bluuebunny/crossref_metadata_2025 huggingface dataset:
МАГНИТОТЕРАПИЯ В ЛЕЧЕНИИ ПЕРЕДНЕЙ ИШЕМИЧЕСКОЙ НЕЙРОПАТИИ
Authors: Иойлева Е.Э. , Засыпкина А.А. , Герасименко М. Ю.
Published: October 2024

Представлен клинический случай редкого послеоперационного осложнения после проведенной факоэмульсификации катаракты с имплантацией заднекамерной интраокулярной линзы (ИОЛ) в виде развития передней ишемической оптической нейропатии. Приведены особенности клиники, диагностики и лечения данного состояния.
ПЕРВЫЕ РЕЗУЛЬТАТЫ ТЕХНОЛОГИИ TRANSPLANT READY DMEK ENDO-IN В УЗБЕКИСТАНЕ
Authors: Каланходжаев Б.A. , Абдуллаев Э.Э. , Джафарова С.М. , Каланходжаев А.Б
Published: October 2024

Актуальность. Учёные активно ищут методы продления молодости и улучшения качества жизни. Ранее сквозная кератопластика (СКП) была основной операцией при патологиях роговицы, включая ЭЭД. Однако сейчас применяется более прогрессивная технология — DMEK, замещающая только изменённый эндотелий и десцеметову мембрану. Но у DMEK есть ограничения, связанные с тем, что мембрана при отделении сворачивается в трубочку. Разработана технология, которая позволяет свернуть мембрану эндотелием внутрь, обеспечивая её правильное расправление в передней камере. Цель исследования. Представить первые результаты новейшей технологии Transplant Ready DMEK Endo-In в Узбекистане. Материалы и методы. Всем пациентам была проведена операция по новой технологии TransplantReady DMEK Endo-In. Результаты. Все трансплантаты десцеметовой мембраны прилегли сразу. Заключение. Новая технология облегчает работу хирурга, а также сокращает реабилитационный период пациента.
РЕЗУЛЬТАТЫ ЭЛЕКТРОФИЗИОЛОГИЧЕКИХ ПОКАЗАТЕЛЕЙ ПРИ ДИАГНОСТИКЕ ПАЦИЕНТОВ С ИШЕМИЧЕСКИМИ ИЗМЕНЕНИЯМИ ОРГАНА ЗРЕНИЯ
Authors: Махкамова Д.К.
Published: October 2024

Актуальность. На сегодняшний день риск развития сердечно – сосудистых заболеваний остается высоким во всем мире, особенно у лиц трудоспособного возраста. Данная патология проявляется обычно постепенным снижением зрительных функций, в итоге, приводя к необратимым грозным осложнениям не только органа зрения, но и организма в целом, поэтому своевременное выявление таких состояний является актуальной проблемой современной медицины. Цель исследования. Изучить параметры электроретинографии для выявления локализации и характера поражения зрительного анализатора при ишемических состояниях органа зрения, а также прогнозирования течения заболевания. Материал и методы. Нами были изучени данные 167 больных с ишемическим поражением магистральных сосудов органа зрения (глазной ишемический синдром (ГИС), хроническая ишемическая нейропатия (ХИН), хроническая ишемическая нейроретинопатия (ХИНР)). Результаты и заключение. При исследовании выявлено удлинение латентности всех пиков на 37% независимо от стадии и характера течения заболевания. Было установлено, что параметры компонентов латентности не являются специфичными для определения стадии и характера течения ишемического поражения органа зрения в то время, как амплитуда вызванных потенциалов прямо коррелировало со стадией, глубиной поражения и характером течения атеросклероза и являлось высокоспецифичным для этой патологии.
КОМПЛЕКСНЫЙ ПОДХОД К ЛЕЧЕНИЮ ЧАСТИЧНОЙ АТРОФИИ ЗРИТЕЛЬНОГО НЕРВА У ДЕТЕЙ
Authors: Назирова З.Р. , Туракулова Д.М. , Абдуллаева З.Б.
Published: October 2024

Актуальность. Частичная атрофия зрительного нерва (ЧАЗН) — тяжелая патология зрительного нерва, приводящая к снижению зрения, слепоте и инвалидности. В Узбекистане ЧАЗН встречается в 1,2–8,6%. Среди слабовидящих – до 30,9%. Цель исследования. Изучить эффективность комплексного подхода в лечении частичной атрофии зрительного нерва различного генеза с использованием транскраниальной магнитной стимуляции.
Материалы и методы Обследовано 36 детей (64 глаза), госпитализированных в глазное отделение клиники ТашПМИ и обследованных в поликлинике центра нейрохирургии. Из всех обращений 17% (6 детей, 9 глаз) - вторичная атрофия диска зрительного нерва. 15 детей (42%) получали транскраниальную магнитную стимуляцию в составе комплексного лечения частичной атрофии, 21 ребенок (58%) — консервативное лечение. Результаты и заключение. В основной группе для лечения больных ЧАЗН применялась традиционная терапия ноотропов, сосудорасширяющие средства, антиоксиданты, ангиопротекторы, полипептиды. В контрольной группе лечение проводилось в сочетании с транскраниальной магнитной стимуляцией. В целом функциональные показатели были достоверно выше у больных, получавших комбинированный метод лечения, по сравнению с группой, получавших традиционный метод. Разработанный оригинальный метод лечения с применением транскраниальной магнитной стимуляцией больных с различными формами частичной атрофии зрительного нерва у детей позволяет улучшить зрительные функции и стабилизировать процесс.
ИЗУЧЕНИЕ ЗРИТЕЛЬНОГО СТАТУСА У ЖЕНЩИН С ОФТАЛЬМОПАТОЛОГИЕЙ ВО ВРЕМЯ БЕРЕМЕННОСТИ И ПОСЛЕ РОДОВОМ ПЕРИОДЕ (ОБЗОР ЛИТЕРАТУРЫ)
Authors: Рузимова Н.Э. , Каримова М.Х.
Published: October 2024

Актуальность. Беременность - состояние, которое сопровождается множеством изменений в различных органах организма женщины, особенно значителные изменения претерпивают гематологические, иммунологические и сердечно-сосудистые системы. Эти изменения безусловно, происходят под влиянием плаценты, а также гормонов эндокринных желез матери надпочечников плода [1]. Как и в случае с другими частями тела, беременность также связана с различными глазными изменениями, которые могут быть как физиологическими так и патологическими или изменения в ранее существоваших условиях. Эти изменения в основном временные, однако некоторые из них могут быть постоянными [2].
Повышение эффективности лечения и профилактика осложнений, развивающихся на фоне гормональных изменений во время беременности, требуют углубленного подхода к беременным. При этом вопрос о тактике ведения беременных женщин с офтальмопатологией и выборе оптимального метода родоразрешения остаётся дискутабельным.
ПОИСКИ ВЗАИМОСВЯЗИ МЕЖДУ СОСТОЯНИЕМ ЦИННОВОЙ ПОДДЕРЖКИ И СИСТЕМНЫМИ ИНВОЛЮЦИОННЫМИ ИЗМЕНЕНИЯМИ В СОЕДИНИТЕЛЬНОЙ ТКАНИ ОРГАНИЗМА
Authors: Сорокин Е.Л. , Буря Р.А.
Published: October 2024

Актуальность. В последнее время наблюдается увеличение частоты спонтанной дислокации комплекса «интраокулярная линза – капсульный мешок» после факоэмульсификации возрастной катаракты. Стандартное предоперационное обследование не всегда эффективно для выявления скрытой слабости цинновой связки. Цель исследования. Оценить состояние цинновой связки у пациентов с возрастной катарактой без явных признаков слабости зонулярной поддержки, имеющих сопутствующие инволюционные изменения соединительной ткани. Материалы и методы. Основная группа – 70 пациентов с ВК на фоне клинических проявлений инволюционных изменений соединительной ткани; группа контроля – 60 человек с ВК без инволюционных изменений соединительной ткани. Оценивалась симметричность дистанции «отростки цилиарного тела – экватор хрусталика» в 2 основных меридианах. Результат. В основной группе асимметрия дистанции «отростки цилиарного тела – экватор хрусталика» обнаружена в 28 глазах (40%), что значительно превышало показатели контрольной группы (5%, p<0,01). Выводы. Наличие асимметрии дистанции «отростки цилиарного тела – экватор хрусталика» свидетельствует о субклинических процессах инволюционных изменений связочного аппарата хрусталика.
ОЦЕНКА ЭФФЕКТИВНОСТИ ПОЗИЦИИ «ПОЛОЖЕНИЕ ЛИЦОМ ВНИЗ» В ПОСЛЕОПЕРАЦИОННОМ ПЕРИОДЕ У ПАЦИЕНТОВ С РЕГМАТОГЕННОЙ ОТСЛОЙКОЙ СЕТЧАТКИ
Authors: Хикматуллаев Б.Х. , Каримова М.Х. , Файзуллоев С.С. , Каланходжаев А.Б.
Published: October 2024

Актуальность: Регматогенная отслойка сетчатки (РОС) часто приводит к пожизненной потере зрения в 1 из 100 случаев, несмотря на высокий анатомический успех восстановления сетчатки. Послеоперационные осложнения возникают в 90% случаев и могут негативно влиять на качество жизни. Для снижения частоты осложнений предложено использовать положение лицом вниз (ПЛВ) после операции. Цель исследования. Оценить влияние ПЛВ на развитие осложнений в послеоперационный период при РОС. Материал и методы. Проведено проспективное клиническое исследование с участием 180 пациентов, разделенных на две группы: основную (с соблюдением ПЛВ) и контрольную (без соблюдения ПЛВ). Пациенты были обследованы на 10-й, 30-й и 90-й дни после операции, включая офтальмологическое обследование и оптическую когерентную томографию. Результаты. На 10-й день у основной группы отмечено снижение дислокации сетчатки, субретинальной жидкости и частоты повторного подъема сетчатки по сравнению с контрольной группой. Частота повышенного внутриглазного давления была выше в основной группе. На 30-й и 90-й дни у основной группы реже наблюдались складки на сетчатке и помутнение хрусталика. Заключение. ПЛВ является важным фактором для предотвращения осложнений после витреоретинальных вмешательств, улучшая результаты лечения и снижая риск рецидивов.
К ВОПРОСУ КЛАССИФИКАЦИИ АНОФТАЛЬМИЧЕСКОЙ ОРБИТЫ
Authors: Ходжаев Д. Х. , Юсупов А.Ф.
Published: October 2024

Актуальность. Анофтальм требует постоянного мониторинга для комфортного ношения глазного протеза. Существующие классификации недостаточно учитывают анатомические изменения, что затрудняет реабилитацию. Новая классификация необходима для улучшения хирургии и протезирования. Цель исследования. Разработать классификацию анофтальмической орбиты с учетом состояния соединительных и костных тканей. Материалы и методы. Исследование включало 457 пациентов с анофтальмом различной этиологии, проходивших осмотр и диагностику. Результаты. Обнаружены различия в состоянии век, конъюнктивальной полости и костей орбиты у пациентов с врожденным и приобретенным анофтальмом. Заключение. Предложенная классификация способствует более точной дифференциации и реабилитации пациентов с анофтальмом.
ЭФФЕКТИВНОСТЬ КОМПЛЕКСНОГО ЛЕЧЕНИЯ ГЛАУКОМНОЙ ОПТИЧЕСКОЙ НЕЙРОПАТИИ НА ОСНОВАНИИ МОРФО-ФУНКЦИОНАЛЬНЫХ ПАРАМЕТРОВ ЗРИТЕЛЬНОГО НЕРВА
Authors: Саидов Т.Т , Янгиева Н.Р.
Published: October 2024

Актуальность. Глаукома является хронической прогрессирующей оптической нейропатией с характерными морфологическими изменениями в головке зрительного нерва и прогрессирующей смертью ганглионарных волокон сетчатки с характерными сужениями полей зрения. Таким образом в виду недостаточной эффективности гипотензивной терапии поиск новых лекарственных средств с целью предотвращения прогрессирования глаукомной оптической нейропатии продолжается. Наиболее перспективным из них является нейропротекторное лечение в сочетании с чрескожной электростимуляцией и эндоназальным электрофорезом, что позволяет максимально защитить нейроны сетчатки и нервные волокна зрительного нерва от повреждающих факторов. Цель. Оценить структурно-функциональные изменения зрительного нерва после комплексного нейропротекторного лечения при глаукоматозной нейропатии зрительного нерва. Материал и методы. Клиническое наблюдение включает 80 (116 глаз) больных ГН в возрасте от 42 до 79 лет. Из них 45 (56,2%) женщин, 35 (43,7%) мужчин с диагнозом ПОУГ II или III стадии и ЗКУГ при компенсации ВГД (21,4±3,1). Результаты. Анализ следующего наблюдения свидетельствует о стабильности данных функциональных показателей, чего не было отмечено в контрольной группе, где данные параметры имели сравнительно недостоверные изменения.
ЭФФЕКТИВНОСТЬ МИКРОИМПУЛЬСНОГО ЛАЗЕРНОГО ВОЗДЕЙСТВИЯ В КОМПЛЕКСНОМ ЛЕЧЕНИИ ЦЕНТРАЛЬНОЙ СЕРОЗНОЙ ХОРИОРЕТИНОПАТИИ
Authors: Туйчибаева Д.М. , Адхамова Л.А.
Published: October 2024

Актуальность. Среди других ретинопатий в структуре заболеваемости центральная серозная хориоретинопатия (ЦСХ) занимает четвертое место после возрастной макулярной дегенерации (ВМД), диабетической ретинопатии и окклюзии ретинальной вены. Цель исследования. Изучение эффективности субпорогового микроимпульсного лазерного воздействия длиной волны 577 нм в комплексном лечении центральной серозной хориоретинопатии. Материал и методы. Клиническое исследование основывается на анализе ХРК 121 глаза 121 пациента с унилатеральным течением ЦСХ. Среди исследуемых: 23 (19%) женщины и 98 (81%) мужчин. Возраст участников составил 40,8 ± 6,4 лет. Результаты и заключение. Разработан и внедрен в клиническую практику протокол СМЛВ длиной волны 577 нм, являющийся безопасным и эффективным в лечении ЦСХ. Отсутствие лазериндуцированных повреждений при использовании энергетических параметров протокола СМЛВ длиной волны 577 нм позволяет проводить лечение в раннем остром периоде ЦСХ, в ФАЗ, при диффузной гиперфлюоресценции, выполнять повторение сеансов при персистировании и рецидивах процесса. Сочетанный анализ данных ОКТ, КВ АФ и ИК-АФ позволяет выявить особенности структурно-функциональных изменений ХРК при ЦСХ, точно установить длительность течения заболевания и прогнозировать функциональный результат после разрешения процесса.
Using Multivariate Analysis within the Vertebral Column to Identify Individual Vertebrae
Authors: Minetz Jolen , Konigsberg Lyle , Ousley Stephen
Published: October 2024

This article demonstrates the utility of a multivariate analysis of vertebrae in an applied context. The human vertebral column is a morphologically complex group of elements. Current methods rely on morphological characteristics to classify isolated vertebrae qualitatively. This research provides a bridge between morphological assumptions for vertebral designations and quantitative classification. These osteometric methods and statistical analyses provide quantifiable information relating to the accuracy of vertebrae classification. The sample used for this analysis consists of osteometric vertebral measurements from intact vertebral columns from 59 individuals. In order to assess the potential for these vertebral measurements to classify vertebrae, regional grouping models based on vertebral column segments were developed and analyzed. The data were tested for multivariate normality and homogeneity of variance–covariance matrices in order to comply with the assumptions required by the statistical analyses used for classification. Linear discriminant function analysis was used for classification. The sensitivity and specificity of each vertebral group prediction were used for evaluation. This research demonstrates that by using osteometric methods and statistical analyses, the accuracy of vertebrae classification is quantifiable. This method has been developed to assist with the sorting and analysis of commingled and fragmentary skeletal remains.
Steven D. Ousley
Authors: Hefner Joseph T. , Jantz Richard
Published: October 2024

Early in his career, Steven D. Ousley established his view on the need for biological anthropologists to provide empirical support for their findings. He, like many of his mentors and contemporaries, became acutely aware of the need for more statistical rigor in forensic anthropology. His research and the scholarly products he produced explored the relationship and linkage between method and theory, between what can be observed, what can be explained, and what can be quantified. Driven by an insatiable curiosity and a commitment to advancing knowledge, Steve’s work has left an indelible mark on the intersection between biological anthropology and statistical approaches to the study of human variation and has enriched our understanding of population affinity estimation. With a career characterized by rigor, innovation, and a passion for discovery, his pioneering insights continue to reverberate through academia and the applied world encompassing forensic anthropology. As we delve into this collection of scholarly works, we embark on a journey through the intellectual landscape Steve so profoundly shaped, celebrating his legacy and the lasting impact of his ideas. We present herewith a compilation of Steve’s works, thesis, dissertation, published papers, grants and awards, software, student mentorships, and data collection activities. These illustrate the breadth of his knowledge and contributions to our discipline.
Aplicação de Modelos Ocultos de Markov para Detecção de Falhas em Componentes de Turbinas Eólicas
Authors: de Sá Fernando , Pinna Danielle , Fernandes Kennedy , de Oliveira Sanderson Gonzaga , Toso Rodrigo , Belloze Kele , Brandão Diego Nunes
Published: October 2024

A demanda crescente por soluções energéticas renováveis destaca a importância das turbinas eólicas na conversão de energia. Monitorar, diagnosticar e prever falhas nesses sistemas e crucial para garantir a produção contínua de energia. Sensores monitoram o funcionamento das turbinas e os dados coletados são usados para criar modelos que identificam sinais precoces de deterioração, permitindo a detecção rápida de falhas e a redução dos custos de manutenção. Este estudo propõe uma abordagem baseada em Modelos Ocultos de Markov para monitorar e diagnosticar falhas em geradores e caixas de velocidade de turbinas eólicas. Os resultados obtidos pela métrica F-Score demonstram a viabilidade da abordagem proposta.
Aplicação de técnicas de Co-Teaching para Limpeza de Dados e Treinamento Robusto de Redes Neurais: um Estudo de Caso de Modelagem de Distribuição de Espécies
Authors: Miyaji Renato O. , Almeida Felipe V. de , Corrêa Pedro L. P.
Published: October 2024

Em tarefas de Classificação, quando existem incertezas associadas aos rótulos do conjunto de dados, o ajuste de Redes Neurais pode ser comprometido. Nesse contexto, técnicas algorítmicas para limpeza de dados são efetivas. Assim, neste trabalho foram aplicadas técnicas de Co-Teaching, em conjunto com métodos para tratar o desbalanceamento entre classes, para identificar instâncias com menor confiança em seus rótulos, limpar os dados e permitir o treinamento robusto de Redes Neurais. Essas técnicas se baseiam no treinamento conjunto de múltiplos modelos e foram aplicadas em um Estudo de Caso de Modelagem de Distribuição de Espécies na região da Bacia Amazônica com um modelo de Redes Neurais, atingindo um incremento de ROC-AUC de 11,8% (aumento de 75,8% para 84,5%).
Estudo Comparativo entre MongoDB e PostgreSQL usando Embedding Documents
Authors: Merlo André , Pavão Jorge N. S. , Freitas Luiz C. de , Soares Jorge , Brandão Diego , Belloze Kele
Published: October 2024

Os Sistemas de Gerenciamento de Banco de Dados (SGBDs) desempenham um papel primordial em e-Science devido à necessidade de gerenciar e analisar grandes volumes de dados de forma eficiente. MongoDB e PostgreSQL são dois dos SGBDs mais utilizados na atualidade, cada um com suas características únicas que atendem a diferentes necessidades de armazenamento e processamento de dados. Este trabalho apresenta uma análise comparativa entre o MongoDB e o PostgreSQL, avaliando o desempenho e a eficiência de ambos os sistemas ao lidar com consultas complexas e cargas de trabalho de análise de dados. Resultados preliminares indicam que, apesar do MongoDB mostrar eficiência em consultas específicas, o PostgreSQL superou em desempenho na maioria dos testes.
Aplicando Lógica Fuzzy na Interpretação de Grandes Volumes de Dados Cromatográficos no Controle de Dopagem
Authors: M. B. Junior Guy , Lopes Giseli Rabello , Serra da Cruz1 Sérgio Manuel
Published: October 2024

Este trabalho apresenta uma proposta computacional para auxiliar na interpretação de grandes volumes de dados baseada na lógica fuzzy e uso do coeficiente de determinação R² aplicados a dados oriundos de cromatógrafos líquidos de alta eficiência acoplados a espectrômetros de massas (HPLC-MS). A abordagem apoia analistas na detecção semiautomatizada de substância alvo nas amostras de urina de atletas submetidos ao controle de dopagem. Os primeiros resultados indicam que o método não apenas acelera a detecção, mas também permite a identificação simultânea de múltiplas substâncias alvo
Padrões de Subnutrição Infantil no Estado do Ceará: Uma Análise Estratificada por Regiões e Grupos Sociais
Authors: Silva Daniel de Amaral da , Medeiros André Filipe de , Braga Antonio Rafael , Gurgel Anna Karine , Gomes Danielo G.
Published: October 2024

A subnutrição infantil continua sendo uma preocupação significativa de saúde pública no Brasil, particularmente no Estado do Ceará. Este artigo apresenta uma análise abrangente dos padrões de subnutrição no Ceará, focando nas suas disparidades regionais e diferenças entre grupos sociais. Utilizando dados do Sistema de Vigilância Alimentar e Nutricional (SISVAN) de 2019 a 2023, examinamos o Déficit de Altura por Idade (DAI) em crianças de 0 a 5 anos em cinco regiões de saúde. Análises estatísticas, incluindo testes qui-quadrado e regressão binomial negativa, sugerem que tanto a localização geográfica quanto a pertença a grupos sociais influenciam significativamente os níveis de subnutrição infantil. Em particular, uma conclusão não óbvia e animadora é que a identidade indígena e a participação em grupos comunitários foram associadas a uma redução nos casos de DAI.
Querying Databases with Natural Language: The use of Large Language Models for Text-to-SQL tasks
Authors: Nascimento Eduardo Roger S. , Casanova Marco Antonio
Published: October 2024

The Text-to-SQL task involves generating SQL queries based on a given relational database and a Natural Language (NL) question. Although Large Language Models (LLMs) show good performance on well-known benchmarks, they are evaluated on databases with simpler schemas. This dissertation first evaluates their effectiveness on a complex and openly available database (Mondial) using GPT-3.5 and GPT-4. The results indicate that LLM-based models perform poorly and struggle with schema linking and joins. To improve accuracy, this work proposes the use of LLM-friendly views and data descriptions. A second experiment on a real-world database confirms that this approach enhances the accuracy of the Text-to-SQL task.
Unified Approach to Trajectory Data Mining and Multi-Aspect Trajectory Analysis with MAT-Tools Framework
Authors: Portela Tarlis Tortelli , Machado Vanessa Lago , Renso Chiara
Published: October 2024

Multiple-aspect trajectory (MAT) data mining requires sophisticated tools to handle the complexity and volume of complex data. This paper introduces MAT-Tools, a comprehensive Python framework for MAT data mining. The framework consists of five main packages: mat-data, which supports data preprocessing and synthetic dataset generation; mat-model, offering model classes tailored for MAT data; mat-similarity, providing methods for similarity measurement; mat-view, visualization tools for MAT data, experimental preparation, and results exploration on a web interface; and mat-classification and mat-clustering, which includes advanced classification and clustering algorithms. Each package addresses specific challenges in MAT data analysis, from preprocessing to modeling and classification. MAT-Tools facilitates efficient and accurate trajectory data analysis, making it invaluable for diverse tasks since exploratory data analysis, anomaly detection, and predictive modeling applications. This framework's integration and extensibility empower researchers and practitioners to gain deeper insights and achieve more reliable results in trajectory data mining.
LLM-MRI Python module: a brain scanner for LLMs
Authors: Costa Luiz , Figênio Mateus , Santanchè André , Gomes-Jr Luiz
Published: October 2024

LLMs (Large Language Models) have demonstrated human-level language and knowledge acquisition skills in several tasks. However, despite the recent success and broad use, understanding how these skills are learned and encoded inside the underlying neural network is still challenging. The goal of the LLM-MRI package is to simplify the study of activation patterns in any transformer-based LLM, similarly to how MRI (magnetic resonance imaging) simplifies with biological brains. The package, written for the Python language, allows the mapping of neural regions using a parameterized reduction of the model's dimensionality. Neural regions can be visualized according to the forward-pass activations stimulated by a set of documents. Similarly, the package enables the creation of graph models representing the interlayer network of connections stimulated by a set of documents. These features allow for qualitative and quantitative assessments of the underlying structure of activations, depending on the type of documents that the LLM model is exposed to.
DataNexus: Uma Ferramenta para Auxiliar a Análise da Modelagem de Bancos de Dados Extensos
Authors: Moraes Gustavo , Carneiro Victor Misael B. F. , Brayner Angelo
Published: October 2024

Bancos de dados estão se tornando cada vez mais complexos, com grandes volumes de dados e tabelas. Ambientes corporativos frequentemente lidam com centenas ou até milhares de tabelas, o que torna desafiadora a visualização e a compreensão de seus esquemas. Este artigo apresenta uma ferramenta que utiliza técnicas de ciência das redes, como detecção de comunidades e centralidade de grau, para oferecer uma visão clara e gerenciável de esquemas de bancos de dados complexos. O objetivo principal é facilitar a compreensão desses esquemas, contribuindo para a gestão mais eficiente de catálogos de bancos de dados e suporte a processos de engenharia reversa, quando os esquemas mal projetados impactam negativamente na performance.
Análise Comparativa de Canais do YouTube utilizando Redes Complexas
Authors: Santos Phelipe Rodovalho dos , Pereira Fabíola S. F.
Published: October 2024

Como comparar dois canais do YouTube? Determinar usuários semelhantes numa rede social é uma tarefa cada vez mais importante, pois permite a personalização da oferta e consumo de conteúdos, com aplicação nas áreas de marketing e inteligência de negócio, por exemplo. A maioria dos métodos existentes na literatura para identificar similaridade entre usuários baseia-se apenas no conteúdo publicado e consumido. Este trabalho propõe um método que utiliza redes complexas para comparar canais do YouTube, implementado em uma ferramenta automatizada. Os resultados obtidos mostram que é possível identificar similaridade com base na capacidade de influência e na interligação entre canais.
Processing Heterogeneous Graphs within Heterogeneous Data Type Embeddings to Enhance Recommender Systems
Authors: Angonese Silvio Fernando , Galante Renata
Published: October 2024

Embeddings represent a viable solution to address the challenge of data and information generation in heterogeneous graphs. This research presents our approach for generating and processing heterogeneous embeddings (AGHE), which are built from various data types such as text, images, and subgraphs embedded in nodes. AGHE comprises several stages, from graph creation to the generation of embedding compositions based on node features and metapaths. In the conducted experiments, simple and embedding compositions were used as input data for the Node Classification task in Recommender Systems, investigating effectiveness metrics. The outcomes achieved in our experiments are encouraging, demonstrating superior results compared to the baseline used.
Uso de Business Intelligence para mensuração de acidentes rodoviários através da base de dados da Polícia Rodoviária Federal
Authors: Carnaúba Lucca , Medeiros Mayson , Camilo Edison , Cardoso Jailton , Medeiros Flávio
Published: October 2024

Este estudo analisa acidentes nas rodovias federais brasileiras com dados do DPRF (2019-2023) com o objetivo de responder a quatro questões de pesquisa. A metodologia utilizada foi a pesquisa exploratória e quantitativa utilizando técnicas de Business Intelligence. Os resultados indicam que as condições climáticas têm em pouca influência na ocorrência de acidentes de trânsito. No entanto, observou-se um aumento significativo no número de acidentes durante os finais de semana e após às 17 horas.
Análise de similaridades e padrões estatísticos em séries temporais de casos de suicídio no Brasil
Authors: Klann Victor William , Sousa Elaine Parros Machado de
Published: October 2024

Suicide is a serious global public health problem and is among the leading causes of death in the world. In Brazil, suicide rates have seen alarming increases and continue to rise. Some public policies have been implemented to prevent the rise in suicide cases. Despite this, studies in the field indicate that such measures are not enough to cause a reduction in cases. The collection and analysis of suicide data are important for creating and reevaluating monitoring and prevention processes for suicide attempts. The goal of this master’s project is to contribute to the recognition of temporal patterns of suicide cases in Brazil from suicide deaths data published by DATASUS, using a time series-based approach. The main steps of this work are the collection of data and modeling of time series, provision of a pre-processed database containing daily suicide cases data from Brazil, exploratory analysis of the time series, similarity search between time series and application of clustering; mining of time series subsequence patterns, integration of multimodal data, and finally, the evaluation of the results obtained. It is expected to identify inherent temporal patterns in the time series to assist in understanding the behavior of suicide cases in the states and regions of Brazil.
Uma Abordagem Multidisciplinar para Detecção de Irregularidades em Compras Públicas
Authors: Costa Lucas G. L. , Dutra Marco Túlio , Oliveira Gabriel P. , Silva Mariana O. , Pappa Gisele P.
Published: October 2024

No Brasil, dados abertos governamentais têm se tornado uma ferramenta fundamental na transparência e na fiscalização das compras públicas. Nesse contexto, este trabalho propõe uma abordagem multidisciplinar para a detecção de irregularidades em compras públicas através da modelagem de trilhas de auditagem. Foram definidas três trilhas para auditar diferentes tipos de processos de compra: dispensas de licitação, cartas convite e inexigibilidades. No geral, os resultados demonstram que metodologia proposta mostrou-se eficaz na identificação e priorização de alertas de irregularidades, oferecendo uma análise inicial que facilita a triagem de grandes volumes de dados.
A Review and Analysis of Recommendation Systems in Collaboration Networks
Authors: Moreira Lara S. , Basso Fábio P. , Lunardi Gabriel M. , Sá Guilherme B.
Published: October 2024

Recommendation systems are widely used to provide personalized suggestions across various domains. In scientific collaboration networks, these systems help identify potential research collaborators by analyzing network data and researcher attributes. This study aims summarize the review findings, and analyze published research on recommendation systems used in scientific collaboration networks. The study provides a comprehensive understanding of the use of recommendation systems in scientific collaboration networks, highlighting patterns, trends, limitations, and research gaps in this field.
Extracting Information from Brazilian Legal Documents with Retrieval Augmented Generation
Authors: Aquino Isabella V. de , M. dos Santos Matheus , Dorneles Carina F. , T. Carvalho Jônata
Published: October 2024

Extracting information from unstructured data is a challenge that has drawn increasing attention over time due to the exponential growth of stored digital data in modern society. Large Language Models (LLMs) have emerged as powerful tools that benefit from this abundance and have shown remarkable capabilities in Natural Language Processing tasks. Nonetheless, these models still encounter limitations on extraction tasks. Retrieval Augmented Generation (RAG) is a novel approach that combines classic retrieval techniques and LLMs to address some of these limitations. This paper proposes a workflow that allows the assessment of RAG experimental setups, including the multiple possibilities of parameters and LLMs, to extract structured data from Brazilian legal documents. We validated our proposal with experiments using forty legal documents and the extraction of two target variables. The best results obtained with our workflow showed an average extraction accuracy of 90\%, significantly outperforming a regular expression strategy, with 58.75\% average accuracy. Furthermore, our results show that each extracted variable potentially holds an optimal combination of parameters, highlighting the context-dependency of each extraction and, therefore, the proposed workflow's usefulness.
The Generative AI Revolution in Immersive Technologies
Authors: G. Filho Arlindo R. , Fanucchi Rodrigo Z. , Winkler Ingrid , Siqueira Alexandre Gomes de
Published: October 2024

Generative Artificial Intelligence has revolutionized various aspects of the modern world, ranging from individual users leveraging tools to answer queries to companies developing advanced chatbots. However, it may still be premature to assert that all the potential applications of Generative AI have been fully explored. Among these unexplored possibilities, the integration with immersive technologies, such as virtual reality, augmented reality, and mixed reality, remains a domain with substantial room for development. This paper explores the opportunities related to Generative AI in the context of immersive technologies, as well as the challenges associated with its broader adoption in virtual worlds.
The Hand Guide: A Virtual Reality Application for Surgical Training
Authors: Cota Neto Amadeo Tato , Teixeira João Marcelo , Moizes João Barbosa , Teichrieb Veronica
Published: October 2024

Surgical practices in controlled and mistake-safe environments are traditionally limited by the tutor’s time availability and by the usage of consumable materials to simulate surgeries in patients. In this context, we developed the “Hand Guide”, a virtual reality application that allows expert surgeons to record their hand movements during a simulated surgical procedure and offer it to a surgical student to try to replicate it. The application provides real-time feedback and also a final score at the end of the recorded procedure. Validation with a specialist revealed that the application may be used to teach theoretical content to students with no surgical background and to demonstrate new techniques to experienced surgeons.
Virtual Reality for Medical Training
Authors: Andrade José Raul B. , Araújo Antonio C. V. , Moraes Ronei M. , Machado Liliane S.
Published: October 2024

The goal of this demonstration is to present an immersive virtual reality environment aimed at training healthcare procedures. The environment was developed to be immersive and interactive and can be executed with the Meta Quest device.
A Case Study on FPS Game Customization Based on Asset Replacement for Child Appropriateness
Authors: Mendes Diego Caracas , Rodrigues Edson Coelho , Barbosa Ícaro da Silva , Neto Joaquim Bento Cavalcante , Vidal Creto Augusto , Leite Junior Antonio José Melo , Gomes George Allan Menezes
Published: October 2024

