Transformation Coefficients Research Articles

Microbiological activity of typical chernozem in cultivation of agricultural crops using agrobiotechnology

The article presents the results of a study of the microbial cenosis of typical slightly eroded chernozem under crops of corn for grain and oats under conditions of the use of agrobiotechnologies, including the treatment of seeds, soil, plants and post-harvest residues with microbiological preparations based on Trichoderma and Lactobacillus, as well as the use of nitrogen fertilizers and lime against the background of surface incorporation by-products. The research was carried out on the territory of the Federal State Budgetary Institution “Kursk FATS” (Medvensky district, Kursky district, Panino village) in two fields. In 2022, in the first field (grain crop rotation) oats (Avena sativa L.) variety “Borets” (predecessor – buckwheat) were cultivated, in the second (grain crop rotation) – hybrid corn (Zea mays L.) variety “Delitop” ( predecessor – winter wheat). The soil of the experimental field is typical low-humus, slightly eroded, heavy loamy chernozem on carbonate loess-like loam. It was established that during the harvesting phase under corn and oats, regardless of the factors (microbiological preparations, nitrogen fertilizers and lime) of the experiment, the soil was characterized by an increase in the number of saprophytic microflora and micromycetes compared to the phase of crop germination. The introduction of microbiological preparations based on Trichoderma and Lactobacillus with nitrogen fertilizers and by-products contributed to an increase in soil biogenicity, which on average during the growing season exceeded the control by 43.9 and 57.2%, respectively, under corn and oats. The maximum number of microscopic fungi was noted with the combined use of Trichoplant, SK and Biogor-Zh with mineral nitrogen, but the soil under oats in terms of the content of fungal microflora was 8.9 thousand CFU/g soils exceeded their amount under corn crops. The highest value of the transformation coefficient of organic matter was observed when by-products were inoculated with microbiological preparations with lime, regardless of the culture and timing of determination (phases of germination and harvesting of crops).

Simultaneous Determination of Transformation Plasticity Coefficients of Bainite and Martensite Transformations by Oil Quenching of Steel Sheet for Machine Structural Use and Accurate Prediction of Distortion by Oil Quenching

Simultaneous Determination of Transformation Plasticity Coefficients of Bainite and Martensite Transformations by Oil Quenching of Steel Sheet for Machine Structural Use and Accurate Prediction of Distortion by Oil Quenching

Aspects of Developing an Innovative, Energy-Efficient, Low-Emission Co-Generator

Purpose. To develop a model and construct an experimental, innovative co-generator with high energy efficiency and low emissions. This involves developing a system that can efficiently generate both electricity and heat simultaneously while minimizing emissions, contributing to sustainability efforts, and addressing energy demands in a more environmentally friendly manner. Methodology. To achieve the goal, a system approach is utilized, enabling the selection of modelling types for the development of an experimental, cutting-edge co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. For this purpose, the following steps were completed: processing and summarizing available literature and patent sources, analysing scientific and technical papers on the selection and application of modelling types in co-generation systems, and considering principles and individual approaches to input data formation for mathematical modelling. This process enables the selection of a mechanism and the creation of simulation models for effective energy production at various enterprises. Findings. Assessment is performed of the energy efficiency of the co-generator system under various operating conditions, comparing it with existing conventional methods of electricity and heat generation. Results are presented of performance testing to determine the system’s capability to simultaneously generate electricity and heat efficiently, considering factors such as output stability, load responsiveness, and overall reliability. Identification and evaluation are performed of innovative technologies and methodologies integrated into the co-generator design, highlighting their effectiveness in enhancing energy efficiency and reducing emissions. Insights into any operational challenges encountered during the construction, testing, and optimization phases, along with proposed solutions or improvements to solve these problems. The analysis of the overall environmental impact of deploying the co-generator showed potential benefits in terms of reduced greenhouse gas emissions and local air quality improvement. Originality. Using a combination of scientific approaches encompassing physics and heat transfer engineering, in accordance with the first law of thermodynamics, such as the conservation laws of energy and entropy, and principles of heat exchange employed to transfer heat between different mediums, gas kinetics have yielded values for the energy transformation coefficient, indicating qualitative characteristics of fuel thermolysis and power generation as the final product. Practical value. The results provide for developing a comprehensive model of an advanced co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. It also entails determining the types of models for mathematical modelling at all management levels and establishing a new method for input data formation for both technologies and their subsystems, incorporating additional technological implementations.

