Properties of Langmuir-Blodgett Films Based on Organosoluble Polyelectrolyte-Surfactant Complex and Oxazine Dye

Preparation procedure of organosoluble stoichiometric polycomplex based on cationic polyelectrolyte and anionic surfactant has been described. It was shown the formation of mixed monolayers consisting of polyelectrolyte-surfactant complex and dye molecules at the water-air interface. Assembling conditions of fluorescent nanosized solid Langmuir-Blodgett films based on polycomplex and dye Nile Red were defined and the spectral-luminescent properties of obtained films were studied. Absorption and fluorescence spectra of mixed Langmuir-Blodgett films revealed that electrostatic interaction between polycomplex and dye molecules is responsible for formation of dimers. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3500

Energy efficiency in buildings

Generally existing buildings are responsible for over 40% of the world's primary energy consumption and account for 34% of world CO2 emissions. Currently, most of the housing fund of Kazakhstan falls on old energy inefficient buildings constructed during the Soviet Union, in a period between 1956-1989, is found to be inefficient by international standards. Their energy consumption reaches 13.5% and 24% of electrical and heating energy consequently. At the same time the statistical data of 2009 shows that the housing fund of Kazakhstan is about 160 million square meters and it projected to continue growing in the next 5 years. The expected increase in housing construction will lead to higher energy consumption coming from space heating and air-conditioning systems, higher GHG emissions, and development of unsustainable energy supply that will result to energy insecurity

Preparation and Properties of Nanofilms Based on Polymethine Dyes by Langmuir-Blodgett Technology

A study of physico-chemical properties of monomolecular layers of amphiphilic cationic polymethine dye - thiacarbocyanine on the surface of the water subphase and the conditions of obtaining of Langmuir- Blodgett (LB) films are presented. The value of area, occupied by one molecule of the dye in different states of monolayer was defined. The spectral-luminescent properties of cationic polymethine dye was studied. An excimer fluorescence in LB films was revealed. It was shown, that in LB films, unlike solutions excimer not forms from monomers, but it forms from dimers of dyes. A possible mechanism of their formation was considered. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3515

DETERMINANTS OF CAPITAL STRUCTURE AND THE 2015 FINANCIAL CRISIS: EVIDENCE FROM KAZAKHSTAN

This study aims to investigate the determinants of capital structure in Kazakhstan and to analyze the e ects of the 2015 nancial crisis in Kazakhstan on the determinants of the capital structure of large rms. The sample used for This study aims to investigate the determinants of capital structure in Kazakhstan and to analyze the e ects of the 2015 nancial crisis in Kazakhstan on the determinants of the capital structure of large rms. The sample used for the following study includes 4000 to 7000 rms from 2009 to 2017. Results obtained show signi cance of tangibility, growth, size and liquidity variables on the leverage and increase in the signi cance of pro tability in the post-crisis period.the following study includes 4000 to 7000 rms from 2009 to 2017. Results obtained show signi cance of tangibility, growth, size and liquidity variables on the leverage and increase in the signi cance of pro tability in the post-crisis period

Model-based recognition of curves and surfaces using tactile data

Model-based object recognition has mostly been studied over inputs including images and range data. Though such data are global, cameras and range sensors are subject to occlusions and clutters, which often make recognition difficult and computationally expensive. In contrast, touch by a robot hand is free of occlusion and clutter issues, and recognition over tactile data can be more efficient.;In this thesis, we investigate model-based recognition of two and three dimensional curved objects from tactile data. The recognition of 2D objects is an invariant-based approach. We have derived differential and semi-differential invariants for quadratic curves and special cubic curves that are found in applications. These invariants, independent of translation and rotation, can be computed from local geometry of a curve. Invariants for quadratic curves are the functions in terms of the curvature and its derivative with respect to arc length. For cubic curves, the derived invariants also involve a slope in their expressions. Recognition of a curve reduces to invariant verification with its canonical parametric form determined along the way. In addition, the contact locations with the robot hand are found on the curve, thereby localizing it relative to the touch sensor. We have verified the correctness of all invariants by simulations. We have also shown that the shape parameters of the recognized curve can be recovered with small errors. The byproduct is a procedure that reliably estimates curvature and its derivative from real tactile data. The presented work distinguishes itself from traditional model-based recognition in its ability to simultaneously recognize and localize a shape from one of several classes, each consisting of a continuum of shapes, by the use of local data.;The recognition of 3D objects is based on registration and consists of two steps. First, a robotic hand with touch sensors samples data points on the object\u27s surface along three concurrent curves. The two principal curvatures at the curve intersection point are estimated and then used in a table lookup to find surface points that have similar local geometries. Next, starting at each such point, a local search is conducted to superpose the tactile data onto the surface model. Recognition of the model is based on the quality of this registration. The presented method can recognize algebraic as well as free-form surfaces, as demonstrated via simulations and robot experiments. One difference in the recognition of these two sets of shapes lies in the principal curvature estimation, which are calculated from the close forms and estimated through fitting, respectively. The other difference lies in data registration, which is carried out by nonlinear optimization and a greedy algorithm, respectively

Towards Image Semantics and Syntax Sequence Learning

Convolutional neural networks and vision transformers have achieved outstanding performance in machine perception, particularly for image classification. Although these image classifiers excel at predicting image-level class labels, they may not discriminate missing or shifted parts within an object. As a result, they may fail to detect corrupted images that involve missing or disarrayed semantic information in the object composition. On the contrary, human perception easily distinguishes such corruptions. To mitigate this gap, we introduce the concept of "image grammar", consisting of "image semantics" and "image syntax", to denote the semantics of parts or patches of an image and the order in which these parts are arranged to create a meaningful object. To learn the image grammar relative to a class of visual objects/scenes, we propose a weakly supervised two-stage approach. In the first stage, we use a deep clustering framework that relies on iterative clustering and feature refinement to produce part-semantic segmentation. In the second stage, we incorporate a recurrent bi-LSTM module to process a sequence of semantic segmentation patches to capture the image syntax. Our framework is trained to reason over patch semantics and detect faulty syntax. We benchmark the performance of several grammar learning models in detecting patch corruptions. Finally, we verify the capabilities of our framework in Celeb and SUNRGBD datasets and demonstrate that it can achieve a grammar validation accuracy of 70 to 90% in a wide variety of semantic and syntactical corruption scenarios.Comment: 21 pages, 22 figures, 5 table

On Inherent Adversarial Robustness of Active Vision Systems

Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise. Since human eyes are robust to such inputs, it is possible that the vulnerability stems from the standard way of processing inputs in one shot by processing every pixel with the same importance. In contrast, neuroscience suggests that the human vision system can differentiate salient features by (1) switching between multiple fixation points (saccades) and (2) processing the surrounding with a non-uniform external resolution (foveation). In this work, we advocate that the integration of such active vision mechanisms into current deep learning systems can offer robustness benefits. Specifically, we empirically demonstrate the inherent robustness of two active vision methods - GFNet and FALcon - under a black box threat model. By learning and inferencing based on downsampled glimpses obtained from multiple distinct fixation points within an input, we show that these active methods achieve (2-3) times greater robustness compared to a standard passive convolutional network under state-of-the-art adversarial attacks. More importantly, we provide illustrative and interpretable visualization analysis that demonstrates how performing inference from distinct fixation points makes active vision methods less vulnerable to malicious inputs

Long-term evolution of Caspian Sea thermohaline properties reconstructed in an eddy-resolving ocean general circulation model

Decadal variability in Caspian Sea thermohaline properties is investigated using a high-resolution ocean general circulation model including sea ice thermodynamics and air–sea interaction forced by prescribed realistic atmospheric conditions and riverine runoff. The model describes synoptic, seasonal and climatic variations of sea thermohaline structure, water balance, and sea level. A reconstruction experiment was conducted for the period of 1961–2001, covering a major regime shift in the global climate during 1976–1978, which allowed for an investigation of the Caspian Sea response to such significant episodes of climate variability. The model reproduced sea level evolution reasonably well despite the fact that many factors (such as possible seabed changes and insufficiently explored underground water infiltration) were not taken into account in the numerical reconstruction. This supports the hypothesis relating rapid Caspian Sea level rise in 1978–1995 with global climate change, which caused variation in local atmospheric conditions and riverine discharge reflected in the external forcing data used, as is shown in the paper. Other effects of the climatic shift are investigated, including a decrease in salinity in the active layer, strengthening of its stratification and corresponding diminishing of convection. It is also demonstrated that water exchange between the three Caspian basins (northern, middle and southern) plays a crucial role in the formation of their thermohaline regime. The reconstructed long-term trends in seawater salinity (general downtrend after 1978), temperature (overall increase) and density (general downtrend) are studied, including an assessment of the influence of main surface circulation patterns and model error accumulation.</p

Introduction to Memristive HTM Circuits

Hierarchical temporal memory (HTM) is a cognitive learning algorithm intended to mimic the working principles of neocortex, part of the human brain said to be responsible for data classification, learning, and making predictions. Based on the combination of various concepts of neuroscience, it has already been shown that the software realization of HTM is effective on different recognition, detection, and prediction making tasks. However, its distinctive features, expressed in terms of hierarchy, modularity, and sparsity, suggest that hardware realization of HTM can be attractive in terms of providing faster processing speed as well as small memory requirements, on-chip area, and total power consumption. Despite there are few works done on hardware realization for HTM, there are promising results which illustrate effectiveness of incorporating an emerging memristor device technology to solve this open-research problem. Hence, this chapter reviews hardware designs for HTM with specific focus on memristive HTM circuits