Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that may lead to significant impairment in social communication, repetitive patterns of behavior, and possible fixed and restricted interests. Applied Behavior Analysis (ABA) is a well-supported and evidence-based treatment for the delays attributed to ASD. Assistive technologies, such as gamification, software apps, computer-based training (Web), and robotics; provide a standardized method of implementing ABA techniques. This review provides a synthesis of the main characteristics of these technologies. The assessed proposals focused on technologies such as Distributed Systems, Image Processing, Gamification, and Robotics. The primary objectives of these tools sought to improve social behavior, attention, communication, and reading skills. Some common limitations found in the literature was a failure to accurately define their target audience, and a failure to comply with the dimensions of ABA as defined by Baer, Wolf, and Risley in 1968.

We employ the NASA Infrared Telescope Facility's near-infrared spectrograph SpeX at 0.8–2.4 μm to investigate the spatial distribution of the stellar populations (SPs) in the four well-known starburst galaxies: NGC 34, NGC 1614, NGC 3310 and NGC 7714. We use the starlight code updated with the synthetic simple SPs models computed by Maraston. Our main results are that the near-infrared light in the nuclear surroundings of the galaxies is dominated by young-/intermediate-age SPs (t ≤ 2 × 10⁹ yr), summing from ∼40 up to 100 per cent of the light contribution. In the nuclear aperture of two sources (NGC 1614 and NGC 3310), we detected a predominant old SP component (t > 2 × 10⁹ yr), while for NGC 34 and NGC 7714 the younger component prevails. Furthermore, we found evidence of a circumnuclear star formation ring-like structure and a secondary nucleus in NGC 1614, in agreement with previous studies. We also suggest that the merger/interaction experienced by three of the galaxies studied, NGC 1614, NGC 3310 and NGC 7714, can explain the lower metallicity values derived for the young SP component of these sources. In this scenario, the fresh unprocessed metal poorer gas from the destroyed/interacting companion galaxy is driven to the centre of the galaxies and mixed with the central region gas, before star formation takes place. In order to deepen our analysis, we performed the same procedure of SP synthesis using the Maraston & Strömbäck evolutionary population synthesis models. Our results show that the newer and higher resolution M11 models tend to enhance the old-/intermediate-age SP contribution over the younger ages.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.

Genetic factors have been pointed out as the primary root associated with the risk of autism. Recent works indicate that approximately 80% of autistic people have inherited the condition from their parents. However, there are no estimates that indicate the likelihood of an autistic parent having an autistic child. Using Hidden Markov Models, together with the data of autism heritability, we developed a model to investigate the likelihood of autistic parents generating autistic children. Hidden Markov Models are a double-layered stochastic process, and it consists of a nonvisible stochastic process (not observable) that can be predicted through a visible one. Our model was built and validated using statistical data from the association of gender with recurrence of autism among siblings, as well as statistical data from the association of genetic factors with autism. Our results suggest that autistic parents may generate autistic children with probabilities of ≈33% for female children and ≈80% for male children. Such estimates could assist parents in some decision making processes according to the estimated risk of autism in their descendants.

Este trabalho apresenta os resultados de uma Revisão Sistemática nos Anais do Congresso Brasileiro de Informática na Educação sobre o uso de Tecnologias Assistivas no apoio ao desenvolvimento de habilidades em pessoas com autismo. Foram analisados estudos primários das últimas 12 edições do evento, identificando as tecnologias empregadas, as abordagens terapêuticas associadas, as habilidades trabalhadas e as limitações comuns. Um total de 60 estudos foram indexados, dos quais 18 foram analisados para responder às questões de pesquisa. Aplicativos e jogos foram as tecnologias mais empregadas; as abordagens comportamentais foram as mais aplicadas, apesar de quase metade dos trabalhos não se fundamentarem em nenhuma abordagem terapêutica; as habilidades mais trabalhadas foram interação social, comunicação e alfabetização; apesar dos avanços, os resultados evidenciaram algumas lacunas, como, por exemplo, a falta de uma melhor caracterização das pessoas envolvidas (dados demográficos, intensidade do diagnóstico etc), não fundamentar a ferramenta em alguma abordagem terapêutica ou estratégia de ensino, e a ausência de profissionais de saúde no processo de desenvolvimento.

A Educação a Distância (EAD) vem crescendo e expandindo as possibilidades de ensino. No entanto, devido a sua natureza, questiona-se a qualidade do ensino nessa modalidade, especialmente em relação a conteúdos complexos. O ensino e aprendizagem da lógica de programação é considerado um tópico complexo, com altos índices de evasão e baixo desempenho em disciplinas correlatas. Esse trabalho objetiva avaliar a eficiência do ensino e aprendizagem da lógica de programação em disciplinas EAD. Nossos resultados indicam que, seguindo uma metodologia adequada e fazendo uso apropriados das tecnologias disponíveis, o desempenho médio e a quantidade de alunos com índice alto de aproveitamento em disciplinas de lógica de programação na modalidade EAD podem ser maiores que em disciplinas estritamente presenciais.