Is average RMSE appropriate for evaluating acoustic-to-articulatory inversion?

The feasibility and accuracy of the risk prediction of gas extraction borehole spontaneous combustion is improved to avoid the occurrence of spontaneous combustion in the gas extraction borehole. A gas extraction borehole spontaneous combustion risk prediction model (PSO-BPNN model) coupling the PSO algorithm with BP neural network is established through improving the connection weight and threshold values of BP neural network by the particle swarm optimization (PSO) algorithm. The prediction results of the PSO-BPNN model are compared and analyzed with that of the BP neural network model (BPNN model), GA-BPNN model, SSA-BPNN model and MPA-BPNN model. The results showed as follows: the average relative error of the PSO-BPNN model was 4.38%; the average absolute error was 0.0678; the root mean square error was 0.0934; and the determination coefficient was 0.9874. Compared with the BPNN model, the average relative error, average absolute error and root mean square error decreased by 9.35%, 0.1707 and 0.2056 respectively; and the determination coefficient increased by 0.1169. Compared with the GA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.19%, 0.0602 and 0.0821 respectively; and the determination coefficient increased by 0.0320. Compared with the SSA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 5.70%, 0.0820 and 0.1100 respectively; and the determination coefficient increased by 0.0474. Compared with the MPA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.50%, 0.0861 and 0.1125 respectively; and the determination coefficient increased by 0.0488, proving that the PSO-BPNN model is more accurate than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model as for prediction. When the PSO-BPNN model was applied to three extraction boreholes A, B, and C in a coal mine of Shanxi, the prediction results were better than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model, proving the accuracy and stability of the PSO-BPNN model in predicting risk of borehole spontaneous combustion in other mine.

The human lung cytochrome P450 2A13 (CYP2A13) activates the nicotine-derived procarcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) into DNA-altering compounds that cause lung cancer. Another cytochrome P450, CYP2A6, is also present in human lung, but at much lower levels. Although these two enzymes are 93.5% identical, CYP2A13 metabolizes NNK with much lower K(m) values than does CYP2A6. To investigate the structural differences between these two enzymes the structure of CYP2A13 was determined to 2.35A by x-ray crystallography and compared with structures of CYP2A6. As expected, the overall CYP2A13 and CYP2A6 structures are very similar with an average root mean square deviation of 0.5A for the Calpha atoms. Like CYP2A6, the CYP2A13 active site cavity is small and highly hydrophobic with a cluster of Phe residues composing the active site roof. Active site residue Asn(297) is positioned to hydrogen bond with an adventitious ligand, identified as indole. Amino acid differences between CYP2A6 and CYP2A13 at positions 117, 300, 301, and 208 relate to different orientations of the ligand plane in the two protein structures and may underlie the significant variations observed in binding and catalysis of many CYP2A ligands. In addition, docking studies suggest that residues 365 and 366 may also contribute to differences in NNK metabolism.

Densitometry measurements have been performed previously using subtracted images. However, digital subtraction angiography (DSA) in coronary angiography is highly susceptible to misregistration artifacts due to the temporal separation of background and target images. Misregistration artifacts due to respiration and patient motion occur frequently, and organ motion is unavoidable. Quantitative densitometric techniques would be more clinically feasible if they could be implemented using unsubtracted images. The goal of this study is to evaluate image recovery techniques for densitometry measurements using unsubtracted images. A humanoid phantom and eight swine (25-35 kg) were used to evaluate the accuracy and precision of the following image recovery techniques: Local averaging (LA), morphological filtering (MF), linear interpolation (LI), and curvature-driven diffusion image inpainting (CDD). Images of iodinated vessel phantoms placed over the heart of the humanoid phantom or swine were acquired. In addition, coronary angiograms were obtained after power injections of a nonionic iodinated contrast solution in an in vivo swine study. Background signals were estimated and removed with LA, MF, LI, and CDD. Iodine masses in the vessel phantoms were quantified and compared to known amounts. Moreover, the total iodine in left anterior descending arteries was measured and compared with DSA measurements. In the humanoid phantom study, the average root mean square errors associated with quantifying iodine mass using LA and MF were approximately 6% and 9%, respectively. The corresponding average root mean square errors associated with quantifying iodine mass using LI and CDD were both approximately 3%. In the in vivo swine study, the root mean square errors associated with quantifying iodine in the vessel phantoms with LA and MF were approximately 5% and 12%, respectively. The corresponding average root mean square errors using LI and CDD were both 3%. The standard deviations in the differences between measured iodine mass in left anterior descending arteries using DSA and LA, MF, LI, or CDD were calculated. The standard deviations in the DSA-LA and DSA-MF differences (both approximately 21 mg) were approximately a factor of 3 greater than that of the DSA-LI and DSA-CDD differences (both approximately 7 mg). Local averaging and morphological filtering were considered inadequate for use in quantitative densitometry. Linear interpolation and curvature-driven diffusion image inpainting were found to be effective techniques for use with densitometry in quantifying iodine mass in vitro and in vivo. They can be used with unsubtracted images to estimate background anatomical signals and obtain accurate densitometry results. The high level of accuracy and precision in quantification associated with using LI and CDD suggests the potential of these techniques in applications where background mask images are difficult to obtain, such as lumen volume and blood flow quantification using coronary arteriography.

Background, aim, and scope The Middle East section of Qilian Mountain was used as the study area, and the temperature interpolation method based on DEM was compared. Materials and methods The temperature average correction method and Anusplin method were used to interpolate the monthly average temperature, seasonal average temperature and annual average temperature of 13 meteorological stations in the study area, and the average absolute error and root mean square error were determined by cross-validation method. The absolute value of the coefficient and index difference evaluates the verification site interpolation results. Results (1) The average absolute error and root mean square error of the vertical temperature correction method ranged from 0.07 °C to 1.36 °C and 0.09 °C to 1.65 °C, and the range of Anusplin ranged from 0.16 °C to 1.3 °C and 0.24 °C. -1.49 °C; (2) Anusplin and temperature vertical correction method on the average absolute error and root mean square error index, the absolute difference between the low temperature season index is 0.09 °C and 0.00 °C, respectively, the high temperature season is 0.30 °C and 0.32 °C. Conclusion The temperature vertical correction method is superior to Anusplin in MAE, RMSE and R2 in July, August and September; the accuracy of temperature vertical correction in summer and autumn is higher than that of Anusplin, and Anusplin is more It is suitable for interpolation in low temperature season, and the vertical temperature correction method is more suitable for interpolation in high temperature season; the error of the two interpolation methods on the annual scale is larger, and the accuracy of temperature and temperature interpolation of Anusplin is much higher than the vertical correction method of temperature. Recommendations and perspectives Different interpolation methods are applicable to different regions, and there is no method that is absolutely suitable for the study area, and only a method that is relatively suitable for the study area. In general, the Anusplim method is more accurate than the temperature vertical correction method for the temperature of the Middle East in Qilian. The former is more suitable for the interpolation of temperature in the study area.

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet‐based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash‐Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as theà trouswavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies––both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process‐based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet‐Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet‐based performance measures, namely the MNSC (Multiscale Nash‐Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash‐Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

BackgroundThe Ki67 Index has been extensively studied as a prognostic biomarker in breast cancer. However, its clinical adoption is largely hampered by the lack of a standardized method to assess Ki67 that limits inter-laboratory reproducibility. It is important to standardize the computation of the Ki67 Index before it can be effectively used in clincial practice.MethodIn this study, we develop a systematic approach towards standardization of the Ki67 Index. We first create the ground truth consisting of tumor positive and tumor negative nuclei by registering adjacent breast tissue sections stained with Ki67 and H&E. The registration is followed by segmentation of positive and negative nuclei within tumor regions from Ki67 images. The true Ki67 Index is then approximated with a linear model of the area of positive to the total area of tumor nuclei.ResultsWhen tested on 75 images of Ki67 stained breast cancer biopsies, the proposed method resulted in an average root mean square error of 3.34. In comparison, an expert pathologist resulted in an average root mean square error of 9.98 and an existing automated approach produced an average root mean square error of 5.64.ConclusionsWe show that it is possible to approximate the true Ki67 Index accurately without detecting individual nuclei and also statically demonstrate the weaknesses of commonly adopted approaches that use both tumor and non-tumor regions together while compensating for the latter with higher order approximations.

To ascertain which percentage of maximal voluntary contractile force of the paraspinal muscles, when tested in a functional position, is most reliable for assessing electromyographic (EMG) fatigue changes. Ten healthy volunteers with no history of low back pain (six males). The surface EMG signal during 60-second isometric contractions of the paraspinal muscles at 40, 50 and 60% levels of maximal voluntary contractile force was captured and analysed. Each contraction level was assessed on two occasions, at least three days apart. The initial median frequency, the decline in median frequency slope and the increase in root mean square values were assessed for between-days reliability, using intraclass correlation coefficients (ICCs) and standard errors of measurements (SEM). Normalized median frequency and root mean square values were also assessed. At 40% of maximal voluntary contraction, little or no EMG fatigue changes occurred in any of the observed parameters. At 50% maximal voluntary contraction the initial mean frequency and root mean square changes proved highly reliable, with ICCs ranging from 0.74 to 0.86 and 0.75 to 1.00 respectively. Normalizing the root mean square data reduced the reliability, but this was still acceptable with ICCs 0.70-0.83. The median frequency decline slope proved less reliable with ICCs 0.24-0.74 for raw and 0.26-0.77 for normalized data. At 60% maximal voluntary contraction the initial mean frequency proved as reliable as initial median frequency at 50% with ICCs 0.70-0.89. The raw and normalized root mean squares (ICCs 0.43-0.89 and 0.30-0.87 respectively) and raw and normalized median frequency (ICCs 0.27-0.51 and 0.24-0.53 respectively) changes were less reliable than at 50% MVC. Overall, the reliability is better at the L4/5 than at the L2/3 level. Outcome measures taken at 50% maximal voluntary contraction are the most reliable in functional testing the paraspinal muscles of healthy volunteers. With initial median frequency and root mean square values being more reliable parameters than median frequency decline. At the L4/5 level, however, all parameters were acceptably reliable at 50% of maximum effort. However the between-subject variability of the median frequency decline and root mean square incline slopes suggest that these parameters are not yet fully suitable for monitoring fatigue changes during prolonged isometric contraction.

Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered.

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Experimental muscle pain changes the spatial distribution of upper trapezius muscle activity during sustained contraction

Developing an improved global solar radiation map for Zimbabwe through correlating long-term ground- and satellite-based monthly clearness index values

BackgroundWearable sensor systems can provide data for at-home gait analyses and input to controllers for rehabilitation devices but they often have reduced estimation accuracy compared to laboratory systems. The goal of this study is to evaluate a portable, low-cost system for measuring ground reaction forces and ankle joint torques in treadmill walking and calf raises.MethodsTo estimate the ground reaction forces and ankle joint torques, we developed a custom instrumented insole and a tissue force sensor. Six healthy subjects completed a collection of movements (calf raises, 1.0 m/s walking, and 1.5 m/s walking) on two separate days. We trained artificial neural networks on the study data and compared the estimates to a multi-camera motion system and an instrumented treadmill. We evaluated the relative strength of each sensor by testing each sensor’s ability to predict the ankle joint torque calculated from a reference inverse kinematics algorithm. We assessed model accuracy through root mean squared error and normalized root mean square error. We hypothesized that the estimation of the models would have normalized root mean square error measures less than 10 %.ResultsFor walking at 1.0 and walking at 1.5 m/s, the single-task, intra-day and multi-task, intra-day predictions had normalized root mean square error less than 10 % for all three force components and both center of pressure components. For the calf raise task, the single-task, intra-day and multi-task, intra-day predictions had normalized root mean square error less than 10 % for only the anterior-posterior center of pressure. The multi-task, intra-day model had similar predictions to the single-task, intra-day model. The normalized root mean square error of predictions from the insole sensor alone were less than 10 % for walking at 1.0 m/s and 1.5 m/s. No sensor was sufficient for the calf raise task. The combination of the insole sensor and the tendon sensor had lower normalized root mean square error than the individual sensors for all three tasks.ConclusionsThe proposed sensor system provided accurate estimates for five of the six components of the ground reaction kinetics during walking at 1.0 and 1.5 m/s and one of the six components during the calf raise task. The normalized root mean square error of the predictions of the ground reaction forces were similar to published studies using commercial devices. The proposed system of low-cost sensors can provide useful estimations of ankle joint torque for both walking and calf raises for future studies in mobile gait analysis.

Comparative evaluation of vertical accuracy of elevated points with ground control points from ASTERDEM and SRTMDEM with respect to CARTOSAT-1DEM

Land surface temperature (LST) retrieval from NOAA-AVHRR data is mainly through so-called split window algorithms. During the last 20 years 17 split window algorithms has been published. These algorithms can be grouped into four categories: emissivity-dependent models, two-factors models, complicated models and radiance model. In this paper we intend to compare these split window algorithms in terms of their computation and accuracy. Two methods are used for the comparison: ground datasets and simulation datasets. Results from comparison shows that different algorithms have different performances under different situations. For simulation datasets, the algorithms of Qin et al. and Sobrino et al. are the best. The average root mean square (RMS) error of the two algorithms is less than 0.3degC. The algorithms of Franca and Cracknell, Prata and Uliverir et al. also have very low RMS errors (0.5-0.7degC). Results from comparison with ground datasets indicates that the algorithms of Qin et al. and Sobrino et al. are among the best for the dataset without precise in situ atmospheric water vapor contents. These algorithms are able to provide LST retrieval with average RMS error less than 1.9degC for the 361 measurements of the two Australian sites. An obvious contrast to the generally higher RMS error for the dataset is the much lower RMS error of the algorithms for the intensive experiments with precise in situ atmospheric water vapor contents. Based on the above two methods for comparison, it can be concluded that, comprehensively, the algorithm of Qin et al. is the best alternative for LST retrieval from AVHRR followed by Sobrino et al., Franca and Cracknell, and Prata when data are available to estimate both emissivity and transmittance

ABSTRACTUpdating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data.We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match.The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.