Abstract: This investigation proposes to develop a methodology based on the mean-variance mapping optimization (MVMO) algorithm to reduce the active power losses of the subtransmission system of the Electric Company Quito (EEQ), determining the best operating point of the system, considering the state of taps of the transformers, the capacitor banks and the contribution of reagents from the generation plants that the company currently has. EEQ. The same ones that, when operating in an adequate manner, lead to reducing the value of active power losses in the network, thus improving the operating conditions of the EEQ Electrical Power System.

Resumen: Esta investigación propone desarrollar una metodología basada en el algoritmo de optimización de mapeo media varianza (MVMO) para reducir las pérdidas de potencia activa del sistema de subtransmisión de la Empresa Eléctrica Quito (EEQ), determinando el mejor punto de operación del sistema, considerando el estado de tap’s de los transformadores, los bancos de capacitores y del aporte de reactivos de las centrales de generación con las que cuenta actualmente la EEQ. Los mismos que al operar de una manera adecuada llevan a reducir el valor de las pérdidas de potencia activa en la red, mejorando así las condiciones operativas del Sistema Eléctrico de Potencia de la EEQ.

Resumen El objetivo fue evaluar la Gestión de la Calidad del aire 5 de las 10 ciudades con mayor contaminación del aire en México y que pertenecen al estado de Guanajuato. El método de investigación consistió en medir la información con Inteligencia Artificial orientada con el modelo LART de Gestión ambiental usando el Procesamiento de Lenguaje Natural en las funciones y estrategias para la gestión de la calidad del aire. Se analizó un corpus de 32 enunciados. Como resultado se obtienen una bolsa de 80 palabras y un vocabulario de 82 N-gramas de longitud 1 a 7 para medir la información del proceso de gestión. Los hallazgos revelan que las mejores gestiones están en Celaya, León y Silao de la Victoria. La originalidad del método radica en que la información encontrada por el algoritmo permite validar parcialmente el modelo LART. Se limita a evaluar la gestión y los estudios siguientes se orientarán al desarrollo de un vocabulario más amplio y un corpus mayor para utilizar el modelo W2V que incruste los N-gramas en un modelo n-dimensional.

Abstract The objective was to evaluate Air Quality Management in 5 of the 10 cities with the highest air pollution in Mexico and that belong to the state of Guanajuato. The research method consisted of measuring the information with Artificial Intelligence oriented with the LART model of Environmental Management using Natural Language Processing in the functions and strategies for air quality management. A corpus of 32 statements was analyzed. The result was a bag of 80 words and a vocabulary of 82 N-grams with length 1 to 7 that measure the information of the management process. The findings reveal that the best managements are in Celaya, León and Silao de la Victoria. The originality of the method lies in the fact that the information found by the algorithm allows partial validation of the LART model . It is limited to evaluating the management and subsequent studies will focus on the development of a broader vocabulary and a larger corpus to use the W2V model that embeds the N-grams in a n-dimensional model.

Abstract Currently, there is technology available to develop treatments at variable rates on crops. The difficulty is to properly identify the weed species and thus be able to adjust the doses of herbicide to the species present at each point of the plot. For this reason, the objective of this work is to carry out a field trial to study the potential of the integration of the available technology: remote sensing using UAV and image analysis, design of variable application maps based on IPMWise recommendations and finally, application with a sprayer capable of applying variable rates nozzle by nozzle. The trials were carried out in two corn fields located at NE Spain. A pre-emergence treatment was carried out with different products to force variability and subsequently, a UAV flight at 40m height using an RGB camera and a multi-spectral camera. AgrisoftPhotoscan software was used to align the images and they were processed using an advanced OBIA algorithm. The dose of herbicide to be applied was calculated according to species and density using the IPMwise DSS for a 1x1m2 grid and the generated map was loaded into an AGRIFAC Condor sprayer. Considering a treatment threshold of 1 plant/m2, herbicide savings of more than 38% were achieved. The effectiveness evaluation was 22 days after the treatment and it was found that the efficiencies obtained were in line with those predicted, although in areas with very low density, no weeds were detected within the crop line.

