Distúrbios psíquicos menores em trabalhadores de enfermagem de hospitais referência no atendimento à Covid-19

Introdução: a pandemia ocasionada pela Covid-19 tem proporcionado ambientes de trabalho estressantes e cansativos para os profissionais de enfermagem, tal situação pode expor-lhes ao desenvolvimento de Distúrbios Psíquicos Menores (DPM). Objetivo: analisar os fatores associados à presença de DPM entre trabalhadores de enfermagem que atuam na área hospitalar durante a na pandemia da Covid-19. Método: trata-se de um estudo multicêntrico, transversal, descritivo e analítico com abordagem quantitativa. Fizeram parte do estudo quatro hospitais que atendem pacientes acometidos pela Covid-19 no estado do Rio Grande do Sul. A amostra foi constituída de 845 trabalhadores de enfermagem de uma população de 6.899 (com nível de confiança de 96%). O formulário do Google Forms foi constituído por questionamentos acerca de dados sociodemográficos, laborais, hábitos de vida e o instrumento Self-Reporting Questionnaire (SRQ-20) para rastrear DPM. Os dados foram analisados pelo programa SPSS. Aplicou-se estatística descritiva, testes de Mann-Whitney e Qui-quadrado para associações entre as variáveis. Na análise multivariada, a força da associação foi analisada por meio do Modelo de Regressão de Poisson e expressa na Razão de Prevalência (IC 95%). Foram consideradas como diferenças estatisticamente significativas os dados com “p” bicaudal menor que 0,05. O projeto foi aprovado pelo ao Comitê de Ética em Pesquisa sob o parecer n° 4.152.027. Resultados: 84,9% dos trabalhadores de enfermagem pertenceram ao sexo feminino, com mediana de idade de 41 (36-48) anos. A prevalência de DPM foi de 49,3% associada ao aumento do consumo de álcool, a não prática de atividade física, ao início de medicação na pandemia, a não ter turno fixo de trabalho e ao medo sentido frente à exposição ao risco de contaminação (p<0,05). Conclusão: Detectou-se elevada prevalência de DPM, associada a hábitos laborais e de vida. É necessária a implantação de estratégias institucionais e políticas públicas com vistas a promover a saúde psíquica dos trabalhadores de enfermagem

Perceived stressors and coping mechanisms of female migrant domestic workers in Singapore.

INTRODUCTION: Worldwide, there are between 50-67 million migrant domestic workers, the majority of whom are women. In many countries, provisions are not in place to protect female migrant domestic workers. These women may be at risk of occupational and social stressors, including exploitation and abuse, which may negatively impact on their quality of life, including psychological health. Research examining the occupational, social, and psychological needs of FMDWs from a public health perspective is critical to guide the development of policies which ensure wellbeing, prevent abuse, and align with international priorities to improve population health. Though there have been a number of high-profile incidents of exploitation and abuse, there has been limited research on the stressors experienced by these communities, their perceived impact, or coping mechanisms. MATERIALS AND METHODS: Thematic analysis was used to analyse qualitative free-text written responses collected as part of a cross-sectional survey on the relationship between social and occupational stressors and the health and quality of life of FMDWs in Singapore. Responses correspond to open-ended questions in the qualitative component of the survey examining three domains: causes of stress, coping strategies, and what people can do to help with stress. RESULTS: Responses from 182 FMDWs were analysed. Key themes were identified around causes of stress (including 'work and agency', 'the pervasiveness of financial need', and 'family and obligation'), coping strategies, and social support. Each theme describes key factors which contribute to the occupational and social stressors experienced and reported by FMDWs. DISCUSSION: This research highlights the stressors FMDWs in Singapore experience, as well as key coping mechanisms. There is a clear need for policies which facilitate FMDWs' ability to utilise these coping resources, and which protect against coercive or exploitative employment conditions. Strategies are also needed to monitor and evaluate policies intended to protect FMDWs, and to strengthen the implementation of global frameworks targeted at improving workplace conditions and workers' rights

Inteligência artificial e sistemas de irrigação por pivô central : desenvolvimento de estratégias e técnicas para o aprimoramento do mapeamento automático

