Possible Impact of Heterogeneous Photocatalysis on the Global Chemistry of the Earth's Atmosphere

Photochemistry is recognized to be important for various physicochemical processes in the atmosphere, such as formation of the ozone layer and smogs, degradation of waste substances, etc. [1]. However, up to the present the emphasis in atmospheric photochemistry has been mainly on the study of photochemical reactions that occur with molecules directly excited by absorption of light quanta. However, the major components and impurities of the earth's atmosphere (such as nitrogen, oxygen, water, carbon dioxide, methane, methane halides, etc.) are totally transparent to most solar radiation. Electronically excited states of these molecules are formed only upon absorption of vacuum ultraviolet light quanta with energy hv ≥ 5 eV (i.e., with wavelength λ ≤ 200 nm). Only a small portion of the energy of solar light is found in this spectral region. In other words, most of the energy of the solar flux cannot participate in such direct photochemical reactions.     

Investigation of the Electronic Structure of Y2Cu2O5–xSx(x — 0.0, 0.25, 0.75) by X-ray Emission Spectroscopy and Xα Scattered Wave Calculation Methods

The investigation of the electronic properties of various copper oxides, containing structural fragments with specific Cu-O polyhedra, has been carried out. The electronic structure of Y₂Cu₂O_5–xS_x compounds (x = 0.0, 0.25, 0.75) has been studied by X-ray emission spectroscopy and the X_α scattered wave (SW) method of calculation. It is shown that substituting apical oxygen by sulfur in the squarepyramid fragments in high-temperature superconductors will result in a decrease of the density of hole carriers in "superconducting" grids of CuO₂ and in the suppression of superconductivity .     

Analysis of the Transient and Steady-State Processes in the Parallel Inverter

This paper deals with the analysis of the transient and steady-state processes in parallel inverters. A set of difference equations, describing the currents and voltages during transient and steady state is obtained and solved. The solution is found in closed form. The roots of the characteristic equations are examined and possible types of transients are discussed. Theoretical and experimental results both for transient and steady-state conditions are compared and satisfactory agreements are found.     

Optimised sensor selection for control and fault tolerance of electromagnetic suspension systems: A robust loop shaping approach

This paper presents a systematic design framework for selecting the sensors in an optimised manner, simultaneously satisfying a set of given complex system control requirements, i.e. optimum and robust performance as well as fault tolerant control for high integrity systems. It is worth noting that optimum sensor selection in control system design is often a non-trivial task. Among all candidate sensor sets, the algorithm explores and separately optimises system performance with all the feasible sensor sets in order to identify fallback options under single or multiple sensor faults. The proposed approach combines modern robust control design, fault tolerant control, multiobjective optimisation and Monte Carlo techniques. Without loss of generality, it's efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.     

Surface functionalization by smart coatings: Stimuli-responsive binary polymer brushes

Heterogeneous binary polymer brushes consist of an assembly of polymer chains of two incompatible polymers that are attached by one end to the surface with sufficient grafting density. They have been investigated experimentally only for a short time. Those brushes can be used in the form of ultrathin polymeric layers as a versatile tool for surface engineering to tune physico-chemical surface characteristics as wettability, surface charge, chemical composition or morphology, and furthermore to create switchable and responsive surface properties. For the fabrication of these layers “grafting-from” (as radical polymerization at the interface) and “grafting-to” (as tethering of the polymer chains from solution) methods were developed and investigated in detail.     

Stress/strain state investigations for extreme points of thin wall cylindrical tanks

In all branches of industry, especially in power-engineering, thin wall steel structures are extensively used. Land vertical cylindrical tanks are examples of such structures. The manufacture and assembling of these structures are usually accompanied by deviations from an ideal cylindrical form. Therefore, the exact evaluation of real local imperfections and common deviations from the analytical model of the tank is very important for such potentially dangerous structures.The main objective of the presented investigations is to identify stress/strain state of the wall tank with local imperfections from the ideal cylindrical surface, taking into account the membrane theory of the shells by using the analytical method, natural experiment and the finite element method.Besides, across the tank wall at the sites of its rigidity variation, rigidity ribs and at places of the load’s significant change, total moment internal forces and stresses arise, which are defined by a solution of the problem of the beam on the elastic ground. The given investigation also suggests the solution of the above-mentioned problem not only for the case when the tank wall is considered as a (long) flexible shell but as a semi-rigid one, when the edge effect is distributed over the whole wall’s height.     

Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

In this work the radial basis function neural network architecture is used to model the dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which do not require knowledge of the governing equations are described and compared. In the first method, the neural network methodology generates the full model of the system that is able to predict the process outputs at any spatial point. Past values of the process inputs and the coordinates of the specific location provide the input information to the model. The second method uses empirical basis functions produced by the Singular Value Decomposition (SVD) on the snapshot matrix to describe the spatial behavior of the system, while the neural network model is used to estimate only the temporal coefficients. The models produced by both methods are then implemented in Model Predictive Control (MPC) configurations, suitable for constrained DPSs. The accuracies of the modeling methodologies and the efficiencies of the proposed MPC formulations are tested in a tubular reactor and produce encouraging results.     

An offset-free neural controller based on a non-extrapolating scheme for approximating the inverse process dynamics

This work presents a novel control scheme based on approximating the inverse process dynamics with a radial basis function (RBF) neural network model, trained with the fuzzy means algorithm. The produced RBF network constitutes an inverse model of the process, which can be applied as an explicit control law. In order to avoid extrapolation in the RBF model predictions, a concept borrowed from chemometrics, namely the applicability domain, is incorporated to the proposed framework. Moreover, an error correction term is added, allowing the inverse neural controller to account for modeling errors and process uncertainty and eliminate offset. The proposed approach is applied to the control of a nonlinear Continuous Stirred Tank Reactor (CSTR) exhibiting multiple equilibrium points, including an unstable one. A comparison with other control schemes on various tests, including set-point tracking, unmeasured disturbance rejection and process uncertainty highlights the advantages of the proposed controller.     

Irreversibility and creativity in neurodynamics

This paper presents an approach to formalization of the concept of creativity in connection with the new architecture of neural networks based upon non-Lipschitzian dynamics. A new nonlinear phenomenon—terminal chaos caused by failure of the Lipschitz condition at equilibrium points of dynamical systems is introduced. It is shown that terminal chaos has a well-organized probabilistic structure which can be predicted and controlled. This gives an opportunity to exploit this phenomenon for information processing. It appears that chaotic states of neurons activity are associated with a higher level of cognitive processes such as generalization and abstraction. The relationship between creativity, irreversibility and unpredictability in neurodynamics are discussed. The theory is illustrated by examples.