Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for standard clinicopathological features such age, grade, and nodal status, yet testing required to elucidate these subtypes not routinely performed. Furthermore, bulk assays RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis therapeutic decision-making can be missed.As more facile readily available...

Traditional monitoring algorithms use the normal data for modeling, which are universal different types of faults. However, these may perform poorly sometimes because lack fault information. In order to further increase detection rate while preserving universality algorithm, a novel dynamic weight principal component analysis (DWPCA) algorithm and hierarchical strategy proposed. first layer, PCA is used diagnosis, if no detected, following DWPCA-based second layer will be triggered....

Process monitoring is an effective means to ensure process safety and improve product quality. On the one hand, it possible that fault will not affect or other every affects both quality simultaneously. To make more purposeful accurate, a novel performance-indicator-oriented concurrent subspace (PIOCS) method containing three subspaces with different degrees of importance proposed in this paper. The first safety-related subspace, second safety-unrelated quality-related third unrelated...

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to closed-loop control, it is possible that fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) proposed, which can not only tell whether occurs but also judge quality in real time. First, reduce process complexity construct an accurate monitoring model, original variable space divided...

Industrial processes are developing towards intelligence and complexity, which brings challenges to intelligent process monitoring. An effective fault diagnosis model plays a vital role in ensuring safety. However, labeling samples is time-consuming costly, make it hard obtain enough labeled train an diagnostic model. This motivates the development of semi-supervised learning basic idea use unlabeled data help limited for training. In this paper, consistency regularization auto-encoder...

To solve the problem of incipient fault detection, a targeted gated recurrent unit-canonical correlation analysis (CCA) method is proposed. First, this article proposed unit (FTGRU) to establish temporal feature extraction model. The features extracted by FTGRU are more sensitive faults, thus increasing accuracy detection Then, model established CCA method. In addition, in order ensure universality model, multilayer strategy At first layer, basic used. When no detected at second layer...

BACKGROUND A significant proportion of cancer patients experience autonomic dysfunction, and treatments such as chemotherapy radiation therapy can exacerbate impairments in the cardiac nervous system. This study sought to investigate characteristics heart rate variability (HRV) individuals with cancer. AIM To evaluate relationship between HRV patients, providing insights references for treatment. METHODS The included 127 available 24-hour dynamic electrocardiogram data. differences were...

In this article, a novel multimode quality-related process monitoring method called multisubspace elastic network (MSEN) is proposed. To make mode partition more precisely, article develops clustering algorithm based on the neighborhood information and subtractive algorithm. each single mode, unlike conventional models that only focus whether fault occurs, model established to judge quality related or not. addition, select most suitable for online data, k-nearest neighbor rule voting...

As industrial technology develops, processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent condition of whole process effect fault on quality indicators. Therefore, a novel multiblock decouple convolutional neural network (multiblock DCN) algorithm is proposed. First, key variables selected, grouped into multiple blocks for following monitoring. Then, in each block, proposed DCN constructs regression model between...

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. processes, the sampling rates of quality variables are always lower than those process variables. Meanwhile, among also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve problem, a deep-learning (DL) model based on multitemporal channels convolutional neural network (MC-CNN) is proposed....

Early treatment with AGD achieved a better clinical outcome in SAP patients.

Abstract The data collected from modern industrial processes always have nonlinear and dynamic characteristics. recently developed deep neural network method, stacked denoising auto‐encoder (SDAE), can extract robust latent variables against noise. However, it leaves the relationship unconsidered. To solve this problem, a novel algorithm named recursive (RSDAE) is proposed. learn relationship, RSDAE focuses on predictability of in recurrence to contain most variations. After variations are...