Nonlinear Dependence Research Articles

МЕТОД РАСЧЕТА ОПТИМАЛЬНОГО РЕЖИМА ДИСПЕРГИРОВАНИЯ РАСТВОРОВ ПОЛИМЕРНЫХ КОМПОЗИТОВ В ТУРБИННОЙ МЕШАЛКЕ

LSTU has developed a turbine stirrer for mixing and dispersing solutions of polymer nanocomposites designed to restore mounting holes in the body parts of equipment. Formula is proposed for calculation of optimal rotation frequency of turbine mixer, which takes into account structural parameters of mixer and physical properties of nanoparticles, which provides effective mixing of nanoparticles of filler in solution of nanocomposite. An experiment was carried out to assess the adequacy of a mathematical model of the turbine stirrer speed. Experimental turbine agitators of three different sizes were used in the experiment. A solution of elastomer nanocomposite F-40S was used for mixing. The dispersion quality was evaluated by the light transmission coefficient. For each turbine stirrer size, experimental non-linear dependences of the transmittance on the stirrer speed were obtained. After approximation of experimental dependences and taking partial derivatives, their extremes are determined, i.e. actual optimal rotation frequencies for each mixer size: n оф1 = 1325 min-1; nof2 = 1113 min-1;, nof3 = 973 min-1. Then the power consumed by stirrers of various sizes at a speed of 500, 1000 and 1500 min-1 was measured. Further, the values of the circumferential velocity coefficients k were calculated for each stirrer using the measured power values. As the stirrer diameter increases from 34 to 90 mm, the corresponding circumferential velocity factor k increases from 0.9534 to 0.9703, indicating an increase in circumferential flow.Further, according to the proposed formula, using the values of the coefficients of peripheral speeds, the calculated values of the optimal speed for stirrers No. 1, No. 2 and No. 3 were obtained: n op1 = 1307 min-1; nop2 = 1100 min-1;, nop3 = 965 min-1. Analysis of variance showed that the calculated Fischer value does not exceed the table value, which confirms the adequacy of the formula for calculating the optimal speed of the turbine stirrer.

Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case-Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy.

Risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons and patients in their joint informed decision making. Recently, a new risk prediction model for 90-day mortality after esophagectomy using the International Esodata Study Group (IESG) database was proposed, allowing for the preoperative assignment of patients into different risk categories. However, given the non-linear dependencies between patient- and tumor-related risk factors contributing to cumulative surgical risk, machine learning (ML) may evolve as a novel and more integrated approach for mortality prediction. We evaluated the IESG risk model and compared its performance to ML models. Multiple classifiers were trained and validated on 552 patients from two independent centers undergoing oncologic esophagectomies. The discrimination performance of each model was assessed utilizing the area under the receiver operating characteristics curve (AUROC), the area under the precision-recall curve (AUPRC), and the Matthews correlation coefficient (MCC). The 90-day mortality rate was 5.8%. We found that IESG categorization allowed for adequate group-based risk prediction. However, ML models provided better discrimination performance, reaching superior AUROCs (0.64 [0.63-0.65] vs. 0.44 [0.32-0.56]), AUPRCs (0.25 [0.24-0.27] vs. 0.11 [0.05-0.21]), and MCCs (0.27 ([0.25-0.28] vs. 0.15 [0.03-0.27]). Conclusively, ML shows promising potential to identify patients at risk prior to surgery, surpassing conventional statistics. Still, larger datasets are needed to achieve higher discrimination performances for large-scale clinical implementation in the future.

Spontaneous anomalous Hall effect in magnetic and non-magnetic systems

Abstract We consider two cases of spontaneous anomalous Hall current. In a non-magnetic system with spin–orbit coupling (SOC), the applied bias results in the appearance of non-equilibrium magnetization and an anomalous Hall current. The latter demonstrates non-linear dependence on the applied bias. In a magnetic system with SOC, an anomalous current appears without applied bias. We perform the calculations in the framework of the free-electron model, whereas the common approach to this type of phenomenon is based on Berry connection. We demonstrate that anomalous currents acquire a measurable magnitude for reasonable model parameters.

Mechanical Properties of Clay-Reinforced Polyamide 6 Nanocomposite Liner Materials of Type IV Hydrogen Storage Vessels.

This study focuses on polyamide 6/organo-modified montmorillonite (PA6/OMMT) nanocomposites as potential liner materials, given the growing interest in enhancing the performance of type IV composite overwrapped hydrogen storage pressure vessels. The mechanical properties of PA6/OMMT composites with varying filler concentrations were investigated across a temperature range relevant to hydrogen storage conditions (-40 °C to +85 °C). Liner collapse, a critical issue caused by rapid gas discharge, was analyzed using an Ishikawa diagram to identify external and internal factors. Mechanical testing revealed that higher OMMT content generally increased stiffness, especially at elevated temperatures. The Young's modulus and first yield strength exhibited non-linear temperature dependencies, with 1 wt. per cent OMMT content enhancing yield strength at all tested temperatures. Dynamic mechanical analysis (DMA) indicated that OMMT improves the storage modulus, suggesting effective filler dispersion, but it also reduces the toughness and heat resistance, as evidenced by lower glass transition temperatures. This study underscores the importance of optimizing OMMT content to balance mechanical performance and thermal stability for the practical application of PA6/OMMT nanocomposites in hydrogen storage pressure vessels.

Modelling incidence and mortality cancer parameters with respect to GLOBOCAN 2020Age standardized world estimates

In this paper, an empirical model with the critical number of incidences and deaths for female cancers as adjustable parameters has been developed using life expectancy data from GLOBOCAN world estimates on cancer types. The model was developed based on the cumulative risk and exponential correlation techniques such that the significance of the adjustable parameters ascertains the strength of this research in two ways. First, it indicates indicates the rapid increase in female cancer morbidity with increase in the number of male cancer cases, regardless of incidence or mortality. Second, it suggests that female cancer cases may approach a virtual limiting value as male cancer cases reach extremely high levels. This projection aligns with the global population trends, indicating a proportional increase in cancer cases each year. Additionally, the cumulative risk of cancer incidences of each sex has been modelled separately against the global cumulative risk of cancer incidences of both sexes which revealed regions that have passed their inflection point and those that are yet to reach the inflection point. There was a curvature change, which indicates an inflection point coinciding with the South-Eastern Region and indicating that for the regions beyond the inflection point, the increase of the cumulative risk of cancer incidences of females against that of males is more accentuated compared with the regions before the inflection point. However, when the cumulative risk of cancer mortality of each sex is modelled separately against the global cumulative risk of cancer mortality of both sexes shows a non-linear dependence and the increase is more accentuated for males than for females. This finding indicates that cumulative risk is influenced by factors beyond the male-female population ratio. This study advances cancer studies as it provides a nuanced understanding of gender-specific cancer trends, crucial for developing targeted cancer prevention and treatment strategies. By elucidating the dynamics of cancer incidences and mortalities across different regions and sexes, the findings can inform public health policies and resource allocation to combat cancer more effectively on a global scale.

Global evidence on the energy–environment dilemma: the role of energy-related uncertainty across diverse environmental indicators

ABSTRACT Several existing studies show that macroeconomic uncertainties intensify global environmental and climate challenges, putting the globe at risk of not being able to achieve the United Nations’ Sustainable Development Goals by 2030. In this study, we provide global evidence on the role of energy-related uncertainty in the energy – environment dilemma between 1996 and 2021. We employ three distinct environmental indicators – load capacity factor (LCF), carbon dioxide emissions (CO2), and ecological footprint (EFP) – alongside a comprehensive global energy-related uncertainty index and time-frequency-quantile methods based on the Wavelet Quantile Correlation, Cross-Quantilogram, and Wavelet Local Multiple Correlation with Dominance. The empirical results suggest negative and strong nonlinear dependencies between energy-related uncertainty and the LCF across periods and quantiles. The results further suggest that the energy-related uncertainty has positive and strong nonlinear dependences not only with CO2 emissions but also EFP across various periods and quantiles. The results further suggest that the dependences between energy-related uncertainty and environmental indicators vary across periods and quantiles, with evidence of stronger dependency structures in the long run. These findings underscore the substantial influence of energy-related uncertainties on contemporary environmental challenges. We suggest that governments and policymakers need to reshape policy directives toward mitigating the environmental effects of energy-related uncertainties.

Nonlinear thermo-electro-mechanical responses and active control of functionally graded piezoelectric plates subjected to strong electric fields

Functionally graded piezoelectric plates (FGPPs) are often exposed to extreme thermal environments and subjected to large amplitude excitation and strong electric fields. Accurately predicting the nonlinear behaviors of FGPPs under large thermo-electro-mechanical loads remains a considerable challenge. This paper proposes a comprehensive nonlinear coupled analysis model that considers geometric nonlinearity, piezoelectric nonlinearity, and the temperature dependence of piezoelectric parameters. The total Lagrangian incremental finite element equations for FGPPs under thermo-electro-mechanical loads are derived using Hamilton's principle, by employing the first-order shear deformation theory and the von Kármán nonlinear strain-displacement relationship. The accuracy of the model is validated through comparative studies. Based on this multi-nonlinear model, we further investigate the effects of geometric nonlinearity, piezoelectric nonlinearity, and temperature dependence on the nonlinear static bending, dynamic responses, and active control of FGPPs. This study demonstrates that considering these factors enables accurate prediction of the static, dynamic, and active control behaviors of FGPPs.

Dependence properties of stochastic volatility models

The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.

ВЛИЯНИЕ ВИБРАЦИИ НА ИЗНОС БУКСОВОГО ПОДШИПНИКА

The study objective is to determine the effect of vibration on the wear of the parts of a rolling axle box bearing. The tasks of the study are to obtain dependences of the speed and wear intensity of bearing elements depending on time, to determine the most unfavorable factors affecting the bearing, to evaluate methods for increasing wear resistance and vibration resistance of parts of a rolling axle box bearing. The bearing is tested on 1K62 screw-cutting lathe under the action and without the action of axial vibration. Wear is calculated for the treadmill using a mass method, by stopping tests and measuring mass loss (with an accuracy of 1g). The friction path of the roller to determine the wear intensity is calculated analytically. The scientific novelty of the work consists in confirming the hypothesis about the adverse effect of vibration on wear and suggesting methods and ways to increase the vibration resistance of rolling axle box bearing parts. The results of the tests and calculations are presented as graphs showing the effect of vibration on the axle box bearing parts in the form of a wear curve, which has a nonlinear dependence on the test time, as well as a wear curve for tests without vibration. The main factors influencing the fretting wear of rolling axle bearings are determined, methods for increasing their vibration resistance and wear resistance are proposed.

Pyramid Cascaded Convolutional Neural Network with Graph Convolution for Hyperspectral Image Classification

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insufficient for multi-scale feature extraction in complex data scenes. To learn the relations among samples in non-grid data, GCNs are employed and combined with CNNs to process HSIs. Nevertheless, most methods based on CNN-GCN may overlook the integration of pixel-wise spectral signatures. In this paper, we propose a pyramid cascaded convolutional neural network with graph convolution (PCCGC) for hyperspectral image classification. It mainly comprises CNN-based and GCN-based subnetworks. Specifically, in the CNN-based subnetwork, a pyramid residual cascaded module and a pyramid convolution cascaded module are employed to extract multiscale spectral and spatial features separately, which can enhance the robustness of the proposed model. Furthermore, an adaptive feature-weighted fusion strategy is utilized to adaptively fuse multiscale spectral and spatial features. In the GCN-based subnetwork, a band selection network (BSNet) is used to learn the spectral signatures in the HSI using nonlinear inter-band dependencies. Then, the spectral-enhanced GCN module is utilized to extract and enhance the important features in the spectral matrix. Subsequently, a mutual-cooperative attention mechanism is constructed to align the spectral signatures between BSNet-based matrix with the spectral-enhanced GCN-based matrix for spectral signature integration. Abundant experiments performed on four widely used real HSI datasets show that our model achieves higher classification accuracy than the fourteen other comparative methods, which shows the superior classification performance of PCCGC over the state-of-the-art methods.

Forecasting the architecture billings index (ABI) using machine learning predictive models

PurposeThis research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.Design/methodology/approachThe architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.FindingsThe experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.Practical implicationsThe architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.Originality/valueThe ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection

Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS.

A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice

This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose–response trends, with cancer being the predominant cause of mortality. SHAP value dose–response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection.

RISK ANALYSIS OF GOOGL & AMZN STOCK CALL OPTIONS USING DELTA GAMMA THETA NORMAL APPROACH

Stocks, as investment products, tend to carry risks due to fluctuations. The tendency of stock prices to rise over time leads investors to opt for call options, which are one of the derivative investment products. However, call options are influenced by several factors that can pose risks and have nonlinear dependence on market risk factors. Therefore, methods are needed to measure the risk of call options, such as Delta Normal Value at Risk and Delta Gamma Normal Value at Risk. Delta and Gamma are part of Option Greeks, parameters that measure the sensitivity of options to various factors used in determining option prices with the Black-Scholes model. This study uses an approach with the addition of Theta, which can measure the sensitivity of options to time. This study aims to analyze Value at Risk with the Delta Gamma Theta Normal approach for call options on Google (GOOGL) and Amazon (AMZN) stocks. The analysis uses closing stock price data from September 7, 2022, to September 7, 2023, and three in-the-money and out-of-the-money call option prices. The study begins by collecting closing stock prices and call option contract components, testing the normality of stock returns, calculating volatility, , Delta, Gamma, and Theta, then calculating the Value at Risk. Based on the analysis, it is found that GOOGL and AMZN call options have a Value at Risk of $0.89588 and $0.92760, respectively, at a 99% confidence level with a strike price of $120. Furthermore, based on the comparison of Value at Risk between in-the-money and out-of-the-money call options, it can be concluded that out-of-the-money call options tend to have larger estimated losses.

Testing for time‐varying nonlinear dependence structures: Regime‐switching and local Gaussian correlation

AbstractThis paper examines nonlinear and time‐varying dependence structures between a pair of stochastic variables, using a novel approach which combines regime‐switching models and local Gaussian correlation (LGC). We propose an LGC‐based bootstrap test for examining whether the dependence structure between two variables is equal across different regimes. We examine this test in a Monte Carlo study, where it shows good level and power properties. We argue that this approach is more intuitive than competing approaches, typically combining regime‐switching models with copula theory. Furthermore, LGC is a semi‐parametric approach, hence avoids any parametric specification of the dependence structure. We illustrate our approach using financial returns from the US–UK stock markets and the US stock and government bond markets, and provide detailed insight into their dependence structures.

High-order brain interactions in ketamine during rest and task: a double-blinded cross-over design using portable EEG on male participants

Ketamine is a dissociative anesthetic that induces a shift in global consciousness states and related brain dynamics. Portable low-density EEG systems could be used to monitor these effects. However, previous evidence is almost null and lacks adequate methods to address global dynamics with a small number of electrodes. This study delves into brain high-order interactions (HOI) to explore the effects of ketamine using portable EEG. In a double-blinded cross-over design, 30 male adults (mean age = 25.57, SD = 3.74) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Ketamine induced an increase in redundancy in brain dynamics (copies of the same information that can be retrieved from 3 or more electrodes), most significantly in the alpha frequency band. Redundancy was more evident during resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine’s HOI and experiences of derealization. Ketamine appears to increase redundancy and HOI across psychometric measures, suggesting these effects are correlated with alterations in consciousness towards dissociation. In comparisons with event-related potential (ERP) or standard functional connectivity metrics, HOI represent an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations between electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes.

Pre-sensor computing with compact multilayer optical neural network.

