DRYING KINETICS AND PARTICLE RESIDENCE TIME IN SPRAY DRYING

A unique experimental equipment for extensive trials on the spray drying kinetics and particles residence time involving “in situ” analysis of the properties of continuous and dispersed phases was designed, built, and tested. Advanced experimental techniques (including laser techniques) to determine current parameters of spray drying process (temperature, humidity, moisture content) and current structure of spray (particle size distribution, particle velocities, etc.) were employed. Full scale spray drying tests of baker's yeast and maltodextrin enabled identification of the effect of process parameters on drying kinetics and spray residence time in the tower. Quantitative relationship describing spray drying kinetics as a function of atomization ratio and drying agent temperature were determined. The experimental results proved that spray residence time was controlled by atomization ratio and airflow rate. Drying kinetics in spray drying process is presented for the first time in the literature.     

An electrically controlled quantum dot based spin current injector

We present a proposal for a fully electrically controllable quantum dot based spin current injector. The device consists of a quantum dot that is strongly coupled to a ferromagnetic electrode on one side and weakly coupled to a nonmagnetic electrode on the other side. The presence of the ferromagnetic electrode results in an exchange field that splits the dot level. We show that this exchange-induced splitting can lead to almost full spin polarization of the current flowing through the device. Moreover, we also demonstrate that the sign of polarization can be changed by the gate or the bias voltage within a switching time in the nanosecond range. Thus, the proposed device can operate as an electrically controlled, rapidly switchable spin current source, which can be realized in various state-of-the-art nanostructures.     

Changes in properties of tar obtained during underground coal gasification process

In this study,the composition of tars collected during a six-day underground coal gasification (UCG) test at the experimental mine 'Barbara'in Poland in 2013 wa...     

Biofilm formation by lactobacilli and resistance to stress treatments

We investigated biofilm formation by six lactobacilli isolates and tested the resistance of biofilm and planktonic Lactobacillus plantarum B1 and Lactobacillus paracasei W1 to ethanol (ET) and acetic acid (AA). The viability of both isolates was considerably affected by ET, with a higher reduction observed in the planktonic cells, whereas AA was harmful only to L. paracasei W1. Cells that had intact (Syto^® 9⁺PI⁻) and compromised (Syto^® 9⁻PI⁺) membranes, and adopted intermediate states with different concentrations of the probes, i.e., Syto^® 9⁺PI⁺ and Syto^® 9⁻PI⁻, were identified following flow cytometry. We linked Syto^® 9⁺PI⁺ to the planktonic cells and clarified on Syto^® 9⁻PI⁻ cells, which after being sorted could regain growth better in biofilm cells. Based on confocal laser scanning microscopy, numerous cells had damaged membranes in the ET-treated biofilm, while half the cells in the AA-treated biofilm maintained their membrane integrity.     

Ensemble Online Classifier Based on the One-Class Base Classifiers for Mining Data Streams

The problem addressed in this study concerns mining data streams with concept drift. The goal of the article is to propose and validate a new approach to mining data streams with concept-drift using the ensemble classifier constructed from the one-class base classifiers. It is assumed that base classifiers of the proposed ensemble are induced from incoming chunks of the data stream. Each chunk consists of prototypes and information about whether the class prediction of these instances, carried-out at earlier steps, has been correct. Each data chunk can be updated by using the instance selection technique when new data arrive. When a new data chunk is formed, the ensemble model is also updated on the basis of weights assigned to each one-class classifier. In this article, two well-known instance-based learning algorithms—the CNN and the ENN—have been adopted to solve the one-class classification problems and, consequently, update the proposed classifier ensemble. The proposed approaches have been validated experimentally, and the computational experiment results are shown and discussed. The experiment results prove that the proposed approach using the ensemble classifier constructed from the one-class base classifiers with instance selection for chunk updating can outperform well-known approaches for data streams with concept drift.