Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecotourism related search for information over the internet: a technology acceptance model perspective

ABSTRACT The current study aims to understand as to how tourists use internet to search for information related to eco-tourism. Besides the mediating role of perceived usefulness, this study also attempts to determine the behavioural intentions and actual usage of tourists by extending the Technology Acceptance Model. A self-administered questionnaire in English was used to collect the data. The findings revealed that perceived ease of use, perceived usefulness, and electronic information quality significantly influence tourists’ intentions to adopt technology which in turn influences their actual usage. The findings shed useful insights for the website/app developers on how they should provide a user-friendly interface along with credible information to influence tourists’ behaviour.
Investor Protection and the Substitution Effect of Corporate Governance and Product Market Competition on Firm Value

International research has firmly established that a substitute relationship exists between corporate governance and product market competition on firm value. In this study, I reexamine this substitution effect in an international setting, allowing the relation to vary with investor protection. As hypothesized, I find that investors place a higher value on the substitution effect of corporate governance and product market competition in countries where the legal and regulatory mechanisms to protect investors are weak. This finding suggests that country-level investor protection has a moderating role in affecting the substitution between product market competition and corporate governance on firm value. These results, which are robust to several sensitivity tests and alternative specifications, are consistent with an equity agency cost benefit, resulting from product market competition and corporate governance, which is valued more by investors where investor protection is weak.
Decriminalization of Drugs as a Harm Reduction Approach in Canada

Ten lives are lost every day in Canada from opioid-related causes of death [1]. 94% of opioidrelated deaths in 2018 were accidental and preventable [2]. Worldwide, opioid-related deaths account for most drug-related deaths with 27 million people suffering from opioid addiction and dependency (OAD) [3,4]. Of particular concern with OAD is fentanyl, a synthetic opioid that is 50 to 100 times more potent than morphine and was first detected in the Canadian illicit drug market in 2013 [5]. This Opinion Editorial will suggest that current drug policies criminalize individuals living with addiction and forces Canadians with OAD to turn to illicit sources. By decriminalizing drugs, criminal penalties associated with drug use and possession would be eliminated [6]. From a harm-reduction (HR) lens, decriminalization rejects forceful, punitive, abstinence-based approaches to drug use and rehabilitation [6].
Organizational health in healthcare organizations – psychometric validation of the Healthcare-OH survey across fifteen acute NHS trusts in England

ABSTRACT Healthcare-OH is the only healthcare-specific measure of organizational health currently in use and for this concept to be truly applied in a healthcare context then reliable and valid measurement is essential. The primary focus of this study is to determine the dimensionality, reliability and so the construct validity of this measure, as applied to its target demographic. The Healthcare-OH survey was disseminated nationwide across acute NHS trusts in the UK. Survey dimensionality and construct validity was examined using confirmatory factor analysis. 5412 participants completed the survey from fifteen trusts covering the breadth of the country. Existing models had considered the Healthcare-OH survey as a series of sub-scales but attempts to fit these models resulted in both poor fit indices and low reliability estimates. An eight factor model, representing the entire 112-item survey, was then devised, with each domain of organizational health denoted as a separate factor. This model demonstrated both acceptable fit indices and a high level of reliability for all factors. We believe that in the future, organizational health measurements, if performed serially and on a large scale, could provide invaluable information for healthcare organizations on their relative strengths and weaknesses, unearthing previously unseen opportunities for targeted organizational change.
Adaptive Neural Motion Control of a Quadrotor UAV

Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities.
Transcriptional and Posttranscriptional Regulation of Cyclooxygenase-2 Expression by Fluid Shear Stress in Vascular Endothelial Cells

Objective— Fluid shear stress induces cyclooxygenase (COX)-2 gene expression in vascular endothelial cells. We investigated the underlying mechanism of this induction. Methods and Results— Exposure of human umbilical vein endothelial cells to laminar shear stress in the physiological range (1 to 30 dyne/cm2) upregulated the expression of COX-2 but not COX-1, a constitutive isozyme of COX. The expression of COX-2 mRNA began to increase within 0.5 hour after the loading of shear stress and reached a maximal level at 4 hours. Roles of the promoter region and the 3′-untranslated region in the human COX-2 gene were evaluated by the transient transfection of luciferase reporter vectors into bovine arterial endothelial cells. Shear stress elevated luciferase activity via the region between −327 and 59 bp. Mutation analysis indicated that cAMP-responsive element (−59/−53 bp) was mainly involved in this response. On the other hand, shear stress selectively stabilized COX-2 mRNA. Moreover, shear stress elevated luc.
Multi-Robot Energy-Efficient Coverage Control with Hopfield Networks