Resumen En la actualidad existe tecnología disponible para realizar tratamientos a dosis variable en los cultivos. La dificultad es identificar adecuadamente las especies de malas hierbas y así poder ajustar las dosis de herbicida a las especies presentes en cada punto de la parcela. Por esto, el objetivo del presente trabajo es realizar un ensayo en campo para estudiar el potencial de la integración de la tecnologia disponible: detección remota utilizado UAV y analisis de imagen, diseño de mapas de aplicación variable basados en las recomendacioens de IPMWise y finalmente, aplicación con un pulverizador capaz de aplicar dosis variable boquilla a boquilla.Los ensayos se llevaron a cabo en dos parcelas de maiz situadas en el NE de España.Se realizó un tratamiento de pre-emergencia con productos diferentes para forzar la variabilidad y posteriormente, un vuelo con UAV a 40m de altura utilizando una camara RGB y otra multi-espectral. Posteriormente se utilizó el software AgrisoftPhotoscan para alinear las imágenes y se procesaron utilizando un algoritmo OBIA avanzado. La dosis de herbicida a aplicar se calculó por especie y densidad utilizando el DSS IPMwise para una cuadrícula de 1x1m2 y el mapa generado se cargó en un pulverizador AGRIFAC Condor. Considerando un umbral de tratamiento de 1planta/m2 se consiguió un ahorro de herbicida superior al 38%. La evaluación de eficacia fue 22 días tras el tratamiento y se constató que las eficacias obtenidas estuvieron en línea con las previstas aunque en zonas con muy baja densidad, no se detectaron malas hierbas en la misma linea del cultivo.

Resumo Mancha-de-Bipolaris é uma das mais importantes doenças foliares do milho. Estratégias de manejo adequadas e o uso de cultivar resistente permitem o controle da doença, mas para tanto, conhecer os aspectos associados ao patógeno e a resposta do hospedeiro é necessário para traçar estratégias eficientes para seleção de genótipos resistentes a essa doença. Neste sentido, objetivou-se realizar a caracterização do isolado de Bipolaris maydis avaliando a patogenicidade em diferentes linhagens de milho-pipoca e os sintomas gerados no hospedeiro a partir da inoculação. A caracterização do isolado consistiu na avaliação macromorfológica das colônias e micromofológica dos conídios em meio nutritivo BDA. Para avaliação da patogenicidade do isolado foi conduzido um experimento em casa de vegetação utilizando 20 linhagens endogâmicas de milho-pipoca, com delineamento em blocos casualizados, com quatro repetições. A inoculação foi realizada por meio de pulverização em folhas, com uma suspensão contendo 1,0 x 104 conídios.ml-1 do isolado CF/UENF 501 de B. maydis. Foi realizada uma avaliação de incidência e três avaliações de severidade dos sintomas da doença, com o intervalo de sete dias para cada avaliação. Os dados da caracterização morfológica do isolado foram analisados por meio da estatística descritiva e para severidade da doença foi aplicado o modelo de regressão linear de primeiro grau. Realizou-se a análise de variância para o coeficiente linear e angular obtido para cada tratamento e quando constatada diferença aplicou-se o agrupamento de médias de Scott-knott a 5% de significância. O isolado apresentou colônias com coloração cinza esverdeado, aspecto algodonoso e forma irregular. As linhagens L353 e L354 e L624 estão entre as linhagens que apresentaram maior resistência no inicio e ao longo das avaliações. A elevada virulência do isolado CF/UENF 501 possibilitou diferenciar as linhagens quanto a intensidade da doença, bem como o padrão dos sintomas apresentados.

Abstract Southern corn leaf blight (SCLB) is one of the most important corn leaf diseases. Appropriate management strategies and the use of resistant cultivars allow disease control. Therefore, knowing the aspects related to the pathogen and the response of hosts makes it possible to design efficient strategies for selecting genotypes resistant to this disease. In this sense, the objective was to carry out the Bipolaris maydis isolate characterization, evaluating the pathogenicity in different popcorn lines and the symptoms generated in the host after inoculation. The isolate characterization consisted of the macromorphological evaluation of the colonies and the micromorphological evaluation of the conidia in the PDA medium. An experiment was carried out in a greenhouse to evaluate the pathogenicity of the isolate, using 20 inbred lines of popcorn in a randomized block design with four replicates. Inoculation was carried out by spraying leaves, with a suspension containing 1.0 x 104 conidia.ml-1 of the CF/UENF 501 isolate of B. maydis. An incidence assessment and three assessments of disease symptom severity were performed, with seven days intervals between evaluations. The morphological characterization data of the isolate were analyzed using descriptive statistics, and for disease severity, the linear regression model was applied the first-degree model. The variance analysis was performed for the linear and angular coefficients obtained for each treatment. When a difference was found, the Scott-Knott clustering algorithm at 5% significance was applied. The isolate had gray-green colonies, a cottony appearance, and an irregular shape. The lines L353, L354, and L624 show more resistance at the beginning and throughout the evaluations. The high virulence of the CF/UENF 501 isolate made it possible to differentiate the lines in terms of disease intensity and the pattern of symptoms presented.

