On-line recognition of abnormal patterns in bivariate autocorrelated process using random forest

It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes led to process abnormalities. This study proposed a pattern recognition model using Random Forest (RF) as pattern model to detect and identify the abnormalities in bivariate autocorrelated process. The simulation experiment results demonstrate that the model is superior on recognition accuracy (RA) (97.96%) to back propagation neural networks (BPNN) (95.69%), probability neural networks (PNN) (94.31%), and support vector machine (SVM) (97.16%). When experimenting with simulated dynamic process data flow, the model also achieved better average running length (ARL) and standard deviation of ARL (SRL) than those of the four comparative approaches in most cases of mean shift magnitude. Therefore, we get the conclusion that the RF model is a promising approach for detecting abnormalities in the bivariate autocorrelated process. Although bivariate autocorrelated process is focused in this study, the proposed model can be extended to multivariate autocorrelated process control

A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data

Identification of abnormal patterns in AR (1) process using CS-SVM

Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of eight patters are generated in experiments for verifying the performance of the proposed model. The results of comparison experiments show that the average recognition rate of the proposed model reaches 96.25% as the autocorrelation coefficient is set equal to 0.5. That is apparently higher than those of the SVM model optimized by the particle swarm optimization (PSO) or the genetic algorithm (GA). Another experiment result demonstrates that the average recognition accuracy of the CS-SVM model also reaches higher than 95% for different autocorrelation levels. At last, a lot of data streams in or out of control are simulated to measure the ARL values. The results turn out that the model has an acceptable online performance. Therefore, we believe that the model can be used as a more effective approach for identification of abnormal patterns in autocorrelation process

Exploiting knowledge graph for multi-faceted conceptual modelling using GCN

The relevant information obtained from multiple sources usually contributes to one intricate phenomenon in the industrial processes. Data fusion of different sources usually leads to more expressive and informative information than that of each single data source. Integrated information has been widely used to model a multi-faceted conceptual phenomenon, which provides a comprehensive and versatile view of understanding of the process. However, the conventional approaches concatenate feature vectors to integrate different facets, not considering the semantic gaps between them. Meanwhile, knowledge graph (KG) receives considerable attention in recent years as it comprises rich relational information among elements. Thus, KG provides a promising way to fuse multiple data sources by bridging the semantic gaps, which can be exploited in the modelling of a multi-faceted phenomenon. Inspired by the advancement of KG, we proposed an approach based on KG and a machine learning algorithm for multi-faceted modelling. Firstly, a domain-specified ontology was built to eliminate the varying distance metrics across facet boundaries, and KGs were generated by populating the data surrounding a multi-faceted phenomenon into this ontology. Secondly, the KGs were fed into a graph convolutional neural network (GCN) to learn the node features and the graph structure for graph embedding simultaneously with the shared parameters. Lastly, with the aim of multi-faceted conceptual modelling, the features obtained from the GCN model were used as inputs for machine learning algorithms to learn the hidden patterns of KGs. An experimental study using real-world data from the cold rolling process was conducted to demonstrate the feasibility of the proposed model

Serum CA72-4 is specifically elevated in gout patients and predicts flares

Objectives Serum CA72-4 levels are elevated in some gout patients but this has not been comprehensively described. The present study profiled serum CA72-4 expression in gout patients and verified the hypothesis that CA72-4 is a predictor of future flares in a prospective gout cohort. Methods To profile CA72-4 expression, a cross-sectional study was conducted in subjects with gouty arthritis, asymptomatic hyperuricaemia, four major arthritis types (OA, RA, SpA, septic arthritis) and healthy controls. A prospective gout cohort study was initiated to test the value of CA72-4 for predicting gout flares. During a 6-month follow-up, gout flares, CA72-4 levels and other gout-related clinical variables were observed at 1, 3 and 6 months. Results CA72-4 was highly expressed in patients with gouty arthritis [median (interquartile range) 4.55 (1.56, 32.64) U/ml] compared with hyperuricaemia patients [1.47 (0.87, 3.29) U/ml], healthy subjects [1.59 (0.99, 3.39) U/ml] and other arthritis patients [septic arthritis, 1.38 (0.99, 2.66) U/ml; RA, 1.58 (0.95, 3.37) U/ml; SpA, 1.56 (0.98, 2.85) U/ml; OA, 1.54 (0.94, 3.34) U/ml; P 6.9 U/ml) was the strongest predictor of gout flares (hazard ratio = 3.889). Prophylactic colchicine was effective, especially for patients with high CA72-4 levels (P = 0.014). Conclusion CA72-4 levels were upregulated in gout patients who experienced frequent flares and CA72-4 was a useful biomarker to predict future flares

Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals <i>SNORA55</i> as a driver of prostate cancer progression

