scholarly journals PHOSPHORUS SORPTION ISOTHERMS IN SOILS OF THE SEMIARID REGION OF BRAZIL

2021 ◽  
Vol 34 (1) ◽  
pp. 166-176
Author(s):  
MONTESQUIEU DA SILVA VIEIRA ◽  
FÁBIO HENRIQUE TAVARES DE OLIVEIRA ◽  
MARCELO TAVARES GURGEL ◽  
HEMMANNUELLA COSTA SANTOS ◽  
HERNANE ARLLEN MEDEIROS TAVARES
Keyword(s):  
Regression Models ◽  
Soil Phosphorus ◽  
Sorption Isotherms ◽  
Semiarid Region ◽  
Model Parameters ◽  
Phosphorus Sorption ◽  
Linear Regression Models ◽  
Equilibrium Solutions ◽  
Phosphorus Storage ◽  
Iron And Calcium

ABSTRACT The soils of the Semiarid region of Brazil lack studies regarding sorption processes and availability of phosphorus (P). Therefore, the objective of this work was to quantify the sorption of P in ten soils representative of the Semiarid region of Brazil and correlate them with the soil phosphorus storage capacity. The P concentrations in the equilibrium solutions used to model the sorption isotherms were: 0, 5, 10, 15, 20, 30, 40, 55, 70, and 80 mg L-1 for the soils Typic Quartzipsamment (Neossolo Quartzarenico), Typic Hapludox (Latossolo Vermelho Amarelo), Typic Hapludult (Argissolo Vermelho Amarelo), Typic Quartzipsamment (Neossolo Flúvico), and Typic Dystrudept (Cambissolo Haplico); and 0, 10, 15, 25, 40, 55, 80, 100, 130, and 150 mg L-1 for the soils Typic Calciudolls (Chernossolo Rendzico), Typic Dystrudept (Cambissolo Haplico), Typic Dystrudept (Cambissolo Haplico), Typic Hapludult (Argissolo Vermelho Amarelo), and Typic Hapludert (Vertissolo Haplico). The Langmuir and Freundlich sorption isotherms were fitted to non-linear regression models and the values of the model parameters were estimated. The sorption isotherms were adequate to quantify the sorption of P in the soils of the Semiarid region of Brazil, with maximum P sorption capacity varying from 50.4 mg kg-1 to 883.5 mg kg-1. The sorption of P was higher in soils with more clayey textures, alkaline, and rich in iron and calcium, denoting the importance of evaluating the effect of these characteristics on the sorption of P in these soils.

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

scholarly journals New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

2016 ◽  
Vol 5 (3) ◽  
pp. 9 ◽  
Author(s):  
Elizabeth M. Hashimoto ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega ◽  
G.G. Hamedani
Keyword(s):  
Regression Model ◽  
Censored Data ◽  
Survival Data ◽  
Regression Models ◽  
Empirical Distribution ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Standard Normal Distribution ◽  
Model Parameters ◽  
Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models. 

scholarly journals Interval forecasting of time series using orderstatistics

2021 ◽  
Vol 2131 (2) ◽  
pp. 022110
Author(s):  
V Misyura ◽  
M Bogacheva ◽  
E Misyura
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Robust Statistics ◽  
Traditional Approach ◽  
Model Parameters ◽  
Regularization Methods ◽  
Linear Regression Models ◽  
Predictive Algorithm ◽  
Point Forecast ◽  
Made In

Abstract In the traditional approach of obtaining time series forecasts based on the selected model, the model parameters are first estimated, then a point forecast using the obtained estimatesis made and then an interval forecast with a given probability is made. In the article the authors propose a nonparametric method for obtaining a single-stage interval forecasting of a time series based on constructing predictive and target variables sets using robust statistics and obtaining the forecast boundaries by constructing linear regression models. The predictive algorithm is based on the problems of estimating the parameters of linear multiple regression using a model regularization methods. The results of forecasting prove the expediency and effectiveness of the proposed method.

Soil phosphorus buffering measures should not be adjusted for current phosphorus fertility

Soil Research ◽  
2008 ◽  
Vol 46 (8) ◽  
pp. 676 ◽  
Author(s):  
L. L. Burkitt ◽  
P. W. G. Sale ◽  
C. J. P. Gourley
Keyword(s):  
Agricultural Soils ◽  
Soil Phosphorus ◽  
Single Point ◽  
Sorption Isotherms ◽  
Phosphorus Sorption ◽  
Soil P ◽  
P Sorption ◽  
P Concentration ◽  
Extractable P ◽  
Fertiliser Application

Soil phosphorus (P) sorption is an important and relatively stable soil property which dictates the equilibrium between sorbed and solution P. Soil P sorption measures are commonly adjusted for the effect of current P fertility on the amount of P a soil sorbs. In the case of highly fertilised agricultural soils, however, this adjustment is likely to be inappropriate as it may mask changes in a soil’s capacity to sorb P, which could affect future P fertiliser applications. A study was undertaken to compare adjusted or unadjusted methods of measuring P sorption using 9 pasture soils sampled from southern Victoria which had previously received P fertiliser and lime. The P sorption assessment methods included: P sorption isotherms, P-buffering capacity (PBC) measures (slope between equilibrium P concentration of 0.25 and 0.35 mg P/L), and single-point P-buffering indices (PBI), with methods either adjusted or unadjusted for current P fertility. A single application of 280 kg P/ha, 6 months before sampling, resulted in a general negative displacement of unadjusted P sorption isotherm curves, indicating reduced P sorption on 8 of the 9 soils. Adding the Colwell extractable P concentration to the amount of P sorbed before calculating the slope (PBC+ColP), tended to negate this fertiliser effect and, in 2 of the 9 soils, resulted in a significant increase in PBC+ColP values. Increasing rates of P fertiliser application (up to 280 kg P/ha) resulted in a consistent trend to decreasing PBI values (unadjusted for Colwell P), which was significant at 4 of the 9 sites after 6 months. However, only minimal changes in PBI values were determined when PBI was adjusted for current P fertility (PBI+ColP). Phosphorus sorption properties appeared reasonably stable over time, although 2 soils, both Ferrosols, indicated significant linear increases in PBI values when these sites remained unfertilised for 30 months. Lime significantly increased both PBI and PBI+ColP values at all sites 6 months after application, but the effect generally diminished after 30 months, suggesting PBI measurements should not be taken immediately after liming. These results demonstrate that unadjusted measures of P sorption are more likely to accurately reflect changes in soil P sorption capacity following P fertiliser applications and suggest that the unadjusted PBI be used in commercial soil testing rather that the currently adjusted PBI+ColP.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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