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Summary Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions., Graphical abstract, Highlights • 3500 primary microarray data from ATH1 Affymetrix chip were normalized using SCAN • A tree with 21,287 Arabidopsis thaliana gene coexpression was constructed using UPGMA • ACT is a web tool that identifies genes which are coexpressed to a driver gene • ACT performs biological term enrichment analysis in coexpressed gene tree clades, Plant genetics; Bioinformatics; Plant bioinformatics

In this work, titanium-free and titanium carbide-strengthened dual-phase steels were studied. While both had the same base composition of Fe–0.1C–1.5Mn–0.5Si–0.6Cr (wt%), the latter included a 0.1 wt% Ti addition for interphase precipitation. Utilizing the Gleeble thermal simulations, both two dual-phase steels were produced with the same ferrite grain size and the same ferrite/martensite volume fractions of 0.7 and 0.3, respectively. Their corresponding microstructures (including deformed structures) and mechanical properties were examined. The results indicate that the titanium carbide-strengthened dual-phase steel possesses an excellent combination of the strengthened ferrite and the weakened martensite, which brings about increased yield and tensile strengths without sacrificing total elongation.

Defective wafer detection is essential to avoid loss of yield due to process abnormalities in semiconductor manufacturing. For most complex processes in semiconductor manufacturing, various sensors are installed on equipment to capture process information and equipment conditions, including pressure, gas flow, temperature, and power. Because defective wafers are rare in current practice, supervised learning methods usually perform poorly as there are not enough defective wafers for fault detection (FD). The existing methods of anomaly detection often rely on linear excursion detection, such as principal component analysis (PCA), k-nearest neighbor (kNN) classifier, or manual inspection of equipment sensor data. However, conventional methods of observing equipment sensor readings directly often cannot identify the critical features or statistics for detection of defective wafers. To bridge the gap between research-based knowledge and semiconductor practice, this paper proposes an anomaly detection method that uses a denoise autoencoder (DAE) to learn a main representation of normal wafers from equipment sensor readings and serve as the one-class classification model. Typically, the maximum reconstruction error (MaxRE) is used as a threshold to differentiate between normal and defective wafers. However, the threshold by MaxRE usually yields a high false positive rate of normal wafers due to the outliers in an imbalanced data set. To resolve this difficulty, the Hampel identifier, a robust method of outlier detection, is adopted to determine a new threshold for detecting defective wafers, called MaxRE without outlier (MaxREwoo). The proposed method is illustrated using an empirical study based on the real data of a wafer fabrication. Based on the experimental results, the proposed DAE shows great promise as a viable solution for on-line FD in semiconductor manufacturing.

Highlights? The paper used the health examinations to uncover risk factors. ? The risk factors used to construct the early warning criteria by data mining. ? The early-warning criteria will lead to detect chronic illnesses earlier. ? The patients can reduce the risk of suffering from the relate complications. Chronic disease has gradually become a major fatality cause in Taiwan. Being afflicted with such illnesses elevate vulnerability to other complications as well. Therefore, this paper adopts a preventative perspective and ascertains the impacts of important physiological indicators and clinical test values for various chronic illnesses. The paper investigates five chronic diseases: hypertension, diabetes, cardiovascular disease, disease of the liver, and renal disease. Utilizing chronic diseases risk factors to establish early-warning criteria may reduce the complication occurrence rate. K-nearest neighbor, linear discriminant analysis, and sequential forward selection are utilized, which is divided into two parts. The first part classifies and screens both healthy persons and those affiliated with the abovementioned chronic illnesses for characteristic value determination. The second part determines the critical value of the important risk factors of each chronic illness and builds early-warning criteria to recognition the chronic illnesses. This paper uses data from a medical center in Taiwan to verify the proposed methodology. The results reveal that classifying materials and screening important factors are both positively efficient with a corrected rate of 80%. Additionally, through the important factors of early-warning criteria, not only can help patients understand the risks of suffering diseases, but also effectively offer diagnosis reference criteria for medical personnel.

In general, the fuzzy Graphical Evaluation and Review Technique (GERT) usually evaluates/analyzes variables with interval arithmetic (α-cut arithmetic) operations, especially those with complicated fuzzy systems. Thus the interval arithmetic operations may occur accumulating phenomenon of fuzziness in complicated systems, and the accumulating phenomenon of fuzziness may make decision-maker that cannot effectively evaluate problems/systems under vague environment. In order to overcome the accumulating phenomenon of fuzziness or credibly reduce fuzzy spreads, this study adopts approximate fuzzy arithmetic operations under the weakest t-norm arithmetic operations (Tω) to evaluate fuzzy reliability models based on fuzzy GERT simulation technology. The approximate fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t-norm arithmetic operations. Therefore, the novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating, under vague environment. In numerical examples the approximate fuzzy arithmetic operations has evidenced that it can successfully calculate results of fuzzy operations as interval arithmetic, and can more effectively reduce fuzzy spreads. In the real fuzzy repairable reliability model the performance also shows that the approximate fuzzy arithmetic operations successfully analyze the reliability problem and obtain more confident fuzzy results.

In many practical situations, the quality of a process, or product, is better characterized and summarized by the relationship between a response variable and one or more explanatory variables. Such a relationship between the response variable and explanatory variables is called a profile. Recently, profile monitoring has become a fertile research field in statistical process control (SPC). To handle the nonlinear profile data, the proposal considered in this paper is that the entire curve is broken into several segments of data points that exhibit a statistical fit to the linear model, and therefore each of them can be monitored separately by using existing linear profile SPC methods. A new method that determines the locations of change points based on the slop change is proposed. Two goodness-of-fit criteria are utilized for determining the best number of change points to avoid over-fitting. Two nonlinear profile examples taken from the literature are used to illustrate the proposed change-point model. Monitoring performances using the existing T 2 and EWMA-based approaches are presented when the nonlinear profile data is fitted by using the proposed change-point model.

Many semiconductor processes have the major problem that die-level signature profiles often look completely different on different devices. During 'in-parallel' and 'in-series' manufacturing control procedures, equipment frequently suffer ageing and tool-wearing effects that induce control model changes and generate unstable control. Therefore, if a control system does not compensate for this variation, the next device will not carry out the required follow-up process control. To solve this problem, we propose a novel control framework that integrates the concepts of self-tuning control, on-line experiments, and linear discriminant analysis (LDA). In this paper, LDA with self-tuning control is used to evaluate process changes and adjust the process recipe. When the system determines that the process has generated a large change, the system applies the design-of-experiment concept continuously to collect process data over an expanded experimental range and uses these data with the least squares error (LSE) method to estimate new model parameters. The proposed control strategy then uses the minimized-total-cost principle and applies a trust-region algorithm to obtain a recipe for the next period. Simulation studies show that the proposed system is able to satisfy the control requirement precisely. Finally, a relevant semiconductor multiple-input multiple-output (MIMO) model is used to illustrate the necessary control procedure for verifying the practicability of the proposed system in a MIMO dynamic process.

During the past decade, a variety of run-to-run (R2R) control techniques have been proposed and extensively used to control various semiconductor manufacturing processes. The R2R control methodology combines response surface modeling, engineering process control, and statistical process control, with the main objective of fine-tuning the recipe so that the process output of each run can be maintained as close to the nominal target as possible. In this paper, the single-input single-output (SISO) model is addressed. To overcome the shortcomings in the traditional R2R EWMA controller, a fuzzy neural network (FNN) control strategy is proposed. When a process has large autoregressive parameters, traditional EWMA control methods cannot establish stable SISO process control. To solve this problem, an SISO process control model based on an FNN was used to build an SISO process control procedure. The analysis results from a numerical simulation indicated that when the coefficient of autocorrelation @f>0.6, the MSE ratio when using the FNN controller was 97.11% lower than when using the EWMA controller and 61.12% lower than when using an adaptive EWMA controller. This showed that the FNN control method established better SISO process control than the EWMA and adaptive EWMA control methods.

As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.