ABSTRACT OBJECTIVE This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location. Paulo denguerelated 201 2019 DATASUS, DATASUS , (DATASUS) IBGE, IBGE (IBGE) WorldClim MapBiomas Warehouse Kmeans K means contexts Then performed variables soil cases characteristics outcomes inhabitant Thus urbanization scale conducted location 20 (DATASUS (IBGE 2

RESUMO OBJETIVO Integrar os conceitos de Saúde Planetária e Big Data ao modelo de Donabedian, para avaliar o Programa de Combate à Dengue no estado de São Paulo. MÉTODOS Foram adotados métodos de Ciência de Dados para integração e análise de dados relacionados à dengue, agregando o contexto aos componentes de estrutura e de resultado do modelo de Donabedian. Esses dados, considerando o período de 2010 a 2019, foram coletados de fontes como Datasus, Instituto Brasileiro de Geografia e Estatística (IBGE), WorldClim e MapBiomas, e integrados em um Data Warehouse. Para a identificação de grupos com contextos similares, foi utilizado o algoritmo K-means. Em seguida, foram realizadas análises estatísticas e visualizações espaciais dos grupos, considerando variáveis socioeconômicas, demográficas, solo, estrutura de saúde e casos de dengue. RESULTADOS Com o uso das variáveis climáticas, o algoritmo K-means identificou quatro grupos de municípios com características similares. A comparação dos seus indicadores revelou certos padrões nos municípios com pior desempenho quanto aos resultados de casos de dengue. Embora tivessem melhores condições econômicas, eles tinham menor número médio de agentes comunitários e de unidades básicas de saúde por habitante. Dessa forma, as condições econômicas não refletiram em melhor estrutura de saúde nos três indicadores avaliados. Outra característica desses municípios é a urbanização. Os municípios de pior desempenho tinham maior taxa de população urbana e de modificações antrópicas relacionadas à urbanização. CONCLUSÕES Por meio desta metodologia, foi possível identificar importantes deficiências nas condições para a execução do programa de combate à dengue no estado de São Paulo. A integração de diversas bases de dados e a utilização de métodos de Ciência de Dados permitiram a avaliação do programa em larga escala, considerando o contexto em que as ações são executadas. Dessa forma, a gestão pública pode utilizar as informações coletadas para planejar ações e investir de acordo com as deficiências de cada local. Donabedian Paulo 201 2019 Datasus IBGE, IBGE , (IBGE) MapBiomas Warehouse similares Kmeans. Kmeans K means. means seguida socioeconômicas demográficas solo climáticas habitante forma avaliados urbanização metodologia escala executadas local 20 (IBGE 2

RESUMO OBJETIVO Integrar os conceitos de Saúde Planetária e Big Data ao modelo de Donabedian, para avaliar o Programa de Combate à Dengue no estado de São Paulo. MÉTODOS Foram adotados métodos de Ciência de Dados para integração e análise de dados relacionados à dengue, agregando o contexto aos componentes de estrutura e de resultado do modelo de Donabedian. Esses dados, considerando o período de 2010 a 2019, foram coletados de fontes como Datasus, Instituto Brasileiro de Geografia e Estatística (IBGE), WorldClim e MapBiomas, e integrados em um Data Warehouse. Para a identificação de grupos com contextos similares, foi utilizado o algoritmo K-means. Em seguida, foram realizadas análises estatísticas e visualizações espaciais dos grupos, considerando variáveis socioeconômicas, demográficas, solo, estrutura de saúde e casos de dengue. RESULTADOS Com o uso das variáveis climáticas, o algoritmo K-means identificou quatro grupos de municípios com características similares. A comparação dos seus indicadores revelou certos padrões nos municípios com pior desempenho quanto aos resultados de casos de dengue. Embora tivessem melhores condições econômicas, eles tinham menor número médio de agentes comunitários e de unidades básicas de saúde por habitante. Dessa forma, as condições econômicas não refletiram em melhor estrutura de saúde nos três indicadores avaliados. Outra característica desses municípios é a urbanização. Os municípios de pior desempenho tinham maior taxa de população urbana e de modificações antrópicas relacionadas à urbanização. CONCLUSÕES Por meio desta metodologia, foi possível identificar importantes deficiências nas condições para a execução do programa de combate à dengue no estado de São Paulo. A integração de diversas bases de dados e a utilização de métodos de Ciência de Dados permitiram a avaliação do programa em larga escala, considerando o contexto em que as ações são executadas. Dessa forma, a gestão pública pode utilizar as informações coletadas para planejar ações e investir de acordo com as deficiências de cada local.

ABSTRACT OBJECTIVE This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.

ABSTRACT This study was conducted to identify the key volatile compounds that characterize whether silage has deteriorated and to investigate the intrinsic link between the key compounds and silage odor. First, silages with different aerobic exposure durations were sampled, and sensory evaluation integrating aerobic stability monitoring was used to distinguish whether the silage had deteriorated. Subsequently, headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) was utilized to determine the relative content of the compounds. Using the relative content of the compounds in each silage as input, relative odor activity value (ROAV) calculations and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to determine the odor contribution of the compounds and the compounds with significant differences in relative content (based on variable importance for the projection, VIP) between the deteriorated and non-deteriorated silages. Next, the key compounds were identified by combining the conditions of average ROAV (aROAV) ≥ 1 and VIP > 1. Finally, the OPLS algorithm was used to analyze the intrinsic link of key compounds with the silage odor. The results showed that three out of 63 compounds—4-ethyl phenol, eugenol, and ethyl linoleate—were key compounds to characterize whether the silages deteriorated or not. In addition, ortho-guaiacol, 4-ethyl guaiacol, and 2-methoxy-4-vinyl phenol were the specific key compounds for deteriorated silage. Eugenol and ethyl linoleate were correlated with fruity, sour, and spicy odors. In addition, guaiacol, 4-ethylphenol, 4-ethyl guaiacol and 2-methoxy-4-vinyl phenol contributed to roasted, musty, and putrid odors. First sampled Subsequently solidphase solid phase chromatographymass chromatography mass HSSPMEGCMS HS SPME GC MS (HS-SPME-GC-MS input (ROAV OPLSDA DA (OPLS-DA based projection nondeteriorated non Next aROAV (aROAV Finally 6 compounds4ethyl compoundsethyl 4 eugenol linoleatewere not addition orthoguaiacol, orthoguaiacol ortho ortho-guaiacol 4ethyl 2methoxy4vinyl methoxyvinyl 2 methoxy vinyl fruity sour odors 4ethylphenol, 4ethylphenol ethylphenol ethylphenol, 4-ethylphenol roasted musty

Abstract A number of studies have focused on the evaluation of the relationship between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. However, given the complexity of potentially confounding risk factors, the use of graphical tools is recommended to identify possible biases. This article aims to propose a theoretical model of causality using the directed acyclic graph between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. Methods: An extensive literature review was performed to identify variables with causal relationships with exposure and/or outcome. The choice of variables for adjustment followed the graphic algorithm that comprises six criteria for selecting a minimum set of potentially confounding variables. Socioeconomic conditions, interpartum interval, maternal age and food consumption pattern were the variables that would have to be adjusted in order to estimate the total effect of pre-pregnancy overweight on polyunsaturated fatty acids content in human milk. The minimum set of variables found in the present study can be used in the analysis of other studies that evaluate this association. prepregnancy pre pregnancy milk However factors biases Methods andor or outcome conditions interval association

Resumo Inúmeros estudos têm se detido na avaliação da associação entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Todavia, diante da complexidade de fatores de risco potencialmente confundidores, é recomendável a utilização de ferramentas gráficas para identificar possíveis vieses. O objetivo deste artigo é propor um modelo teórico de causalidade utilizando o gráfico acíclico direcionado entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Foi realizada ampla revisão da literatura para identificar as variáveis com relações causais com a exposição e/ou desfecho. A escolha das variáveis para ajuste seguiu o algoritmo gráfico que compreende seis critérios para a seleção de um conjunto mínimo de variáveis potencialmente confundidoras. Condições socioeconômicas, intervalo interpartal, idade materna e padrão de consumo alimentar foram as variáveis ajustadas a fim de se estimar o efeito total do excesso de peso pré-gestacional sobre o conteúdo dos ácidos graxos poli-insaturados no leite humano. O conjunto mínimo de variáveis encontrado pelo presente estudo pode ser utilizado na análise de outros estudos que avaliem essa associação. prégestacional pré gestacional poliinsaturados poli insaturados humano Todavia confundidores vieses eou ou desfecho confundidoras socioeconômicas interpartal

