Rainfall patterns in a major wheat-growing region of Australia

1993 ◽  
Vol 44 (4) ◽  
pp. 609 ◽  
Author(s):  
R Boer ◽  
DJ Fletcher ◽  
LC Campbell
Keyword(s):  
Prediction Error ◽  
Summer Rainfall ◽  
Important Variable ◽  
Regression Equations ◽  
Winter Rainfall ◽  
Rainfall Prediction ◽  
The Mean ◽  
Production Areas ◽  
Growing Region ◽  
Mean Prediction Error

Rainfall is an important variable in the wheat production areas of Australia. This analysis examines, firstly, the pattern of rainfall over 2.3 million ha of a high-quality wheat-producing region, and secondly, develops regression equations for rainfall prediction over this region. Most of the variation in rainfall pattern across the region is accounted for by differences in October-to-March (summer) rainfall and in April-to-September (winter) rainfall. The summer rainfall differences account for over two thirds of the variation. Based on these two rainfall periods, a partitioning of the study area reveals five distinct regions. The second part of the analysis uses multiple regression to provide a set of equations for rainfall prediction at any location in the region, for a number of rainfall periods. These equations use altitude, longitude and latitude as predictors. Nearly all of the equations explain between 80% and 94% of the variation in rainfall. Differences between regions are accounted for in the analysis, making the equations widely applicable. The validity of the mean rainfall equations was tested on three further sites: the mean prediction error was 6.9%. This approach may be applicable where large land masses with similar geographical features occur.

scholarly journals Does Every Calculation Formula Fit for All Types of Intraocular Lenses? Optimization of Constants for Tecnis ZA9003 and ZCB00 Is Necessary

Medicina ◽  
2021 ◽  
Vol 57 (4) ◽  
pp. 319
Author(s):  
Ivajlo Popov ◽  
Veronika Popova ◽  
Juraj Sekac ◽  
Vladimir Krasnik
Keyword(s):  
Prediction Error ◽  
Poor Performance ◽  
Intraocular Lenses ◽  
Power Calculation ◽  
Universal Formula ◽  
Aspheric Iol ◽  
Different Types ◽  
The Mean ◽  
Clinically Significant ◽  
Mean Prediction Error

Background and Objectives: To evaluate the performance of intraocular lenses (IOLs) using power calculation formulas on different types of IOL. Materials and Methods: 120 eyes and four IOL types (BioLine Yellow Accurate Aspheric IOL (i-Medical), TECNIS ZCB00, TECNIS ZA9003 (Johnson & Johnson) (3-piece IOL) and Softec HD (Lenstec)) were analyzed. The performance of Haigis, Barret Universal II and SKR-II formulas were compared between IOL types. The mean prediction error (ME) and mean absolute prediction error (MAE) were analyzed. Results: The overall percentage of eyes predicted within ±0.25 diopters (D) was 40.8% for Barret; 39.2% Haigis and 31.7% for SRK-II. Barret and Haigis had a significantly lower MAE than SRK-II (p < 0.05). The results differed among IOL types. The largest portion of eyes predicted within ±0.25 D was with the Barret formula in ZCB00 (33.3%) and ZA9003 (43.3%). Haigis was the most accurate in Softec HD (50%) and SRK-II in Biolline Yellow IOL (50%). ZCB00 showed a clinically significant hypermetropic ME compared to other IOLs. Conclusions: In general, Barret formulas had the best performance as a universal formula. However, the formula should be chosen according to the type of IOL in order to obtain the best results. Constant optimizations are necessary for the Tecnis IOL ZCB00 and ZA9003, as all of the analyzed formulas achieved a clinically significant poor performance in this type of IOL. ZCB00 also showed a hypermetropic shift in ME in all the formulas.

Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method

2019 ◽  
Vol 26 (3) ◽  
pp. 543-548
Author(s):  
Toshihisa Nakashima ◽  
Takayuki Ohno ◽  
Keiichi Koido ◽  
Hironobu Hashimoto ◽  
Hiroyuki Terakado
Keyword(s):  
Cancer Patients ◽  
Prediction Error ◽  
Bayesian Method ◽  
Predictive Accuracy ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Squared Prediction Error ◽  
Trough Concentrations ◽  
The Mean ◽  
Mean Prediction Error

Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of predictive accuracy between the Sawchuk–Zaske and Bayesian methods in Japanese cancer patients is not completely understood. Objective To retrospectively compare the accuracy of predicting vancomycin concentrations between the Sawchuk–Zaske method and the Bayesian method in Japanese cancer patients. Methods Using data from 48 patients with various malignancies, the predictive accuracy (bias) and precision of the two methods were assessed by calculating the mean prediction error, the mean absolute prediction error, and the root mean squared prediction error. Results Prediction of the trough and peak vancomycin concentrations by the Sawchuk–Zaske method and the peak concentration by the Bayesian method showed a bias toward low values according to the mean prediction error. However, there were no significant differences between the two methods with regard to the changes of the mean prediction error, mean absolute prediction error, and root mean squared prediction error. Conclusion The Sawchuk–Zaske method and Bayesian method showed similar accuracy for predicting vancomycin concentrations in Japanese cancer patients.

scholarly journals External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 834
Author(s):  
J.J. van Kleef ◽  
H.G. van den Boorn ◽  
R.H.A. Verhoeven ◽  
K. Vanschoenbeek ◽  
A. Abu-Hanna ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prediction Model ◽  
Oesophageal Cancer ◽  
Prediction Error ◽  
Prediction Models ◽  
Mean Difference ◽  
Survival Prediction ◽  
Cancer Model ◽  
The Mean ◽  
Mean Prediction Error

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was −2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

scholarly journals Rainfall Changes over Southwestern Australia and Their Relationship to the Southern Annular Mode and ENSO

2014 ◽  
Vol 27 (15) ◽  
pp. 5801-5814 ◽  
Author(s):  
Bhupendra A. Raut ◽  
Christian Jakob ◽  
Michael J. Reeder
Keyword(s):  
Southern Oscillation ◽  
Summer Rainfall ◽  
Southern Annular Mode ◽  
Winter Rainfall ◽  
Sea Level Pressure ◽  
Annular Mode ◽  
Southwestern Australia ◽  
Synoptic Conditions ◽  
The Mean ◽  
Rainfall Patterns

Abstract Since the 1970s, winter rainfall over coastal southwestern Australia (SWA) has decreased by 10%–20%, while summer rainfall has been increased by 40%–50% in the semiarid inland area. In this paper, a K-means algorithm is used to cluster rainfall patterns directly as opposed to the more conventional approach of clustering synoptic conditions (usually the mean sea level pressure) and inferring the associated rainfall. It is shown that the reduction in the coastal rainfall during winter is mainly due to fewer westerly fronts in June and July. The reduction in the frequency of strong fronts in June is responsible for half of the decreased rainfall in June–August (JJA), whereas the reduction in the frequency of weaker fronts in June and July accounts for a third of the total decrease. The increase in rainfall inland in December–February (DJF) is due to an increased frequency of easterly troughs in December and February. These rainfall patterns are linked to the southern annular mode (SAM) index and Southern Oscillation index (SOI). The reduction in coastal rainfall and the increase in rainfall inland are both related to the predominantly positive phase of SAM, especially when the phase of ENSO is neutral.

scholarly journals A Wavelet-Based Robust Relevance Vector Machine Based on Sensor Data Scheduling Control for Modeling Mine Gas Gushing Forecasting on Virtual Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Wang Ting ◽  
Cai Lin-qin ◽  
Fu Yao ◽  
Zhu Tingcheng
Keyword(s):  
Prediction Error ◽  
Relevance Vector Machine ◽  
Wavelet Function ◽  
Sensor Data ◽  
Percentage Error ◽  
Data Scheduling ◽  
The Mean ◽  
Mine Gas ◽  
Mean Prediction Error ◽  
Better Than

It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5%, and the mean prediction error of mine gas gushing of the RVM model is more than 2.5%, it can be seen that the prediction accuracy for mine gas gushing of the WRRVM model is better than that of the RVM model.

scholarly journals Description of epidemics of coffee rust with neural networks

2002 ◽  
Vol 27 (5) ◽  
pp. 517-524 ◽  
Author(s):  
AUGUSTO CARLOS S. PINTO ◽  
EDSON A. POZZA ◽  
PAULO E. DE SOUZA ◽  
ADÉLIA A. A. POZZA ◽  
VIVIANE TALAMINI ◽  
...  
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Coffea Arabica ◽  
Regression Models ◽  
Stepwise Regression ◽  
Climatic Variables ◽  
Summary Statistics ◽  
Coffee Rust ◽  
The Mean ◽  
Mean Prediction Error

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Impact of changing rainfall conditions on surface and groundwater resources in an experimental watershed in Greece

2013 ◽  
Vol 12 (2) ◽  
pp. 119-125
Keyword(s):  
Groundwater Resources ◽  
Annual Runoff ◽  
Annual Rainfall ◽  
Rainfall Regime ◽  
Winter Rainfall ◽  
Rainfall Depth ◽  
Rainfall Volume ◽  
The Mean ◽  
Groundwater Reserves ◽  
The Impact

The present study concerns the impact of a change in the rainfall regime on surface and groundwater resources in an experimental watershed. The research is conducted in a gauged mountainous watershed (15.18 km2) that is located on the eastern side of Penteli Mountain, in the prefecture of Attica, Greece and the study period concerns the years from 2003 to 2008. The decrease in the annual rainfall depth during the last two hydrological years 2006-2007, 2007-2008 is 10% and 35%, respectively, in relation to the average of the previous years. In addition, the monthly distribution of rainfall is characterized by a distinct decrease in winter rainfall volume. The field measurements show that this change in rainfall conditions has a direct impact on the surface runoff of the watershed, as well as on the groundwater reserves. The mean annual runoff in the last two hydrological years has decreased by 56% and 75% in relation to the average of the previous years. Moreover, the groundwater level follows a declining trend and has dropped significantly in the last two years.

Field assessment of partial resistance to mycosphaerella blight in Pisum subspecies accessions

2012 ◽  
Vol 92 (2) ◽  
pp. 289-296 ◽  
Author(s):  
R. L. Conner ◽  
B. D. Gossen ◽  
S. F. Hwang ◽  
K. F. Chang ◽  
K. B. McRae ◽  
...  
Keyword(s):  
Partial Resistance ◽  
Field Trials ◽  
Field Pea ◽  
Mycosphaerella Pinodes ◽  
Detached Leaf ◽  
High Severity ◽  
Field Assessment ◽  
Ascochyta Pinodes ◽  
The Mean ◽  
Production Areas

Conner, R. L., Gossen, B. D., Hwang, S. F., Chang, K. F., McRae, K. B. and Penner, W. C. 2012. Field assessment of partial resistance to mycosphaerella blight in Pisum subspecies accessions. Can. J. Plant Sci. 92: 289–296. Mycosphaerella blight, caused by Mycosphaerella pinodes (Berk. & Bloxam) Vestergr., the teleomorph of Ascochyta pinodes Jones, is an important foliar disease of field pea in the major production areas of the world. Partial resistance to mycosphaerella blight has been reported in some field pea cultivars, but, at best, they are only moderately susceptible. A 3-yr field study was conducted to evaluate the mycosphaerella blight reactions of 28 accessions from a number of subspecies of Pisum sativum L. and one accession of P. fulvum Sibth. A few of the accessions carried mutations for the genes af, tl, and st that affect the morphology of the leaflets, stipules and tendrils. Reactions to mycosphaerella blight were characterized based on the mean of the severity ratings taken on the two final assessment dates before the crop matured and also on the change in mycosphaerella blight severity between these two dates. In many of the accessions, severity ratings were similar to that of the moderately susceptible check cultivar, CDC Peko, while a few had high severity ratings similar to those of the susceptible check cultivars. The accession PI 512079, which has small stipules, branched petioles with many leaflets but no tendrils, had the lowest ratings for mycosphaerella blight severity. Four other accessions exhibited the smallest change in mycosphaerella blight severity at the end of the growing season. Differences in leaf morphology likely influenced the change in disease severity, since all the semi-leafless and leafless accessions had smaller changes in mycosphaerella blight severity than the susceptible check cultivars. In a detached leaf assay with two isolates of Mycosphaerella pinodes (Berk. & Bloxam) Vestergr., the smallest lesions formed on PI 512079, but otherwise the results failed to show a relationship with the observed severity values in the field trials.

scholarly journals Generalized predictive model of estimation of inhibition of muscarinic receptors M1-M5

2020 ◽  
Vol 3 (3) ◽  
pp. e00129
Author(s):  
A.V. Mikurova ◽  
V.S. Skvortsov ◽  
V.V. Grigoryev
Keyword(s):  
Predictive Model ◽  
Muscarinic Receptors ◽  
Three Dimensional ◽  
Dynamics Simulation ◽  
Dimensional Structure ◽  
Regression Equations ◽  
Energy Characteristics ◽  
Value Prediction ◽  
M3 Receptor ◽  
The Mean

A general predictive model for assessing the inhibition constant (K<sub>i</sub>) value of human acetylcholine muscarinic receptors M1-M5 by potential ligands has been constructed. We used information on the three-dimensional structure of human M1, M2, M4, and M5 receptors, as well as a model of the M3 receptor constructed according to homology based on the structure of the rat M3 receptor. A set of complexes of known inhibitors with the target receptor constructed by means of molecular docking, was selected using an additional option: the coincidence of the spatial position of 4 pharmacophore points of a tested inhibitor and tiotropium, for which the position in the crystal structure was known. For five types of M receptors 199 complexes with known K<sub>i</sub> values were selected. Based on the data obtained during molecular dynamics simulation of these complexes by means of the MM-PBSA/MM-GBSA methods, their energy characteristics were calculated. They were used as independent variables in linear regression equations for pK<sub>i</sub> value prediction. The R<sup>2</sup> prediction for the generalized equation was 0.7, and the mean prediction error was 0.55 logarithmic units with a range for pK<sub>i</sub>=4.7.

scholarly journals Prediksi Tinggi Badan Ditentukan dari Panjang Rentang Lengan dan Panjang Telapak Kaki Pada Mongoloid Feminina Khususnya Etnis Jawa

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Nur Mujaddidah Mochtar
Keyword(s):  
Clinical Medicine ◽  
Pearson Correlation ◽  
Body Height ◽  
Regression Equation ◽  
The Body ◽  
Regression Equations ◽  
Foot Length ◽  
Arm Span ◽  
Knee Height ◽  
The Mean

Background: There are various circumstances where measurements are not actually possible, replacement parameters can be used to estimate body height. Many characteristics of body height measurement and how to measure it. These include anthropometric measurements that can be used for the identification of medicolegal-forensic processes. Body height in clinical medicine and in the field of scientific research can be easily estimated using various anthropometric parameters such as arm span, knee height, foot length and foot breadth, and others. The arm span and foot length has proved to be one of the most reliable predictors. This study was conducted to estimate of body height from arm span and foot length using the regression equation and to determine the correlation between the body height and arm span and foot length.Methods: This study was conducted at Universitas Muhammadiyah Surabaya with 182 Javanese female students. Stature, arm span and foot length measured directly using anthropometric technique and measuring tape. The data obtained were then analyzed with SPSS version 16. The regression equation was derived for the estimate of body height and the relationship between stature, arm span and foot length determined by the Pearson correlation.               Results: We found that the mean body height of Javanese women was 1534,45 ± 47,623  mm, mean of arm span 1543,25 ± 60,468 mm and the mean of foot length 226,14 ± 9,586 mm. The correlation between stature and arm span was positive and significant (r = 0,715  , p <0,05). The correlation between stature and foot length was positive and significant (r = 0,726 , p <0,05). The correlation between stature and arm span and foot length was positive and significant (r = 0,798, p <0,05).               Conclusion: Body height correlates well with the arm span and foot length so that it can be used as a reliable marker for high estimates using regression equations.

Sign in / Sign up

Export Citation Format

Share Document