Non-Gaussian Geostatistical Modeling using (skew) t Processes

We propose a new model for regression and dependence analysis when addressing spatial data with possibly heavy tails and an asymmetric marginal distribution. We first propose a stationary process with $t$ marginals obtained through scale mixing of a Gaussian process with an inverse square root process with Gamma marginals. We then generalize this construction by considering a skew-Gaussian process, thus obtaining a process with skew-t marginal distributions. For the proposed (skew) $t$ process we study the second-order and geometrical properties and in the $t$ case, we provide analytic expressions for the bivariate distribution. In an extensive simulation study, we investigate the use of the weighted pairwise likelihood as a method of estimation for the $t$ process. Moreover we compare the performance of the optimal linear predictor of the $t$ process versus the optimal Gaussian predictor. Finally, the effectiveness of our methodology is illustrated by analyzing a georeferenced dataset on maximum temperatures in Australi

Gate induced enhancement of spin-orbit coupling in dilute fluorinated graphene

We analyze the origin of spin-orbit coupling (SOC) in fluorinated graphene using Density Functional Theory (DFT) and a tight-binding model for the relevant orbitals. As it turns out, the dominant source of SOC is the atomic spin-orbit of fluorine adatoms and not the impurity induced SOC based on the distortion of the graphene plane as in hydrogenated graphene. More interestingly, our DFT calculations show that SOC is strongly affected by both the type and concentrations of the graphene's carriers, being enhanced by electron doping and reduced by hole doping. This effect is due to the charge transfer to the fluorine adatom and the consequent change in the fluorine-carbon bonding. Our simple tight-binding model, that includes the SOC of the $2p$ orbitals of F and effective parameters based on maximally localized Wannier functions, is able to account for the effect. The strong enhancement of the SOC induced by graphene doping opens the possibility to tune the spin relaxation in this material.Comment: 9 pages, 8 figure

Diffusion of fluorine adatoms on doped graphene

We calculate the diffusion barrier of fluorine adatoms on doped graphene in the diluted limit using Density Functional Theory. We found that the barrier $\Delta$ strongly depends on the magnitude and character of the graphene's doping ($\delta n$): it increases for hole doping ($\delta n<0$) and decreases for electron doping ($\delta n>0$). Near the neutrality point the functional dependence can be approximately by $\Delta=\Delta_0-\alpha\, \delta n$ where $\alpha\simeq6\times10^{-12}$ meVcm$^2$. This effect leads to significant changes of the diffusion constant with doping even at room temperature and could also affect the low temperature diffusion dynamics due to the presence of substrate induced charge puddles. In addition, this might open up the possibility to engineer the F dynamics on graphene by using local gates.Comment: 4 pages, 4 figure

Effect of stress and/or field annealing on the magnetic behavior of the „Co77Si13.5B9.5…90Fe7Nb3 amorphous alloy

Variations of coercive field, induced magnetic anisotropy, and saturation magnetostriction constant in sCo77Si13.5B9.5d90Fe7Nb3 amorphous ribbons submitted to stress and/or axial magnetic-field annealing are reported. The annealing was carried out by using the Joule-heating effect saverage temperature values of the sample corresponding to the intensity of the electrical current were 273, 378, 409, and 445 °Cd and the applied stress and axial magnetic field during the thermal treatments were 500 MPa and 750 A/m, respectively. As a result of these treatments, a uniaxial in-plane magnetic anisotropy, which affects drastically the soft magnetic character of the samples, was developed.Ministerio de Ciencia y Tecnología de España-MAT2001-0082-C04-0

Black adrenal adenoma

Indexación: Web of Science; Scielo.Introduction: The black adenoma is a rare tumor of the adrenal gland. Clinical case: A male patient treated for lung carcinoma, was found to have an incidental adrenal mass. Due to a suspicious of metastatic disease, a laparoscopic adrenalectomy was done. The biopsy showed a black adenoma. After 11 years of follow-up, there is no evidence of recurrence. Conclusion: The black adenoma of the adrenal gland is a rare, benign and non-functioning tumor. The accurate diagnosis is done only by histological studies. Key words: Incidentaloma, black adenoma, adrenal gland.Introducción: El adenoma negro de la glándula suprarrenal, o Black adenoma, es una patología de baja frecuencia dentro las masas suprarrenales. Caso clínico: Presentamos el caso de un paciente tratado por un Cáncer pulmonar, con el diagnóstico incidental de una masa suprarrenal izquierda que requirió extirpación laparoscópica por la sospecha de metástasis. La biopsia confirmó la presencia de un Adenoma negro. El paciente se encuentra vivo 11 años después. Conclusión: El adenoma negro es un tumor suprarrenal raro, benigno, no funcionante, cuyo diagnóstico es solamente histológico. Palabras clave: Adenoma suprarrenal, metástasis, adenoma negro.http://ref.scielo.org/sb747

COVIDetect: A Desktop Application as a Diagnostic Tool for Novel Coronavirus (COVID-19) Pneumonia in Chest X-ray Images Using Convolutional Neural Network

The COVID-19 pandemic has heavily affected the well-being of people worldwide. Current diagnostic tools, like the RT-PCR, are expensive and time-consuming; thus, there is a need for cheaper and faster means of COVID-19 detection. This study proposes using a desktop application with a convolutional neural network (CNN) and visual analysis as a supplementary diagnostic tool for detecting COVID-19 pneumonia in chest X-ray images. The CNN used is a sequential Keras model that was trained and tested through eight epochs using an augmented dataset. Random data augmentation techniques applied were rotation and horizontal flipping, which increased the total images used to 13,584. Visual analysis was created using the Grad-CAM algorithm to determine patterns in chest X-ray images. These were implemented in a desktop application and evaluated by a professional pulmonologist. Results showed that the CNN achieved an average accuracy rate of 97.96% among the three classes, which was superior among related studies. The CNN also achieved a precision, recall, and F1-score of 99.67%, 99.62%, and 99.64% respectively for COVID-19 pneumonia, 99.26%, 94.83%, and 96.99% respectively for viral pneumonia, and 95.12%, 99.42%, and 97.22% respectively for normal chest X-ray images. Meanwhile, the visual analysis was also accurate, as evaluated by a professional pulmonologist, where patterns of haziness were determined. Hence, this could serve as an effective supplementary diagnostic tool for healthcare professionals for faster and more accurate diagnosis of COVID-19 and viral pneumonia patients