ABSTRACT BACKGROUND AND OBJECTIVES: Lumbar disorders, which contribute to significant workplace absenteeism and chronic disability, are associated with a considerable financial and social burden. Although a conservative approach provides satisfactory pain relief, biomechanical improvement and is associated with a low risk of adverse effects, there is lack of consensus in the literature regarding the best therapeutic strategy in such cases. METHODS: This retrospective longitudinal study used secondary data from the institutional medical records of patients who completed a multidisciplinary program for the treatment of low back pain between 2019 and 2021. Data regarding pain levels and motor skills were obtained from patients who completed the care program at a private hospital in Bento Gonçalves, RS. The following step-wise treatment algorithm was used: evaluation by a specialist physician for the etiological diagnosis of pain, pharmacological management and dry needling, followed by standard rehabilitation intervention performed by the physiotherapy team and exercises by the physical education team. The visual analogue scale (VAS) was used to measure pain at the start and at the completion of the intervention, and the Oswestry Disability Index (ODI) was used to measure motor skills at the start and at 6 and 12 months following the multiprofessional intervention for rehabilitation. RESULTS: A reduction in pain and motor disability in patients who completed all stages of the treatment program was observed. Pain by the VAS presented the following scores: baseline 7 [5-8] and after treatment 2 [0-4]; and the scores of the ODI were: at baseline 0.34 [0.26 - 0.40], at 6 months 0.16 [0.08 - 0.26] and after treatment 0.12 [0.04 - 0.21]. CONCLUSION: The treatment program reduced the pain and disability associated with low back pain and can serve as the basis for further studies carried out to confirm the effectiveness of this intervention. OBJECTIVES disorders burden relief effects cases METHODS 201 2021 Gonçalves RS stepwise step wise needling (VAS (ODI 1 RESULTS observed 58 5 8 [5-8 04 0 4 [0-4] 034 34 0.3 0.26 026 26 [0.2 0.40, 040 0.40 , 40 0.40] 016 16 0.1 0.08 008 08 [0.0 012 0.04 004 0.21. 021 0.21 . 21 0.21] CONCLUSION 20 202 [5- [0-4 03 3 0. 0.2 02 [0. 0.4 01 0.0 00 [5 [0- [0 [

RESUMO JUSTIFICATIVA E OBJETIVOS: As doenças lombares, que contribuem para um absenteísmo significativo no local de trabalho e para a incapacidade crônica, estão associadas a um encargo financeiro e social considerável. Embora a abordagem conservadora proporcione alívio satisfatório da dor, melhore a biomecânica e esteja associada a baixo risco de efeitos adversos, não há consenso na literatura sobre a melhor estratégia terapêutica nesses casos. MÉTODOS: Neste estudo longitudinal retrospectivo, foram utilizados dados secundários dos prontuários médicos institucionais de pacientes que completaram um programa multidisciplinar para tratamento de dor lombar entre 2019 e 2021. Dados sobre níveis de dor e habilidades motoras foram obtidos de pacientes que completaram o programa assistencial de um hospital privado de Bento Gonçalves, RS. Foi utilizado o seguinte tratamento passo a passo: avaliação por médico especialista para diagnóstico etiológico da dor, manejo farmacológico e agulhamento a seco, seguido de intervenção de reabilitação padrão realizada pela equipe de fisioterapia e exercícios pela equipe de educação física. A escala analógica visual (EAV) foi utilizada para medir a dor no início e após a conclusão da intervenção, e o Índice de Incapacidade de Oswestry (ODI) foi usado para medir as habilidades motoras no início e aos 6 e 12 meses após a intervenção multiprofissional para reabilitação. RESULTADOS: Observou-se redução na dor e na incapacidade motora em pacientes que completaram todas as etapas do programa de tratamento. A intensidade da dor medida pela EAV apresentou as seguintes pontuações: basal 7 [5-8] e após tratamento 2 [0-4]; enquanto o ODI apresentou as pontuações: basal 0,34 [0,26 – 0,40], até 6 meses 0,16 [0,08 – 0,26] e após o tratamento 0,12 [0,04 – 0,21]. CONCLUSÃO: O programa de tratamento reduziu a dor e a incapacidade associadas à dor lombar e pode servir de base para novos estudos realizados para confirmar a eficácia desta intervenção. OBJETIVOS lombares crônica considerável adversos casos MÉTODOS retrospectivo 201 2021 Gonçalves RS seco física (EAV (ODI 1 RESULTADOS Observouse Observou se pontuações 58 5 8 [5-8 04 0 4 [0-4] 034 34 0,3 0,26 026 26 [0,2 0,40, 040 0,40 , 40 0,40] 016 16 0,1 0,08 008 08 [0,0 012 0,04 004 0,21. 021 0,21 . 21 0,21] CONCLUSÃO 20 202 [5- [0-4 03 3 0, 0,2 02 [0, 0,4 01 0,0 00 [5 [0- [0 [

Abstract Introduction: under neoliberal capitalism, worker domination has shifted from the subjection of bodies to the control of subjectivity. Among the management technologies involved in this process are the performance evaluation instruments. Given the centrality of the financial system in capitalist society, technological innovations with the potential to spread throughout society usually occur in companies in this field. Objective: to identify the underlying rationality of these technologies, this article analyzes a performance evaluation instrument used by a Brazilian private bank. Methods: a documentary analysis of the explanatory manual was conducted. Results: the instrument, by means of a mathematical algorithm and a 360-degree evaluation, determines a minimum percentage of workers with unsatisfactory performance and limits the maximum percentage of workers who can achieve the highest performance. Workers are thus encouraged to compete among themselves to occupy pre-determined positions. Conclusion: we identified in the instrument a paradigm illustrative of the managerial rationality underlying the most diverse management technologies which encourage competition among workers, contributing to an unhealthy work environment. Introduction capitalism subjectivity instruments field Objective bank Methods conducted Results 360degree degree 360 predetermined pre determined positions Conclusion environment 36 3

