Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending language learning beyond the EFL classroom through WhatsApp

Abstract WhatsApp is a powerful tool for L2 learning because of its capacity to promote interaction between teacher and student, as well as among students. Its use has also been shown to support ubiquitous learning. In an attempt to extend language learning beyond the classroom, this study reports on the use of instant messaging through WhatsApp by a group of EFL learners (n = 23) who were regularly sent optional language-learning tasks by their teacher during an intensive summer course. By the end of a five-week-period, 764 messages had been produced, which are explored through a qualitative analysis in the present study, with particular attention to changes of participation as a result of time. Most of the messages in the WhatsApp group were produced by the students in the context of the teacher-initiated tasks and through them students had the chance to practice the language as well as engage in real communication, even though student participation tended to decrease over time. The WhatsApp group was also used for informal communication among the students and with the teacher (a total of 289 off-task messages) which, in contrast to on-task messages, grew over time. The nature of the interactions also tended to be more spontaneous and symmetrical, at times with more of a presence of textese. Negotiation of form and content was scarce, but both on-task and off-task messages turned out to be equally productive in engaging students to use English beyond the walls of the classroom.
Characterisation of Atlantic meridional overturning hysteresis using Langevin dynamics

Abstract. Hysteresis diagrams of the Atlantic meridional overturning circulation (AMOC) under freshwater forcing from climate models of intermediate complexity are fitted to a simple model based on the Langevin equation. A total of six parameters are sufficient to quantitatively describe the collapses seen in these simulations. Reversing the freshwater forcing results in asymmetric behaviour that is less well captured and appears to require a more complicated model. The Langevin model allows for comparison between models that display an AMOC collapse. Differences between the climate models studied here are mainly due to the strength of the stable AMOC and the strength of the response to a freshwater forcing.
Angular Superresolution Based on A Priori Information

The results of theoretical studies and mathematical modeling indicate the possibility of obtaining the angular superresolution and its limits by using algebraic methods of imaging. The necessary algorithms are created on the basis of solution to inverse problems. The limiting levels of the achieved angular superresolution are found depending on the signal‐to‐noise ratio for objects of various types. A number of specific advantages of the method are shown as applied to the solution of two‐dimensional problems. Capabilities of the method are illustrated by examples. High performance of the proposed approach allows one to use it in real time when scanning and tracking targets.
Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms

ABSTRACT Forecasting transportation demands can aid online car-hailing platforms to dispatch their service vehicles in advance to areas with more potential orders. This results in a reduction in passengers’ waiting time and better utilisation of transportation resources. However, the complexity and dynamics of multi-dimensional influential factors make the forecasting and dispatching procedures challenging. This paper addresses these issues by using machine learning techniques and an effective probabilistic dispatching strategy. Multiple influential factors were identified in spatial, temporal, and meteorological dimensions, and effective machine learning algorithms were applied to predict the number of passenger orders. The fusion of the multi-dimensional features enables the proposed algorithms to better reveal the spatiotemporal characteristics and their correlations. A sensing-area-based strategy was introduced to dispatch available service vehicles to high demand-intensity regions efficiently with respect to the global demand-supply-balance and the individual probability of receiving orders. Finally, extensive experiments with large-scale real-world datasets were conducted to evaluate the performance of the machine learning algorithms and the effectiveness of the dispatching strategy. Overall, this paper extensively studies the forecasting of the spatiotemporal demand in multiple cities using point-of-interest data and the dispatching of available service vehicles based on such information for online car-hailing platforms.
Flow Model Selection for Water Soluble Polymer Solutions

This research deals with the effect of type and concentration of different polymer solutions on rheological behavior and apparent viscosity. All polymers studied in this work are water soluble, which are: XC-polymer, Carboxymethyl cellulose (two types), Hydroxyethyl cellulose and Polyvinyl alcohol. 55 experiments were performed with different polymer types and concentrations at 30 C ( 1 C). By using the Solver Add-in in Microsoft Excel®, the power law flow model was found to be the best fits the experimental results. The apparent viscosity of each polymer used at different concentrations was decreased with shear rates. It was found that XC-polymer solutions have higher shear stresses than other polymers. Also it was found that XC-polymer solutions have a higher viscosity, and its viscosity decreases much more than other polymer solutions used in this study.
Experiential education as a strategy to preserve Maqasid Al-Shariah by identifying and addressing stigmatic views held by pharmacy students of patients with substance use disorders.

Introduction: Stigmatising people with a history of substance use disorders (PHSUDs) is discriminatory, causes harm by hindering access to health services and promotes relapse. It jeopardises the preservation of Maqasid Al-Shariah, particularly the protection of life and well-being. Experiential education (EE) is a potential strategy to overcome stigma through direct experience with PHSUDs. This study aimed to identify stigmatised views of PHSUD's held by pharmacy students and, in accord with Maqasid Al-Shariah, the effectiveness of EE in changing those views. Materials and methods: Interviews were conducted with seven undergraduate pharmacy students who provided care to PHSUDs through an EE programme. A semi-structured interview guide was used to explore students' perceptions on stigma. Each interview was audio-taped, transcribed verbatim and translated into English. Data were manually sorted and coded using Microsoft Excel 2016 and subjected to thematic analysis. Results: The following themes related to stigma were identified: 1) individuals are to blame 2) moral versus biological views of addiction 3) stereotypes of unpredictability and dangerousness 4) lack of didactic education/training for pharmacy students regarding PHSUDs and 5) lack of face-to-face experiential education with PHSUDs. Less stigma and greater empathy towards PHSUDs were reported post-intervention attributable to increased knowledge about substance abuse and face-toface interactions with PHSUDs. Conclusion: Pharmacy students have pre-conceived stigmatic views regarding PHSUDs modifiable through experiential education. Contact by pharmacy students with stigmatised patients promotes health care without discrimination as advocated in the teaching of Islam. Future interventions to reduce stigma are required to preserve Maqasid Al-Shariah.
Constitutive relation of stiffened square CFST Columns

Using the constitutive model of a square CFST column with binding bars, the constrained mechanism of the stiffened square CFST columns is analyzed in great detail. The equivalent constitutive model under uniaxial compression is established for square CFST columns. The model is then used to describe the stress-strain curve process for concrete and to calculate the bearing capacity. Models of ten concrete-filled square steel tubular columns under axial compression specimens are created with constitutive relation. The results are compared with the axial compression test of an ordinary square CFST specimen column and stiffened square CFST specimens. Our results show that the bearing capacity and stress-strain curves under axial compression are consistent with the actual test measurements. Analysis can be applied to the square stiffened CFST columns, the square CFST columns with binding bars, and the ordinary square CFST columns.
Generalised predictive current‐mode control of passive front‐end boost‐type converters

In this work, an average current-mode control strategy based on a generalised predictive control formulation for passive front-end three-phase boost-type converters is proposed. A novel design procedure for the generalised predictive control strategy is introduced which considers both the cost function and disturbance model as design parameters to set the controller’s dynamic response and robustness against component variations. A maximum robustness criterion was used for achieving stability up to a 70 % inductance reduction with maximum possible bandwidth. The proposed strategy was compared against both a PI and a predictive deadbeat average current-mode control using both simulations and experimental results on a 2 − kW converter. The generalised predictive control presented less performance variations between different operating points than the PI controller. Also, the proposed strategy is more robust than the predictive deadbeat strategy, showing a better transient response with a 50 % inductance reduction and remained stable for a 71 % inductance reduction, while the predictive deadbeat could not. Finally, the proposed strategy achieved a 1 . 4 % output voltage load transient response for a 595 W load power step, and a 2 . 8 % output voltage line transient response for a 100 V input voltage step, outperforming existing state-of-the-art strategies.
Polypyrrole-Based Composite Materials for Electromagnetic Wave Absorption

Abstract With the development of electronic communication technology, the issues of electromagnetic (EM) wave pollution have become increasingly prominent, and the requirements for EM wave absorbing materials are rising. Polypyrrole (PPy), an important type of conductive polymers, has attracted increasing attention in the applications of EM wave absorbing due to their facile synthesis process, low density, good conductivity, corrosion resistance, and stable chemical properties. Various PPy-based composite materials exhibit excellent electromagnetic absorbing properties and have been widely reported in the literature. In this review, the recent development and understanding of PPy and PPy-based composites materials in the applications of EM wave absorption are summarized in detail. In particular, as a conductive loss-type absorbing material, we focus on the design and fabrication of PPy-based composites and explore the significant impact of EM parameters on the EM wave absorbing properties. Finally, perspectives are provided for future directions of advanced PPy and PPy-based composites for microwave absorbers.
Molecular Characterization of Sewage Bacteriophage and Their Efficiency in Treatment of Antibiotic Resistant Bacteria

Background: The increasing prevalence of multi-drug resistant of bacteria strains, has become a globalproblem and therefore alternative method to antibiotics must be found. Bacteriophages are considered analternative agent to control infection and contamination of the bacteria. Method: We used RAPD-PCRtechnique in present study, and used restriction endonuclease conformed presence unknown DNA fragments.Results: In this study, we described the isolation and character development of lytic bacteriophages capableof infecting bacteria specifically. Lytic bacteriophages, specific to Escherichia coli, Klebsiella pneumonia,Salmonella typhi, clinical strains, was first isolated from sewage. We have analyzed the DNA (RAPD)-PCR technique to produce unique and reproducible band from 21 different bacteriophages infecting E.coli,K. pneumonia, S. typhi . Used restriction endonuclease conformed presence unknown DNA fragments.Conclusion: Phages was capable of eliciting efficient lysis of studied bacteria, revealing its potential as anon-toxic sanitizer for controlling bacterial infection and contamination in both hospital and other publicenvironments.
Instantaneous Pre-Fire Biomass and Fuel Load Measurements from Multi-Spectral UAS Mapping in Southern African Savannas

Landscape fires are substantial sources of (greenhouse) gases and aerosols. Fires in savanna landscapes represent more than half of global fire carbon emissions. Quantifying emissions from fires relies on accurate burned area, fuel load and burning efficiency data. Of these, fuel load remains the source of the largest uncertainty. In this study, we used high spatial resolution images from an Unmanned Aircraft System (UAS) mounted multispectral camera, in combination with meteorological data from the ERA-5 land dataset, to model instantaneous pre-fire above-ground biomass. We constrained our model with ground measurements taken in two locations in savanna-dominated regions in Southern Africa, one low-rainfall region (660 mm year−1) in the North-West District (Ngamiland), Botswana, and one high-rainfall region (940 mm year−1) in Niassa Province (northern Mozambique). We found that for fine surface fuel classes (live grass and dead plant litter), the model was able to reproduce measured Above-Ground Biomass (AGB) (R2 of 0.91 and 0.77 for live grass and total fine fuel, respectively) across both low and high rainfall areas. The model was less successful in representing other classes, e.g., woody debris, but in the regions considered, these are less relevant to biomass burning and make smaller contributions to total AGB.
Identification of Disturbances in Power System and DDoS Attacks using Machine Learning

Power system disturbances are the most common problem that often occurs due to human-made or natural events. The most challenging issue is finding the main cause of power disturbance and making appropriate decisions in response to it. The accuracy of the human judgement is not good in predicting the power disturbance; it is not easy for operators to monitor the system’s situation. To make human findings easier, we incorporate machine learning techniques to differentiate between the categories of power disruptions and focus more on DDoS attacks, which are its main cause. We evaluate various machine learning techniques to discriminate between power disturbances and to discuss the advantage of incorporating machine learning to enhance the productivity of the power system design.
Keterampilan Berpikir Kritis Mahasiswa PGSD Melalui Pembelajaran Read-Answer-Discuss-Explain-And Create (RADEC) yang Berorientasi Penyelidikan

Abstrak. Penelitian ini bertujuan untuk memperoleh gambaran secara komprehensif mengenai keterampilan berpikir kritis siswa yang memperoleh pembelajaran RADEC berorientasi penyelidikan. Metode penelitian yang digunakan yaitu mixed methods, dengan sampel sebanyak 29 orang mahasiswa PGSD. Instrumen yang digunakan yaitu tes keterampilan berpikir kritis mengacu pada 5 aspek Critical Thinking Abilities yang dikembangkan Ennis (2011). Hasil penelitian menunjukkan bahwa keterampilan berpikir kritis mahasiswa PGSD pada materi perkembangbiakan pada tumbuhan melalui pembelajaran RADEC berorientasi penyelidikan memiliki kategori cukup (53,4%). Adapun keterampilan berpikir kritis tertinggi pada aspek the bases for a decision (59,1%) dan persentase terendah pada aspek basic clarification (44,5%). Hasil uji hipotesis menggunakan software SPSS 22 menunjukkan bahwa tidak terdapat perbedaan yang signifikan keterampilan berpikir kritis antara mahasiswa PGSD perempuan dan laki-laki. Namun demikian, nilai rata-rata keterampilan berpikir kritis perempuan (55,3) lebih besar daripada laki-laki (45,8), maka dapat disimpulkan keterampilan berpikir kritis mahasiswa PGSD perempuan lebih tinggi daripada keterampilan berpikir kritis mahasiswa PGSD laki-laki.Kata kunci: Keterampilan berpikir kritis, RADEC, Penyelidikan
THE EFFECT OF SOCIO-ECONOMIC VARIABLES ON PUBLIC SECTOR PERFORMANCE: AN EMPIRICAL ANALYSIS ON PUBLIC EXPENDITURE IN TURKEY

Public sector performance is regarded as the impact of government activities on certain key economic and social indicators, that is, the outcome of public sector activities. In this respect, the share of public expenditures, which is considered as one of the most important indicators of the size of the public sector, in the gross domestic product is also considered as a powerful tool in terms of revealing the public sector performance. There are many studies in the literature examining the effect of public expenditures on socioeconomic variables. In this study, different from other studies in the literature, the effects of socio-economic variables on public expenditures were investigated. Thus, it is aimed to determine the effects of socioeconomic variables on public performance through public expenditures. For this purpose, the effects of the net public debt stock, the human development index, and the unemployment rate on public expenditure were analyzed by cointegration tests for the 1990-2018 period in Turkey. Empirical results obtained reveal that there is a long run relationship between variables. Accordingly, increases in net public debt stock and human development index increase public expenditures, while increases in the unemployment rate create a decrease in public expenditures.
FRONTAL AND NON-FRONTAL FACE DETECTION USING DEEP NEURAL NETWORKS

Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.
Gender Gaps in Venture Capital Performance

Abstract We explore gender differences in performance in a comprehensive sample of venture capital investments in the United States. Investments by female venture capital investors have significantly lower success rates than investments by their male colleagues when controlling for personal characteristics, including employment and educational history, and portfolio companies’ characteristics. The gender differences in investment outcomes are not due to female investors being less skilled but, rather, are largely attributable to female investors receiving less benefit from the track records of their colleagues. Performance differences disappear in older, larger firms and firms with other female investors. This supports the view that formal feedback mechanisms and hierarchies are potentially useful in ameliorating the female performance gap.
The Effect of Nostalgia Proneness on Ad-Evoked Nostalgia, Brand Attitude and Purchase Intention

Nostalgia is generally defined as a longing for the past, and from a marketing perspective it can be described as an emotion that drives the purchase behaviors of consumers. Companies have started to make use of the element of nostalgia in their marketing strategies more frequently in order to be more successful than their competitors in today’s intensely competitive market. The aim of the study is to examine the effect of nostalgia proneness on ad-evoked nostalgia, attitude and purchase intention as well as to evaluate it with regard to the purchase decision making process of the consumer. The structural equation model was used for testing the developed hypothesis with a sample group comprised of 381 consumers. The study puts forth that sense of nostalgia has a strong effect on ad-evoked nostalgia, attitude and purchase intention. The findings of the study point out to the marketers that nostalgic signs in advertisements may have positive effects on the brand attitude and the purchase intention of the targeted groups.
Modified multi-verse optimiser used for global optimisation

: In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named as white hole, black hole and wormhole. Mathematical modelling of this concept has been carried out to acquire exploitation, exploration and local search. Modification in MVO has been made by introducing concept of dynamic variation in population size (universe). Modified multi-verse optimiser (MMVO) was tested on 16 benchmark functions having different complexity. Statistical comparisons of other algorithms outcomes is depicted that MMVO performs better than other algorithms.
Simultaneous removal of Cr(VI) and orange G6 by polyaniline/attapulgite supported nano zero-valent iron activate persulfate

In this paper, the polyaniline/attapulgite composite supported nano zero-valent iron (nZVI/PANI/ APT) composites were synthesized by one-step oxidation using Fe3+ as oxidant and iron source. nZVI/PANI/APT was used as a catalyst to activate persulfate (Ps) for the simultaneous removal of Cr(VI) and orange G6 (OG6) from aqueous solutions. The main factors (different composite materials, pH value, Ps concentration, etc.) affecting the removal performance of Cr(VI) and OG6 were evaluated as well. The experimental results revealed that the nZVI/PANI/APT composites can effectively remove Cr(VI) and OG6 simultaneously at a relatively low Ps concentration, the removal efficiencies of Cr(VI) and OG6 were 99.7% and 99.4% after 60 min, respectively. PANI/APT as the supporter matrix could decrease the aggregation of nZVI and increase its reactivity. A good synergistic effect between Cr(VI) reduction and OG6 oxidation was achieved by nZVI/PANI/APT combined Ps system. nZVI not only can be as a reductant for removal Cr(VI) and OG6 but also play as a catalyst to activate Ps and generate radicals for the degradation OG6. The presence of persulfate could not significantly decline nZVI reactivity for Cr(VI) reduction but remarkably promote OG6 oxidation. Moreover, kinetic and thermodynamic analyses were used to study the removal process, and the possible removal mechanism of Cr(VI) and OG6 was discussed by using the UV-Vis spectrum and liquid chromatography-mass spectrometry techniques. The nZVI/PANI/APT-persulfate heterogeneous Fenton system combines the nZVI reduction with the Fenton-like oxidation, which can be used for the treatment of industrial wastewater containing heavy metal ions and organic dyes.