Moving computation units closer to sensors is becoming a promising approach to addressing bottlenecks in computing speed, power consumption, and data storage. Pre-sensor computing with optical neural networks (ONNs) allows extensive processing. However, the lack of nonlinear activation and dependence on laser input limits the computational capacity, practicality, and scalability. A compact and passive multilayer ONN (MONN) is proposed, which has two convolution layers and an inserted nonlinear layer, performing pre-sensor computations with designed passive masks and a quantum dot film for incoherent light. MONN has an optical length as short as 5 millimeters, two orders of magnitude smaller than state-of-the-art lens-based ONNs. MONN outperforms linear single-layer ONN across various vision tasks, off-loading up to 95% of computationally expensive operations into optics from electronics. Motivated by MONN, a paradigm is emerging for mobile vision, fulfilling the demands for practicality, miniaturization, and low power consumption.

Regression dependence of the number growth dynamics of the biogas plants in Ukraine

The global environmental crisis forces the countries of the world to introduce and use new alternative means of energy production, in particular methods of extraction and processing of biogas to provide energy to the population. Therefore, the determination of the regression dependence, which describes the dynamics of the increase in the number of biogas plants and can be used when forecasting the number of such plants, is an urgent scientific and technical task. The purpose of the research is to determine the regression dependence, which describes the dynamics of the increase in the number of biogas plants and can be used when forecasting the number of such plants, with the aim of increasing the prevalence of the use of renewable energy sources, saving fossil energy sources and simultaneously reducing the intensity of environmental pollution. During the research, the method of regression analysis of the results of one-factor experiments and other paired dependencies was used with the selection of a rational type of function from the sixteen most common options according to the criterion of the maximum value of the correlation coefficient. Regression was carried out on the basis of linearizing transformations, which allow reducing the non-linear dependence to a linear one. The coefficients of the regression equations were determined by the method of least squares using the developed computer program "RegAnalyz", which is protected by a certificate of copyright registration for the work. An adequate regression relationship was obtained, which describes the dynamics of the increase in the number of biogas plants and can be used when forecasting the number of such plants. A graphical dependence was built that describes the dynamics of the increase in the number of biogas plants and allows to visually illustrate this dynamic and show sufficient convergence of theoretical results with actual data. It was established that the increase in the number of biogas plants in Ukraine during 2012-2019 grew according to a hyperbolic dependence.

A mixer architecture using GaN-based split-gate nanowire transistor

Frequency mixer is an essential block in radio-frequency signal processing for frequency translation and phase comparison. The most common mixers are fabricated using passive elements which suffer from significant conversion loss and low isolation. Mixers using active devices are used less frequently and rather less matured on GaN technology. Here, we demonstrate a mixer based on GaN split-gate nanowire transistor, allowing low conversion loss and high isolation. A constriction is formed by electrostatic modulation of the effective gate width. The threshold voltage of the transistor is modified by one of the gate voltages through the width variation, while the other gate voltage biases the transistor in the saturation region. The nonlinear dependency of the transistor characteristics on the two gate voltages facilitates frequency translation. The mixing characteristics of this architecture are verified both experimentally and theoretically. The output power spectral density peaks at the difference frequency with a minimal conversion loss. Extremely high isolation is measured using three-port S-parameter measurements. The proposed architecture shows multiple benefits, additionally facilitating monolithic mixers on the GaN platform.

Diagnostics of power cable insulation by using the methods of dielectric spectroscopy: overview of physical basis and features of practical application

The article provides an overview of the physical foundations and features of the application of diagnostic methods for the insulation of power cables using dielectric spectroscopy methods in the frequency and time domains. The relationship between the depolarization currents and the frequency spectrum of the complex dielectric constant is shown. An analysis of the factors determining the frequency dependence of the tangent angle of dielectric losses of paper and polyethylene insulation of power cables is given. The main types of reaction of dielectric materials with different levels of development of water channels at the level and frequency of the applied voltage are listed. According to the results of the analysis, it is shown that for high-voltage measurements, one of the factors that complicates the diagnosis of the insulation of power cables by analyzing the frequency dependences of the capacitance and the tangent of the dielectric loss angle is the distortion of the sinusoidal current through the dielectric due to the nonlinear dependence of the complex dielectric constant on the level of the applied voltage. A comparative analysis of dielectric spectroscopy methods in the frequency domain, involving high-voltage and low-voltage measurements of the complex dielectric constant, was carried out. The correlation between the tangent of the dielectric loss angle of paper insulation and its mechanical characteristics is shown