Resumo Introdução: a dominação sobre os trabalhadores na fase neoliberal do capitalismo se desloca da sujeição dos corpos para o controle da subjetividade. Entre as tecnologias gerenciais que auxiliam esse processo, estão os instrumentos de avaliação de desempenho. Pela centralidade do sistema financeiro na sociedade capitalista, é em empresas desse ramo que costumam ocorrer inovações tecnológicas com maior potencial para se disseminar por toda a sociedade. Objetivo: para identificar a racionalidade subjacente a essas tecnologias, neste artigo estudamos o instrumento de avaliação de desempenho utilizado por um banco privado brasileiro. Métodos: foi realizada uma análise documental do manual explicativo do instrumento. Resultados: o instrumento, por meio de um algoritmo matemático e do uso de avaliação 360 graus, por um lado, determina um percentual mínimo de trabalhadores com desempenho insatisfatório, e, por outro, limita a porcentagem máxima de trabalhadores que poderão atingir o mais alto desempenho. Desse modo, os trabalhadores são estimulados permanentemente a concorrerem entre si para ocuparem posições predeterminadas. Conclusão: identificamos no instrumento um paradigma ilustrativo da racionalidade gerencial subjacente às mais diversas tecnologias gerenciais que, por meio do fomento à concorrência generalizada entre os trabalhadores, contribuem para um ambiente de trabalho adoecedor. Introdução subjetividade processo capitalista Objetivo brasileiro Métodos Resultados 36 graus lado insatisfatório outro modo predeterminadas Conclusão adoecedor 3

Abstract A number of studies have focused on the evaluation of the relationship between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. However, given the complexity of potentially confounding risk factors, the use of graphical tools is recommended to identify possible biases. This article aims to propose a theoretical model of causality using the directed acyclic graph between pre-pregnancy overweight and polyunsaturated fatty acids content in human milk. Methods: An extensive literature review was performed to identify variables with causal relationships with exposure and/or outcome. The choice of variables for adjustment followed the graphic algorithm that comprises six criteria for selecting a minimum set of potentially confounding variables. Socioeconomic conditions, interpartum interval, maternal age and food consumption pattern were the variables that would have to be adjusted in order to estimate the total effect of pre-pregnancy overweight on polyunsaturated fatty acids content in human milk. The minimum set of variables found in the present study can be used in the analysis of other studies that evaluate this association.

Resumo Inúmeros estudos têm se detido na avaliação da associação entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Todavia, diante da complexidade de fatores de risco potencialmente confundidores, é recomendável a utilização de ferramentas gráficas para identificar possíveis vieses. O objetivo deste artigo é propor um modelo teórico de causalidade utilizando o gráfico acíclico direcionado entre o excesso de peso pré-gestacional e os ácidos graxos poli-insaturados no leite humano. Foi realizada ampla revisão da literatura para identificar as variáveis com relações causais com a exposição e/ou desfecho. A escolha das variáveis para ajuste seguiu o algoritmo gráfico que compreende seis critérios para a seleção de um conjunto mínimo de variáveis potencialmente confundidoras. Condições socioeconômicas, intervalo interpartal, idade materna e padrão de consumo alimentar foram as variáveis ajustadas a fim de se estimar o efeito total do excesso de peso pré-gestacional sobre o conteúdo dos ácidos graxos poli-insaturados no leite humano. O conjunto mínimo de variáveis encontrado pelo presente estudo pode ser utilizado na análise de outros estudos que avaliem essa associação.

Abstract Objective: To describe the accuracy of HealthVCF, a software product that uses artificial intelligence, in the detection of incidental moderate-to-severe vertebral compression fractures (VCFs) on chest and abdominal computed tomography scans. Materials and Methods: We included a consecutive sample of 899 chest and abdominal computed tomography scans of patients 51–99 years of age. Scans were retrospectively evaluated by the software and by two specialists in musculoskeletal imaging for the presence of VCFs with vertebral body height loss > 25%. We compared the software analysis with that of a general radiologist, using the evaluation of the two specialists as the reference. Results: The software showed a diagnostic accuracy of 89.6% (95% CI: 87.4–91.5%) for moderate-to-severe VCFs, with a sensitivity of 73.8%, a specificity of 92.7%, and a negative predictive value of 94.8%. Among the 145 positive scans detected by the software, the general radiologist failed to report the fractures in 62 (42.8%), and the algorithm detected additional fractures in 38 of those scans. Conclusion: The software has good accuracy for the detection of moderate-to-severe VCFs, with high specificity, and can increase the opportunistic detection rate of VCFs by radiologists who do not specialize in musculoskeletal imaging. Objective HealthVCF intelligence moderatetosevere moderate severe (VCFs Methods 89 5199 51 99 51–9 age 25 25% reference Results 896 6 89.6 95% 95 (95 CI 87.4–91.5% 874915 87 4 91 5 738 73 8 73.8% 927 92 7 92.7% 948 94 94.8% 14 42.8%, 428 42.8% , 42 (42.8%) 3 Conclusion 519 9 51– 2 89. (9 87.4–91.5 87491 73.8 92.7 94.8 1 42.8 (42.8% ( 87.4–91. 8749 73. 92. 94. 42. (42.8 87.4–91 874 (42. 87.4–9 (42 87.4– (4 87.4 87.

Resumo Objetivo: Descrever a acurácia do software HealthVCF na detecção incidental de fraturas compressivas de corpos vertebrais moderadas a graves em exames de tomografia computadorizada do tórax e abdome. Materiais e Métodos: Foram incluídos 899 exames consecutivos de pacientes com idades entre 51 e 99 anos. As imagens foram retrospectivamente avaliadas pelo software e por dois radiologistas especializados em musculoesquelético que investigaram fraturas compressivas de corpos vertebrais com perda da altura somática > 25%. A análise comparativa foi realizada entre o software e um radiologista geral, usando a avaliação do especialista como referência. Resultados: O software apresentou uma acurácia de 89,6% (IC 95%: 87,4–91,5%) para fraturas compressivas moderadas a graves, com sensibilidade de 73,8%, especificidade de 92,7% e valor preditivo negativo de 94,8%. Entre as 145 tomografias positivas detectadas pelo software, o radiologista geral deixou de relatar as fraturas em 62 (42,8%) e o algoritmo detectou fraturas adicionais em 38 dessas tomografias. Conclusão: O software possui boa acurácia na detecção de fraturas compressivas moderadas a graves, com alta especificidade, podendo aumentar a taxa de detecção oportunística dessas fraturas por radiologistas não especializados em musculoesquelético. Objetivo abdome Métodos 89 5 9 anos 25 25% referência Resultados 896 6 89,6 IC 95% 95 87,4–91,5% 874915 87 4 91 738 73 8 73,8% 927 92 7 92,7 948 94 94,8% 14 42,8% 428 42 (42,8% 3 Conclusão 2 89, 87,4–91,5 87491 73,8 92, 94,8 1 42,8 (42,8 87,4–91, 8749 73, 94, 42, (42, 87,4–91 874 (42 87,4–9 (4 87,4– ( 87,4 87,

ABSTRACT BACKGROUND: cardiorespiratory arrest (CRA) is a severe public health concern, and clinical simulation has proven to be a beneficial educational strategy for training on this topic. OBJECTIVE: To describe the implementation of a program for pediatric cardiac arrest care using rapid-cycle deliberate practice (RCDP), the quality of the technique employed, and participants' opinions on the methodology. DESIGN AND SETTING: This descriptive cross-sectional study of pre- and post-performance training in cardiopul monary resuscitation (CPR) techniques and reaction evaluation was conducted in a hospital in São Paulo. METHODS: Multidisciplinary groups performed pediatric resuscitation in a simulated scenario with RCDP mediated by a facilitator. The study sample included professionals working in patient care. During the simulation, the participants were evaluated for their compliance with the CRA care algorithm. Further, their execution of chest compressions was assessed pre- and post-intervention. RESULTS: In total, 302 professionals were trained in this study. The overall quality of CPR measured pre-intervention was inadequate, and only 26% had adequate technique proficiency, whereas it was 91% (P < 0.01) post-intervention. Of the participants, 95.7% responded to the final evaluation and provided positive comments on the method and their satisfaction with the novel simulation. Of these, 88% considered that repetition of the technique used was more effective than traditional simulation. CONCLUSIONS: The RCDP is effective for training multidisciplinary teams in pediatric CPR, with an emphasis on the quality of chest compressions. However, further studies are necessary to explore whether this trend translates to differential performances in practical settings. BACKGROUND (CRA concern topic OBJECTIVE rapidcycle rapid cycle RCDP, , (RCDP) employed methodology SETTING crosssectional cross sectional pre postperformance post performance (CPR Paulo METHODS facilitator algorithm Further postintervention. postintervention intervention. intervention post-intervention RESULTS total 30 preintervention inadequate 26 proficiency 91 P 0.01 001 0 01 957 95 7 95.7 these 88 CONCLUSIONS However settings (RCDP 3 2 9 0.0 00 95. 8 0.

