Root cause prediction for failures in semiconductor industry, a genetic algorithm-machine learning approach

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component's flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product's quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.

Machine learning techniques on homological persistence features for prostate cancer diagnosis

The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike’s phase contrast microscopy and Gabor’s holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%.

Prognostic factors after adrenalectomy for adrenal metastasis

PURPOSE

Very few studies have sought prognostic factors after adrenalectomy for metastasis. The aim of this study was to assess prognostic factors for oncological outcomes after adrenalectomy for adrenal metastasis.

METHODS

All adrenalectomies for metastases performed in seven centers between 2006 and 2016 were included in a retrospective study. Recurrence-free survival (RFS) and cancer-specific survival (CSS) were estimated using the Kaplan-Meier method. Prognostic factors for CSS and RFS were sought by Cox regression analyses.

RESULTS

106 patients were included. The primary tumors were mostly renal (47.7%) and pulmonary (32.3%). RFS and CSS estimated rates at 5 years were 20.7% and 63.7%, respectively. In univariate analysis, tumor size (HR 3.83; p = 0.04) and the metastasis timing (synchronous vs. metachronous; HR 0.47; p = 0.02) were associated with RFS. In multivariate analysis, tumor size (HR 8.28; p = 0.01) and metastasis timing (HR 18.60; p = 0.002) were significant factors for RFS. In univariate analysis, the renal origin of the primary tumor (HR 0.1; p < 0.001) and the disease-free interval (DFI; HR 0.12; p = 0.02) were associated with better CSS, positive surgical margins with poorer CSS (HR 3.4; p = 0.01). In multivariate analysis, the renal origin of the primary tumor vs. pulmonary (HR 0.13; p = 0.03) and vs. other origins (HR 0.10; p = 00.4) and the DFI (HR 0.01; p = 0.009) were prognostic factors for CSS.

CONCLUSION

In this study, tumor size and synchronous occurrence of the adrenal metastasis were associated with poorer RFS. Renal origin of the primary tumor and longer DFI were associated with better CSS. These prognostic factors might help for treatment decision in the management of adrenal metastasis.

Selection of discriminant mid-infrared wavenumbers by combining a naïve Bayesian classifier and a genetic algorithm: Application to the evaluation of lignocellulosic biomass biodegradation

Infrared spectroscopy provides useful information on the molecular compositions of biological systems related to molecular vibrations, overtones, and combinations of fundamental vibrations. Mid-infrared (MIR) spectroscopy is sensitive to organic and mineral components and has attracted growing interest in the development of biomarkers related to intrinsic characteristics of lignocellulose biomass. However, not all spectral information is valuable for biomarker construction or for applying analysis methods such as classification. Better processing and interpretation can be achieved by identifying discriminating wavenumbers. The selection of wavenumbers has been addressed through several variable- or feature-selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune, and others require additional information, such as concentrations. This paper proposes a new approach by combining a naïve Bayesian classifier with a genetic algorithm to identify discriminating spectral wavenumbers. The genetic algorithm uses a linear combination of an a posteriori probability and the Bayes error rate as the fitness function for optimization. Such a function allows the improvement of both the compactness and the separation of classes. This approach was tested to classify a small set of maize roots in soil according to their biodegradation process based on their MIR spectra. The results show that this optimization method allows better discrimination of the biodegradation process, compared with using the information of the entire MIR spectrum, the use of the spectral information at wavenumbers selected by a genetic algorithm based on a classical validity index or the use of the spectral information selected by combining a genetic algorithm with other methods, such as Linear Discriminant Analysis. The proposed method selects wavenumbers that correspond to principal vibrations of chemical functional groups of compounds that undergo degradation/conversion during the biodegradation of lignocellulosic biomass.

Evaluation of Lignocellulosic Biomass Degradation by Combining Mid- and Near-Infrared Spectra by the Outer Product and Selecting Discriminant Wavenumbers Using a Genetic Algorithm

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy provide useful information on the molecular composition of biological systems. Because they are sensitive to organic and mineral components, there is a growing interest in these techniques for the development of biomarkers that reflect intrinsic characteristics of plants and their mode of degradation. Due to their complexity and complementary nature, an important challenge is the combining of MIR and NIR information to identify discriminating wavenumbers in each wavenumber region, with the ultimate goal of assessing the biodegradation process of a lignocellulosic biomass at different time scales. This work investigates the potential of using the outer product to combine MIR and NIR spectra to highlight the connections between fundamental molecular vibrations and their combinations and bonds. Because this operation yields high-dimensional spectra, we propose to use a genetic algorithm to select the most discriminant wavenumbers within the degradation process. The results from two lignocellulosic biomasses with different biodegradation kinetics, miscanthus aerial parts and maize roots, confirm that the outer product combination of MIR and NIR spectral information allows a better discrimination of the biodegradation kinetic compared with the simple concatenation of MIR and NIR spectra or with the use of MIR or MIR spectral information separately. We show that the genetic algorithm selects wavenumbers that correspond to principal vibrations of chemical functional groups of compounds that undergo degradation/conversion during the biodegradation of the lignocellulosic biomass.

MDM2 restrains estrogen-mediated AKT activation by promoting TBK1-dependent HPIP degradation

Restoration of p53 tumor suppressor function through inhibition of its interaction and/or enzymatic activity of its E3 ligase, MDM2, is a promising therapeutic approach to treat cancer. However, because the MDM2 targetome extends beyond p53, MDM2 inhibition may also cause unwanted activation of oncogenic pathways. Accordingly, we identified the microtubule-associated HPIP, a positive regulator of oncogenic AKT signaling, as a novel MDM2 substrate. MDM2-dependent HPIP degradation occurs in breast cancer cells on its phosphorylation by the estrogen-activated kinase TBK1. Importantly, decreasing Mdm2 gene dosage in mouse mammary epithelial cells potentiates estrogen-dependent AKT activation owing to HPIP stabilization. In addition, we identified HPIP as a novel p53 transcriptional target, and pharmacological inhibition of MDM2 causes p53-dependent increase in HPIP transcription and also prevents HPIP degradation by turning off TBK1 activity. Our data indicate that p53 reactivation through MDM2 inhibition may result in ectopic AKT oncogenic activity by maintaining HPIP protein levels.

Synthesis and molecular structures of some new titanium(IV) aryloxides

The coordination chemistry of model phenolic ligands (pyrocatechol, salicylic acid, and 2,2'-biphenol) that are able to form respectively five-, six-, or seven-membered rings with titanium(IV) alkoxides is investigated. With pyrocatechol, a polynuclear complex containing 10 Ti atoms was characterized with a not very common doubly bridging mu3-(O,O,O',O') coordination mode. With salicylic acid, a monomeric tris(chelate) complex was obtained. With 2,2'-biphenol, a polynuclear complex containing six Ti atoms was obtained showing both mu2-(O,O') and mu2-(O,O,O') coordination modes for the ligands. Intermolecular interactions in the solid state for these three new compounds are also quantitatively discussed using the partial charge model.