Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of the Factor Structure and Internal Consistency of the ‘Conceptions of Learning’ and ‘Preferences for Teaching’ Measures in American Occupational Therapy Students

When planning to use measurement scales in new samples and contexts, examining the scales’ psychometric properties is an important initial step. This study examined the factor structure and internal consistency of two measures that are part of the Approaches and Study Skills Inventory for Students (ASSIST) – the Conceptions of learning and Preferences for teaching and courses – in a sample of American occupational therapy students. The students (n = 115) completed the measures and provided basic sociodemographic information. Scale structure was examined with Principal Components Analysis (PCA), while consistency between scale items was assessed with mean inter-item correlations. For the Conceptions of learning measure, one item was removed due to cross-loading between factors. The subsequent analysis revealed two factors, representing deep and surface conceptions of learning, on which the items – with one exception – loaded in line with theory. For the Preferences for teaching and courses measure, two factors were found, representing preferences denoted in theory as supporting understanding and transmitting information, respectively. The items showed good fit with the two theoretically proposed factors. The scales’ mean inter-item correlations were satisfactory, ranging 0.27-0.36. One item on the Conceptions of learning measure appears to be problematic due to crossloading, and another may be interpreted in a different way than originally proposed. After removing the problematic item, all scales showed satisfactory psychometric properties for assessing conceptions of learning and preferences for teaching.
A Systematic Evaluation of Shallow Convolutional Neural Network on CIFAR Dataset

—Convolutional Neural Network (CNN) classiﬁer is a very popular classiﬁer used to solve many problems, including image classiﬁcation and object recognition. The CNN classiﬁer usually improved by designing a deeper and bigger classiﬁer which needs more memory and computational power to run the classiﬁer. In this paper, we analyze and optimize the use of small and shallow CNN classiﬁer on CIFAR dataset. Karpathy ConvNetJS CIFAR10 model was used as a base network of our classiﬁer and extended by adding max-min pooling method. The max-min pooling is used to explore the negative and positive response of the convolution process which in theory will be trained the classiﬁer more effectively. We choose several different conﬁgurations to analyze the effectiveness of the classiﬁer by combining the training algorithm, batch normalization conﬁguration, weights initialization methods, dropout regularization conﬁguration, and heavy data augmentation. To ensure that the classiﬁer we designed is still small and shallow CNN classiﬁer, we limit the maximum number of layers in our CNN classiﬁer to 15 layers. Experiments on CIFAR10 and CIFAR100 dataset shows that by compacting the kernel on each layer, the classiﬁer can achieve good accuracy and comparable with another state-of-the-art classiﬁer with a relatively same number of layers with an error rate of 6.99% on the CIFAR10 dataset and 29.41% on the CIFAR100 dataset.
Seasonal stratospheric ozone trends over 2000–2018 derived from several merged data sets

Abstract. In this work, we analyze the seasonal dependence of ozone trends in the stratosphere using four long-term merged data sets, SAGE-CCI-OMPS, SAGE-OSIRIS-OMPS, GOZCARDS, and SWOOSH, which provide more than 30 years of monthly zonal mean ozone profiles in the stratosphere. We focus here on trends between 2000 and 2018. All data sets show similar results, although some discrepancies are observed. In the upper stratosphere, the trends are positive throughout all seasons and the majority of latitudes. The largest upper-stratospheric ozone trends are observed during local winter (up to 6 % per decade) and equinox (up to 3 % per decade) at mid-latitudes. In the equatorial region, we find a very strong seasonal dependence of ozone trends at all altitudes: the trends vary from positive to negative, with the sign of transition depending on altitude and season. The trends are negative in the upper-stratospheric winter (−1 % per decade to −2 % per decade) and in the lower-stratospheric spring (−2 % per decade to −4 % per decade), but positive (2 % per decade to 3 % per decade) at 30–35 km in spring, while the opposite pattern is observed in summer. The tropical trends below 25 km are negative and maximize during summer (up to −2 % per decade) and spring (up to −3 % per decade). In the lower mid-latitude stratosphere, our analysis points to a hemispheric asymmetry: during local summers and equinoxes, positive trends are observed in the south (+1 % per decade to +2 % per decade), while negative trends are observed in the north (−1 % per decade to −2 % per decade). We compare the seasonal dependence of ozone trends with available analyses of the seasonal dependence of stratospheric temperature trends. We find that ozone and temperature trends show positive correlation in the dynamically controlled lower stratosphere and negative correlation above 30 km, where photochemistry dominates. Seasonal trend analysis gives information beyond that contained in annual mean trends, which can be helpful in order to better understand the role of dynamical variability in short-term trends and future ozone recovery predictions.
Research Progress on Chemical Constituents and Anticancer Pharmacological Activities of Euphorbia lunulata Bunge

Euphorbia lunulata Bunge (ELB) is a traditional Chinese medicine possessing the functions of expectoration, cough relief, asthma relief, detoxification, and itching relief. Modern pharmacological studies have showed that ELB exhibits a variety of activities, such as antitumor, antibacterial, and antioxidant activities. In particular, the anticancer activities of ELB have attracted much attention. In this review, we summarize the recent research progress on the chemical constituents and anticancer activities of ELB by searching the PubMed, Web of Science, and China National Knowledge Infrastructure databases. The results show that more than 151 components have been identified from extracts of ELB, including 73 terpenoids, 28 flavonoids, 8 phenylpropanoids, 7 steroids, 19 phenols, and 5 alkaloids. ELB has been shown to exhibit significant inhibitory effects on lung, cervical, gastric, breast, and liver cancers, and its anticancer effects are mainly manifested in the 3 aspects, including cell cycle arrest, cell apoptosis, and inhibition of the migration of cancer cells.
Housing and output growth in Turkey

This paper investigates the housing-output growth nexus in Turkey by using quarterly data on real gross domestic product, real house prices and real gross fixed capital formation. The main aim of this paper is to identify indicators, for example, output and real capital formation, which potentially has strong causal relationship with housing prices in Turkey. The data set for this study spans the period; 1987:Q1 to 2016:Q2. Using full sample bootstrap Granger causality test and parameter stability test, we examine the stability of estimated Vector Autoregressive Regressions (VAR), and thus determined whether the full-sample Granger causality inference might be invalid. When the stability of the VAR model is rejected, the Granger non-causality tests are not valid. For this reason, the bootstrap rolling window estimation technique was used to evaluate the Granger causality between the housing variables and the output growth rate. The bootstrap rolling window estimation results show that there is no Granger causality relation in full sample. However, in some sub-periods, these variables have predictive ability for each other.
Faculty of Science STRESSED SNAKES: INVESTIGATING A POSSIBLE LINK BETWEEN BASELINE LEVELS OF CORTICOSTERONE AND BODY CONDITION IN THE WESTERN RATTLESNAKE

ii ACKNOWLEDGEMENTS.iii TABLE OF CONTENTS.iv LIST OF FIGURES.v INTRODUCTION.1 METHODS.13 Statistical analyses.15 RESULTS.16 DISCUSSION.20 LITERATURE CITED.24
Performance Appraisal and Employee Engagement: Does Tenure Matters? Evidence from South-West Nigeria

