Percentage Prediction Error Research Articles

Effects of two equine digestive aid supplements on hindgut health.

Gastrointestinal disease is the number one killer of horses. Little is known about the maintenance of microbes in the equine hindgut and how to distinguish a healthy gut in a live horse. Utilization of internal and external digestibility markers and starch fermentation has been extensively studied in ruminants and is the basis for research conducted on horses. The aims of this study were to investigate the effects of two equine feed digestive aid supplements on hindgut health (HGH) as reflected in fecal pH and digestibility and to compare and validate DM digestibility measurements through the use of internal and external markers such as chromium oxide (CR), lignin (Lig), indigestible ADF (iADF), indigestible NDF (iNDF), and indigestible lignin (iLig). Nine mature Quarter horses (six geldings, three mares) were used in a crossover design, three feeding periods of 17 d (51 d total), using three treatments: control, no feed additive (CON), Smartpak (SP; Plymouth, MA), or Platinum Performance (PP; Buellton, CA). Both SP and PP contained a strain of Lactobacillus, whereas SP further supplied mannanoligosaccharides (MOS) and fructooligosaccharides (FOS) and PP supplied Saccharomyces boulardii. Within the 17-d period, horses were offered orchard grass hay and sweet cob grain and the assigned treatment daily and four CR cookies to deliver 8 g/d of CR for the last 7 d of each period. Total feces were collected from 15 to 17 d. Feed and fecal samples were dried, ground, and sent to ANALAB (Fulton, IL) for nutrient analysis. Duplicate samples of feed and feces were placed in ruminally cannulated cows for in situ determination of iADF, iNDF, and iLig to estimate digestibility. Estimated CR fecal output, CR DMI, and DM digestibilities were evaluated using the root mean square prediction error percentage of the observed mean (RMSPE), concordance correlation coefficient (CCC), and Nash–Sutcliffe efficiency methods. Marker predictive ability tests showed iADF to have the least amount of bias with the smallest RMSPE (4%), largest CCC (0.43), and the largest amount of random bias (error of dispersion = 0.45). Supplementation of PP decreased CR DM digestibility (P < 0.02). Smartpak increased fecal pH (P < 0.09), but PP had no effect on fecal pH. Therefore, SP had a beneficial effect on HGH that is believed to be due to MOS and FOS.

Development of in vitro - in vivo correlations for newly optimized Nimesulide formulations.

Use of the human volunteers in bioequivalence studies is being discouraged by the Food and drug administration after the introduction of biowaiver approaches. In-vitro in-vivo correlation (IVIVC) with the level A is accepted for the registration of new molecules. In the present study deconvolution technique with numeric approaches was applied after compressing and in vitro validating the 100mg Nimesulide immediate, intermediate and slow release tablets. Single centered, crossover, randomized study was conducted in four phases with a two-week washout period to obtain the plasma drug concentration data after administrating test and reference products in male healthy volunteers. KineticaTM 4.4.1 (Thermoelectron corp, USA) was used for the calculation of two ways ANOVA with 90% CI from both log transformed and non- transformed data and Phoenix WinNonlin 7 and it's IVIVC toolkit version 7.0 was used for the application of numeric approaches of IVIVC. Results revealed that the individual internal percentage prediction error for AUCinf and Cmax were found to be < 15% while their average values were < 10% in all medium. Numeric values of % PE at pH 6.8 and pH 7.4 (50 rpm in USP II and 100 rpm in USP I and II apparatus) were found to be (2.5842, 2.9789 and, 7.1732; 7.0944, 2.4721 and 4.350) for AUCinf and (2.5842, 0.5736 and 4.6928; 5.6214, 3.0551 and -2.4711) values for Cmax respectively. The low values of prediction errors demonstrate that the correlation model is projecting the in vivo response of each formulation. Percentage External error (% PE) was not required because individual values of percentage internal error (%PE) of Cmax and AUClast were not >15. In order to predict point to point correlation between fraction drug dissolved and drug absorbed, their mean r2 value was found to be > 0.9112 which showed a linear correlation in slightly alkaline pH.