Experimental study of performance of building heating system based on HPS and equipped with thermal potential recovery system and heat load prediction

The results of experimental studies of the heating system performance of a building located in the area of temperate continental climate with temperatures in the heating period down to −30 °C are discussed. The studied heating system comprises an air–liquid heat pump, a liquid–liquid heat pump with a ground circuit, and an electric boiler for peak demands.The paper presents the results of the influence of the ground thermal potential recovery system on the liquid–liquid heat pump performance. Also the influence of the used method of heat load prediction on the system efficiency and indoor air temperature stability is shown.Implementation of the thermal potential recovery system allows to increase the average value of the transformation coefficient for the liquid–liquid heat pump from 2.7 to 2.9 during three years of operation.Heat load prediction leads to stable indoor air temperature in the range of 22 ± 1 °C. Also it allows to level out the dependence of indoor temperature fluctuations on outdoor air temperature fluctuations.

Implementation of Energy-saving Technologies in House construction

Objective. Over the past decades, heat pumps have gained great popularity due to their advantages in energy efficiency and environmental friendliness, becoming one of the most attractive alternatives to replace traditional heating systems running on fossil fuels. Heat pump efficiency is a decisive factor that should be taken into account when selecting and optimizing a heat pump system. The aim of the study was to evaluate the efficiency of air source heat pumps of the air-to-water system in the climatic conditions typical of the regions of the Central Federal District. Method. To evaluate the efficiency of air source heat pumps, a mathematical model is used that takes into account the multifactorial nature of the process under study. Result. To adequately compare the obtained research results with the passport performance indicators, the transformation coefficient (COP) was used, showing the ratio of the energy received to the expended work. As a result, the method of heating rooms with an air source heat pump with a combined operation mode was recognized as more preferable. Conclusion. Currently, the potential of heat pumps in the field of energy saving and CO2 emission reduction remains largely untapped, which sets the vector for further research.

Investigation of the sintering behavior of nanoparticulate SiC by molecular dynamics simulation

Sintering is a valuable processing technique utilized to create bulk materials from powder compacts. In recent years, sintering has a garnered significant attention in the research community due to its versatility and applicability in various fields, including additive manufacturing. Herein, we investigate the sintering kinetics and mechanism of SiC nanoparticulate using molecular dynamics simulations. The surface morphology, microstructure, radial distribution function, mean square displacement, atomic displacement distribution, structural transformation, and diffusion coefficient are analyzed in detail. The free surface surrounding the neck undergoes a reconstruction to eventually reach an equilibrium dihedral angle. The neck rapidly grows and aligns with the surface, causing the SiC particle to transform from an original spherical shape into a twists shape. The concentration of shear stress results in a change of the local lattice structure from the face-centered cubic phase to amorphous phase in the surface. With an increasing sintering time, the dominant diffusion mechanism gradually shifts from surface diffusion to interface diffusion. This trend leads to a rise in the atomic migration, the consumption of interface energy, and the degree of particle fusion. Ultimately, the sintered SiC particles have the spherical shape, low surface energy, and stable structure. Additionally, the high mobility of melted atom causes a gradual dissolution of SiC particle into the large particle from the surface towards the interior. This process leads to high-altitude concentration structures that facilitate atomic migration. These results shed light on an atomic sintering mechanism in SiC nanoparticulate for applications in the preparation of nanostructured materials, and additive manufacturing.

Investigation of the third harmonic generation in a high-current relativistic Ka-band gyrotron

Theoretical and experimental investigations of radiation generation in the frequency multiplication mode at the third harmonic in a high-current Ka-band gyrotron have been carried out. Within the framework of three-dimensional PIC-simulation, it is shown that the nonlinear transformation coefficient in the W-band can reach values of 0.5%. Experimental data on registration of radiation in this range are presented.

Influence of phase transformation coefficient on thermomechanical modeling of laser powder bed fusion for maraging 300 steel

Manufacturing maraging steel components using laser-based powder bed fusion (LPBF) presents an attractive proposition for industries due to the material's combination of mechanical properties such as hardness, wear resistance, toughness and the capability to produce parts with complex geometries and high precision. Despite these advantages, the LPBF process induces defects such as distortion and residual stress associated with the complex thermal cycles, compromising final part quality. Numerical simulations have been developed to predict these defects. However, LPBF simulations remain challenging due to the complexity of the process and the substantial computational resources required. For maraging steel, for example, the occurrence of phase transformation promotes compressive stress that interferes in results and makes simulations inaccurate. This research aims to simulate a geometry with a circular inner channel and investigate distortion, volume fraction of martensite and equivalent stress results. Modeling was performed by varying phase transformation rate parameters to assess the impact of transformations on simulation outcomes. Results showed minimal impact of these parameters on distortions and equivalent stress. Equivalent stress results were compared with literature findings, while distortion results were validated against experimental data to validate the accuracy of the model.

