Atmospheric downward longwave radiation flux (L↓) is a variable that directly influences the surface net radiation and consequently, weather and climatic conditions. Measurements of L↓ are scarce, and the use of classical models depending on some atmospheric variables may be an alternative. In this paper, we analyzed L↓ measured over the Brazilian Pampa biome. This region is located in a humid subtropical climate zone and characterized by well defined seasons and well distributed precipitation. Furthermore, we evaluated the performance of the eleven classical L↓ models for clear sky with one-year experimental data collected in the Santa Maria experimental site (SMA) over native vegetation and high relative humidity throughout the year. Most of the L↓ estimations, using the original coefficients, underestimated the experimental data. We performed the local calibration of the L↓ equations coefficients over an annual period and separated them into different sky cover classifications: clear sky, partly cloudy sky, and cloudy sky. The calibrations decreased the errors, especially in cloudy sky classification. We also proposed the joint calibration between the clear sky emissivity equations and cloud sky correction function to reduce errors and evaluate different sky classifications. The results found after these calibrations presented better statistical indexes. Additionally, we presented a new empirical model to estimate L↓ based on multiple regression analysis using water vapor pressure and air temperature. The new equation well represents partial and cloudy sky, even without including the cloud cover parameterization, and was validated with the following five years in SMA and two years in the Cachoeira do Sul experimental site (CAS). The new equation proposed herein presents a root mean square error ranging from 13 to 21 Wm⁻² and correlation coefficient from 0.68 to 0.83 for different sky cover classifications. Therefore, we recommend using the novel equation to calculate L↓ over the Pampa biome under these specific climatic conditions.

Knowledge of soil thermal properties (diffusivity (k) and conductivity (λ)) is important to understand the soil–plant–atmosphere interaction related to the physical and biological processes associated with energy transfer and greenhouse gas exchanges. The incorporation of all the physical processes that occur in the energy transfer in the soil is a challenge in order to correctly estimate soil thermal properties. In this work, experimental measurements of soil temperature and soil heat flux obtained in a silty clay loam soil covered by native grassland located in the Brazilian Pampa biome were used to estimate soil thermal properties using different methods including the influence of the soil water content at different soil depths in heat transfer processes. The λ was estimated using the numerical solution of the Fourier equation by the Gradient and Modified Gradient methods. For the surface layer, the results for both models show large variability in daily values, but with similar values for the annual mean. For λ at different soil depths, both models showed an increase of approximately 50% in the λ value in the deeper layers compared to the surface layer, increasing with depth in this soil type. The k was estimated using analytical and numerical methods. The analytical methods showed a higher variability and overestimated the values of the numerical models from 15% to 35%. The numerical models included a term related to the soil water content. However, the results showed a decrease in the mean value of k by only 2%. The relationship between thermal properties and soil water content was verified using different empirical models. The best results for thermal conductivity were obtained using water content in the surface layer (R² > 0.5). The cubic model presented the best results for estimating the thermal diffusivity (R² = 0.70). The analyses carried out provide knowledge for when estimating soil thermal properties using different methods and an experimental dataset of soil temperature, heat flux and water content, at different soil depths, for a representative soil type of the Brazilian Pampa biome.

Soil thermal conductivity is an important parameter for understanding soil heat transfer. It is difficult to measure in situ with available instruments. This work aims to propose a numerical model to estimate the thermal conductivity from the experimental measurements of soil heat flux and soil temperature. The new numerical model is based on the Fourier Law adding a constant empirical parameter to minimize the uncertainties contained in the data from field experiments. Numerically, the soil thermal conductivity is obtained by experimental linear data fitting by the Least Squares Method (LSM). This method avoids numerical indetermination when the soil temperature gradient or soil heat flux is very close to zero. The new model is tested against the different numerical methodology to estimate the soil heat flux and validated with field experimental data. The results indicate that the proposed model represents the experimental data satisfactorily. In addition, we show the influence of the different methodologies on evaluating the dependence of the thermal conductivity on the soil water content.

Parkinson's disease (PD) presents symptoms based mainly on motor changes; however, psychomotor and non-motor manifestations, such as anxiety-like behavior, learning, and memory deficits, also have a relevant impact on the individual's quality of life. Recently, 7-chloro-4-(phenylselanyl) quinoline (4-PSQ) showed positive effects on motor and biochemical behavioral parameters in a model of PD, in Drosophila melanogaster. The present study aimed to evaluate the effects of 4-PSQ in the psychomotor and non-motor alterations in a mimetic model of PD induced by rotenone (ROT) in Drosophila melanogaster. Flies of both sexes with two days of age were used and divided into four groups: [1] control group, [2] 4-PSQ (25 μM) group, [3] ROT (500 μM) group, and [4] 4-PSQ (25 μM) + ROT (500 μM) group, exposed to a diet with ROT and/or 4-PSQ for seven days, according to each group. The open field and equilibrist test, aversive phototaxis suppression assay, light/dark exploration test, and the acetylcholinesterase (AChE) activity were performed. The treatment with 4-PSQ was effective in reversing the ROT-induced deficits in flies, improving spontaneous locomotor activity, learning, memory, and anxiety-like behavior. And the 4-PSQ was able to prevent the increase in the AChE activity in the head and body of flies exposed to ROT. The treatment with 4-PSQ was effective in the prevention of psychomotor and non-motor deficits in this mimetic model of PD, with effects possibly related to its anticholinesterase activity, associated with the multi-target action of 4-PSQ.

O Pensamento Computacional (PC) pode ser visto como uma metodologia para solucionar problemas das mais diversas áreas com fundamentação em conceitos da Ciência da Computação. A Base Nacional Comum Curricular já faz referências a essa metodologia e habilidades relacionadas, porém de forma pontual e, principalmente, relacionadas à unidade temática Álgebra da Matemática. Neste trabalho, são apresentadas habilidades que relacionam diferentes áreas temáticas da Matemática do primeiro ano do Ensino Fundamental e alguns conceitos do PC com o objetivo de ilustrar a abrangência e a viabilidade da integração desta metodologia no currículo desde os primeiros anos do ensino.