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Emotional reactions impact on people's health and quality of life. With the advent of neuroscience in the 1990s, much research has focused on the relationship between neural circuits and emotional reactions. In this sense, we have identified a 2031% growth in research linking emotions and health between 1990 and 2022. Using the Text mining tool called "Biotext Tools", we performed a literature search, focusing on diseases of old age and covid-19. Emotion molecules (EMs), produced by the endocrine system, impact the homeostasis of the body, affecting the biological balance of the body. We related the EMs with diseases of the elderly and Covid-19, in order to analyze the impact of emotional states on the development and treatment of these pathologies. To this end, the bidirectional relationship between anxiety, depression, heart disease, stroke, and Alzheimer's and Parkinson's diseases was analyzed. Finally, Coronavirus severity and mortality levels were related to cortisol, testosterone, and serotonin levels.

Este trabalho apresenta um estudo de caso sobre a utilização da Metodologia de Gerenciamento de Incidentes, e sua aplicação na Agência de Tecnologia da Informação e Comunicação (AGTIC) da Universidade Federal do Paraná. Para isto, é analisado o sistema de chamados de uma Instituição de Ensino Superior, onde foi investigado se este está seguindo as boas práticas de governança em Tecnologia da Informação (TI). Estas são estabelecidas no Gerenciamento de Incidentes, presentes no ITIL V3 (Information Technology Infrastructure Library).

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a `Gesture' or `Non-Gesture'. The ILSP counterparts of the frames labeled as "Gesture" are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.

In this paper we extend the Shared Biological Image Manager (SBIM) system by incorporating a content-based image retrieval (CBIR) technique using machine learning concepts. For the classification it was applied a multiclass support vector machine (SVM) schema. Given a query image, the SVM approach performs its classification and the Euclidean distance is used to compute the similarity between the query and the images in the same class. The computational experiments show satisfactory classification accuracy and suitable retrieval results. Besides, the new SBIM was validated using integration tests which show that the methods implemented attend the proposed functionalities.

Panoramic x-ray images are very popular as a first tool for diagnosis in odontological protocols. Automating the process of analysis of such images is important in order to help dentist procedures. In this process, teeth segmentation of the radiographic images is an essential step. In this paper, we propose a segmentation approach based on a supervised learning technique for texture recognition. Firstly, feature extraction is performed by computing moments and statistical features. The obtained data are the input to a Bayesian classifier that, after training, can distinguish two classes of pixels: active (inside the target texture) or inactive (outside the teeth). In the experimental results we show that the methodology is a promising one for teeth segmentation in panoramic x-ray images and discuss its limitations.

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsu’s thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.

With the Internet evolution, there has been a huge increase in the amount of images stored in electronic format particularly in the case of biological and medical image applications. Nowadays, hospitals and research centers can acquire large image databases which poses fundamental requirements for storage, processing and sharing data. In order to fulfill these requirements we have proposed the Shared Biological Image Manager (SBIM) system which has been developed using the programming languages PHP and Javascript as well as the Database Management System PostgreSQL. In this chapter, we propose an extension of the SBIM functionalities by incorporating data mining and image retrieval facilities. We describe an unified solution for both these services inside the Shared Biological Image Manager (SBIM) through Support Vector Machine (SVM) frameworks. Data mining is implemented using discriminant weights given by SVM separating hyperplanes to select the most discriminant features for two-class classification problems. For image retrieval, we consider an SVM ensemble based on the “one-against-all” SVM multi-class approach. The user specifies an initial feature space, the training set and the SVM (ensemble) configuration. After training, the SVM ensemble can be used to retrieve relevant data once given a query image. Finally, we discuss some details about the implementation of the content-based image retrieval (CBIR) and discriminant features discovery approaches inside the SBIM system.

Nowadays applications that use face images as input for people identification have been very common. In general, the input image must be preprocessed in order to fit some normalization and quality criteria. In this paper, we propose a computational framework composed of digital image quality computation, segmentation of the damaged regions on face images by statistical decision, morphological operators and image restoration by inpainting techniques. Additionally, in this work we propose a new method for digital inpainting that considers as relevant information for such restoration the neighboring pixel intensities as well as prior information extracted from an image database. To assess the efficiency of the computational framework proposed, we have used 2D face images from public databases. The results show that our inpainting method performs similarly to the traditional ones on quasi-homogeneous regions and gives better results when the damaged areas includes complex image patterns.

Este artigo trata a necessidade da implantação de um Sistema de Gestão da Manutenção para as micro e pequenas empresas. A manutenção de equipamentos, quando bem programada e bem realizada, é algo que gera bons resultados para as empresas. Para tanto se faz necessáriocompreender plenamente os conceitos de manutenção preventiva, corretiva e preditiva. Para que estas possam ser realizadas de acordo, é necessário que as mesmas sejam elaboradas e administradas pela gerência administrativa que tem o real poder de intervir em uma possível mudança no cronograma de produção fabril. A manutenção realizada de acordo com as atividades estabelecidas gera uma maior disponibilidade dos equipamentos, e em conseqüência tem-se um aumento no nível de produção e diminuição do custo unitário. O sistema proposto permite organizar as informações referentes à manutenção de equipamentose com isso é capaz de sugerir uma gestão de manutenção de equipamentos. A escolha para a utilização de um sistema baseado na web é devido ao fato de a mesma permitir que oconteúdo esteja disponível para qualquer pessoa que utilize a internet como meio de busca e aprendizado, e que não se restrinja as micros e pequenas empresas.