Multiple Linear Regression-based Models Research Articles

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

Passive stiffness of fibrotic skeletal muscle in mdx mice relates to collagen architecture.

The amount of fibrotic material in dystrophic mouse muscles relates to contractile function, but not passive function. Collagen fibres in skeletal muscle are associated with increased passive muscle stiffness in fibrotic muscles. The alignment of collagen is independently associated with passive stiffness in dystrophic skeletal muscles. These outcomes demonstrate that collagen architecture rather than collagen content should be a target of anti-fibrotic therapies to treat muscle stiffness. Fibrosis is prominent in many skeletal muscle pathologies including dystrophies, neurological disorders, cachexia, chronic kidney disease, sarcopenia and metabolic disorders. Fibrosis in muscle is associated with decreased contractile forces and increased passive stiffness that limits joint mobility leading to contractures. However, the assumption that more fibrotic material is directly related to decreased function has not held true. Here we utilize novel measurement of extracellular matrix (ECM) and collagen architecture to relate ECM form to muscle function. We used mdx mice, a model for Duchenne muscular dystrophy that becomes fibrotic, and wildtype mice. In this model, extensor digitorum longus (EDL) muscle was significantly stiffer, but with similar total collagen, while the soleus muscle did not change stiffness, but increased collagen. The stiffness of the EDL was associated with increased collagen crosslinking as determined by collagen solubility. Measurement of ECM alignment using polarized light microscopy showed a robust relationship between stiffness and alignment for wildtype muscle that broke down in mdx muscles. Direct visualization of large collagen fibres with second harmonic generation imaging revealed their relative abundance in stiff muscles. Collagen fibre alignment was linked to stiffness across all muscles investigated and the most significant factor in a multiple linear regression-based model of muscle stiffness from ECM parameters. This work establishes novel characteristics of skeletal muscle ECM architecture and provides evidence for a mechanical function of collagen fibres in muscle. This finding suggests that anti-fibrotic strategies to enhance muscle function and excessive stiffness should target large collagen fibres and their alignment rather than total collagen.

Dielectric Spectroscopy to Improve the Production of rAAV Used in Gene Therapy

The insect cell-baculovirus expression vector system is an established method for large scale recombinant adeno-associated virus (rAAV) production, largely due to its scalability and high volumetric productivities. During rAAV production it is critical to monitor process parameters such as Spodoptera frugiperda (Sf9) cell concentration, infection timing, and cell harvest viabilities since they can have a significant influence on rAAV productivity and product quality. Herein we developed the use of dielectric spectroscopy as a process analytical technology (PAT) tool used to continuously monitor the production of rAAV in 2 L stirred tank bioreactors, achieving enhanced control over the production process. This study resulted in improved manufacturing robustness through continuous monitoring of cell culture parameters, eliminating sampling needs, increasing the accuracy of infection timing, and reliably estimating the time of harvest. To increase the accuracy of baculovirus infection timing, the cell growth/permittivity model was coupled to a feedback loop with real-time monitoring. This system was able to predict baculovirus infection timing up to 24 h in advance for greatly improved accuracy of infection and ensuring consistent high rAAV productivities. Furthermore, predictive models were developed based on the dielectric measurements of the culture. These multiple linear regression-based models resulted in correlation coefficients (Q2) of 0.89 for viable cell concentration, 0.97 for viability, and 0.92 for cell diameter. Finally, models were developed to predict rAAV titer providing the capability to distinguish in real time between high and low titer production batches.

Pharmacophoric sites of anticancer metal complexes located using quantum topological atomic descriptors

In this paper, we present a new set of topological atomic descriptors (TAD) to identify pharmacophoric sites, using the anti-tumoral activities of metal copper (II) complexes. To this end, multiple linear regression-based models were built using TAD. These descriptors are related with the atomic electronic population, localization and delocalization, dipolar moment and quadrupole moment, and describe the way in which the atoms donate, accept or share electron density and how the electron density is polarized within an atomic domain. The regression models were built to reproduce the antiproliferative activity on two tumor human cells lines. Usually, the activity analysis of a metal complex focusses on the metal center features, but the ligands have a definitive role on the molecular recognition of the complexes by the biomolecules. In this case, the best models selected are those involving the changes of the delocalization and dipole moments of atoms within the C–N bonds of the ligands. These changes correlate with the antiproliferative activity and, thus, it allows to identify the C–N bonds as the pharmacophoric site of the copper complexes. These bonds are determinant for the recognition site of copper complexes by DNA backbone, as found in a previous molecular dynamics study performed by our group.

Development of multiple linear regression-based models for fatigue life evaluation of automotive coil springs

Abstract This paper discusses the establishment of multiple linear regression (MLR)-based spring durability models for predicting the fatigue life of automotive coil springs based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. These models were developed in order to simplify the design and development process of vehicle suspension systems, which is both time-intensive and cost-intensive. The simulated force-time histories were processed to obtain the fatigue life of the automotive coil spring based on the strain-life models whereas the acceleration-time histories were weighted according to the ISO-2631-1:1997 standard to determine the vertical vibrations of the vehicle. MLR was used to establish the spring durability models and the goodness of fit, linearity, normality, and homoscedasticity of the models were assessed. The highest coefficient of determination at 0.8820 was obtained for the Morrow MLR-based spring durability model, with the mean square error of 0.5855. The models were validated by comparing the fatigue life values predicted by the models with those predicted from strain measurements. The results show a good agreement between the predicted and experimental values, indicating the suitability of these models in predicting the fatigue life of automotive coil springs.

Multiple Linear Regression Modeling To Predict the Stability of Polymer-Drug Solid Dispersions: Comparison of the Effects of Polymers and Manufacturing Methods on Solid Dispersion Stability.

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two manufacturing techniques, spray-drying and melt extrusion. Each formulation underwent a six-month stability study at accelerated conditions, 40 °C and 75% relative humidity (RH). Significant differences in times to crystallization (onset of crystallization) were observed between both the different polymers and the two processing methods. Stability from zero days to over one year was observed. The extensive experimental data set obtained from this stability study was used to build multiple linear regression models to correlate physicochemical properties of the active pharmaceutical ingredients (API) with the stability data. The purpose of these models is to indicate which combination of processing method and polymer carrier is most likely to give a stable solid dispersion. Six quantitative mathematical multiple linear regression-based models were produced based on selection of the most influential independent physical and chemical parameters from a set of 33 possible factors, one model for each combination of polymer and processing method, with good predictability of stability. Three general rules are proposed from these models for the formulation development of suitably stable solid dispersions. Namely, increased stability is correlated with increased glass transition temperature ( Tg) of solid dispersions, as well as decreased number of H-bond donors and increased molecular flexibility (such as rotatable bonds and ring count) of the drug molecule.

River Flow Model Using Artificial Neural Networks

The use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of hydrology and water resources problems. In this research, an ANN was developed and used to model the rainfall-runoff relationship, in a catchment located in a semiarid and Mediterranean climate in Algeria. The performance of the developed neural network-based model was compared against multiple linear regression-based models using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this research, artificial neural network modeling appears to be a promising technique for the prediction of flow for catchments in semi-arid and Mediterranean regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate.

Modeling of Furfural and 5-Hydroxymethylfurfural Content of Fermented Lotus Root: Artificial Neural Networks and a Genetic Algorithm Approach

Abstract The aim of this study was to investigate the effect of different pretreatment and reducing sugar content on furfural (F) and 5-hydroxymethylfurfural (HMF) contents of fermented lotus root by vinegar. The lotus root samples were fermented using vinegar for 15 days, at different solution concentrations and temperatures. The processing conditions were considered as inputs of neural network to predict the F and HMF contents of lotus root. Genetic algorithm was applied to optimize the structure and learning parameters of ANN. The developed genetic algorithm-artificial neural network (GA-ANN) which included 23 and 17 neurons in the first and second hidden layers, respectively, gives the lowest mean squared error (MSE). The correlation coefficient of ANN was compared with multiple linear regression-based models. The GA-ANN model was found to be a more accurate prediction method for the F and HMF contents of fermented lotus root than linear regression-based models. In addition, sensitivity analysis and Pearson’s correlation coefficient were also analyzed to find out the relation between input and output variables.