Background: The level of employee engagement globally is declining. Evidences points to low level of employee engagement in Nigeria. In this study, performance appraisal and performance appraisal feedback are hypothesized as factors that increase the level of employee engagement. <br><br>Objective: The objective of the study was to investigate the influence of performance appraisal and performance appraisal feedback on the employee engagement among workers in South-West Nigeria. <br><br>Methods: The study adopted descriptive research approach. Respondents were sampled from employees of one State University, wire industries, and oil and gas sector in Osun State capital, Nigeria. Stratified and simple random sampling techniques were used to select 250 respondents. The questionnaire was used for data collection. Only 139 (54.51%) questionnaires were found useful for the analysis. Descriptive statistics, correlation and regression analyses were used for the data analysis. <br><br>Results: The study found out that performance appraisal had positive significant influence on employee engagement but the relationship between performance appraisal feedback and employee engagement was found to be statistically insignificant. Tenure was found to moderate the influence on the relationships. <br><br>Conclusions: Based on the findings of the study, the study concluded that employee tenure was an important moderator in the relationship between performance appraisal, performance appraisal feedback and employee engagement. <br><br>Implications: The implication of the findings of this study was that employee characteristics are important factors for the consideration of Human resource practitioners for organization effectiveness and productivity. <br><br>Recommendations: The paper recommended that HR managers should pay more attention to the employee metrics in implementing HR policies.
The changing impact of religiosity on agentic political engagement in Nigeria

Abstract This study assessed whether the impact of religiosity on agentic political engagement in Nigeria is changing. Survey method was used for the study. Primary data were generated using a 7-item structured questionnaire created and administered via SurveyMonkey platform, and responded to by a sample size of 348 persons from across Nigeria. Results revealed that the greater percentage of the respondents have become aware of their position as social agents who should, in spite of their religious affiliations, decide their political leaders, determine social changes, participate in political protests, and oppose political leaders whenever the need arises. The study concluded that religion is losing its grip on Nigerians in terms of their sense of personal agency and political engagement. The study therefore recommends that civil society organizations (CSO) need to seize the opportunity of this growing awareness of the sense of agency to engage in demands for social change.
UNIVERSIDADE FEDERAL DO CEARÁ CENTRO DE TECNOLOGIA DEPARTAMENTO DE ENGENHARIA QUÍMICA PROGRAMA DE PÓS-GRADUAÇÃO EM EMGENHARIA QUÍMICA RAFAEL AUGUSTO MORALES OSPINO ASSESSMENT OF TEMPERATURE SWING ADSORPTION CONFIGURATIONS FOR CO2 CAPTURE ON ZEOLITE 13X FORTALEZA 2018 RAFAEL AUGUSTO MORALES OSP

In this study, two configurations of Temperature Swing Adsorption (TSA) were assessed with the aim of evaluating their efficacy on CO2 capture on commercial adsorbent zeolite 13X within a postcombustion scenario. A Fixed Bed Laboratory Unit (FBU) with a single column was employed to obtain experimentally breakthrough curves (adsorption) and the subsequent depletion profiles (desorption). Considering a dry desulfurized flue gas stream, breakthrough curves for CO2-N2 (15/75 % v/v) in Helium were performed at 25, 50 and 75 °C. The desorption step was accomplished following two TSA regeneration strategies: a two phase desorption arrangement (configuration 1) consisting of a purging phase and then a heating-purging phase, and a one phase desorption arrangement (configuration 2) involving only the heating-purging phase. Adsorption equilibrium isotherms were also obtained for pure CO2 and N2 on zeolite 13X at the same temperatures of the dynamic tests in the range of 0-10 bar. Finally, a mathematical model formulation, considering isotherm modeling and transport phenomena balances, was performed to predict the whole adsorption-desorption profile. The results obtained on breakthrough curves showed that CO2 separation from N2 on zeolite 13X is accomplished by adsorption under the studied conditions with a meaningful preference on CO2. In regard with the desorption phase, configuration 1 may not be proper for an integration of adsorption-desorption steps once only the purge phase duplicates the time of the adsorption stage. On the other hand, configuration 2 is likely to synchronize the whole adsorption –desorption process since the regeneration time was significantly reduced by this strategy. However, configuration 1 managed to obtain full CO2 recovery with all the temperatures tested during the heating step, whereas configuration 2 reached recovery values around 92 %. Moderate temperatures (e.g. 125-150 °C) are encouraged to be used for configuration 2 regeneration strategy to avoid energy penalties. Simulations were able to reproduce well the experimental breakthrough curves, however for the desorption profiles, some discrepancies were observed.
Investigating the Impacts of Brand Social Media Posts' Linguistic Styles on Consumer Engagement