Conjugate gradient descent learned ANN for Indian summer monsoon rainfall and efficiency assessment through Shannon-Fano coding

Work reported in the present paper demonstrates a neurocomputing based predictive model for the average rainfall in India during the season of summer monsoon. Backpropagation method with Conjugate Gradient Descent algorithm has been implemented to develop the neurocomputing model. After three runs of the model, it is found that a high prediction yield is available. Finally, Shannon-Fano coding has been implemented and the coding efficiency has been measured by dividing the error percentage of prediction into various classes. The efficiency of Conjugate Gradient Descent algorithm for multilayer ANN has been finally established through Shannon-Fano coding.

Modeling of greenhouse agro-ecosystem using optimally designed bootstrapping artificial neural network

The greenhouse environment is a complex multi-scale integrated nonlinear system. This agro-ecosystem is composed of crop and greenhouse climate which are based on the existence of two different timescales. The effect of temperature plays a vital role in the variation of phenotypic data in crop growth. Thus, two different models, one for capturing the climate dynamics inside the greenhouse and other for crop dynamics, are essential. First, the neural network is used to predict the inside environment, given the outside conditions and the operation of the control equipment. The inputs of the network are meteorological parameters, whose measurements are costly and time consuming to acquire. So, instead of measuring all the parameters used in the physical modeling, the most significant relevant input parameters which give same modeling efficiency are identified. To avoid overfitting of the data and to realize the best prediction results with the simplest structure, an enhanced pruning algorithm is implemented for topology optimization of the artificial neural network. The pruning algorithm is discussed and exemplified via simulations. By plotting the training error, test error, and final prediction error (FPE) estimates over the course of pruning the network weights, it is inferred that a network with minimum of 15 weights is reliable to model greenhouse environment dynamics. With the Optimal Brain Surgeon (OBS) algorithm, a reduction of the number of weights from 141 to 76 (46%) in the first step and finally to 15 (89%) was achieved and the percentage prediction error is reduced from 13.13% for the complete network structure to 4.35% for final pruned network. Secondly, in order to study the progress of crop ontogeny, bootstrap resampling-based artificial neural network is developed with limited destructive measurements. The notion of prediction performance and the efficiency of the bootstrapped crop phenotypic neural network model are evaluated by root mean square error (RMSE), mean square error (MSE) and Nash and Sutcliffe efficiency (NSE) criteria. The net assimilation rate which determines the growth rate of the plant inside the greenhouse environment is calculated. The resulting model can be used for growth assessment, understanding crop physiology and yield prediction.

Soft computing model for analysing the effect of friction stir processing parameters on the intergranular corrosion susceptibility of aluminium alloy AA5083

Abstract Aluminium alloy AA5083 is prone to intergranular corrosion in marine environments. In an attempt to reduce the intergranular corrosion, AA5083 was subjected to friction stir processing (FSP). The FSP experimental trials were conducted as per face-centered central composite design with three levels of variation in FSP process parameters viz. tool rotation speed (TRS), tool traverse speed (TTS) and tool shoulder diameter (SD). Intergranular corrosion susceptibility of the processed specimens was assessed by performing nitric acid mass loss test. The mass loss of the specimens was correlated with the intergranular corrosion susceptibility as per the standard ASTM G67-13. The experimental results indicate that FSP had significantly reduced the intergranular corrosion susceptibility of the AA5083 alloy. Soft computing techniques namely Artificial Neural Network, Mamdani Fuzzy system, and Sugeno Fuzzy system were used to predict the intergranular corrosion (IGC) susceptibility (mass loss) of the friction stir processed specimens. Among the developed models, Sugeno fuzzy system displayed minimum percentage error in prediction. So Sugeno fuzzy system was used to analyze the effect of friction stir processing process parameters on the IGC of the processed specimens. The results suggest that stir processing of AA5083 at a TRS of 1300 rpm, TTS of 60 mm/min and SD of 21 mm would make the alloy least susceptible to intergranular corrosion.

