The potential to control the number of the spin-wave band gaps of a magnonic crystal (MC) by variation of its geometry is investigated by numerical simulations. The magnonic crystal is represented by a micro-sized planar ferromagnetic waveguide with periodically variable width. By choosing a step-like or sinusoidal variation of the width, the magnonic crystal reveals multiple or single band gaps, respectively. This allows for additional degrees of freedom in the design of MCbased microwave filters and phase shifters with desired characteristics. The MCs band gaps have been studied in the space and frequency domains exploring the spin-wave spectrum dependence on the probing position inside the magnonic crystal.
In this chapter, a novel technique for investigation of natural and laboratory cyanobacterial cultures is presented. The technique is based on a strict relation between the intrinsic singlecell fluorescence emission spectra of cyanobacteria and the physiological state of the whole culture. It will be shown else that the single-cell fluorescence spectra for different species are steady enough to conduct a taxonomic analysis of cyanobacterial cultures based on a common statistical data evaluation among the parameters extracted from a set of such spectra. Several examples are given to illustrate the power and simplicity of a new technique, which can become a promising tool for automation of production in the cyanobacterial biotechnology, as well as give a valuable contribution to the development of innovative approaches in environmental monitoring of harmful algal blooms.
In this article a feedforward error backpropagation artificial neural network is investigated and the analysis of its illogical behaviour is presented. The problem of illogical behavior arises in various models of artificial neural networks. In the presented work a classifying artificial neural network (CANN) is considered and several learning algorithms were implemented and compared. CANN was designed for automatic differentiaition of cyanobacterial strains during environmental monitoring and some of trained networks demonstrated illogical behavior in further testing. Several original techniques were elaborated for estimation of the quality and accuracy of classification in addition to the traditional ones. Novel visualization methods were suggested for classification and generalization results representation.
Self-fluorescence of light-harvesting complex is a powerful tool for investigation of living photosynthetic microorganisms. As the physiological state of single cells of such microorganisms is closely related to the operation and activity of photosynthetic system, any variations in spectroscopic properties of their self-fluorescence indicate the changes in their physiological state. In this chapter, we present several applications of confocal laser scanning microscopy (CLSM) for investigation of living photosynthetic cells. A set of ordinary CLSM techniques will be applied for studying of cyanobacteria (or blue-green algae) such as 3D imaging, spectral imaging, microscopic spectroscopy, and fluorescence recovery after photobleaching (FRAP). Cyanobacteria were chosen as a model microorganism due to their great importance for different scientific and biotechnological applications. Cyanobacteria are the most ancient photosynthetic microorganisms on Earth. Nowadays, cyanobacteria are one of the most wide-spreaded organisms in nature, and the ecological aspect in their investigation is quite valuable. On the other hand, thousand strains belonging to different species are cultivated in biolaboratories all over the world for different biotechnological applications such as biofuel cells, food production, pharmaceuticals, fertilizers, etc. Thus, the noninvasive spectroscopic methods are quite important for monitoring of physiological state of cyanobacterial cultures and other photosynthetic microorganisms.
It is well-known that photosynthetic cells of various microalgae species display distinct fluorescent properties. The efficiency of self-fluorescence excitation and emission at different wavelengths depends on the structure of photosynthetic system and particularly on the structure of antenna complex of specific strains. The peculiar structure of blue-green algae light-harvesting complex allows to discriminate and classify known and new cells up to species/strain level by means of microscopic spectroscopy. In this chapter, a novel fluorescent spectroscopic technique for microalgae species discrimination will be presented. This method is based on a special data processing of a set of fluorescent spectra, obtained from a single photosynthetic cell of microalgae, particularly from cyanobacterial cells. According to the presented technique, single-cell self-fluorescence spectra are recorded by means of confocal laser scanning microscopy (CLSM), and data processing is conducted via linear discriminant analysis (LDA) and artificial neural networks (ANN).
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