: The control problem of the multi-robots with different actuation capabilities has caught the attention of the robotics researchers over the last years. The algorithm proposed in the paper not only makes use of the energy-efficient coverage optimal control scheme but also utilizes Hopfield Neural Networks (HNN) in order to perform collaboration among the agents according to their different actuation performances without knowing them beforehand. The agents estimate their own performances by using HNN estimator and calculate the Power Voronoi Diagram (PVD or PD) weights for each agent. The robots achieve the optimal coverage configuration autonomously. The algorithm can assign the regions from the workspace and in resultant configuration, the robots with strong actuators obtain wider areas allocated from the workspace while the weaker robots obtain smaller areas. Also, by making use of the energy-efficient optimal coverage control scheme, a trade-off between the coverage time and energy consumption of the agents can be made. The MATLAB simulation and experimental results regarding the proposed online and distributed algorithm are given. The novelty of this work lies in the using of HNN-based multi-robot coverage collaboration according to the different actuation capabilities and energy-efficient coverage control laws at the same time. Significant improvements compared with the method utilized in (Pierson et al., 2017) have been observed.
Burnout and turnover intention among electronics manufacturing employees in South Africa

Globalisation and technological innovation have triggered an increase in the number of electronics manufacturing companies that are experiencing major organisational changes such as acquisitions, mergers, downsizings and retrenchments (e.g. Cartwright & Holmes, 2006, p. 199). The selected company in the present study is an electronics manufacturing plant in South Africa that has a history of downsizings and retrenchments that have contributed to higher job demands due to role overload, role conflict and role ambiguity, and lower job resources due to loss of human capital and social support. Higher job demands and lower job resources may increase the levels of burnout experienced by electronics manufacturing employees (Hu, Schaufeli, & Taris, 2017, p. 642; Maslach, Schaufeli, & Leiter, 2001, p. 407; Wang, Huang, & You, 2016, p. 255). Previous research found evidence that major organisational changes are significant predictors of turnover intention (Holtom, Mitchell, Lee, & Inderrieden, 2005, p. 345). Orientation: Although previous research found evidence that burnout is associated with turnover intention, few studies explore this phenomenon among electronics manufacturing employees in South Africa.
Tendências de Pesquisas em Mineração de Dados Educacionais em MOOCs: um Mapeamento Sistemático

ResumoMassive Open Online Courses (MOOCs) são uma modalidade de curso que utilizam plataformas online e atraem diferentes perfis de estudantes, ofertando oportunidades de qualificação - seja formal ou informal - em um formato bastante dinâmico. Uma característica das plataformas que ofertam tais cursos é a capacidade de armazenar uma grande quantidade de dados, o que possibilitou a exploração destes dados por meio de técnicas de Mineração de Dados Educacionais (MDE). Nesse contexto, foi conduzido um mapeamento sistemático de literatura em cinco bases de dados com o propósito de verificar quais as vertentes de estudos em destaque quanto ao uso de MDE em MOOCs. A busca compreendeu o período de 2015 a 2020, sendo que 158 foram selecionados. Os resultados revelaram que estudos relativos à Análise de Comportamento, Predição (de Desempenho, de Abandono, de Conclusão), Mineração de Texto e Sistemas de Recomendação são os mais frequentes. Além disso, foram identificadas áreas com potencial de exploração, como Social Network Analysis (SNA), Digital Learning Ecosystem (DLE) e Análise de Mind Wandering (MW). Ademais, foram levantados métodos e ferramentas usados nas pesquisas, bem como desafios do uso de MDE na pesquisa sobre MOOC. Conclui-se que a questão do desequilíbrio de classes, provocado pela baixa adesão aos cursos, é um dos maiores desafios.Palavras-Chave: MOOCs, Mineração de Dados Educacionais, Mapeamento Sistemático. AbstractMassive Open Online Courses (MOOCs) use online platforms and attract different student profiles, offering qualification opportunities - whether formal or informal - in a very dynamic format. A characteristic of the platforms that offer such courses is the ability to store a large amount of data, which made it possible to explore it through Educational Data Mining (EDM) techniques. In this context, a systematic scoping was conducted in five databases with the purpose of discover research trends regarding to the use of EDM in MOOCs. The search covered the period from 2015 to 2020, selecting 158 papers. The results revealed that studies related to Behavior Analysis, Prediction (Performance, Abandonment, Conclusion), Text Mining and Recommendation Systems are the most frequent. Promissing researches were also identified, such as Social Network Analysis (SNA), Digital Learning Ecosystem (DLE) and Mind Wandering Analysis (MW). Methods and tools used in research were listed, as well as challenges in the use of EDM in research on MOOC. We concluded that the issue of class imbalance, caused by low adherence to courses, is one of the biggest challenges.Keywords: MOOCs, Educational Data Mining, Systematic Scoping.
Collaborate or Separate? Distributed Service Caching in Mobile Edge Clouds