In recent years, with the decreasing costs of Virtual Reality equipment, some first-person shooter (FPS) video games have employed this technology to provide greater immersion for players, but they rarely offer experiences appropriate for children. This work explores the customization of a first-person shooter video game in Virtual Reality by changing its visual and sound assets and the behavior of weapons shots to make it suitable for a younger age group.
Virtual Reality and Eye Tracking in Usability Testing for Industry 4.0
Authors: Leão Felipe , Brito Gabriel de , Miguez Rafael , Winkler Ingrid
Published: October 2024

Virtual reality has emerged as one of the most promising technologies in the context of Industry 4.0. With technological advancements, there is a notable need to modify usability testing in industrial processes to be more practical, safe, and cost-effective, with virtual reality being a key solution. Virtual reality provides a sophisticated solution to these challenges. This work describes the development of a virtual reality-based environment to analyze eye-tracking data in the context of industrial usability testing. To this end, tests were conducted using the PICO 4 Enterprise headset, analyzing user attention in a simulated environment created in Unreal Engine.
Estratégias de Undersampling para Redução de Viés em Classificação de Texto Baseada em Transformers
Authors: Fonseca Guilherme , Prenassi Gabriel , Cunha Washington , Gonçalves Marcos André , Rocha Leonardo
Published: October 2024

Automatic Text Classification (ATC) in unbalanced datasets is a common challenge in real-world applications. In this scenario, one (or more) class(es) is overrepresented, which usually causes a bias in the learning process towards these majority classes. This work investigates the effect of undersampling methods, which aim to reduce instances of the majority class, on the effectiveness of recent ATC methods. Through a systematic mapping of the literature, we selected and implemented 15 undersampling strategies. We also propose two new strategies and compare all 17 methods using RoBERTa as sentiment analysis classifier. Our results suggest that a set of undersampling approaches is capable of significantly reducing the learning bias of ATC methods towards the majority class on imbalanced datasets, without incurring any effectiveness loss, and with improvements in efficiency and reduction of carbon emissions.
Multi-Domain Spatio-Temporal Deformable Fusion model for video quality enhancement
Authors: Silveira Júnior Garibaldi da , Kreisler Gilberto , Zatt Bruno , Palomino Daniel , Correa Guilherme
Published: October 2024

Lossy video compression introduces artifacts that can degrade the perceived visual quality of the video. Improving the quality of compressed videos involves mitigating these artifacts through filtering techniques. Deep neural network (DNN) models have emerged as powerful tools for this task, demonstrating effectiveness in artifact reduction. However, traditional approaches typically evaluate these models using videos compressed by a single coding standard, limiting their applicability across diverse codecs. To address this limitation, this study proposes a novel multi-domain architecture built upon the Spatio-Temporal Deformable Fusion technique. This innovative approach enables the development of models capable of enhancing videos compressed by various codecs, ensuring consistent performance across different standards. Experimental results showcase the efficacy of the proposed method, yielding significant improvements in average Peak Signal-to-Noise Ratio (PSNR) for videos compressed with HEVC, VVC, VP9, and AV1, with enhancements of 0.764 dB, 0.448 dB, 0.736 dB, and 0.228 dB, respectively. The code of our MD-STDF approach is available at https://github.com/Espeto/md-stdf
Elderly Fall Monitoring in Smart Homes Using Wearable Device
Authors: Moreira Júlia M. P. , Bettio Raphael W. , Freire André P. , Santos Luciano M. , Pereira Marluce R.
Published: October 2024

The constant progress of technology, especially in the area of health, brings numerous benefits, one of which is the increase in human life expectancy. However, problems that occur recurrently among the elderly age group are now on the radar of studies that also seek to improve the quality of life of these people. The number of cases of falls among elderly people is worrying, even more so as this is a portion of the population that tends to live alone. In the context of smart homes, several solutions have emerged for monitoring elderly people to increase safety and provide faster assistance, if necessary. One of these solutions is the use of wearable devices responsible for identifying the person’s movements. This work presents the study and development of a wearable device capable of detecting falls and, if they occur, automatically notifying the necessary people through alert messages via the Telegram application so that they can help the person who has suffered a fall. In this work, a Wi-Fi network, MQTT protocol, accelerometer and gyroscope inertial sensors and an ESP32 board programmed using the Arduino IDE were used. Preliminary tests indicated good performance in recognizing falls, based on tilt angle analysis, gyroscope readings and accelerometer readings. The proof of concept and preliminary tests carried out demonstrate the potential for using low-cost technologies for wearable resources for application in smart homes and monitoring the health of elderly people.
Uma Investigação sobre Técnicas de Data Augmentation Aplicadas a Tradução Automática Português-LIBRAS
Authors: Silva Marcos André Bezerra da , Lima Manuella Aschoff C. B. , Silva Diego Ramon Bezerra da , Souza Daniel Faustino L. de , Costa Rostand Edson O. , Araújo Tiago Maritan U. de
Published: October 2024

The automatic translation from Portuguese to LIBRAS is extremely important for accessibility and inclusion of deaf individuals in society, but the scarcity of data and the high cost of building an authentic corpora pose significant challenges. Data Augmentation in Neural Machine Translation is the process of generating synthetic sentences to increase the quantity and diversity of the training set. This work investigates the use of data augmentation techniques to improve the performance of Portuguese-LIBRAS automatic translation using the BLEU metric. Among the techniques analyzed, back-translation and its combination with synonym substitution using part-of-speech tagging stood out as the most effective in enhancing the translation model and can be used to increase the diversity of underrepresented datasets.
Cuidado Ubíquo de Pacientes com Doenças Crônicas Através de um Modelo de Análise do Comportamento Humano
Authors: Dias Lucas Pfeiffer Salomão , Barbosa Jorge Luis Victória
Published: October 2024

