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This article is about the improvement of the mechanisms of corporate governance with the use of information technology moderna in a scanning environment. At the beginning of the article explains the basic concepts of corporate governance, which in essence would be: its basic definition, the rules and articulate these standards, the corporate governance as a value generator, among others. Later, in the article dealing with the subject of the linking governments and corporate enterprises, the role of boards of directors, and of course corporate governance in the digital age. Taking into account some existing platforms that are designed to provide directors and managers of the technology, information, and processes that enable them to work safely and promote good corporate governance. At the end of the article make it discusses some improved solutions to existing emphasis in the Clouds of Government.

The purpose of this study is to evaluate the accuracy of predictions regarding the work capacity of CO2 and the selectivity of MOF, using machine learning methodologies in relation to CO2/N2. A dataset was used that includes numerous characteristics of MOFs for the development of a neural network model. The factors that determined the operational capacity of CO2 and the CO2/N2 selectivity included pore size, surface area, chemical composition, among others. The model demonstrated its work capacity by evaluating the selectivity of CO2; the mean absolute errors for the CO2/N2 selectivity were 25 and 0.8 mmol/g, respectively. The correlation Analysis showed a fairly negative correlation (-0.014) between the operational capacity of CO2 and its chemical makeup and a very positive correlation ( 0.029) between the surface area and amount of pore size. Thus, the gas absorbability is not top-dependent exclusively; pore size and surface area of a material contribute to the capacity as well. More research should be carried out to evaluate a machine learning capability on predicting the nature of different Flow Object Models (MOFs) with an aim of increasing efficiency, precision and dependability of the models.

The effect of different amounts of supplemental cementitious materials (SCMs) on the physical and mechanical characteristics of lightweight concrete is examined in this study. SCMs include Fly Ash, Rice Husk Ash (RHA), Ground Granulated Blast-furnace Slag (GGBS), and Silica Fume. Cube crushing strength, flexural strength, density and water absorption tests were performed on eight mix proportions. The current study also established that, when 20% Fly Ash was incorporated in the concrete as a replacement, the compressive strength was 30 MPa and the flexural strength was 4 MPa, the highest compressive strength of 32 MPa and the flexural strength of 4.2 MPa were however obtained when 20% Silica Fume was incorporated in the concrete as a replacement. In the present study only small increment in water absorption was recorded for mixtures containing GGBS and RHA while Fly Ash and Silica Fume shown relatively less water absorption than the control specimen. So, according to the results obtained Fly Ash and Silica Fume are good additives to lightweight concrete since the material becomes more stronger and durable and at the same time has low density.

Austenitic Stainless Steel 316L is one among most frequent ASS scores utilized in modern industry. It is more resistant to corrosion in normal atmospheric conditions in severe settings such as seawater with salt and situations that require protection against chloride corrosion and pitting.While operating effectively at high temperatures, it can also retain because to its resilience and strength at low temperatures, it is a great option for use in the automotive, nuclear, water treatment, marine, and aerospace industries.The mechanical metrics yield quality (YS), percentage elongation, and ultimate tensile strength (UTS) were assessed and compared in this studyusing experimental data through uniaxial isothermal tensile testing.

Motion or inertial sensors, like the accelerometer and gyroscope that are frequently found in smartphones and smartwatches, can measure the acceleration and angular velocity of bodily movements and be used to teach bots so that they may guess human activities. These models can be selected to a variety of fields, including biometrics and remote patient health monitoring. Because deep learning-based methods employ representing teaching methods, those may automatically identify hidden patterns in data and generate optimal objects from basic information generated from sensors without human intervention, they got popular in earlier in recognizing human activities. Along with recognize human activity, this paper suggests a novel called HDDN-model called CNN-GRU, which combines convolutional units. This model exhibited accuracy that is suggestively better than other state-of-the-art DNN models like Inception Time and Deep Conv LSTM developed using Auto ML, and was successfully verified on the WISDM dataset.

The robustness, hardness, and longevity of high strength conventional concrete have long been sought after qualities. Fibre reinforced concrete (FRC) was created to address shortcomings. The purpose of fibres used is to control cracking and to enhance tensile strength, flexural strength, toughness, impact strength, and failure mode by enhancing post-cracking ductility. To study the influence of specimen size on the flexural behavior of high strength reinforced cement concrete of 60 MPa with hybrid fibres . The current work aimed to investigate the size effect of high strength, polypropylene and steel fibre reinforced RC beams under flexure loading more realistically in addition to that how specimen size affects the flexural behaviour of 60 MPa high strength hybrid fibre reinforced cement concrete. Correspondingly three cubes of size 150 mm x 150 mm x 150 mm and the cylinders of size 100 mm diameter, 200 mm height are also cast. Three RC beams of size 150 mm x 200 mm x 1000 mm (1A), 150 mm x 300 mm x 1500 mm (2A) and 150 mm x 400 mm x 2000 mm (3A) are cast. Three hybrid fibre reinforced concrete beams (HFRC) of size 150 mm x 200 mm x 1000 mm (1B), 150 mm x 300 mm x 1500 mm(2B) and 150 mm x 400 mm x 2000 mm(3B) are cast. All beams are provided with HYSD longitudinal reinforcement of 0.52% of the cross section at 0% and 1% fibres were added. Tests Such as Compressive strength test ,Split tensile strength test and Flexural strength test was conducted.

In this present study we have studied about the strength enhancement of autoclaved aerated block by using natural pozzolanas. Fly ash has been partially replaced with GGBS. GGBS has been used in place of fly ash at percentages of 6%, 12%, and 18% of the total amount of fly ash plus super plasticizer. GGBS improves resistance to damage from alkali silica reaction. It also reduces the thermal cracking. The role of super plasticizer is to reduce the water content in block.we have used 1% of super plastizer to the 1 litre of water. Each block's compressive strength is measured and computed for varying fly ash and GGBS ratios. It has been observed that replacing GGBS with 12% yields higher compressive strength than replacing it with 6% or 18%.

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.

Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.

Evaluating the performance of university faculty is a hard task because of the diversity of the work performed. The authors assume that the founder of the university evaluates the effectiveness of the university according to performing the teaching staff. The aim of the study is to improve the monitoring of key performance indicators of the university teaching staff based on data management. The authors present an information system to support decision-making related to the teaching staff at Kemerovo State University. The authors of the paper describe creating such a system using Business Intelligence technologies step by step. The authors identified data sources, designed the structure of the data mart and built ETL-processes for its filling, implemented various analytical dashboards. Implementing the information system in the daily activities of the university allows responding promptly to changes in the key indicators, forecasting their further change, deciding on activation of efforts in the chosen direction or types of work.

The main aim of this work was to determine the possibility of reliable identification of phenolic compounds, in particular flavonoids, specific for very popular type of medicinal plant material Bidens (B. tripartita, B. frondosa, B. cernua), their qualitative and quantitative determination by various methods: TLC, HPLC and absorption spectrophotometry (UV method). TLC method was used to isolate a specific compound from B. cernua sample, and by using HPLC method this compound was identified as quercitrin in comparison with a reference sample. A study of 8 series of the bur-marigold herb has shown that the presence of significant amounts of quercitrin in the sample indicates an unacceptable impurity of B. cernua in the pharmacopeial samples of B. tripartita. The main components of B. tripartita were identified as: luteolin-7-glucoside, unidentified luteolin glycoside, luteolin, derivatives of caffeic acid, in particular chlorogenic acid, polyacetylenes, chalcones. The correlation between the results of quantitative determination of the sum of flavonoids by the UV method and the results of HPLC determination of main components was shown.

This article deals with the problem of choosing the most optimal teaching model for teaching Russian as a foreign language. The purpose of the research is to identify and analyze possible models for the introduction of a virtual educational environment in the process of teaching Russian as a foreign language, and to determine the criteria for the effectiveness of these models. The article is based on the following practical methods: observation of classroom and extracurricular communication between teachers and foreign students, questioning of teachers and students, the method of experiment. Five models of the introduction of virtual educational environment into the practice of teaching Russian as a foreign language are identified and analyzed, and the functional capabilities of platforms and applications used in online learning are described in detail. The main problems that arise for foreign students while fully converting Russian language teaching into a distance format are identified. Russian language teaching quality is determined by three main criteria that affect the quality of teaching Russian to foreign students in a distance format using a virtual educational environment: the level of Russian language proficiency, the motivation of the student, and the language/discipline section being studied. The mixed learning model, which combines traditional classroom learning with parallel online broadcasting of classes in a virtual space, is currently the most optimal model for implementing a virtual educational environment in the teaching process. When using the mixed learning model, the necessary competencies are acquired by both students studying in the classroom and those students who, due to various circumstances, study in a distance format. The mixed education model allows the student to make a deliberate choice between traditional and innovative models, making it possible to switch from one model to another directly in the learning process.

In 2020, the President of Russian Federation outlined new national goals in the form of achieving “digital maturity” of key sectors of the economy and social sphere (Presidential Decree No. 474 of July 21, 2020 “On the national development goals of Russian Federation for the period up to 2030”) … At the meeting of working group of State Council in January 2020, the head of the Ministry of Telecom and Mass Communications named seven areas of development of digital economy in the interests of citizens, business and the state, including “Digital economy - to business: drastic reduction of costs” and “Digital economy - to business: reduction of administrative pressure”. To create the conditions for introduction of new digital technologies in construction activities within the framework of “Digital Economy” program, a subgroup “Digital technologies for managing the development of territories and urban planning policy” was created. The current period - the period of a pandemic (its consequences) and introduction of a number of restrictions for a number of sectors of the economy - is also becoming a new stage for the implementation of digitalization in construction activities. The COVID-19 pandemic is one of those external threats that are unpredictable. It is impossible to predict their appearance, peculiarities of the course. As noted in the economic literature, this greatly complicates the counteraction, since it is impossible to develop a coherent and effective strategy of this kind in advance. Moreover, it is impossible to predict the duration of consequences of this threat, as well as the list of risks and threats to the economic security of construction enterprises caused by the COVID-19 pandemic.

There are presented results of developing a conceptual trigger chart of the functioning mechanism of the decision support system. The suggested model of visualizing algorithms as a trigger net of states of the computer decision support system provides for interaction of power objects of mining and metallurgical complexes and regions. The authors introduce new interpretation of components of the network trigger model. The model is interactively connected with both the user-operator’s actions and states of power system components. With that, the state of the automatic model is associated with realizing a set of metarules to control the logic output. The authors elaborate a new formalism of representing algorithms of controlling knowledgebases interacting with the outer environment which aggregates primitives of conditions, triggers and transactions of operations and greatly generalizes standard languages of algorithm visualization. It enhances elaboration of standardized smart systems interacting with the external environment. This allows description of functioning algorithms of knowledgebases and the event-driven output to ensure development of reliable standardized smart systems interacting with control objects of power systems in mining and metallurgical regions.

Among many tasks to be done by the state, the most significant is a social-economic one i.e. provision of its citizens with affordable, long-lasting, energy- efficient and economical housing. One of the ways of doing the given task is the application of concrete-filled steel tubular (CFST) structures, for the elements of a building, of circular, square or rectangular type. It is also possible to use such structures not only for a new housing stock but in the reconstruction of present one at the expense of additional storeys (about 20); without demolition and total resettlement of tenants of the house under reconstruction. Alongside with this, there is an opportunity to increase total area of residential houses and make new types of comfortable place of various purpose.

The article defines the quantitative staff characteristics of road transport and vehicles of economic entities of various organizational and legal forms and departmental subordination. The dependences of the personnel number on the vehicles number were obtained, which made it possible to determine the need for the motor complex of the Orenburg region in labor resources, also the share of road transport workers in the total structure of the working population was determined. The work establishes the values of the staffing characteristics of motor transport enterprises, taking into account the levels of education and forms of ownership, which made it possible to determine the need for the motor transport complex of the Orenburg region in personnel.