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.A irrigação é o principal responsável pelo aumento da produtividade dos cultivos. Os sistemas de irrigação por pivô central (SIPC) são líderes em irrigação mecanizada no Brasil, com expressivo crescimento nas últimas décadas e projeção de aumento de mais de 134% de área até 2040. O método mais utilizado para identificação de SIPC é baseado na interpretação visual e mapeamento manual das feições circulares, tornando a tarefa demorada e trabalhosa. Nesse contexto, métodos baseados em Deep Learning (DL) apresentam grande potencial na classificação de imagens de sensoriamento remoto, utilizando Convolutional Neural Networks (CNN’s). O uso de DL provoca uma revolução na classificação de imagens, superando métodos tradicionais e alcançando maior precisão e eficiência, permitindo monitoramento regional e contínuo com baixo custo e agilidade. Essa pesquisa teve como objetivo aplicação de técnicas de DL utilizando algoritmos baseados em CNN’s para identificação de SIPC em imagens de sensoriamento remoto. O presente trabalho foi dividido em três capítulos principais: (a) identificação de SIPC em imagens Landsat-8/OLI, utilizando segmentação semântica com três algoritmos de CNN (U-Net, Deep ResUnet e SharpMask); (b) detecção de SIPC usando segmentação de instâncias de imagens multitemporais Sentinel-1/SAR (duas polarizações, VV e VH) utilizando o algoritmo Mask-RCNN, com o backbone ResNeXt-101-32x8d; e (c) detecção de SIPC utilizando imagens multitemporais Sentinel-2/MSI com diferentes percentuais de nuvens e segmentação de instâncias utilizando Mask-RCNN, com o backbone ResNext-101. As etapas metodológicas foram distintas entre os capítulos e todas apresentaram altos valores de métricas e grande capacidade de detecção de SIPC. As classificações utilizando imagens Landsat-8/OLI, e os algoritmos U-Net, Depp ResUnet e SharpMask tiveram respectivamente 0,96, 0,95 e 0,92 de coeficientes Kappa. As classificações usando imagens Sentinel-1/SAR apresentaram melhores métricas na combinação das duas polarizações VV+VH (75%AP, 91%AP50 e 86%AP75). A classificação de imagens Sentinel-2/MSI com nuvens apresentou métricas no conjunto de 6 imagens sem nuvens (80%AP e 93%AP50) bem próximas aos valores do conjunto de imagens com cenário extremo de nuvens (74%AP e 88%AP50), demonstrando que a utilização de imagens multitemporais, aumenta o poder preditivo no aprendizado. Uma contribuição significativa da pesquisa foi a proposição de reconstrução de imagens de grandes áreas, utilizando o algoritmo de janela deslizante, permitindo várias sobreposições de imagens classificadas e uma melhor estimativa de pivô por pixel. O presente estudo possibilitou o estabelecimento de metodologia adequada para detecção automática de pivô central utilizando três tipos diferentes de imagens de sensoriamento remoto, que estão disponíveis gratuitamente, além de um banco de dados com vetores de SIPC no Brasil Central.Irrigation is primarily responsible for increasing crop productivity. Center pivot irrigation systems (CPIS) are leaders in mechanized irrigation in Brazil, with significant growth in recent decades and a projected increase of more than 134% in area by 2040. The most used method for identifying CPIS is based on the interpretation visual and manual mapping of circular features, making the task time-consuming and laborious. In this context, methods based on Deep Learning (DL) have great potential in the classification of remote sensing images, using Convolutional Neural Networks (CNN's). The use of Deep Learning causes a revolution in image classification, surpassing traditional methods and achieving greater precision and efficiency, allowing regional and continuous monitoring with low cost and agility. This research aimed to apply DL techniques using algorithms based on CNN's to identify CIPS in remote sensing images. The present work was divided into three main chapters: (a) identification of CIPS in Landsat-8/OLI images, using semantic segmentation with three CNN algorithms (UNet, Deep ResUnet and SharpMask); (b) CPIS detection using Sentinel-1/SAR multitemporal image instance segmentation (two polarizations, VV and VH) using the Mask-RCNN algorithm, with the ResNeXt-101-32x8d backbone; and (c) SIPC detection using Sentinel2/MSI multitemporal images with different percentages of clouds and instance segmentation using Mask-RCNN, with the ResNext-101 backbone. The methodological steps were different between the chapters and all presented high metric values and great CPIS detection capacity. The classifications using Landsat-8/OLI images, and the U-Net, Depp ResUnet and SharpMask algorithms had respectively 0.96, 0.95 and 0.92 of Kappa coefficients. Classifications using Sentinel-1/SAR images showed better metrics in the combination of the two VV+VH polarizations (75%AP, 91%AP50 and 86%AP75). The classification of Sentinel-2/MSI images with clouds presented metrics in the set of 6 images without clouds (80%AP and 93%AP50) very close to the values of the set of images with extreme cloud scenario (74%AP and 88%AP50), demonstrating that the use of multitemporal images increases the predictive power in learning. A significant contribution of the research was the proposition of reconstruction of images of large areas, using the sliding window algorithm, allowing several overlaps of classified images and a better estimation of pivot per pixel. The present study made it possible to establish an adequate methodology for automatic center pivot detection using three different types of remote sensing images, which are freely available, in addition to a database with CPIS vectors in Central Brazil

