Classical Error Research Articles

Universal crossover in quantum circuits governed by a proximate classical error correction transition

We formulate a semi-classical circuit model to clarify the role of quantum entanglement in the recently discovered encoding phase transitions in quantum circuits with measurements. As a starting point we define a random circuit model with nearest neighbor classical gates interrupted by erasure errors. In analogy with the quantum setting, this system undergoes a purification transition at a critical error rate above which the classical information entropy in the output state vanishes. We show that this phase transition is in the directed percolation universality class, consistent with the fact that having zero entropy is an absorbing state of the dynamics; this classical circuit cannot generate entropy. Adding an arbitrarily small density of quantum gates in the presence of errors eliminates the transition by destroying the absorbing state: the quantum gates generate internal entanglement, which can be effectively converted to classical entropy by the errors. We describe the universal properties of this instability in an effective model of the semi-classical circuit. Our model highlights the crucial differences between information dynamics in classical and quantum circuits.

Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic

The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman’s two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.

Psychometric properties of the Caring Efficacy Scale among personal care attendants working in residential aged care settings.

This study assessed the psychometric properties of the Caring Efficacy Scale (CES) among personal care attendants providing care to older residents in residential aged care settings. This cross-sectional study was completed in Australia in 2020-2021. Confirmatory factor analysis (CFA) of the 30-item original CES (Model 1) and 28-item CES validated in registered nurses (Model 2) was conducted to assess the goodness of fit of these models in our study population. Due to unsatisfactory fit indices for both models, exploratory factor analysis (EFA) was conducted to examine the dimensionality and underlying structure of the original CES among personal care attendants. Internal consistency of the final scale and subscales identified was examined using item-total correlations and Cronbach's alpha coefficients. Two hundred and eighty personal care attendants participated in the study. The model fit indices such as Comparative Fit Index and Tucker Lewis Index of both models were less than 0.90, while the Standardised Root Mean Square Residual and Root Mean Square of Approximation values were greater than or equal to 0.08 and 0.06, respectively. The EFA identified a two-factor structure, and 22 items of the 30 in the original scale were retained. Item-total correlations amongst items retained in the scale and subscales were greater than 0.3. Cronbach's alpha for the abbreviated scale was 0.85, with 0.83 and 0.79, respectively, for the two subscales. The modified CES can be used as a robust tool to assess the self-efficacy of personal care attendants in providing care to older residents in residential aged care settings.

EM-Gaze: eye context correlation and metric learning for gaze estimation

In recent years, deep learning techniques have been used to estimate gaze—a significant task in computer vision and human-computer interaction. Previous studies have made significant achievements in predicting 2D or 3D gazes from monocular face images. This study presents a deep neural network for 2D gaze estimation on mobile devices. It achieves state-of-the-art 2D gaze point regression error, while significantly improving gaze classification error on quadrant divisions of the display. To this end, an efficient attention-based module that correlates and fuses the left and right eye contextual features is first proposed to improve gaze point regression performance. Subsequently, through a unified perspective for gaze estimation, metric learning for gaze classification on quadrant divisions is incorporated as additional supervision. Consequently, both gaze point regression and quadrant classification performances are improved. The experiments demonstrate that the proposed method outperforms existing gaze-estimation methods on the GazeCapture and MPIIFaceGaze datasets.

Longitudinal study investigating serum metabolites and their association with type 2 diabetes risk in a Korean population.

The lack of longitudinal metabolomics data and the statistical techniques to analyse them has limited the understanding of the metabolite levels related to type 2 diabetes (T2D) onset. Thus, we carried out logistic regression analysis and simultaneously proposed new approaches based on residuals of multiple logistic regression and geometric angle-based clustering for the analysis in T2D onset-specific metabolic changes. We used the sixth, seventh and eighth follow-up data from 2013, 2015 and 2017 among the Korea Association REsource (KARE) cohort data. Semi-targeted metabolite analysis was performed using ultraperformance liquid chromatography/triple quadrupole-mass spectrometry systems. As the results from the multiple logistic regression and a single metabolite in a logistic regression analysis varied dramatically, we recommend using models that consider potential multicollinearity among metabolites. The residual-based approach particularly identified neurotransmitters or related precursors as T2D onset-specific metabolites. By using geometric angle-based pattern clustering studies, ketone bodies and carnitines are observed as disease-onset specific metabolites and separated from others. To treat patients with early-stage insulin resistance and dyslipidaemia when metabolic disorders are still reversible, our findings may contribute to a greater understanding of how metabolomics could be used in disease intervention strategies during the early stages of T2D.

Use of single cell DNA sequencing to characterize somatic mosaicism in a patient with two signal transduction and activator of transcription 1 (STAT1) gain-of-function variants

There is increasing recognition that immune defects caused by somatic mosaicism are often clinically indistinguishable from classical germline inborn errors of immunity. Here we demonstrate the utility of single cell DNA sequencing in defining the cellular genotypes and specific cellular populations involved in the pathogenesis of immune dysregulation in a patient with two novel STAT1 variants and a clinical presentation consistent with STAT1 gain-of-function.A 5-month-old male presented with Pneumocystis jiroveci pneumonia, CMV viremia, and immune-mediated hepatitis. Lymphocyte subset analysis was not consistent with a combined immunodeficiency. Sequencing of genomic DNA using targeted panel of 407 inborn error of immunity genes revealed two heterozygous variants in the coiled-coil region of STAT1 (c.496_504del, p.Leu166_Asp168del; c.489_497del, p. Glu164_Leu166del). Functional analysis was consistent with STAT1 gain-of-function – patient peripheral blood mononuclear cells displayed increased IFN-alpha and IFN-beta-mediated STAT1 phosphorylation, and increased expression of downstream IFN-stimulated genes including IFI44, ISG15, and Ly6E.Further examination of the patient’s STAT1 sequencing data revealed that the two in-frame deletion variants were present on distinct alleles, and surprisingly, that a WT allele was also detected. Based on the presence of three distinct alleles, somatic mosaicism was suspected. Parental testing failed to detect either variant, suggesting that the variants were de novo. Sequencing of DNA extracted from a buccal swab of the patient revealed only wild type STAT1 sequence.Single cell DNA sequencing analysis demonstrated that the two in-frame deletions comprised three different genotypes –a compound heterozygous population comprising 9% of leukocytes, a heterozygous p. Leu166_Asp168del/wild type population comprising of 82% of leukocytes, and a WT population comprising 9% of leukocytes. This analysis additionally revealed that all three genotypes were uniformly distributed among both myeloid and lymphoid populations. Functional analysis of the two in-frame deletions demonstrated that both promote increased STAT1 activity.That both STAT1 variants occur at nearly identical locations, and the uniform distribution among leukocytes, suggests a still undefined genetic event occurring prior to the divergence of common myeloid and lymphoid progenitor cells in hematopoietic stem cell ontogeny. This case demonstrates the power of single cell DNA sequencing in precisely defining disease pathogenesis in patients with somatic mosaicism.

Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE.

New Evidence on the Effect of Human Capital on Economic Growth of Pakistan

The importance of human capital in the wealth of developed countries has increased significantly as it has transitioned from being a mere cost component to becoming the primary factor contributing to economic growth and societal development. This study attempts to assess Pakistan's human capital-led growth between 1973 and 2021. This has been accomplished by using a human capital index based on years of education, returns to education, and economic growth to ascertain the link in the long run and short run, the Auto Regressive Distributive Lagged (ARDL) co-integration methodology and error correction methods are utilized in this context. The model's stability is examined using CUSUMSQ and CUSUM. The results demonstrated that the human capital index has a significant and positive effect on economic growth over the long term, but it has a detrimental impact in the short term. Capital accumulation, population growth, and foreign direct investment have positive, long-term effects on GDP per capita. As human capital is an important factor in economic development. Therefore government should invest more in promoting more human capital.

Accuracy of shoreline forecasting using sparse data

Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.

Potassium Hydroxide Activation as an Adsorbent for Kinetic Models of Hexavalent Chromium Adsorptions onto <i>Garcinia mangosteen</i> Shell

The utilized adsorption kinetic models, i.e., pseudo-zero order, pseudo-first-order, pseudo-second order, intraparticle diffusion, Bangham’s pore diffusion, liquid film diffusion, modified Freundlich and Elovich models have been cultivated the hexavalent chromium adsorption process. A high correlation coefficient (R2) and a small residual root mean square error (RMSE) and Chi-square (χ2) can be depicted in the pseudo-first order for the kinetic adsorption process of hexavalent chromium adsorption on the activated mangosteen shell with potassium hydroxide and carbonized at 400 °C for 2 h as a biochar adsorbent which has a specific surface area of 164 m2/g.

Evaluation of Station Timing Errors from Demeaned Arrival Time Delays

ABSTRACT We present an analytical solution for the evaluation of timing errors at seismological stations. The method makes use of differential P- and S-wave arrival time measurements demeaned over a network that recorded a set of densely located seismic events. In this configuration, one can assume coincident P and S ray paths between sources and receivers, and cancel out dependencies associated with absolute event origin times, event locations, and P- or S-wave velocities in the problem. Relative timing errors can be obtained by linear inversion, using only a limited amount of input data: differential P- and S-wave arrival times, and a local VP/VS ratio. By setting at least one reference station in the network, supposed to be devoid of any timing error, one can retrieve reliable timing errors for other stations. We validate the approach against synthetic and real data. We also analyze the sensitivity of results on errors in the input data. Although picking uncertainties do affect the variability of estimates, we also identified a significant bias when an incorrect VP/VS ratio is used. However, this bias can be reduced if one uses the optimal VP/VS value that minimizes the root mean square of travel-time residuals. Application of the method to a collection of manually picked arrival times for the 2002–2003 Tricastin, France, earthquake, swarm allowed us to identify nonstationary timing errors from tenth to tens of seconds during the monitoring campaign.

A three-dimensional Oral health-related quality of life framework for temporomandibular joint disorders: A structural analysis of the Oral Health Impact Profile-14

Objectivesthe objective is to analyze the structure and relationship between the components of the Oral health-related quality of life using the Oral Health Impact Profile-14 (OHIP-14) for patients with temporomandibular joint disorders (TMJD). Methodstwo studies were examinated by means of factor analysis, multidimensional scaling and their spatial display in a cross-sectional design (n = 153). Resultsthree-dimensional solutions with an adequate fit were found: residual distribution, root mean square of residuals (RMSR) = 0.04, Goodness of Fit Index (GFI) = 0.96, and Adjusted Goodness of Fit Index (AGFI) = 0.93; and Stress I = 0.023, respectively. The overall ordinal alpha coefficient was 0.92 (CI=0.90 to 0.94). The ordinal alpha coefficients for functional impact, psychosocial impact and psychological impact factors were 0.87 (CI=0.84 to 0.90), 0.88 (CI=0.84 to 0.91) and 0.78 (CI= 0.72 to 0.83), respectively. Item 9 was the most central, followed by 7 and 11. Items 1 and 2 had minor associations. There was no evidence of differences between centers: edge weight differences (M = 0.226, p = 0.94), global strength invariance test (S = 1.88, p = 0.1), centrality invariance test (p >0.05), edge invariance test (p >0.05). Conclusionsthe links between the dimensions and the items seem to involve psychological components. Oral health-related quality of life emerged as a three-dimensional structure (Functional Impact, Psychosocial Impact and Psychological Impact) of functional and psychosocial elements in which physical, psychological, and social disability were essential, while theoretical functional limitation was least important. Clinical significancethe three-dimensional OHRQoL system for temporomandibular disorders (TMD) is a worthwhile alternative to interpret psychological and psychosocial aspects.

Simulating Parametric and Nonparametric Models

The purpose of this paper was to investigate the performance of the parametric bootstrap data generating processes (DGPs) methods and to compare the parametric and nonparametric bootstrap (DGPs) methods for estimating the standard error of simple linear regression (SLR) under various assessment conditions. When the performance of the parametric bootstrap method was investigated, simple linear models were employed to fit the data. With the consideration of the different bootstrap levels and sample sizes, a total of twelve parametric bootstrap models were examined. Three hypothetical and one real datasets were used as the basis to define the population distributions and the “true” SEEs. A bootstrap paper was conducted on different parametric and nonparametric bootstrap (DGPs) methods reflecting three levels for group proficiency differences, three levels of sample sizes, three test lengths and three bootstrap levels. Bias of the SLR, standard errors of the SLR, root mean square errors of the SLR, were calculated and used to evaluate and compare the bootstrap results. The main findings from this bootstrap paper were as follows: (i) The parametric bootstrap DGP models with larger bootstrap levels generally produced smaller bias likewise a larger sample size. (ii) The parametric bootstrap models with a higher bootstrap level generally yielded more accurate estimates of the standard error than the corresponding models with lower bootstrap level. (iii) The nonparametric bootstrap method generally produced less accurate estimates of the standard error than the parametric bootstrap method. However, as the sample size increased, the differences between the two bootstrap methods became smaller. When the sample size was equal to or larger than 3,000, say 10000, the differences between the nonparametric bootstrap DGP method and the parametric bootstrap DGP model that produced the smallest RMSE were very small. (4) Of all the models considered in this paper, parametric bootstrap DGP models with higher bootstrap performed better under most bootstrap conditions. (5) Aside from method effects, sample size and test length had the most impact on estimating the Simple Linear Regression.

Joint-product representation learning for domain generalization in classification and regression

In this work, we study the problem of generalizing a prediction (classification or regression) model trained on a set of source domains to an unseen target domain, where the source and target domains are different but related, i.e, the domain generalization problem. The challenge in this problem lies in the domain difference, which could degrade the generalization ability of the prediction model. To tackle this challenge, we propose to learn a neural network representation function to align a joint distribution and a product distribution in the representation space, and show that such joint-product distribution alignment conveniently leads to the alignment of multiple domains. In particular, we align the joint distribution and the product distribution under the L^{2}-distance, and show that this distance can be analytically estimated by exploiting its variational characterization and a linear variational function. This allows us to comfortably align the two distributions by minimizing the estimated distance with respect to the network representation function. Our experiments on synthetic and real-world datasets for classification and regression demonstrate the effectiveness of the proposed solution. For example, it achieves the best average classification accuracy of 82.26% on the text dataset Amazon Reviews, and the best average regression error of 0.114 on the WiFi dataset UJIIndoorLoc.

Systematic errors in the maximum-likelihood regression of Poisson count data: introducing the overdispersed χ2 distribution

ABSTRACT This paper presents a new method to estimate systematic errors in the maximum-likelihood regression of count data. The method is applicable in particular to X-ray spectra in situations where the Poisson log-likelihood, or the Cash goodness-of-fit statistic, indicate a poor fit that is attributable to overdispersion of the data. Overdispersion in Poisson data is treated as an intrinsic model variance that can be estimated from the best-fit model, using the maximum-likelihood Cmin statistic. The paper also studies the effects of such systematic errors on the ΔC likelihood-ratio statistic, which can be used to test for the presence of a nested model component in the regression of Poisson count data. The paper introduces an overdispersed χ2 distribution that results from the convolution of a χ2 distribution that models the usual ΔC statistic, and a zero-mean Gaussian that models the overdispersion in the data. This is proposed as the distribution of choice for the ΔC statistic in the presence of systematic errors. The methods presented in this paper are applied to XMM–Newton data of the quasar 1ES 1553+113 that were used to detect absorption lines from an intervening warm-hot intergalactic medium (WHIM). This case study illustrates how systematic errors can be estimated from the data, and their effect on the detection of a nested component, such as an absorption line, with the ΔC statistic.

Optimization of Biofuel Production Process Using Design of Experiments (Doe)

This study focuses on optimizing the process of biofuel production from citrus peel using the Design of Experiments (DOE) technique. This study aims to determine the optimal values for the variables that have a significant impact on the production of biofuel. The variance within and between data groups was determined using the analysis of variance (ANOVA) table. The ANOVA table shows how much of the response variable's variation (biofuel production) can be explained by the independent variables (A, B, C, D, E, AB, AC, AD, AE, and BJ) and how much is caused by random error. The ANOVA table comprises of three primary parts: the F-statistic, the p-value, the df, the mean square (MS), the source of variation, and the sum of squares (SS). The wellspring of variety alludes to the beginning of the information variety, which can be either the lingering or the model. The amount of squares estimates the information's changeability, with the absolute amount of squares addressing the amount of the squared deviations of the genuine qualities from the mean worth. The residual is the sum of the squared deviations from the predicted values of the actual values, while the model's sum of squares is the sum of the squared deviations from the mean of the predicted values. The model has 10 degrees of freedom (the number of independent variables) and the residual has 4 degrees of freedom (the number of observations minus the number of independent variables). These degrees of freedom represent the number of independent pieces of information used to estimate a parameter. The mean square, which indicates the typical amount of variation for each variation source, is calculated by dividing the sum of squares by the degrees of freedom. The degree to which the model explains the variation in the data is indicated by the F-statistic, which is the ratio of the model's mean square to the residual's mean square. The probability of obtaining an F-statistic that is as large as the one observed if the null hypothesis is true is represented by the p-value. The independent variables' insignificant impact on biofuel production is the null hypothesis in this instance. The model's p-esteem in this study is under 0.05, demonstrating that the free factors essentially affect biofuel creation and that the model is genuinely huge. In addition, the model is significant because the F-statistic is relatively large in comparison to the F-distribution for the 10 and 4 degrees of freedom, respectively. The estimated coefficients for the linear regression model used to investigate the production of biofuel from citrus peel can be found in the ANOVA coefficients table. The table provides a list of the intercept and independent variables' coefficients, standard errors, t-values, and p-values. When all of the independent variables are zero, the intercept has a coefficient of 0.0672, indicating the estimated value of the response variable. The fact that the intercept does not differ significantly from zero is supported by the fact that its p-value is not significant. The fact that the coefficients of the independent variables A, E, AC, AD, AE, and BJ are not statistically significant indicates that these variables have little impact on the response variable. On the other hand, the positive coefficients and significant p-values of the independent variables B and C suggest that an increase in their values could result in an increase in the production of biofuel from citrus peel. In conclusion, the key variables that influence the production of biofuel from citrus peel have been identified thanks to the use of the Design of Experiments (DOE) method. According to the findings of this study, an increase in the production of biofuel from citrus peel may result from an increase in the values of the independent variables B and C. The development of environmentally friendly energy sources and the optimization of biofuel production processes will benefit greatly from these findings

Spatial Analysis of Greenspace and Mental Health in North Carolina: Consideration of Rural and Urban Communities.

Greenspace positively impacts mental health. Previous research has focused on the greenspace-mental health relationship in urban areas. Yet, little work has looked at rural areas despite rural communities reporting similar rates of poor mental health outcomes and higher rates of suicide mortality compared with urban areas. This ecological research study examined the following research questions: (1) Do public and/or private greenspaces affect the spatial distribution of mental health outcomes in North Carolina? (2) Does this relationship change with rurality? Emergency department data for 6 mental health conditions and suicide mortality data from 2009 to 2018 were included in this analysis. Spatial error and ordinary least squares regressions were used to examine the influence of public and private greenspace quantity on mental health in rural and urban communities. Results suggest greenspace benefits mental health in rural and urban communities. The strength of this relationship varies with urbanity and between public and private greenspaces, suggesting a more complex causal relationship. Given the high case counts and often lower density of mental health care facilities in rural areas, focusing attention on low-cost mental health interventions, such as greenspace, is important when considering rural mental health care.

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

AbstractThe research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072‐AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs‐BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs‐BRS is well‐established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec, magnetic interaction parameter M, suction parameter S, and heat generation parameter Q are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter.

Solution predictive Bayesian networks knacks for entropy optimized second-order slip velocity free convective nanofluid flow along Darcy-Forchheimer porous medium

The aim of this study is to present incompressible, steady entropy optimized second-order slip velocity free convective nanofluid flow along Darcy–Forchheimer porous medium through neural network backpropagation with Bayesian Regularization Approach (NNBPBRA) under the influence of heat sink/source and viscous dissipation. The viscous dissipation effects and radiative heat flux are also employed in the presented fluid flow system. Nanomaterials are silicon dioxide and molybdenum disulfide, using water as the base fluid. To obtain the reference dataset for NNBPBRA, the ODEs which are obtained after simplifications of original fluid flow system in terms of PDEs with the appropriate application of transformation mechanism are solved numerically using state of the art numerical method. The outcome in terms of solution and error analyses plots for the modification of different parameters are interpreted using these reference datasets. The error histograms, regression analyses, and MSE indices are used to validate the performance of NNBP-BRA. In the presence of slip parameters and stratification variables, the skin friction coefficient, i.e. surface drag force, and heat transfer rate, i.e. Nusselt number, are also evaluated. The outcomes show that as the Brinkman number increases, the rate of entropy formation increases, but the temperature falls as the stratification parameter increases.

Intelligent identification algorithm and key point detection of abnormal vibration of transmission tower based on machine learning

Abstract In some special geological areas, the inclination and displacement of transmission line towers are relatively common, which should be analyzed from various factors. Transmission towers are the support structure for overhead transmission lines and play a pivotal role in the safe operation of the power grid. Transmission lines are widely distributed. Transmission poles and towers are generally built in areas with poor geological conditions such as goaf, river bed and slope. Under severe weather conditions, the force on the tower may be affected to a certain extent, causing the tower to tilt, deform, or even collapse, and thus causing a wide range of power system failures, which have a great impact on people’s production and life. Based on this objective problem, this paper has mainly studied the intelligent recognition methods and key technologies based on machine learning. In the experimental study of support vector machine (SVM) model based on machine learning, the average training time of genetic algorithm support vector regression (GA-SVR) was the longest, reaching 1.462 s. The average training duration of double chain quantum genetic algorithm-least squares support vector regression (DCQGA-LSSVR) was the shortest, with 0.156 s. The average pose error of double chain quantum genetic algorithm-support vector regression (DCQGA-SVR) was the smallest, only 0.136, while the average attitude error of genetic algorithm-least squares support vector regression (GA-LSSVR) was the highest, reaching 0.45. Therefore, it is of the great significance to analyze abnormal vibration of the transmission towers based on machine learning method.