Determinantes do cumprimento das despesas com pessoal e do resultado primário nos estados brasileiros: uma análise de 2017 a 2022 / Determinantes del cumplimiento de los gastos de personal y el resultado primario en los estados brasileños, un análisis de 2017 a 2022 / Determinants of compliance with personnel expenses and primary result in Brazilian States: an analysis from 2017 to 2022

Resumo O artigo científico avalia os determinantes da gestão fiscal representada pelo cumprimento dos limites da Lei de Responsabilidade Fiscal (LRF) e o cumprimento da meta do resultado primário. Para atender aos objetivos propostos, fez-se uma abordagem econométrica, na qual se utilizou a variável binária para cumprimento da meta de despesa total com pessoal e do resultado primário por parte das unidades federativas brasileiras. Para a análise dos fatores determinantes, usaram-se dados sociais e contábeis, características do gestor e uma análise de regressão múltipla para dados em painel, examinando o período de 2017 a 2022. No modelo para a despesa total com pessoal, as transferências da União para os estados e a receita tributária favorecem o não cumprimento dos limites da LRF. Por outro lado, para a meta do resultado primário, as transferências, os restos a pagar, a capacidade de pagamento, a escolaridade e a idade do gestor favorecem o cumprimento da meta.

Resumen Este artículo científico evalúa los determinantes de la gestión fiscal representados por el cumplimiento de los límites de la Ley de Responsabilidad Fiscal (LRF) y el cumplimiento de la meta de resultado primario. Para alcanzar los objetivos propuestos, se adoptó un enfoque econométrico, utilizando la variable binaria para el cumplimiento de la meta de gasto total en personal y la variable binaria para el cumplimiento de la meta de resultado primario por las unidades federales brasileñas. Además, se utilizaron datos sociales y contables y características de los gestores para analizar los factores determinantes, así como un análisis de regresión múltiple para datos de panel, examinando el período de 2017 a 2022. En el modelo para el gasto total de personal, las transferencias del gobierno federal a los estados y los ingresos fiscales favorecen el incumplimiento de los límites de la LRF. Por otro lado, para la meta de resultado primario, las transferencias, las deudas impagadas, la capacidad de pago, el nivel educativo y la edad del gestor favorecen el cumplimiento de la meta.

Abstract This scientific article evaluates the determinants of fiscal management represented by compliance with the thresholds of the Fiscal Responsibility Law and the primary result target. The study adopted an econometric approach, using binary variables regarding the Brazilian states' compliance with the total personnel expenditure target and the primary result target. Social and accounting data and the manager's characteristics were used to analyze the determining factors. In addition, a multiple regression analysis for panel data was conducted, examining the period from 2017 to 2022. In the model for total personnel expenditure, transfers from the federal government to the states and tax revenue favor non-compliance with the thresholds of the Fiscal Responsibility Law. On the other hand, for the primary result target, transfers, accounts payable, payment capacity, and the managers' education level and age favor compliance with the target.

Functional traits and performance of woody species on oil-affected soils of the Ecuadorian Amazon / Rasgos funcionales y comportamiento de especies leñosas en suelos afectados por petróleo de la Amazonía ecuatoriana

Abstract Introduction: Plant functional traits are widely used to predict community productivity. However, they are rarely used to predict the performance (in terms of growth diameter, growth height, survival, and integral response index) of woody species planted in degraded soils. Objective: To evaluate the relationship between the functional traits and the performance of 25 woody species planted in disturbed soils affected by oil extraction activities in Ecuadorian Amazon. Methods: Eighteen permanent sampling plots were established and five 6-month-old seedlings of each 25 species were randomly planted in each plot (125 individuals per plot), at a distance of 4×4 m. Eight quantitative functional traits (leaf size, specific leaf area, leaf nitrogen concentration, leaf phosphorus concentration, leaf minimum unit, leaf dry matter content, stem specific density and leaf tensile strength) were determined for each species. Results: The woody species with high performance shows greater leaf size, specific leaf area and Stem Specific Density than those showing low performance. Leaf nitrogen concentration and stem specific density had a direct relationship with the integral response index. The leaf size, leaf phosphorus concentration, leaf dry matter content and leaf tensile strength showed a negative relationship with the integral response index. Conclusions: Our study demonstrated that the performance of woody species o disturbed soils can be predicted satisfyingly by leaf and stem functional traits, presumably because these traits capture most of environmental and neighborhood conditions.

Resumen Introducción: Los rasgos funcionales de las plantas han sido ampliamente utilizados para predecir la productividad (en términos de crecimiento en diámetro, crecimiento en altura, sobrevivencia e índice de respuesta integral) de las comunidades vegetales. Sin embargo, rara vez han sido utilizados para predecir el desempeño de las especies leñosas plantadas en suelos degradados. Objetivo: Evaluar la relación entre el desempeño y los rasgos funcionales de 25 especies leñosas plantadas en suelos afectados por actividades de extracción de petróleo en la Amazonía ecuatoriana. Métodos: Se establecieron 18 parcelas permanentes de muestreo y en cada parcela se sembraron aleatoriamente cinco plántulas de 6 meses de las 25 especies (125 individuos por parcela), a una distancia de 4×4 m. Se determinaron ocho rasgos funcionales (área foliar, área foliar específica, concentración de nitrógeno foliar, concentración de fósforo foliar, unidad mínima foliar, contenido foliar de materia seca, densidad específica del fuste y fuerza tensil foliar) de cada especie. Resultados: Las especies leñosas con alto desempeño presentaron mayor área foliar, área foliar específica y densidad específica del fuste que las especies de bajo desempeño. La concentración de nitrógeno foliar y la densidad específica del fuste mostraron una relación directa. El área foliar, la concentración de fósforo foliar, el contenido de materia seca foliar y la fuerza tensil foliar presentaron una relación inversa con el Índice de Respuesta Integral. Conclusión: Se demostró que el desempeño de las especies leñosas plantadas en suelos alterados puede predecirse satisfactoriamente por rasgos funcionales de hoja y de tallo, debido posiblemente a que los rasgos influyen en el crecimiento y supervivencia de las especies, y reflejan la mayoría de las condiciones ambientales.

Prediction of total phosphorus in reservoir cascade systems.

Reservoir cascade systems have attracted the attention of scientists worldwide. The present study investigates the cascade of five reservoirs (R1, R2, R3, R4, and R5) along a 192-km water channel system located in the state of Ceará, in the Brazilian semiarid region. This cascade system was implemented in 2012 to promote water availability and security to the capital of Ceará and the strategic industry and port complex of the region. However, these reservoirs have faced a progressive degradation of water quality, which has resulted in intense eutrophication and high-water treatment costs. The study evaluates the dynamics of water quality from 2013 to 2021 along this reservoir cascade (from R1 to R5). The results revealed that water quality did not improve along the cascade system, differently from previous studies on reservoirs interconnected by natural rivers. This was attributed to the low water residence time and low capacity of pollutant removal along the man-made water channel system, as well as to the high internal phosphorus loads of the reservoirs. Multiple regression models involving the explanatory variables of total phosphorus, total nitrogen, chlorophyll-a, cyanobacteria, transparency, rainfall, and volume from upstream reservoirs were obtained to determine total phosphorus concentration in downstream reservoirs, considering different combinations of reservoir pairs in the cascade and different time delays. A clear trend of R2 decline with the distance between the upstream and downstream reservoirs was observed. For example, the R2 values for the correlations adjusted between R1 and R2 (48 km), R1 and R3 (172 km), R1 and R4 (178 km), and R1 and R5 (192 km) were 0.66, 0.32, 0.22, and 0.12, respectively. On the other hand, the adoption of time delays of the order of the cumulative residence times of the reservoirs promoted a significant improvement in the R2 values. For instance, the best correlation adjusted between R1 and R5 improved from R2 = 0.12 to 0.69 by considering a time delay of 21 months. This suggests that previous data from upstream reservoirs can be used to predict current and future total phosphorus concentration in downstream reservoirs. The results from this study are important to better understand the spatiotemporal dynamics of water quality in reservoir cascade systems and thus improve water resources management, especially in drylands.

Assessment of soil respiration process in a mangrove swamp of Panama's Bay.

Studies reveal that mangroves have the ability to store underground carbon more than a tropical forest, and this function is classified as the second most important to mitigate the effects of climate change. However, part of the carbon fixed returns to the atmosphere, and this is done through soil respiration. The present study seeks to quantify the total soil efflux (a subrogate of total soil respiration) that includes both autotrophic and heterotrophic soil efflux, emitted by a Panama's mangrove swamp, as well as to investigate what drivers are important. Firstly, 3 plots were established with predominant mangroves species, such as salty mangrove tree (Avicennia bicolor Standl.) and black mangrove tree (Avicennia germinans L.). Secondly, a forest inventory was carried out in one ha, resulting in 371 trees ha-1, where the salty mangrove tree prevailed with 219 individuals in front of the black mangrove tree, with 152 trees. In addition, tree level measurements were performed such as diameter at breast height (DBH), crown diameter and distance between trees. Third, using a Licor 6400XT infrared gas analyzer system and a meteorological tower, soil CO2 fluxes and air and soil temperature were measured respectively. Results showed a total of 33.61 t of CO2 ha-1 emitted by the soil of the mangrove in 3.5 months.

Application of Path Analysis and Structural Equation Modeling in Nutrition and Dietetics.

This is one of a series of monographs on research design and analysis. The purpose of this article is to describe a set of statistical procedures or techniques used to develop and test structural models that characterize the relationships and interrelationships between a group of concepts and variables. These procedures include multiple regression, exploratory and confirmatory factor analysis, path analysis, and structural equation modeling. The article describes the purpose of each of these procedures and how they relate to and build on one another. It also covers the different types of variables examined, including the distinction between endogenous, exogenous, and mediating variables, along with the distinction between measured and unmeasured (or latent) variables. Each procedure results in a set of statistical estimates, and the article presents the interpretation of these estimates, including regression coefficients (standardized and unstandardized), path coefficients, factor loadings, and coefficients of determination (or R2 values). The article presents examples of how each procedure has been used in practice, along with additional resources for readers who wish to learn more.

Normatização da Bateria de Provas de Raciocínio (BPR-5) usando a Teoria de Resposta ao Item / Standardization of the Reasoning Tests Battery (BPR-5) using Item Response Theory / Estandarización de la Batería de Pruebas de Razonamiento (BPR-5) usando la teoría de respuesta al ítem

A Bateria de Provas de Raciocínio (BPR-5) é um instrumento bastante utilizado para avaliação da inteligência no Brasil, possuindo três formas: infantil (1º ao 6º ano do Ensino Fundamental), Forma A (7º ao 9º ano do Ensino Fundamental) e Forma B (Ensino Médio e Superior). Visando a possibilidade de comparar os resultados entre sujeitos que respondem a formas diferentes, o presente estudo objetivou: (a) calibrar os itens das três formas pelo modelo de Rasch, realizando a equalização por itens comuns de modo a obter uma escala equiparável para as três formas, (b) atualizar as normas expandindo a representatividade das amostras e produzindo-se normas para diferentes combinações de idade, escolaridade e sexo, (c) descrever padrões de desenvolvimento da inteligência a partir da escala, comum, produzida entre 6 e 52 anos. Dois estudos foram relatados, o primeiro sobre a calibração dos parâmetros segundo o modelo de Rasch indicando um bom ajuste ao modelo. O segundo estudo exemplificou a utilização de regressão múltipla para criação de normas para testes psicológicos, considerando as variáveis idade, escolaridade e sexo como preditoras dos escores na BPR-5. Essas três variáveis tiveram efeitos significativos explicando 8% (Raciocínio Abstrato), 13% (Raciocínio Verbal), 12% (Raciocínio Espacial), 8% (Raciocínio Numérico) e 22% (Raciocínio Mecânico).(AU)

The Reasoning Tests Battery (BPR-5) is widely used in Brazil for the assessment of intelligence. It has three different forms: children (1st to 6th grade of elementary school), Form A (7th to 9th grade of elementary school) and Form B (high school and higher education). This study describes the steps followed to create a common metric across the forms. It aimed to: (a) calibrate the items of the three forms using Rasch model, link items and equate subjects' scores across forms using the anchoring of common items method, (b) update the norms by expanding the representativeness of the samples by producing norms for different combinations of age, education and sex, and (c) describe the developmental patterns of the BPR-5 subtests across a wide age range, from 6 to 52 years. We present two studies, the first reporting the calibration of item and person parameters with the Rasch model and a good fit to the model. The second illustrated the use of multiple regression analysis to create norms for the psychological tests considering the variables age, education and gender as predictors of the BPR-5 scores. These three variables had significant effects explaining 8% (Abstract Reasoning), 13% (Verbal Reasoning), 12% (Spatial Reasoning), 8% (Numerical Reasoning) and 22% (Mechanical Reasoning) of the variance. (AU)

La Batería de Pruebas de Razonamiento (BPR-5) es un instrumento muy utilizado para la evaluación de la inteligencia en Brasil, disponiendo de tres formas: infantil (1° al 6° año de la Enseñanza Fundamental), Forma A (7° al 9° año de la Enseñanza Fundamental) y Forma B (Bachillerato y Educación Superior). Teniendo como finalidad la posibilidad de comparar los resultados entre sujetos que responden a diferentes versiones, el presente estudio tuvo como objetivo: (a) calibrar los ítems de las tres formas por el modelo de Rasch, realizando la igualación por ítems comunes a fin de obtener una escala equiparable para las tres formas, (b) actualizar las normas ampliando la representatividad de las muestras y produciendo normas para diferentes combinaciones de edad, escolarización y sexo, (c) describir patrones de desarrollo de la inteligencia a partir de la escala, común, producida entre los 6 y los 52 años. Dos estudios fueron reportados, el primer sobre la calibración de los parámetros de acuerdo con el modelo de Rasch, indicando un buen ajuste al modelo. El segundo estudio ejemplificó la utilización de regresión múltiple para crear normas para tests psicológicos, considerando las variables edad, educación y sexo como predictivos de los escores en el BPR-5. Esas tres variables tuvieron efectos significativos explicando 8% (Razonamiento Abstracto), 13% (Razonamiento Verbal), 12% (Razonamiento Espacial), 8% (Razonamiento Numérico), y 22% (Razonamiento Mecánico).(AU)

Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica.

Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi-urban systems that use water sources as springs, surfaces, and a mixture of both, in three provinces of Costa Rica from April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and a mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 and 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC), and ultraviolet absorbance at 254 nm (UV254) showed a significant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-fit and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R2, standard error (SE), mean square error (MSE), and mean absolute error (MAE), spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHM concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems.

Spatial distribution of soil δ13C in the central Brazilian savanna.

Stable carbon isotope ratios (δ13C) of soil record information regarding C3 and C4 plants at the landscape scale that can be used to document vegetation distribution patterns. The Central Brazilian savanna (locally called the Cerrado) has a substantial potential to develop studies of patterns of dynamics and distribution of soil δ13C, due to its environmental diversity. The purpose of this work was to develop a spatial model of soil δ13C (soil δ13C isoscape) to the Cerrado, based on multiple linear regression analysis, and compare the results with the existing model to obtain greater detail of the soil δ13C distribution. The model used 219 soil samples (0-20 cm depth) and a set of climatic, pedological, topographic, and vegetation correlations. The soil δ13C isoscape model presented amplitude between -29‰ and -13‰, with the highest estimated values in the southern and the lowest values in the northern of the Cerrado. Results indicate that soil δ13C, by reflecting the relative contribution of C3 and C4 species to plant community productivity, served as a proxy indicator of the vegetation history at the landscape scale for the Central Brazilian savanna. Despite the large sampling effort, there are still regions with some gaps that the model could not estimate. However, the soil δ13C isoscape model filled most the existing gaps and provided greater detail of some unique local aspects of the Cerrado.

Predicting the chemical composition of the body and the carcass of hair sheep using body parts and carcass measurements.

Determination of the chemical composition in the body and carcass of ruminants is important for both nutritional requirement studies and the meat industry. This study aimed to develop equations to predict the body and carcass chemical composition of hair sheep using the chemical composition of body parts, carcass measurements and shrunk BW as predictors. A database containing 107 individual records for castrated male hair sheep ranging from 24 to 43 kg BW was gathered from two body composition studies. The empty body, carcass and body parts were analyzed for water, ash, fat and protein contents (%). The body parts used to estimate body and carcass composition were fore leg, hind leg and 9-11th rib section. The carcass measurements used were leg length, thoracic circumference, hind circumference, hind width, thoracic width, thoracic depth and chest width. Each model performance was evaluated using a leave-one-out cross-validation. Multiple regression analysis considering the study as a random effect revealed that body parts in association with carcass measurements were significant for predicting the chemical composition in the body of castrate male sheep. However, the use of the chemical composition of hind leg produced the best models for predicting the ash and fat contents in the empty body, whereas the water and protein contents in the empty body were better predicted when using the chemical compositions of 9-11th rib section and fore leg, respectively. Multiple regression analysis also revealed that most body parts were suitable for predicting the carcass composition, except for 9-11th rib section whose chemical composition did not produce significant prediction equations for ash and protein carcass contents. The use of the chemical composition of hind leg in association with carcass measurements produced the best models for predicting the water and fat contents in the carcass, while the ash and protein contents in the carcass were better predicted when using the chemical composition of fore leg. In conclusion, precision, accuracy and goodness-of-fit of the equations drove the selection of the chemical composition of hind leg and carcass measurements in a multivariate approach, as the most suitable predictors of the chemical composition of the body and carcass of hair sheep. However, the chemical composition of fore leg may be used as well. The developed equations could improve the accuracy of the empty body and carcass composition estimations in sheep, optimizing the estimation of nutrient requirements, as well as the carcass quality evaluation for this species.

Meteorological factors responsible for the growth and development of sugarcane at two locations in Rio Grande do Sul, Brazil / Fatores meteorológicos responsáveis pelo crescimento e desenvolvimento da cana-de-açúcar em dois locais do Rio Grande do Sul

This study determined the meteorological variable that most contribute to the productivity of sugarcane stalks in the northwest and central regions of Rio Grande do Sul. The following sugarcane genotypes were used: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, and IAC87-3396. The UFSM cultivars originate from a mutation process in the breeding program conducted at the Federal University of Santa Maria, Frederico Westphalen campus, and have low temperature tolerance. The productivity-associated morphological characters included in the models were average stem diameter, average stem number per meter of furrow, and average stem height. The following meteorological variables were used: minimum air temperature, precipitation, incident solar radiation, and accumulated thermal sum. Pearsons correlation, canonical correlations, and Stepwise regression were performed between morphological characters and meteorological variables: minimum air temperature had the greatest influence on sugarcane productivity in the studied regions, and accumulated thermal sum showed the highest correlation and contributed most to stem diameter and average stem height. Thus, the models indicated that the growth of sugarcane is positively associated with the accumulated thermal sum, and sugarcane can be cultivated at the studied regions.(AU)

O objetivo deste trabalho foi determinar a variável meteorológica com maior contribuição na produtividade de colmos de cana-de-açúcar na região noroeste e central do Rio Grande do Sul. Os genótipos de cana-de-açúcar utilizados foram: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, IAC87-3396. As cultivares UFSM são provenientes do processo de mutação do programa de melhoramento da Universidade Federal de Santa Maria, campus de Frederico Westphalen, e possuem tolerância a baixas temperaturas. Os caracteres morfológicos responsáveis pela produtividade utilizados nos modelos foram diâmetro médio de colmo, número médio de colmos por metro de sulco e estatura média de colmos. As variáveis meteorológicas utilizadas foram a temperatura mínima do ar, precipitação pluviométrica, radiação solar incidente e soma térmica acumulada. Foi realizada a correlação de Pearson, correlações canônicas e regressão de Stepwise entre os caracteres morfológicos e as variáveis meteorológicas, nos quais foi constado que a temperatura mínima do ar é a variável com maior influência na produtividade da cana-de-açúcar nas regiões estudadas e, que a soma térmica acumulada é a variável com maior correlação e contribuição no diâmetro de colmo e estatura média dos colmos. Desse modo, os modelos indicam que o crescimento da cana-de-açúcar responde de forma positiva a soma térmica acumulada, e as regiões em estudo apresentam potencial para o cultivo da cana-de-açúcar.(AU)

Meteorological factors responsible for the growth and development of sugarcane at two locations in Rio Grande do Sul, Brazil / Fatores meteorológicos responsáveis pelo crescimento e desenvolvimento da cana-de-açúcar em dois locais do Rio Grande do Sul

ABSTRACT: This study determined the meteorological variable that most contribute to the productivity of sugarcane stalks in the northwest and central regions of Rio Grande do Sul. The following sugarcane genotypes were used: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, and IAC87-3396. The UFSM cultivars originate from a mutation process in the breeding program conducted at the Federal University of Santa Maria, Frederico Westphalen campus, and have low temperature tolerance. The productivity-associated morphological characters included in the models were average stem diameter, average stem number per meter of furrow, and average stem height. The following meteorological variables were used: minimum air temperature, precipitation, incident solar radiation, and accumulated thermal sum. Pearson's correlation, canonical correlations, and Stepwise regression were performed between morphological characters and meteorological variables: minimum air temperature had the greatest influence on sugarcane productivity in the studied regions, and accumulated thermal sum showed the highest correlation and contributed most to stem diameter and average stem height. Thus, the models indicated that the growth of sugarcane is positively associated with the accumulated thermal sum, and sugarcane can be cultivated at the studied regions.

RESUMO: O objetivo deste trabalho foi determinar a variável meteorológica com maior contribuição na produtividade de colmos de cana-de-açúcar na região noroeste e central do Rio Grande do Sul. Os genótipos de cana-de-açúcar utilizados foram: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, IAC87-3396. As cultivares UFSM são provenientes do processo de mutação do programa de melhoramento da Universidade Federal de Santa Maria, campus de Frederico Westphalen, e possuem tolerância a baixas temperaturas. Os caracteres morfológicos responsáveis pela produtividade utilizados nos modelos foram diâmetro médio de colmo, número médio de colmos por metro de sulco e estatura média de colmos. As variáveis meteorológicas utilizadas foram a temperatura mínima do ar, precipitação pluviométrica, radiação solar incidente e soma térmica acumulada. Foi realizada a correlação de Pearson, correlações canônicas e regressão de Stepwise entre os caracteres morfológicos e as variáveis meteorológicas, nos quais foi constado que a temperatura mínima do ar é a variável com maior influência na produtividade da cana-de-açúcar nas regiões estudadas e, que a soma térmica acumulada é a variável com maior correlação e contribuição no diâmetro de colmo e estatura média dos colmos. Desse modo, os modelos indicam que o crescimento da cana-de-açúcar responde de forma positiva a soma térmica acumulada, e as regiões em estudo apresentam potencial para o cultivo da cana-de-açúcar.

Meteorological factors responsible for the growth and development of sugarcane at two locations in Rio Grande do Sul, Brazil / Fatores meteorológicos responsáveis pelo crescimento e desenvolvimento da cana-de-açúcar em dois locais do Rio Grande do Sul

This study determined the meteorological variable that most contribute to the productivity of sugarcane stalks in the northwest and central regions of Rio Grande do Sul. The following sugarcane genotypes were used: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, and IAC87-3396. The UFSM cultivars originate from a mutation process in the breeding program conducted at the Federal University of Santa Maria, Frederico Westphalen campus, and have low temperature tolerance. The productivity-associated morphological characters included in the models were average stem diameter, average stem number per meter of furrow, and average stem height. The following meteorological variables were used: minimum air temperature, precipitation, incident solar radiation, and accumulated thermal sum. Pearson’s correlation, canonical correlations, and Stepwise regression were performed between morphological characters and meteorological variables: minimum air temperature had the greatest influence on sugarcane productivity in the studied regions, and accumulated thermal sum showed the highest correlation and contributed most to stem diameter and average stem height. Thus, the models indicated that the growth of sugarcane is positively associated with the accumulated thermal sum, and sugarcane can be cultivated at the studied regions.

O objetivo deste trabalho foi determinar a variável meteorológica com maior contribuição na produtividade de colmos de cana-de-açúcar na região noroeste e central do Rio Grande do Sul. Os genótipos de cana-de-açúcar utilizados foram: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, IAC87-3396. As cultivares UFSM são provenientes do processo de mutação do programa de melhoramento da Universidade Federal de Santa Maria, campus de Frederico Westphalen, e possuem tolerância a baixas temperaturas. Os caracteres morfológicos responsáveis pela produtividade utilizados nos modelos foram diâmetro médio de colmo, número médio de colmos por metro de sulco e estatura média de colmos. As variáveis meteorológicas utilizadas foram a temperatura mínima do ar, precipitação pluviométrica, radiação solar incidente e soma térmica acumulada. Foi realizada a correlação de Pearson, correlações canônicas e regressão de Stepwise entre os caracteres morfológicos e as variáveis meteorológicas, nos quais foi constado que a temperatura mínima do ar é a variável com maior influência na produtividade da cana-de-açúcar nas regiões estudadas e, que a soma térmica acumulada é a variável com maior correlação e contribuição no diâmetro de colmo e estatura média dos colmos. Desse modo, os modelos indicam que o crescimento da cana-de-açúcar responde de forma positiva a soma térmica acumulada, e as regiões em estudo apresentam potencial para o cultivo da cana-de-açúcar.

Risk factors of pain, physical function, and health-related quality of life in elderly people with knee osteoarthritis: A cross-sectional study.

Data on the precise mechanisms of the complex interactions of factors related to clinical impact of knee osteoarthritis (KOA) in the elderly population remain limited. To find predictors that explain pain intensity, physical function, and quality of life in elderly KOA subjects, we performed a cross-sectional analysis of the baseline data from a randomized trial. The trial included 104 subjects (aged ≥60) with KOA pain and dysfunctional endogenous pain-inhibitory system activity assessed by conditioned pain modulation (CPM). Three multiple linear regression models were performed to understand the independent predictors of Brief Pain Inventory (BPI), WOMAC function subscale (WOMACFunc), and SF-12 physical subscale (SF12-PCS). Model 1 showed that BPI pain score was predicted by low CPM response, high von-Frey light touch threshold, worse radiological severity as indexed by Kellgren-Lawrence grade (KL), high von-Frey punctate pain intensity and high levels of anxiety (adjusted R2 = 27.1%, F (6,95) = 7.27, P < 0.0001). In model 2, von-Frey light touch threshold, KL, depressive symptoms indexed by Beck Depression Inventory (BDI), level of sleepiness and pain pressure threshold were risk factors for SF12-PCS (adjusted R2 = 31.9%, F (5,96) = 10.5, P < 0.0001). Finally, model 3 showed that WOMACFunc was predicted by BDI, KL and BPI (adjusted R2 = 41%, F (3,98) = 24.42, P < 0.0001). Our data provides an interesting framework to understand the predictors of KOA pain in the elderly and highlights how its related outcomes are affected by disease-specific factors, somatosensory dysfunction and emotional factors.

Phenotypic selection of Brahman bulls: Adaptive typification for breeding programs.

The purpose of this study was to evaluate the relationship between adaptive and productive performance and determine which characteristics related to adaptation could be included as criteria for the selection of Brahman bulls managed in a tropical region. Fifty Brahman bulls were evaluated in relation to productive performance and adaptive capacity to a hot environment. The animals were grouped according to the following characteristics: Respiratory rate (RR), Sweating rate (SR) and coat traits. Three clusters were formed with these different characteristics related to thermoregulation after that these clusters were compared with productive performance that includes weighing of animals, carcass and scrotal circumference. The high adapted group corresponded to the animals with higher productive performance, with heavier animals in terms of weight at 550 days and carcass weight. The principal components analysis (PCA1) showed that carcass weight (0.95), weight at 550 days (0.92) and gain weight (0.71) were characteristics strongly correlated with the performance of the animals. These sets of characteristics were analyzed together (productive characteristics) in a multiple regression. The animals with better productive performance presented shorter hair, a greater capacity to heat dissipate by sweating and a lower rectal temperature. These characteristics were most suitable to inclusion in the breeding programs, resulting in higher economic gains. It is possible to simultaneously select resistant animals to thermal stress as well as to maintain the productivity of the flocks.

A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil.

OBJECTIVE: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. METHODS: We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. RESULTS: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41-96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51-97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). CONCLUSION: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends.

OBJECTIF: Le diagnostic du COVID-19 est un problème critique, principalement dû au manque ou au retard dans les résultats du test. Nous visions à obtenir un modèle pour prédire l'infection par le SRAS-CoV-2 chez les patients suspects signalés au système de surveillance brésilien. MÉTHODES: Nous avons analysé les patients suspects signalés au Système National de Surveillance qui correspondaient à la définition de cas suivante: patients présentant des symptômes respiratoires et de la fièvre, qui se sont rendus dans des régions à transmission locale ou communautaire ou qui ont eu des contacts étroits avec un cas suspect ou confirmé. Sur la base de variables collectées en routine, nous avons obtenu un modèle multiple en utilisant la régression logistique. L'aire sous la courbe caractéristique de fonctionnement du récepteur (AUC) et les indicateurs de précision ont été utilisés pour la validation. RÉSULTATS: Nous avons décrit 1.468 cas de COVID-19 (confirmés par RT-PCR) et 4.271 patients atteints d'autres maladies. Avec un sous-ensemble de données comprenant 80% de patients de Sao Paulo (SP) et de Rio de Janeiro (RJ), nous avons obtenu une fonction qui atteignait une AUC de 95,54% (IC95%: 94,41% - 96,67%) pour le diagnostic de COVID- 19 et une précision de 90,1% (sensibilité 87,62% et spécificité 92,02%). Dans un ensemble de données de validation incluant les 20% restants de patients de SP et de RJ, ce modèle présentait une AUC de 95,01% (92,51% - 97,5%) et une précision de 89,47% (sensibilité 87,32% et spécificité 91,36%). CONCLUSION: Nous avons obtenu un modèle adapté au diagnostic clinique du COVID-19 sur la base des données de surveillance collectées en routine. Les applications de cet outil comprennent l'identification précoce pour un traitement et un isolement spécifiques, l'utilisation rationnelle des tests de laboratoire et des données pour modéliser les tendances épidémiologiques.

Statistical model for heating value of municipal solid waste in Brazil based on gravimetric composition.

Municipal solid waste (MSW) management is a serious problem for public administrations, especially in terms of treatment and final disposal. These wastes have a high energy content and one of the possibilities of treatment is the recovery of energy through thermochemical processes. The parameter used to measure the amount of useful energy available in collected waste when submitted to thermochemical processes is called the lower heating value (LHV), which is usually determined in the laboratory or through empirical models from the literature. To this end, this paper aims to present two models for prediction of the LHV in the municipal solid waste of the municipality of Santo André. Samples were collected from 36 garbage trucks in the above-mentioned city, from September 2015 to January 2016. The models were developed based on the results of the gravimetric composition and laboratory analysis. The technique used to develop the models was the multiple linear regression by least squares method. As a result, the models obtained mean absolute percentage error (MAPE) indexes of 5.09% and 5.52%, considered excellent according to the literature classification. In addition, the calculated LHV of the Santo André municipal waste was 7.03 MJ/kg, which indicates a great potential for energy recovery using thermochemical processes. These are the first LHV prediction models developed in Brazil, which has been a significant accomplishment in Brazil. The proposed models were developed using empirical measurements of moisture in the solid waste and the LHV on samples collected with a statistically representative sampling method.

Impact of economic factors on formal milk collection by the dairy industry in the main producing states in Brazil: a cointegration analysis

The objective of this study was to measure the short- and long-term elasticities of the Gross Domestic Product (GDP), productivity, prices paid to producers, milk powder imports, minimum wages, and exchange ratios in milk collection between 1999 and 2016. For the measurement, the cointegration technique for data in a balanced panel was used, contemplating five states with great representativeness in the Brazilian industrial milk collection: Minas Gerais, Paraná, Rio Grande do Sul, Goiás, and São Paulo. These states corresponded to 73.3% of all formal milk collection in 2016. Results indicated that GDP, productivity, and imports impacted long-term milk collection. Productivity and GDP were the variables with the greatest long-term impact, with elasticities of 1.395 and 1.297, respectively. Minimum wages impacted milk collection to a lesser scale, −0.802; and exchange ratio and imports showed lower coefficients, that is, 0.177 and −0.078, respectively. In the short term, minimum wages was the significant variable to explain the milk collection and presented a coefficient of −0.213. The variables with the greatest impact on formal milk collection during the period analyzed were productivity, which made the production process more efficient, and variation in the Brazilian consumer's income, represented by the GDP variable, which increased the demand for dairy products.(AU)

A simulação da biomassa de aveia por elementos climáticos, nitrogênio e regulador de crescimento / The simulation of the oat biomass by climatic elements, nitrogen and growth regulator

A produção de biomassa de aveia voltada à elaboração de silagem de qualidade é dependente de elementos climáticos e nitrogênio sem ocorrência de acamamento. O objetivo do presente estudo é a definição da dose ideal do regulador de crescimento que possibilite, no máximo, 5% de acamamento de plantas de aveia, bem como a identificação das variáveis potenciais para composição do modelo de regressão linear múltipla com simulação da produtividade de biomassa à elaboração de silagem nas condições de uso do regulador, em reduzida, alta e muito alta fertilização com nitrogênio. O estudo foi conduzido em 2013, 2014 e 2015, em delineamento de blocos ao acaso, com quatro repetições em esquema fatorial 4x3, para doses de regulador (0, 200, 400 e 600mL ha-1) e doses de nitrogênio (30, 90 e 150kg ha-1), respectivamente. A dose de 495mL ha-1 de regulador se mostra eficiente na redução do acamamento de plantas de aveia em condição de reduzida, alta e muito alta fertilização com nitrogênio. A soma térmica, a precipitação, a radiação, a dose de regulador e o nitrogênio qualificam a composição do modelo de regressão linear múltipla, tornando eficiente a simulação da produtividade de biomassa da aveia para silagem ao longo do ciclo.(AU)