Optimization of KPC impeller using computational fluid dynamics (CFD)

Orientador: José Roberto NunhezDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Um impelidor ideal para suspensões deve prover a suspensão completa ou homogeneização utilizando o mínimo de energia. O fluxo em um tanque agitado depende da geometria do impelidor, do diâmetro e da localização deste, do diâmetro e do fundo do tanque e da geometria de internos. A Fluidodinâmica Computacional (CFD) é uma ferramenta poderosa para predizer fluxos tridimensionais e distribuição de concentração de sólidos produzidos por impelidores de qualquer configuração geométrica. Este estudo pretende aumentar a capacidade de bombeamento de um impelidor de bombeamento axial, chamado KPC, enquanto mantém o baixo consumo de potência. Para avaliar a capacidade de bombeamento do KPC, diferentes ângulos entre a raiz e a ponta das pás foram estudados. O ângulo da raiz foi mantido em 45º, enquanto o ângulo da ponta da pá foi modificado, resultando em diferentes pás. As simulações em CFD foram feitas com água e com água e areia. O modelo de turbulência utilizado foi o Shear Stress Transport (SST) e foi utilizada a técnica de simulação Sliding Grid. O modelo escolhido para as simulações foi validado experimentalmente. O descolamento da camada limite na ponta das pás dos impelidores foi visualizado, e o objetivo principal do trabalho, encontrar o impelidor KPC otimizado, foi atingido. Este impelidor possui 45º na raiz da pá e 10º na ponta da pá. O impelidor otimizado confirmou seu melhor desempenho quando comparado ao impelidor inicial na simulação de uma suspensão de areia e águaAbstract: An ideal impeller for solid suspension should provide complete suspension or homogenization consuming a minimum of energy. The flow in a stirred tank depends on impeller design, diameter and the location of impellers, vessel diameter, bottom design and internals. Computational Fluid Dynamics (CFD) is a powerful tool to predict the three dimensional flow and solids concentration distribution produced by different impellers of any geometric configuration. This study aims to improve the pumping of an axial pumping impeller, named KPC, while maintaining low power consumption. In order to evaluate the pumping capacity of the KPC impeller, differents angles between the root and the tip of the blades have been studied. The angle at the root was maintained at 45º, and the angle at the blade tip has been modified, resulting in different blades. CFD simulations were performed for water and for water and sand. The model used the Shear Stress Transport (SST) turbulence model and the Sliding Grid simulation strategy. The model was validated against experimental data. The impelller blade flow separation was visualized, and the main objective of the work, which was to find the optimized KPC impeller, was reached. This impeller possess 45º in the blade root and 10º in the tip of the blade. The optimized impeller confirmed its better performance when compared to the initial impeller in a simulation of a suspension of sand and waterMestradoDesenvolvimento de Processos QuímicosMestre em Engenharia Químic

Childhood Internalizing and Externalizing Problems Predict the Onset of Clinical Panic Attacks over Adolescence: The TRAILS Study

