Miracle-dz/UniFAR-FTUs huggingface dataset:
Brazilian craft breweries and internationalization in the born global perspective

Purpose –This paper aims to analyze how the internationalization process of Brazilian microbreweries takes place, especially in factors of propensity to internationalization, competition and survival in the national and international market. Design/methodology/approach –The authors adopted qualitative approach research, using amultiple case study strategy, analyzing seven Brazilian craft breweries. The authors developed this study through documentary research, semi-structured interviews and nonparticipant observation. Findings – The research findings made it possible to characterize the internationalization process motivated by the background and international experience of entrepreneurs that adopted different strategies for expansion in the international market. The authors noticed that the networks developed in production partnerships, festivals and international missions represent an increase in the ease of internationalization and helps to reduce the risk perception. Research limitations/implications – The contributions refer to the conjecture that the born global’s evolution related to the high competitiveness of themarket is influenced by the international background of the founders and by the influence of the international network, overcoming institutional and market challenges in emerging countries, assuming risks associated with rapid and early entry into the international arena. Practical implications – The paper is potentially useful for managerial practice and policymakers by providing insights into the characteristics of entrepreneurs and companies considering their propensity to internationalize, presenting how entrepreneurs and managers can seek resources and develop their skills and capacities to manage such companies and processes of internationalization. Born global international © Fabricio Stocker, Gustavo Abib, Ronaldo de Oliveira Santos Jhunior and Helio Arthur Reis Irigaray. Published in Revista de Gest~ao. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode This work was supported in part by grants from CAPES Brazilian Federal Agency for Support and Evaluation of Superior Education within the Ministry of Education of Brazil, and CNPq Brazilian National Council for Scientific and Technological Development within the Ministry of Science and Technology of Brazil. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald. com/insight/2177-8736.htm Received 22 January 2021 Revised 14 April 2021 Accepted 23 April 2021
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

Motion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this study, we propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. The system built is cost-effective and easy-to-use and requires much fewer computational resources than traditional video-based analysis on monitoring motions of players during training. The system built includes a customized wireless wearable sensing device (WWSDs), a mobile application, and a data processing interface-based cloud with an ankle attitude angle analysis model. Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. The experimental outcome demonstrates the proposed motion tracking system based on AIoT and MEMS sensing technologies capable of recognizing different motions and assessing the players' skills. The talent selection function can partition the elite and amateur players at an accuracy of 93%. This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels.
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in the"forward"(source to target) direction can successfully map words in the"reverse"(target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how BDMA reduces the overall size of the model.
SHAPE AND SIZE EFFECTS OF THE PORE ON THE MECHANICAL PROPERTIES OF NANOPOROUS GRAPHENE MEMBRANES

In this work, effects of shape and size of the pores on the mechanical properties of nanoporous graphene (NPG) membranes are studied. Molecular dynamics simulations were performed to study the mechanical and structural responses under uniaxial traction. The results of the stress-strain curves of NPG membranes show an elastic linear behavior for small strain (<0.03) independent of the chiral direction. The chiral anisotropy (armchair and zigzag direction) is notable as deformation increases to the point of fracture. The NPG membranes with hexagonal and rectangular pores present a higher fracture stress (65 GPa and 81 GPa, respectively). Furthermore, Young’s elastic modulus decreases as pore size increases (porosity). This study is expected to provide practical application as high performance membrane filters.
Metadiscourse and Counterargument Integration in Student Argumentative Papers

Incorporating counterarguments can be challenging for many student-writers, including those for whom English is a second or other language. In this study, we present findings that may lead to improvements in students’ understanding of the benefits of integrating counterarguments. In our study, expert readers were presented with excerpts of student academic writing in order to assess the degree to which counterarguments could be identified through student deployment of metadiscoursal features. The results suggest that student-writers deploy metadiscoursal features with insufficient frequency and consistency. The results further suggest that when metadiscoursal features are used in counterarguments, their deployment is largely restricted to the sentence-initial position. The study also considers the pedagogical and technological issues associated with the findings.
Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review

The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions.
Calibrating Trust in AI-Assisted Decision Making

