE TRANSITIONS: APPLICATIONS TO INNOVATIVE TECHNOLOGIES

Phase transitions are critical in many of the new technologies of interest to chemical engineers. Applications to materials processing and pharmaceuticals manufacture are among the uses for phase transition dynamics investigated with the methods of population balance modeling for clustering processes. The underlying phenomenon of clustering occurs during condensation processes such as crystallization from solution. Self-assembly of molecules or supramolecules is likewise spontaneous clustering, often through non-covalent interactions. Vapor-liquid and solid–liquid condensations usually involve nucleation from a metastable state, accompanied by particulate growth and Ostwald ripening (with denucleation). The nucleation process is bypassed under certain conditions, such as in spinodal decomposition, glass transition, and gelation. It is proposed that the unifying concept for all these transformations is cluster kinetics and dynamics. Using population balance modeling, we outline how phase transition processes can be quantitatively modeled by cluster size distributions evolving in time. We also discuss how generic cluster processes apply to granular systems, synchronizing oscillators, and polymorphic transformations.
Impact of Population Growth, Poverty and Unemployment on Economic Growth

This research explores the impact of population growth, poverty and unemployment on economic growth in Nigeria using Auto Regressive Distributed Lag (ARDL). The study employed an econometric procedure; unit root test which involved the use of Augmented Dickey Fuller test (ADF) and Phillip-Perron test (PP). The cointegration test technique used in the study is Auto Regressive and Distributed Lag (ARDL). The study variables are real GDP, population, poverty, unemployment and foreign direct investment has control variable. The null hypothesis stated that there is presence of a unit root was failed to be rejected at levels but rejected at first difference according to the two tests (ADP and PP) employed. The study found that some of the variables are stationary at level I(0) while others are stationary at first difference I(1). The results of the cointegration test showed that there exist cointegrating equation between explanatory variables and economic growth. The ECT speed of adjustment to the normal equilibrium confirms their long run relationship of the variables. Finally, the study found that population and FDI have a positive impact while poverty and unemployment has negative impact on GDP. Based on these findings recommend that policy makers should grow the real economic sectors to improve and enhance productivity, exports, job creation, curb inflation and reduce poverty and rapid economic growth and substitute the non-productive imports with domestic products and develop enabling environment to attract foreign private investors.
A Carbon-13 Nuclear Magnetic Resonance Study of Chemical Processes Involved in the Isolation of Klason Lignin

Klason lignins were isolated from 15 samples of hardwoods, softwoods and grasses. The various stages of the procedure were monitored by solid-state C NMR with cross Polarisation and magic-angle spinning. The results confirm sources of errors identified by other methods of analysis. In particular, lignin-like material was removed during the extraction steps while tannins remained in the residue after digestion with sulphuric acid. The NMR spectra provided rate constants for hydrolysis of ß-O-4 ether linkages. Most linkages involving syringyl groups were hydrolysed within minutes of exposure to 72% sulphuric acid at room temperature, while about half of those involving guaiacyl groups remained intact after 2 hours. This observation explains in part the tendency for woods with a high syringyl/guaiacyl ratio to release relatively high concentrations of acid-soluble lignin.