Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process

Due to the recent rapid growth of advanced sensing and production technologies, the monitoring and diagnosis of multivariate process operating performance have drawn increasing interest in process industries. The multivariate statistical process control (MSPC) chart is one of the most commonly used tools for detecting process faults. However, an out-of-control MSPC signal only indicates that process faults have intruded the underlying process. Identifying which of the monitored quality variables is responsible for the MSPC signal is fairly difficult. Pinpointing the responsible variable is vital for process improvement because it effectively determines the root causes of the process faults. Accordingly, this identification has become an important research issue concerning recent multivariate process applications. In contrast with the traditional single classifier approach, the present study proposes hybrid modeling schemes to address problems that involve a large number of quality variables in a multivariate normal process. The proposed scheme includes multivariate adaptive regression splines (MARS), logistic regression (LR), and artificial neural network (ANN). By applying MARS and LR techniques, we may obtain fewer but more significant quality variables, which can serve as inputs to the ANN classifier. The performance of our proposed approaches was evaluated by conducting a series of experiments

Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan

Crude oil is the most important nonrenewable energy resource and the most key element for the world. In contrast to typical forecasts of oil price, this study aims at forecasting the demand of imported crude oil (ICO). This study proposes different single stage and two-stage hybrid stages of forecasting models for prediction of ICO in Taiwan. The single stage forecasting modeling includes multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), and extreme learning machine (ELM) approaches. While the first step of the two-stage modeling is to select the fewer but more significant explanatory variables, the second step is to generate predictions by using these significant explanatory variables. The proposed two-stage hybrid models consist of integration of different modeling components. Mean absolute percentage error, root mean square error, and mean absolute difference are utilized as the performance measures. Real data set of crude oil in Taiwan for the period of 1993–2010 and twenty-three associated explanatory variables are sampled and investigated. The forecasting results reveal that the proposed two-stage hybrid modeling is able to accurately predict the demand of crude oil in Taiwan

Immune Responses in Pigs Induced by Recombinant DNA Vaccine Co-Expressing Swine IL-18 and Membrane Protein of Porcine Reproductive and Respiratory Syndrome Virus

In this study, two DNA vaccines, which express the membrane (M) protein of porcine respiratory and reproductive syndrome virus (PRRSV) (pEGFP-M) and co-express both M and swine IL-18 (pEGFP-IL18-M), were constructed and their abilities to induce humoral and cellular responses in piglets were comparatively evaluated. Experimental results showed that both recombinant DNA vaccines could not elicit neutralizing antibodies in the immunized piglets. However, both DNA vaccines elicited Th1-biased cellular immune responses. Notably, pigs immunized with the plasmid pEGFP-IL18-M developed significantly higher levels of IFN-γ and IL-2 production response and stronger specific T-lymphocyte proliferation response than the pigs inoculated with the plasmids pEGFP-M and pEGFP-IL18 (P < 0.05). These results illustrated that co-expression of M and IL-18 proteins could significantly improve the potency of DNA vaccination on the activation of vaccine-induced virus-specific cell-mediated immune responses in pigs, which may be used as a strategy to develop a new generation of vaccines against highly pathogenic PRRSV

StemCellNet: an interactive platform for network-oriented investigations in stem cell biology.

Stem cells are characterized by their potential for self-renewal and their capacity to differentiate into mature cells. These two key features emerge through the interplay of various factors within complex molecular networks. To provide researchers with a dedicated tool to investigate these networks, we have developed StemCellNet, a versatile web server for interactive network analysis and visualization. It rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer. The StemCellNet web server is freely accessible at http://stemcellnet.sysbiolab.eu

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A simple approximation for the distribution of the weighted combination of non-independent or independent probabilities

This paper presents a simple approximation for the distribution of the weighted Fisher's statistic , which is suitable for use when P1,P2,...,Pk are non-independent or independent tail-area probabilities arising from k continuous distributions, and w1,w2,...,wk are constants.Combination of significance tests Fisher's method of combining probabilities Weighted combination

Integrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray

With the recent development of biotechnologies, cDNA microarray chips are increasingly applied in cancer research. Microarray experiments can lead to a more thorough grasp of the molecular variations among tumors because they can allow the monitoring of expression levels in cells for thousands of genes simultaneously. Accordingly, how to successfully discriminate the classes of tumors using gene expression data is an urgent research issue and plays an important role in carcinogenesis. To refine the large dimension of the genes data and effectively classify tumor classes, this study proposes several hybrid discrimination procedures that combine the statistical-based techniques and computational intelligence approaches to discriminate the tumor classes. A real microarray data set was used to demonstrate the performance of the proposed approaches. In addition, the results of cross-validation experiments reveal that the proposed two-stage hybrid models are more efficient in discriminating the acute leukemia classes than the established single stage models

Hybrid artificial neural networks modeling for faults identification of a stochastic multivariate process,”

Due to the recent rapid growth of advanced sensing and production technologies, the monitoring and diagnosis of multivariate process operating performance have drawn increasing interest in process industries. The multivariate statistical process control (MSPC) chart is one of the most commonly used tools for detecting process faults. However, an out-of-control MSPC signal only indicates that process faults have intruded the underlying process. Identifying which of the monitored quality variables is responsible for the MSPC signal is fairly difficult. Pinpointing the responsible variable is vital for process improvement because it effectively determines the root causes of the process faults. Accordingly, this identification has become an important research issue concerning recent multivariate process applications. In contrast with the traditional single classifier approach, the present study proposes hybrid modeling schemes to address problems that involve a large number of quality variables in a multivariate normal process. The proposed scheme includes multivariate adaptive regression splines (MARS), logistic regression (LR), and artificial neural network (ANN). By applying MARS and LR techniques, we may obtain fewer but more significant quality variables, which can serve as inputs to the ANN classifier. The performance of our proposed approaches was evaluated by conducting a series of experiments

Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance

Due to its importance in process improvement, the issue of determining exactly when faults occur has attracted considerable attention in recent years. Most related studies have focused on the use of the maximum likelihood estimator (MLE) method to determine the fault in univariate processes, in which the underlying process distribution should be known in advance. In addition, most studies have been devoted to identifying the faults of process mean shifts. Different from most of the current research, the present study proposes an effective approach to identify the faults of variance shifts in a multivariate process. The proposed mechanism comprises the analysis of variance (ANOVA) approach, a neural network (NN) classifier, and an identification strategy. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments is conducted, and the best results from our proposed approach are addressed