A Study on the Design of Incentive Contracts for Platform Economy Regulation Based on Dual Principal–Agents

A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation.

Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing.

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%.

Diffusion-thermo and thermo-diffusion impacts on MHD natural convection flow of maxwell nanofluids over a stretching sheet

The present study carried out significant importance in its magnetic field and crosswise diffusion influenced the Maxwell nanofluid flow past a nonlinear stretching sheet. This investigation aimed to shed light on the behavior of critical parameters, particularly in the presence of thermophoresis and Brownian motion. The primary aim is to investigate the impact of thermal diffusion, diffusion-thermo, and various influencing factors on a Maxwell nanofluid near a stretching sheet in the magnetic field, and the impact of three distinct slip conditions (velocity, thermal, and solutal). The partial differential equations by the nonlinear coefficients are utilized in obtaining the most important equations. These equations are distorting to beneficial nonlinear ordinary differential equations making use of the appropriate transformations variables and transformation coefficients. To investigating the computational solutions for the reducing sets of the nonlinear ordinary differential equations, these were developing and utilized the Keller box methodology. The simulations are carried out the nanofluids velocity, temperature, concentration, skin friction coefficients, the rate of temperature transport, in addition to the rate of mass transportation, amongst previous variables. The validations of this methodology are displayed via the comparison of the present outputs through the preceding judgments into the literatures. The major conclusions drawn from this study are the concentration profiles exhibited opposing behaviours as concentration slip parameter increased. However, the temperature profile increases for Brownian motion parameter and thermophoresis parameter parameters. Secondly, concentration displayed an increasing behavior as the Soret number increased.

НАЦІОНАЛЬНА БЕЗПЕКА ТА УМОВИ ЕНЕРГОЗАБЕЗПЕЧЕННЯ СУБ’ЄКТІВ НАЦІОНАЛЬНОЇ ЕКОНОМІКИ: ЦІЛІ І ЗАВДАННЯ СИСТЕМИ ПУБЛІЧНОГО УПРАВЛІННЯ ТА АДМІНІСТРУВАННЯ

The article consistently proves the fact of direct dependence of the national security of the State of Ukraine on the state of energy supply of the subjects of its national economy and proposes their interpretation in the form of a model of a typical resource and economic link, which transforms a certain list of resources (including energy) into results useful for society. In this case, the transformation coefficient of a typical link is the ratio of the cost of all resources used by it to the cost of the results generated and useful for society. On this basis, the author substantiates the needs and conditions for the necessary changes in the intellectual and mental state of the bodies and personnel responsible for the energy sector of the national system of public administration and management, human resources of a number of energy supplying and consuming enterprises, as well as the entire population of Ukraine, since all these categories are now characterized by «rudiments of Soviet thinking» caused by the long stay of human resources under the influence of ideologies of the Eastern institutional X-matrix (centralised control, communist ideology and non-market economy), as well as the lack of awareness of the need for fundamental intellectual and mental changes due to the needs of the global energy transition of humanity to a «green economy». It is proved that the intellectual and mental transition to new goals and objectives of activity should take place as a priority among the personnel of the public administration system with subsequent fundamental changes in the organization of relations between sets of enterprises-suppliers and enterprises-consumers of energy based on the ideologies of the Western institutional Y-matrix (decentralized management, individual ideology and market economy), the conditions for the transition of enterprises to reduce their energy dependence on external resources are demonstrated.

Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson's Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression-NLR, Linear Regression-LR, Gaussian Mixture Models-GMMs, Expectation Maximization-EM, Logistic Regression-LoR, Softmax Discriminant Classifier-SDC, and Support Vector Machine with Radial Basis Function kernel-SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

Application of Harmonized Elliptic Fourier Transform Coefficients for Comparing the Shapes of Biological Structures (Example of the Attachment Organs of Monogenea)

Application of Harmonized Elliptic Fourier Transform Coefficients for Comparing the Shapes of Biological Structures (Example of the Attachment Organs of Monogenea)

Boosting integral-based human pose estimation through implicit heatmap learning

Human pose estimation typically encompasses three categories: heatmap-, regression-, and integral-based methods. While integral-based methods possess advantages such as end-to-end learning, full-convolution learning, and being free from quantization errors, they have garnered comparatively less attention due to inferior performance. In this paper, we revisit integral-based approaches for human pose estimation and propose a novel implicit heatmap learning framework. The framework learns the true distribution of keypoints from the perspective of maximum likelihood estimation, aiming to mitigate inherent ambiguity in shape and variance associated with implicit heatmaps. Specifically, Simple Implicit Heatmap Normalization (SIHN) is first introduced to calculate implicit heatmaps as an efficient and effective representation for keypoint localization, which replaces the vanilla softmax normalization method. As implicit heatmaps may introduce potential challenges related to variance and shape ambiguity arising from the inherent nature of implicit heatmaps, we thus propose a Differentiable Spatial-to-Distributive Transform (DSDT) method to aptly map those implicit heatmaps onto the transformation coefficients of a deformed distribution. The deformed distribution is predicted by a likelihood-based generative model to unravel the shape ambiguity quandary effectively, and the transformation coefficients are learned by a regression model to resolve the variance ambiguity issue. Additionally, to expedite the acquisition of precise shape representations throughout the training process, we introduce a Wasserstein Distance-based Constraint (WDC) to ensure stable and reasonable supervision during the initial generation of implicit heatmaps. Experimental results on both the MSCOCO and MPII datasets demonstrate the effectiveness of our proposed method, achieving competitive performance against heatmap-based approaches while maintaining the advantages of integral-based approaches. Our source codes and pre-trained models are available at https://github.com/ducongju/IHL.

STUDY RE-ANALYSIS OF HIGH WAVE DEFORMATION IN RE-DESIGN COASTAL REVETMENT PROTECTION OF RAJABASA BEACH KALIANDA

The eruption of Mount Krakatau in 2018, along with high waves, has impacted the coastal conditions of Kalianda Beach. The parameters for planning the revetment include significant wave height, highest high water level (HHWL), refraction and shoaling coefficients, wave set-up, and SLR. Wave data processing was conducted using the FT Type I. The extreme wave 50-year period has a height (Hr) of 2.22 m and a period (Tr) of 14.67 s. The wave transformation coefficients for refraction and shoaling, which are 0.74 and 1.44 respectively, lead to a deformation wave height of 2.19 m at a depth of 5 m. Admiralty tide analysis yielded a tidal range element at MSL of 0.94 m, which serves as the datum for elevation point 0. The HHWL from MSL is 0.73 m, wave set-up is 0.58 m due to breaking waves at 3.43 m, and SLR is 0.16 m, leading to a DWL of 1.46 m. The wave run-up varies according to the type of revetment, boulder type elevations id 4.60 m, tetrapod type is 3.80 m, and dolos type is 4.40 m. Differences elevation are attributed to different wave run-up every type of revetment material, run-up of the type material boulder is 2.83 m, run-up of tetrapod is 2.08 m, and dolos is 2.67 m.

Capacity estimation for series-connected battery pack based on partial charging voltage curve segments

Accurate estimation of battery pack capacity is crucial in determining electric vehicle driving range and providing valuable suggestions for battery health management. This article proposes an improved capacity co-estimation framework for cells and battery pack using partial charging process. The transformation characteristics of cell capacity difference within the battery pack on the external voltage curve are discussed. The charging voltage curve is segmented according to the feature points extracted from battery incremental capacity curve, and the linear correlation between the voltage curve segments and capacity decline is analyzed to obtain the optimal transformation part, which is verified through a series of cycle aging data. Subsequently, by optimizing the transformation coefficients, we achieve capacity estimation for each cell within the battery pack utilizing only a partial charging voltage profile at approximately 30 % SOC. We further establish a connection between the battery pack and its series cells to enable pack capacity estimation. The proposed method is verified based on two sets of battery pack tests comprising 60 cells in series and with severe capacity inconsistency. The results show that the estimation errors of cells are <2 % and the errors of battery packs are <1 %.

Influence of biodestructors on sequestration of labile carbon compounds in the soil

Ukraine stands as a testament to agricultural wealth due to its fertile soils, most notably, its significant share of the world’s chornozem. Rich soil is fundemantal to agricultural production, and therefore the success of the agricultural industry heavily relies on the fertility of the soil. However, modern agricultural practices in Ukraine, have led to increased anthropogenic impact on soils, affecting their biodiversity and humus content. In this regard, it is becoming increasingly important to perform comprehensive studies of microbiological, biochemical and chemical processes in the soil in response to different agricultural interventions. The urgent need to restore natural ecosystems to preserve their biodiversity and ensure ecological stability poses new scientific challenges, emphasizing the importance of urgent measures aimed at protecting the environment from pollution and degradation. This research aims to explore the effects of biodestructors on the deposition of labile carbon compounds in the soil during long-term consistent corn cultivation. The research was conducted during 2020–2023 within the framework of a stationary field experiment of the scientific research department of the «Druzhba Nova» in Varva village, Chernihiv region. The soil is a typical low-humus chornozem with the experiment focusing on a monoculture corn crop rotation. The DKC 4351 corn hybrid was sown at a density of 82 thousand units per hectare, employing a three-part experiment design: 1) a UAN control at 32–28 l/ha, 2) Ecostern Classic at 2 l/ha combined with UAN 32–28 l/ha, and 3) Ecostern Bacterial at 2 l/ha alongside Ecostern Trichoderma at 1 l/ha and UAN 32–28 l/ha. In the present study, the influence of biodestructors on soil’s agrochemical and microbiological indicators, as well as corn yield was evaluated. In a long-term stationary field experiment conducted on a typical low-humus chornozem, we investigated the influence of microbial preparations — plant residue biodestructors — on the deposition of labile carbon compounds in the soil under continuos corn cultivation. The application of Ecostern Classic and a combination of Ecostern Bacterial + Ecostern Trichoderma biodestructors resulted in an increase of labile carbon content by 0.11% and 0.18%, respectively, compared to the control. The aim of further analysis was to determinate the dynamics of the organic matter transformation coefficient, which is a marker of enhanced microbiological activity in the soil and a predominance of organic matter synthesis over its mineralization. In treatments utilizing biodestructors, this indicator was significantly higher than control values throughout the study. The survival of the fungal bioagent from Ecostern Classic and Ecostern Trichoderma bioproducts in the soil was monitored by assessing the population dynamics of the Trichoderma genus species. Results demonstrated an increase by an average of 19 thousand CFU/g with Ecostern Classic and by 34 thousand CFU/g with the combined use of Ecostern Bacterial with Ecostern Trichoderma, compared to 28 thousand CFU/g in control soil samples. This may indirectly indicate a successful colonization of the bioagent in treated soils. Using BIOTREX technology to evaluate the soil’s ecophysiological diversity, we found that the diversity index increased from 3.66 in control samples to between 4.87 and 5.61 in treatments with biodestructors. According to the further BIOTREX assessment, the use of biodestructors not only increased soil biodiversity, but also improved soil health. The application of these biodestructors also significantly boosted corn grain yield with an increase of 0.32 t/ha with Ecostern Classic treatment and 0.18 t/ha with the combined use of Ecostern Bacterial with Ecostern Trichoderma, compared to control.

A comparative Analysis of Image Enhancement Using Autoencoders and Generative Adversarial Networks

The purpose of this study, which focuses on image enhancement methods, is to investigate how various methods contribute to higher-quality photos in particular applications. Tracing the history of image improvement, encompassing the technological evolution of early photography from the 19th century to the present. The foundation of conventional image enhancement technology, which directly affects image elements and transformation coefficients, respectively, emphasizes frequency domain and spatial domain processing. It is noted that the application of autoencoders and generative adversarial networks to image enhancement as a deep learning training strategy has shown effectiveness in picture classification, target recognition, and image segmentation. This article primarily examines and contrasts autoencoders and generative adversarial networks, summarizing their applicability, constraints, benefits, and drawbacks and talking about the patterns in their upcoming development. The study has guiding value for advancing the development of picture technology and offers significant theoretical and empirical support for the field of image enhancement.

Transform coefficient distribution modeling based frame-level adaptive rate control for VVC

Rate control is a key issue in the research of video coding optimization. In this work, we propose a statistical modeling based frame-level distortion estimation model and a distortion dependency based rate control optimization algorithm for Versatile Video Coding (VVC). VVC has incorporated into more advanced coding tools, resulting in the distribution of transform coefficient more sharper. In response to this phenomenon, the TCD of VVC is modeled by the probability density function (PDF) of Cauchy distribution and generalized Gaussian distribution (GGD) individually. Then, a sliding window-based frame-level distortion model is proposed, which is used for derivation of inter-frame distortion dependency factor. According to the derived distortion dependency factor, a frame-level rate control optimization algorithm is presented to update the coding parameters in practical coding process. Experiments have shown that the proposed method could effectively improve rate–distortion performance, achieving an average rate distortion performance improvement of 0.72% in sequences blow 4K resolution, and 1.47% in 4K sequences in low delay P frame (LDP) configuration.