Use of deep multi-target prediction to identify learning styles

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students105Applied machine learnin

Proposal of a Model for IT Service Continuity Management

Micro and small companies in the software area now represent more than 90% of organizations in the country and grow 6% annually on average. However, the lack of planning in these companies in business continuity management contributes to their mortality when problems occur, such as disasters and disruption of system services. The implementation of IT Service Continuity Management as a tool for planning would be the solution. This research presents the current level of maturity of these companies in a Micro scenario and Small IT Companies in Paraná, at Londrina and Cascavel region

Técnicas de Extração de Informação para Avaliação da Qualidade de Páginas Web com o Uso de Ontologias

A qualidade dos conteúdos das páginas Web podem ser determinadas parcialmente através de indicadores como autoria do página, presença de referencias e de propriedade. Este artigo discute a aplicação de técnicas de extração de informação sobre a identificação de indicadores de qualidade, especificamente autoria. Ao contrário de outras técnicas de extração, as técnicas desenvolvidas neste trabalho não utilizam a estrutura da página como principal elemento de análise, voltando sua atenção para o conteúdo extraido. O objetivo final do trabalho é criar uma ferramenta que possibilita a avaliação da qualidade de sites de saúde. Com as entidades extraídas é populado uma ontologia onde estão definidos os critérios de qualidade para as páginas Web

Retrospective clinical and epidemiological analysis of scorpionism at a referral hospital for the treatment of accidents by venomous animals in Alagoas State, Northeast Brazil, 2007-2017

Scorpionism has a high incidence rate in Brazil. It is considered a serious public health problem mainly in tropical and subtropical regions around the world. The number of scorpion accidents have increased over the years and the highest frequencies have been reported mainly in the Brazilian Northeast region. Therefore, in this study we report a retrospective clinical and epidemiological analysis of scorpion stings from 2007 to 2017 in Alagoas State, Northeast Brazil, at a referral hospital for assistance and treatment of accidents by venomous animals. During the analyzed period, the referral hospital treated 27,988 cases, and an increase in the number of cases has taken place over the years. The highest frequency of scorpion stings was observed in females, and the age range most affected was from 20 to 29 years old. The most stung body site was the foot, followed by finger, toe or hand. Regarding the severity, most severe cases were reported in children up to 4 years old (69.4%) and 50% of the total cases treated with serotherapy corresponded to patients in this age range. Interestingly, it was also found that the occurrence of systemic manifestations and the severity of the cases were significantly associated with pediatric patients. In this way, this study highlights the scorpionism as an environmental public health problem in Alagoas State, Northeast Brazil, as well as the need to intensify the epidemiological surveillance and educational campaigns to prevent and control scorpion accidents throughout the year

Meningite Grave por Listeria monocytogenes em adulto imunossuprimido: relato de caso

Meningite por Listeria monocytogenes em adultos é rara, acometendo principalmente indivíduos imucomprometidos e produzindo quadro clínico grave com elevada letalidade. O tratamento deve ser realizado por tempo prolongado, sendo a ampicilina a droga de escolha. Complicações neurológicas são mais comuns quando comparadas a meningites por outros agentes, aumentando a complexidade e a morbimortalidade destes casos. Relatamos um caso complexo de meningite por L. monocytogenes com sucessivas e graves complicações. Após internação prolongada com quadros de infecções respiratórias, abscesso extradural cervical com necessidade de drenagem e persistência de paraplegia como sequela, a preservação do nível de consciência e alta hospitalar foi considerada um desfecho positivo diante do cenário desafiador. &nbsp

Monitoramento navegacional do aluno para descoberta de padrões de preferências de aprendizagem no Moodle

A análise de padrões de comportamento na Web tem por objetivo a descoberta automática ou semi-automática de padrões de acesso gerados por usuários de sites Web de tal forma que esta informação possa ser utilizada em sistemas de recomendação ou sistemas voltados à personalização de ensino ou de acesso a conteúdos.  No caso da personalização, a adaptação é realizada relacionando-se informações sobre o domínio da aplicação com informações sobre o perfil de navegação dos usuários. Neste contexto, o presente trabalho descreve uma arquitetura para a aquisição automática de perfis de classes de usuários, a partir da captura de eventos gerados por ações de alunos no ambiente virtual de aprendizagem Moodle. Ao final, apresenta-se a metodologia para adaptação do estilo navegacional às preferências do aluno, desde a preparação dos objetos de aprendizagem até a detecção e monitoramento dinâmico dos eventos configurados no ambiente

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost