Multiple Nonlinear Regression Research Articles

Development of predictive model for compressive strength of eco-brick masonry walls using numerical method

The usage of waste plastics in the form of eco-bricks for the construction of buildings is gradually gaining traction. However, the performance of eco-brick masonry walls is not sufficiently known, and the process of setting up experimental rigs for the conduct of compressive strength test of masonry are usually cumbersome and expensive. This study investigated the compressive strength performance of eco-brick masonry walls and developed a predictive model for evaluating the strength of eco-brick masonry walls. Waste polyethylene terephthalate (PET) bottles were physically recycled and utilised to manufacture four samples of eco-bricks, strength grades 40.5, 21.02, 13.52 and 3.05 N/mm2 using stone dust, sharp sand and laterite at predetermined moisture contents. The eco-bricks were laid with seven grades of mortar to build 54 eco-brick masonry prisms. A quasi-static compressive loading test was carried out on the masonries, and predictive models were developed using multiple non-linear regression. The study found that eco-brick masonry prisms supported large loads and failed due to the propagation of tensile cracks and debonding of the units. The study also found that the compressive strength of eco-brick masonry is a power function of the compressive strength of eco-bricks and the mortar used for the bonding. It was determined that the mean ratio of the expected to experimental prism strength was 1.002. The mathematical model developed in this study for predicting the strength of eco-brick masonries can significantly evaluate the compressive strength of eco-brick masonry.

Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques

This paper presents a novel method for determining the fundamental frequency and modeling slender masonry structures, which is particularly relevant in the context of structural dynamics. This research evaluates the effectiveness of machine learning techniques, specifically artificial neural networks (ANN), and regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNLR), in predicting the fundamental frequency of structures, considering both their geometrical and mechanical properties. The objective is to provide a comprehensive comparison of these methods, emphasizing their benefits, limitations, and potential applications in structural health monitoring. The ANN model demonstrated superior performance to linear and non linear regression methods. The findings could significantly impact both theoretical understanding and practical applications in this field. Moreover, this work paves the way for future research, potentially leading to the creation of more accurate and efficient predictive models for slender masonry structures.This methodology ensures a thorough understanding of the model’s behavior and its responsiveness to variations in input parameters.

Prediction of key quality attributes in Salvia miltiorrhiza standard decoction using a Gaussian process regression model.

Nonstationary, nonlinear mass transfer in traditional Chinese medicine (TCM) extraction poses challenges to correlating process characteristics with quality parameters, particularly in defining clear parameter ranges for the process. The aim of the study was to provide a solution for quality consistency analysis in TCM preparation processes. Salvia miltiorrhiza was taken as an example for 15 batches of standard decoction. Using aqueous extract, alcoholic extract, and the content of salvianolic acid B as herb material key quality attributes, multiple nonlinear regression, Gaussian process regression, and artificial neural network models were employed to predict the key quality attributes including the paste yield, the content of salvianolic acid B, and the transfer rate. The evaluation criteria were root mean square error, mean absolute percentage error, and R2. The Gaussian process regression model had the best prediction effect on the paste yield, the content of salvianolic acid B, and the transfer rate, with R2 being 0.918, 0.934, and 0.919, respectively. Utilizing Gaussian process regression model confidence intervals, along with Shewhart control and intervals optimized through process capability index analysis, the quality control range of the standard decoction was determined as follows: paste yield, 25.14%-33.19%; salvianolic acid B content, 2.62%-4.78%; and transfer rate, 56.88%-64.80%. This study combined the preparation process of standard decoction with the Gaussian process regression model, accurately predicted the key quality attributes, and determined the quality parameter range by using process analysis tools, providing a new idea for the quality consistency standard of TCMprocesses.

Design of melting parameters for safety airbag labels based on hot air welding technology: CFD simulation and experimental validation

This paper focuses on researching the various factors that influence the material melting rate during the hot air melting process. To achieve this purpose, the paper proposes a melting model for Acrylonitrile-Butadiene-Styrene (ABS) plastic under hot air heating based on Computational Fluid Dynamics (CFD). Orthogonal experiments were conducted based on the parameter range of each factor obtained in the pre-experiment. The results showed that the outlet distance, hot air velocity, hot air temperature, and their interaction items are important factors that affect the melting rate of ABS materials. Based on the simulation results, a multiple nonlinear regression model was constructed to predict the melting rate under different parameters. Comparing the simulation values with the predicted values of the multiple nonlinear regression model, the error between the two was no >10%, indicating the effectiveness of the prediction model. The research results can help guide the hot air welding melting process for different materials and characteristics, and provide a certain reference value for the development of hot air welding technology.

Regression and Neural Network Analysis of the System of Cardiorespiratory Synchronism Parameters to Predict the Minimum Limit of the Synchronization Range

In order to improve the existing step-by-step principle of determining the limits of the range of cardiorespiratory synchronization (CRS) and the regulatory and adaptive capabilities of the human body, a regression analysis of measurements of the CRS parameters system was carried out. Statistical prediction of the minimum limit of the synchronization range in the CRS method was performed using linear, nonlinear multiple regression models, as well as neural network regression. The following parameters were used as the basis for predicting the minimum limit of the synchronization range: initial heart rate, initial respiratory rate, age and gender. The variable initial heart rate contributed the most to all regression models. A comparative analysis of the quality indicators of all models showed that the prediction of the minimum limit of the synchronization range can be carried out both by linear multiple regression and using neural network regression implemented by means of a 3-4-1 multilayer perceptrons. However, having already had a NN-regression forecast of maximum limit of the synchronization range, as well as a NN-classification of the level of regulatory-adaptive status, the use of NN-regression will be proposed as the main forecasting method for its implementation in the software of the CRS measurement system.

A NONLINEAR REGRESSION MODEL FOR EARLY LOC ESTIMATION OF OPEN-SOURCE KOTLIN-BASED APPLICATIONS

Context. The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps based on the Box-Cox four-variate normalizing transformation to increase the confidence in early LOC estimation of these apps. Method. For early LOC estimation in open-source Kotlin-based apps, the model, confidence, and prediction intervals of nonlinear regression were constructed using the Box-Cox four-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating multivariate normalizing transformations, account for the dependencies between variables in non-Gaussian data scenarios. As a result, this method tends to reduce the mean magnitude of relative error (MMRE) and narrow confidence and prediction intervals compared to models utilizing univariate normalizing transformations. Results. An analysis has been carried out to compare the constructed model with nonlinear regression models employing decimal logarithm and Box-Cox univariate transformation. Conclusions. The nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps is constructed using the Box-Cox four-variate transformation. Compared to the other nonlinear regression models, this model demonstrates a larger multiple coefficient of determination, a smaller value of the MMRE, and narrower confidence and prediction intervals. The prospects for further research may include the application of other data sets to construct the nonlinear regression model for early LOC estimation of open-source Kotlin-based apps for other restrictions on predictors.

Modelling fuel oil transformation on geographically different seacoasts and assessing their self-cleansing capacity.

The present paper considers the results of long-term (up to 17years) in situ and laboratory research carried out on oiled French, Spanish, and Russian seacoasts. The objective of this research is to quantify the influence of geographical factors on the rates of natural transformation of the heavy fuel oil stranded ashore and to develop an empirical statistical model in order to evaluate the self-cleansing capacity of the coastal environment. In a number of field campaigns, 363 samples of weathered oil slicks and tar balls have been collected and analysed with the use of thin-layer chromatography combined with optical and gravimetric methods. The results obtained have been subjected to multiple nonlinear regression analyses. It has been shown that heavy fuel oil natural attenuation is more active in continental or estuarine environments influenced by nutrient-rich freshwater runoff and characterised by a higher number of sunny days, solar irradiation, and large temperature fluctuations. On the oceanic coasts, especially in sectors with low hydrodynamic energy, these processes take more time. The resulting model allows for the identification and mapping of the most vulnerable seacoasts, characterised by a low potential to degrade oil pollution. This information may be used in the contingency plans in order to optimise clean-up techniques and associated costs.

