Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical match performance and injuries in professional soccer before and after the COVID-19 break

ABSTRACT Purpose To examine possible differences in physical match performance and the injury occurrence before and after the COVID-19 induced interruption of the 2019/2020 season of the German Bundesliga. Due to the interruption, matches and soccer-specific training were suspended for nine weeks, matches after the resumption of play occurred congested, and the playing situation changed, regarding, e.g., an increase to five substitutions. Methods For this purpose, the 25 match-days before (pre) and the 9 match-days after (post) the COVID-19 induced interruption were examined regarding total distance, sprinting distance, average peak velocity, high-intensity distance, and injury occurrence. Results Average peak velocity increased slightly for the comparison of pre and post with a small effect size (MD = 0.28 km/h, 95% CI: 0.11–0.45, p < 0.01, ES = 0.30, 95% CI: 0.12–0.48). There were no relevant differences in total distance, sprinting distance, high-intensity distance, and injury occurrence (0.11 ≤ p ≤ 0.82; 0.02≤ ES≤0.15). Discussion In terms of physical match performance and injury occurence results suggest the opportunity to recover and work on individual physical weaknesses during the interruption alongside with an increased number of substitutions was sufficient to compensate for the short soccer-specific preparation phase before the commencement of matches and the congested match schedule after the COVID-19 break.
Análise de Sentimentos: Avaliando o Desempenho de Pré-Processamento e de Algoritmos de Aprendizagem de Máquina sobre o Dataset TweetSentBR

A análise de sentimentos é o campo de estudo responsável por analisar as opiniões contidas em aplicações ou serviços, os quais estão sendo muito utilizados pela sociedade. Neste trabalho, foi realizada uma análise de sentimentos no dataset TweetSentBR, na qual foram utilizadas técnicas de aprendizado de máquina para identificação dos sentimentos contidos nos textos. Foram aplicadas abordagens de pré-processamento para os algoritmos os classificarem. Os melhores resultados apresentaram uma Medida F com predição de 82% para a polaridade binária utilizando SVM.
SHWS: Stochastic Hybrid Workflows Dynamic Scheduling in Cloud Container Services

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms. Note to Practitioners—This article investigates a stochastic hybrid workflows scheduling problem in cloud container services. We propose a stochastic hybrid workflows scheduling algorithm, which is named SHWSA. The SHWSA is designed to jointly schedule off-line batch workflows (DIWs) and online stream workflows (DSWs) for minimizing the cost and improving resource utilization in cloud container services. The basic idea is to assign tasks subdeadlines and prioritize tasks for guaranteeing workflows deadlines constraints and the processing dependence requirements of tasks. Experiments show that SHWSA outperforms some state-of-the-art algorithms.
Optimizing HER2-positive breast cancer therapy through neoadjuvant platforms: controversies and concepts

: HER2-positive breast cancer is a highly aggressive subtype that once had a poor prognosis. While the last two decades have seen progress in multiple HER2-targeting agents that lead to improved patient outcomes, these benefits are accompanied by increased toxicity and great cost. In addition, as not all patients have the same risk of disease, individualized treatment regimens are required. Neoadjuvant therapy can allow surgical de-escalation while also providing efficacy and prognostic information, and for HER2-positive breast cancer, chemotherapy combined with dual HER2-blockade regimens can achieve 42−68% pathologic complete response (pCR) and translate to excellent outcomes. The KATHERINE study showed that adjuvant trastuzumab emtansine (T-DM1) ameliorated invasive disease-free survival (iDFS) significantly in patients with residual disease. However, the question remains as to whether treatment can be tailored for patients with pCR or early response, and several ongoing trials such as CompassHER2 and PHERGain are attempting to address this. Preoperative therapy is currently recommended by the American National Comprehensive Cancer Network (NCCN) and by Chinese Guidelines for HER2-positive breast cancer patients with ≥ T2 or ≥ N1. For now, we can call neoadjuvant therapy a promising platform for optimizing HER2-positive breast cancer treatment. However, it is an ongoing challenge to individualize management of anti-HER2 regimens, and this narrative review will discuss current trials and comment on several controversial topics based on therapeutic strategies and clinical practices in China.
Proposta metodológica para previsão de impactos decorrentes de acidentes com barragens de rejeito

