Average Goodness Of Fit Research Articles

Prediction of hourly air temperature based on CNN–LSTM

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods—a convolutional neural network (CNN) and long short-term memory (LSTM)—are integrated into a network model (CNN–LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN–LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN–LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN–LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN–LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Burn injuries can decrease the quality of life of a patient tremendously, because of esthetic reasons and because of contractions that result from them. In severe case, skin contraction takes place at such a large extent that joint mobility of a patient is significantly inhibited. In these cases, one refers to a contracture. In order to predict the evolution of post-wounding skin, several mathematical model frameworks have been set up. These frameworks are based on complicated systems of partial differential equations that need finite element-like discretizations for the approximation of the solution. Since these computational frameworks can be expensive in terms of computation time and resources, we study the applicability of neural networks to reproduce the finite element results. Our neural network is able to simulate the evolution of skin in terms of contraction for over one year. The simulations are based on 25 input parameters that are characteristic for the patient and the injury. One of such input parameters is the stiffness of the skin. The neural network results have yielded an average goodness of fit (R^2) of 0.9928 (± 0.0013). Further, a tremendous speed-up of 19354X was obtained with the neural network. We illustrate the applicability by an online medical App that takes into account the age of the patient and the length of the burn.

A method of identifying cell suspension concentration based on bioimpedance spectroscopy

Based on bioimpedance spectroscopy technology, a method of automatically identifying the cell suspension concentration is proposed. This method combines multiple linear regression algorithm and bioimpedance spectroscopy technology, which can identify the concentration of cell suspension quickly and accurately. Firstly, a strategy of random distribution of cell locations is proposed to simulate the true existence of cells. Secondly, 2400 groups of normal, cancerous and mixed cell models with different concentrations are generated by numerical simulation and their bioimpedance spectroscopy data are calculated.Thirdly, the multiple linear regression algorithm (MLR), support vector machine (SVM), and gradient boosting regression algorithm (GBR) are used to identify the concentration of cancerous cells. The simulation results show that the MLR is the best regression model for cell suspension concentration identification and its average goodness of fit and mean square error are 0.9997 and 0.0008respectively. Finally, the MLR is applied to the identification of red blood cell suspensions with different concentrations, the experimental results show that the average goodness of fit and mean square error are 0.9998 and 0.0079, respectively, indicating that this method has a greater ability to identify cell suspension concentrations.

HANDY: a device for assessing resistance to mechanical crushing of maize kernel

BackgroundHow to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called “HANDY”, which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY.ResultsBreakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties.ConclusionsThe HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting.

Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting

In order to address the challenge of labor shortages, and to reduce costs of apple harvesting, a targeted shake-and-catch technique is being developed at Washington State University for fresh market apple harvesting. This technique is showing promising results for some varieties of apples trained to a formal, fruiting wall tree architecture. However, the operators are still required to manually engage the shaker on target branches. To further improve the shake-and-catch apple harvesting system, a multi-class object detection algorithm was developed in this study for automatically detecting apples, branches and trunks in the natural environment using a Faster R-CNN (Regions-Convolutional Neural Network) model. This study deployed transfer learning and fine-tuning for the pre-trained networks (Alexnet, VGG16 and VGG19) and activated the feature of different layers to realize the detection of these objects. The Precision and Recall (PR) curve, F1-score and mean Average Precision (mAP) were used to evaluate the performance of Faster R-CNN in detecting different object classes. VGG19 achieved the highest mAP of 82.4%, which was 10.8% higher than Alexnet and 0.4% higher than VGG16 respectively. The computational time consumed by the entire algorithm was also assessed in this study; Faster R-CNN completed the detection of one image, on average, in 0.45 s. Based on the multi-class object detection results, a polynomial fitting method was used to predict the skeleton equation of branches and trunks. The average Goodness of Fit (R2), Root Mean Squared Error (RMSE) and correlation coefficient (r) between the predicted and reference skeleton were calculated to represent the accuracy of skeleton fitting. VGG16 and VGG19 both achieved higher accuracy than Alexnet for the skeleton fitting of branches and trunks. An algorithm was then developed to estimate shaking locations on the branches using the results of previous steps. Compared with the human experts’ input, a total of 72.7% of shaking locations estimated by the algorithm were considered appropriate. This study provided a foundation and possibility for developing a fully automated shake-and-catch apple harvesting system.

Assessment of computational fracture models using Bayesian method

We present a methodology to evaluate the uncertainty in several popular models for modelling damage and material failure, i.e. a gradient damage model, nonlocal model, phase field approach and cohesive zone model; the latter one is used in the context of the phantom node method though it can easily be used in the context of other computational methods for discrete fracture. The objective is to evaluate and compare the uncertainties in the current models and correlate them to practical observations. The Bayesian method is exploited to achieve this purpose based on experimental reference measurements. The developed methodology has been tested on mode-I fracture in concrete beams through well established three point bending test though other benchmark problems can be adopted for the comparison as well. The results from the current study are compared to the published experimental results. The methodology is implemented in three different steps. Firstly, a sensitivity analysis is performed to quantify the influence of uncertainties in the model parameters. Secondly, the coefficient of variation and average goodness of fit are calculated to evaluate the discrepancy of the predictions with respect to the corresponding measured experimental data. Finally, the posterior probability of models are updated to incorporate the uncertainties in both the model and the parameters, leading to an estimation of the model complexity. Based on the results, the gradient-enhanced damage is found to be the most probable model class with the lowest total model uncertainty. The present study can serve as a platform for future investigations on uncertainties associated with damage modelling and hence the concerned countermeasures.

Big data GIS analysis for novel approaches in building stock modelling

Building heat demand is responsible for a significant share of the total global final energy consumption. Building stock models with a high spatio-temporal resolution are a powerful tool to investigate the effects of new building policies aimed at increasing energy efficiency, the introduction of new heating technologies or the integration of buildings within an energy system based on renewable energy sources. Therefore, building stock models have to be able to model the improvements and variation of used materials in buildings. In this paper, we propose a method based on generalized large-scale geographic information system (GIS) to model building heat demand of large regions with a high temporal resolution. In contrast to existing building stock models, our approach allows to derive the envelope of all buildings from digital elevation models and to model location dependent effects such as shadowing due to the topography and climate conditions. We integrate spatio-temporal climate data for temperature and solar radiation to model climate effects of complex terrain. The model is validated against a database containing the measured energy demand of 1845 buildings of the city of St. Gallen, Switzerland and 120 buildings of the Alpine village of Zernez, Switzerland. The proposed model is able to assess and investigate large regions by using spatial data describing natural and anthropogenic land features. The validation resulted in an average goodness of fit (R2) of 0.6.

Magnetoencephalographic Identification of Epileptic Focus in Children With Generalized Electroencephalographic (EEG) Features but Focal Imaging Abnormalities.

Children with generalized seizures are often excluded as epilepsy surgery candidates. This prospective study was conducted to evaluate the utility of magnetoencephalography (MEG) to refine the location of the "irritative zone" in children with single lesions on magnetic resonance imaging (MRI) but with generalized ictal electroencephalographic (EEG) findings. Patients admitted with refractory epilepsy with imaging studies showing focal or hemispheric abnormalities but scalp video EEG showing generalized or multiregional epileptiform abnormalities were included. Patients were encouraged into natural sleep, and simultaneous whole-head MEG/EEG was recorded. Source localization of epileptic spikes on MEG was carried out while blinded to other results. Acceptable dipoles were classified into 3 groups: focal, hemispheric clusters, and single focal cluster with additional widespread dipoles. Nine patients (4 female, 5 males; ages 10 months to 15 years) were included. Two had focal features on clinical semiology, whereas all had generalized or multiregional interictal and ictal EEG. Etiologies included tuberous sclerosis complex (2), postencephalitic sequelae (1), focal cortical dysplasia (1), and unknown (2). Five patients had clear focal lesions on brain MRI whereas the other 2 had focal positron emission tomography (PET) abnormalities. An average of 38 spikes were accepted (average goodness of fit = 85.3%). A single tight cluster of dipoles was identified in 5 patients, 1 had dipoles with propagation from left occipital to right temporal. One patient had 2 distinct dipole clusters. MEG demonstrated focal findings 9 times more often than the simultaneously recorded scalp EEG, and 3 times more often than the associated multiday video EEG recordings. This study shows that neurophysiologic evidence of focal epileptiform abnormalities in patients with focal brain lesions and generalized EEG findings can be strengthened using MEG. Further feasibility of surgical candidacy should be evaluated in these patients.

Feasibility study of transfer function model on electrocardiogram change caused by acupuncture

BackgroundAcupuncture treatments that regulate the heart are used to treat various clinical disorders and conditions. Although many studies have been conducted to measure quantitatively the effects of acupuncture, thus far, models that describe these effects have not been established. The purpose of this study was to derive a transfer function model of acupuncture stimulation within the electrocardiograms based on the periods before, during, and after acupuncture.MethodsFourteen healthy subjects were included in this clinical trial. Five-minute electrocardiograms were captured before, during, and after acupuncture at HT7. For each period, signal-averaged electrocardiograms were created from all of the subjects’ 5-min electrocardiograms for that period. Individual transfer functions, which has the highest average goodness of fit, were derived for each period pair. By averaging individual transfer functions, generalized transfer functions were derived.ResultsThe transfer function with the highest average goodness of fit was a fraction with 4th order numerator and 5th order denominator. Fourteen individual transfer functions were derived separately for each pair of periods: before and during acupuncture, during and after acupuncture, and before and after acupuncture. Three generalized transfer functions were derived by averaging individual transfer functions for each period pair.ConclusionThe three generalized transfer functions that were derived may reflect the electrocardiogram changes caused by acupuncture. However, this clinical trial included only 14 subjects. Further studies with control groups and more subjects are needed.This clinical trial has been registered on the Clinical Research Information Service, Republic of Korea (No. KCT0001944). The first enrolment of subject started at 2 June 2015 and this trial was retrospectively registered at 14 June 2016

The model training of knowledge-based radiotherapy treatment planning and its preliminary application

Objective Knowledge-based radiotherapy (KBRT) can reduce the plan quality variability induced by different experiences between physicians and improve the quality of treatment plans. Methods The Varian Rapid Plan system was used to train a dose-volume histogram (DVH) prediction model. The obtained model was preliminarily applied to semi-automatic design of the preoperative treatment plans for rectal cancer. Eighty high-quality volumetric modulated arc therapy plans were imported into the model training set of the Rapid Plan system. The structures of the plans were matched to the corresponding labels and codes as listed in the library. The training started after the verification of prescription. The residual plots, regression curves, geometric plots for organ at risk (OAR), in-field DVH plots, and model training logs were examined. After removal of the mismatch, the original plans were assessed to rule out outliers and influential data points. More similar plans may be added for another round of training. Ten KBRT plans were designed using the final model and compared with the clinical plans. Results For the two major OARs, the femoral head and bladder, the average goodness of fit of the principal component were 0.999415/1.0 and 0.999963/1.0 for the DVH model, and 0.999651/1.0 and 0.999945/1.0 for geometry-based expected dose model, respectively. In all the plans, 11 had Cook’s distance values exceeding the tolerance and 4 had studentized residual values exceeding the tolerance. The outliers were all kept in the training set to generalize the scope of the model. The 10 KBRT plans had significantly improved homogeneity indices for PGTV and PTV than the original plans (P=0.00, 0.04). The 10 KBRT plans also had significantly reduced D50% to the femoral head and bladder as well as significantly reduced mean doses to the bladder than the original plans (P=0.042, 0.000, 0.005). Conclusions In this study, the Rapid Plan system is used to train a KBRT model for design of preoperative radiotherapy plans for rectal cancer. The results of preliminary application meet the clinical requirements. Key words: Radiotherapy planning; Model training; Model verification

Fast SAR Sea Surface Distribution Modeling by Adaptive Composite Cubic Bézier Curve

We address the problem of sea surface distribution modeling in a synthetic aperture radar (SAR) image by developing an innovative nonparametric method to tackle the main weakness of the traditional Parzen window kernel method, i.e., relatively low computation speed. We derive an explicit analytical solution of modeling sea surface distribution by a composite cubic Bezier curve and propose an adaptive segmentation strategy to improve the modeling precision. A comparative study validates that the average computation time of the proposed method is only 1/60 of the Parzen window kernel method and about 1/6 of the k-root and G0 methods. More importantly, in terms of modeling performance, the proposed method can achieve more adaptability and stability to different SAR sensors, resolutions, and sea scenes. The average goodness of fit tested on eight sea scenes of the proposed method, measured by $\overline{|\hat{R}^{2}|}$ (the smaller the better), is only 0.0006 and outperforms that of the Parzen window kernel method (0.0059), k-root (0.0390), and G0 (0.0678).

Alternate Tool for Comparative Analysis of Diesel Engine Emission with Simple Mathematical Model using Response Surface Methodology

Growth of pollutants in atmospheric air leads to hazardous impacts on human health and all living things. The urbanization is spreading all over the world rapidly which leads to rise the pollutants in the environment. The major air pollutants are nitrogen (nitrogen oxide and all amounts of nitrogen dioxide) carbon monoxide and organic compounds which are the unburned or partially burned hydrocarbons. Many techniques like exhaust gas recirculation (EGR), catalytic converters, water injection methods, thermal reactors, introduction of various materials in the path of exhaust gasses and etc are available to reduce pollution rate. In this experimental investigation, the compounds used are NaCl, lime stone, egg shell and urea in the path of exhaust. It is found that the rate of CO2, CO, HC, NOx are reduced to considerable level. Moreover, the mathematical model was developed to reduce the emission by NaCl, lime stone, egg shell and urea using Response surface methodology (RSM) by the experimental results. A visual studio tool was used to generate alternative tool. Finally, the experimental results and RSM mathematical model results have been compared by considering different load and different chemical reactant and discussed. The final average goodness of fit (GOF) values indicated by RSM mathematical model analys is found to be 99% of fit for internal combustion engine emission analysis.

Simulating streamflow and water table depth with a coupled hydrological model

A coupled model integrating MODFLOW and TOPNET with the models interacting through the exchange of recharge and baseflow and river-aquifer interactions was developed and applied to the Big Darby Watershed in Ohio, USA. Calibration and validation results show that there is generally good agreement between measured streamflow and simulated results from the coupled model. At two gauging stations, average goodness of fit (R2), percent bias (PB), and Nash Sutcliffe efficiency (ENS) values of 0.83, 11.15%, and 0.83, respectively, were obtained for simulation of streamflow during calibration, and values of 0.84, 8.75%, and 0.85, respectively, were obtained for validation. The simulated water table depths yielded average R2 values of 0.77 and 0.76 for calibration and validation, respectively. The good match between measured and simulated streamflows and water table depths demonstrates that the model is capable of adequately simulating streamflows and water table depths in the watershed and also capturing the influence of spatial and temporal variation in recharge.

MCG source reconstruction based on greedy sparse method

Current source reconstruction, i.e., reconstructing current dipole distribution through measured array signals of cardiac magnetic field on body surface, is a method for non-invasively study on the heart electrical activity. In this paper, the relationship between measured magnetic signals and current dipole distribution is described by a linear equation, and a sparse solution of current source reconstruction is achieved using a fast greedy method. This method can significantly decrease the computational complexity of or- thogonal matching pursuit (OMP) algorithm by means of approximating orthogonalisation and improving the selection vector strategy per iteration. Thereby, the sources with large dipole strength can be fast searched out with high accuracy. A set of magnetocardiogram (MCG) data of normal subject is used to demonstrate the effectiveness of this method that the trajectory of reconstructed dominant sources, whose strengths are more than 65%, is almost consistent with conduction process in depolarization and repolarization. The average goodness of fit (GOFs) of measured MCG and the magnetic field map generated by the reconstructed current sources during QRS complex and ST-T segment are 99.36% and 99.78%, respectively.

Comparative analysis of current and magnetic multipole graphical models

In recent year, a multipole graphical model, which is constructed by using individual MCG measurements based on the equivalent current dipole (ECD) or equivalent magnetic dipole (EMD) source model, has been developed with the aim of instead of the volume conductor model in the inverse solution of cardiac source estimation. In this paper, two graphical models known as the double magnetic dipole source model (DMD) and the dual current dipole source model (DCD) are introduced. The simulation results and the comparison of two evaluation criteria, i.e. average GOF (Goodness of Fit) and average RMSE (Root Mean Square Error), indicated that both multipole graphical models can provide a good representation of dynamic magnetic field from the noninvasively detected MCG- recordings, even when the heart is of the dilation. The time-averaged sources localization error and the RMSE for both models are demonstrated, and the characteristic of two multipole models is discussed.

Genetic and least squares algorithms for estimating spectral EIS parameters of prostatic tissues

We employed electrical impedance spectroscopy (EIS) to evaluate the electrical properties of prostatic tissues. We collected freshly excised prostates from 23 men immediately following radical prostatectomy. The prostates were sectioned into 3 mm slices and electrical property measurements of complex resistivity were recorded from each of the slices using an impedance probe over the frequency range of 100 Hz to 100 kHz. The area probed was marked so that following tissue fixation and slide preparation, histological assessment could be correlated directly with the recorded EIS spectra. Prostate cancer (CaP), benign prostatic hyperplasia (BPH), non-hyperplastic glandular tissue and stroma were the primary prostatic tissue types probed. Genetic and least squares parameter estimation algorithms were implemented for fitting a Cole-type resistivity model to the measured data. The four multi-frequency-based spectral parameters defining the recorded spectrum (ρ∞, Δρ, fc and α) were determined using these algorithms and statistically analyzed with respect to the tissue type. Both algorithms fit the measured data well, with the least squares algorithm having a better average goodness of fit (95.2 mΩ m versus 109.8 mΩ m) and a faster execution time (80.9 ms versus 13 637 ms) than the genetic algorithm. The mean parameters, from all tissue samples, estimated using the genetic algorithm ranged from 4.44 to 5.55 Ω m, 2.42 to 7.14 Ω m, 3.26 to 6.07 kHz and 0.565 to 0.654 for ρ∞, Δρ, fc and α, respectively. These same parameters estimated using the least squares algorithm ranged from 4.58 to 5.79 Ω m, 2.18 to 6.98 Ω m, 2.97 to 5.06 kHz and 0.621 to 0.742 for ρ∞, Δρ, fc and α, respectively. The ranges of these parameters were similar to those reported in the literature. Further, significant differences (p < 0.01) were observed between CaP and BPH for the spectral parameters Δρ and fc; this is especially important since current prostate cancer screening methods do not reliably differentiate between these two tissue types.

Fit of Wood's function to weekly records of milk yield, total digestible nutrient intake and body weight changes in early lactation of multiparous Holstein cows in Japan

Abstract The aim of this study was to determine the interrelationships between milk yield, total digestible nutrient (TDN) intake and body weight. A total of 14,900 records of weekly (from the 1st week to 20th week after calving) weights of milk yield and TDN intake and body weight from 745 multiparous Holstein cows in Japan were used. In addition, attempts were made to predict energy requirement in early lactation of dairy cows using metabolizable energy (ME) system. The Wood's function ( y t = at b e − ct ) was fitted to each animal record to derive secondary traits (parameters, weeks at peak or bottom, maximum–minimum values and predicted daily body weight gain). The average goodness of fit, as assessed by the R 2 value, was highest for TDN intake (0.82) and lowest for body weight (0.59). The weeks at peak milk yield and TDN intake and minimum weight were 6.0, 10.0 and 5.5, respectively. Phenotypic correlations within the same trait were negative between parameters a and b and positive between b and c . Relatively large correlations between traits were obtained for a values and maximum–minimum traits (maximum milk yield and TDN intake and minimum body weight). In addition, the phenotypic correlations between predicted daily gain and a value for milk yield were negative during the first 5 weeks after calving. A comparison of ME intakes estimated from Wood's function and ME intake predicted from energy systems showed that the mean-square prediction error (MSPE) was largest in the first week and smallest in the fourth week. The results suggest that the Wood's function is inadequate in predicting body weight changes in early lactation.

Determination of methylnaltrexone in clinical samples by solid-phase extraction and high-performance liquid chromatography for a pharmacokinetics study

A high-performance liquid chromatographic (HPLC) method with electrochemical detection and solid-phase extraction (SPE) using cartridges of weak cation-exchange capacity as the primary retention mechanism is described for the separation and determination of methylnaltrexone (MNTX) in small clinical samples of plasma or urine. The procedure was performed using a Phenomenex Prodigy ODS-2, 5 μm, 150×3.2 mm analytical column and 50 m M potassium acetate buffer, with 11% methanol as organic modifier at pH* 4.5 at a flow-rate of 0.5 ml/min. The detection potential was 700 mV. The six-point standard calibration curves were linear over three consecutive days in the range from 2 to 100 ng/ml. The average goodness of fit ( r) was 0.9993. The lower limit of detection (LOD) and limit of quantification (LOQ) were found to be 2.0 and 5.0 ng/ml, respectively. At the LOQ, the coefficient of variation for the entire method was 8.0% and the accuracy was 10.0% ( n=10). Recovery of the drug from plasma was in the region of 94%. The method was applied to a pharmacokinetics study of methylnaltrexone after subcutaneous administration and in numerous assays of analytes in blood plasma and urine. The pharmacokinetics parameters for a single dose of 0.1 or 0.3 mg/kg in plasma were C max=110 (±55) and 287 (±101) ng/ml and t max=16.7 (±10.8) and 20.0 (±9.5) min, respectively. The method is simple, yet sensitive for the detection and determination of methylnaltrexone in biological samples at the level of the physiological response.