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Two types of grounding systems are recommended for use in the international standard IEC 62305-3, Part 3: Physical damage to structures and life hazard. One of these is a radial-based grounding system (type-A), which is used in soil resistivities of up to 3000 Ω m and is considered in this paper. It is a well-known fact that during lightning strikes, only a part of the grounding wire contributes to dissipating the lightning current into the surrounding soil. This effective part of the grounding system depends on several features, such as soil resistivity, burial depth, and rise time of the dissipated lightning current. The effect of all of these features on the effective length of the type-A grounding system is explored in this paper. A suitable supervised machine learning regression model is developed, which will enable readers to accurately approximate the effective length of the type-A grounding system for realistic values of input features. The trained model in the paper yielded an R 2 value of 0.99998 on the test set. In addition, two simple mathematical formulas are also provided, which produce similar but less accurate results ( R 2 values of 0.989883 and 0.998557, respectively). [ABSTRACT FROM AUTHOR]

The quality factor (Q) is a parameter reflecting the physical properties of reservoirs. Accurate estimation of the quality factor plays an important role in improving the resolution of seismic data. Spectral-ratio method is a widely used traditional method based on the linear least-squares fitting to extract the quality factor, but is sensitive to noise. This is the main reason preventing this method from being widely used. Some supervised deep-learning methods are proposed to extract Q in which the construction of training labels is a key link. The proposed method is based on the spectral-ratio method to create training labels, avoiding errors in generating them. In contrast to the least-squares method, this paper proposes to use a nonlinear regression algorithm based on a fully connected network to fit the spectral logarithmic ratio and frequency. Meanwhile, the empirical equation is applied to constrain prediction results. The proposed method can effectively overcome the influence of noise and improve the accuracy of prediction results. Tests on the synthesized data of vertical seismic profile and common middle profile show that the proposed method has better generalization ability than the spectral-ratio method. Applying the method to the field vertical seismic profile data successfully extracts the quality factor, which can provide effective information for dividing stratigraphic layers. [ABSTRACT FROM AUTHOR]

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) networks represent established tools for nonlinear regression modeling with numerous applications in various fields. Because their standard training is vulnerable with respect to the presence of outliers in the data, several robust methods for RBF network training have been proposed recently. This paper is interested in robust regularized RBF networks. A robust inter-quantile version of RBF networks based on trimmed least squares is proposed here. Then, a systematic comparison of robust regularized RBF networks follows, which is evaluated over a set of 405 networks trained using various combinations of robustness and regularization types. The experiments proceed with a particular focus on the effect of variable selection, which is performed by means of a backward procedure, on the optimal number of RBF units. The regularized inter-quantile RBF networks based on trimmed least squares turn out to outperform the competing approaches in the experiments if a highly robust prediction error measure is considered. [ABSTRACT FROM AUTHOR]

The incorporation of conformal cooling systems has significantly enhanced the efficiency and quality of injection molding process. While several automated methods have been developed for creating conformal cooling channels in injection molds, the current optimization process for conformal cooling design parameters is hindered by labor-intensive iterative thermal simulation processes and the substantial reliance on empirical human knowledge. This paper presents an innovative machine learning method to assess the thermal performance of conformal cooling systems by employing a combination of a non-linear regression model and a neural network. By employing a logarithmic regression model describing the temperature graph and a neural network predicting the coefficients of the logarithmic regression model, the thermal performance of specified conformal cooling systems can be assessed and predicted precisely. This methodology empowers designers to evaluate the thermal efficiency of conformal cooling systems efficiently and effectively to further optimize the conformal cooling design parameters without relying on tedious manual thermal and fluid simulation processes. [ABSTRACT FROM AUTHOR]

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at  U E  and  B S , this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only  10 %  of the beams from the  U E  and  B S  codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at  U E  and  B S  vary from  128 × 128  to  1024 × 1024 . [ABSTRACT FROM AUTHOR]

Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently. [ABSTRACT FROM AUTHOR]

Buried pipelines, as the most common method of natural gas transportation, are prone to pipeline leakage accidents and are difficult to detect due to their harsh and concealed environment. This paper focused on the problem regarding the free dissipation of residual gas in buried gas pipelines and soil after closing the gas supply end valve after a period of leakage by numerical simulation. A multiple non-linear regression model was established based on the least squares method and multiple regression theory, and MATLAB 2016b mathematical calculation software was used to solve the problem. The research results indicated that compared to the gas leakage diffusion stage, the pressure and velocity distribution during the free dissipation stage were significantly reduced. The increase in leakage time, pipeline pressure, leakage size, and pipeline burial depth led to a large accumulation of natural gas, which increased the concentration and distribution range of gas on the same free dissipation stage monitoring line. A prediction model for natural gas concentration in the free dissipation stage was established with an average error of 7.88%. A calculation model for the safety repair time of buried gas pipeline leakage accidents was further derived to determine the safety repair time of leakage accidents. [ABSTRACT FROM AUTHOR]

In this paper, a novel ensemble learning model named EWT-EnsemLSTM-SSA, which assembles long short-term memory (LSTM), support vector regression (SVR), and sparrow search algorithm (SSA), is a proposed to deal with long term metro passenger flow volume prediction, which is an essential content of traffic flow prediction problems. Firstly, the empirical wavelet transform (EWT) method is introduced to decompose the original dataset into five wavelet time-sequence data for further prediction. Then, a cluster of LSTMs with diverse hidden layers and neuron counts are employed to explore and exploit the implicit information of the EWT-decomposed signals. Next, the output of LSTMs is aggregated into a nonlinear regression method SVR. Lastly, SSA is utilized to optimize the SVR automatically. The proposed EWT-EnsemLSTM-SSA model is applied in three case studies, using the data collected from the passengers' amount in the Minneapolis, America metro, divided into one hour in one day. Experiment results, which compare the proposed EnsemLSTM-SSA model with five conventional time series forecasting models, show that the proposed model can achieve a better performance than the traditional prediction algorithms. [ABSTRACT FROM AUTHOR]

In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging. However, traditional methods for microseismic velocity inversion face challenges in terms of precision and computational efficiency. In this paper, we use deep learning (DL) algorithms to achieve precise and efficient real-time microseismic velocity modeling, which holds significant importance for ensuring engineering safety and preventing geological disasters in microseismic monitoring. Given that this task was approached as a non-linear regression problem, we adopted and modified the Attention U-Net network for inversion. Depending on the degree of coupling among microseismic events, we trained the network using both single-event and multi-event simulation records as feature datasets. This approach can achieve velocity modeling when dealing with inseparable microseismic records. Numerical tests demonstrate that the Attention U-Net can automatically uncover latent features and patterns between microseismic records and velocity models. It performs effectively in real time and achieves high precision in velocity modeling for Tilted Transverse Isotropy (TTI) velocity structures such as anticlines, synclines, and anomalous velocity models. Furthermore, it can provide reliable initial models for traditional methods. [ABSTRACT FROM AUTHOR]

The paper is devoted to the formal description of the running time of the user task on some virtual nodes in the computing network. Based on the probability theory framework, this time represents a random value with a finite mean and variance. For any class of user task, these moments are the functions of the node resources, task numerical characteristics, and the parameters of the current node state. These functions of the vector arguments can be treated as some surfaces in the multidimensional Euclidean spaces, so the proposed models are called the stochastic time complexity surfaces. The paper also presents a class of functions suitable for the description of both the mean and variance. They contain unknown parameters which should be estimated. The article includes the statement of the parameter identification problem given the statistical results of the node stress testing, recommendations concerning the test planning, and preprocessing of the raw experiment data. To illustrate the performance of the proposed model, the authors design it for an actual database application—the prototype of the passengers' personal data anonymization system. Its application functions are classified into two user task classes: the data anonymization procedures and fulfillment of the statistical queries. The authors identify the stochastic time complexity surfaces for both task types. The additional testing experiments confirm the high performance of the suggested model and its applicability to the solution of the practical providers' problems. [ABSTRACT FROM AUTHOR]

The objective this paper is determine the highest performance of fresh mass and number of fruits in tomato crops using different nutrient solution. An absolute hybrid with a semi-determined growth was used. It was grown in a protected environment using a fertigated substrate inside plastic bags containing ten liters of solution. The experiment was completely randomized with four levels of fertilization and five replications. Two experiments were carried out in two cultivation cycles (spring 2018 and autumn 2019) by performing an analysis of variance and Scott & Knott test and estimating the parameters of nonlinear logistic model and its critical points for both variables in each treatment. The mean fruit mass per plant was 3.70 kg for the spring experiment and 3.80 kg for the autumn experiment. The mean number of fruits per plant was 10.50 and 10.70 fruits for spring and autumn, respectively. There are significant differences between the treatments KO46, KO45 and KO56 compared to KO69 for fruit mass in the autumn experiment. For the other variables and cultivation cycles, the treatments did not show statistical differences. The logistic growth model fitted the weight and number of tomato fruits according to days after transplanting the seedlings and evidenced production cycle data, highlighting the main differences between the nutrient solutions. The nutritional solutions KO46, KO45 and KO56 are recommended for growing Gaúcho tomatoes in substrate. The nutrient solution KO56 has the best performance because it has a higher K availability, meets the balance of loads and antagonism between nutrients, provides equal response of means mass and number of fruits, and has a lower N:K ratio and balance of K over Ca and Mg, thus favoring fruit production, precocity, and development. [ABSTRACT FROM AUTHOR]

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance. [ABSTRACT FROM AUTHOR]

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system. [ABSTRACT FROM AUTHOR]

Logistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields. In this paper, we first aimed to introduce the concept of uncertain logistic regression based on the uncertainty theory, as well as investigating the likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters. [ABSTRACT FROM AUTHOR]

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts' forecasts. Four oil price scenarios are considered for expository purposes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explain the effects of innovation, specialisation, qualifications and sectoral structure on the resilience of German regions (municipal level) facing the Great Recession in 2008/2009. Design/methodology/approach: To calculate the effects of various variables on the resilience of German regions against the Great Recession, the authors use quantile regressions. To measure resilience, the authors create a number of indexes representing different parts of the economy: resistance performance index, recovery performance index, shift-share resistance index, shift-share recovery index, manufacturing resistance index, manufacturing recovery index, service resistance index and service recovery index. Findings: The results of this study confirm that locations with employment growth before the crisis and with a good industry structure show better employment dynamics during and after the crisis. The authors find evidence for positive relationship between innovativeness, qualification, the share of the service sector, specialisation and resistance. The authors obtain positive results for related variety and both resistance and recovery. The share of the manufacturing sector only shows a positive relationship with recovery. Originality/value: The authors expand the existing literature in three aspects: First, instead of using regions as observation units, the authors conduct the analyses on the basis of municipalities and their surroundings. By doing so, the authors reduce the modifiable area unit problem because the authors do not rely on regions defined for administrative reasons. Second, the authors apply quantile regressions to detect nonlinear effects. Third, in addition to the resilience of the whole economy, the authors also study the resilience of the manufacturing and service sectors separately and examine the resilience of the local shift effect. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset. [ABSTRACT FROM AUTHOR]

Multicollinearity among independent variables is one of the most common problems in regression models. The aftereffects of this problem, such as ill-conditioning, instability of estimators, and inflating mean squared error of ordinary least squares estimator (OLS), in the multivariate linear regression model (MLRM) are the same that of linear regression models. To combat multicollinearity, several approaches have been presented in the literature. Liu estimator (LE), as a well known estimator in this connection, has been used in linear, generalized linear, and nonlinear regression models by researchers in recent years. In this paper, for the first time, LE and jackknifed Liu estimator (JLE) are investigated in MLRM. To improve estimators in the sense of mean squared error, two known resampling methods, i.e., jackknife and bootstrap, are used. Finally, OLS, LE, and JLE are compared by a simulation study and also using a real data set, by resampling methods in MLRM. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method. [ABSTRACT FROM AUTHOR]

The use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). The registration problem and three-dimensional transformation of coordinates still require further research. The paper attempts to guide the reader through the vast field of existing registration methods so that he can choose the appropriate approach for his particular problem. Furthermore, the article contains a regression method that enables the estimation of the covariance matrix of the transformation parameters and the calculation of the uncertainty of the estimated points. This makes it possible to extend existing registration methods with uncertainty estimates and to improve knowledge about the performed registration. The paper's primary purpose is to present a survey of known methods and basic estimation theory concepts for the point cloud registration problem. The focus will be on the guiding principles of the estimation theory: ICP algorithm; Normal Distribution Transform; Feature-based registration; Iterative dual correspondences; Probabilistic iterative correspondence method; Point-based registration; Quadratic patches; Likelihood-field matching; Conditional random fields; Branch-and-bound registration; PointReg. The secondary purpose of this article is to show an innovative statistical model for this transformation problem. The new theory needs known covariance matrices of identical point coordinates. An unknown rotation matrix and shift vector have been estimated using a nonlinear regression model with nonlinear constraints. The paper ends with a relevant numerical example. [ABSTRACT FROM AUTHOR]

