Mean Relative Prediction Error Research Articles

A single Bayesian estimator for iohexol clearance estimation in ICU, liver failure and renal transplant patients.

Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients. Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into development (n = 136) and validation (n = 36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performance for GFR estimation was evaluated in the validation set. A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9h exhibiting a relative mean prediction error (MPE) (RMSE) = -3.7% (14.3%) and better performance than the Bröchner-Mortensen formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients (MPE (RMSE) = 3.7% (18.8%)) while the best performances were obtained for renal transplant patients (MPE (RMSE) = 1.0% (5.8%)). All LSS that included samples before 9hours for the third sample were associated with an increased imprecision. A single MAP-BE of iohexol based on a three-sample LSS for four heterogeneous populations was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients.

How much are Canadians willing to pay for clean surface and ground water? A meta-analysis of the Canadian non-market valuation literature

Three decades of non-market water quality valuation (NMWQV) studies in Canada are analyzed to generate a generic benefits transfer function. Contrary to the large valuation literature focusing on water and wilderness-based recreation in Canada, the number of studies related to water quality is limited. NMWQV studies lack a common design, including consistent adherence to a Canada-specific water quality ladder (WQL). Despite the high degree of data heterogeneity, values extracted from the literature show an increasing step function when relating them to the Resources for the Future WQL. Meta-regression models (MRMs) explain a large share of the variation in value estimates based on the type of water resources, population and methodological characteristics. Baseline water quality and the size of the water quality change are significant determinants of the estimated non-market values. With a relative mean prediction error of no more than 20 percent, the predictive power of the estimated MRMs is high. As such, they are an important step forward in the development of a policy-relevant water quality valuation model. However, there is a clear need for the development of more coherent non-market valuation guidelines in the Canadian water context.

Model-Based Estimation of Iohexol Plasma Clearance for Pragmatic Renal Function Determination in the Renal Transplantation Setting

BackgroundIohexol plasma clearance-based glomerular filtration rate (GFR) determination provides an accurate method for renal function evaluation. This technique is increasingly advocated for clinical situations that dictate highly accurate renal function assessment, as an alternative to conventional serum creatinine-based methods with limited accuracy or poor feasibility. In the renal transplantation setting, this particularly applies to living renal transplant donor eligibility screening, renal transplant function monitoring and research purposes. The dependency of current iohexol GFR estimation techniques on extensive sampling, however, has limited its clinical application. We developed a population pharmacokinetic model and limited sampling schedules, implemented in the online InsightRX precision dosing platform, to facilitate pragmatic iohexol GFR assessment.MethodsIohexol concentrations (n = 587) drawn 5 min to 4 h after administration were available from 67 renal transplant recipients and 41 living renal transplant donor candidates with measured iohexol GFRs of 27–117 mL/min/1.73 m2. These were split into a model development (n = 72) cohort and an internal validation (n = 36) cohort. External validation was performed with 1040 iohexol concentrations from 268 renal transplant recipients drawn between 5 min and 4 h after administration, and extended iohexol curves up to 24 h from 11 random patients with impaired renal function. Limited sampling schedules based on one to four blood draws within 4 h after iohexol administration were evaluated in terms of bias and imprecision, using the mean relative prediction error and mean absolute relative prediction error. The total deviation index and percentage of limited sampling schedule-based GFR predictions within ± 10% of those of the full model (P10) were assessed to aid interpretation.ResultsIohexol pharmacokinetics was best described with a two-compartmental first-order elimination model, allometrically scaled to fat-free mass, with patient type as a covariate on clearance and the central distribution volume. Model validity was confirmed during the internal and external validation. Various limited sampling schedules based on three to four blood draws within 4 h showed excellent predictive performance (mean relative prediction error < ± 0.5%, mean absolute relative prediction error < 3.5%, total deviation index < 5.5%, P10 > 97%). The best limited sampling schedules based on three to four blood draws within 3 h showed reduced predictive performance (mean relative prediction error < ± 0.75%, mean absolute relative prediction error < 5.5%, total deviation index < 9.5%, P10 ≥ 85%), but may be considered for their enhanced clinical feasibility when deemed justified.ConclusionsOur online pharmacometric tool provides an accurate, pragmatic, and ready-to-use technique for measured GFR-based renal function evaluation for clinical situations where conventional methods lack accuracy or show limited feasibility. Additional adaptation and validation of our model and limited sampling schedules for renal transplant recipients with GFRs below 30 mL/min is warranted before considering this technique in these patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-021-00998-z.

Mapping high resolution national daily NO2 exposure across mainland China using an ensemble algorithm.