RESUMO O rompimento de barragens de rejeitos é alvo de preocupação em função do alto dano potencial associado, sendo necessária uma avaliação de previsão de impactos que permita melhor caracterização da área a jusante, e que a coleta de dados seja realizada pelos órgãos fiscalizadores, e não por parte dos empreendedores. Este trabalho teve por objetivo propor uma classificação dos impactos decorrentes de rompimentos desses barramentos, tendo como referência as avaliações de danos adotadas pelas legislações de segurança de barragens no Brasil. Para isso, foi elaborada uma tabela de critérios e subcritérios com os parâmetros que influenciam na determinação dos impactos causados, os quais foram hierarquizados por meio da aplicação do Analytic Hierarchy Process (AHP), proposto por Saaty (1991). Para determinação dos valores de importância de cada impacto, utilizou-se a técnica Ad-Hoc por meio da dinâmica do brainstorming, com profissionais que realizaram julgamentos a partir de comparações paritárias dos critérios adotados. A ocupação humana na área a jusante, como observado nas legislações, apresentou o maior peso. O volume do reservatório e os impactos no meio socioeconômico apresentaram pesos inferiores aos impactos nos meios físico e biótico, contrastando com as legislações. A aplicação da metodologia em barragem do Quadrilátero Ferrífero comprovou a coerência do índice de impactos ambientais (IA) proposto, indicando as fragilidades da área atingida. O detalhamento da área a jusante permitiu identificar aspectos que podem potencializar ou amortizar os impactos, como periculosidade do rejeito, volume do reservatório e proximidade de centros urbanos.
The divergence-free condition in axisymmetric magnetohydrodynamic models

Axisymmetric magnetohydrodynamic (MHD) models are useful in studies of magnetized winds and non-linear Alfvén waves in solar and stellar atmospheres. We demonstrate that a condition often used in these models for the determination of a nearly vertical magnetic field is applicable to a radial field instead. A general divergence-free condition in curvilinear coordinates is self-consistently derived and used to obtain the correct condition for the variation of a nearly vertical magnetic field. The obtained general divergence-free condition along with the transfield equation completes the set of MHD equations in curvilinear coordinates for axisymmetric motions and could be useful in studies of magnetized stellar winds and non-linear Alfvén waves.
Various Approaches Proposed for Eliminating Duplicate Data in a System

Resume The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.
CALT: Uma Ferramenta Automática para Cobrança do ICMS em Operações Interestaduais

Devido a mudanças constantes na legislação e a complexidade do sistema tributário brasileiro, determinar a tributação de um produto não é uma tarefa trivial. Com isso, este artigo apresenta uma proposta de ferramenta para o cálculo automático dos tributos devidos nas operações interestaduais destinadas ao estado de Alagoas. Mediante o uso técnicas de agrupamento e classificação, o trabalho proposto pretende aprender os padrões de tributação em notas fiscais previamente calculadas e, deste modo, fornecer um modelo capaz de predizer a tributação de um produto. Dessa forma, o cálculo automático oferece uma maneira facilitada de obter a cobrança do ICMS com uma melhora imensurável no tempo de processamento das notas eletrônicas em relação ao cálculo manual, que de forma prática não era realizado em todas as notas.
Seismic Evidence for Thermal and Chemical Heterogeneities in D″ Region Beneath Central America From Grid Search Modeling

Large lateral variations in the depth, sharpness, and magnitude of the D″ discontinuity are observed beneath Central America and the Caribbean using a new innovative grid search modeling method. The strong correlation between the D″ topography and the underlying seismic velocities suggests a large lateral gradient in the thermal condition in the lowermost mantle. Low‐velocity patches were identified among the high‐velocity areas, coinciding with regions showing a disruption in the D″ discontinuity. The low‐velocity zones may be attributed to the heat trapped in the middle of the Farallon slab remnant while those farther to the west are likely to be associated with the upwelling generated at the edge of the ancient slab. A broad range in the sharpness of the D″ discontinuity was also observed, suggesting the existence of different chemical components in the region, as a result of a disparate mixture of basalt, harzburgite, and pyrolite.
ActS – Service design based on human activity sets

PurposeThis paper aims to develop and apply a service design method that allows for stronger recognition and integration of human activities into the front-end stages of the service design process.Design/methodology/approachFollowing a discussion of different service design perspectives and activity theory, the paper develops a method called activity-set mapping (ActS). ActS is applied to an exploratory service design project to demonstrate its use.FindingsThree broad perspectives on service design are suggested: (1) the dyadic interaction, (2) the systemic interaction and (3) the customer activity perspectives. The ActS method draws on the latter perspective and focuses on the study of human activity sets. The application of ActS shows that the method can help identify and visualize sets of activities.Research limitations/implicationsThe ActS method opens new avenues for service design by zooming in on the micro level and capturing the set of activities linked to a desired goal achievement. However, the method is limited to activities reported by research participants and may exclude unconscious activities. Further research is needed to validate and refine the method.Practical implicationsThe ActS method will help service designers explore activities in which humans engage to achieve a desired goal/end state.Originality/valueThe concept of “human activity set” is new to service research and opens analytical opportunities for service design. The ActS method contributes a visualization tool for identifying activity sets and uncovering the benefits, sacrifices and frequency of activities.
Ultrasonic Pretreatment for Anaerobic Digestion: a Study on Feedstock, Methane Yield, and Energy Balance

Ultrasonic Pretreatment for Anaerobic Digestion: a Study on Feedstock, Methane Yield, and Energy Balance Maxime Moisan Co-Advisor: Dr. Animesh Dutta University of Guelph, 2012 Dr. Bill Van Heyst The research represents a first approach to measure the utilization potential of ultrasonic pretreatment on six different substrates: fat, oil and grease (FOG), paper sludge, ground switch grass, ground hay, ground wheat straw, and cut wheat straw. Several laboratories techniques were applied to determine the influence of ultrasonication on biogas production and yield, biogas quality, and digestibility ratio. With the data, mathematical definitions of Net Energy Balance and Net Economy Balance were computed to draw a first justification or rejection of the use of this pretreatment technology for the specific substrates. Ultrasonic pretreatment has a significant effect on biogas production and yield as well as digestibility ratio (p-value < 0.0001) from the early stages of digestion until as far as 50 days of digestion. Ultrasonication and macro particle size management did not influence significantly the methane (CH4) content in the biogas (pvalue = 0.1793). Also, the impact of ultrasonication on the substrate varies between all studied feedstock. Most of the ultrasonicated digestion cases studied provided a negative Net Energy and Economic Balance except for FOG where a certain window of utilization was found. In the context of an ultrasonication process retrofit upgrade, the technology looks to be more useful for substrates that are hard to digest when the retention time is, unfortunately, longer than common retention time. In the context of a new facility, a design that includes an understood ultrasonication technology has yet a small potential success depending on several variables. The ultrasonication technology for anaerobic digestion is hard to recommend due to its energy consumption that, in many cases, overshadows the energy surplus derived from its use.
Modeling the emissions of a gasoline engine during high-transient operation using machine learning approaches

To perform a suitable optimization method in terms of emission and efficiency for an internal combustion engine, first highly accurate and possible real-time capable modeling for the transient operations should be provided. In this work, the modeling of NO x and HC raw emission (before exhaust aftertreatment systems) in a six-cylinder gasoline engine under highly transient operation was performed using machine learning approaches. Three different machine learning methods, namely Artificial Neural Network, Long Short-Term Memory, and Random Forest were used and the results of these models were compared with each other. In general, the results show a significant improvement in accuracy compared to other studies that have modeled transient operations. Furthermore, the shortcoming of Artificial Neural Network for the prediction of the HC emission by the transient operation is observed. The coefficient of determination (R2) for the best model for NO x prediction is 0.98 and 0.97 for the training data and test data, respectively. This value is 0.9 and 0.89 for the best HC prediction model.
Enforcing Intelligent Learning-Based Security in Internet of Everything

The exponential growth of the Internet of Everything (IoE), in recent times, has revealed many underlying security vulnerabilities of the nodes forming IoE networks. The extension of conventional security protocol to these devices has been greatly complicated by the prevalence of restricted computational hardware and limited battery life. Modern learning-based algorithms have shown the potential to secure the IoE networks without undue duress on the nodes’ limited capabilities. In this article, a machine learning-based architecture has been proposed to identify malicious and benign nodes in an IoE network operating with big data. A novel approach for the cooperation of XGBoost and deep learning models along with a genetic particle swarm optimization (GPSO) algorithm to discover the optimal architectures of individual machine learning models has been proposed. Through simulations, it is shown that GPSO-based learning algorithms provide reliable, robust, and scalable solutions. The proposed model significantly outperforms other security protocols in the classification of malicious and benign nodes forming an IoE network.
Climate Change Knowledge and Political Identity in Australia

National data from the 2018 Australian Survey of Social Attitudes show that knowledge of climate change is positively associated with the scientific consensus position on anthropogenic climate change. Responses to factual quiz questions that include climate trigger terms such as “greenhouse gas” or reference to increased ocean temperature and acidification are influenced by one’s political party identification, with Liberal and National party identifiers tending to score lower than Labor partisans on climate knowledge scales. Yet, responses to climate-related factual questions sans trigger terms are not influenced by political partisanship. Climate skeptics tend to score lower on climate knowledge scales than those who accept anthropogenic climate change, although skeptics also tend to have inflated confidence in their factual knowledge of climate change.
Using Technology for Foreign Language Learning: The Teacher’s Role

The spread of information technology has changed the role of language teachers considerably. Being a good educator and an expert in their field are not enough anymore, but teachers are expected to be modern, which means, to possess the ability to design interactive classes (often by using digital tools) and use teaching methods that engage students in a creative way. Today it is a general requirement for teachers to know their way around technology and to possess the know-how of implementing it in a way that fosters language learning. To this purpose teachers need to take into account all facets of technology use, including the advantages and disadvantages of technology-mediated tasks, their usefulness for language learning (e.g. if they are related to the topic of the lesson, are challenging enough for students), helpful resources for students, etc. Technology is regarded as a supplementary instrument to traditional teaching methods that can impact students’ motivation to learn in a positive way, provided it is used for activities that are in line with their needs and expectations. Task-based activities are considered to be especially useful in this regard, allowing students to practice their language skills in an authentic context and also develop creative thinking and problem solving abilities. Web 2.0 technologies (e.g. software programs for creating quizzes and polls, language learning websites, chat programs, wikis, etc.) offer a variety of valuable resources both for activities in the classroom and for practice at home.
TV series adaptations and their repercussions on consumers: insights from two complementary qualitative analyses

ABSTRACT The study explores the nature of adaptations made on an experiential product, a TV series, that enters a foreign market, and investigates the perceptions of foreign consumers concerning the adapted experiential product in two parts. The first part content analyses the nature of adaptations based on comparisons of the contents of the original and adapted versions of the TV series, while the second part content analyses consumers’ perceptions of the adapted TV series based on their social media entries. The first part reveals three broad categories of adaptations, namely contextual, cultural, and competitive. The second part indicates that the familiarity with the original TV series and the distinct genre were influential on consumers’ evaluation of and satisfaction with the adapted TV series.
Bioinspired triangular ZnO nanoclusters synthesized by Argyreia nervosa nascent leaf extract for the efficient electrochemical determination of vitamin C

This work deals with the synthesis of bioinspired triangular ZnO nanoclusters (bT-ZnO NCs) from Argyreia nervosa nascent leaf extract for their effective antibacterial activity and further utilization as a platform for the electrocatalytic determination of ascorbic acid (AA; vitamin C) for applications in the agricultural domain. The structural, optical, and morphological characteristics of the synthesized bT-ZnO NCs were analyzed by UV-vis, FT-IR, XRD, AFM, SEM, TEM, HR-TEM, and EDX techniques. After this, bT-ZnO NCs were electrophoretically deposited onto an indium-tin-oxide (ITO) glass substrate and assessed for the electro-oxidation of AA by cyclic voltammetry (CV), and from this it was proven that bT-ZnO NCs had a very high electrochemical sensitivity of 29.88 μA cm−2 toward AA and a low limit of detection of 0.5321 mM under the optimized experimental conditions. Thus, it provides a potential sensing platform for electrochemical studies to detect AA. Moreover, bT-ZnO NCs were preliminarily investigated for their antibacterial activity, and the obtained results showed that the bT-ZnO NCs have potency as an antibacterial agent.
Farmers’ preferences and willingness to pay for climate-smart agricultural technologies on rice production in Nigeria

ABSTRACT Climate change is a critical environmental issue that has threatened sustainable rice production in Nigeria. The application of climate-smart agricultural (CSA) technologies by smallholder farmers is vital for sustaining rice production in the face of climate change impacts. We investigate rice farmers’ preferences and elicit willingness to pay (WTP) for CSA technologies, using the contingent valuation method (CVM). We find that most farmers do not utilize innovative CSA technologies. The CSA technologies most preferred by farmers are drip irrigation, drainage management, and weather-based rice agro-advisories. At the same time, they are willing to pay $115.63 annually for CSA, notably: $40.25 for knowledge-smart, $31.02 for water-smart, $17.97 for nutrient-smart, $17.72 for weather-smart, $6.82 for carbon-smart, and $1.85 for energy-smart technologies. Age, gender, access to credit, education, extension visit, farm size, and social group membership are significant predictors of farmers’ choices. Key policy insights Identifying and prioritizing farmers’ preferred CSA technologies would enable them and other relevant stakeholders to make investment decisions. Given farmers’ low willingness to pay for CSA technologies, incentives such as subsidies and interest-free loans should be provided to boost uptake. Committing a substantial amount of money to credit facilities would help scale up CSA technologies’ adoption. A robust framework on the revitalization of agricultural extension services would increase extension visits, helping to scale up farmers’ adoption of CSA technologies.