Metastasis is the primary cause of death in prostate cancer (PCa) patients. Small nucleolar RNAs (snoRNAs) have long been considered "housekeeping" genes with no relevance for cancer biology. Emerging evidence has challenged this assumption, suggesting that snoRNA expression is frequently modulated during cancer progression. Despite this, no study has systematically addressed the prognostic and functional significance of snoRNAs in PCa. We performed RNA Sequencing on paired metastatic/non-metastatic PCa xenografts derived from clinical specimens. The clinical significance of differentially expressed snoRNAs was further investigated in two independent primary PCa cohorts (131 and 43 patients, respectively). The snoRNA demonstrating the strongest association with clinical outcome was quantified in PCa patient-derived serum samples and its functional relevance was investigated in PCa cells via gene expression profiling, pathway analysis and gene silencing. Our comparison revealed 21 differentially expressed snoRNAs in the metastatic vs. non-metastatic xenografts. Of those, 12 were represented in clinical databases and were further analyzed. SNORA55 emerged as a predictor of shorter relapse-free survival (results confirmed in two independent databases). SNORA55 was reproducibly detectable in serum samples from PCa patients. SNORA55 silencing in PCa cell lines significantly inhibited cell proliferation and migration. Pathway analysis revealed that SNORA55 expression is significantly associated with growth factor signaling and pro-inflammatory cytokine expression in PCa. Our results demonstrate that SNORA55 up-regulation predicts PCa progression and that silencing this non-coding gene affects PCa cell proliferation and metastatic potential, thus positioning it as both a novel biomarker and therapeutic target

Singlemode-Multimode-Singlemode Fiber Structures for Sensing Applications – A Review

A singlemode-multimode-singlemode (SMS) fiber structure consists of a short section of multimode fiber fusion-spliced between two SMS fibers. The mechanism underpinning the operation of an SMS fiber structure is multimode interference and associated self-imaging. SMS structures can be used in a variety of optical fiber systems but are most commonly used as sensors for a variety of parameters, ranging from macro-world measurands such as temperature, strain, vibration, flow rate, RI and humidity to the micro-world with measurands such as proteins, pathogens, DNA, and specific molecules. While traditional SMS structures employ a short section of standard multimode fiber, a large number of structures have been investigated and demonstrated over the last decade involving the replacement of the multimode fiber section with alternatives such as a hollow core fiber or a tapered fiber. The objective of replacing the multimode fiber has most often been to allow sensing of different measurands or to improve sensitivity. In this paper, several different categories of SMS fiber structures, including traditional SMS, modified SMS and tapered SMS fiber structures are discussed with some theoretical underpinning and reviews of a wide variety of sensing examples and recent advances. The paper then summarizes and compares the performances of a variety of sensors which have been published under a number of headings. The paper concludes by considering the challenges faced by SMS based sensing schemes in terms of their deployment in real world applications and discusses possible future developments of SMS fiber sensors

Identification of Amino Acids in HA and PB2 Critical for the Transmission of H5N1 Avian Influenza Viruses in a Mammalian Host

Since 2003, H5N1 influenza viruses have caused over 400 known cases of human infection with a mortality rate greater than 60%. Most of these cases resulted from direct contact with virus-contaminated poultry or poultry products. Although only limited human-to-human transmission has been reported to date, it is feared that efficient human-to-human transmission of H5N1 viruses has the potential to cause a pandemic of disastrous proportions. The genetic basis for H5N1 viral transmission among humans is largely unknown. In this study, we used guinea pigs as a mammalian model to study the transmission of six different H5N1 avian influenza viruses. We found that two viruses, A/duck/Guangxi/35/2001 (DKGX/35) and A/bar-headed goose/Qinghai/3/2005(BHGQH/05), were transmitted from inoculated animals to naïve contact animals. Our mutagenesis analysis revealed that the amino acid asparagine (Asn) at position 701 in the PB2 protein was a prerequisite for DKGX/35 transmission in guinea pigs. In addition, an amino acid change in the hemagglutinin (HA) protein (Thr160Ala), resulting in the loss of glycosylation at 158–160, was responsible for HA binding to sialylated glycans and was critical for H5N1 virus transmission in guinea pigs. These amino acids changes in PB2 and HA could serve as important molecular markers for assessing the pandemic potential of H5N1 field isolates

Students’ Experiences of English-Medium Instruction at the Postgraduate Level: Challenges and Sustainable Support for Success

More and more students are exploring overseas destinations and English-Medium Instruction (EMI) environments for their postgraduate studies. While it is known that students can often struggle in an EMI environment, the challenges faced by postgraduate students, and the support they receive or need, are not fully understood. By adopting a two-stage qualitative sequential data collection approach, this study explored the experiences and perceptions of full-time postgraduate students from Mainland China studying in a one-year Master of Education programme at a Hong Kong university during their first semester. Data were collected through an online survey (N = 73) and three in-depth group interviews (N = 12). The analysis of data offered a holistic understanding of the students’ challenges, needs, and struggles. The findings provide suggestions for support that teachers and programmes can provide to postgraduate students, as well as student self-help support strategies. Several sustainable support strategies are proposed to assist students in adjusting and succeeding in the EMI context at the postgraduate level

SII_MDP_LLaMA

This article introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different models and approaches, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use as input in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and new material development.</p