With the development of 5G technology, mobile edge computing is emerging as an enabling technique to promote Quality of Service (QoS) of network services. In particular, the response latency of network services can be significantly reduced by deploying cloudlets at 5G base stations in mobile edge clouds. Network service providers that usually deploy their services in remote clouds now shift their services from the remote clouds to the network edge in the proximity of users. However, the permanent placement of their services into edge clouds may not be economic, since computing and bandwidth resources in edge clouds are limited and relatively expensive. A smart way is to cache the services that are frequently requested by mobile users in edge clouds. In this paper, we study the problem of service caching in mobile edge network under a mobile service market with multiple network service providers completing for both computation and bandwidth resources of the edge cloud. We propose an Integer Linear Program (ILP) and a randomized rounding algorithm, for the problem without resource sharing among the network service providers. We also devise a distributed and stable game-theoretical mechanism for the problem with resource sharing among the network service providers, with the objective to minimize the social cost of all network service providers, by introducing a novel cost sharing model and a coalition formation game. We analyze the performance of the mechanism by showing a good guaranteed gap between the solution obtained and the optimal one, i.e., Strong Price of Anarchy (SPoA). We finally evaluate the performance of our algorithms by extensive simulations, and the obtained results show that the social cost of all players can be reduced significantly via allowing cooperation among network service providers in service caching.
Langkah-Langkah dan Teknik Evaluasi Hasil Belajar Pendidikan Agama Islam

Untuk mengetahui hasil belajar siswa dalam pembelajaran pendidikan Agama Islam, seorang guru harus memiliki pengetahuan tentang evaluasi hasil belajar, diantaranya adalah teknik dan langkah-langkah evaluasi hasil belajar, sehingga evalusi yang dilakukan dapat terukur. Penelitian ini lakukan untuk mengkaji apa saja Langka-Langkah dan Teknik Eveluasi Hasil Belajar Pendidikan Agama. Penelitian bersifat kualitatif dengan dengan menganalisi beberapa buku literatur dan sebagai sumber data utama. Adapun hasil penelitianya yaitu Langkah-langkah Evaluasi Hasil Belajar yaitu, Menyusun rencana evaluasi hasil belajar, Menghimpun data, Melakukan verifikasi data, Mengolah dan menganalisis data, Memberikan interpretasi dan menarik kesimpulan, Tidak lanjut hasil evaluasi. Sedangkan Teknik Evaluasi Hasil Belajar yaitu Teknik Tes, Teknik Objektif dan Teknik Non Tes.
High-extraction-efficiency operation of a midinfrared free electron laser enabled by dynamic cavity desynchronization

The extraction efficiency is one of the key parameters of an oscillator-type free electron laser (FEL). An extraction efficiency higher than 5% was observed with the midinfrared (MIR) FEL at Kyoto University using a dynamic cavity desynchronization method. The observed efficiency is the highest extraction efficiency realized in MIR-FEL oscillators driven by normal conducting linacs. A numerical simulation was performed, and the evolution of the electron energy distribution in the macropulse was compared with the experimental result. The details of the evolution of the electron energy distribution were investigated to reveal the physics of this high-efficiency lasing.
A Compact C-Band Bandpass Filter with an Adjustable Dual-Band Suitable for Satellite Communication Systems