The article presents the results of research on the management of cooperation in the innovative environment of the region. The research, the results of which are presented in the article, is aimed at finding a solution to the problem of assessing the level of development of the innovative environment of the region and the selection of cooperation tools to achieve the best results of innovation activity for companies. The methodological approach has been developed for assessing the level of development of the innovative environment of the region in the context of technological and regional unevenness. A mathematical toolkit is proposed to describe the process of development of the innovative environment of the region. An algorithm for managing cooperation in an innovative environment is described. The results of approbation of the methodological approach on the materials of three regions of the Russian Federation are revealed: the Belgorod region, the Republic of Bashkortostan and the Udmurt Republic. Brief recommendations for the management of cooperation in the innovative environment of the regions are formulated.

This paper proposes the use of a self-propelled pneumatic mini-seeder with replaceable mechanical seeding devices for selection production conditions. As a result of the research carried out, operating high-tech designs of seeding devices with a continuous dosing principle of auger and torsion-pin types have been designed and manufactured. The sowing devices allow sowing loose and difficult-to-flow seeds when used as dispensers on a self-propelled pneumatic mini-seeder for sowing herbs in breeding production. The developed seeding devices will significantly reduce the range of seeders and expand the range of sown seeds of various agricultural plants with one brand of seeding machine.

The article substantiates the need of development of a portable operating table designed for performing medical manipulations, both in and out veterinary clinics. On the basis of the analysis of existing veterinary operating tables, the design of the portable operating table is proposed the parameters of the main elements are justified by theoretical calculations. The use of the portable operating table due to improvements of its ergonomic characteristics allows reducing the amount of time and improving the quality of operations.

The article describes patterns of transportation of hard-running seeds by working bodies of the torsion-pin sowing machine. An analytical expression for determining the volumetric supply of hardrunning seeds by working bodies of the torsion-pin sowing unit at the stage of their transportation is substantiated. Graphical dependences of the volumetric feed on the number and height of vertical pins are provided.

The combined unit is used for tillage with simultaneous introduction of protective and stimulating agents. A special feature of the unit is the use of pneumatic slot sprayers, they use a jet-forming device, which is necessary to create a highly dispersed air-drop jet. Agrotechnical requirements for such spraying ensure high quality distribution of the working fluid to the soil surface and supply the required rate of its consumption. The main task of the study of pneumatic slot sprayer was to determine the performance of this sprayer.

Improving the technical, economic, and environmental performance of the power plant is the most important direction for improving the characteristics of the ship’s propulsive complex. This issue can be solved most effectively by improving the design, repair, and maintenance of the fuel supply system, which significantly affects the quality of the fuel equipment and determines the working process of the steam boiler. Performance indicators of fuel equipment are determined not only by the stability of its design and adjustment parameters and parameters of the fuel supply process, but also by the physical and chemical composition of the fuel.

Free-stream turbine is an alternative to classical high-head turbines. On one hand, it produces less power, on the other hand, it requires less ser-vice and construction and might be an eco-friendlier option. We have modelled the start-up of a free-stream turbine, which consists of a wheel and a generator, using CFD methods. Unsteady RANS method with k-ω SST model was employed to describe turbulence. We have used finite-volume solver Ansys FLUENT on unstructured mesh. Moving mesh approach was applied for the wheel rotation. We have based the blade sys-tem design on several simple assumptions. Wheel inertia and generator load were taken into account through simple moment equations. Velocity fields during different moments of time were obtained. We have obtained both momentum and power of the wheel curves over time and reviewed cases with generator presence and absence. Angular velocity curves comparison over time is presented for two cases.