Nitrogen dioxide (NO2) is an important air pollutant and highly related to air quality, short- and long-term health effects, and even climate. A national model was developed using the extreme gradient boosting algorithm with high-resolution tropospheric vertical column NO2 densities from the Sentinel-5 Precursor/Tropospheric Monitoring Instrument and general meteorological variables as input to generate daily mean surface NO2 concentrations across mainland China. Model-derived daily NO2 estimates were high accuracy with sample-based cross-validation coefficient of determination of 0.83, a root-mean-square error of 7.58μg/m3, a mean prediction error of 5.56μg/m3, and a mean relative prediction error of 18.08%. It has good performance in NO2 estimations at both regional and individual site scale. The model also performed well in terms of estimating monthly, seasonal, and annual mean NO2 concentrations across China. The model performance appears to better than or comparable to most previous related studies. The seasonal and annual spatial distributions of surface NO2 across China and several regional NO2 hotspots in 2019 were derived from the model and analyzed. Also evaluated were the population exposure levels of NO2 for cities in and provinces of China. At the national scale, about 12% of the population experienced annual mean NO2 concentrations exceeding the Chinese national air quality standard. The nationwide model with conventional predictors developed here can derive high-resolution surface NO2 concentrations across China routinely, benefitting air epidemiological and environmental related studies.

Threshold values to discriminate grazing activity of dairy cows by an uni-axial accelerometer as affected by grazing season and herbage mass

Recording grazing time is important because the time allocated for each grazing event is one of the limitations to achieving high intake levels. Grazing time can be measured via visual observation or, more recently, by devices calibrated for this purpose. The Kenz Lifecorder Plus (Suzuken Co. Ltda., Nagoya, Japan) has been used to record daily grazing time with high accuracy, but none of the previous studies have analyzed how pasture characteristics and grazing management influence the recording sensitivity of this device. Thus, the aim of this study was to evaluate the effect of pre-grazing herbage mass and season of the year on the activity level recorded by the Lifecorder Plus and its resulting threshold value used to determine a grazing event for dairy cows. A randomized 2 × 2 factorial design was used to evaluate the effect of two pre-grazing herbage masses (HM), medium herbage mass (MHM) and high herbage mass (HHM), in two seasons, winter (WIN) and spring (SPR), on the observed and predicted grazing time. In each period, two groups of six Holstein Friesian cows were fitted with a Lifecorder Plus in a collar attached to their neck. The pasture characteristics and observed and predicted grazing times were measured. There was no effect (P > 0.05) of HM on the observed grazing time. The grazing time in SPR was significantly higher (P < 0.001) than that in WIN. There was no effect (P> 0.05) of HM on the threshold value. The threshold value for SPR was significantly higher (P < 0.001) than that for WIN. The mean relative prediction error of all treatments was 0.07, which implies a precision of 0.93 in grazing detection and demonstrates a high level of precision in the estimation of daily grazing time. Determining an independent threshold value for each treatment was necessary to obtain a high degree of precision in estimating grazing time. In conclusion, changes in the structural sward characteristics caused by grazing management or the annual cycle of growth of the pasture affect both the activity level recorded using a Lifecorder Plus and the threshold value obtained to detect grazing events in dairy cows. When studies incorporate factors that might alter grazing behavior, a specific threshold value for the study conditions needs to be determined to estimate the grazing time of dairy cows with higher precision.

Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics.

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080cm-1 showed best results for prediction sample sets in terms of high R2 and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.

Evaluation of Extrapolation Methods to Predict Trough Concentrations to Guide Therapeutic Drug Monitoring of Oral Anticancer Drugs.

For oral anticancer drugs, trough concentration (Cmin) is usually used as a target in therapeutic drug monitoring (TDM). Recording of Cmin is highly challenging in outpatients, in whom there is typically a variability in sample collection time after dosing. Various methods are used to estimate Cmin from the collected samples. This simulation study aimed to evaluate the performance of 3 different methods in estimating the Cmin of 4 oral anticancer drugs for which TDM is regularly performed. Plasma concentrations of abiraterone, dabrafenib, imatinib, and pazopanib at a random time (Ct,sim) and at the end of the dosing interval (Cmin,sim) were simulated from population pharmacokinetic models including 1000 patients, and the values were converted into simulated observed concentrations (Ct,sim,obs and Cmin,sim,obs) by adding a residual error. From Ct, sim,obs, Cmin was predicted (Cmin,pred) by the Bayesian estimation (method 1), taking the ratio of the Ct,sim,obs and typical population concentration and multiplying this ratio with the typical population value of Cmin,sim (method 2), and log-linear extrapolation (method 3). Target attainment was assessed by comparing Cmin,pred with the proposed pharmacokinetic targets related to efficacy and calculating the positive predictive and negative predictive values. The mean relative prediction error and root mean squared relative prediction error results showed that method 3 was out-performed by method 1 and 2. Target attainment was adequately predicted by all 3 methods (the respective positive predictive value of method 1, 2, and 3 was 92.1%, 92.5%, and 93.1% for abiraterone; 87.3%, 86.9%, and 99.1% for dabrafenib; 79.3%, 79.3%, and 75.9% for imatinib; and 72.5%, 73.5%, and 67.6% for pazopanib), indicating that dose adjustments were correctly predicted. Both method 1 and 2 provided accurate and precise individual Cmin,pred values. However, method 2 was easier to implement than method 1 to guide individual dose adjustments in TDM programs.

