Plekhanov Russian University of Economics

The climate has changed significantly under the influence of human behavior. And first of all, this is due to the change in the proportionality and concentration of greenhouse gases in the atmosphere (water vapor, carbon dioxide, methane, ozone, PFC (perfluorocarbons). This paper analyzes the dynamics of greenhouse gas emissions. Climate change has many consequences on human health throughout the world, especially in African countries. The growth of greenhouse gas emissions is viewed as a cause of infectious and non-infectious diseases, negative effects on nutrition, water security and other social disruptions. The global average temperature gradually increases, and the atmospheric CO2 concentration has exceeded 400 ppm due to the intensification of greenhouse effect. The method of energy balance was featured to simulate the trends in Greenhouse Gas Emission Forecast in different sectors until 2030. Through sensitivity analysis, we found that the reduction of anthropogenic CO2 emissions from people (cars and households) would deescalate the consequences of the above trends. Emissions are mostly associated with industries, which can be reduced if local Government will want to achieve the Paris Agreement goal.

The electrical power sector plays an important role in the economic growth and development of every country around the world. Total global demand for electric energy is growing both in developed and developing economies. The commitment to the decarbonization of economies, which would mean replacing fossil fuels with renewable energy sources (RES) as well as the electrification of transport and heating as a means to tackle global warming and dangerous climate change, would lead to a surge in electricity consumption worldwide. Hence, it appears reasonable that the electric power sector should embed the principles of sustainable development into its functioning and operation. In addition, events such as the recent European gas crisis that have emerged as a result of the massive deployment of renewables need to be studied and prevented. This review aims at assessing the role of the renewable energy in the sustainable development of the electrical power sector, focusing on the energy providers and consumers represented both by businesses and households that are gradually becoming prosumers on the market of electric energy. Furthermore, it also focuses on the impact of renewables on the utility side and their benefits for the grid. In addition, it identifies the major factors of the sustainable development of the electrical power sector.

Along with industrialization and rapid urbanization, environmental remediation is globally a perpetual concept to deliver a sustainable environment. Various organic and inorganic wastes from industries and domestic homes are released into water systems. These wastes carry contaminants with detrimental effects on the environment. Consequently, there is an urgent need for an appropriate wastewater treatment technology for the effective decontamination of our water systems. One promising approach is employing nanoparticles of metal oxides as photocatalysts for the degradation of these water pollutants. Transition metal oxides and their composites exhibit excellent photocatalytic activities and along show favorable characteristics like non-toxicity and stability that also make them useful in a wide range of applications. This study discusses some characteristics of metal oxides and briefly outlined their various applications. It focuses on the metal oxides TiO2, ZnO, WO3, CuO, and Cu2O, which are the most common and recognized to be cost-effective, stable, efficient, and most of all, environmentally friendly for a sustainable approach for environmental remediation. Meanwhile, this study highlights the photocatalytic activities of these metal oxides, recent developments, challenges, and modifications made on these metal oxides to overcome their limitations and maximize their performance in the photodegradation of pollutants.

Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced a improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease. The researchers proposed the Optimal Feature Selection based Medical Image Classification using DL model by incorporating preprocessing, feature selection and classification. The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, Opposition-based Crow Search (OCS) algorithm is proposed. The OCS algorithm picks the optimal features from pre-processed images, here Multi-texture, grey level features were selected for the analysis. Finally, the optimal features improved the classification result and increased the accuracy, specificity and sensitivity in the diagnosis of medical images. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches. The proposed model achieved the maximum performance in terms of accuracy, sensitivity and specificity being 95.22%, 86.45 % and 100% for the applied set of images.

Energy is the fundamental requirement of all physical, chemical, and biological processes which are utilized for better living standards. The toll that the process of development takes on the environment and economic activity is evident from the arising concerns about sustaining the industrialization that has happened in the last centuries. The increase in carbon footprint and the large-scale pollution caused by industrialization has led researchers to think of new ways to sustain the developmental activities, whilst simultaneously minimizing the harming effects on the enviroment. Therefore, decarbonization strategies have become an important factor in industrial expansion, along with the invention of new catalytic methods for carrying out non-thermal reactions, energy storage methods and environmental remediation through the removal or breakdown of harmful chemicals released during manufacturing processes. The present article discusses the structural features and photocatalytic applications of a variety of metal oxide-based materials. Moreover, the practical applicability of these materials is also discussed, as well as the transition of production to an industrial scale. Consequently, this study deals with a concise framework to link metal oxide application options within energy, environmental and economic sustainability, exploring the footprint analysis as well.

In the 21st century, we observe an increasingly aware of a series of global, technological and scientific advancement that create a need of good performance in science education at all levels of schooling. These challenges, among them rapid science and technological changes, a rise of information technology use, and continuing movement towards a knowledge-based society all highlight the need for deep education in science including biology, chemistry, environmental science, physics, and sustainability. In fact, the impact of teacher characteristics of self-efficacy level is important for science education and students’ learning outcomes in science. In an effort to highlight this, this study investigated the impacts of teacher efficacy and motivation on students’ academic achievement in science education in secondary and high schools located in Iran and Russia using motivation for academic performance scale (α = 0.89) and teacher self-efficacy scale (α = 0.91) as measuring instruments and achievement test in science education. Two hypotheses were tested using the statistical programme. For evaluating the demographical differences of the students in terms of their academic achievement, comparative analyses were performed using t-test. Results showed that gender difference was not significant but nationality difference was significant in terms of students’ academic achievement in science education. Also other findings reported significant impact of teacher self-efficacy and motivation on academic achievement in science education. Implications, suggestions and recommendations for students, teachers, school administrators, parents, government, education counselors, etc. were discussed and presented.

At present times, the real-time requirement on the multiaccess healthcare monitoring system, information mining, and efficient disease diagnosis of health conditions is a difficult process. The recent advances in information technology and the internet of medical things (IoMT) have fostered extensive utilization of the smart system. A complex, 24/7 healthcare service is needed for effective and trustworthy monitoring of patients on a daily basis. To accomplish this need, edge computing and cloud platforms are highly required to satisfy the requirements of smart healthcare systems. This paper presents a new effective training scheme for the deep neural network (DNN), called ETS-DNN model in edge computing enabled IoMT system. The proposed ETS-DNN intends to facilitate timely data collection and processing to make timely decisions using the patterns that exist in the data. Initially, the IoMT devices sense the patient's data and transfer the captured data to edge computing, which executes the ETS-DNN model to diagnose it. The proposed ETS-DNN model incorporates a Hybrid Modified Water Wave Optimization (HMWWO) technique to tune the parameters of the DNN structure, which comprises of several autoencoder layers cascaded to a softmax (SM) layer. The SM classification layer is placed at the end of the DNN to perform the classification task. The HMWWO algorithm integrates the MWWO technique with limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS). Once the ETS-DNN model generates the report in edge computing, then it will be sent to the cloud server, which is then forwarded to the healthcare professionals, hospital database, and concerned patients. The proposed ETS-DNN model intends to facilitate timely data collection and processing to identify the patterns exist in the data. An extensive set of experimental analysis takes place and the results are investigated under several aspects. The simulation outcome pointed out the superior characteristics of the ETS-DNN model over the compared methods.

This review describes new reactions catalyzed by recently discovered types of metal complexes and catalytic systems (catalyst + co-catalyst). Works of recent years (mainly 2010–2016) devoted to the oxygenations of saturated, aromatic hydrocarbons and other carbon–hydrogen compounds are surveyed. Both soluble metal complexes and solid metal compounds catalyze such transformations. Molecular oxygen, hydrogen peroxide, alkyl peroxides, and peroxy acids were used in these reactions as oxidants.

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

Bioenergy is a renewable energy source that saves from fossil fuel dependence. Therefore, it is important to increase the efficiency of bioenergy investments to create an environmentally sustainable energy supply. This paper aims to identify economic indicators significant in forecasting the supply of bioenergy. Considering this goal, an integrated evaluation has been performed for 17 developed economies using the Random Forest method and the Fuzzy Decision-Making Trial and Evaluation Laboratory (QROF-DEMATEL) method. The main contribution of this study is conducting analysis by using both quantitative and qualitative data. Additionally, the coherence of the results made with the QROF-DEMATEL method is also verified by implementing a sensitivity analysis. The results of both approaches are quite similar and provide information about the reliability of the findings. This situation demonstrates that for the development of bioenergy investments, firstly, countries’ macroeconomic conditions should be improved. Consequently, economic growth and unemployment (weighting results - 0.159 and 0.155) should be primarily considered for the bioenergy supply forecast.

MXenes are a family of two-dimensional (2D) composite materials based on transition metal carbides, nitrides and carbonitrides that have been attracting attention since 2011. Combination of electrical and mechanical properties with hydrophilicity makes them promising materials for biomedical applications. This review briefly discusses methods for the synthesis of MXenes, their potential applications in medicine, ranging from sensors and antibacterial agents to targeted drug delivery, cancer photo/chemotherapy, tissue engineering, bioimaging, and environmental applications such as sensors and adsorbents. We focus on in vitro and in vivo toxicity and possible mechanisms. We discuss the toxicity analogies of MXenes and other 2D materials such as graphene, mentioning the greater biocompatibility of MXenes. We identify existing barriers that hinder the formation of objective knowledge about the toxicity of MXenes. The most important of these barriers are the differences in the methods of synthesis of MXenes, their composition and structure, including the level of oxidation, the number of layers and flake size; functionalization, test concentrations, duration of exposure, and individual characteristics of biological test objects Finally, we discuss key areas for further research that need to involve new methods of nanotoxicology, including predictive computational methods. Such studies will bring closer the prospect of widespread industrial production and safe use of MXene-based products.

The tutorial describes the basic concepts and concepts of financial management. The fundamentals of financial mathematics, methods of accounting for risk factors in financial calculations. The methods of financial analysis in the financial management of the enterprise, as well as the use of methods of financial planning, forecasting and formation of the financial strategy of the enterprise. The manual contains two sections. For each topic are control questions and tasks for self-test. Prepared on the basis of the Federal state educational standard of higher education in the specialty 38.05.01 "Economic security (level of specialization)".

Purpose The purpose of this paper is to determine the priorities of formation of competencies during training of digital personnel for industry 4.0. Design/methodology/approach The author performs two experiments for determining the scenario according to which industry 4.0 develops and will develop: the first experiment is aimed at determining the influence of the number of robots at unemployment level in 2019 and 2022 with the help of regression and correlation analysis (regression curves are built). The second experiment is connected to evaluation of the ratio of the number of robots to the number of population in 2019 and 2022. The research objects are countries with the highest number of robots in the world – i.e. with the highest level of development of industry 4.0; the information and empirical basis is materials of the International Federation of Robotics and the International Monetary Fund for 2019 and their forecasts for 2022. Findings The results of the performed experiments showed that in 2019 and 2022 the level of robotization of socio-economic systems of the countries of the world will be very low, and robotization will not cause growth of unemployment. Based on this, it is concluded that industry 4.0 will be developing according to the scenario of moderate automatization and robotization with preservation of domination of human labor in most business processes and spheres of economy. Communications with people will constitute the basis of the activities of digital personnel, and social competencies (with obvious significance of technical competencies) will be of top priority for them. Originality/value It is substantiated that technical competencies, with their large importance, will move to the background, while the key task will be society’s adaptation to the new technological mode and making social competencies the highest priority. The social and technical competencies of digital personnel in view of the performed tasks for industry 4.0 are determined.

Skeletal muscle tissue demonstrates global hypermethylation with age. However, methylome changes across the time-course of differentiation in aged human muscle derived cells, and larger coverage arrays in aged muscle tissue have not been undertaken. Using 850K DNA methylation arrays we compared the methylomes of young (27 ± 4.4 years) and aged (83 ± 4 years) human skeletal muscle and that of young/aged heterogenous muscle-derived human primary cells (HDMCs) over several time points of differentiation (0, 72 h, 7, 10 days). Aged muscle tissue was hypermethylated compared with young tissue, enriched for; pathways-in-cancer (including; focal adhesion, MAPK signaling, PI3K-Akt-mTOR signaling, p53 signaling, Jak-STAT signaling, TGF-beta and notch signaling), rap1-signaling, axon-guidance and hippo-signalling. Aged cells also demonstrated a hypermethylated profile in pathways; axon-guidance, adherens-junction and calcium-signaling, particularly at later timepoints of myotube formation, corresponding with reduced morphological differentiation and reductions in MyoD/Myogenin gene expression compared with young cells. While young cells showed little alterations in DNA methylation during differentiation, aged cells demonstrated extensive and significantly altered DNA methylation, particularly at 7 days of differentiation and most notably in focal adhesion and PI3K-AKT signalling pathways. While the methylomes were vastly different between muscle tissue and HDMCs, we identified a small number of CpG sites showing a hypermethylated state with age, in both muscle tissue and cells on genes KIF15, DYRK2, FHL2, MRPS33, ABCA17P. Most notably, differential methylation analysis of chromosomal regions identified three locations containing enrichment of 6-8 CpGs in the HOX family of genes altered with age. With HOXD10, HOXD9, HOXD8, HOXA3, HOXC9, HOXB1, HOXB3, HOXC-AS2 and HOXC10 all hypermethylated in aged tissue. In aged cells the same HOX genes (and additionally HOXC-AS3) displayed the most variable methylation at 7 days of differentiation versus young cells, with HOXD8, HOXC9, HOXB1 and HOXC-AS3 hypermethylated and HOXC10 and HOXC-AS2 hypomethylated. We also determined that there was an inverse relationship between DNA methylation and gene expression for HOXB1, HOXA3 and HOXC-AS3. Finally, increased physical activity in young adults was associated with oppositely regulating HOXB1 and HOXA3 methylation compared with age. Overall, we demonstrate that a considerable number of HOX genes are differentially epigenetically regulated in aged human skeletal muscle and HDMCs and increased physical activity may help prevent age-related epigenetic changes in these HOX genes.

Phenotypes of athletic performance and exercise capacity are complex traits influenced by both genetic and environmental factors. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status summarises recent advances in sports genomics research, including findings from candidate gene and genome-wide association (GWAS) studies, meta-analyses, and findings involving larger-scale initiatives such as the UK Biobank. As of the end of May 2023, a total of 251 DNA polymorphisms have been associated with athlete status, of which 128 genetic markers were positively associated with athlete status in at least two studies (41 endurance-related, 45 power-related, and 42 strength-related). The most promising genetic markers include the AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. It should be appreciated, however, that elite performance still cannot be predicted well using only genetic testing.

Saffron (Crocus sativus L., family Iridaceae) is used traditionally for medicinal purpose in Chinese, Ayurvedic, Persian and Unani medicines. The bioactive constituents such as apocarotenoids, monoterpenoids, flavonoids, phenolic acids and phytosterols are widely investigated in experimental and clinical studies for a wide range of therapeutic effects, especially on the nervous system. Some of the active constituents of saffron have high bioavailability and bioaccessibility and ability to pass the blood-brain barrier. Multiple preclinical and clinical studies have supported neuroprotective, anxiolytic, antidepressant, learning and memory-enhancing effect of saffron and its bioactive constituents (safranal, crocin, and picrocrocin). Thus, this plant and its active compounds could be a beneficial medicinal food ingredient in the formation of drugs targeting nervous system disorders. This review focuses on phytochemistry, bioaccessibility, bioavailability, and bioactivity of phytochemicals in saffron. Furthermore, the therapeutic effect of saffron against different nervous system disorders has also been discussed in detail.

MXenes, two-dimensional transition metal carbides or nitrides, have recently shown great promise for gas sensing applications. We demonstrate that the sensitivity of intrinsically metallic Ti3C2Tx MXene can be considerably improved via its partial oxidation in air at 350 °C. The annealed films of MXene sheets remain electrically conductive, while their decoration with semiconducting TiO2 considerably improves their chemiresistive response to organic analytes at low-ppm concentrations in dry air, which was used to emulate practical sensing environments. We demonstrate that partially oxidized MXene has a faster and a qualitatively different sensor response to volatile analytes compared to pristine Ti3C2Tx. We fabricated multisensor arrays of partially oxidized Ti3C2Tx MXene devices and demonstrate that in addition to their high sensitivity they enable a selective recognition of analytes of nearly the same chemical nature, such as low molecular weight alcohols. We investigated the oxidation behavior of Ti3C2Tx in air in a wide temperature range and discuss the mechanism of sensor response of partially oxidized MXene films, which is qualitatively different from that of pristine Ti3C2Tx.

The paper proposes a machine-learning approach to predict oil price. Market participants can forecast prices using such factors as: US key rate, US dollar index, S&P500 index, VIX index, US consumer price index. After analyzing the results and comparing the accuracy of the model first, we can conclude that oil prices in 2019-2022 will have a slight upward trend and will generally be stable. At the time of the fall in June 2012 the price of Brent fell to a minimum of 17 months. The reason for this was the weak demand for oil futures, which was caused by poor data on the state of the US labor market. Keywords: oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&P500 index. JEL Classification: C51, C58, F31, G12, G15 DOI: https://doi.org/10.32479/ijeep.7597

Abstract A series of highly energetic organic salts comprising a tetrazolylfuroxan anion, explosophoric azido or azo functionalities, and nitrogen‐rich cations were synthesized by simple, efficient, and scalable chemical routes. These energetic materials were fully characterized by IR and multinuclear NMR ( 1 H, 13 C, 14 N, 15 N) spectroscopy, elemental analysis, and differential scanning calorimetry (DSC). Additionally, the structure of an energetic salt consisting of an azidotetrazolylfuroxan anion and a 3,6,7‐triamino‐7 H ‐[1,2,4]triazolo[4,3‐ b ][1,2,4]triazolium cation was confirmed by single‐crystal X‐ray diffraction. The synthesized compounds exhibit good experimental densities (1.57–1.71 g cm −3 ), very high enthalpies of formation (818–1363 kJ mol −1 ), and, as a result, excellent detonation performance (detonation velocities 7.54–8.26 kms −1 and detonation pressures 23.4–29.3 GPa). Most of the synthesized energetic salts have moderate sensitivity toward impact and friction, which makes them promising candidates for a variety of energetic applications. At the same time, three compounds have impact sensitivity on the primary explosives level (1.5–2.7 J). These results along with high detonation parameters and high nitrogen contents (66.0–70.2 %) indicate that these three compounds may serve as potential environmentally friendly alternatives to lead‐based primary explosives.

The article deals with the development of one of the service sectors, which is considered an element of the economic basis of the post-industrial development stage. The author considers the main indicators of the sector growth in the most competitive OECD countries, as well as compares the main macroeconomic structural and dynamic indicators of business and professional services in the USA and Kazakhstan, which made it possible to identify the main positions of Kazakhstan's lag. A comprehensive analysis of the dynamics and structure of the development of post-industrial services in Kazakhstan was carried out in all the significant parameters of the development of the types of services of this group that are available in domestic statistics. This made it possible to identify a group of development leaders; activities characterized by stability and a high market share, but weak dynamics; shrinking sectors. The negative trends in the development of the post-industrial services sector, determined as a result of the analysis and assessment, allow us to form a vision for further steps to develop them.