A face das emoções na pandemia: um estudo exploratório com recurso ao F-M FACS 3.0

O reconhecimento das emoções é um aspeto de alta na relevância na comunicação entre indivíduos, tem uma elevada importância na vida social de qualquer pessoa visto que é quase impossível passarmos um dia sem comunicar com ninguém (Hybiner & Azevedo, 2021). As vantagens de um bom reconhecimento de emoções são imensas, tanto para melhorar o fator social como para nos conhecermos melhor para saber como reagir a certas circunstâncias. A pandemia afetou significativamente a forma como comunicamos, este estudo tem como finalidade verificar se a máscara impede a comunicação emocional, e se sim se é possível quantificar. Verificar se o género feminino, apesar de a literatura confirmar que apresenta vantagens emocionais em relação ao género masculino, consegue percecionar as emoções de uma forma mais correta que o género masculino. Como também verificar que emoções são mais afetadas com a máscara cirúrgica a esconder a face inferior. Para a realização deste estudo foi necessário recolher uma base de dados, na qual estavam representadas as 8 emoções básicas universais (alegria, tristeza, surpresa, medo, raiva, aversão, desprezo e dor). Depois da recolha da base de dados e autorizada pelo FEELab, foi necessária a criação de um questionário para recolher as respostas através do publico geral. Os participantes tiveram que olhar para um total de 48 questões em formato de segmentos estáticos (fotografias com uma emoção representada), 24 delas sem máscara e as outras 24 com máscara. Apenas havia uma resposta correta associada à emoção representada. Com a recolha de respostas realizada foi necessário analisar os resultados, o software utilizado para esta análise foi o “SPSS Statistics” (versão 27.0) Os resultados obtidos vão de encontro com a literatura, o sexo feminino teve um Índice Percentual de Acertos cerca de 4% superior ao do sexo masculino. Existem emoções que foram mais afetadas do que outras, mas não foi o caso da raiva, a qual apresentou um aumento no reconhecimento de cerca de 40% com a máscara cirúrgica a esconder a face inferior. A máscara a afetou o Índice Percentual de Acerto em cerca de 9%. A máscara provoca ruído comunicacional, e algumas emoções são mais afetadas do que outras, mas de uma forma geral não se apresenta como fator impeditivo para a comunicação social e emocional. Este presente estudo vem servir de rampa de lançamento para outros projetos relacionados com a temática emoções e pandemia.Emotion recognition is a highly relevant aspect in the communication between individuals, and has a high importance in the social life of any person, since it is almost impossible to spend a day without communicating with anyone (Hybiner & Azevedo, 2021). The advantages of a good emotion recognition are immense, both to improve the social factor and to know ourselves better so we know how to react in certain circumstances. The pandemic has significantly affected the way we communicate, this study aims to verify if the mask hinders emotional communication, and if so if it can be quantified. To verify if the female gender, despite the literature confirming that it has emotional advantages over the male gender, can perceive emotions more correctly than the male gender. As well as verify which emotions are more affected with the surgical mask hiding the lower face. To conduct this study, it was necessary to collect a database, in which were represented the 8 universal basic emotions (happiness, sadness, surprise, fear, anger, disgust, contempt and pain). After the database was collected and authorized by FEELab/UFP, a questionnaire had to be created to collect responses from the general public. The participants had to look at a total of 48 questions in static segment format (pictures with an emotion represented), 24 of them unmasked and the other 24 masked. There was only one correct answer associated with the represented emotion. With the answers collected it was necessary to analyze the results, the software used for this analysis was the "SPSS Statistics" (version 27.0) The results obtained are in agreement with the literature, the female gender had a Percent Correctness Index about 4% higher than the male gender. There are emotions that were more affected than others, but this was not the case for anger, which showed an increase in recognition of about 40% with the surgical mask hiding the lower face. The mask affected the Percent Correctness Index by about 9%. The mask causes communicational noise, and some emotions are more affected than others, but in general it does not present itself as an impeding factor for social and emotional communication. This study will serve as a launching pad for other projects related to the subject of emotions and the pandemic

CARACTERIZAÇÃO AMBIENTAL DA UNIDADE DE PLANEJAMENTO E GERENCIAMENTO DO RIO AMAMBAÍ

O objetivo  é caracterizar o meio físico da Unidade de Planejamento e Gestão do rio Amambaí e analisar a cobertura vegetal  e uso da terra do ano de 2007. Foram utilizados dados secundários e efetuado mapeamento da cobertura vegetal e uso da terra. O meio físico é  diversificado. A vegetação natural ocorre em apenas 19% da UPG. O antropismo em 80% da região, com pastagem plantada e agricultura anual ocupado 91% desta área. O desmatamento está além do permitido pela legislação atual, portanto sugere-se que o Governo adote medidas para reverter o atual quadro de degradação ambiental na região

GEOTECNOLOGIAS APLICADAS NA CARACTERIZAÇÃO E DIAGNÓSTICO DA PAISAGEM DA UPG DO RIO APORÉ, MS

Este trabalho objetivou realizar a caracterização e o diagnóstico ambiental da paisagem na Unidade de Planejamento e Gerenciamento Aporé/MS, na perspectiva de contribuir com informações que subsidiem o planejamento ambiental e desenvolvimento territorial

Assessment of honey bee cells using deep learning

Temporal assessment of honey bee colony strength is required for different applications in many research projects. This task often requires counting the number of cells with brood and food reserves multiple times a year from images taken in the apiary. There are thousands of cells in each frame, which makes manual counting a time-consuming and tedious activity. Thus, the assessment of frames has been frequently been performed in the apiary in an approximate way by using methods such as the Liebefeld. The automation of this process using modern imaging processing techniques represents a major advance. The objective of this work was to develop a software capable of extracting each cell from frame images, classify its content and display the results to the researcher in a simple way. The cells’ contents display a high variation of patterns which added to light variation make their classification by software a challenging endeavor. To address this challenge, we used Deep Neural Networks (DNNs) for image processing. DNNs are known by achieving the state-of-art in many fields of study including image classification, because they can learn features that best describe the content being classified, such as the interior of frame cells. Our DNN model was trained with over 60,000 manually labeled images whose cells were classified into seven classes: egg, larvae, capped larvae, honey, nectar, pollen, and empty. Our contribution is an end-to-end software capable of doing automatic background removal, cell detection, and classification of its content based on an input image. With this software the researcher is able to achieve an average accuracy of 94% over all classes and get better results compared with approximation methods and previous techniques that used handmade features like color and texture.This research was funded through the 2013-2014 BiodivERsA/FACCE-JPJ joint call for research proposals,witht he national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

DYNAMICS AND ENVIRONMENTAL STATE OF VEGETABLE COVERAGE AND LAND USE IN LANDSCAPE REGIONS OF THE SOUTHWESTERN PORTION OF THE BRAZILIAN STATE OF MATO GROSSO

The objective of this article is to investigate the space-time dynamics of vegetation cover and land use and the Environmental State of the landscape regions of the southwestern portion of the Brazilian state of Mato Grosso.The vegetation cover and land use maps were generated from the Landsat 5 satellite images from 1984, and Landsat 8 from 2013 in the SPRING software. Map quantifications and layouts were elaborated withArcGis. The regionalization and analysis of the environmental state of the landscape were made through a regional geoecological matrix. From the results obtained, it was verified that the anthropic uses in the period of study were expanded by 134.08% while the vegetal coverings were reduced by 21.66% and the water bodies by 39%. Pasture is the predominant land use in the region, 24.09% (31,335.86 km²), mainly occupying the flat and smooth wavy terrain. Forest cover totaled 66.36% (84,967.12 km2), being found mainly in forest fragments, in which the larger territorial dimensions are either protected by environmental legislation or located in indigenous lands. Eight landscape regions were delimited in the southwest portion of Mato Grosso, including the Paraguay River Depression, which presents the landscape with the highest percentage of anthropic uses, predominantly the Degraded Environmental State. It was concluded that there is a need to adopt land use practices that minimize the environmental degradation of landscape regions, considering that during the period under investigation, the expansion of anthropic uses, mainly Livestock, directly influenced the suppression of vegetation cover

