This paper presents the current stage of the development of EA-MOSGWA – a tool for identifying causal genes in Genome Wide Association Studies (GWAS). The main goal of GWAS is to identify chromosomal regions which are associated with a particular disease (e.g. diabetes, cancer) or with some quantitative trait (e.g height or blood pressure). To this end hundreds of thousands of Single Nucleotide Polymorphisms (SNP) are genotyped. One is then interested to identify as many SNPs as possible which are associated with the trait in question, while at the same time minimizing the number of false detections.

The software package MOSGWA allows to detect SNPs via variable selection using the criterion mBIC2, a modified version of the Schwarz Bayesian Information Criterion. MOSGWA tries to minimize mBIC2 using some stepwise selection methods, whereas EA-MOSGWA applies some advanced evolutionary algorithms to achieve the same goal. We present results from an extensive simulation study where we compare the performance of EA-MOSGWA when using different parameter settings. We also consider using a clustering procedure to relax the multiple testing correction in mBIC2. Finally we compare results from EA-MOSGWA with the original stepwise search from MOSGWA, and show that the newly proposed algorithm has good properties in terms of minimizing the mBIC2 criterion, as well as in minimizing the misclassification rate of detected SNPs.

The electron field and photo-field emission from GaN nanostructures has been analyzed in this review. In order to explain the obtained experimental results, a model was proposed taking into account the change in carrier concentration distribution in the main and the satellite valley during the emission process. The lowering of work function (due to the increased number of carriers in the satellite valley) can explain the decrease in the Fowler-Nordheim plot slope. It was shown that the energy difference between the main and satellite valley in GaN was decreased in the case of quantum confinement, thus increasing the probability of electron transition from Γ to X valley at same electric fields.

Investigations of electron photo-field emission demonstrated that the Fowler–Nordheim plots of the emission current have different slopes for nonilluminated and illuminated devices. A model based on the electron emission from valleys having different specific electron affinities is proposed to explain the experimental results. In the absence of illumination the emission takes place only from the lower valley. Upon UV illumination and presence of a high electric field at the emitter tip, the upper valley of the conduction band appears to be occupied by electrons generated at the valence band.

The aim of this article is to present a modern method of convective drying intensification caused by the external action of ultrasound. The purpose of this study is to discover the mechanism of ultrasonic interaction between the solid skeleton and the moisture in pores. This knowledge may help to explain the enhancement of drying mechanism affected by ultrasound, particularly with respect to biological products like fruits and vegetables. The experimental kinetics tests were conducted in a hybrid dryer equipped with a new ultrasonic generator. The drying kinetics curves determined on the basis of drying model developed by the author were validated with those by the ones obtained from experimental tests. The intensification of heat and mass transfer processes due to ultrasound induced heating effect and vibration effect are analysed. The obtained results allow to state that ultrasound makes drying processes more effective and enhance the drying efficiency of biological products without significant elevation of their temperature.

Simulations of turbulent mixing in two types of jet mixers were carried out using two CFD models, large eddy simulation and κ-ε model. Modelling approaches were compared with experimental data obtained by the application of particle image velocimetry and planar laser-induced fluorescence methods. Measured local microstructures of fluid velocity and inert tracer concentration can be used for direct validation of numerical simulations. Presented results show that for higher tested values of jet Reynolds number both models are in good agreement with the experiments. Differences between models were observed for lower Reynolds numbers when the effects of large scale inhomogeneity are important.

Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.