Abstract
The Power Normal (PN) family of distributions is obtained by inverting the Box-Cox (BC) transformation over a truncated normal (TN) (or for some cases normal) random variable. In this paper we explore the PN distribution. We give a formula for the ordinary moments and considering the bivariate PN (BPN) distribution we calculate the marginal and conditional probability density functions (pdf). We prove that they are not univariate PN distributed. We also calculate the correlation curve and we fit a power law model.

Abstract

Abstract
This study aimed to estimate the daily crop evapotranspiration (ETc) of soilless cut 'Red Naomi' roses, cultivated in a commercial glass greenhouse, using climatic and crop predictors. A multiple stepwise regression technique was applied for estimating ETc using the daily relative humidity, stem leaf area and number of leaves of the bended stems. The model explained 90% of the daily ETc variability (R-2 = 0.90, n = 33, P < 0.0001) measured by weighing lysimeters. The mean relative difference between the observed and the estimated daily ETc was 9.1%. The methodology revealed a high accuracy and precision in the estimation of daily ETc.

Abstract
Rheumatic Heart Disease is the serious consequence of repeated episodes of Acute Rheumatic Fever, an autoimmune reaction of a group A streptococcal infection. It's the major cause of heart valve damage, responsible for thousands of deaths per year. The key evidences are changes in the thickness, shape and mobility of the mitral valve leaflets. In this work, an automatic approach is proposed for pathology classification based on motion patterns anterior mitral leaflet, as observed in an echocardiographic video, is proposed. The motion points are extracted from the morphological skeleton of the anterior mitral leaflet and motion velocity and directionality is observed. This data is processed with the Discrete Fourier Transform, and used by several types of classifiers to perform the pathology classification. A Random Forest classifier utilizing the processed data archived a 71% precision and negative predicated value of 58%.

Abstract
In this article, we present a solution for training the mouse input device handling, in order to improve the users' digital skills. Knowing that the widespread access to the computer and the Internet is still made by the use of traditional devices, such as, the keyboard and mouse device (and these devices does not provide natural interaction between people and technology), it is necessary to train for the correct handling. Specifically, mouse usage requires movement control, hand posture and finger positioning, as well as, precision clicks for different functions (one click, two clicks, drag and click) This training is often done in real interaction situations, that could led to inefficiency of use the digital environments and also user's frustration and discouragement. Thus, using simple and intuitive interfaces and providing a set of activities, we allow users to learn and train the motor coordination and basic movements with the mouse. The main results of the study showed that users had a pleasant experience of interaction, considered the interfaces intuitive and affirmed their availability to use it as a training tool.

Abstract