Coefficient Estimation Method Research Articles

Assessment of inbreeding coefficients and inbreeding depression on complex traits from genomic and pedigree data in Nelore cattle.

Nelore cattle play a key role in tropical production systems due to their resilience to harsh conditions, such as heat stress and seasonally poor nutrition. Monitoring their genetic diversity is essential to manage the negative impacts of inbreeding. Traditionally, inbreeding and inbreeding depression are assessed by pedigree-based coefficients (F), but recently, genetic markers have been preferred for their precision in capturing the inbreeding level and identifying animals at risk of reduced productive and reproductive performance. Hence, we compared the inbreeding and inbreeding depression for productive and reproductive performance traits in Nelore cattle using different inbreeding coefficient estimation methods from pedigree information (FPed), the genomic relationship matrix (FGRM), runs of homozygosity (FROH) of different lengths (> 1Mb (genome), between 1 and 2Mb - FROH 1-2; 2-4 Mb FROH 2-4 or > 8 Mb FROH >8) and excess homozygosity (FSNP). The correlation between FPed and FROH was lower when the latter was based on shorter segments (r = 0.15 with FROH 1-2, r = 0.20 with FROH 2-4 and r = 0.28 with FROH 4-8). Meanwhile, the FPed had a moderate correlation withFSNP (r = 0.47) and high correlation withFROH >8 (r = 0.58)and FROH-genome (r = 0.60). The FROH-genome was highly correlated with inbreeding based on FROH>8 (r = 0.93) and FSNP (r = 0.88). The FGRM exhibited a high correlation with FROH-genome (r = 0.55)and FROH >8 (r = 0.51) and a lower correlation with other inbreeding estimators varying from 0.30 for FROH 2-4 to 0.37 for FROH 1-2. Increased levels of inbreeding had a negative impact on the productive and reproductive performance of Nelore cattle. The unfavorable inbreeding effect on productive and reproductive traitsranged from 0.12 to 0.51 for FPed, 0.19-0.59 for FGRM, 0.21-0.58 for FROH-genome, and 0.19-0.54 for FSNP per 1% of inbreeding scaled on the percentage of the mean. When scaling the linear regression coefficients on the standard deviation, the unfavorable inbreeding effect varied from 0.43 to 1.56% for FPed, 0.49-1.97% for FGRM, 0.34-2.2% for FROH-genome, and 0.50-1.62% for FSNP per 1% of inbreeding.The impact of the homozygous segments on reproductive and performance traits varied based on the chromosomes. This shows that specific homozygous chromosome segments can be signs of positive selection due to their beneficial effects on the traits. The low correlation observed between FPed and genomic-based inbreeding estimates suggests that the presence of animals with one unknown parent (sire or dam) in the pedigree does not account for ancient inbreeding. The ROH hotspots surround genes related to reproduction, growth, meat quality, and adaptation to environmental stress. Inbreeding depression has adverse effects on productive and reproductive traits in Nelore cattle, particularly on age at puberty in young bulls and heifer calving at 30 months, as well as on scrotal circumference and body weight when scaled on the standard deviation of the trait.

Spatial non-equilibrium and distribution dynamic evolution of the development level of national physical fitness in China's provinces.

Physical health is fundamental to a country's socio-economic advancement. An in-depth exploration of the spatial distribution and dynamic evolution of national physical fitness across China is crucial for enhancing the country's overall physical health. This study aims to provide valuable insights into the geographical and temporal patterns of physical fitness, informing strategies for national physical fitness improvement. Employing data from China's national physical fitness monitoring of 31 provinces, cities, and districts for the years 2005, 2010, and 2015, this study utilizes the Gini coefficient, its decomposition, and nonparametric density estimation methods. These techniques are applied to analyze the spatial disparities and temporal trends in national physical fitness levels among different demographics, including the overall population, males, females, and urban and rural residents. The study reveals that the regional disparity in China's national physical fitness initially narrowed and then expanded across the general population, females, and in both urban and rural areas, while consistently increasing among males. In terms of geographical distribution, the East, Middle, and West regions show significant heterogeneity, with the East-West gap being the most pronounced (Gini coefficients of 0.0249, 0.0230, 0.0263). The contribution rate of regional gaps was highest (54.40% -64.69%), followed by regional disparities (24.78% -27.15%), and the contribution of hypervariable density difference was the smallest (10.53% -19.75%). Despite a slight improvement in overall national physical fitness, the absolute regional disparities have further widened. Provinces with lower levels of national physical fitness demonstrate a 'club convergence' trend, indicating regional clustering of similar fitness levels. Additionally, a 'catch-up effect' is evident in rural areas, particularly in provinces with historically lower levels of national physical fitness. These findings suggest the need for region-specific public health strategies to address the growing disparities in national physical fitness across China.