A narrowband dual-band bandpass filter (BPF) with independently tunable passbands is presented through a systematic design approach. A size-efficient coupling system is proposed with the capability of being integrated with additional resonators without increasing the size of the circuit. Two flag-shaped resonators along with two stepped-impedance resonators are integrated with the coupling system to firstly enhance the quality response of the filter, and secondly to add an independent adjustability feature to the filter. The dual passband of the filter is centered at 4.42 GHz and 7.2 GHz, respectively, with narrow passbands of 2.12% and 1.15%. The lower and upper passbands can be swept independently over 600 MHz and 1000 MHz by changing only one parameter of the filter without any destructive effects on the frequency response. According to United States frequency allocations, the first passband is convenient for mobile communications and the second passband can be used for satellite communications. The filter has very good in- and out-of-band performance with very small passband insertion losses of 0.5 dB and 0.86 dB as well as a relatively strong stopband attenuation of 30 dB and 25 dB, respectively, for the case of lower and upper bands. To verify the proposed approach, a prototype of the filter is fabricated and measured showing a good agreement between numerically calculated and measured results.
SENSORY AND PHYSICOCHEMICAL EVALUATION OF TRADITIONAL POWDER COFFEE

Coffee is one of the most important products for the Brazilian economy. Due to this fact, its sensory acceptance is extremely important for both the consumer and the producer. The objective of this study was to evaluate the acceptance of six brands of traditional roasted coffee in the southern region of Minas Gerais. The samples were evaluated by 124 consumers through the following tests: acceptance for appearance, aroma, taste, texture and overall impression, ideal for bitter taste and body and, finally, purchase intention. In addition, colorimetric and soluble solids analyses were performed. According to the results obtained, sample D had the highest frequency of positive purchase intention, while sample A had the highest frequency of negative purchase intention. Sample F had the closest ideal bitter taste and, in relation to the body of the drink, sample C was considered the closest to the ideal. Sample F presented the highest acceptance for all attributes evaluated in the acceptance test.
Cardiac Arrhythmia classification using deep learning

The present work aims to obtain an automated program based on deep learning to detect and classify cardiac arrhythmia using electrocardiogram (ECG) data. One of the main obstacles in working with medical dataset is the limited availability of public medical data and the fact that only a small fraction of data represents non-normal medical conditions. In the present work we will compare the two convolutional neural network models inspired by VGGNet and ResNet in predicting of cardiac arrhythmia. Our calculations show that the two methods are equally good in predicting the overall accuracy for large dataset. However, ResNet shows a slightly better performance in predictions for much smaller dataset.
Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms

In this study, an efficient model for the intrusion detection has been developed with TF.IDF and C4.5 algorithms . The model has been tested on a dataset for validation and results show a significant increase in accuracy in comparison with Multi Layer Perceptron (MLP) and Naive Bayes (NB). Aim In this study
Rapid increase in summer surface ozone over the North China Plain during 2013–2019: a side effect of particulate matter reduction control?

Abstract. While the elevated ambient levels of particulate matters with aerodynamic diameter of 2.5 µm or less (PM2.5) are alleviated largely with the implementation of effective emission control measures, an opposite trend with a rapid increase has been seen in surface ozone (O3) in the North China Plain (NCP) region over the past several years. It is critical to determine the real culprit causing such a large increase in surface O3. In this study, 7-year surface observations and satellite retrieval data are analyzed to determine the long-term change in surface O3 as well as driving factors. Results indicate that anthropogenic emission control strategies and changes in aerosol concentrations as well as aerosol optical properties such as single-scattering albedo (SSA) are the most important factors driving such a large increase in surface O3. Numerical simulations with the National Center for Atmospheric Research (NCAR) Master Mechanism (MM) model suggest that reduction of O3 precursor emissions and aerosol radiative effect accounted for 45 % and 23 % of the total change in surface O3 in summertime during 2013–2019, respectively. Planetary boundary layer (PBL) height with an increase of 0.21 km and surface air temperature with an increase of 2.1 ∘C contributed 18 % and 12 % to the total change in surface O3, respectively. The combined effect of these factors was responsible for the rest of the change. Decrease in SSA or strengthened absorption property of aerosols may offset the impact of aerosol optical depth (AOD) reduction on surface O3 substantially. While the MM model enables quantification of an individual factor's percentage contributions, it requires further refinement with aerosol chemistry included in the future investigation. The study indicates an important role of aerosol radiative effect in development of more effective emission control strategies on reduction of ambient levels of O3 as well as alleviation of national air quality standard exceedance events.
Machine Learning in Internet Search Query Selection for Tourism Forecasting

Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality.