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Photosynthesis is a process which converts light energy into energy contained in the chemical bonds of organic compounds by photosynthetic pigments such as chlorophyll (Chl a, b, c, d, f) or bacteriochlorophyll. It occurs in phototrophic organisms, which include higher plants and many types of photosynthetic bacteria, including cyanobacteria. In the case of the oxygenic photosynthesis, water is a donor of both electrons and protons, and solar radiation serves as inexhaustible source of energy. Efficiency of energy conversion in the primary processes of photosynthesis is close to 100%. Therefore, for many years photosynthesis has attracted the attention of researchers and designers looking for alternative energy systems as one of the most efficient and eco-friendly pathways of energy conversion. The latest advances in the design of optimal solar cells include the creation of converters based on thylakoid membranes, photosystems, and whole cells of cyanobacteria immobilized on nanostructured electrode (gold nanoparticles, carbon nanotubes, nanoparticles of ZnO and TiO2). The mode of solar energy conversion in photosynthesis has a great potential as a source of renewable energy while it is sustainable and environmentally safety as well. Application of pigments such as Chl f and Chl d (unlike Chl a and Chl b), by absorbing the far red and near infrared region of the spectrum (in the range 700-750 nm), will allow to increase the efficiency of such light transforming systems. This review article presents the last achievements in the field of energy photoconverters based on photosynthetic systems.

Introduction. The paper presents the key aspects of constructing a management system for the state of multi-operation computer-controlled machines based on the information-control module of e-Mind Machine and the apparatus of fuzzy sets and fuzzy logic. It is shown that the input effects of the control system are formed due to the sets of inheriting and operating components of the status parameters. The work objective is to develop a system for monitoring the condition, detecting dimensional wear, and determining the period of tool life on the basis of the fuzzy logic methods. Materials and Methods. A new algorithm for constructing an expert system based on the fuzzy logic methods is proposed. The applicability of fuzzy neuron methods for solving the problems on determining the service life of the instrument through comparing the calculated values to the data of the manufacturing firms is demonstrated. The study is based on the application of the concept of electronic services using expert systems. Research Results. The basic principles of the construction and application of the status monitoring system are substantiated. They provide the possibility, under managing, to adapt to the emerging situation and to predict state changes when processing the parts. The monitoring functions include not only the processing of data obtained from the test units of the mechatronic system and external equipment, but both the forecasting of the residual dimensional tool life and the durability for the period of normal wear and tear. The decision-making process on managing the tool status is presented in the form of an algorithm for the expert system activity based on the use of a fuzzy neuron controller. Discussion and Conclusions. The results obtained can be applied in the parts production where accuracy is one of the key parameters. Automated control systems for the machine condition allow reducing costs due to equipment downtime, and monitoring the tool status can reduce the rejection rate. The characteristic examples of decision-making in the fuzzy neuron system are given.

He work objective is the presentation of the main aspects of the flexible monitoring system of the mechatronic processing machines state including the multioperational computer-controlled machines. The basis of such a system is considered the abandonment of the response strategy to failure in favor of a flexible proactive and predictive maintenance. A variant of the flexible system implementation based on the intelligent technologies is proposed. The basic principles of the flexible monitoring system that makes possible the adaptive capability to the unfolding situation are proved. They include combined neuro-fuzzy subsystems to implement such features as the selection of various types of technical maintenance, the determination of the urgency of its carrying out, the choice of the service facilities, and making the recommendations on the feasibility of the technological mode change at work within the border area of the machine runnability.

The concept of e-Mind Machine, a system that monitors the status of multioperational machines, is considered. The existing international and domestic monitoring systems, as well as the essential aspects of monitoring specifying e-Mind Machine features are analyzed. The structure of the e-Mind Machine module organization is presented. The need for converting test data into a knowledge system when the perspective monitoring systems operate at the multioperational machines is justified. Benefits of the forecasting methods application are determined. New concepts such as fuzzy boundary of operability/reliability and fuzzy boundary of parametric uptime are introduced. It is shown that the use of the intelligent control opens up fresh opportunities for the monitoring automation. The application of the e-Mind Machine module as an onboard intelligent system maximizes the part-processing efficiency and reliability in combination with the machine self-service.