From global to national: The role of urban agglomerations in China's new development paradigm.

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes.

Peak pressures on high-rise buildings roof: A dual approach through validated LES and wind tunnel experiments with uncertainty quantification

Understanding the accuracy of predicted peak pressure in wind engineering is crucial for effective risk assessment and structural design. This study investigates the capacity of large eddy simulation (LES) in predicting peak pressure coefficients on the flat roof and facade of a high-rise building, incorporating uncertainty quantification from 11-hour equivalent full-scale experimental measurements. The isolated and group configurations under 0° and 45° wind angles are analysed.The performance of two estimation methods for peak pressure coefficients – the traditional “epochal” approach and peaks over threshold (POT) – is evaluated, determining the optimal set of parameters that minimises uncertainty. While the stationarity test reaches satisfactory residuals for a 75-minute equivalent full-scale duration, 25- and 37.5-minute durations are also analysed. The traditional approach is preferred for its reliability and consistency, although POT notably excels in capturing higher peak pressure coefficients.Numerical results demonstrate negligible differences across all analysed durations and align well with experimental findings. A 25-minute equivalent full-scale duration is found to be a suitable representative of the entire signal. However, notable discrepancies near the upwind corners on the roof, with larger uncertainty in experimental data, underscore challenges in prediction accuracy for these regions. It emphasises importance of individual treatment and validation for each case.

A comparative study on hydrocarbon detection using cepstrum–based methods

Amplitude anomaly caused by a high–frequency component is attenuated more rapidly than a low–frequency component for a seismic trace in sediments susceptible to gas influence and is always used as a direct indicator of hydrocarbons. Recently, cepstrum–based hydrocarbon detection methods have become an important exploration pathway for detecting amplitude anomalies in the gas–bearing formation more sensitively and accurately. In this study, we compared three cepstrum–based hydrocarbon detection methods. We compared their fluid identification abilities in gas reservoirs and the noise robustness. They were further compared with the traditional absorption coefficient estimation method. We also indicate how the choice of the selection of the sliding–window length influences the performance of the three cepstrum–based hydrocarbon detection methods. Model test results and real data applications show that for thick gas reservoirs, the hydrocarbon detection methods using the Berthil–based cepstrum and the wavelet–based cepstrum can better discriminate the upper and lower interfaces than the hydrocarbon detection method using the Fourier–based cepstrum. For thin gas reservoirs, the hydrocarbon detection method using the wavelet–based cepstrum can identify the lower interface of the gas–bearing reservoir more obviously than the upper interface. The hydrocarbon detection method using the Berthil cepstrum cannot identify the upper and lower interfaces of the gas–bearing reservoir, but it shows higher temporal and spatial resolution compared to the hydrocarbon detection method using the Fourier–based cepstrum. For noise robustness, the hydrocarbon detection method using the wavelet–based cepstrum has the strongest noise robustness property, and the hydrocarbon detection method using the Fourier–based cepstrum is the most sensitive method to the noise. For time–consumption, the hydrocarbon detection method using the wavelet–based cepstrum is the most time–consuming, and the hydrocarbon detection method using the Fourier–based cepstrum is the fastest.

An experimental estimation method of diffusion coefficients in ternary and multicomponent systems from a single diffusion profile

Until recently, it was textbook knowledge that the diffusion coefficients could not be estimated in a multi-component system following the widely practised diffusion couple method. The recently proposed constrained diffusion couple method largely solved the problems. The need to produce two intersecting diffusion paths in a multi-component space with very well-controlled compositions of the diffusion couple end members may be tricky in certain situations, depending on the complications of diffusion paths. In this study, we have shown the estimation of all types of diffusion coefficients from a single diffusion profile without neglecting Onsager's cross terms in concentrated ternary Ni-Co-Fe, Fe-rich quaternary Fe-Ni-Co-Cr and concentrated Ni-Co-Fe-Cr-Al quinary alloys. This is applicable in ternary and multicomponent component systems (n≥3) at a stable Kirkendall marker plane position. One can even estimate the impurity diffusion coefficients utilizing the composition profiles at the ends of the diffusion couples following the Hall method, which has been rarely practised in multicomponent systems until now. We have further demonstrated the importance of estimating the tracer and intrinsic diffusion coefficients in a concentrated or multi-principal element alloy in which interdiffusion coefficients can be vague and misleading for understanding the elements' diffusional interactions and relative mobilities. We have also shown the importance of considering the vacancy wind effect in concentrated alloys. The method proposed in this study will help to generate a mobility database with relative ease in various multicomponent systems important for various applications, which was considered impossible until recently.

Method development for estimation of bleed momentum coefficient using surface pressure measurements and in situ hot wire calibration

Recently, as a passive flow control method, bleeding technique allowing flow of upstream towards suction side by interior passages, has been utilized on non-slender delta wing planforms, where effective geometries can enhance the flow field characteristics. In order to quantify the effect of the flow control, estimation of the momentum induced by the technique is of significant importance in addition to quantified aerodynamic favor. In the current work, passive bleeding momentum coefficient estimation method is developed and presented based on surface pressure measurements by utilizing Bernoulli equation and loss coefficient for a 45° swept non-slender delta wing. For the characterization of the presented technique in-situ hot wire measurements were performed allowing to extract across bleed slot pressure loss coefficient. Results represent that proposed approach can be used to compute the bleeding momentum where further advancements might lead to an important control parameter for potential active bleeding systems in different flow regimes.

Nonparametric statistical inference for stochastic optimal control problems and its applications for financial investment

Against a backdrop of financial decision-making, this study examines nonparametric statistical inference problems in stochastic optimal control problems. First, we construct a nonparametric estimation model using the wavelet estimation method for drift and diffusion coefficients. In this study, we focus on stochastic linear–quadratic (SLQ) problems, solved using Riccati equations, and apply this to a cash management problem within the linear–quadratic framework. The results show the effectiveness of the wavelet method using a numerical case.

Modeling and Clamping Force Tracking Control of an Integrated Electric Parking Brake System Using Sliding-Mode-Based Observer

This article proposes a hierarchical control strategy to address semi-ABS control as well as the precise clamping force control problems for an integrated electric parking brake (iEPB) system. To this end, a detailed system model, including modeling of the motor, transmission mechanism, friction and braking torque, is constructed for controller and observer design, and a sliding-mode-based observer (SMO) is proposed to estimate the load torque by using the motor rotational speed without installing a force sensor. In addition, a stable and reliable tire–road friction coefficient (TRFC) estimation method is adopted, and the desired slip ratio (DSR) is observed as the target that the rear wheels cycle around. At the upper level of the hierarchical control structure, the desired clamping forces of the rear wheels are generated using a sliding mode control (SMC) technique, and the control objective is to track the DSR to make full use of the road condition. At the lower level, the motor is controlled to track the desired clamping force generated from the upper controller. The hardware-in-the-loop (HIL) experimental results demonstrate the effectiveness and high tracking precision of the proposed strategy under different road conditions, and the estimation parameters based on the proposed observers are timely and accurate to satisfy the control requirements.

A Reflection Coefficient Estimation Method for Power Cable Defects Based on Three-Point Interpolated FFT

A Reflection Coefficient Estimation Method for Power Cable Defects Based on Three-Point Interpolated FFT

A review of road surface recognition and tyre-road friction coefficient estimation methods

A review of road surface recognition and tyre-road friction coefficient estimation methods

Representation and effectiveness based on big data analytics: optimizing the construction of leadership team structures

Abstract The optimization of leadership team structure is the research object of this paper, and the regression model in big data analysis is explored as the research method. Neighboring sample information is introduced through the network structure diagram, the regression model with neighboring sample information is constructed, the corresponding estimation method of regression coefficients is proposed, and the proof of the error bounds of the proposed estimation is given. Finally, the method is applied to analyze the results after optimizing the corporate leadership team structure. It includes regression analysis of the non-heterogeneous, heterogeneous, and overall structures of the leadership team and examines the moderating effect of salary heterogeneity on the results. The results show that the number of applications for invention patents and the number of authorized patents in the enterprise reaches 3 times to 5 times the number before the change, respectively, and in the regression analysis of the heterogeneous structure of the leadership team on the development of the enterprise R 2 =0.134, F value=2.186, which indicates that the heterogeneity of the years of working experience has a very significant positive effect on the development of the enterprise.

Dynamic hybrid observer-based early slipping fault detection for high-speed train wheelsets

Early slipping fault detection of High-speed Train (HST) wheelsets on ramped railway lines has been a challenging problem due to the difficulty of achieving accurate detection in a limited time-frame. A dynamic hybrid observer-based early slipping fault detection strategy is proposed. Firstly, a hybrid observer-based adhesion coefficient estimation method is proposed. The hybrid observer is constructed in an interconnected configuration, which integrates the estimated ramped line angle into the adhesion coefficient estimation. Then, a multivariate evaluation function (MEF)-based parameter dynamic adjustment method is proposed. The MEF is constructed to reconcile the trade-off of the speediness and smoothness of the hybrid observer. Finally, a dynamic adhesion coefficient-based early slipping fault detection method for HST wheelsets is proposed. And the proposed strategy is verified on the hardware-in-the-loop(HIL)-based platform. The test results show that the proposed strategy can actualize millisecond-level early slipping fault detection and reduce the false detection rate on ramped railway lines.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China.

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute β spatial convergence and conditional β spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.

A phase plane H∞ controller for distributed drive electric vehicles with stability enhancement based on tire road friction coefficient estimation

An H∞ control strategy based on the phase plane method (phase plane H∞ controller) tracking two degrees of freedom (DOF) ideal vehicle trajectory scheme is designed for distributed drive electric vehicles with stability enhancement. Firstly, an Extended Kalman Filter (EKF) tire road friction coefficient estimation method based on Keras neural network is presented to accurately and efficiently identify the tire road friction coefficient, taking into account the huge influence of the tire road friction coefficient on vehicle equilibrium point and stability region. Secondly, the phase plane method is applied to provide a dynamic stability boundary for the switching control strategy of direct yaw moment for different tire road friction coefficients; Furthermore, based on the dynamic stability boundary, the weighted phase stability is applied to achieve more realistic stability evaluation criteria, and the fuzzy control strategy is adopted to carry out the feedback regulator of the target side slip angle and yaw rate on the purpose of limiting its maximum value max ( β) and max ( ωr). Then the torque of the four-wheel was optimized by the quadratic programming method. Finally, the presented method is verified and the results indicate that: (1) the phase plane H∞ control has the advantage in terms of stability and maneuvering performance. More importantly, under a low tire road friction coefficient, the amplitude of the side slip angle is decreased by 26.52% over the H∞; (2) the designed EKF based on Keras neural network parameter correction has a quick and accurate performance in identifying the tire road friction coefficient, and the steady-state error does not exceed 3.25%.

Tire-Road Friction Coefficient Estimation for Distributed Drive Electric Vehicles Using PMSM Sensorless Control

Many tire-road friction coefficient estimation methods require the wheel angular speed sensor to provide a signal. In this paper, a tire-road friction coefficient estimation algorithm for distributed drive electric vehicles using permanent magnet synchronous motors(PMSM) sensorless control is proposed to reduce the use of wheel angular speed sensors. The wheel angular speed signal is replaced by the rotor speed signal obtained from PMSM sensorless control. First, the three degrees of freedom vehicle dynamics model and the PMSM mathematical model are established, and a strong tracking cubature Kalman filtering algorithm based on orthogonal transformation (T-STCKF) is derived to overcome the problem that the STCKF algorithm is prone to nonlocal sampling when dealing with high dimensional systems. Second, a PMSM sensorless control system based on the adaptive exponent reaching law of sliding mode control and the T-STCKF algorithm is established, and confirmed its validity. Finally, the feasibility of the proposed algorithm is demonstrated through multicase simulation and experiment and the results shows that the T-STCKF algorithm is more accurate than the STCKF algorithm.

Robust explicit estimators using the power-weighted repeated medians

This paper consists of two parts. The first part of the paper is to propose an explicit robust estimation method for the regression coefficients in simple linear regression based on the power-weighted repeated medians technique that has a tuning constant for dealing with the trade-offs between efficiency and robustness. We then investigate the lower and upper bounds of the finite-sample breakdown point of the proposed method. The second part of the paper is to show that based on the linearization of the cumulative distribution function, the proposed method can be applied to obtain robust parameter estimators for the Weibull and Birnbaum-Saunders distributions that are commonly used in both reliability and survival analysis. Numerical studies demonstrate that the proposed method performs well in a manner that is approximately comparable with the ordinary least squares method, whereas it is far superior in the presence of data contamination that occurs frequently in practice.

Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Accurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning. Firstly, a vehicle-based data feedback system combined with a vehicle-road network cloud is introduced, and CarSim simulation is used to expand the data set and train the model effectively. Then, the dynamic analysis of the whole vehicle is carried out, and the vehicle operation data related to the adhesion coefficient are obtained as the input of the estimation model. Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. Finally, the model is verified by the simulation data and the actual vehicle-based data. The results show that the vehicle-based data feedback system combined with the vehicle-road network Ccloud is effective, and compared with other commonly used model, the estimation model proposed in this paper can effectively predict the road adhesion coefficient.

Regional difference and dynamic evolution of development quality of power industry in China

To achieve the goals of carbon peaking and carbon neutrality and maintain high-quality economic growth, China is currently striving to improve the quality of development of its power sector. In this regard, revealing the regional differences and evolutionary trends in the development quality of China’ power sector has a high value to inspire the next improvement direction toward how to integrate regional power recourses to an overall optimization level. Motived by this purpose, this paper uses the entropy method to evaluate the comprehensive and subsystem indices of the development quality of the power industry, and reveals their regional differences and evolutionary trends with the help of the Dagum Gini coefficient and Kernel density estimation methods. The findings show that: There are obvious regional differences in the development quality of China’s power industry, and the differences are steadily declining in all regions except the West. Regional differences are mainly derived from inter-regional differences, with the largest inter-regional differences in the East-Northeast region. Intra-regional differences show a distribution pattern of East > West > Northeast > Center.

Machine-learning-based estimation of absorption coefficients from transfer functions modeled by equivalent sources

In the field of room acoustics, it is important to find the absorption coefficients of the wall surface, which is a boundary condition for modeling the room acoustic field. However, it is not easy to measure the acoustic impedances of the entire room because it requires many measurement points near the wall surface. Recently, a method to estimate the acoustic impedance and absorption coefficients by using both measurement and simulation methods has been proposed. However, a large number of measurement points are required to obtain sufficient estimation accuracy. In this study, we proposed estimation method of the sound absorption coefficients using machine learning with virtually increasing the number of microphones. First, the transfer functions at the virtual microphones are obtained from small number of transfer functions based on the sound field modeling by sparse equivalent sources. Then, the both transfer functions at the virtual and real microphones are used as the training data for machine learning. To evaluate estimation accuracy of the proposed method, we conducted the two-dimensional simulation experiments based on the boundary element method.