Multilinear Regression Method Research Articles

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy (H) contains the combined effects of both soil moisture (w) and soil temperature (T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different (positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer (i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation (P) prediction, the Huanghe–Huaihe Basin (HHB) and Southeast China (SEC), with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H–P and T–P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer (June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors (e.g., T and w).

The effect of budget goal clarity, organizational commitment, accounting control, and adherence to laws on the perception of government performance of Central Lombok Regency

This research was conducted to examine the effect of budget goal clarity, organization-al commitment, accounting control and adherence to laws on the perceptions of gov-ernment performance. The respondents consists of the structural positions in a region-al government such as the chair person of a body, an agency, a division, a subdivision, and the team of government performance report of Central Lombok Regency. It used a purposive sampling method to get the respondents. They were asked to respond a set of questions in a questionnaire. Of the 90 questionnaires distributed, only 78 question-naires were valid for analysis. The data were analyzed using multi-linear regression method with SPSS 18.0. The results show that budget goal clarity and organizational commitment have an effect on the perception of government performance. The ac-counting control and adherence to laws have no effect on the perceptions of govern-ment performance. It implies that is important to have observation of planning and budget execution as well as the local government commitment to make the financial transaction control and adherence of laws run effectively. The local government shall obey the existing laws in order to integrate the strategic plan system, governance accounting system, budgeting system and exchequer system into an integrated system by improving human resource and information technology.

Improved petrographic-coded model and its evaluation to determine a thermal conductivity log

Thermal conductivity is one of the crucial properties for thermal modelling as well as tunnelling or geological modelling. Available data are mainly from laboratory measurements. Therefore, additional ways, such as correlations with other properties to derive the petrophysical parameter, will be an advantage. The research presented here continues and improves the petrographic-coded model concept with an increased set of data, including a variety of lithologies, and, furthermore, the correlations, including the electrical resistivity. Input parameters are no longer taken from the literature, but are derived directly from measurements. In addition, the results are compared with other published approaches. Results show good correlations with measured data. The comparison with the multi-linear regression method shows acceptable outcome, in contrast to a geometric-mean method, where data scatter. In summary, it can be said that the improved model delivers for both correlation (compressional wave velocity and electrical resistivity with thermal conductivity) positive results.

Higher order partial least squares for object tracking: A 4D-tracking method

Object tracking has a wide range of applications and great efforts have been spent to build the object appearance model using image features encoded in a vector as observations. Since a video or image sequence is intrinsically a multi-dimensional matrix or a high-order tensor, these methods cannot fully utilize the spatial-temporal correlations within the 2D image ensembles and inevitably lose a lot of useful information. In this paper, we propose a novel 4D object tracking method via the higher order partial least squares (HOPLS) which is a generalized multi-linear regression method. To do so, we first represent each training and testing example as a set of image instances of a target or background object. Then, we view object tracking as a multi-class classification problem and construct the 4D data matrix and 2D labeling matrix for HOPLS. Furthermore, we use HOPLS to adaptively learn low-dimensional discriminative feature subspace for object representation. Finally, a simple yet effective updating schema is used to update the object appearance model. Experimental results on challenging video sequences demonstrate the robustness and effectiveness of the proposed 4D tracking method.

Rapid anthropogenic changes in CO2 and pH in the Atlantic Ocean: 2003–2014

AbstractThe extended multilinear regression method is used to determine the uptake and storage of anthropogenic carbon in the Atlantic Ocean based on repeat occupations of four cruises from 1989 to 2014 (A16, A20, A22, and A10), with an emphasis on the 2003–2014 period. The results show a significant increase in basin‐wide anthropogenic carbon storage in the North Atlantic, which absorbed 4.4 ± 0.9 Pg C decade−1 from 2003 to 2014 compared to 1.9 ± 0.4 Pg C decade−1 for the 1989–2003 period. This decadal variability is attributed to changing ventilation patterns associated with the North Atlantic Oscillation and increasing release of anthropogenic carbon into the atmosphere. There are small changes in the uptake rate of CO2 in the South Atlantic for these time periods (3.7 ± 0.8 Pg C decade−1 versus 3.2 ± 0.7 Pg C decade−1). Several eddies are identified containing ~20% more anthropogenic carbon than the surrounding waters in the South Atlantic demonstrating the importance of eddies in transporting anthropogenic carbon. The uptake of carbon results in a decrease in pH of ~0.0021 ± 0.0007 year−1 for surface waters during the last 10 years, in line with the atmospheric increase in CO2.

Development and analysis of a metamodel to represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings

This paper describes the development and focus in the analysis of the Brazilian metamodels used in the residential labeling system. The metamodels represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings. A computational simulation experiment using the EnergyPlus program was conducted taking into account naturally ventilated and artificially air-conditioned residential buildings. Several parameters were considered for parametric simulations such as thermal transmittance, thermal solar absorptance, shading device, ventilation opening factor. The multi-linear regression method was used to predict the cooling degree hours and the annual energy consumption for heating and cooling for dormitories and living room. An assessment on the accuracy of the Brazilian metamodels for residential buildings was analyzed and showed that the regressions include standard errors that may influence the metamodels accuracy. Therefore, artificial neural networks (ANNs) are presented as an alternative to improve the metamodels used in the Brazilian regulation. Comparing both statistical method multi-linear regression and artificial neural network method it can be seen that ANN presented lower results for standard deviation end root mean square errors. Therefore, ANNs are recommended to improve the accuracy of the prediction in further behavior associated with the labeling system.

Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes

The multilinear regression method is applied for quality prediction and quality-relevant monitoring in batch processes. Four multilinear partial least squares (PLS) models are investigated, including three higher-order PLS (HOPLS) models, termed as HOPLS-Tucker, HOPLS-RTucker and HOPLS-CP, and the N-way PLS (N-PLS) model. These multilinear PLS methods have two advantages as compared to the unfold-PLS method. Firstly, they retain the inherent three-way representation of batch data and avoid the disadvantages caused by data unfolding, resulting in more stable process models. Secondly, they summarize the main information on each mode of data and describe the three-way interactions between them, and therefore have better modeling accuracy and intuitive interpretability. Online quality prediction and quality-relevant monitoring methods are developed by combining multilinear PLS with the moving data window technique. These methods are tested in a fed-batch penicillin fermentation process. The results indicate that the multilinear PLS method has higher predictive accuracy, better anti-noise capability and monitoring performance than the unfold-PLS method.

On the concept of sloped motion for free-floating wave energy converters.

A free-floating wave energy converter (WEC) concept whose power take-off (PTO) system reacts against water inertia is investigated herein. The main focus is the impact of inclining the PTO direction on the system performance. The study is based on a numerical model whose formulation is first derived in detail. Hydrodynamics coefficients are obtained using the linear boundary element method package WAMIT. Verification of the model is provided prior to its use for a PTO parametric study and a multi-objective optimization based on a multi-linear regression method. It is found that inclining the direction of the PTO at around 50° to the vertical is highly beneficial for the WEC performance in that it provides a high capture width ratio over a broad region of the wave period range.

Liquid chromatography coupled to quadrupole-time of flight tandem mass spectrometry based quantitative structure–retention relationships of amino acid analogues derivatized via n-propyl chloroformate mediated reaction

In the current study, quantitative structure-retention relationships (QSRR) were constructed based on data obtained by a LC–(ESI)-QTOF-MS/MS method for the determination of amino acid analogues, following their derivatization via chloroformate esters. Molecules were derivatized via n-propyl chloroformate/n-propanol mediated reaction. Derivatives were acquired through a liquid–liquid extraction procedure. Chromatographic separation is based on gradient elution using methanol/water mixtures from a 70/30% composition to an 85/15% final one, maintaining a constant rate of change. The group of examined molecules was diverse, including mainly α-amino acids, yet also β- and γ-amino acids, γ-amino acid analogues, decarboxylated and phosphorylated analogues and dipeptides. Projection to latent structures (PLS) method was selected for the formation of QSRRs, resulting in a total of three PLS models with high cross-validated coefficients of determination Q2Y. For this reason, molecular structures were previously described through the use of descriptors. Through stratified random sampling procedures, 57 compounds were split to a training set and a test set. Model creation was based on multiple criteria including principal component significance and eigenvalue, variable importance, form of residuals, etc. Validation was based on statistical metrics Rpred2, QextF22,QextF32 for the test set and Roy's metrics rm(Av)2 and rm(δ)2, assessing both predictive stability and internal validity. Based on aforementioned models, simplified equivalent were then created using a multi-linear regression (MLR) method. MLR models were also validated with the same metrics. The suggested models are considered useful for the estimation of retention times of amino acid analogues for a series of applications.

