The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Thermodynamics of Ising model with infinite-range interactions by generalized canonical ensemble

In this work we present the idea of how generalized ensembles can be used to simplify the operational study of non-additive physical systems. As alternative of the usual methods of direct integration or mean-field theory, we show how the solution of the Ising model with infinite-range interactions is obtained by using a generalized canonical ensemble. We describe how the thermodynamical properties of this model in the presence of an external magnetic field are founded by simple parametric equations. Without impairing the usual interpretation, we obtain an identical critical behaviour as observed in traditional approaches.

The log-exponentiated generalized gamma regression model for censored data

For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.

The negative binomial-beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial-beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.

Assessment of a Neurophysiological Model of the Mandibular Branch of the Facial Nerve in Rats by Electromyography

Objectives: Our objective was to develop an experimental model for the noninvasive and objective evaluation of facial nerve regeneration in rats using a motor nerve conduction test (electromyography). Methods: Twenty-two rats were submitted to neurophysiological evaluation using motor nerve conduction of the mandibular branch of the facial nerve to obtain the compound muscle action potentials (CMAPs). To record the CM APs, we used two needle electrodes that were inserted into the lower lip muscle of the rat. A supramaximal electrical stimulus was applied, and the values of CMAP latency, amplitude, length, area, and stimulus intensity obtained from each side were compared by use of the Wilcoxon test. Results: There was no significant difference (all p > 0.05) in latency, amplitude, duration, area, or intensity of stimuli between the two sides. The amplitudes ranged between 1.61 and 8.30 mV, the latencies between 1.03 and 1.97 ms, and the stimulus intensities between 1.50 and 2.90 mA. Conclusions: This is a noninvasive, easy, and highly reproducible method that contributes to an improvement of the techniques previously described and may contribute to future studies of the degeneration and regeneration of the facial nerve.

Independent predictors and a prognostic model for surgical outcome in refractory frontal lobe epilepsy

Purpose: Refractory frontal lobe epilepsy (FLE) remains one of the most challenging surgically remediable epilepsy syndromes. Nevertheless, definition of independent predictors and predictive models of postsurgical seizure outcome remains poorly explored in FLE. Methods: We retrospectively analyzed data from 70 consecutive patients with refractory FLE submitted to surgical treatment at our center from July 1994 to December 2006. Univariate results were submitted to logistic regression models and Cox proportional hazards regression to identify isolated risk factors for poor surgical results and to construct predictive models for surgical outcome in FLE. Results: From 70 patients submitted to surgery, 45 patients (64%) had favorable outcome and 37 (47%) became seizure free. Isolated risk factors for poor surgical outcome are expressed in hazard ratio (H.R.) and were time of epilepsy (H.R.=4.2; 95% C.I.=.1.5-11.7; p=0.006), ictal EEG recruiting rhythm (H.R. = 2.9; 95% C.I. = 1.1-7.7; p=0.033); normal MRI (H.R. = 4.8; 95% C.I. = 1.4-16.6; p = 0.012), and MRI with lesion involving eloquent cortex (H.R. = 3.8; 95% C.I. = 1.2-12.0; p = 0.021). Based on these variables and using a logistic regression model we constructed a model that correctly predicted long-term surgical outcome in up to 80% of patients. Conclusion: Among independent risk factors for postsurgical seizure outcome, epilepsy duration is a potentially modifiable factor that could impact surgical outcome in FLE. Early diagnosis, presence of an MRI lesion not involving eloquent cortex, and ictal EEG without recruited rhythm independently predicted favorable outcome in this series. (C) 2011 Elsevier B.V. All rights reserved.

Longitudinal familial analysis of blood pressure involving parametric (co)variance functions

Abstract Background For analyzing longitudinal familial data we adopted a log-linear form to incorporate heterogeneity in genetic variance components over the time, and additionally a serial correlation term in the genetic effects at different levels of ages. Due to the availability of multiple measures on the same individual, we permitted environmental correlations that may change across time. Results Systolic blood pressure from family members from the first and second cohort was used in the current analysis. Measures of subjects receiving hypertension treatment were set as censored values and they were corrected. An initial check of the variance and covariance functions proposed for analyzing longitudinal familial data, using empirical semi-variogram plots, indicated that the observed trait dispersion pattern follows the assumptions adopted. Conclusion The corrections for censored phenotypes based on ordinary linear models may be an appropriate simple model to correct the data, ensuring that the original variability in the data was retained. In addition, empirical semi-variogram plots are useful for diagnosis of the (co)variance model adopted.

Effect of parametric uncertainties on the performance of a piezoelectric energy harvesting device

The use of piezoelectric materials for the development of electromechanical devices for the harvesting or scavenging of ambient vibrations has been extensively studied over the last decade. The energy conversion from mechanical (vibratory) to electrical energy is provided by the electromechanical coupling between mechanical strains/stresses and electric charges/voltages in the piezoelectric material. The majority of the studies found in the open literature present a tip-mass cantilever piezoelectric device tuned on the operating frequency. Although recent results show that these devices can be quite effective for harvesting small amounts of electrical energy, little has been published on the robustness of these devices or on the effect of parametric uncertainties on the energy harvested. This work focuses on a cantilever plate with bonded piezoelectric patches and a tip-mass serving as an energy harvesting device. The rectifier and storage electric circuit was replaced by a resistive circuit (R). In addition, an alternative to improve the harvesting performance by adding an inductance in series to the harvesting circuit, thus leading to a resonant circuit (RL), is considered. A coupled finite element model leading to mechanical (displacements) and electrical (charges at electrodes) degrees of freedom is considered. An analysis of the effect of parametric uncertainties of the device on the electric output is performed. Piezoelectric and dielectric constants of the piezoelectric active layers and electric circuit equivalent inductance are considered as stochastic parameters. Mean and confidence intervals of the electric output are evaluated.