scholarly journals Wiskundige modellering van die prestasiekoëffisiënt van ’n direkte-uitbreiding grootmaatmelkverkoeler op ’n plaas: ’n Veelvoudige lineêre regressie-benadering

2021 ◽  
pp. 94-102
Author(s):  
R Mhundwa ◽  
M Simon
Keyword(s):  
Mlr Model

In hierdie studie word ’n metode vir die voorspelling van die prestasiekoëffisiënt (COP) van ’n direkte uitbreiding grootmaatmelkverkoeler (DXBMC) aangebied in die vorm van ’n meervoudige lineêre regressie (MLR) model. Die eksperimentele data wat gebruik is om die model te bou en te ontwikkel, is versamel vanaf ’n DXBMC van 21 m3 met behulp van ’n dataverwerwingstelsel wat temperatuursensors, ’n omgewingstemperatuur- en relatiewe humiditeitsensor en ’n kragmeter bevat. Die studie het aan die lig gebring dat die COP van ’n DXBMC op die plaas met hoë akkuraatheid voorspel kan word. Daar is gevind dat die R2-waardes vir die voorspelling van die COP 0.957 is. Verder is die ontwikkelde model statisties beduidend, met ’n p-waarde van 1,31 × 10-120. Die model kan die COP met ’n relatiewe hoë akkuraatheid voorspel, soos aangedui deur ’n lae wortel gemiddelde kwadraatfout (RMSE) wat 0,0406 is met ’n standaardfout van 0,0392 vir die gebruik van die model om die COP te voorspel, wat aandui die eksperimentele data lewer ’n goeie pasvorm. Die ReliefF-algoritme en 2D-simulasiegrafieke het aangedui dat energieverbruik en volume melk primêr bydra tot die COP. Daarenteen was melktemperatuur, omgewingstemperatuur en relatiewe humiditeit sekondêre bygedraende faktore. Die studie het bevind dat elektriese energie die belangrikste faktor is wat die COP van die DXBMC op ’n plaas beïnvloed. Dus kan energie-doeltreffendheidsinisiatiewe in melkboerderye help om die energieverbruik te optimaliseer.

NLEAP Computer Model and Multiple Linear Regression Prediction of Nitrate Leaching in Vegetable Systems

2002 ◽  
Vol 12 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Hudson Minshew ◽  
John Selker ◽  
Delbert Hemphill ◽  
Richard P. Dick
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nitrate Leaching ◽  
Cover Crops ◽  
N Uptake ◽  
N Leaching ◽  
Residual Soil ◽  
Vegetable Crops ◽  
Crop N Uptake ◽  
Mlr Model

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

scholarly journals Factors Associated with Attrition of Girls Students from School in Bangladesh

2020 ◽  
Vol 12 (1) ◽  
pp. 29-38
Author(s):  
M. N. Hasan
Keyword(s):  
Wealth Index ◽  
Fifth Grade ◽  
Child Marriage ◽  
Multiple Logistic Regression ◽  
Factors Associated ◽  
School Based ◽  
Stepwise Method ◽  
Multiple Indicator ◽  
Girls School ◽  
Mlr Model

Many girls who enrolled in a school but didn’t complete elementary or secondary education, have become a serious problem in the last few decades in Bangladesh. Several studies have been conducted to identify the determinants of school dropout by constructing bivariate and multiple logistic regression (MLR) model. Bangladesh multiple indicator cluster surveys (MICS) 2012 data were selected in this investigation. This study was based on girls aged between 15 and 17 years since all these girls should have been in school or have completed primary education. The backward stepwise method was used for model selection and fitting to the dataset. From 4800 girls, 29.1% were out of school and 70.9% were attending school. Backward stepwise method confirmed that girl’s marital status, area, division, wealth index, religion, mothers and father’s aliveness and household education were the major reasons of girl’s dropout and these covariates are only considered in the analysis. The MLR analysis showed that married girls were significantly (OR 11.06; 95% CI 9.05–13.56) more likely to attrition compared to unmarried girls. School-based programs aimed at preventing child marriage should target girls from the fifth grade because of their escalated risk, and they need to prioritize girls from disadvantaged groups.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

scholarly journals Research on carbonate reservoir interwell connectivity based on a modified diffusivity filter model

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 306-312
Author(s):  
Wei Wang ◽  
Jun Yao ◽  
Yang Li ◽  
Aimin Lv
Keyword(s):  
Carbonate Reservoir ◽  
Correction Coefficient ◽  
Signal Attenuation ◽  
Inversion Result ◽  
Filter Model ◽  
Dual Porosity Model ◽  
Interwell Connectivity ◽  
Dynamic Connectivity ◽  
Porosity Model ◽  
Mlr Model

AbstractAccording to the solution of dual-porosity model, a diffusivity filter model of carbonate reservoir was established, which can effectively illustrate the injection signal attenuation and lag characteristic. The interwell dynamic connectivity inversion model combines a multivariate linear regression (MLR) analysis with a correction coefficient to eliminate the effect of fluctuating bottom-hole pressure (BHP). The modified MLR model was validated by synthetic field with fluctuating BHP. The method was applied to Tahe oilfield which showed that the inversion result was reliable. The interwell dynamic connectivity coefficients could reflect the real interwell connectivity of reservoir. The method is easy to use and proved to be effective in field applications.

scholarly journals Prediction of Parallel Artificial Membrane Permeability Assay of Some Drugs from their Theoretically Calculated Molecular Descriptors

2011 ◽  
Vol 8 (3) ◽  
pp. 1074-1085
Author(s):  
E. Konoz ◽  
Amir H. M. Sarrafi ◽  
S. Ardalani
Keyword(s):  
Neural Network ◽  
Membrane Permeability ◽  
Selection Procedure ◽  
Structure Property ◽  
Artificial Membrane ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Artificial Neural ◽  
Variable Subset Selection ◽  
Mlr Model

Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural network model in the prediction of membrane permeability of drugs.

scholarly journals Aplikasi Analisis Multivariat Berdasarkan Warna untuk Memprediksi Brix dan pH pada Pisang

2017 ◽  
Vol 37 (1) ◽  
pp. 109 ◽  
Author(s):  
Yohanita Maulina Akbar ◽  
Dr. Rudiati Evi Masithoh ◽  
Nafis Khuriyati
Keyword(s):  
Multivariate Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Conventional Method ◽  
Calibration Model ◽  
Validation Data ◽  
Determination Coefficient ◽  
Mlr Model

In this research, Multiple Linear Regression (MLR) model was used to predict Brix and pH of banana based on RGB and Lab color values. Banana samples varied in color and ripening level from less ripen to ripen. RGB and Lab values were measured non-destructively using colormeter, while Brix and pH were determined using conventional method in laboratory. Multivariate analysis was done using the Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, and trial version). Results showed that calibration model using MLR was able to predict Brix and pH of banana based on RGB and Lab color values. Furthermore, validation data were used to test the selected models. MLR model to predict Brix based on RGB and Lab validation resulted in 0.8 and 0.84 of determination coefficient between observation and prediction data. The model was also able to predict pH based on RGB and Lab values with 0.71 and 0.79 of determination coefficient between observation and prediction data. ABSTRAKPada penelitian ini, model Multiple Linear Regression (MLR) digunakan untuk memprediksi Brix dan pH pada buah pisang berdasarkan nilai warna Red Green Blue (RGB) dan Lab. Pisang yang dianalisis mempunyai variasi warna dari kurang masak sampai masak. Parameter warna RGB dan Lab dilakukan secara non-destruktif dengan menggunakan colormeter, sedangkan pengukuran kualitas internal yaitu Brix dan pH ditentukan secara destruktif atau dengan prosedur konvensional di laboratorium. Aplikasi analisis multivariat yang digunakan adalah Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, versi trial). Analisis data menunjukkan bahwa model kalibrasi MLR dapat digunakan untuk memprediksi Brix dan pH berdasarkan parameter warna RGB dan Lab pada buah pisang. Selanjutnya, data validasi digunakan untuk menguji model MLR terpilih. Model kalibrasi MLR dapat memprediksi Brix berdasarkan nilai RGB dan Lab dengan nilai koefisien determinasi (R2) sebesar 0,8 dan 0,84, secara berurutan. Sedangkan koefisien determinasi (R2) untuk pH berdasarkan warna RGB dan Lab adalah 0,71 dan 0,79.