Integrating factors associated with complex wound healing into a mobile application: findings from a cohort study

Complex, chronic or hard-to-heal wounds are a prevalent health problem worldwide, with significant physical, psychological and social consequences. This study aims to identify factors associated with the healing process of these wounds and develop a mobile application for wound care that incorporates these factors. A prospective multicentre cohort study was conducted in nine health units in Portugal, involving data collection through a mobile application by nurses from April to October 2022. The study followed 46 patients with 57 wounds for up to 5 weeks, conducting six evaluations. Healing time was the main outcome measure, analysed using the Mann–Whitney test and three Cox regression models to calculate risk ratios. The study sample comprised various wound types, with pressure ulcers being the most common (61.4%), followed by venous leg ulcers (17.5%) and diabetic foot ulcers (8.8%). Factors that were found to impair the wound healing process included chronic kidney disease (U = 13.50; p = 0.046), obesity (U = 18.0; p = 0.021), non-adherence to treatment (U = 1.0; p = 0.029) and interference of the wound with daily routines (U = 11.0; p = 0.028). Risk factors for delayed healing over time were identified as bone involvement (RR 3.91; p < 0.001), presence of odour (RR 3.36; p = 0.007), presence of neuropathy (RR 2.49; p = 0.002), use of anti-inflammatory drugs (RR 2.45; p = 0.011), stalled wound (RR 2.26; p = 0.022), greater width (RR 2.03; p = 0.002), greater depth (RR 1.72; p = 0.036) and a high score on the healing scale (RR 1.21; p = 0.001). Integrating the identified risk factors for delayed healing into the assessment of patients and incorporating them into a mobile application can enhance decision-making in wound care.info:eu-repo/semantics/publishedVersio

Automatic detection and classification of honey bee comb cells using deep learning

In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).This research was developed in the framework of the project “BeeHope - Honeybee conservation centers in Western Europe: an innovative strategy using sustainable beekeeping to reduce honeybee decline”, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

ESTUDO DE CASO DA CONSTRUÇÃO EM ALVENARIA ESTRUTURAL DE UM PRÉDIO DE 18 PAVIMENTOS EM ANÁPOLIS-GOIÁS

O artigo objetiva mostrar o estado da arte da construção em alvenaria estrutural e analisar, utilizando o método de sobrepor para compatibilização dos projetos e fichas de verificação de serviço, se as ações tomadas por uma construtora da cidade de Anápolis, Goiás, seguem as boas práticas recomendadas pelas literaturas sobre alvenaria estrutural. De início o estudo foi realizado através de uma perspectiva qualitativa, identificando por meio de levantamentos bibliográficos os principais aspectos necessários para garantir qualidade na elaboração de projetos e na execução de edifícios em alvenaria estrutural. Posteriormente, de maneira quantitativa foi realizado um estudo de caso para verificar o grau de atendimento de uma construtora às boas práticas relacionadas à execução e controle de obra de um edifício de 18 pavimentos construído em alvenaria estrutural. A coleta de dados foi feita por meio de entrevistas, registros fotográficos e observação direta dos procedimentos de controle e serviço da obra investigada. Em escritório, os dados obtidos foram organizados e analisados. Nesse sentido, obteve-se que a empresa seguiu 78% dos procedimentos de execução e 100% dos procedimentos de controle de obras recomendados pela literatura. De forma geral, conclui-se que as potencialidades da alvenaria estrutural não foram totalmente alcançadas pelas normas e pela construtora. Palavras-chave: Estudo de caso. Alvenaria estrutural. Edifícios. Boas práticas

Conhecimento, Responsabilidade, Visão e Futuro: bases programáticas da Lista F para o 3.º mandato do Conselho Geral da Universidade de Évora 2016-20

O presente documento inclui o programa de acção e os elementos de campanha da Lista F, candidata ao 3.º mandato do Conselho Geral da Universidade de Évora (2016-20)