A new experimental and numerical simulation method for measuring ash slag fluidity using VOF based on OpenFOAM

A numerical computational model of heated ash flow is proposed using the Volume of Fluid (VOF) method based on OpenFOAM. Established numerical model can accurately calculate the critical flow temperature (Tc) and critical flow velocity (Vc) of ash slag with the error of 1.5 % and 19 % from the experiment, respectively. Forty-five types of ash slag with widely varied properties in China were calculated based on the established model. Results show that high levels (>30 wt%) of Fe2O3 and CaO improve the flowability of the ash slag, and low levels of Al2O3 (10 wt%) also contribute to the flowability of the ash slag. Tc and Vc of the ash were distributed in the range of 1150 ∼ 1400 °C and 0.1 ∼ 1.0 mm/min, respectively. Tc and Vc change in opposite trends with the component content increases, indicating that a higher Tc corresponds to a lower Vc and fluidity reduction. Mathematical models for calculating the Tc and Vc of ash were obtained based on non-linear multiple regression and correlation coefficient, and prediction accuracy was within 10 % and 30 % for Tc and Vc, respectively. The proposed numerical simulation method and prediction model can provide an accurate reference for a wide range of ash fluidity. Also, it was demonstrated that elemental Na has a positive effect on ash flowability based on the model, which is important for measuring slag flowability in slag-tap furnaces.

Tensile and shear capacity of post-installed chemical adhesive anchors in lightweight concrete

Due to construction efficiency and performance, chemical anchors have been widely applied to long-span or high-rise structures or infrastructures. This study investigates the capacity of post-installed anchors in concrete with lightweight aggregates. Extensive static experiments are conducted to investigate tensile and shear capacities of chemical adhesive anchors in lightweight concrete. Both confined and unconfined tensile conditions are considered. In total, 102 tests are performed with various anchor diameters (12–30 mm), embedment depths (80–300 mm), and types of concrete. The test results show that cone breakout and combined failures dominate the unconfined tensile tests, which have about 40% capacity for the anchors in normal-weight concrete. Chemical adhesive anchor failure models in lightweight concrete are also summarized with respect to the input parameters. Finally, multiple nonlinear regression analyses are further performed, and revised empirical and analytical equations are proposed for lightweight concrete to accommodate different failure modes.

Comparison of proxy-Shortwave Cloud albedo from SBUV observations with CMIP6 models

Abstract We construct a long-term record of Top of Atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340 nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an Equilibrium Climate Sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7 to 5.1°C. Our ECS estimates are sensitive to the instrument calibration which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California. To produce our SW cloud+aerosol albedo we first retrieve a Black-sky Cloud Albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then we estimate the broadband proxy albedo using multiple non-linear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Extended Pathfinder Atmospheres (Patmos-x) observational dataset.

DEM simulation and optimization of crushing chamber shape of gyratory crusher based on Ab-t10 model

To study and optimize the crushing chamber performance of the gyratory crusher, the impact of the crushing chamber shape on the gyratory crusher performance is explored in this paper. The discrete element method (DEM) analysis model of ore particle and gyratory crusher is established, where ore particle crushing is realized based on the Ab-t10 breakage model, and its validity is verified based on actual working conditions. On this basis, the performance of the gyratory crusher under different crushing chamber shapes is studied by combining the DEM simulation with the response surface method (RSM), and the performance prediction model is obtained based on multiple nonlinear regression. Further, the productivity is considered as the main optimization objective, the mass percentage of effective particles and the crushing force are used as auxiliary optimization objectives. The multi-objective optimization is implemented and the optimized effects are analysed by DEM simulation, and the optimized quartic equation coefficients of the concave curve are obtained. Finally, the results show that the productivity, mass percentage of effective particles, and crushing force are increased to varying degrees. Meanwhile, the clogged layer area increases to some extent, which promotes the crushing of ore particles.

An empirical method for estimating TBM penetration rate using tunnelling specific energy

Establishing a suitable tunnel boring machine (TBM) performance estimation model is benefit to the machine type selection, project scheduling and budgeting. Although various models have been developed to predict TBM performance, most of these models use a relatively small specific database, which is difficult to be applied to engineering practice. In this study, some equations for estimating TBM penetration rate (PR) applicable to different geological conditions are proposed. Firstly, a TBM construction performance database containing 159 tunnel sections is built according to four different tunnel projects originating from China, which covers rock mass properties, TBM specification parameters and performance information. By eliminating the effects of some TBM specification and operating parameters, a normalized energy index is proposed for assessing the rock mass boreability. On this basis, a series of statistical analysis, i.e. linear multiple regression (LMR) and non-linear multiple regression (NLMR) are used to develop some empirical equations for estimating the PR by employing rock mass properties and TBM specification parameters, and more than 84% of the PR can be accurately predicted. Finally, to further evaluate the prediction performance of these equations, they are compared and discussed with fifteen published typical models, and the results proved that the models developed in this study are more reasonable and reliable. It should be noted that the input parameters of these equations are readily available at the project planning stage, thus they are very simple and practical for designers and constructors.

Influence of Weather Factors on the Incidence of Major Insect Pests of Sesame (Sesamum indicum L.)

Population dynamics of major pests viz., web worm, Antigastra catalaunalis (Duponchel), leaf hoppers Orosius albicinctus (Distant) and gallfly Asphondilia sesami (Felt) infesting sesame was studied in Coimbatore, Tamil Nadu, India during Rabi and Kharif seasons of the year 2020. Correlation between the pest population, percent damage and weather parameters was analyzed and regression equations were developed. The incidence of leaf hoppers, web worm and gallfly on sesame crop during Rabi season started on 4th, 3rd and 7th Standard meteorological week (SMW) respectively. The peak attained on 8th,11th, and 11th SMW with population of leafhoppers and damage by web worm and gallfly (2.01/plant,60.04 percent and 49.68 percent) respectively during Rabi season. In case of leafhopper and web worm, minimum temperature (r=0.031r=0.373) had non-significant positive correlation whereas mean relative humidity (r=-0.02) registered non-significant negative correlation with leaf hopper and web worm significant negative correlation Gall fly incidence showed significant positive correlation with maximum and minimum temperature. Whereas in Kharif season, incidence of leafhoppers, web worm and gallfly on sesame crop started on 33rd, 32nd and 37th SMW respectively. The peak activity and incidence was noticed during 37th, 39th and 40th SMW for leafhoppers, web worm and gallfly respectively. The correlation between weather parameters and incidence of major pests followed a similar pattern to kharif season. Regression analysis data revealed that the multiple non-linear regression equations are sufficient to predict the pest population with prevailing weather parameters.

Multiple non-Linear Regression and its Applications

The non-linear slope reflects the features of the parameters of the model to estimate the values of the approved variable, and the number of independent variables are two or more, the non-Linear model requires complex approximation operation in self non-Linear models, which cannot be converted into α sign by performing known conversions.

Comparison of Some Selected Models of Daily Water Supply in Abuja, Federa Capital Territory (FCT) Of Nigeria.

Water is considered as an essential commodity in human society. It has been observed that the basic standard of living of any Country can be measure through the effective portable water supply for consumption, sanitation and hygiene for the citizen. Its production and supply in the Federal Capital Territory (FCT), Abuja, Nigeria is a challenge, as dominant percentages of lower- class workforce live outside the FCTWB existing water network, and depend on borehole or tanker for water. The demand on the water supply system in FCT, Abuja has exceeded its initial design population, and the system is incomplete and operating with only 720 million liter of water per day (MLD) functioning plant. On this basis, this study seeks to identify the best model that fit daily water supply data in FCT, Abuja and to forecast the future daily water supply in this area. The comparing models were artificial neural, multiple linear and non-linear regression model were applied to the data set of daily water production, daily water treated, daily water supply, daily water leakage, Number of household connected to Federal Capital Territory Water board water network, Number of Commercial area connected to Federal Capital Territory Water board water network, and the Estimated population of Federal Capital Territory ,Abuja from (2013 to 2020), and it was emerged that the most adequate model for the data was Multiple linear regression model (MLR), with (R2 =1.000), (RMSE = 0.00282), and (MAE = 6.540). Artificial neural network records the high value of (RMSE = 432.4321), (MAE = 549.444) and (R2 =0.921), and multiple nonlinear regression record the highest value of (RMSE = 806.687), (MAE = 666.421) and (R2 = 0.999). From the statistical measure of goodness of fit, the three models compared results indicate that MLR model forecast are closely matching with actual observations values, artificial neural network model over-forecast some days volume of daily water supply by FCTWB, cooperation and multiple nonlinear regression model under-forecast some days volume of daily water supply by FCTWB, corporation. The three models compared depicts the influential variables of FCTWB Corporation daily water supply, (ANN = DWP, DWT), (MLR = DWT, DWL) and (MNLR = DWP, DWL), and their forecast graphs shows downward daily water supply in FCT, Abuja. It is recommended that the FCT, Abuja water Board Corporation should consider multiple linear regression (MLR) model and its forecasted values as a road map in its operations and planning toward water supply sustainability in FCT, Abuja.

Inhibition activity of triazoles as a new family for the inhibition of the Indoleamine 2,3-dioxygenase 1 IDO1 protein using 2D-QSAR approach

Protein IDO1 (indoleamine 2,3-dioxygenase) occupies a critical position in the regulation of the immune system and is involved in cancer progression and the development of immune diseases. Being a therapeutic target for such critical diseases, we aimed to investigate the IDO1 inhibition activity of thirty-nine triazole derivatives using a quantitative structure-activity relationship. The dataset was under principal component analysis, multiple linear regression, and multiple non-linear regression from which two models were generated. The best 2D-QSAR model was generated using linear regression, demonstrating a determination coefficient of R2=0.680, a good acceptable internal cross-validated coefficient of R2cv=0.700, an error of MSE=0.074, and a good predictive potential of R2test=0.809. The QSAR model was further investigated using the applicability domain, which showed that all molecules were within the applicability domain, hence the absence of an outlier. Overall, the obtained results provide a reliable and highly predictive model for the design and prediction of new IDO1 inhibitors thereby influencing cancer progression and autoimmune disease development.

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Efisiensi Faktor Produksi Pada Usahatani Padi Sawah Peserta dan Non-Peserta SL-PTT di Kecamatan Poelang Utara Kabupaten Bombana

Research to determine the influence of production factors and their efficiency on rice farming of SL-PTT participants and non-SL-PTT participants and non-SL-PTT participants in North Poleang Bombana District. Carried out in May – July 2011. Samples were determined using simple cluster sampling based on villages, as many as 2 villages (50% of the village population). Respondents from each village consisted of 15 participants and 15 non-participants of SL-PTT in a simple random manner (60 samples). Analysis using multiple non-linear regression using the Cobb-Douglas function and price efficiency analysis. Research results: (a) Production Factors; Land area, amount of use of seeds, fertilizer, labor and pesticides are closely related and simultaneously influence the production of lowland rice farming (R; l 0.889 and R2 = 0.790). (b) partially, except for the number of seeds, all production factors have a positive effect, but only fertilizer has a real effect, even at a confidence level of 85%. (c) Efficiency analysis shows that the use of fertilizer, labor and pesticides is not efficient so they can still be added to improve, while seeds are not efficient.

Определение зависимости кажущейся плотности керамических образцов от формовочной влажности глинистого сырья и давления прессования на основе регрессионных моделей

The paper proposes to use white clay with the highest apparent density to improve the production of wire-ceramic resistors. Preliminary studies of white clay have shown that it is more sensitive to drying than red clay. The intensity of moisture output from the center of the product to its surface during the manufacturing process and the drying characteristics of white clay give greater shrinkage and strength of dry samples. Linear and nonlinear multiple regression models with and without multicollinearity are considered to find the dependence of the apparent density of clay raw materials on humidity and pressing pressure. To clarify the model structures, a two-stage approach is used, which involves building a model for residuals. It is found that the combination of a multiple regression model taking into account multicollinearity with an AR model constructed for residues makes it possible to calculate the apparent density of clay raw materials with the smallest standard deviation from experimental data. The obtained model structures will be used to determine the optimal parameters of forming ceramic samples: the moisture content of clay raw materials and the compaction pressure, at which the greatest apparent density of samples is achieved. This will improve the production technology of wire-ceramic resistors. The use of wire-ceramic resistors based on white clay with the highest apparent density will speed up one of the stages of the technological process – drying and reduce their hygroscopicity. As a result of the use of wire-ceramic resistors with the highest apparent density, the mechanical strength of the products will increase, which will improve the operational characteristics and prolong the life of the products.