In order to adapt to the development of modern education and provide education practitioners with decision‐making suggestions, it is necessary to understand the relationship between the public's attention to basic chemistry education (BCE) and academic attention. However, many existing research is based on a single platform to study social hot information, such as Google Index, Baidu Index, Web of Science, and so on. But they ignore the relationship that exists between the Baidu Index and related academic platforms, and ignore the common information reflected between them. This paper takes advantage of the big data method, through the big data of Baidu Index and the big data obtained from China National Knowledge Internet (CNKI) database, to study the network attention and academic attention of BCE, and propose a CVS‐LSP‐GP framework. It first selects keywords through correlation analysis, secondly uses the data obtained from the first step to construct a nonlinear regression model, and finally combines the results of gray prediction to predict the academic attention of CNKI related to BCE. The research results show that the BCE is mainly affected by the micro‐lecture teaching mode, and relevant education practitioners should integrate the micro‐lecture mode into teaching for more further research and practice. [ABSTRACT FROM AUTHOR]

Background: The wheelchair is a widely used rehabilitation device, which is indispensable for people with limited mobility. In the process of using a wheelchair, they often face the situation of sitting for a long time, which is easy to cause fatigue of the waist muscles of the user. Therefore, this paper hopes to provide more scientific guidance and suggestions for the daily use of wheelchairs by studying the relationship between the development of muscle fatigue and sitting posture. Methods: First, we collected surface Electromyography (sEMG) of human vertical spine muscle and analyzed it in the frequency domain. The obtained Mean Power Frequency (MPF) was used as the dependent variable. Then, the pose information of the human body, including the percentage of pressure points, span, and center of mass as independent variables, was collected by the array of thin film pressure sensors, and analyzed by a multivariate nonlinear regression model. Results: When the centroid row coordinate of the cushion pressure point is about 16(range, 7.7-16.9), the cushion pressure area percentage is about 80%(range, 70.8%-89.7%), and the cushion pressure span range is about 27(range, 25-31), the backrest pressure point centroid row coordinate is about 15(range, 9.1-18.2), the backrest pressure area percentage is about 35%(range, 11.8%-38.7%), and the backrest pressure span range is about 16(range, 9-22). At this time, the MPF value of the subjects decreased by a small percentage, and the fatigue development of the muscles was slower. In addition, the pressure area percentage at the seat cushion is a more sensitive independent variable, too large or too small pressure area percentage will easily cause lumbar muscle fatigue. Conclusion: The results show that people should sit in the middle and back of the seat cushion when riding the wheelchair, so that the Angle of the hip joint can be in a natural state, and the thigh should fully contact the seat cushion to avoid the weight of the body concentrated on the buttocks; The back should be fully in contact with the back of the wheelchair to reduce the burden on the waist, and the spine posture can be adjusted appropriately according to personal habits, but it is necessary to avoid maintaining a chest sitting position for a long time, which will cause the lumbar spine to be in an unnatural physiological Angle and easily lead to fatigue of the waist muscles. [ABSTRACT FROM AUTHOR]

Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income. [ABSTRACT FROM AUTHOR]

This paper presents the measurement and evaluation of the surfaces of molds produced using additive technologies. This is an emerging trend in mold production. The surfaces of such molds must be treated, usually using laser-based alternative machining methods. Regular evaluation is necessary because of the gradually deteriorating quality of the mold surface. However, owing to the difficulty in scanning the original surface of the injection mold, it is necessary to perform surface replication. Therefore, this study aims to describe the production of surface replicas for in-house developed polymer molds together with the determination of suitable descriptive parameters, the method of comparing variances, and the mean values for the surface evaluation. Overall, this study presents a new summary of the evaluation process of replicas of the surfaces of polymer molds. The nonlinear regression methodology provides the corresponding functional dependencies between the relevant parameters. The statistical significance of a neural network with two hidden layers based on the principle of Rosenblatt's perceptron has been proposed and verified. Additionally, machine learning was utilized to better compare the original surface and its replica. [ABSTRACT FROM AUTHOR]

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. [ABSTRACT FROM AUTHOR]

Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy. [ABSTRACT FROM AUTHOR]

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients’ multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy. [ABSTRACT FROM AUTHOR]

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Predicting the arrival aircraft's flight time plays a critical role in effectively optimizing and scheduling spatial‐temporal resources in the terminal airspace. This paper focuses on a data‐driven method for predicting the arrival flight time. First, based on the existing research, a feature set is constructed from four aspects: initial state, arrival pressure, sequencing pressure, and wind information, which are believed to affect arrival flight time significantly. Second, eight widely used models are developed to predict flight time, including linear regression models, nonlinear regression models, and tree‐based ensemble models. Furthermore, the stacking technique is adopted to improve the prediction performance. Finally, take Guangzhou Baiyun International Airport as a study case to verify the proposed method's effectiveness. The results indicate that the arrival pressure (describing the arrival traffic demand) and the sequencing pressure (sketching the arrival traffic distribution) could effectively improve the prediction accuracy. The mean absolute percentage error of the predicted flight time via ATAGA and IGONO can be increased by 1%. Besides, the proposed method of extracting wind data could also improve the prediction performance. The mean absolute error of the predicted flight time via GYA can be reduced by 4.85 s. [ABSTRACT FROM AUTHOR]

This work presents a global surrogate modelling of mechanical systems with elasto-plastic material behaviour based on support vector regression (SVR). In general, the main challenge in surrogate modelling is to construct an approximation model with the ability to capture the non-smooth behaviour of the system under interest. This paper investigates the ability of the SVR to deal with discontinuous and high non-smooth outputs. Two different kernel functions, namely the Gaussian and Matèrn 5/2 kernel functions, are examined and compared through one-dimensional, purely phenomenological elasto-plastic case. Thereafter, an essential part of this paper is addressed towards the application of the SVR for the two-dimensional elasto-plastic case preceded by a finite element method. In this study, the SVR computational cost is reduced by using anisotropic training grid where the number of points are only increased in the direction of the most important input parameters. Finally, the SVR accuracy is improved by smoothing the response surface based on the linear regression. The SVR is constructed using an in-house MATLAB code, while Abaqus is used as a finite element solver. [ABSTRACT FROM AUTHOR]

Gender poverty differences in households are likely to affect female-headed households more than male-headed households. This paper examined the evolution of the gender poverty rate gap and identified the factors that underlie differences in poverty rates between female-headed households and male-headed households using the most recent representative household surveys conducted by the Kenya National Bureau of Statistics in 2005/06 and 2015/16. An extended Blinder-Oaxaca decomposition analysis with nonlinear regression was performed. The findings indicate that poverty rates for female-headed households and male-headed households declined from 38.56 to 32.73% in 2005/06 to 30.23 and 26.04% in 2015/16, respectively. Although female-headed households (1.12) have a higher chance of falling into poverty than male-headed households (0.95), the decline in the poverty rate was higher for female-headed households (8.33%) than for male-headed households (6.69%). Therefore, the results do not support the feminization of poverty hypothesis in Kenya. Factors that have bridged the gender poverty gap include cash transfers that explain 11.02% of the gaps, literacy (53.97%), university education (10.39%), secondary education (40.84%), employment in public and private sectors (26.66%) and business employment (10.58%). Recommended policies include the implementation of the gender policy and affirmative action, enhancing literacy levels, and secondary and university enrolment. [ABSTRACT FROM AUTHOR]

Research question: Is there a non-linear relationship between public debt and economic growth in North Macedonia, in the form of an inverted U-shape? Motivation: Government consumption plays an important role in the stability of the national economy, especially in periods of economic crisis. However, a rapidly growing public debt is a concerning issue nowadays, since it might jeopardize economic growth perspectives. Economic theory suggests that public debt has nonlinear impact on economic growth in the form of an inverted U-shape. In other words, it is believed that after a certain threshold, the public debt will have deleterious impact on economic growth. Idea: Given that such threshold varies significantly across countries, the aim of this paper is to calculate the turning point of the public debt impact in the Republic of North Macedonia. Tools: For this purpose, we use non-linear multiple regression model for the real GDP growth rate as a dependent variable, general government public debt-to-GDP ratio (in nominal and squared terms) as a key independent variable, as well as several other controlling variables. Since theory also suggests reverse causality between economic growth and public debt, we use two different estimation techniques (Ordinary Least Squares and Generalized Method of Moments) to deal with potential endogeneity, and to cross-validate the results. Data: In this regard, we use annual data for the period 1998 – 2019, for 14 variables in total, obtained from several different data sources. Findings: Our results show that general government debt in the Republic of North Macedonia positively affects economic growth until it reaches around 30% of GDP, whereas further indebtedness after that turning point will most likely have a negative impact. Contribution: Given that current debt level is far above the estimated turning point, the need of urgent fiscal consolidation inevitably arises. This is especially important in the light of the ongoing COVID-19 crisis, which imposed the need for strong government intervention and pointed out the importance of the fiscal space for such matter. [ABSTRACT FROM AUTHOR]

As an extension of the ongoing long term research work of the first author on the detailed mathematical and statistical analysis of the budget of the Bangalore Municipal Corporation (see, A. Sathyavathi https://ssrn.com/abstract=4403863 and A. Sathyavathi https://ssrn.com/abstract=4461494), in the present paper we propose two possible regression models - the first one a polynomial regression model and the other one a nonlinear regression model to explain the observed trends of the revenue receipts and the revenue expenditures of the municipal corporations of the various Indian States including the Union Territory of Chandigarh for the 2017-18 Accounts Category, which pertains to the data of finances of municipal corporations of the various Indian States during the financial year 2017-2018. We also compare these two models of ours. The entire data used by us in this study is gratefully taken by us from the Report on Municipal Finances which was published by the Reserve Bank of India recently on 10 November, 2022 (see, https://m.rbi.org.in/scripts/AnnualPublications.aspx?head=Report%20on%20Municipal%20Finances,https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/RMF101120223A34C4F7023A4A9E99CB7F7FEF6881D0.PDF). [ABSTRACT FROM AUTHOR]

Photosynthetic rate prediction models can provide guidance for crop photosynthetic process optimization, which has been widely used in the precise regulation of the protected environment. The photosynthetic capacity of crops continuously changes during their whole growth process. Previous studies on photosynthetic models mainly consider the interaction between a crop's photosynthetic rate and its outer environmental conditions and have been able to predict a crop's photosynthetic rate in a certain growth period. However, photosynthetic rate prediction models for whole growth periods have not been proposed yet. To solve this question, this paper introduces growing time into a variable set and proposes a method for building a cucumber photosynthetic rate prediction model of whole growth periods. First, the photosynthetic rate of cucumber leaves under different environmental conditions (light, temperature, and CO2 concentration) during the whole growth period was obtained through a multi-gradient nested test. With the environmental data and the cultivation time as the inputs, a photosynthetic rate prediction model was built using the Support Vector Regression algorithm. In order to obtain better modeling results, multiple kernel functions were used for pretraining, and the parameters of the Support Vector Regression algorithm were optimized based on multiple population genetic algorithms. Compared with a Back Propagation neural network and Non-linear Regression method, the Support Vector Regression model optimized had the highest accuracy, with the coefficient of determination of the test set was 0.998, and the average absolute error was 0.280 μmol·m−2·s−1, which provides a theoretical solution for the prediction of the cucumber photosynthetic rate during the whole growth period. [ABSTRACT FROM AUTHOR]

An unbiased, widely accepted estimate of the rate of occurrence of new cases of autism over time would facilitate progress in understanding the causes of autism. The same may also apply to other disorders. While incidence is a widely used measure of occurrence, birth prevalence—the proportion of each birth year cohort with the disorder—is the appropriate measure for disorders and diseases of early childhood. Studies of autism epidemiology commonly speculate that estimates showing strong increases in rate of autism cases result from an increase in diagnosis rates rather than a true increase in cases. Unfortunately, current methods are not sufficient to provide a definitive resolution to this controversy. Prominent experts have written that it is virtually impossible to solve. This paper presents a novel method, time-to-event birth prevalence estimation (TTEPE), to provide accurate estimates of birth prevalence properly adjusted for changing diagnostic factors. It addresses the shortcomings of prior methods. TTEPE is based on well-known time-to-event (survival) analysis techniques. A discrete survival process models the rates of incident diagnoses by birth year and age. Diagnostic factors drive the probability of diagnosis as a function of the year of diagnosis. TTEPE models changes in diagnostic criteria, which can modify the effective birth prevalence when new criteria take effect. TTEPE incorporates the development of diagnosable symptoms with age. General-purpose optimization software estimates all parameters, forming a non-linear regression. The paper specifies all assumptions underlying the analysis and explores potential deviations from assumptions and optional additional analyses. A simulation study shows that TTEPE produces accurate parameter estimates, including trends in both birth prevalence and the probability of diagnosis in the presence of sampling effects from finite populations. TTEPE provides high power to resolve small differences in parameter values by utilizing all available data points. [ABSTRACT FROM AUTHOR]

Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets. [ABSTRACT FROM AUTHOR]

Treatment of wastewater and reuse of purified water in an industrial process can provide an alternative source of fresh water as well as reduce pollution load by discharging a lower quantity of wastewater. When adsorption is used for treatment, the regeneration of the used adsorbent may also account for a large portion of the operational cost and cause secondary pollution. This problem may be solved by cyclic repetition of adsorption/regeneration cycles using a column method. In this paper, a total of nine successive cycles of zinc capture and zeolite bed regeneration using a column method have been investigated. The derived form of the breakthrough curve was used for analysing mass transfer in the column. For that purpose, the Dose–response, the Thomas, the Bohart-Adams, the Yoon-Nelson and the Wolborska models were used for modelling the breakthrough curve by nonlinear regression analysis. Simulation results and mathematical similarities between the models were discussed. This is the first study that used derived form of Dose–response model to analyse the inflection points of the breakthrough curve and mass transfer during nine consecutive sorption-regeneration cycles of zinc ions on natural zeolite. Obtained peak shape rate profiles were analysed for all cycles. Optimal operation conditions were evaluated with respect to the inflection point, the model parameters, and the residence time. [ABSTRACT FROM AUTHOR]

Recently, steel fiber–reinforced magnesium phosphate cement–based concrete (SFRMPCC) has attracted much attention due to its excellent properties. To apply this new type of concrete to practical engineering, this paper evaluates the effect of steel fiber (SF) content and curing age on the water and sulfate resistance of magnesium phosphate cement–based concrete (MPCC). Specimens containing fly ash and metakaolin and with different SF volume contents were prepared and subjected to curing in air, water, and 5% Na2SO4 solution. The mass change, cubic compressive strength (CCS), strength retention rate, deterioration factor, microstructure, and phase composition were analyzed. Empirical formulas for strength prediction are proposed using range, variance, and nonlinear regression analysis methods. The results showed that the specimens immersed in water and Na2SO4 solution had good mass retention. The compressive strength of air-cured specimens gradually increased with the curing age, and the addition of a proper amount of SF could improve the strength under air curing. However, the addition of SF had a negative effect on the compactness and strength of specimens immersed in water and sulfate solution. SFRMPCC and MPCC had good resistance to sulfate attack, which may be related to the new reaction in the magnesium phosphate cement (MPC) matrix. [ABSTRACT FROM AUTHOR]

Introduction: Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. Methods: The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. Results: The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Discussion: Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands. [ABSTRACT FROM AUTHOR]

This paper presents the relationship between the efficiency of the process of onion peeling and the amount of waste generated in the process. The research was carried out on a pilot test stand for onion peeling. The process variables were the value of the compressed air pressure (p) and the value of the flow opening time through the control valve (t). The experiment took into account the influence of the onion diameter (d0) and its hardness (H). The obtained results were subjected to statistical analysis. Standard deviations were of the percentage loss of onion mass in the form of the peel removed in the onion peeling process in relation to the obtained average values. Tukey's multiple comparison test was performed in order to identify the importance of individual process variables on the final effect of onion peeling. This was the basis for the development of a predictive model in the form of a non-linear regression Mp = f(p,t,d0,H), which is a mathematical description of the onion peeling process. Finally, the response surface area of the relationship between analyzed variables was determined. The results of research show that the peeling efficiency of the onion and waste of peel mass depend on the compressed air pressure. Extending the onion blowing time does not improve the process efficiency, while the hardness and size of the onion are irrelevant to the process. [ABSTRACT FROM AUTHOR]

To improve the sowing performance of the spoon wheel maize seeding machinery, in this paper, two varieties of maize seed are selected as examples. The maize spoon precision seed metering device, a core component of the spoon wheel seeding machinery, is used as the research object. The maize seed assembly model is first established based on the maize seed assembly modeling method. Its validity is verified by the sowing experiment and corresponding DEM simulation under the different revolving speeds of the seed metering wheel. Secondly, the performance of the spoon precision seed metering device is optimized by integrating the maize seed assembly model and multivariate nonlinear regression method. Therefore, the number of sub-spheres of the horse tooth, spherical cone, and spheroid maize seed model are 10–14, 18, and 6, respectively. The results show that the performance of the seed metering device improved when the revolving speed of the seed metering wheel, handing angle, and seed spoon radius are 25 r/min, 40°, and 7 mm, respectively. There is good agreement between the expected results and experimental ones with relative errors of less than 5%, and the optimized seed metering device facilitates the process of seed guiding and seed delivery during the sowing process. [ABSTRACT FROM AUTHOR]

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region's ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks' filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R 2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result ( R 2 = 0.710). [ABSTRACT FROM AUTHOR]

The bore hole is sealed from a sealing hole: the surrounding coal fracture permeability and grout cementation form a new consolidated body and coal material. In this paper, the characteristics of the macroscopic compressive strength, microscopic interface bending, porosity, and fractal dimension of the consolidated body were studied, and the structure strength relationship between loading rates, porosity, fractal dimension, and uniaxial compressive strength (UCS) was established. The results show that the loading rates had a great and consistent effect on the macro- and micro-mechanical properties of the consolidated body. Macroscopically, in the range of 0.1~0.4 mm/min, the UCS and elastic modulus of the solidified body increased with the increase in the loading rate, and there was a critical loading rate (η = 0.4 mm/min). At the microscale, with the increase in loading rates, the interface bending phenomenon, porosity, fractal dimension, and UCS of the grout and coal were consistent, showing a trend of increasing first and then decreasing. The fractal dimension was linearly correlated with the UCS and porosity. The loading rates, porosity, fractal dimension, and UCS had a multivariate nonlinear regression distribution. [ABSTRACT FROM AUTHOR]

In many scientific fields, the dynamics of the system are often known, and the main challenge is to estimate the parameters that model the behavior of the system. The question then arises whether one can use experimental measurements of the system response to derive the parameters? This problem has been addressed in many papers that focus mainly on data from a deterministic model, but few efforts have been made to use stochastic data instead. In this paper, we address this problem using the following procedure: first, we build the probabilistic stochastic differential models using a natural extension of the commonly used deterministic models. Then, we use the data from the stochastic models to estimate the model parameters by solving a nonlinear regression problem. Since the stochastic solutions are not differentiable, we use the well-known Nelder–Mead algorithm. Our numerical results show that the fitting procedure is able to obtain good estimates of the parameters requiring only a few sample data. [ABSTRACT FROM AUTHOR]

The thermal decomposition and sintering kinetics of microfine magnesite were investigated to inaugurate a new One-step process for preparing high-density sintered magnesia. The optimal parameters for the discharging particle size of magnesite through ball milling were determined using an orthogonal experiment and Three-Way Measures ANOVA. The model for the thermal decomposition process of microfine magnesite was established through TGA-DSC and nonlinear regression analysis. Meanwhile, by describing the sintering process of microfine magnesite in detail through microstructure and CTE, the kinetic mechanisms of grain growth at different sintering stages were obtained. The results revealed that increasing the ratio of ball to powder can quickly obtain microfine magnesite which the bulk density reaches 3.45 g·cm−3 after One-step sintering. The thermal decomposition process required an activation energy of only 124.98 kJ·mol−1, which is much lower than existing technology and followed the Two-dimensional phase boundary reaction mechanism R 2. Additionally, the densification process was dominated alternatively by grain boundary diffusion and volume diffusion. This paper provided a detailed theoretical basis and leads a new direction in the One-step preparation of high-density sintered magnesia. [ABSTRACT FROM AUTHOR]