ABSTRACT The saturated hydraulic conductivity (Ks) is an essential property for modeling water and contaminants movement into aquifers. However, Ks is extremely variable, even when considering nearby locations, which poses a challenge for modeling at catchment scales. Field measurements of Ks are most of the time expensive, time-consuming and labor-intensive. This study aimed to obtain, for modeling purposes, and using pedotransfer functions (PTFs), a composite value of Ks at a catchment scale, in a recharge area of the Guarani Aquifer System. Soil samples were taken across the study area, and the Ks for each sampling point were determined by several PTF methods. At the same locations, Ks field measurements were taken using a Guelph permeameter. Average values of Ks for all the sampling points calculated by PTFs were similar to the average value obtained by field measurements. The use of PTFs proved to be a faster and simpler method to efficiently determine the Ks value for the watershed and to capture the stochastic variation in terms of soil pore combination at the watershed scale.

RESUMO A condutividade hidráulica saturada (Ks) é uma propriedade essencial para modelagem do fluxo de água e contaminantes da zona não saturada em direção aos aquíferos. Entretanto, Ks é extremamente variável, mesmo considerando-se locais muito próximos, o que torna a modelagem em escala de bacia de drenagem um desafio. Medições de Ks em campo são na maioria das vezes dispendiosas, demoradas e trabalhosas. O objetivo desse estudo é obter, para fins de modelagem computacional, e por meio de funções de pedotransferência (FPTs), um valor representativo de Ks para uma área de recarga do Sistema Aquífero Guarani. Amostras de solo foram coletadas por toda área de estudo e os valores de Ks em cada ponto de amostragem determinados por meio de FPTs. Nos mesmos pontos, foram feitas medições de Ks por meio de um permeâmetro Guelph. Os valores médios de Ks calculados por meio de FPTs para todos os pontos de amostragem foram semelhantes aos valores médios obtidos pelas medições de campo. O uso de FPTs provou ser um método mais rápido e simples para determinar com eficiência o valor de Ks da área de recarga e para capturar a variação estocástica em termos de combinação de poros do solo na escala considerada.

This study evaluated the biomechanical influence of apical bone anchorage on a single subcrestal dental implant using three-dimensional finite element analysis (FEA). Four different bone anchorage designs were simulated on a posterior maxillary segment using one implant with platform switching and internal Morse taper connection as follows: 2 mm subcrestal placement with (SW) or without (SO) the implant apex engaged into the cortical bone or position at bone level with anchorage only in the crestal cortical (BO) bone or with bicortical fixation (BW). Each implant received a premolar crown, and all models were loaded with 200 N to simulate centric and eccentric occlusion. The peak tensile and compressive stress and strain were calculated at the crestal cortical, trabecular, and apical cortical bone. The vertical and horizontal implant displacements were measured at the platform level. FEA indicated that subcrestal placement (SW and SO) created lower stress and strain in the crestal cortical bone compared with crestal placement (BO and BW models). The SW model exhibited lesser vertical and horizontal implant micromovement compared with the SO and BO models under eccentric loading; however, stress and strain were higher in the apical cortical bone. The BW model exhibited the lowest implant displacement. These results indicate that subcrestal placement decreases the stress in the crestal cortical bone of dental implants, regardless of apical anchorage; however, apical cortical anchorage can be effective in limiting implant displacement. Further studies are required to evaluate the effects of possible remodeling around the apex on the success of subcrestal implants.

The Gaussian curve is a mathematical model for the distribution, i.e, it is an idealized description, which offers a concise picture of the overall pattern of data. In conducting an analysis is interesting to use graphics to explore data, to assess the relationships between variables, summarize findings and facilitate the interpretation of statistical results. From the data collected by weighing matchsticks presented a simple example for teaching normal distribution in the discipline of Statistics Applied Chemistry from the description of data and inferences, providing principles and methodologies that assist in the observation and participation by students in the teaching-learning process. The data collected is a good example of the normal distribution of data.

La curva de Gauss es un modelo matemático para la distribución, es decir, es una descripción idealizada, que ofrece una visión concisa del patrón general de los datos. En la realización de un análisis es interesante el uso de gráficos para explorar los datos, para evaluar las relaciones entre las variables, resumir los resultados y facilitar la interpretación de los resultados estadísticos. De los datos recogidos por el peso de cerillas se presentó un ejemplo sencillo para la enseñanza de la distribución normal en la disciplina de la Estadística Aplicada a Química proporcionando principios y metodologías que auxilian en la observación y participación de los alumnos en el proceso enseñanza y aprendizaje. Los datos recogidos son un buen ejemplo de la distribución normal de los datos.