Rapid acidification of mode and intermediate waters in the southwestern Atlantic Ocean

Abstract. Observations along the southwestern Atlantic WOCE A17 line made during the Dutch GEOTRACES-NL programme (2010–2011) were compared with historical data from 1994 to quantify the changes in the anthropogenic component of the total pool of dissolved inorganic carbon (ΔCant). Application of the extended multi-linear regression (eMLR) method shows that the ΔCant from 1994 to 2011 has largely remained confined to the upper 1000 dbar. The greatest changes occur in the upper 200 dbar in the Subantarctic Zone (SAZ), where a maximum increase of 37 μmol kg−1 is found. South Atlantic Central Water (SACW) experienced the highest rate of increase in Cant, at 0.99 ± 0.14 μmol kg−1 yr−1, resulting in a maximum rate of decrease in pH of 0.0016 yr−1. The highest rates of acidification relative to ΔCant, however, were found in Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW). The low buffering capacity of SAMW and AAIW combined with their relatively high rates of Cant, increase of 0.53 ± 0.11 and 0.36 ± 0.06 μmol kg−1 yr−1, respectively, has lead to rapid acidification in the SAZ, and will continue to do so whilst simultaneously reducing the chemical buffering capacity of this significant CO2 sink.

Quality prediction based on HOPLS-CP for batch processes

A new multilinear regression method, called the higher-order partial least squares based on CP (canonical decomposition/parallel factor analysis) decomposition (HOPLS-CP), is proposed. Unlike the unfold-PLS (i.e., MPLS) which needs to unfold tensors into matrices for modeling, HOPLS-CP maintains the tensorial representation of data by using the tensor decomposition. Therefore, HOPLS-CP avoids the drawbacks induced by the data unfolding operation and can summarize the multi-way interaction in the data. HOPLS-CP ensures a maximize correlation between two tensors by projecting them into a common latent subspace and independent loading subspaces, where the optimal latent vectors and loading vectors are obtained by performing the CP decomposition on the cross-covariance tensor of two tensors. A generalized HOPLS-CP model is then developed to build a regression model for uneven tensors with varying sizes on one mode. An outstanding advantage of GHOPLS-CP is that it naturally solves the uneven data length problem with no need for data alignment, guaranteeing a better modeling ability and the intuitive interpretability. HOPLS-CP and GHOPLS-CP are applied for the quality prediction in a benchmark fed-batch penicillin fermentation process. Their advantages, including well predictive ability and anti-noise capability, are illustrated by two case studies.

The QSPR Study for the Net Heat of Combustion of Esters Based on Ant Colony Optimization

A quantitative structure–property relationship (QSPR) model for predicting the standard net heat of combustion () was developed based on the ant colony optimization (ACO) method coupled with the partial least square (PLS) for variable selection. Five molecular descriptors were screened out as the parameters of the model, which were finally constructed using multi-linear regression (MLR) method. A reliable model of five parameters for predicting the of esters was established, which can provide some help for engineering to predict the based on only their molecular structures.

Prediction of the Adsorption Capability onto Activated Carbon of Liquid Aliphatic Alcohols using Molecular Fragments Method

Quantitative structure-property relationship (QSPR) for estimating the adsorption of aliphatic alcohols onto activated carbon were developed using substructural molecular fragments (SMF) method. The adsorption capacity of activated carbon (gr/100grC) for 150 aliphatic alcohols onto activated carbon (AC) is studied under equilibrium conditions. Forward and backwards stepwise regression variable selection and multilinear regression (MLR) are combined to describe the effect of molecular structure on the adsorption capacity of activated carbon according to the QSPR method. To quantitatively relate adsorption capacity (Qe) with the molecular structure MLR analysis is performed on the set of 15 substructural molecular fragments (SMF) provided by the software ISIDA. The five fragments selected by variable subset selection, all belonging to the subfragments, adequately represent the structural factors influencing the affinity of alcohols to AC in the adsorption process. Finally, a QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 30 representative compounds excluded from model calibration. The prediction results from the MLR models are in good agreement with the experimental values. By applying MLR method we can predict the test set (30 compounds) with squared cross validated correlation coefficient (Q 2 ext) of 0.9538 and root mean square error (RMSE) of 2.0832.

DNA cleavage, antimicrobial studies and a DFT-based QSAR study of new antimony(III) complexes as glutathione reductase inhibitor

New antimony(III) complexes, [Sb(2-aminopyridine)2Cl3] (1a), [Sb(2-aminopyridine)2Br3] (1b), [Sb(5-methyl-2-aminopyridine)2Cl3] (2a), [Sb(5-methyl-2-aminopyridine)2Br3] (2b), [Sb(2-aminopyrimidine)2Cl3] (3a), [Sb(2-aminopyrimidine)2Br3] (3b), [Sb(4,6-dimethoxy-2-aminopyrimidine)2Cl3] (4a), [Sb(4,6-dimethoxy-2-aminopyrimidine)2Br3] (4b), [Sb(2-amino-1,3,5-triazine)2Cl3] (5a), [Sb(2-amino-1,3,5-triazine)2Br3] (5b), [Sb(2-guanidinobenzimidazole) Cl3] (6a), [Sb(2-guanidinobenzimidazole)Br3] (6b) [Sb(2- benzyl-2-thiopseudeourea)2Cl3] (7a) and [Sb(2- benzyl-2-thiopseudeourea)2Br3] (7b) were synthesized. Their structures were characterized by elemental analysis, molecular conductivity, FT-IR, 1H NMR, LC–MS techniques. Glutathione reductase inhibitor activity, antimicrobial activity and DNA cleavage studies of the complexes were determined. The geometrical structures of the complexes were optimized by DFT/B3LYP method with LANL2DZ as basis set. Calculation results indicated that the equilibrium geometries of all complexes have square pyramidal shape. About 350 molecular descriptors (constitutional, topological, geometrical, electrostatic and quantum chemical parameters) of the complexes were calculated by DFT/B3LYP/LANL2DZ method with CODESSA software. Calculated molecular parameters were correlated to glutathione reductase inhibitory activity values (pIC50) of all complexes by Best Multi-Linear Regression (BMLR) method. Obtained two-parameter QSAR equation shows that increase in “maximum partial charge for a H atom” and decrease in HOMO–LUMO gap would be favorable for the glutathione reductase inhibitory activity.

SBDP: Bandwidth prediction mechanism towards server demands in P2P-VoD system

Migrating popular videos into cloud is an efficient mechanism to decrease the costs of VoD services, but how to get the set of popular videos is a key problem. In this paper, we propose a prediction mechanism named SBDP to evaluate future server bandwidth demands on each video. Firstly, SBDP adopts time-series analysis techniques and history dataset to predict online population in advance. Then in order to obtain initial online population, the curve shape similar (CSS) and Gaussian process regression (GPR) methods are introduced. And finally a multi linear regression (MLR) method with the online population as a main factor is proved and applied in predicting seeds’ upload bandwidth. The proposed methods are simulated on large dataset collected from a popular VoD services in China and the simulation results demonstrate that the prediction data are close to real-world data.

Moisture Diffusivity and Shrinkage of Fruit and Cladode of Opuntia ficus-indica during Infrared Drying

Drying behaviour of prickly pear cladodes and fruits was studied with an Infrared dryer. The volume shrinkage for Opuntia ficus-indica products is calculated and a linear relation was established to describe the experimental variation of shrinkage of the product versus its moisture content. Effective diffusion coefficient of moisture transfer was determined using the Fick law at three drying temperatures (40, 50, and 60°C). Shrinkage was also included into the diffusion model for the determination of the effective diffusion coefficient. The obtained results of the effective moisture diffusivity, for the cladode and the fruit, were evaluated in the range of 1.77 × 10−10–5.07 × 10−10 m2/s and 2.53 × 10−10–7.6 × 10−10 m2/s, respectively. The values of the activation energies for cladode and fruit were estimated to be 45.39 and 47.79 kJ/mol, respectively. However, these values of moisture diffusivity were estimated independently of the evolution of moisture content during drying process. Therefore, a correlation (full quadratic equation) for moisture diffusivity as a function of moisture content and temperature was developed. The parameters are obtained by a multilinear regression method. This equation was found satisfactory to describe the diffusivity evolution function of moisture content and temperature with correlation coefficients of 91.5 and 95%.

STUDY ON ANTI-HIV ACTIVITY OF DIARYLANILINE DERIVATIVES USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR)

Study on anti-HIV activity of diarylaniline derivative compounds by using quantitative structure-activity relationship (QSAR) has been done. The compounds structure and their anti-HIV activities were obtained from literature. Molecular and electronic parameters were calculated by Austin Model 1 (AM1), Parameterized Model 3 (PM3), Hartree-Fock (HF), and density functional theory (DFT) methods. QSAR analysis was performed using multilinear regression method. The result shows that HF method can produce the best model as follows:log EC50 = 46.418 + (99.360 × qC4) - (67.189 × qC9) - (278.869 × qC15) + (782.466 × qC19) - (127.463 × qO7)n = 20; r2 = 0.815; SEE = 0.393; Fcal/Ftab = 4.185; PRESS = 2.160Those model can predict a good inhibitory activity (log EC50) value of -0.3359 to compound N1-(4′-Cyanophenyl)-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy)-4-hydroxyethylbenzene-1,2-diamine).

Relationship between antimold activity and molecular structure of cinnamaldehyde analogues

A quantitative structure–activity relationship (QSAR) modeling of the antimold activity of cinnamaldehyde analogues against of Aspergillus niger and Paecilomyces variotii was presented. The molecular descriptors of cinnamaldehyde analogues were calculated by the CODESSA program, and these descriptors were selected by best multi-linear regression method (BMLR). Satisfactory multilinear regression models of Aspergillus niger and Paecilomyces variotii were obtained with R2=0.9099 and 0.9444, respectively. The models were also satisfactorily validated using internal validation and leave one out validation. The QSAR models provide the guidance for further synthetic work.

A New Method for Predicting the Net Heat of Combustion of Organic Compounds

A quantitative structure–property relationship (QSPR) model for prediction of standard net heat of combustion (ΔH0c) was developed based on the ant colony optimization (ACO) method coupled with the partial least square (PLS) for variable selection. For developing this model, a diverse set of 1650 organic compounds were used, and 1481 molecular descriptors were calculated for every compound. Four molecular descriptors were screened out as the parameters of the model, which was finally constructed using multi-linear regression (MLR) method. The squared correlation coefficient R2of the model was 0.995 for the training set of 1322 compounds. For the test set of 328 compounds, the corresponding R2was 0.996. The results of this study showed that an accurate prediction model for ΔH0ccould be obtained by using the ant colony optimization method. Moreover, this study can provide a new way for predicting the ΔH0cof organic compounds for engineering based on only their molecular structures.

An improved dynamic test method for solar collectors

A comprehensive improvement of the mathematical model for the so called transfer function method is presented in this study. This improved transfer function method can estimate the traditional solar collector parameters such as zero loss coefficient and heat loss coefficient. Two new collector parameters t and mfCf are obtained. t is a time scale parameter which can indicate the heat transfer ability of the solar collector. mfCf can be used to calculate the fluid volume content in the solar collector or to validate the regression process by comparing it to the physical fluid volume content if known. Experiments were carried out under dynamic test conditions and then test data were processed using multi-linear regression method to get collector parameters with statistic analysis. A comparison of the collector parameters obtained from the improved transfer function (ITF) method and the quasi-dynamic test (QDT) method is carried out. The results show that the improved transfer function method can accurately obtain reasonable collector parameters. The influence of different averaging time intervals is investigated. Based on the investigation it is recommended to use on line calculation if applicable for the second-order differential term with 6–9min as the best averaging time interval. The measured and predicted collector power output of the solar collector are compared during a test of 13days continuously both for the ITF method and the QDT method. The maximum and averaging error is 53.87W/m2 and 5.22W/m2 respectively of the ITF method while 64.13W/m2 and 6.22W/m2 of the QDT method. Scatter and relative error distribution of the measured power output versus the predicted power output is also plotted for the two methods. No matter in either error analysis or scatter distribution, the ITF method is more accurate than the QDT method in predicting the power output of a solar collector.In conclusion, all the results show that the improved transfer function method can accurately and robustly estimate solar collector parameters and predict solar collector thermal performance under dynamic test conditions.