Effect of deriving periosteal and endosteal contours from microCT scans on computation of cross-sectional properties in non-adults: the femur.

Derivation of periosteal and endosteal contours taken from transversal long bone cross-sections limits the accuracy of calculated biomechanical properties. Although several techniques are available for deriving both contours, the effect of these techniques on accuracy of calculated cross-sectional properties in non-adults is unknown. We examine a sample of 86 non-adult femora from birth to 12years of age to estimate the effect of error in deriving periosteal and endosteal contours on cross-sectional properties. Midshaft cross-sections were taken from microCT scans and contours were derived using manual, fully automatic, spline, and ellipse techniques. Agreement between techniques was assessed against manually traced periosteal and endosteal contours using percent prediction error (%PE), reduced major axis analysis, and limits of agreement. The %PEs were highest in the medullary area and lowest in the total area. Mean %PEs were sufficiently below the 5% level of acceptable error, except for medullary areas, but individual values can greatly exceed this 5% boundary given the high standard deviation of %PE means and wide minimum-maximum range of %PEs. Automatic processing produces greater errors than does combination with manual, spline, and ellipse processing. Although periosteal contour is estimated with stronger agreement compared with endosteal contour, error in deriving periosteal contour has a substantially greater effect on calculated section moduli than does error in deriving endosteal contours. We observed no size effect on the resulting bias. Nevertheless, cross-sectional properties in a younger age category may be estimated with greater error compared with in an older age category. We conclude that non-adult midshaft cross-sectional properties can be derived from microCT scans of femoral diaphyses with mean error of <5% and that derivation of endosteal contour can be simplified by the ellipse technique because fully automatic derivation of endosteal contour may increase the resulting error, especially in small samples.

Evaluation of Multiple Linear Regression–Based Limited Sampling Strategies for Enteric-Coated Mycophenolate Sodium in Adult Kidney Transplant Recipients

Although multiple linear regression-based limited sampling strategies (LSSs) have been published for enteric-coated mycophenolate sodium, none have been evaluated for the prediction of subsequent mycophenolic acid (MPA) exposure. This study aimed to examine the predictive performance of the published LSS for the estimation of future MPA area under the concentration-time curve from 0 to 12 hours (AUC0-12) in renal transplant recipients. Total MPA plasma concentrations were measured in 20 adult renal transplant patients on 2 occasions a week apart. All subjects received concomitant tacrolimus and were approximately 1 month after transplant. Samples were taken at 0, 0.33, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 6, and 8 hours and 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2, 3, 4, 6, 9, and 12 hours after dose on the first and second sampling occasion, respectively. Predicted MPA AUC0-12 was calculated using 19 published LSSs and data from the first or second sampling occasion for each patient and compared with the second occasion full MPA AUC0-12 calculated using the linear trapezoidal rule. Bias (median percentage prediction error) and imprecision (median absolute prediction error) were determined. Median percentage prediction error and median absolute prediction error for the prediction of full MPA AUC0-12 were <15% for 4 LSSs, using the data from the same (second) occasion. One equation (1.583C1 + 0.765C2 + 0.369C2.5 + 0.748C3 + 1.518C4 + 2.158C6 + 3.292C8 + 3.6690) showed bias and imprecision <15% for the prediction of future MPA AUC0-12, where the predicted AUC0-12 from the first occasion was compared with the full AUC0-12 from the second. All LSSs with an acceptable predictive performance included concentrations taken at least 6 hours after the dose. Only one LSS had an acceptable bias and precision for future estimation. Accurate dosage prediction using a multiple linear regression-based LSS was not possible without concentrations up to at least 8 hours after the dose.

Morphometric panel regression equations for predicting body mass in immature humans.

Predicting body mass is a frequent objective of several anthropological subdisciplines, but there are few published methods for predicting body mass in immature humans. Because most reference samples are composed of adults, predicting body mass outside the range of adults requires extrapolation, which may reduce the accuracy of predictions. Prediction equations developed from a sample of immature humans would reduce extrapolation for application to small-bodied target individuals, and should have utility in multiple predictive contexts. Here, we present two novel body mass prediction equations derived from 3468 observations of stature and bi-iliac breadth from a large sample of immature humans (n = 173) collected in the Harpenden Growth Study. Prediction equations were generated using raw and natural log-transformed data and modeled using panel regression, which accounts for serial autocorrelation of longitudinal observations. Predictive accuracy was gauged with a global sample of human juveniles (n = 530 age- and sex-specific annual means) and compared to the performance of the adult morphometric prediction equation previously identified as most accurate for human juveniles. While the raw data panel equation is only slightly more accurate than the adult equation, the logged data panel equation generates very accurate body mass predictions across both sexes and all age classes of the test sample (mean absolute percentage prediction error = 2.47). The logged data panel equation should prove useful in archaeological, forensic, and paleontological contexts when predictor variables can be measured with confidence and are outside the range of modern adult humans.

Use of Fourier transform infrared spectroscopy for monitoring the shelf life of ham slices packed with probiotic supplemented edible films after treatment with high pressure processing

The aim of the present study was to investigate the potential use of Fourier transform infrared (FTIR) spectroscopy to quantify biochemical changes occurring in ham slices packed with probiotic supplemented edible films and treated with High Pressure Processing (HPP), in monitoring spoilage. Details regarding the data collection and experimental procedure were presented by Pavli et al. (2017). A series of Partial Least Squares (PLS) models were developed to correlate spectral data from FTIR analysis with ham spoilage during storage under vacuum at different temperatures (4, 8 and 12°C). FTIR spectra were collected from the surface of the ham samples in parallel with microbiological analysis of total viable counts (TVC) and lactic acid bacteria (LAB). Qualitative interpretation of spectral data was based on a sensory evaluation, using a hedonic scale, classifying the samples in three quality classes, fresh, semi-fresh and spoiled. The scope of the modeling approach was to discriminate the ham slices in their respective quality class and additionally to predict the microbial population directly from spectral data. The results obtained demonstrated that the processing of the samples affected the performance of classification in the sensory classes, with better results observed in the case of for ham slices packed with probiotic supplemented (PS) edible films and of control samples without HPP. The performance of PLS regression models on providing quantitative estimations of microbial counts were based on specific figures of merit (bias factor, accuracy factor, root mean square error, percentage of prediction error). Bias and accuracy factors were close to unity for both microbial groups tested for samples without HPP, whereas for HPP treated samples the values of these indices ranged from 0.963 to 1.332, depending on the case and indice. The results of this study demonstrated for the first time that although FTIR can be used reliably for the rapid assessment of sliced ham, additional processes such as HPP can affect its performance.

Clinical validation study of dried blood spot for determining everolimus concentration in patients with cancer

PurposeEverolimus treatment is seriously hampered by its toxicity profile. As a relationship between everolimus exposure and effectiveness and toxicity has been established, early and ongoing concentration measurement can be key to individualize the dose and optimize treatment outcomes. Dried blood spot (DBS) facilitates sampling at a patients’ home and thereby eases dose individualization. The aim of this study is to determine the agreement and predictive performance of DBS compared to whole blood (WB) to measure everolimus concentrations in cancer patients.MethodsPaired DBS and WB samples were collected in 22 cancer patients treated with everolimus and analyzed using UPLC-MS/MS. Bland-Altman and Passing-Bablok analysis were used to determine method agreement. Limits of clinical relevance were set at a difference of ± 25%, as this would lead to a different dosing advice. Using DBS concentration and Passing-Bablok regression analysis, WB concentrations were predicted.ResultsSamples of 20 patients were suitable for analysis. Bland-Altman analysis showed a mean ratio of everolimus WB to DBS concentrations of 0.90, with 95% of data points within limits of clinical relevance. Passing-Bablok regression of DBS compared to WB revealed no constant bias (intercept 0.02; 95% CI 0.93–1.35) and a small proportional bias (slope 0.89; 95% CI 0.76–0.99). Predicted concentrations showed low bias and imprecision and 90% of samples had an absolute percentage prediction error of < 20%.ConclusionsDBS is a valid method to determine everolimus concentrations in cancer patients. This can especially be of value for early recognition of over- or underexposure to enable dose adaptations.

Assessing the accuracy of body mass estimation equations from pelvic and femoral variables among modern British women of known mass

Femoral head diameter is commonly used to estimate body mass from the skeleton. The three most frequently employed methods, designed by Ruff, Grine, and McHenry, were developed using different populations to address different research questions. They were not specifically designed for application to female remains, and their accuracy for this purpose has rarely been assessed or compared in living populations. This study analyzes the accuracy of these methods using a sample of modern British women through the use of pelvic CT scans (n = 97) and corresponding information about the individuals' known height and weight. Results showed that all methods provided reasonably accurate body mass estimates (average percent prediction errors under 20%) for the normal weight and overweight subsamples, but were inaccurate for the obese and underweight subsamples (average percent prediction errors over 20%). When women of all body mass categories were combined, the methods provided reasonable estimates (average percent prediction errors between 16 and 18%). The results demonstrate that different methods provide more accurate results within specific body mass index (BMI) ranges. The McHenry Equation provided the most accurate estimation for women of small body size, while the original Ruff Equation is most likely to be accurate if the individual was obese or severely obese. The refined Ruff Equation was the most accurate predictor of body mass on average for the entire sample, indicating that it should be utilized when there is no knowledge of the individual's body size or if the individual is assumed to be of a normal body size. The study also revealed a correlation between pubis length and body mass, and an equation for body mass estimation using pubis length was accurate in a dummy sample, suggesting that pubis length can also be used to acquire reliable body mass estimates. This has implications for how we interpret body mass in fossil hominins and has particular relevance to the interpretation of the long pubic ramus that is characteristic of Neandertals.

Solid formulation of a supersaturable self-microemulsifying drug delivery system for valsartan with improved dissolution and bioavailability.

In order to improve the dissolution and oral bioavailability of valsartan (VST), and reduce the required volume for treatment, we previously formulated a supersaturable self-microemulsifying drug delivery system (SuSMEDDS) composed of VST (80 mg), Capmul® MCM (13.2 mg), Tween® 80 (59.2 mg), Transcutol® P (59.2 mg), and Poloxamer 407 (13.2 mg). In the present study, by using Florite® PS-10 (119.1 mg) and Vivapur® 105 (105.6 mg) as solid carriers, VST-loaded solidified SuSMEDDS (S-SuSMEDDS) granules were successfully developed, which possessed good flow properties and rapid drug dissolution. By introducing croscarmellose sodium (31 mg) as a superdisintegrant, S-SuSMEDDS tablets were also successfully formulated, which showed fast disintegration and high dissolution efficiency. Preparation of granules and tablets was successfully optimized using D-optimal mixture design and 3-level factorial design, respectively, resulting in percentage prediction errors of <10%. In pharmacokinetic studies in rats, the relative bioavailability of the optimized granules was 107% and 222% of values obtained for SuSMEDDS and Diovan® powder, respectively. Therefore, we conclude that novel S-SuSMEDDS formulations offer great potential for developing solid dosage forms of a liquefied formulation such as SuSMEDDS, while improving oral absorption of drugs with poor water solubility.

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.

Body mass estimation in hominoids: Age and locomotor effects

While there are a number of methods available for estimation of body mass in adult nonhuman primates, very few are available for juveniles, despite the potential utility of such estimations in both analyses of fossils and in museum collection based research. Furthermore, because of possible scaling differences, adult based body mass estimation equations may not be appropriate for non-adults. In this study, we present new body mass estimation equations for both adult and immature nonhuman hominoids based on joint and metaphyseal dimensions. Articular breadths of the proximal and distal femur, distal humerus and tibial plateau, and metaphyseal breadths of the distal femur and humerus were collected on a reference sample of 159 wild Pan, Gorilla, Pongo, Hylobates, and Symphalangus specimens of known body mass from museum and research collections. Scaling of dimensions with body weight was assessed in both the adult and the ontogenetic sample at several taxonomic levels using reduced major axis regression, followed by regression of each dimension against body mass to generate body mass estimation equations. Joint dimensions were found to be good predictors of body mass in both adult and immature hominoids, with percent prediction errors of 10–20%. However, subtle scaling differences between taxa impacted body mass estimation, suggesting that phylogeny and locomotor effects should be considered when selecting reference samples. Unlike patterns of joint growth in humans, there was little conclusive evidence for consistently larger joints relative to body mass in the non-adult sample. Metaphyseal breadths were strong predictors of body mass and, with some exceptions, gave more precise body mass estimates for non-adults than epiphyseal breadths.

Dried Blood Spot Sampling for Tacrolimus and Mycophenolic Acid in Children: Analytical and Clinical Validation.

Tacrolimus and mycophenolic acid (MPA) are the backbone of immunosuppressive therapy after pediatric kidney transplantation. Dosing of these drugs is individualized by therapeutic drug monitoring. Dried blood spot (DBS) sampling may prove beneficial over conventional venous sampling. We aimed to develop and clinically validate a DBS method for tacrolimus and MPA in children. A joint DBS liquid chromatography-mass spectrometry assay for tacrolimus and MPA was developed. DBS-specific items included the hematocrit effect and influence of spot volume. Subsequently, a clinical validation study among children aged 2-18 years was performed to assess the agreement between observed and DBS-predicted venous concentrations. Agreement of the methods was assessed with Passing-Bablok regression, Bland-Altman plots, and quantification of the DBS predictive performance in terms of bias (median percentage prediction error) and precision (median absolute percentage prediction error), both should be <15%. A total of 40 tacrolimus and 32 MPA samples were available from 28 children. Conversion factors were used to predict venous concentrations from DBS. For tacrolimus, 95% of the individual ratios of predicted and observed concentrations were within a range of 0.74-1.28, with 85% of these ratios between 0.80 and 1.20 (Bland-Altman plots). For MPA, the 95% limits of agreement represented a broader range of 0.49-1.49%, and 72% of individual ratios were between the 0.80 and 1.20 limits. Median percentage prediction error and median absolute percentage prediction error were less than 15% for both drugs. A DBS assay was developed for tacrolimus and MPA. Tacrolimus venous concentrations could be adequately predicted from DBS. DBS analysis of MPA seemed to be a semiquantitative measurement at the most when compared with conventional plasma analysis, considering the high variability between observed and predicted concentrations. Next, home-based DBS sampling of tacrolimus for the purpose of therapeutic drug monitoring will be implemented into routine clinical care.

Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors

Based on the 212 re-measured permanent plots for natural Betula platyphylla fore-sts in Daxing'an Mountains and Xiaoxing'an Mountains and 30 meteorological stations data, an individual tree growth model based on meteorological factors was constructed. The differences of stand and meteorological factors between Daxing'an Mountains and Xiaoxing'an Mountains were analyzed and the diameter increment model including the regional effects was developed by dummy variable approach. The results showed that the minimum temperature (Tg min) and mean precipitation (Pg m) in growing season were the main meteorological factors which affected the diameter increment in the two study areas. Tg min and Pg m were positively correlated with the diameter increment, but the influence strength of Tg min was obviously different between the two research areas. The adjusted coefficient of determination (Ra2) of the diameter increment model with meteorological factors was 0.56 and had an 11% increase compared to the one without meteorological factors. It was concluded that meteorological factors could well explain the diameter increment of B. platyphylla. Ra2 of the model with regional effects was 0.59, and increased by 18% compared to the one without regional effects, and effectively solved the incompatible problem of parameters between the two research areas. The validation results showed that the individual tree diameter growth model with regional effect had the best prediction accuracy in estimating the diameter increment of B. platyphylla. The mean error, mean absolute error, mean error percent and mean prediction error percent were 0.0086, 0.4476, 5.8% and 20.0%, respectively. Overall, dummy variable model of individual tree diameter increment based on meteorological factors could well describe the diameter increment process of natural B. platyphylla in Daxing'an Mountains and Xiaoxing'an Mountains.

Alternative methods for calculating percentage prediction error and their implications for predicting body mass in fossil taxa

Since body mass covaries with many ecological aspects of an animal, body mass prediction of fossil taxa is a frequent goal of paleontologists. Body mass prediction often relies on a body mass prediction equation (BMPE): a bivariate relationship between a predictor variable (e.g., molar occlusal area, femoral head breadth) and body mass as observed in extant taxa. A variety of metrics have been used to assess the reliability of BMPEs, including percentage prediction error (%PE), which involves predicting body masses of a test sample comprising individuals with associated masses. A mean %PE can be calculated in two ways: 1) as the mean %PE of multiple individual predictions (%MPE), or 2) as the %PE of mean body mass generated from the mean predictor value of multiple individuals (here termed %PEM). Differences between these two approaches have never been formally examined and no formal protocols have been recommended. Using a large sample of cercopithecoid primates (406 individuals from 50 species/subspecies) with associated body masses, body mass is predicted with six previously published interspecific BMPEs. Both %MPE and %PEM are calculated and compared. For all BMPEs, the distributions of differences between %MPE and %PEM exhibit positive skew and have medians significantly greater than zero, indicating that the examined prediction equations are more accurate at predicting mean body mass when they are applied to mean predictor values. The decreased predictive accuracy of %MPE relative to %PEM likely stems from changing the unit of analysis from mean values (in the reference sample) to individual values (in the test sample) when calculating %MPE. Empirical results are supported with a simulated dataset. Implications for body mass prediction in fossil species are discussed.

A Comparison Study of Learning Algorithms for Estimating Fault Location

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current signal acted as traveling wave fault location technique. Six types commonly used backpropagation training algorithm were selected including the Levenberg-Marquardt, Bayesian Regulation, Conjugate gradient backpropagation with Powell-Beale restarts, BFGS quasi-Newton, Conjugate gradient backpropagation with Polak-Ribiere updates and Conjugate gradient backpropagation with Fletcher-Reeves updates. The proposed fault location method is tested with varying fault location, fault types, fault resistance and inception angle. The performance of each training algorithm is evaluated by goodness-of-fit (R&lt;sup&gt;2&lt;/sup&gt;), mean square error (MSE) and Percentage prediction error (PPE). Simulation results show that the best of training algorithm for estimating fault location is Bayesian Regulation (R&lt;sup&gt;2 &lt;/sup&gt;= 1.0, MSE = 0.034557 and PPE = 0.014%).

Reuniting the “head hunted” Corythosaurus excavatus (Dinosauria: Hadrosauridae) holotype skull with its dentary and postcranium

A Corythosaurus skull (UALVP 13) was collected in 1920 from what is now Dinosaur Provincial Park in southern Alberta, Canada and was designated the holotype of Corythosaurus excavatus, a new hadrosaur species by Gilmore (1923). In 1992, a previously uncovered, weathered, headless skeleton was found. This was collected in 2012 by the University of Alberta as there was potential it could belong to the holotype skull. In addition, an isolated hadrosaur dentary had been found in 1992 close to the articulated postcranial skeleton and may be one of the missing jaws of Corythosaurus excavatus. The hypothesis that it may be the skeleton of the holotype of Corythosaurus excavatus is tested using anatomical information and statistical analyses. Statistical comparisons suggest it is possible that the skull and dentary belong to the same specimen. Furthermore, bivariate plots and percentage prediction errors also indicate that the postcranial material could belong to the UALVP 13 skull. Because many large vertebrate fossil specimens (even types) are not always collected in their entirety, this method may be used as a line of evidence to determine whether independently collected specimens potentially belong to the same individual. The problems described herein highlight the need to collect specimens in their entirety, and for good field documentation, including the spatial and stratigraphic context of all finds.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs.