To improve the color stability of facial prosthesis silicone rubber, this paper studied the effect of nano titanium oxide with different surface treatments on the color stability of red ...pigment-colored silicone rubber. Under the simulated sunlight aging condition, this paper takes MDX4-4210 silicone rubber as the matrix, silicon aluminum-coated nano TiO2 as the shading agent, and cadmium red oil paint as the colorant, and it observes the values of silicon aluminum-coated nano-TiO2 silicone rubber film with 1 mm thickness and different concentrations (0, 0.05%, 0.10%, and 0.15%) before and after aging. The experimental results showed that in the four concentrations of silicon aluminum-coated nano-TiO2 film, the ΔE,ΔL∗, Δa∗, Δb∗ values gradually decreased with the increase of the concentration of silicon aluminum-coated nano-TiO2. The lowest was in the 0.10% group; however, it increased in the 0.15% group. There was a significant difference among the concentration groups P<0.05. The method of covering nano-TiO2 silicone rubber film with different concentrations of silicon aluminum has a certain effect on delaying the discoloration of prosthetic silicone rubber, and it provides a new idea for improving the color stability of the prosthetic silicone rubber.
Industrial process plants are instrumented with a large number of redundant sensors and the measured variables are often contaminated by random noises. Thus, it is significant to discover the general ...trends of data by latent variable models in the probabilistic framework before soft sensor modeling. However, traditional probabilistic latent variable models such as probabilistic principal component analysis are mostly static linear approaches. The process dynamics and nonlinearities have not been well considered. In this paper, a novel weighted linear dynamic system (WLDS) is proposed for nonlinear dynamic feature extraction. In WLDS, two kinds of weights are proposed for local linearization of the nonlinear state evolution and state emission relationships. In this way, a weighted log-likelihood function is designed and expectation-maximization algorithm is then used for parameter estimation. The feasibility and effectiveness of the proposed method is demonstrated with a numerical example and an industrial process application.
Deep learning is a recently developed feature representation technique for data with complicated structures, which has great potential for soft sensing of industrial processes. However, most deep ...networks mainly focus on hierarchical feature learning for the raw observed input data. For soft sensor applications, it is important to reduce irrelevant information and extract quality-relevant features from the raw input data for quality prediction. To deal with this problem, a novel deep learning network is proposed for quality-relevant feature representation in this article, which is based on stacked quality-driven autoencoder (SQAE). First, a quality-driven autoencoder (QAE) is designed by exploiting the quality data to guide feature extraction with the constraint that the potential features should largely reconstruct the input layer data and the quality data at the output layer. In this way, quality-relevant features can be captured by QAE. Then, by stacking multiple QAEs to construct the deep SQAE network, SQAE can gradually reduce irrelevant features and learn hierarchical quality-relevant features. Finally, the high-level quality-relevant features can be directly applied for soft sensing of the quality variables. The effectiveness and flexibility of the proposed deep learning model are validated on an industrial debutanizer column process.
In modern industrial processes, soft sensors have played increasingly important roles for effective process monitoring, control and optimization. Deep learning has shown excellent ability for ...hierarchical nonlinear feature representation in soft sensors. However, the existing deep learning based soft sensors are mostly trained offline and applied online without updating mechanism. This may cause their performance degradation in time-varying processes. To deal with this problem, an adaptive updating framework is proposed for deep learning, which is based on just-in-time fine-tuning of stacked autoencoder (JIT-SAE). In JIT-SAE, an offline SAE model is first trained with layer-wise unsupervised pre-training and supervised fine tuning. For online prediction, the network is dynamically fine-tuned upon the query sample. For each query sample, the most relevant labeled samples are selected to form a fine-tuning dataset from the historical labeled database, which is regularly augmented once new labeled samples are available from laboratory analysis. Moreover, each relevant sample is assigned with a weight according to its similarity with the query sample. Then, the deep network is fine-tuned with these relevant labeled samples by designing a weighted loss function. Thus, JIT-SAE is able to track the newest process running state timely and match the data pattern accurately. Case study on an industrial hydrocracking process is provided to demonstrate the effectiveness of the JIT-SAE framework.
Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. However, traditional JITL approaches mainly focus on equal sample sizes between process ...(input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised weighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and weight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding weights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results.
In recent years, proinflammatory cytokine interleukin-1β (IL-1β) was considered to play a critical role in the pathogenesis of depression. In addition, P2X7 receptor (P2X7R), a member of the ...purinergic receptor family, which is predominantly present on microglia, as well as on astrocytes and neurons in lesser amounts in the central nervous system, was suggested to be involved in the processing and releasing of IL-1β. Here, we investigated the role of P2X7R in the pathogenesis of depression.
Male Sprague-Dawley rats were subjected to chronic unpredictable stressors (CUS) for 3 weeks. At the end of week 1, 2, and 3, extracellular ATP, caspase 1, IL-1β, and components and activation of NLRP3 inflammasome (nucleotide-binding, leucine-rich repeat, pyrin domain containing 3) were evaluated as biomarker of neuroinflammation. In separate experiments, the rats were microinjected with P2X7R agonists ATP, BzATP, and saline into the hippocampus, respectively, or exposed to CUS combined with hippocampal microinjection with P2X7R antagonist, BBG and A438079, and saline, respectively, for 3 weeks, followed by exposed to forced swimming test and open-field test. Moreover, we also evaluated the depressive and anxiety-like behavior of P2X7-null mice in forced swimming test, open-field test, and elevated plus maze.
Along with stress accumulation, extracellular ATP, cleaved-caspase 1, IL-1β, and ASC were significantly enhanced in the hippocampus, but P2X7R and NLRP3 were not. Immunoprecipitation assay indicated that along with the accumulation of stress, assembly of NLRP3 inflammasome and cleaved caspase 1 in NLRP3 inflammasome were significantly increased. Moreover, antagonists of P2X7R, either BBG or A438079, prevented the development of depressive-like behaviors induced by chronic unpredictable stress in rats. Meanwhile, we could not observe any depressive-like or anxiety-like behaviors of P2X7-null mice after they had been exposed to CUS. The results implied that P2X7 knockout could impede the development of depressive-like and anxiety-like behaviors induced by CUS. In contrast, chronic administration of agonists of P2X7R, either ATP or BzATP, could induce depressive-like behaviors.
The activation of P2X7R and subsequent NLRP3 inflammasome in hippocampal microglial cells could mediate depressive-like behaviors, which suggests a new therapeutic target for the prevention and treatment of depression.
Wide band gap (WBG) power electronic devices, such as silicon carbide metal–oxide–semiconductor field-effect transistors (SiC MOSFETs) and gallium–nitride high-electron-mobility transistors (GaN ...HEMTs) have been widely used in various fields and occupied a certain share of the market with rapid momentum, owing to their excellent electrical, mechanical, and thermal properties. The reliability of WBG power electronic devices is inseparable from the reliability of power electronic systems and is a significant concern for the industry and for academia. This review attempts to summarize the recent progress in the failure mechanisms of WBG power electronic semiconductor chips, the reliability of WBG power electronic packaging, and the reliability models for predicting the remaining life of WBG devices. Firstly, the typical structures and dominant failure mechanisms of SiC MOSFETs and GaN HEMTs are discussed. This is followed by a description of power electronic packaging failure mechanisms and available packaging materials for WBG power electronic devices. In addition, the reliability models based on physics-of-failure (including time-dependent dielectric breakdown models, stress–strain models, and thermal cycling models), and data-driven models are introduced. This review may provide useful references for the reliability research of WBG power devices.
Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for ...the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.
In this work, a simple, label-free, low cost electrochemical biosensor for highly sensitive and selective detection of Escherichia coli has been developed on the basis of rolling circle amplification ...(RCA) coupled peroxidase-mimicking DNAzyme amplification. A aptamer-primer probe (APP) containing anti-E. coli aptamer and a primer sequence complementary to a circular probe, which includes two G-quadruplex units, is used for recognizing target and triggering RCA-based polymerase elongation. Due to RCA coupled DNAzyme amplification strategy, the presence of target E. coli leads to the formation of numerous G-quadruplex oligomers on electrode, which folds into G-quadruplex/hemin complexs with the help of K+ and hemin, thus generating extremely strong catalytic activity toward H2O2 and giving a remarkably strong electrochemical response. As far as we know, this work is the first time that RCA coupled peroxidase-mimicking DNAzyme amplification technique have been integrated into electrochemical assay for detecting pathogenic bacteria. Under optimal conditions, the proposed biosensor exhibits ultrahigh sensitivity toward E. coli with detection limits of 8cfumL−1 and a detection range of 5 orders of magnitude. Besides, our biosensor also shows high selectivity toward target E. coli and has the advantages in its rapidness, low cost, simplified operations without the need of electrochemical labeling steps and additional labile reagents. Hence, the RCA coupled peroxidase-mimicking DNAzyme amplification-based electrochemical method might create a useful and practical platform for detecting E. coli and related food safety analysis and clinical diagnosis.
•A label-free electrochemical biosensor for detecting E. coli has been developed.•RCA coupled DNAzyme amplification has been integrated into electrochemical assay.•DNAzyme is used as catalytic tag, making our approach technically label-free.
Optimal Mass Transport for Shape Matching and Comparison Su, Zhengyu; Wang, Yalin; Shi, Rui ...
IEEE transactions on pattern analysis and machine intelligence,
2015-Nov.-1, 2015-Nov, 2015-11-1, 20151101, Letnik:
37, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Surface based 3D shape analysis plays a fundamental role in computer vision and medical imaging. This work proposes to use optimal mass transport map for shape matching and comparison, focusing on ...two important applications including surface registration and shape space. The computation of the optimal mass transport map is based on Monge-Brenier theory, in comparison to the conventional method based on Monge-Kantorovich theory, this method significantly improves the efficiency by reducing computational complexity from O(n 2 ) to O(n). For surface registration problem, one commonly used approach is to use conformal map to convert the shapes into some canonical space. Although conformal mappings have small angle distortions, they may introduce large area distortions which are likely to cause numerical instability thus resulting failures of shape analysis. This work proposes to compose the conformal map with the optimal mass transport map to get the unique area-preserving map, which is intrinsic to the Riemannian metric, unique, and diffeomorphic. For shape space study, this work introduces a novel Riemannian framework, Conformal Wasserstein Shape Space, by combing conformal geometry and optimal mass transport theory. In our work, all metric surfaces with the disk topology are mapped to the unit planar disk by a conformal mapping, which pushes the area element on the surface to a probability measure on the disk. The optimal mass transport provides a map from the shape space of all topological disks with metrics to the Wasserstein space of the disk and the pullback Wasserstein metric equips the shape space with a Riemannian metric. We validate our work by numerous experiments and comparisons with prior approaches and the experimental results demonstrate the efficiency and efficacy of our proposed approach.
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