Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

Machine Learning for Scientific Data Analysis

Over the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, and driving discovery across many scientific domains such as chemistry, biology, and genomics. However, the specific challenges posed by scientific data demand to adapt machine learning techniques to new requirements. We investigate machine learning-driven scientific data analysis, focusing on a set of key requirements. These include the management of uncertainty for complex data and models, the estimation of system properties starting from low-volume and imprecise collected data, the support to scientific model development through large-scale analysis of experimental data, and the machine learning-driven integration of complementary experimental technologies.

Settlement system in the Angara coastal area in the 17th21st centuries in terms of strategic planning

One of the fundamental urban planning tasks is currently a study of the settlement system properties. In Russian and foreign historical and urban planning science, settlement is studied according to the hierarchical location of settlements. Small and medium-sized settlements are considered as elementary lower units of large cities, their structure and formation processes are not studied. Accordingly, they are rarely considered in elaborating strategic programs of the regional development. The paper proposes the urban retrospective method, which provides a deep and large-scale analysis of the settlement system in the coastal area of the Angara River.Research is based on the cartographic sources developed by governmental institutions whose the activity depends on statistical data. Here belong Ministry of Internal Affairs, Ministry of Agriculture, Ministry of Railways. The comparative analysis of the sources provides modeling and identification of the settlement system with respect to small settlements in the coastal area of the Angara River in different periods. Significant results include the quantitative data on small settlements, since they are not interesting to urban planners of today.

Oxygen Stable Electrochemical CO2 Capture and Concentration through Alcohol Additives

Current methods for CO2 capture and concentration (CCC) are energy intensive due to their reliance on thermal cycles, which are intrinsically Carnot limited in efficiency. In contrast, electrochemically driven CCC (eCCC) can operate at much higher theoretical efficiencies. However, most reported systems are sensitive to O2, precluding their practical use. In order to achieve O2 stable eCCC, we pursued the development of molecular redox carriers with reduction potentials positive of the O2/O2- redox couple. Prior efforts to chemically modify redox carriers to operate at milder potentials resulted in a loss in CO2 binding. To overcome these limitations, we used common alcohols additives to anodically shift the reduction potential of a quinone redox carrier, 2,3,5,6-tetrachloro-p-benzoquinone (TCQ), by up to 350 mV, conferring O2 stability. Intermolecular hydrogen-bonding interactions to the dianion and CO2-bound forms of TCQ were correlated to alcohol pKa to identify ethanol as the optimal additive, as it imparts beneficial changes to both the reduction potential and CO2 binding constant, the two key properties for eCCC redox carriers. We demonstrate a full cycle of eCCC in aerobic simulated flue gas using TCQ and ethanol, two commercially available compounds. Based on the system properties, an estimated minimum of 21 kJ/mol is required to concentrate CO2 from 10% to 100%, or twice as efficient as state-of-the-art thermal amine capture systems and other reported redox carrier-based systems. Furthermore, this approach of using hydrogen-bond donor additives is general and can be used to tailor the redox properties of other quinones/alcohol combinations for specific CO2 capture applications.

Complex system properties in the spreading of COVID-19 pandemic

The objective of the present study was to show that the spread of the COVID-19 pandemic around the world shows complex system properties such as lognormal laws, temporal fluctuation scaling, and time correlation. First, the daily cumulative number of confirmed cases and deaths is distributed among countries as lognormals such that the time series exhibit a temporal fluctuation scaling. Second, the daily return time series of cases and deaths per day have associated Levy stable distributions and they have time correlation. The idea was to draw attention to the fact that the spread of the COVID-19 pandemic can be seen as a complex system, and, thus, contribute to the identification of the structural properties of this system, which is relevant as it is expected that future stochastic models describing the spread of the COVID-19 pandemic from a microscopic dynamics perspective should be able to explain the emergence of the structural properties identified here.

Analysis of sensorimotor abilities and nervous system properties of players of different e-sports disciplines

The article presents the results of the study of the properties of the nervous system, the speed of simple and complex reactions, short-term visual memory of first-person shooter CS: GO and multiplayer battle arena DOTA 2. Relevance of the description of individual typological personality traits of participants relationships with indicators of gaming activity is associated with the possibility of creating psychodiagnostic tools to determine the propensity to certain disciplines of e-sports, the development of psychological foundations to improve the effectiveness of training and competitive activities of e-sportsmen based on their individuality, prevention of psychological risks of e-sports. Purpose: to identify and analyze individual sensorimotor abilities and properties of the nervous system of players who prefer different e-sports genres. Research methods: theoretical analysis of literature sources, pedagogical testing, methods of mathematical statistics. The study involved e-sports players Counter-Strike: Global Offensive (n = 18) and DOTA 2 (n = 10) aged 18-20 years. According to the playing experience, the number of playing hours per week, the regularity of participation in competitions and their level, these players were classified as amateurs. It is established that the main factors determining the effectiveness of e-sports are sensorimotor coordination, nervous system properties, cognitive properties, technical and tactical skills, social communication skills and more. In games of both genres, the speed of simple and complex reactions has been found to be important, but it is most effective in combination with a high level of technical and tactical training. Analysis of the results of the study showed that players in different e-sports disciplines have some differences in sensorimotor abilities, but at the level of amateurs guilt does not have a significant difference. At the same time, they have significant differences in the type of nervous system.

Analysis of LLL System Properties for Different Excitation Parameters

Photoluminescent strips forming a Low Location Lighting (LLL) system are the primary method for marking escape routes on passenger ships. The LLL system can be built as a self-luminous system (powered by electricity) or made as a series of strips made of photoluminescent materials, which glow and indicate the escape route after the loss of basic and emergency lighting. To ensure correct visual guidance, these strips must be installed at specific locations in the passageways and achieve appropriate photometric parameters after a certain time from their activation. The properties of the LLL system depend on the type of luminescent material used, the excitation source, and the exposure parameters. This paper presents the results of laboratory tests on two types of photoluminescent materials used for the construction of LLL systems. We recorded the change in luminance after the loss of excitation and measured the luminance values obtained 10 and 60 min after the loss of excitation under exposure to light sources commonly used for interior lighting on passenger ships. It turns out that replacing fluorescent lamps with LED lamps can reduce the luminance of the LLL system.