The production of oat biomass focused on the development of quality silage is dependent on climatic elements and nitrogen without lodging occurrence. The objective of the study is to define the optimal dose of growth regulator that allows a maximum of 5% oat plant lodging and identify potential variables for composition of multiple linear regression model with productivity simulation of biomass to the preparation of silage in the conditions of low, high and very high fertilization with nitrogen. The study was conducted in 2013, 2014, and 2015 in the randomized block design with four replications in a factorial 4x3, for regulator doses (0, 200, 400 and 600ml ha-1) and nitrogen doses (30, 90 and 150kg ha-1), respectively. The dose of 495mL ha-1 regulator is efficient in reducing the oat plant lodging in condition reduced, high, and very high fertilization with nitrogen. Thermal time, precipitation, radiation, regulator dose and nitrogen dose qualify the composition of the multiple linear regression model, making efficient the biomass oat productivity simulation for silage over the cycle.(AU)

A simulação da biomassa de aveia por elementos climáticos, nitrogênio e regulador de crescimento / The simulation of the oat biomass by climatic elements, nitrogen and growth regulator

A produção de biomassa de aveia voltada à elaboração de silagem de qualidade é dependente de elementos climáticos e nitrogênio sem ocorrência de acamamento. O objetivo do presente estudo é a definição da dose ideal do regulador de crescimento que possibilite, no máximo, 5% de acamamento de plantas de aveia, bem como a identificação das variáveis potenciais para composição do modelo de regressão linear múltipla com simulação da produtividade de biomassa à elaboração de silagem nas condições de uso do regulador, em reduzida, alta e muito alta fertilização com nitrogênio. O estudo foi conduzido em 2013, 2014 e 2015, em delineamento de blocos ao acaso, com quatro repetições em esquema fatorial 4x3, para doses de regulador (0, 200, 400 e 600mL ha-1) e doses de nitrogênio (30, 90 e 150kg ha-1), respectivamente. A dose de 495mL ha-1 de regulador se mostra eficiente na redução do acamamento de plantas de aveia em condição de reduzida, alta e muito alta fertilização com nitrogênio. A soma térmica, a precipitação, a radiação, a dose de regulador e o nitrogênio qualificam a composição do modelo de regressão linear múltipla, tornando eficiente a simulação da produtividade de biomassa da aveia para silagem ao longo do ciclo.(AU)

The production of oat biomass focused on the development of quality silage is dependent on climatic elements and nitrogen without lodging occurrence. The objective of the study is to define the optimal dose of growth regulator that allows a maximum of 5% oat plant lodging and identify potential variables for composition of multiple linear regression model with productivity simulation of biomass to the preparation of silage in the conditions of low, high and very high fertilization with nitrogen. The study was conducted in 2013, 2014, and 2015 in the randomized block design with four replications in a factorial 4x3, for regulator doses (0, 200, 400 and 600ml ha-1) and nitrogen doses (30, 90 and 150kg ha-1), respectively. The dose of 495mL ha-1 regulator is efficient in reducing the oat plant lodging in condition reduced, high, and very high fertilization with nitrogen. Thermal time, precipitation, radiation, regulator dose and nitrogen dose qualify the composition of the multiple linear regression model, making efficient the biomass oat productivity simulation for silage over the cycle.(AU)

Influencia de las estrategias cognitivas de regulación emocional sobre la ansiedad y depresión en universitarios: análisis preliminar / Influence of cognitive emotion regulation strategies on anxiety and depression in college students: a preliminary analysis

Resumen Objetivo: Determinar la influencia de las estrategias cognitivas de regulación emocional sobre la ansiedad y depresión en universitarios. Materiales y métodos: Esta investigación se llevó a cabo con una muestra de 127 individuos (69.3 % mujeres) entre 18 y 57 años. Fue implementado un análisis de regresión múltiple para ansiedad (GAD-2) y depresión (PHQ-2), con las subescalas del CERQ como predictores. Resultados: En ambos casos, los predictores explican una variabilidad elevada de los criterios (R 2 > 50 %). Las estrategias cognitivas de Rumiación y Catastrofización predicen en mayor grado la ansiedad; y Catastrofizacion y Reinterpretación Positiva predicen positiva y negativamente, de forma respectiva, la depresión. Conclusiones: La influencia de las estrategias cognitivas de regulación emocional sobre la ansiedad y depresión es significativa. Se discuten las consecuencias prácticas y las limitaciones del estudio.

Abstract Objective: Determine the influence of cognitive emotion regulation strategies on anxiety and depression in college students Material and methods: This research was conducted on a sample of 127 individuals (69.3 % women) between ages 18 and 57 years old. Was implemented a regression multiple analysis on anxiety (GAD-2) and depression (PHQ-2), with the subscales of CERQ as predictors. Results: In both cases, predictors explain high variability of the criteria (R 2 > 50 %). Cognitive strategies rumination and catastrophizing predicted anxiety; Positive Reinterpretation and catastrophizing and predict positively and negatively, respectively form, depression. Conclusions: The influence of cognitive emotion regulation strategies on anxiety and depression is significant. The practical consequences and limitations are discussed.