ABSTRACT Traditional hydrological models have been widely used in hydrologic studies, providing credible representations of reality. This paper introduces a hybrid model that combines the traditional hydrological model Soil Moisture Accounting Procedure (SMAP) with the machine learning algorithm XGBoost. Applied to the Sobradinho watershed in Brazil, the hybrid model aims to produce more precise streamflow forecasts within a three-month horizon. This study employs rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs of the SMAP to produce streamflow forecasts. The study evaluates NMME forecasts, corrects bias using quantile mapping, and calibrates the SMAP model for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO). Model evaluation covers the period from 2011 to 2022. An XGBoost model predicts SMAP residuals based on the past 12 months, and the hybrid model combines SMAP's streamflow forecast with XGBoost residuals. Notably, the hybrid model outperforms SMAP alone, showing improved correlation and Nash-Sutcliffe index values, especially during periods of lower streamflow. This research highlights the potential of integrating traditional hydrological models with machine learning for more accurate streamflow predictions. studies reality (SMAP Brazil threemonth three month horizon (NMME mapping 198 201 PSO. PSO . (PSO) 2022 1 months SMAPs s Notably alone NashSutcliffe Nash Sutcliffe values predictions 19 20 (PSO 202 2

RESUMO Os modelos hidrológicos tradicionais têm sido amplamente utilizados em estudos hidrológicos, fornecendo representações credíveis da realidade. Este artigo introduz um modelo híbrido que combina o modelo hidrológico tradicional Soil Moisture Accounting Procedure (SMAP) com o algoritmo de aprendizado de máquina XGBoost. Aplicado à bacia de Sobradinho no Brasil, o modelo híbrido tem como objetivo produzir previsões de vazão mais precisas em um horizonte de três meses. Este estudo utiliza previsões de chuvas do North America Multi Model Ensemble (NMME) como entradas do SMAP para produzir previsões de vazão. O estudo avalia as previsões do NMME, corrige viés usando mapeamento de quantis e calibra o modelo SMAP para a região de estudo de 1984 a 2010 usando a Otimização por Enxame de Partículas (PSO). A avaliação do modelo abrange o período de 2011 a 2022. Um modelo XGBoost prevê os resíduos do SMAP com base nos últimos 12 meses, e o modelo híbrido combina a previsão de vazão do SMAP com os resíduos do XGBoost. Notavelmente, o modelo híbrido supera o SMAP sozinho, mostrando melhor correlação e valores do índice Nash-Sutcliffe, especialmente durante períodos de menor vazão. Esta pesquisa destaca o potencial da integração de modelos hidrológicos tradicionais com aprendizado de máquina para previsões de vazão mais precisas. realidade (SMAP Brasil meses NMME (NMME 198 201 PSO. PSO . (PSO) 2022 1 Notavelmente sozinho NashSutcliffe, NashSutcliffe Nash Sutcliffe, Sutcliffe Nash-Sutcliffe 19 20 (PSO 202 2

Abstract Dental fillings, frequently used in dentistry to address various dental tissue issues, may pose problems when not aligned with the anatomical contours and physiology of dental and periodontal tissues. Our study aims to detect the prevalence and distribution of normal and overhanging filling restorations using a deep CNN architecture trained through supervised learning, on panoramic radiography images. A total of 10480 fillings and 2491 overhanging fillings were labeled using CranioCatch software from 2473 and 1850 images, respectively. After the data obtaining phase, validation (80%), training 10%), and test-groups (10%) were formed from images for both labelling. The YOLOv5x architecture was used to develop the AI model. The model’s performance was assessed through a confusion matrix and sensitivity, precision, and F1 score values of the model were calculated. For filling, sensitivity is 0.95, precision is 0.97, and F1 score is 0.96; for overhanging were determined to be 0.86, 0.89, and 0.87, respectively. The results demonstrate the capacity of the YOLOv5 algorithm to segment dental radiographs efficiently and accurately and demonstrate proficiency in detecting and distinguishing between normal and overhanging filling restorations. issues tissues learning 1048 249 247 185 respectively phase 80%, 80 80% , (80%) 10%, 10 10% 10%) testgroups test groups (10% labelling YOLOvx YOLOv x models s F calculated 095 0 95 0.95 097 97 0.97 0.96 096 96 086 86 0.86 089 89 0.89 087 87 0.87 104 24 18 8 (80% 1 (10 09 9 0.9 08 0.8 2 (80 (1 0. (8 (