Miracle-dz/UniFAR-FTUs huggingface dataset:
Transversal Study Describing the Applicability of the Conicity Index in the Age Group of Children

O excesso de massa corporal predominantemente na regiao abdominal do corpo favorece o surgimento de varias doencas, dentre elas as dislipidemias e os disturbios cardiovasculares. Nesse contexto, o indice de conicidade (IC) e uma alternativa viavel para indicar obesidade abdominal. O objetivo deste estudo foi verificar se o indice de massa corporal (IMC) e o IC apresentam alguma correlacao e se o IC das criancas pode ser inserido na faixa de corte, utilizando sua formula original. Tratou-se de uma metodologia quantitativa analitica e descritiva, com delineamento transversal. Criancas de ambos os sexos entre 9 e 11 anos de idade participaram deste estudo. A circunferencia da cintura-CC (m), altura (m) e indice de massa corporal (kg) foram utilizados como variaveis ​​para o calculo do IC e IMC. Apos a coleta dessas variaveis, foi realizada a tabulacao, e posteriormente, o software estatistico SAS® Studio foi utilizado para realizar as analises estatisticas. Sessenta e seis criancas (27 homens e 39 mulheres) foram avaliadas. Observou-se que os dados de IC e IMC nao se correlacionam. No entanto, os valores obtidos pelo calculo do intervalo de confianca de 95% da media das criancas estao contidos no intervalo 1,00 e 1,73.
Doing Nothing is Sometimes Worse: Comparing Avoidant versus Approach Coping Strategies with Peer Victimization and Their Association to Depression and Suicide Ideation

ABSTRACT Peer victimization is a major stressor adolescents often face in the school environment, and has been linked to depression and suicidal risk. This study analyzed the associations between three behavioral coping strategies (avoidance, seeking social support from adults/peers, and retaliation) and depression and suicidal ideation. Participants included 4,254 victimized students who were part of the Saving and Empowering Young Lives in Europe (SEYLE) study that compared three school-based prevention interventions and collected information on health risk behaviors in adolescents from 11 European countries. Results showed behavioral avoidance to be associated with an increase in levels of depression, as compared to the other coping strategies (adult social support, peer social support, retaliation). Seeking social support from adults was associated with lower depression and suicidal ideation rates. A more than 25% increase in suicidal ideation was associated with behavioral avoidance, compared to seeking social support from adults. By contrast, retaliation to peer victimization was not found to be associated with increased depression or suicidal ideation. The findings support the Coping Deficit model and may suggest that abstaining by doing nothing when victimized can be associated with certain adverse emotional outcomes.
HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.
Do Immigrant Entrepreneurs Have Natural Cognitive Advantages for International Entrepreneurial Activity?

Are entrepreneurs with an immigration background more willing to internationalize their entrepreneurial firms compared to their nonimmigrant peers? Previous research has already emphasized that immigrant entrepreneurs possess superior human resources and social capabilities—such as international experience, networks and language skills—that help them to better identify and evaluate international entrepreneurial opportunities. However, literature other than capability-based explanations is rare, and thus it is not yet understood in what sense immigrant entrepreneurs differ from other entrepreneurs in terms of their cognitive characteristics for international entrepreneurial activity. This study addresses this gap by involving a quantitative study of Berlin-based high-tech founding entrepreneurs to empirically investigate how immigrant entrepreneurs vary in their cognitive characteristics, and how this affects their intention to engage in international entrepreneurship. The findings show that immigrant entrepreneurs possess higher levels of proactive behavior, which lead to a more favorable evaluation of international business opportunities, and in turn positively affect the willingness to engage in international entrepreneurial activity. This contributes to a deeper understanding of immigrant entrepreneurs’ natural cognitive advantages and the role they play for entrepreneurial growth, success and national economic development.
Reversal No Longer Matters: Attention-Based Arrhythmia Detection with Lead-Reversal ECG Data

In this paper, we propose an attention-based multi-scale neural network for arrhythmia detection with lead-reversal electrocardiogram data. Electrocardiogram with a set of 12 waveforms(known as 12-lead ECG) measures myocardial electro-physiological activity, which is important in clinical diagnosis of arrhythmia. However, lead reversals caused by electrode interchange may cause great interference to the interpretation, leading to significant accuracy decline of automated algorithms and possible faulty diagnosis by cardiologists. To address this problem, we design a multi-scale neural network using attention mechanism to reduce the influence of lead reversals. The proposed model is evaluated on a dataset which consists of ECG data from 3,658 patients. In experiments, the proposed method shows high performance on both lead-reversal data and normal data, which proves great robustness of the method.
Multi-Strategy Dynamic Service Composition in Opportunistic Networks

Distributed computing pervasiveness is nowadays undeniable, and will continue to grow as the usage of device-to-device communications and the number of connected things populating our daily environment increase. Due to the connectivity disruptions induced by the mobility of devices communicating through short-range wireless interfaces and by the sleep phases of devices, it is often difficult to exploit the resources offered by the connected things forming these pervasive environments through the services they provide, and even harder to compose these services together so as to provide users with more useful and sophisticated services. This paper presents a service composition system adapted to opportunistic networks. This composition system relies on a service provision platform that exploits opportunistic networking and computing techniques to cope with connectivity disruptions. Service composition is performed dynamically, according to users’ interests. A multi-strategy scheme is used for the invocation of composite services, and a recovery mechanism is possible through partial invocation. This paper also presents the evaluation of the proposed composition system on two different scenarios: one involving people roaming in an open area, and another one involving spectators of a running event.
Statistical models for large-scale comparative metagenome analysis

Metagenomics, as a culture-independent approach, enables the exploration of complex heterogeneous microbial communities under natural conditions by massive sequencing of community-specific DNA. Metagenomic data sets, derived from various environments, provide new insights into microbial life. Large-scale projects like the Human Microbiome Project or the Earth Microbiome Project emphasize the increasing importance of metagenomics for biomedical and ecosystem research. However, such projects are currently challenging bioinformatics due to the explosive increase in sequencing data. New computationally efficient and statistically adequate methods are required to answer the essential questions “Who is in there? ” and “What are they doing? ”. In this thesis, I developed the Mixture-of-Pathways (MoP) model and Tax4Fun approach. Both methods link the taxonomic profile to a set of pre-computed reference profiles to predict the metabolic repertoire of the microbial community. Since the taxonomic profile is normally estimated to answer the question “Who is in there? ”, the further use of the taxonomic profile avoids additional costs for answering the question “What are they doing? ”. Tax4Fun is specifically designed for the output of 16S rRNA analysis pipelines using the SILVA database as reference, whereas the MoP model is especially conceived for metagenome sequence data and provides a robust statistical basis to describe the metabolic potential of a microbial community. The adequate metabolic modeling of metagenomes provides a concise summary of the functional variation of metagenomes across many samples, enabling the identification of relevant metabolic differences in comparative analyses. For comparative metagenomics, the identification of similar metagenomes to a newly obtained dataset is of growing importance. For an efficient large-scale identification of closely related metagenomes within a database retrieval context, I conducted a detailed evaluation of a k-nearest-neighbor search utilizing different biological feature profiles and metrics. I demonstrated that different features and metrics can be chosen for a convenient interpretation of results in terms of the underlying features. The integration of the k-nearest-neighbor search into metagenome annotation and comparison systems is beneficial to automatically identify additional metagenomes for comparative analyses as well as to detect mislabeled or contaminated datasets by unexpected neighboring habitat labels. The MoP approach and k-nearest-neighbor search are available to the scientific community as part of the CoMet-Universe web server application. Additionally, the MoP and Tax4Fun approach are provided as R Package.
Modal and Forced Response Analysis of Vehicle Systems With Latent Vibration Modes Due to Hydraulic Mounts

Hydraulic mounts used in vehicles for better isolation of vibrations were often approximated by lumped or mechanical mass-damper-spring (m-c-k) models, although deficiency in such modeling was pointed out and “hydraulic” modeling was proposed as an alternative. In this paper, a brief review on the mechanical m-c-k modeling and “hydraulic” modeling of the hydraulic mounts is presented. A simplest system consisting of a single mass and a hydraulic mount is used to illustrate both equivalence and difference in a closed form between the two modeling approaches. Then, modal analyses are done on an apparently three degrees-of-freedom (DOF) quarter car with a hydraulic mount, where the key idea is to use an internal variable for the movement of fluid mass which is responsible for a “latent” vibration mode. Equations of motion for the apparently 3DOF system, 4DOF system in fact, by the two modeling are formulated. Modal parameters by the proposed “hydraulic” modeling of the hydraulic mount are compared with those by the m-c-k modeling. Forced responses to transient base excitations are also compared between the two modeling approaches to illustrate how much errors can arise in the frequency and time domain analysis. To be more realistic, the modal and forced response analysis on a full car of an apparently 10DOF (3DOF for powertrain, 3DOF for car body, and 4DOF for knuckles and tires) with two more DOF internally for two hydraulic mounts between the powertrain and car body is presented.
Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.
Parents’ Knowledge as a Risk Factor of Tuberculosis in Children

—Tuberculosis (TB) cases in Indonesia ranked second after India. According to Indonesia Health Profile, TB prevalence in 2016 was 351,893 cases and the number increased to 360,770 (2.5%) in 2017. Sintang regency is one of the regencies in Indonesia which is endemic for TB. Sintang had 272 TB cases in 2016 and it decreased to 161 in 2017. On the other hand, Kebong Health Center recorded that TB cases in children increased from 10 cases in 2016 to 40 in 2017. It meant that 24.84% of TB cases in Sintang occurred in children. The aim of this study was to determine the risk factors of TB in children incidence at Kebong Health Center. This study was analytic observational research with a case control design. The samples were 40 cases and 40 controls. The result showed that there was a significant relationship between knowledge (p value= 0,00; OR= 36,00) and incidence of TB occurring to children. There were no relationships between exclusive breastfeeding (p value = 0.666), economic status (p value = 0.329), occupancy density (p value = 0.421), room humidity (p value = 0.561) and room temperature (p value = 1,000) with TB incidence. It could be concluded that the parents’ knowledge of TB was the risk factor of TB at Kebong Health Centre. Subsequently, the cases would increase 6 times higher if there was insufficient knowledge of TB in children.
European Industrial Energy Intensity: Innovation, Environmental Regulation, and Price Effects

We investigate the direct role of technological innovation and other factors influencing industrial energy intensity across 17 EU countries over 1995-2009. We develop an innovative industry-level patent dataset and find compelling evidence that patent stock negatively influences industrial energy intensity. In particular, we find a much stronger effect of patent stock on energy-intensive industries with an estimated coefficient of -0.138 which almost double that of less energy-intensive industries (estimated at -0.085). While our results show that energy price remains the major determinant of energy intensity, the chemicals industry, which is not covered by the EU Emissions Trading Scheme (ETS) during the sample period, appears more susceptible to energy prices relative to other energy-intensive industries that are covered by the EU ETS. Exploring regional differences in carbon taxation, we find a significant decline in energy intensity in Northern Europe owing to the carbon tax policy implemented in the early 1990s across the Nordic countries.
The role of Chlamydia trachomatis and Mycoplasma genitalium in infertile couples.

Chlamydia trachomatis is one of the most prevalent sexually transmitted diseases. Previous work has shown that C. trachomatis IgG antibody is correlated with tubal factor infertility and reduced pregnancy rates. There has been little research on Mycoplasma genitalium and the incidence in infertile couples remains unknown. This study has investigated 250 infertile couples as well as 250 fertile women. The presence of these organisms was detected by polymerase chain reaction and an analysis of serum IgA, IgM and IgG antibodies to C. trachomatis. Low prevalence of C. trachomatis, low level of concordance within couples and no evidence of M. genitalium was found. Elevation of IL6 and 8 were observed in C. trachomatis positive men and this varied with diagnostic method. Seminal IL-8 was correlated negatively with semen volume and positively with age. Sperm DNA fragmentation (TUNEL) and chromatin structure (AB, AO & CMA3) were not correlated with C. trachomatis infection. TUNEL results correlated negatively with semen volume and the percentage of protamination was related to age. Urethritis was more common in men diagnosed with C. trachomatis by urine DNA. Men who work as drivers were more likely to be positive for IgG. PCOS was more common in women with positive serology for IgM and IgG. C. trachomatis infection in men or women did not affect pregnancy rate and pregnancy outcome in either assisted or spontaneous conception. In conclusion, this thesis does not support the hypothesis that there is a strong relationship between C. trachomatis infection and infertility. Nor does it support the idea that C. trachomatis is linked to poor semen quality. Study limitations include the lack of fertile men as control group and difficulties with some methodology. A number of future studies are suggested along with some recommendations of C. trachomatis screening programmes both in Iran and the world.
HOTS Android-Based student worksheets to practice creative thinking ability of vocational school students

This study aims to describe the development of an Android-based HOTS Student Worksheet to train the creative thinking skills of vocational students. The type of research used is Research and Development (R & D) using the ADDIE model which consists of five stages: 1) analyze, 2) design, 3) development, 4) implementation, and 5) evaluation. The research subjects were class XI students in Online and Marketing Business (BDP) majoring in SMK Negeri 1 SekarBojonegoro, totaling 33 students. The research instruments used were learning media validation sheets, test questions, and student response questionnaires. The results of this study are HOTS-based Android LKS learning media that meet very valid criteria with an average of 90.22%, practically with an average student response questionnaire of 80.30% and effective to train creative thinking skills of class XI students of linear program material.
Maximality of noncommutative rings over orders

La teoria de ordenes ha sido ampliamente estudiada desde la ultima parte del siglo XX. En el contexto no conmutativo, la maximalidad de ordenes ha sido revisada para objetos clasicos de tipo polinomial como las extensiones de Ore y las extensiones PBW, entre otras, y mas recientemente para anillos de Ore-Rees. En este trabajo extendemos algunos resultados encontrados en la literatura a las extensiones PBW torcidas.
An Updated Classification System and Review of the Lipooligosaccharide Biosynthesis Gene Locus in Campylobacter jejuni

Lipooligosaccharide (LOS) is an integral component of the Campylobacter cell membrane with a structure of core oligosaccharides forming inner and outer core regions and a lipid A moiety. The gene content of the LOS core biosynthesis cluster exhibits extensive sequence variation, which leads to the production of variable cell surface LOS structures in Campylobacter. Some LOS outer core molecules in Campylobacter jejuni are molecular mimics of host structures (such as neuronal gangliosides) and are thought to trigger neuronal disorders (particularly Guillain–Barré syndrome and Miller Fisher syndrome) in humans. The extensive genetic variation in the LOS biosynthesis gene cluster, a majority of which occurs in the LOS outer core biosynthesis gene content present between lgtF and waaV, has led to the development of a classification system with 23 classes (A–W) and four groups (1–4) for the C. jejuni LOS region. This review presents an updated and simplified classification system for LOS typing alongside an overview of the frequency of C. jejuni LOS biosynthesis genotypes and structures in various C. jejuni populations.
Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.