PLS Method Research Articles

Investigating knowledge sharing behaviour on virtual community members: integration of technological, individual and contextual factors

Knowledge contributing behaviour as well as knowledge collecting behaviour is essential and necessary in optimising knowledge sharing behaviour. By integrating social cognitive theory SCT model and IS success model theory, this research identified and analysed three dimensions of influential factors of knowledge sharing behaviour, namely individual factors, technological factors and contextual factors. Data were collected from virtual community members of information technology in Indonesia and subsequently analysed by using partial least squares PLS method. The result showed that the information quality, the norm of reciprocity, the expectation outcomes, the enjoyment in helping others and the interpersonal trust have significant influence in knowledge sharing behaviour. In addition, the result also concluded that knowledge contributing behaviour and knowledge utilisation are particularly important for community promotion activities.

Multirate Partial Least Squares for Process Monitoring

In most chemical processes, variables are sampled at different rates which brings great challenges to traditional process monitoring methods that are built upon single sampling rate. In this paper, a multi-rate partial least squares algorithm is proposed. Compared to the traditional PLS method, the proposed algorithm takes use of the incomplete data samples through a modification of both of the covariance matrix of the input dataset and the covariance matrix between the input and output datasets. Iteration is used in the model training step to avoid the calculation of same parameters which requires complete training datasets. Then the fault detection and online prediction strategy is proposed based on this algorithm. A case study on TE process shows that the proposed method had an enhanced performance on both monitoring and online prediction, compared to the traditional PLS method.

IJARCCE - Computer and Communication Engineering

2 Abstract: An intrusion is essentially an attack on security layer in a TCP/IP protocol stack. Such attacks have definite signature for instance specific ports or RTT. Hence such attacks can be detected by cross verifying current communication signature with attack signatures. However attack signatures are evolutionary. Therefore using a string matching technique is neither robust nor fast. Hence several machine learning techniques are developed which are mainly based on classifier. These classifiers lack generalization capabilities which result in less performance leading to high false positives. However a specific attack can have wide range of signatures and a signature may of wide range of attacks. Therefore conventional classifiers like Neural Network needs frequent training when a new signature is discovered. Again discovery of such new signature also needs a regression with existing signature database. In order to avoid exploding the training nodes of Neural Network, it is important that a benchmark is set of introduce new nodes. In this paper we use Artificial Immune System mark the signatures as genes. A packet or network level signature is verified for closeness with existing model. In case of significant diversification is detected, the signature is marked as new which is regressed with the existing signature model to automate the grouping of the signature. Signature similar to existing ones is subjected to regression using PLS method and is then classified by neural network. This paper evaluates the performance of technique using the publicly available KDD Cup dataset and compares the result with conventional Neural Network Based Classifier, Support Vector Machine based Classifier, pure regression based technique and conventional string matching technique. Further we investigate the real time applicability of the technique by using PLS Regression to detect anomaly in CIT college router dataset. We consider that the connections by peer clients should only be accessing internet. Other activities like using Bittorrent are considered as anomaly. First we take router log and extract the features. We then select a specific data row and classify it using auto regression.

A consensus successive projections algorithm – multiple linear regression method for analyzing near infrared spectra

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.

Travel-Time Ultrasonic Computed Tomography Applied to Quantitative 2-D Imaging of Standing Trees: A Comparative Numerical Modeling Study

The quality of the Ultrasonic Computed Tomography (UCT) imaging device, in term of spatial resolution, sensitivity or dynamic, is a function of the wavelength, the number of transducers, the ultrasonic field generated and the inversion algorithm. This latter factor was studied using four inversion techniques for the tomography of trees. Two new methods, Partial Least Squares method - PLS, and Layer Stripping - LS, are compared to two classical methods, Filtered Backprojection - FBP, and Simultaneous Iterative Reconstruction Technique - SIRT. An original numerical phantom of tree was used and the effects of the variation of the number of transducers and the noise level were analyzed. The PLS was found to converge slowly but reached a high performance for the reconstruction of the projection values (slowness). PLS method was also characterized by a fast computation time. However, the PLS method gave a poor image quality (high sensitivity to noise level and presence of outlier values). The main advantage of the LS method was its high robustness to the noise level. The computing time was however the weakest point of LS method. A combination of the two classic FBP-SIRT methods seemed the most suitable except for the computation time. However, an improvement of the convergence conditions of PLS method (very fast computing time; including also curved rays), with the use of SIRT method, would constitute a promising solution.

핵심 거래처 지원활동이 제조업체의 성과에 미치는 영향

Key account management (KAM) is an approach adopted by manufacturers to build strong relationship with their key accounts. To build strong relationship manufacturers offer their key accounts tailored product/service packages on a continuing basis. Because of technological changes and emergence of larger retailers, the importance of key accounts to manufacturers are increasing. However, related research about key account management is still rare in domestic market setting. Our study focused on the effects of the manufacturers’support activities toward their key accounts. To investigate the support activities, we suggest five different support dimensions-product, incentive, service, information, human. The effects of support activities on manufacturers’ performance are measured by relationship performance and economic performance. Additionally, we examined the firm size influence on such support activities. To test our hypothesis, the data was collected from marketing and sales department personnel of manufacturers and PLS method was used to analyze the data. Our finding shows that supports related to product, services, and human have positive effects on the key account’s trust towards the manufacturers. Incentive and information supports have positive effects on the manufacturer’s economic performance. Also, our result reveals that higher level of key account’s trust towards the manufacturer has positive effects on economic performance of manufacturers. We also finds that effective support activities vary depending on the size of manufacturers. Our study contributes to the literature in the following ways. First, this research is the first attempt of trying to understand the effects of key account management in domestic market. Second, unlike previous studies which have treated support activities as one dimension, our study suggests five different dimensions of manufacturer’s support activities and try to clarify the effects of each dimension of support activities. We hope our result can provide more specific guidance to practitioners as they can understand the effects of each of their support activities.

Enhancing Business Performance through Learning Organization and Knowledge Sharing Capability: the Mediating Effect of Innovation Capability using PLS Method

This research aims to analyze descriptive condition of Learning Organization, Knowledge Sharing Capability, Innovation Capability, and Business Performance perceived by employees at PT SIT GLOBAL SYSTEMS, an IT consultant company. Besides, research is to analyze influence of Learning Organization and Knowledge Sharing Capability (exogenous variables) on Innovation Capability and Business Performance (endogenous variables) in the company. This research used descriptive statistics and associative methods, with primary and secondary data obtained from observation, interview, and questionnaire to 50 employees as respondents. The data then were analyzed with SmartPLS 2.0 software by measuring loading factor (convergent validity), cross loading (discriminant validity), and reliability for outer model, and interpretation of R2 value, path coefficient, and total effect for inner model. Research found that the descriptive condition of Learning Organization and Knowledge Sharing Capability was on average level, whereas the descriptive condition of Innovation Capability and Business Performance was perceived well by the employees. Research concluded that Learning Organization has positive and significant influence on Innovation Capability; Knowledge Sharing Capability has positive and significant influence on Innovation Capability; Innovation Capability has positive and significant influence on Business Performance; Learning Organization has positive and significant influence on Business Performance, directly and indirectly through Innovation Capability; and Knowledge Sharing Capability has positive and significant influence on Business Performance, directly and indirectly through Innovation Capability.

Structural requirements of some 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide derivatives as poly (ADP-ribose) polymerase (PARP) for the treatment of cancer: QSAR approach.

The present study is aimed to elucidate the structural features of substituted 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide required for poly (ADP-ribose) polymerase inhibition and to obtain predictive 2D QSAR models to guide the rational synthesis of novel poly (ADP-ribose) polymerase inhibitors. The statistical analysis has shown that excellent results are obtained by using partial least regression based on simulated annealing method. The best model was selected based on the highest correlation coefficient r2 = 0.8590 and cross validated squared correlation coefficient q2 = 0.7875 with external predictive ability of pred_r2 = 0.7407 was developed by stepwise PLS method with the descriptors like T_N_F_1, SdsCHcount, and Rotatable Bond Count. The generated models provide insight into the influence of various interactive fields on the activity and, thus, can help in designing and forecasting the inhibition activity of novel (ADP-ribose) polymerase molecules.

Data Mining of Chemogenomics Data Using Bi-Modal PLS Methods and Chemical Interpretation for Molecular Design.

Chemogenomics is a new strategy in drug discovery for interrogating all molecules capable of interacting with all biological targets. Because of the almost infinite number of drug-like organic molecules, bench-based experimental chemogenomics methods are not generally feasible. Several in silico chemogenomics models have therefore been developed for high-throughput screening of large numbers of drug candidate compounds and target proteins. In previous studies, we described two novel bi-modal PLS approaches. These methods provide a significant advantage in that they enable direct connections to be made between biological activities and ligand and protein descriptors. In this special issue, we review these two PLS-based approaches using two different chemogenomics datasets for illustration. We then compare the predictive and interpretive performance of the two methods using the same congeneric data set.

Rapid Quantification of Vitamin C in Navel Oranges by Near-Infrared Diffuse Reflectance Spectroscopy

In this paper, NIR diffuse reflectance spectroscopy as well as the PLS method was adopted for rapid determination of vitamin C in navel oranges. The GN method was employed to select calibration and validation samples. The spectral data in the region 900-1600nm were used for modeling. The coefficient of determination of the validation set is 0.8690, and RMSECV is 1.039mg/100g. The results suggest that NIR diffuse reflectance spectroscopy may serve as a strong tool for developing an on-line fruit sorting system.

Simultaneous determination of atrazine and metabolites (DIA and DEA) in natural water by multivariate electronic spectroscopy

A simple and rapid analytical method involving solid-phase extraction (SPE) and multivariate electronic spectroscopy for the determination of atrazine (ATZ) and its main metabolites desethylatrazine (DEA) and deisopropylatrazine (DIA) in aqueous solution was proposed. The predictive capability of the multivariate method developed by partial least squares regression was satisfactory, allowing mean errors between 0.33 and 10% in the simultaneous determination of ATZ, DIA and DEA in aqueous solution. The SPE procedure was optimized by factorial design, using ATZ as a model substrate. Under optimized experimental conditions, the recoveries of ATZ from aqueous standard solutions ranged from 83 to 116%. The developed method was applied in the analysis of spiked natural waters (river and spring) with recoveries in the range of 73–115% for ATZ in spring water. In the analysis of these complex samples, an important matrix effect was observed probably because of significant effects in both the extraction and quantification processes. Although the multivariate spectroscopy in the analysis of natural waters was largely dependent on the application of preliminary cleanup techniques guided to the removal of organic matter, the SPE–UV–PLS method was simple, fast and operated with low running costs.

Analysis of Data Mining Method for Near Infrared Spectral Data of Dairy Products Based Orthogonality

There are advantages such as real time, quick, multi-constituents simultaneous measurement, green and no pollution, and so on, of using near infrared spectral method to detect concentration information of dairy products constituents. From time domain and frequency domain, near infrared spectral data mining methods based on orthogonality were separately researched to realize noise filtering and useful information extracting. For milk spectrum, methods in the time domain including Orthogonal Signal Correction and Direct Orthogonalization, Wavelet Transform de-noising method in the frequency domain, were separately explored and used in spectral preprocessing, PLS method was used to build calibration model, and processing effect of different data mining methods was compared with and analyzed. It is showed that in the time domain, using the orthogonality methods to make data mining for milk spectrum, the complex interfering signal and noise information uncorrelated with the measured constituents can be can reduced effectively, and the correlated information with the measured constituents is reserved in maximal limitation, prediction capability of calibration model is improved, furthermore, the Orthogonal Signal Correction method is better than the Direct Orthogonalization method, moreover, in the frequency domain, wavelet de-noising is better than the orthogonality methods within the time domain.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares (SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene (PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged. As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor. The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model

Microspheres have been developed as drug carriers in controlled drug delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and drug loading rate in risperidone poly(d,l-lactide-co-glycolide) (PLGA) microspheres. Various batches of risperidone PLGA microspheres were designed and prepared successfully. The particle size and drug-loading rate of all the samples were determined by a laser diffraction particle size analyzer and high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was applied to identify the outliers and choose the numbers of calibration set. Furthermore, a series of preprocessing methods were performed to remove signal noise in NIR spectra. Moving window PLS and radical basis function neural network (RBFNN) methods were employed to establish calibration model. Our data demonstrated that PLS-developed model was only suitable for drug loading analysis in risperidone PLGA microspheres. Comparatively, RBFNN-based predictive models possess better fitting quality, predictive effect, and stability for both drug loading rate and particle size analysis. The correlation coefficients of calibration set (Rc2) were 0.935 and 0.880, respectively. The performance of optimum RBFNN models was confirmed by independent verification test with 15 samples. Collectively, our method is successfully performed to monitor drug-loading rate and particle size during risperidone PLGA microspheres preparation.

Application of orthogonal L-shaped PLS to chemogenomic data and its chemical interpretation from predictive and orthogonal latent variables

We carried out a validation study of the orthogonal L-shaped PLS (OLPLS) method using chemogenomic data based on adenosine receptor inhibitor activity measurements. Using OLPLS, the ligand and protein descriptors could be connected to eight adenosine receptor inhibitor activities. The fingerprints representing specific chemical substructures on the ligands were used as the ligand descriptors, while z-scales were used as the protein descriptors. Three clusters were observed in the chemical and protein spaces from the predictive scores and loadings. From these, the predictive and orthogonal ligand structure fragments towards three adenosine receptors could be successfully elucidated. The predictive fragment for the human adenosine 2A receptor was confirmed by comparison to the X-ray crystal structure. As expected, the orthogonal fragments contained no physicochemical features required for specific interaction with the adenosine receptors.

The Effects of Chance Correlations on Partial Least Squares Path Modeling

Partial least squares path modeling (PLS) has been increasing in popularity as a form of or an alternative to structural equation modeling (SEM) and has currently considerable momentum in some management disciplines. Despite recent criticism toward the method, most existing studies analyzing the performance of PLS have reached positive conclusions. This article shows that most of the evidence for the usefulness of the method has been a misinterpretation. The analysis presented shows that PLS amplifies the effects of chance correlations in a unique way and this effect explains prior simulations results better than the previous interpretations. It is unlikely that a researcher would willingly amplify error, and therefore the results show that the usefulness of the PLS method is a fallacy. There are much better ways to compensate for the attenuation effect caused by using latent variable scores to estimate SEM models than creating a bias into the opposite direction.

Applications Of Opls Statistical Method In Medicine

Studies related to prognosis in medicine result in a large volume of variables if clinical and laboratory variables are simultaneously accompanied with new imaging techniques; this issue causes problems for classical statistical methods such as logistic and linear regression. Among these cases, emergence of multicollinearity or close linear correlation between regression variables when the number of regression variables is high can be pointed out. Emergence of multicollinearity is inappropriate for ordinary least squares of regression model. PLS is a well-known method for connecting two X and Y data matrices using a multicollinearity model. OPLS is the product of a change which has occurred on PLS method in recent years. Considering application problems of linear regression method, applying an alternative method is a requirement. Using OPLS method can reduce model complexity and develop its power.

Assessing the effect of salinity on the oil quality parameters of Indian mustard (<em>Brassica juncea</em> L. <em>Czern & Coss</em>) using Fourier Transform Near-Infrared Reflectance (FT-NIR) spectroscopy

Calibration models were developed for Fourier Transform Near Infrared Reflectance spectroscopy using PLS method and coefficients of determination (r 2 ) for oil, protein, erucic acid and crude fiber contents were estimated 0.907, 0.922, 0.902, 0.903, respectively. Effects of the salinity on these traits were studied under normal and saline environments in the field (ECe 10.7dS·m -1 ) and nethouse using saline irrigation levels (EC iw 0, 9, 12, 15, 18 dS/m) during 2011–12 and 2012–13. At higher salinity; oil, protein and crude fiber contents decreased by 7.27, 14.78, 34.25% and 5.78, 29.31, 20.45% in nethouse and field conditions respectively, whereas erucic acid content increased by 72.43 and 12.20%. Thus, FT-NIRS may be useful for quick and nondestructive estimations of oil quality parameters in Indian mustard.

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR) of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC50 values (Log of LC50) against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R2) values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q2 (0.74).

The impact of treatment of cereal mashes with hydrolases of non-starch polysaccharides and pullulanase on the chemical composition of the obtained distillates

The distilling industry has been looking for more efficient technologies for spirit production. The pressureless starch liberation method (PLS) is an energy-saving alternative to high-pressure treatment of starch raw materials. Also, one of the promising solutions is the use of supportive enzymatic preparations during the mashing of starch raw materials. Rye and corn mashes obtained by the PLS method were treated with enzymes (pullulanase, xylanase, cellulases and cellobiase). The composition of volatile compounds in the distillates of the fermentation mashes was determined. Acetaldehyde was the dominant aliphatic carbonyl compound found in the raw distillates. Rye-based fermentation distillates contained small amounts of propionaldehyde, isobutyraldehyde and isovaleraldehyde, which were not found in any of the corn distillates. The treatment of mashes with cellulolytic preparations caused an increase in the methanol content of the obtained distillates. All of the mashes treated with auxiliary enzymes yielded raw distillates with much higher levels of higher alcohols. The application of hydrolases of non-starch polysaccharides, or pullulanase preparations, during the mashing of the starch raw materials affects the chemical composition of the distillates. Xylanase and pullulanase can be used in the processing of rye and corn mashes for subsequent use in distilled ethanol beverage production. Copyright © 2014 The Institute of Brewing & Distilling