Enhancing consumer engagement with brand posts on social media is challenging to digital marketers. However, it is unclear what contents work better for which brand and in what way. This paper investigates the impacts of three brand post linguistic styles (i.e., emotionality, complexity, and informality) and finds that brand posts’ linguistic styles can impact consumer engagement. The findings improve our understanding of the role that language plays in brand communications on social media.
IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Rapid prototyping tools turn the design of smart toys faster and easier for creative teams. Appropriate tools for smart toys should meet a list of requirements, which include distributed data collection and adaptability for assorted toy shapes and size. The IoT4Fun toolkit innovates by mixing the embedded, modular, and plug-and-play approaches. It supports motion tracking data, wireless communication, and contactless identification. IoT4Fun demonstrates its effectiveness to design a variety of smart toy solutions by fitting into a hula-hoop toy until spherical, cubic, and wearable shapes. Solutions connect with either mobile applications or other toys and play rules range from open-ended to closed behaviors. End-users exhaustively tested developed solutions, and technical assessment evaluates their integrity after playtesting sessions. Results show comparative data on battery consumption and vulnerabilities threats for data security and privacy of each design. Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements.
Assessing Big Data Analytics Capability and Sustainability in Supply Chains

Big data analytics capability (BDAC) is a technologybased capability, which can influence sustainability performance of firms in supply chains. By using BDAC strategically, supply chains could improve their responses to social, environmental, and social changes taking place in uncertain business environments. This paper presents a detailed literature review on the two ends of the equation: BDACs and sustainability in supply chains performance (SSCP). The theoretical perspective of the dynamic capabilities helps us to understand BDAC holistically, a combination of non-human and human capabilities. Then, we adapt the three-bottom-line approach: economic, environmental, and social performance in order to offer a comprehensive measurement of SSCP Based on the overview of the literature, the paper offers metrics to be used in assessing both BDAC and SSCP that can advance the understanding of the relationship between them.
I Spy with my Little Sensor Eye - Effect of Data-Tracking and Convenience on the Intention to Use Smart Technology

The increasing number of smart objects in private households leads to a profound invasion of privacy. Based on privacy calculus theory, we assume that many users accept tracking in exchange for full functioning and convenience. However, privacy calculus has not yet been tested in an area where privacy protection is a binary decision: to either use a product or not. Therefore, we examined the effect of convenience and tracking on the intention to use a smart device in a 2 x 2 betweensubjects online experiment (N = 209). While convenience is a major factor for the willingness to deploy smart technology, users do not seem to care whether these devices track their personal data or not.
Towards Trust-Aware Human-Automation Interaction: An Overview of the Potential of Computational Trust Models

Several computational models have been proposed to quantify trust and its relationship to other system variables. However, these models are still under-utilised in human-machine interaction settings due to the gap between modellers’ intent to capture a phenomenon and the requirements for employing the models in a practical context. Our work amalgamates insights from the system modelling, trust, and human-autonomy teaming literature to address this gap. We explore the potential of computational trust models in the development of trust-aware systems by investigating three research questions: 1At which stages of development can trust models be used by designers? 2how can trust models contribute to trust-aware systems? 3which factors should be incorporated within trust models to enhance models’ effectiveness and usability? We conclude with future research directions.
A Probabilistic Model for Malicious User and Rumor Detection on Social Media

Rumor detection in recent years has emerged as an important research topic, as fake news on social media now has more significant impacts on people’s lives, especially during complex and controversial events. Most existing rumor detection techniques, however, only provide shallow analyses of users who propagate rumors. In this paper, we propose a probabilistic model that describes user maliciousness with a two-sided perception of rumors and true stories. We model not only the behavior of retweeting rumors, but also the intention. We propose learning algorithms for discovering latent attributes and detecting rumors based on such attributes, supposedly more effectively when the stories involve retweets with mixed intentions. Using real-world rumor datasets, we show that our approach can outperform existing methods in detecting rumors, especially for more confusing stories. We also show that our approach can capture malicious users more effectively.
The Impact of Motivation and Strategy Use on Course Outcome in Digital Learning Environment - A Case Analysis of a Business School Course

Using the Motivated Strategies for Learning Questionnaire (MSLQ) multiple times in an undergraduate business school course in a University in New Zealand, 188 students completed the questionnaire to explore how students’ motivation and strategy use changed as they progressed and how these constructs predict students’ course outcome. In contrast to other studies, our results showed varying levels of motivation as well as increasing strategy usage as the course progressed. Our exploratory analysis identified three subgroups within the class, each of which reported differently in terms of motivation and strategy use. From this analysis and course outcome data we infer how these finding may contribute to theory and classroom practice.
Multilevel Design for Smart Communities - The Case of Building a Local Online Neighborhood Social Community

Smart cities and communities aim for social wellbeing. Mobilizing and integrating various institutions, actors, and resources are crucial when building and instantiating smart community initiatives. The design of such an arrangement is a complex phenomenon, difficult to conduct systematically and to observe empirically. We address this challenge by applying a multilevel design framework for service systems to an ongoing design science research project. The research project pursues the goal of building a neighborhood community as an instantiation of smart communities by activating and leveraging local institutions, actors, and resources on an IT-enabled engagement platform. We demonstrate how this multilevel perspective informs the design process for building smart communities. Based on micro-level observations, the interdependence of engagement-stimulating mechanisms related to the platform ’ s design at the meso-level, and design implications for the institutional arrangement at the macro-level are emphasized as inseparable design activities for mobilizing and integrating actors and resources.
Detecting Political Bots on Twitter during the 2019 Finnish Parliamentary Election

In recent years, the impact of bots used for manipulating public opinion has become an increasingly prevalent topic in politics. Numerous sources have reported about the presence of political bots in social media sites such as Twitter. Compared to other countries, the influence of bots in Finnish politics has received little attention from media and researchers. This study aims to investigate the influence of bots on Finnish political Twitter, based on a dataset consisting of the accounts following major Finnish politicians before the Finnish parliamentary election of 2019. To identify the bots, we extend the existing models with the use of user-level metadata and state-of-art classification models. The results support our model as a suitable instrument for detecting Twitter bots. We found that, albeit there is a huge amount of bot accounts following major Finnish politicians, it is unlikely resulting from foreign entities’ attempts to influence the Finnish parliamentary election.
Assessment of the quality of mobile voluntary counseling and testing services in Addis Ababa and Adama organization for social services for AIDS (OSSA) branches, Ethiopia

Mobile or outreach type of voluntary counseling and testing approach is a useful way to reach high risk groups and rural population that are far away from facility based voluntary counseling and testing (VCT) service. Little has been done to assess the quality of mobile VCT services in Ethiopia. This study aims to assess the quality of mobile VCT services and client satisfaction in the cities of Addis Ababa and Adama. This study used both qualitative and quantitative study designs. Structured questionnaire was administered to 279 client respondents. The qualitative data was analyzed by thematic areas, and the quantitative data was analyzed and interpreted by using statistical packages for social sciences (SPSS) package of version 13.0. Overall service satisfaction of the clients was very high in this study. The competence of the counselors was good. Short duration of counseling time, inconvenience of counseling rooms, high number of clients seen by the counselors amongst other factors were findings from the qualitative study that can affect the quality of the mobile VCT services. To improve the promotional activities, the referral linkages and the feedback problem, there should be work integration with government health institutions, non-governmental organization (NGOs) and kebele (the smallest administrative unit) HIV focal persons. Key words: Quality, satisfaction, quality of VCT counselors, providers, client, quality of VCT site,