Background: Panic attacks are a source of individual suffering and are an independent risk factor for later psychopathology. However, much less is known about risk factors for the development of panic attacks, particularly during adolescence when the incidence of panic attacks increases dramatically. We examined whether internalizing and externalizing problems in childhood predict the onset of panic attacks in adolescence. Method: This study is part of the TRacking Adolescents' Individual Lives Survey (TRAILS), a Dutch longitudinal population cohort study (N = 1,584). Internalizing and Externalizing Problems were collected using the Youth Self-Report (YSR) and the parent-report Child Behavior Checklist (CBCL) at baseline (age 10-12). At age 18-20, DSM-IV defined panic attacks since baseline were assessed with the Composite International Diagnostic Interview (CIDI). We investigated whether early adolescent Internalizing and Externalizing Problems predicted panic attacks between ages 10-20 years, using survival analysis in univariate and multivariate models. Results: There were N = 314 (19.8%) cases who experienced at least one DSM-IV defined panic attack during adolescence and N = 18 (1.2%) who developed panic disorder during adolescence. In univariate analyses, CBCL Total Problems, Internalizing Problems and three of the eight syndrome scales predicted panic attack onset, while on the YSR all broad-band problem scales and each narrow-band syndrome scale predicted panic attack onset. In multivariate analyses, CBCL Social Problems (HR 1.19, p<.05), and YSR Thought Problems (HR 1.15, p<.05) and Social Problems (HR 1.26, p<.01) predicted panic attack onset. Conclusion: Risk indicators of panic attack include the wide range of internalizing and externalizing problems. Yet, when adjusted for co-occurring problem behaviors, Social Problems were the most consistent risk factor for panic attack onsets in adolescence

Parenting behaviors that shape child compliance: A multilevel meta-analysis

Background What are the parenting behaviors that shape child compliance? Most research on parent-child interactions relies on correlational research or evaluations of “package deal” interventions that manipulate many aspects of parenting at the same time. Neither approach allows for identifying the specific parenting behaviors that shape child compliance. To overcome this, we systematically reviewed and meta-analyzed available evidence on the effects of experimentally manipulated, discrete parenting behaviors—a niche in parent-child interaction research that contributes unique information on the specific parenting behaviors that shape child behavior. Methods We identified studies by systematically searching databases and through contacting experts. Nineteen studies (75 effect sizes) on four discrete parenting behaviors were included: praise, verbal reprimands, time-out, and ignore. In multilevel models, we tested for each parenting behavior whether it increased child compliance, including both observed and parent-reported measures of child compliance. Results Providing “time-out” for noncompliance robustly increased both observed and parent-reported child compliance (ds = 0.84–1.72; 95% CI 0.30 to 2.54). The same holds for briefly ignoring the child after non-compliance (ds = 0.36–1.77; 95% CI 0.04 to 2.90). When observed and parent-reported outcomes were combined, but not when they were examined separately, verbal reprimands also increased child compliance (d = 0.72; 95% CI 0.26 to 1.19). Praise did not increase child compliance (ds = –0.27–1.19; 95% CI –2.04 to 1.59). Conclusion Our findings suggest that of the discrete parenting behaviors that are experimentally studied in multiple trials, especially time-out and ignore, and to some extent verbal reprimands, shape child compliance

Higher and Lower Order Factor Analyses of the Temperament in Middle Childhood Questionnaire.

The Temperament in Middle Childhood Questionnaire (TMCQ) is a widely used parent-report measure of temperament. However, neither its lower nor higher order structures has been tested via a bottom-up, empirically based approach. We conducted higher and lower order exploratory factor analyses (EFAs) of the TMCQ in a large ( N = 654) sample of 9-year-olds. Item-level EFAs identified 92 items as suitable (i.e., with loadings ≥.40) for constructing lower order factors, only half of which resembled a TMCQ scale posited by the measure\u27s authors. Higher order EFAs of the lower order factors showed that a three-factor structure (Impulsivity/Negative Affectivity, Negative Affectivity, and Openness/Assertiveness) was the only admissible solution. Overall, many TMCQ items did not load well onto a lower order factor. In addition, only three factors, which did not show a clear resemblance to Rothbart\u27s four-factor model of temperament in middle childhood, were needed to account for the higher order structure of the TMCQ