Chronic diseases are among 7 out of the 10 leading causes of death worldwide. The main chronic diseases are heart disease, cancer, chronic respiratory diseases, and diabetes. Heart disease alone causes 9 million deaths a year. Lifestyle changes can prevent many chronic diseases’ deaths and their risk factors. In addition, machine learning and wearable devices have been used for behavior analysis. Therefore, this research proposes B-Track, a computational model for assistance in chronic diseases care through the analysis of behaviors that attenuate or worsen the risk factors associated with chronic diseases, working with user behavior profiles and recommendations for healthier behaviors. The B-Track collects data from different data sources for current and future human behavior analysis through the usage of data fusion and machine learning models. These data comprise the patients’ context histories, which include sensor data and data from self-management surveys. The scientific contribution of B-Track model is the analysis of human behaviors directly associated with risk factors and their susceptibility to the development of NCDs. The model was evaluated through a prototype, which was used within 10 patients during your treatment. Three patients achieved changes in some behaviors over an extended period. Overall, according to the TAM Model evaluation, 83% of users agreed that B-Track was useful, and 80% found it easy to use.
A Domain-Specific Language for Multimedia Service Function Chains based on Virtualization of Sensors
Authors: Quico Franklin Jordan Ventura , Battisti Anselmo L. E. , Muchaluat-Saade Débora , Delicato Flavia C.
Published: October 2024

Language is a dynamic aspect of our culture that changes when expressed in different technologies and/or communities. On the Internet, social networks have enabled the diffusion and evolution of different dialects, including African American English (AAE). However, this increased usage of different dialects is not without barriers. One particular barrier, the focus of this paper, is on how sentiment (Vader, TextBlob, and Flair) and toxicity (Google’s Perspective and models from the open-source Detoxify) scoring methods present biases towards utterances with AAE expressions. In particular, AI tools cannot understand the re-appropriation of the terms, leading to false positive scores and biases. Here, we study the bias of Toxicity and Sentiment Analysis models based on experiments performed on Web-and spoken English datasets.
Exploring Visual and Multimodal Interaction in NCL Authoring
Authors: Borges Paulo Victor , Moraes Daniel de S. , dos Santos Joel , Muchaluat-Saade Débora C. , Colcher Sérgio
Published: October 2024

This paper introduces two innovative tools for enhancing interactive multimedia authoring using the Nested Context Language (NCL): (i) a visual extension that supports more traditional interactions with mouse and keyboard and (ii) a multimodal extension that incorporates gesture recognition and voice commands. These tools were implemented as Visual Studio Code extensions and aim to streamline the editing process, making it more intuitive and accessible. We present an evaluation of the usability and acceptance of both tools with developers in an experiment with three tasks for creating and manipulating spatial regions in hypermedia documents. By exploring the potential of multimodal interfaces, this work sets the stage for more efficient and user-friendly document editing.
Providing Interoperability between Wearable Devices and FHIR-based Healthcare Systems
Authors: Cabral Arthur T. , Fischer Gabriel Souto , Righi Rodrigo da Rosa , Costa Cristiano A. da , Roehrs Alex , Rigo Sandro José , Mello Blanda
Published: October 2024

The manufacturers of IoT-based wearable devices present different ways to deliver the captured vital signs. Healthcare third-party systems need to adapt to the different approaches, rising complexity and acts against any idea of seamless integration. In addition to the interoperability problem, we observed that the state-of-the-art does not present alternatives that capture and analyze vital signs in such a way that they can be used to diagnose the user’s health status automatically. This article presents the HealthTranslator model, which provides interoperability by integrating multiple wearable devices and delivering their data through an HL7 FHIR healthcare standard format. More precisely, our contribution combines different user-oriented periodicity strategies to collect vital signs with data enrichment over an FHIR file, effortlessly delivering valuable insights regarding the user’s health status. We developed a microservices-based prototype that collects data on different APIs from different companies/wearables. The resources were encouraging, where data from Xiaomi, Apple, Garmin, and Samsung devices were read and delivered in an FHIR format. Thinking at the intelligent cities level, the provided interoperability is essential to generate the new vision of healthcare, where users and patients will be 24/7 monitored from their own homes.
Um Estudo Aprofundado sobre Grupos Semânticos de Palavras - CluWords - em tarefas de PLN
Authors: Viegas Felipe , Rocha Leonardo , Gonçalves Marcos André
Published: October 2024

This Ph.D. dissertation focused on proposing, designing and evaluating a novel textual document representation that exploits the “best of two worlds”: efficient and effective frequentist information (TFIDF representations) with semantic information derived from word embedding representations. In more details, our proposal – called CluWords – groups syntactically and semantically related words into clusters and applies domain-specific and applicationoriented filtering and weighting schemes over them to build powerful document representations especially tuned for the task in hand. We apply our novel Cluword concept to four Natural Language Processing (NLP) applications, related to topics from WebMedia interest: topic modeling, hierarchical topic modeling, sentiment lexicon building, and sentiment analysis. Some of the novel contributions of this dissertation include: (i) the introduction of a new data representation; (ii) the design of CluWords’ components capable of improving the effectiveness of Topic Modeling, Hierarchical Topic Modeling and Sentiment Analysis applications; (iii) the proposal of two new topic quality metrics to assess the topical quality of the hierarchical structures. Our extensive experimentation demonstrates that CluWords produce the current state-of-the-art topic modeling and hierarchical topic modeling. For sentiment analysis, our experiments show that CluWords filtering and weighting can mitigate semantic noise, surpassing powerful Transformer architectures in the task. Our results were published in some of the most important conferences in journals of the field, as detailed in this document. Our work was supported by two Google Research Awards.