This study focuses on the problem of relationships between personality traits and stylistic characteristics of professional interaction between a teacher and student, the consideration of personal determinant of the stylistic peculiarities of their interaction, as well as the styles of professional activity of a teacher. The model of interaction of subjects of educational process, from the point of view of innovative personality-oriented education is considered. And the result of this interaction is the transformation of educational features into personal and the transition to personal meanings. It is the personal importance of the subject of interaction and the interaction itself that contributes to the formation of the subjective position of the student and the teacher. The main emphasis is placed on the subject-subject interaction of participants of the educational process, personal determinants of style features of psychological and pedagogical interaction, which contribute to the development of the success of professional activity of the teacher.

Reorganization is one of the fundamental processes of development of modern society. The infographic model of the reorganization cycle, also known as the “development wheel”, allows us to divide development of this process into four stages: settlement, disorganization, reorganization and co-organization. These stages have been researched on by numerous Russian and foreign researchers. In the sphere of construction, the most interesting is the restructuring, which has a special relevance at the present stage of development of the Russian Federation and requires creative approaches, models and methods of scientific research of vision of this important branch of economy. In this article, the model of reorganization process is structured with use of the theory of multipoint logic and the practice of infographic modeling, possible impacts and interactions leading to well-defined and formalized results are considered. Models of loading the interactions of individual components of the model of reorganization by control actions and the results obtained therewith have been developed.

At present the types of seepage-control elements made of non-soil materials are worked out for construction of ultra-high embankment dams. We made assessment of workability of a number of new structural designs of seepage-control elements for embankment dams such as concrete core, grout curtain, massive face, combined seepage-control elements. The assessment was made by analyzing the results of numerical modeling of more than 200 m high dam stress-strain state. It was revealed that the most perspective way of constructing high embankment dams is use of combined seepage-control elements. The operative structures are structures consisting of a reinforced concrete face and a vertical structure (like a diaphragm or a curtain), made of clay and cement mix. Use of internal seepage-control elements made of rigid materials in high embankment dams is dangerous.

The aim of the work is to choose a method of a solar car battery cooling. The student engineering team of Peter the Great Petersburg Polytechnic University designs the car. The analysis of the electrical circuit of the battery is carried out, the heat release is estimated due to three factors. According to the conditions of reliable operation of the battery, it is necessary to maintain its temperature range below 45°C, which requires cooling. The paper analyzes the possibilities of liquid, air-cooling, compares the free and forced methods of convective heat transfer. For the normal operating mode of the electric vehicle, environmental temperature at the level up to 38°C, a criterion thermal engineering calculation of the forced air-cooling of the corridor assembly of 405 battery cells providing the required heat dissipation is performed. It is shown that relatively high values of the heat transfer coefficient are provided under turbulent flow conditions characterized by Reynolds criteria above 103. On the basis of an analysis of the steady-state stationary heat-removal regime, it was concluded that an air flow provides a temperature gradient, sufficient for cooling the lithium-ion battery of a Solar Car «Polytech Solar».

This paper discusses the considerations for production modernization, replacement of imported spare parts manufactured in-house and redistribution of items manufactured spare parts between managed societies, with a goal to improve the quality and reliability of products, to reduce its cost, and work. Noted that the process of acquiring new foreign equipment and modern materials are complicated. There are examples of restrictive measures of the sanctions impact on industrial products and the dynamics of dependency on imports of industrial products. We used analytic and statistical research methods. Authors conducted a comparative analysis of the engineering assets of «Metalloinvest» holding that showed that all businesses have an impressive fleet of metal-working equipment on all groups of machines on the basis of an analysis of the needs for them to transport spare parts.

The article considers the formation of a strategic regional industrial policy in conditions of the new technological order occurrence on the basis of trends in the global technological development, conducts a strategic analysis of industry of the Republic of Bashkortostan on types of economic activities: mining, production of petroleum products, chemical production, manufacture of rubber and plastic products and construction, identified positive and negative trends and proposed future directions of development taking into account assumed scenarios of regional, national and global markets development. The study proposed options for the sectoral and cross-sectoral clusters establishment, the estimation of the resulting synergies in terms of creating inter-industry construction cluster based on the element wise estimation method of clustering efficiency and performed the benefits of the cross-sectoral cluster formation for all market participants.