scholarly journals Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Rachodbun Srimanus ◽  
Wechapraan Srimanus ◽  
Sarima Niampradit ◽  
Nopadol Preecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Local Level ◽  
Winter Season ◽  
Summer Season ◽  
Weather Data ◽  
Good Prediction ◽  
Mlr Model

Abstract Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Livelihood Assets and Income Generating Activities: A Comparative Analysis in the Scheduled and Non-Scheduled Areas of Jharkhand

2021 ◽  
pp. 097370302110649
Author(s):  
Ashish Aman Sinha ◽  
Hari Charan Behera ◽  
Ajit Kumar Behura ◽  
Amiya Kumar Sahoo ◽  
Utpal Kumar De
Keyword(s):  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Primary Source ◽  
Animal Husbandry ◽  
Livelihood Assets ◽  
Income Generating Activities ◽  
Daily Wage ◽  
Activity Cluster ◽  
Mlr Model

The main objective of the article is to identify different types of livelihood assets, income generating activities (IGAs) and choices of these activities by households across social groups in the Fifth and non-Fifth Scheduled areas of Jharkhand in eastern India. It is based on a primary survey of 785 households randomly selected across caste and Scheduled Tribe groups in Giridih and Latehar districts of Jharkhand. K-means clustering is applied for determination of latent class activity clusters and Multinomial Logistic Regression (MLR) model used for understanding the importance of livelihood assets in determining livelihood activity cluster (LC) for income generation. Further, discriminant analysis is applied to obtain probability of choice of individual households in determining livelihood generating activity. The analysis shows that forest-based activity remains a better livelihood support system in the Fifth Scheduled areas, which is less significant and further diminishing in the non-Fifth Scheduled areas. Rural households engaged in a diverse set of IGAs to obtain additional income to reduce risk and maintain a balanced consumption. Occupational transition is marked by the decline of agriculture and increasing reliance on daily-wage activities as the primary source of income. Other traditional livelihood activities such as animal husbandry and the collection of forest produce have less scope for income in the absence of institutional support.

Predictors of radiation dose for uterine artery embolisation are angiography system-dependent

2022 ◽  
Author(s):  
Don Johnson Nocum ◽  
John Robinson ◽  
Mark Halaki ◽  
Magnus Bath ◽  
John D. Thompson ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Uterine Artery ◽  
Predictive Accuracy ◽  
Uterine Artery Embolisation ◽  
System A ◽  
Group I ◽  
Reduce Radiation Dose ◽  
Area Product ◽  
Dose Measurements ◽  
Mlr Model

Abstract This study sought to achieve radiation dose reductions for patients receiving uterine artery embolisation (UAE) by evaluating radiation dose measurements for the preceding generation (Allura) and upgraded (Azurion) angiography system. Previous UAE regression models in the literature could not be applied to this centre’s practice due to being based on different angiography systems and radiation dose predictor variables. The aims of this study were to establish whether radiation dose is reduced with the upgraded angiography system and to develop a regression model to determine predictors of radiation dose specific to the upgraded angiography system. A comparison between Group I (Allura, n = 95) and Group II (Azurion, n = 95) demonstrated a significant reduction in KAP (kerma-area product) and Ka, r (reference air kerma) by 63% (143.2 Gy·cm2 vs 52.9 Gy·cm2; P < 0.001, d = 0.8) and 67% (0.6 Gy vs 0.2 Gy; P < 0.001, d = 0.8), respectively. The multivariable linear regression (MLR) model identified the UAE radiation dose predictors for KAP on the upgraded angiography system as total fluoroscopy dose, Ka, r, and total uterus volume. The predictive accuracy of the MLR model was assessed using a Bland-Altman plot. The mean difference was 0.39 Gy·cm2 and the limits of agreement (LoA) were +28.49 and -27.71 Gy·cm2, and thus illustrated no proportional bias. Our findings validated the upgraded angiography system and its advance capabilities to significantly reduce radiation dose for our patients. Interventional radiologist and interventional radiographer familiarisation of the system’s features and the implementation of the newly established MLR model would further facilitate dose optimisation for all centres performing UAE procedures using the upgraded angiography system.

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