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling.

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO2 in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.

QSPR Models for Predicting of the Melting Points and Refractive Indices for Inorganic Substances

The QSPR methodology is very promising for the creation of new materials, including materials based on inorganic compounds. However, the majority of QSPR descriptor systems are applicable only for organic molecules. In this work the 1D - QSPR descriptor system is proposed for analysis of the properties of various inorganic compounds. These descriptors are easily accessible, as they describe the most fundamental atom properties. The combinatorial schemes for computing these descriptors provide for their wide variety. The effectiveness of the proposed approach has been demonstrated to study the refractive indices and melting points of various inorganic compounds - components of potential optical film-forming materials. The developed QSPR models are suitable for the evaluative virtual screening of inorganic compounds; the mean relative error of prediction is 6 - 15%. The interpretation of the developed models reflects the nature of interatomic interactions in compounds with ionic structure.

Real-time modelling of indoor particulate matter concentration in poultry houses using broiler activity and ventilation rate

Measuring particulate matter concentration in poultry houses remains as a difficult task, primarily because aerosol analysers are expensive, require specialist knowledge to operate and are labour intensive to maintain. However, it is well known that high concentrations of particulate matter causes health and welfare problems with livestock, farm workers and people living in the vicinity of the farm premises. In this work, a data-based mechanistic model is developed to relate broiler activity and ventilation rate with indoor particulate matter concentration. For six complete growing cycles, in a U.K. commercial poultry farm, broiler activity was monitored using a camera-based flock monitoring system (eYeNamic®) and ventilation rate was measured. Indoor particulate matter concentration was continuously monitored by measuring size-segregated mass fraction concentrations with the aerosol analyser DustTrak™. A discrete-time multi-input single-output time-invariant parameters Transfer Function model was developed to determine the particulate dynamics within each day of the growing cycle in the poultry house using broiler activity and ventilation rate as inputs. This model monitored indoor particulate matter concentration with an average accuracy of R T 2 = ( 51 ± 26 ) % . A dynamic linear regression modelling with time-variant parameters improved average accuracy with R T 2 = ( 97.7 ± 1.3 ) % . It forecasted one sample-ahead the indoor particulate matter concentration level, using a time window of 14 samples, with a mean relative prediction error, M R P E = ( 4.6 ± 3.2 ) % . Thus, dynamic modelling with time-variant parameters has the potential to be part of a control system to manage in real-time indoor particulate matter concentration in broiler houses.

Design and simulation of a three-detector instrument for accurate neutron H*(10) and energy distribution calculation

Many efforts have been made to improve the energy response of neutron rem meters to match the fluence to ambient dose equivalent conversion coefficients(DCC) accurately. In this work, a design of a portable instrument based on three thermal neutron detectors is proposed and simulated. The detectors are embedded in different depth along the cylindrical high density polyethylene moderator and their count rates are recorded simultaneously. Two different H*(10) calculation methods have been introduced. One is linear correction method based on the combination of detector responses to approximate the DCC curve provided in ICRP74. The other is spectrum unfolding method which unfold the neutron spectrum at the measuring point for further H*(10) calculation. Monte Carlo simulations have been applied to calculate the response matrices of the three detectors. And standard neutron spectra are used to evaluate the performance of the instrument. As the results indicate, linear correction method is accurate and stable for H*(10) estimation, with mean relative prediction error of 8.45% for tested spectra. Spectrum unfolding method is not stable and have large H*(10) estimation error in some cases, but it can simultaneously derive neutron energy distributions with acceptable approximate degree. The instrument is highly suitable for H*(10) measurement for one-directional parallel neutron beam as well as isotropic neutron source with determined location. And by applying an absorber shell or detector arrays, the angular influence for the instrument is effectively improved to obtain more accurate H*(10) estimation results.

Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence.

Sensor faults can impede the functionality of monitoring and control systems for bioprocesses. Hence, suitable systems need to be developed to validate the sensor readings prior to their use in monitoring and control systems. This study presents a novel approach for online validation of sensor readings. The basic idea is to compare the original sensor reading with predictions for this sensor reading based on the remaining sensor network's information. Deviations between original and predicted sensor readings are used to indicate sensor faults. Since especially batch processes show varying lengths and different phases (e.g., lag and exponential phase), prediction models that are dependent on process time are necessary. The binary particle swarm optimization algorithm is used to select the best prediction models for each time step. A regularization approach is utilized to avoid overfitting. Models with high complexity and prediction errors are penalized, resulting in optimal predictions for the sensor reading at each time step (5% mean relative prediction error). The sensor reliability is calculated by the Kullback-Leibler divergence between the distribution of model-based predictions and the distribution of a moving window of original sensor readings (moving window size = 10 readings). The developed system allows for the online detection of sensor faults. This is especially important when sensor data are used as input to soft sensors for critical quality attributes or the process control system. The proof-of-concept is exemplarily shown for a turbidity sensor that is used to monitor a Pichia pastoris-batch process.

Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day−1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

Real-time modelling of individual weight response to feed supply for fattening pigs

Precision feeding is a promising technique to achieve better feed efficiencies for livestock and realise production results closer to the genetic potential of the animal. Continuous and automatic monitoring of growth of individual finishing pigs is an essential element of a precision feeding system. For optimal pig growth, the feed nutrients need to be adjusted at different moments in time throughout the fattening period. In an integrated system, the key element is the prediction of the process output (weight) to a variation of the process input (feed supply). During the course of this study, three experiments were performed, with 80 pigs on average in each of them, to measure and model their growth responses to step changes in feed amount and/or feed composition along the production cycle. The individual dynamic responses of pig growth to feed changes are monitored by gathering daily weight and feed supply data for each individual pig. The time-series data is analysed using Transfer Function (TF) and Dynamic Linear Regression (DLR) models. On one hand, the average TF model fitting agreement is (RT2=94 ± 4)%. On the other hand, the Mean Relative Prediction Error (MRPE) of applying the DLR approach with a window size of four and seven days was found to be, MRPE = (1.0 ± 0.4)%, and MRPE = (3.3 ± 1.3)% for a forecasting horizon of one and seven days, respectively. Moreover, a parameter of the DLR model resembles the feed efficiency of the pig, exhibiting a coefficient of correlation, r = (0.9 ± 0.2). While the current models may need further validation, the approach seems promising to be implemented in an individual integrated pig feeding system.

Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate

This article presents parametric analysis and subsequent optimization of a CO2 trans-critical chiller system operating in a warm climate. High side pressure and gas cooler face velocities are two controllable parameters investigated. COP of the system is analyzed and optimized using a developed and validated mathematical model. The mean relative error of prediction in COP is found to be within ±10% for physics-based model and within ±1% for Artificial Neural Network (ANN) based model of the experimental findings. The validated mathematical model is utilized to predict optimal high side pressure as well as gas cooler face velocity for the varying ambient and evaporation conditions to achieve best possible COP. A possibility of 5.31% improvement in COP is found based on the optimization of parameters. The proposed methodology is deemed suitable for design and testing of control system for maximization of energy efficiency.

Monitoring of soil moisture and irrigation forecast under the condition of drip irrigation in greenhouse

The accurate prediction of the soil moisture in greenhouse can improve the accuracy of irrigation forecast, thus saving water and increasing production. In this paper, the monitoring data were obtained from the greenhouse in Agricultural Demonstration Park of Northland Spring, Wuqing District, Tianjin. The soil moisture was predicted based on the GM (1,1) model, and the effect of water-saving and increased production was tested with the results of the economic irrigation research in the same area. The results show that the GM (1,1) model has a higher accuracy of soil moisture prediction. The correlation coefficient (R2) of the predicted value and the measured value is higher than 0.9; the small error probability P is 1; the mean relative prediction errors are all less than 0.1. Therefore, the GM (1,1) model can be used to predict soil moisture content.

Characterization of the Temporal Trends in the Rate of Cattle Carcass Condemnations in the US and Dynamic Modeling of the Condemnation Reasons in California With a Seasonal Component.

Based on the 2016 National Cattlemen's Beef Association statistics, the cattle inventory in the US reached 93.5 million head, from which 30.5 million were commercial slaughter in 2016. California ranked fourth among all the US states that raise cattle and calves, with 5.15 million head and approximately 1.18 million slaughtered animals per year. Approximately 0.5% of cattle carcasses in the US are condemned each year, which has an important economic impact on cattle producers.In this study, we first described and compared the temporal trends of cattle carcass condemnations in all the US states from Jan-2005 to Dec-2014. Then, we focused on the condemnation reasons with a seasonal component in California and used dynamic harmonic regression (DHR) models both to model (from Jan-2005 to Dec-2011) and predict (from Jan-2012 to Dec-2014) the carcass condemnations rate in different time horizons (3 to 12 months).Data consisted of daily reports of 35 condemnation reasons per cattle type reported in 684 federally inspected slaughterhouses in the US from Jan-2005 to Dec-2014 and the monthly slaughtered animals per cattle type per states. Almost 1.5 million carcasses were condemned in the US during the 10 year study period (Jan 2005-Dec 2014), and around 40% were associated with three condemnation reasons: malignant lymphoma, septicemia and pneumonia. In California, emaciation, eosinophilic myositis and malignant lymphoma were the only condemnation reasons presenting seasonality and, therefore, the only ones selected to be modeled using DHRs. The DHR models for Jan-2005 to Dec-2011 were able to correctly model the dynamics of the emaciation, malignant lymphoma and eosinophilic myositis condemnation rates with coefficient of determination () of 0.98, 0.87 and 0.78, respectively. The DHR models for Jan-2012 to Dec-2014 were able to predict the rate of condemned carcasses 3 month ahead of time with mean relative prediction error of 33, 11, and 38%, respectively. The systematic analysis of carcass condemnations and slaughter data in a more real-time fashion could be used to identify changes in carcass condemnation trends and more timely support the implementation of prevention and mitigation strategies that reduce the number of carcass condemnations in the US.

Studies of the relationship between rice stem composition and lodging resistance

AbstractPlant height and lodging resistance can affect rice yield significantly, but these traits have always conflicted in crop cultivation and breeding. The current study aimed to establish a rapid and accurate plant type evaluation mechanism to provide a basis for breeding tall but lodging-resistant super rice varieties. A comprehensive approach integrating plant anatomy and histochemistry was used to investigate variations in flexural strength (a material property, defined as the stress in a material just before it yields in a flexure test) of the rice stem and the lodging index of 15 rice accessions at different growth stages to understand trends in these parameters and the potential factors influencing them. Rice stem anatomical structure was observed and the lignin content the cell wall was determined at different developmental stages. Three rice lodging evaluation models were established using correlation analysis, multivariate regression and artificial radial basis function (RBF) neural network analysis, and the results were compared to identify the most suitable model for predicting optimal rice plant types. Among the three evaluation methods, the mean residual and relative prediction errors were lowest using the RBF network, indicating that it was highly accurate and robust and could be used to establish a mathematical model of the morphological characteristics and lodging resistance of rice to identify optimal varieties.

External validation of the GrazeIn model of pasture dry matter intake and milk yield prediction for cows managed at different calving dates and stocking rates

The aim was to evaluate the prediction accuracy of pasture dry matter intake (PDMI) and milk yield (MY) predicted by the GrazeIn model using a database representing 124 PDMI measurements at paddock level and 2232 MY measurements at cow level. External validation of the model was conducted using data collected from a trial carried out with Holstein-Friesian cows (n=72) while grazed 28 paddocks and were managed in a 2×2 factorial design by considering two calving dates (CD), with different number of days in milk (DIM), early (E, 29 DIM) vs. middle (M, 167 DIM), and two stocking rates (SR), medium (M, 3.9 cows ha-1) vs. high (H, 4.8 cows ha-1), under a rotational grazing system. Cows were randomly assigned to four grazing scenarios (EM, EH, MM and MH). The mean observed PDMI of the total database was 14.2 kg DM cow-1 day-1 while GrazeIn predicted a mean PDMI for the database of 13.8 kg DM cow-1 day-1. The mean bias was −0.4 kg DM cow-1 day-1. GrazeIn predicted PDMI for the total database with a relative prediction error (RPE) of 10.0% at paddock level. The mean observed MY of the database was 23.2 kg cow-1 day-1 while GrazeIn predicted a MY for the database of 23.1 kg cow-1 day-1. The mean bias was –0.1 kg cow-1 day-1. GrazeIn predicted MY for the total database with a mean RPE of 17.3% at cow level. For the scenarios investigated, GrazeIn predicted PDMI and MY with a low level of error which made it a suitable tool for decision support systems.

Hybrid Prediction Model of the Temperature Field of a Motorized Spindle

The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy.