Insight into the BiFeO3 flash sintering process by in-situ energy dispersive X-ray diffraction (ED-XRD)

The sintering mechanism of BiFeO₃ has been investigated in-situ by energy dispersive X-ray diffraction (ED-XRD) using a high-energy white collimated X-ray beam from the Advanced Photon Source (Argonne National Laboratories). Such radiation is very penetrating thereby allowing measurements of the sample even when placed inside the flash sintering set up. Additionally, the fast ED-XRD measurements permit monitoring the flash sintering process by providing information about phase composition and sample temperature in real time. Moreover, profile scans, obtained by moving the stage vertically while recording the ED-XRD spectra, permit investigating the homogeneity of the flash for the entire length of the sample. All experiments have been complemented by ex-situ studies. It has been concluded that flash sintering of BiFeO₃ is a homogeneous process without any directionality effects. Furthermore, flash sintering takes place at quite low temperatures (below the Tc $\approx$ 830?°C), which may be related to the high quality of the samples, as pure, highly insulating ceramics without evidence of secondary phases with a homogenous nanostructured grain size distribution are obtained by this technique. Moreover, it is also evidenced that the rapid heating of the sample does not seem to justify, at least by itself, the densification process. Therefore, it appears that the electric current should play a role in the enhanced mobility during the sintering process.     

The Covariance Least-Squares Algorithm for Spectral Estimation of Processes of Short Data Length

A new method for generating the autoregressive (AR) process parameters for spectral estimation is introduced. The method fits AR models to the data optimally in the sense of minimizing the sum of squares of the error covariance function within the model prediction region, and is thus designated as the Covariance Least-Squares (CLS) algorithm. This minimization is shown to be identical with minimizing the weighted average one-step, linear prediction errors with adaptive weights corresponding to the energy of the data within the prediction region. The CLS algorithm is compared to the Least-Squares (LS) algorithm [1], [2] by simulation and asymptotic properties. It is shown that the CLS method combines all the desirable properties of the comparison algorithm with improved robustness in the presence of nonstationarity, namely, additive transients and envelope modulation. It is also shown that the CLS algorithm provides asymptotically unbiased AR parameters, a property also shared by the comparison LS algorithm.     

Maximum likelihood parameter estimation from incomplete data viathe sensitivity equations: the continuous-time case

This paper deals with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (EM) algorithm. It is shown that the EM algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically implemented. The methodology exploits the relationships between incomplete and complete data, log-likelihood and its gradient     

Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines

Cardiotocography is the main method used for fetal assessment in every day clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting, therefore, a promising new automatic methodology for the prediction of metabolic acidosis.     

Two iterative image restoration algorithms with applications to nuclear medicine

Two methods for recovering an image that has been degraded while being processed are presented. The restoration problem is formulated as a constrained optimization problem in which a measure of smoothness based on the second derivatives of the restored image is maximized subject to the constraint that noise energy is equal to the energy in the difference between the distorted and blurred images. The approach is based on the Lagrange multiplier method. The first algorithm reduces the problem to the computation of few discrete Fourier transforms and allows control of the degree of sharpness and smoothness of the restored image. The second algorithm with weight matrices included allows the handling of edges and flat regions in the image in a pleasing manner for the human visual system. In this case the iterative conjugate gradient method is used in conjunction with the discrete Fourier transform to minimize the Lagrangian function. The application of these algorithms to nuclear medicine images is presented.     

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.     

A Simulation Tool to Predict the Impact of Soil Topologies on Coupling Between a Light Rail System and Buried Third-Party Infrastructure

The production of stray currents by DC light rail systems leads to the corrosion of the supporting and third-party infrastructure in close proximity to the rail system. This paper simulates two parallel tracks that are occupied by two trains: one on each track. This type of modeling constitutes a case study that is utilized to investigate the effect of soil topologies on the corrosion performance of a floating DC light rail system focusing on the supporting and third-party infrastructure. The modeling technique used involves the accurate computation of the shunt and series parameters for use in a resistive-type model using a commercially available software package. The results demonstrate the importance that soil resistivity has on the corrosion risk to traction system and third-party infrastructure. Such information could ultimately be used to vary the level of stray current protection across a light rail system to ensure a consistent lifetime across the whole system.