Generated power of thermal power plant is closely related to efficient work of cooling towers, via condenser pressure affected by output temperature of cooling water. Performance characteristics of cooling system can be rated via several parameters such as: thermal effectiveness, Merkel number, number of transfer units, and overall heat and mass transfer coefficient. Results gathered during acceptance test of cooling system of thermal power plant Kakanj (unit 7) which consists of 12 wet counterflow induced draft cooling towers, are used to evaluate its most important performance characteristics. It is shown that some tower performance characteristic vary during the day more than others due to their dependence on climatic parameters, particularly air wet bulb temperature. Different approaches and methods (analytical and empirical) for evaluation of tower performance are discussed in order to define the most appropriate performance characteristic and calculation method which can be used for establishing the optimal working mode of analysed cooling system.

Due to limited computational resources, acoustic models of early automatic speech recognition ( ASR ) systems were built in low-dimensional feature spaces that incur considerable information loss at the outset of the process. Several comparative studies of automatic and human speech recognition suggest that this information loss can adversely affect the robustness of ASR systems. To mitigate that and allow for learning of robust models, we propose a deep 2 D convolutional network in the waveform domain. The first layer of the network decomposes waveforms into frequency sub-bands, thereby representing them in a structured high-dimensional space. This is achieved by means of a parametric convolutional block defined via cosine modulations of compactly supported windows. The next layer embeds the waveform in an even higher-dimensional space of high-resolution spectro-temporal patterns, implemented via a 2 D convolutional block. This is followed by a gradual compression phase that selects most relevant spectro-temporal patterns using wide-pass 2 D filtering. Our results show that the approach significantly outperforms alternative waveform-based models on both noisy and spontaneous conversational speech ( 24% and 11% relative error reduction, respectively). Moreover, this study provides empirical evidence that learning directly from the waveform domain could be more effective than learning using hand-crafted features. by means of a non-parametric 2 D convolutional layer. is followed

Introduction. Human monocytes are heterogeneous and plastic cell population with the ability to undergo phenotypic and functional changes as a response to a stimulus from a local microenvironment. Our aim was to determine the potential of human monocytes to differentiate into different cell populations depending on two different cytokines (IL-4 and IL-6) added to cultures as well as to compare their phenotypical and functional characteristics. Methods. Peripheral blood mononuclear cells (PBMNC) were isolated from buffy coats of healthy donors. Monocytes, which were separated from PBMNC by plastic adherence, had been cultivated in Dendritic cell (DC), serum free medium for 5 days, either with granulocyte/macrophage colony-stimulating factor (GM-CSF) alone or with GM-CSF, with addition of interleukin 4 (IL-4) or interleukin 6 (IL-6), respectively. After cultivation, phenotypic characteristics of these cells were analyzed by flow cytometry, whereas the levels of produced cytokines in culture supernatants were quantified by ELISA. The potential of differentiated cells to modulate the proliferation of allogeneic T cells was examined by co-cultivation of these cells with PBMNC. Results. GM-CSF differentiated monocytes into M0/M1 macrophages (MØ). The combination of GM-CSF and IL-4 favoured differentiation of immature DC, whereas GM-CSF and IL-6 transformed monocytes into monocytic myeloid derived suppressor cells (M-MDSC). All cell populations expressed typical monocyte/macrophage markers such as CD14, CD11b, CD16 and CD33, HLA-DR, CD209 and CD86, a co-stimulatory marker. DC and M-MDSC expressed CD1a and CD11c, in contrast to M0/M1 MØ. The expression of HLA-DR, CD1a, CD209 and CD86 was highest on DC. The expression of CD33 and CD16 was highest on M-MDSC, followed by lowest expression of HLA-DR. The potential of promoting T-cell proliferation was highest in cultures of PBMNC with DC, whereas M-MDSC had the opposite, suppressive, effect. These differences correlated with highest production of immunosuppressive cytokines such as IL-10, IL-27 and TGF-b by M-MDSC. Conclusion. This study confirmed the differentiation plasticity of human monocytes, which are influenced by cytokines added in cultures. Phenotypic characteristics of these cells correlated with the production of cytokines involved in modulation of T-cell proliferation.