With the proliferation of AI products, humans and AI are increasingly working in partnership with each other to make decisions. For this type of collaboration to be successful, humans need to understand AI capability in order to effectively calibrate their trust. In these partnerships, it’s critical to explain decisions and predictions in a manner that can be understood by humans in order to encourage trust calibration. The field of explainable AI is focused on integrating explainability into AI, but is geared towards making AI models more interpretable. As a result, this research often approaches explanations from a model-centric perspective, as opposed to a human-centered perspective. At the same time, industry researchers have developed guidelines to help interface designers effectively generate user-friendly explanations. However, these guidelines are typically too broad to be effective for the day-to-day work of industry designers. Our research addresses this gap through two approaches: an empirical experiment to investigate how people respond to explanations and what types of explanations are most helpful for trust calibration, and an educational resource for industry designers to help them understand what questions users might have, and how the context of use influences what explanations they may use. Findings from our experiment indicate that explanations do not always aid trust calibration, and can actually hurt it, especially in the face of novice users who have low self-competence. Our exploratory interviews and usability testing with industry designers reveal that there is a desire for a comprehensive but accessible educational resource that translates research such as our experiment and guides the design of explainable interfaces for AI products.
Research on the Effect of Peer Feedback Training in English Writing Teaching—A Case Study of Students in Business English Major

Based on peer feedback, this paper further explores the application of peer feedback training to English writing teaching in China. From the theoretical perspective, compared with teacher feedback, peer feedback is beneficial to motivate students to take the initiative in learning, practice the student-centered concept, and promote cooperative learning among students. Peer feedback training, namely peer feedback under teacher intervention in this paper, combines the advantages of teacher feedback and peer feedback, which can not only be accepted by learners but also achieve significant pragmatic effects. From a practical point of view, peer feedback training can be applied to teach large groups of students, thus reducing the pressure and burden of teachers and improving the quality of peer feedback. The results also show that peer feedback training mainly works during the training stage rather than the modification stage. It is crucial to pay attention to the mechanism of peer feedback training and apply it to practice to promote the quality of English writing teaching.
Synthesis, physiochemical property and antibacterial activity of gemini quaternary ammonium salts with a rigid spacer

A novel series of adamantane-based gemini quaternary ammonium salts (GQASs) named as m-Ad-m (bromides, alkyl chain length, m = 12, 14, 16) are synthesized from 1,3-adamantanedicarboxylic acid. Their chemical structures are confirmed by 1H NMR, FT-IR and ESI-MS. The surface activities in aqueous solutions of m-Ad-m are evaluated by surface tension. Unlike conventional GQASs with a flexible spacer, m-Ad-m possessing a rigid adamantane spacer show lower surface tension (γCMC) values as compared to conventional GQASs bearing the same alkyl chains. The thermodynamic properties of m-Ad-m are examined by electrical conductivity measurements at different temperatures, including molecular interaction parameters (β), standard Gibbs free energy (ΔG0m), enthalpy (ΔH0m) and entropy (ΔS0m), which show that the micellization of m-Ad-m is entropy-driven. TEM study exhibits that the aggregate morphologies of m-Ad-m change from micelles and spheroidal vesicles of varying size to network aggregates and then to globular vesicles with the increase in m-Ad-m concentration. Furthermore, the antimicrobial efficacy of m-Ad-m against both Gram-positive bacteria (S. aureus and B. subtilis) and Gram-negative bacteria (E. coli, P. aeruginosa and V. parahaemolyticus) is systematically studied by minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) methods, which indicates the favorable antibacterial activity of 12-Ad-12 against the studied bacteria.
A Prospective Analysis of Security Vulnerabilities within Link Traversal-Based Query Processing

The societal and economical consequences surrounding Big Data-driven platforms have increased the call for decentralized solutions. However, retrieving and querying data in more decentralized environments requires fundamentally different approaches, whose properties are not yet well understood. Link Traversal-based Query Processing (LTQP) is a technique for querying over decentralized data networks, in which a client-side query engine discovers data by traversing links between documents. Since decentralized environments are potentially unsafe due to their non-centrally controlled nature, there is a need for client-side LTQP query engines to be resistant against security threats aimed at the query engine's host machine or the query initiator's personal data. As such, we have performed an analysis of potential security vulnerabilities of LTQP. This article provides an overview of security threats in related domains, which are used as inspiration for the identification of 10 LTQP security threats. Each threat is explained, together with an example, and one or more avenues for mitigations are proposed. We conclude with several concrete recommendations for LTQP query engine developers and data publishers as a first step to mitigate some of these issues. With this work, we start filling the unknowns for enabling querying over decentralized environments. Aside from future work on security, wider research is needed to uncover missing building blocks for enabling true decentralization.
A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models.
Devising a method for recognizing the causes of deviations in the development of the plant Aloe arborescens L. using machine learning capabilities

This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values
Water Availability, Quality And Treatment Methods In Malawi: A Review

Water is essential in all aspects of life such as health, economic growth, food production and support for the environment. Water is scarce mainly in developing countries and pollution of the water resources is a major challenge contributed by the discharge of wastewater from industries and municipal, agriculture runoff, household wastes and others. The mainly used water resources in African countries are Lakes, boreholes, rivers, shallow wells, streams and springs. People consume water from unprotected sources without treatment leading to an outbreak of waterborne diseases. The water resources recorded high pollution levels with contaminants concentrations above the acceptable limits by the Malawi Bureau of Standards (MBS) and World Health Organization (WHO) for drinking water. Parameters such as turbidity, Faecal coliforms and bacteria counts in the shallow wells for the sampled and analyzed water recorded a high-value range above the permissible limits for drinking water. The drinking water treatment methods used are disinfection, filtration and boiling by a large percentage of pollution at the household level. Total dissolved solids, electrical conductivity and nitrates in the analysis done for the water from the wells and boreholes were within the allowable for drinking water standards. The TSS in another study analyzed for the drinking water recorded value above the WHO and MBS standards caused by the direct discharge of the wastewater to water bodies. The water treatment plants treat surface water and groundwater recorded raw water quality of some of the parameters within the allowable limits with and exemption of Liwonde water treatment plant recorded high turbidity water above drinking water standards. Water treatment plants also recorded better removal of the pollutants in the water through coagulation, flocculation, sedimentation, filtration and disinfection processes. The water quality index (WQI) as a key method for regulating water quality for domestic use and the overall effects of quality of water parameters that pose risk to water usage. The WQI provides valuable information to water resource managers for it is efficiently reliable and useful method for communicating and evaluating the overall quality water information. In terms of total hardness, the water quality was rated as mild hard to very hard water, while in salinity the water recorded high concentration of total dissolved solids and electric conductivity. Therefore, this review paper, determined the water resources, water quality, and the treatment techniques for the drinking water, hence a cost-effective, feasible and affordable water treatment method is suggested for the water treatment plant in the treatment of the drinking water.
HOW COSTLY IS ENERGY CONSERVATION? THE ENERGY-GDP RELATIONSHIP RE-EXAMINED FOR TURKEY

Economic structure, technology, consumption habits, cultural and similar values vary greatly between countries. Energy consumption included in this structure is theoretically thought to be related to growth output. Could slowing the growth be the cost of reducing energy consumption? In this study, using annual data between 1970 and 2019, it was investigated whether per capita energy consumption affects per capita income asymmetrically or not by using the NARDL model. The feature that makes this study different from similar studies is that it interprets the short and long-term asymmetric effects with the analysis model used and makes a unique contribution to the literature. The findings gave us the conclusion that income is affected in the same way by shocks experienced in energy consumption, and it has been observed that the effect of positive shocks is greater in the long run. However, we conclude that negative shocks are more effective than positive shocks in the short term. Thus, we see that the increase in energy consumption in the long term increases the per capita income, and the decrease in energy consumption has a high as well as negative impact on income per capita in the short term. Based on the fact that the conservation approach is managed in a balanced way, one of the reasons for the slowdown in the national income rates of the countries may be due to the decrease in energy consumption in the long term. Accepting that the conservation approach is managed in a balanced way, one of the reasons for the slowdown in the national income rates of the countries may be due to the decrease in energy consumption in the long term.