Related Matrix Research Articles

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces.

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Genetic Diversity Study of Cinnamomum cassia Blume Using Random Amplified Polymorphic DN A Markers (RAPD)

Aims: To investigate Genetic diversity study of Cinnamomum cassia Blume in Vietnam.
 Methodology: Fresh leaves of Cinnamomum cassia Blume sample were grinded in liquid nitrogen and total genomic DNA was extracted and purified by CTAB method. The primer was amplified via the polymerase chain reaction on a Gene Amp PCR system 9700. The binary data set was utilized to compute the pairwise Jaccard similarity index and to construct the related similarity matrix. All analyses were performed by using the NTSYS-PC software application.
 Results: Twenty-four accessions of Cinnamomum cassia Blume belongs to Cinnamomum species collected from some mountainous areas of the Yenbai province, Vietnam were examined to assess genetic variation using the RAPD method. The accessions included 4 in Lucyen, 5 in Tranyen, 5 in Vanchan, 6 in Vanyen and 4 in Yenbinh. Eighty-one out of 99 bands detected by 15 selected RAPD primers were polymorphic. The average number of band/primer was 6.6, in which the polymorphic bands/primer was 5.4, corresponding to 82 %. The similarity indexes among accessions fluctuate between 0.67 to 0.91 The coefficient of estimated PIC/primer range from 0.371 to 0.883, with an average of 0.756. The hight polymophylic and similarity indexes showed large modification of genome. Grouping accessions in phylogenetic tree reflected the fact that the different areas may used the same or different cultivars of Cinnamomum cassia Blume for cultivation.
 Conclusion: Cinnamomum cassia Blume belongs to Cinnamomum species was introduced in Yenbai from multiple sources and possesses hight genetic diversity and variation and suggests that necessary to establish conservation plan, evaluation and selection of the best gene resources for commercial purposes.

Analysis of differences in intestinal flora associated with different BMI status in colorectal cancer patients

BackgroundOverweight is known to be an important risk factor for colorectal cancer (CRC), and the differences in intestinal flora among CRC patients with different BMI status have not been clearly defined. The purpose of this study was to elucidate the differences in the abundance, composition and biological function of intestinal flora in CRC patients with different BMI status.MethodA total of 170 CRC patients were included and grouped according to the BMI data of CRC patients. BMI ≥ 24 kg/m2 was defined as overweight group, and BMI within the range of 18.5–23.9 kg/m2 was defined as normal weight group. Preoperative stool collection of patients in both groups was used for 16S rRNA sequencing. Total RNA was extracted from 17 CRC tumor tissue samples for transcriptome sequencing, and then CIBERSORT algorithm was used to convert the transcriptome data into the relative content matrix of 22 kinds of immune cells, and the correlation between different intestinal flora and immune cells and immune-related genes under different BMI states was analyzed. Finally, we identified BMI-related differential functional pathways and analyzed the correlation between these pathways and differential intestinal flora.ResultThere was no significant difference in α diversity and β diversity analysis between overweight group and normal weight group. Partial least square discriminant analysis (PLS-DA) could divide the flora into two different clusters according to BMI stratification. A total of 33 BMI-related differential flora were identified by linear discriminant effect size analysis (LEfSe), among which Actinomyces, Desulfovibrio and Bacteroides were significantly enriched in overweight group. ko00514: Other types of O-glycan biosynthesis are significantly enriched in overweight group. There was a significant positive correlation between Clostridium IV and Macrophages M2 and T cells regulatory (Tregs). There was a significant negative correlation with Dendritic cells activated and T cells CD4 memory activated.ConclusionsThe richness and diversity of intestinal flora of CRC patients may be related to different BMI status, and the enrichment of Actinomyces, Desulphurvibrio and Bacteroides may be related to overweight status of CRC patients. The tumor microenvironment in which BMI-related differential flora resides has different immune landscapes, suggesting that some intestinal flora may affect the biological process of CRC by regulating immune cell infiltration and immune gene expression, but further experiments are needed to confirm this.

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

ADAM8 silencing suppresses the migration and invasion of fibroblast-like synoviocytes via FSCN1/MAPK cascade in osteoarthritis

ObjectiveOsteoarthritis (OA) is a degenerative joint disease that affects elderly populations worldwide, causing pain and disability. Alteration of the fibroblast-like synoviocytes (FLSs) phenotype leads to an imbalance in the synovial inflammatory microenvironment, which accelerates the progression of OA. Despite this knowledge, the specific molecular mechanisms of the synovium that affect OA are still unclear.MethodsBoth in vitro and in vivo experiments were undertaken to explore the role of ADAM8 playing in the synovial inflammatory of OA. A small interfering RNA (siRNA) was targeting ADAM8 to intervene. High-throughput sequencing was also used.ResultsOur sequencing analysis revealed significant upregulation of the MAPK signaling cascade and ADAM8 gene expression in IL-1β-induced FLSs. The in vitro results demonstrated that ADAM8 blockade inhibited the invasion and migration of IL-1β-induced FLSs, while also suppressing the expression of related matrix metallomatrix proteinases (MMPs). Furthermore, our study revealed that inhibiting ADAM8 weakened the inflammatory protein secretion and MAPK signaling networks in FLSs. Mechanically, it revealed that inhibiting ADAM8 had a significant effect on the expression of migration-related signaling proteins, specifically FSCN1. When siADAM8 was combined with BDP-13176, a FSCN1 inhibitor, the migration and invasion of FLSs was further inhibited. These results suggest that FSCN1 is a crucial downstream factor of ADAM8 in regulating the biological phenotypes of FLSs. The in vivo experiments demonstrated that ADAM8 inhibition effectively reduced synoviocytes inflammation and alleviated the progression of OA in rats.ConclusionsADAM8 could be a promising therapeutic target for treating OA by targeting synovial inflammation.

A fuzzy comprehensive evaluation method based on the joint distribution dynamic membership degree for classification problems

In this study, we propose a new classification method by adopting some ideas originating from the fuzzy comprehensive evaluation (FCE). To make the FCE be a classifier, the class labels in classification problems are regarded as the evaluation remarks in the FCE, and the attributes in these two domains are regarded to be consistent. Then, to implement the FCE model B = W ∘ R and obtain an accurate classification result, on the one hand, a learning algorithm, which is based on the joint distribution of attribute values and is dynamic, is proposed to construct the fuzzy relational matrix R; on the other hand, equal weight is considered to constitute the weight vector W. Meanwhile, for a continuous dataset, the discretization method and the determination of the discretization class number corresponding to the proposed classifier are discussed. The proposed classifier not only innovatively extends the FCE to data mining but also has its own classification advantages, that is, it is easy to operate and has good interpretability. Finally, we perform some numerical experiments using publicly available datasets, and the experimental results demonstrate that the proposed classifier outperforms some existing classifiers.

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The input parameters of ηq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta _q$$\\end{document} leading-twist distribution amplitude ϕ2;ηq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi _{2;\\eta _q}$$\\end{document} at the initial scale μ0∼1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu _0\\sim 1$$\\end{document} GeV are fixed by using those moments. The QCD sum rules for the 0th\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0_{\ extrm{th}}$$\\end{document}-order moment can also be used to fix the magnitude of ηq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta _q$$\\end{document} decay constant, giving fηq=0.141±0.005\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_{\\eta _q}=0.141\\pm 0.005$$\\end{document} GeV. As an application of ϕ2;ηq,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi _{2;\\eta _q},$$\\end{document} we calculate the transition form factors B(D)+→η(′)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B(D)^+ \\rightarrow \\eta ^{(\\prime )}$$\\end{document} by using the QCD light-cone sum rules up to twist-4 accuracy and by including the next-to-leading order QCD corrections to the leading-twist part, and then fix the related CKM matrix element and the decay width for the semi-leptonic decays B(D)+→η(′)ℓ+νℓ.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B(D)^+ \\rightarrow \\eta ^{(\\prime )}\\ell ^+ \ u _\\ell .$$\\end{document}

Identification of composite power quality disturbances based on relative position matrix

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one-dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convolutional neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods.

ECS-SC: Long-tailed classification via data augmentation based on easily confused sample selection and combination

The long-tailed distribution data poses many challenges for machine learning because the tail classes are extremely scarce. Long-tailed data augmentation is a powerful technique for enriching the tail class diversity. However, existing methods often treat each class independently, assuming that classes are isolated from each other. These approaches overlook the presence of easily confused tail classes, making it challenging for models to distinguish between them accurately. In this paper, we propose a long-tailed classification method based on data augmentation, which utilizes multi-granularity knowledge to select and combine easily confused tail samples, thereby enhancing the classification performance of these samples. First, we utilize multi-granularity knowledge and semantic relation trees to build a class relation matrix. This matrix records the relationship between classes and helps the model search for easily confused classes from bilateral branch samplers. Second, we crop and combine the easily confused head and tail class samples in a foreground–background manner to generate new samples, thereby augmenting the model training. The extensive head class knowledge is transferred to the scarce tail class samples through the combination of fore-background, and the discriminative and generalized abilities of the model are improved. The experimental results affirm the effectiveness of our proposed method.

An evaluation method of open-pit slope stability based on Poset theory

To achieve an objective and reasonable judgment on slope stability, the partially ordered set (Poset) theory is applied to the slope stability evaluation against the engineering background of an open pit slope. From the four aspects of rock mass structural characteristics, slope morphology, environment conditions and engineering conditions, 24 typical evaluation indices such as cohesion, internal friction angle, elastic modulus and Poisson’s ratio are selected. Based on the Poset theory, a Poset evaluation model is established. According to the slope stability evaluation indices and the risk grade classification criteria, the slope stability risk grade is divided into four grades: stable (Grade I), generally stable (Grade II), slightly stable (Grade III) and unstable (Grade IV). The index data are normalized and the weight ranking information is used. Implicit weighting is carried out by means of accumulation. The comparison relation matrix, the Hasse matrix and the Hasse diagram are constructed for each factor layer and comprehensive index layer, respectively. This method was utilized to evaluate the risk grade of an engineering example, determine the slope risk grade, and compare with the actual situation. The results show that the slope to be evaluated is generally stable (Grade II) under the comprehensive evaluation indices of various factors, and the assessed stability condition is consistent with the actual situation. These verify the rationality and applicability of the proposed method and provide a new insight for accurately identifying the stability condition of open-pit slopes.

Approximation approach for backward stochastic Volterra integral equations

<p>In this paper, we focus on studying a specific type of equations called backward stochastic Volterra integral equations (BSVIEs). Our approach to approximating an unknown function involved using collocation approximation. We used Newton's technique to solve a particular BSVIE by employing block pulse functions (BPFs) and the related stochastic operational matrix of integration. Additionally, we developed considerations for Lipschitz and linear growth, along with linearity conditions, to illustrate error and convergence analysis. We compared the solutions we obtain the values of exact and approximate solutions at selected points with a defined absolute error. The computations were performed using MATLAB R2018a.</p>

An Analysis of the Values of Project-Based Learning in English Language Teaching and the Cultivating Functions it Displays

Abstract In this paper, an English teaching cultivation evaluation model based on RBF neural network is designed to solve the problems of poor generalization ability, low evaluation accuracy, and correlation coefficient of the current teaching evaluation model. Before establishing the evaluation model, conditional information entropy is used to screen the evaluation indexes of the English teaching cultivation function, and six dimensions of core literacy and five dimensions of thinking ability are selected as evaluation indexes. A new relative merit matrix is constructed under the constraint of relative merit by using the screened conditional attributes. Selection, crossover, and mutation operations are used in the RBF neural network to select the optimal parameters through the introduction of the genetic algorithm. Finally, the assessment data of students after project-based English teaching are analyzed for values and nurturing functions using the evaluation model. According to the findings, project-based English teaching led to the highest average score of 29.15 for students’ values compared to the dimensions of interest, ability, and personality. In terms of the six dimensions of core literacy and the five dimensions of thinking ability, the score difference between the experimental class and the control class is more than 1.2 points, and the Sig value of each dimension is less than 0.05, which indicates that English teaching based on Project-Based Learning promotes students’ values, core literacy and thinking ability.

Some new characterizations of normal matrices

This paper mainly studies some properties of normal matrix and gives the relation between the general solution of related matrix equations and normal matrices.

The method of judging satisfactory consistency of linguistic judgment matrix based on adjacency matrix and 3-loop matrix

<abstract> <p>Language phrases are an effective way to express uncertain pieces of information, and easily conforms to the language habits of decision makers to describe the evaluation of things. The consistency judgment of a linguistic judgment matrices is the key to analytic hierarchy process (AHP). If a linguistic judgment matrix has a satisfactory consistency, then the rank of the decision schemes can be determined. In this study, the comparison relation between the decision schemes is first represented by a directed graph. The preference relation matrix of the linguistic judgment matrix is the adjacency matrix of the directed graph. We can use the $ n - 1 $ st power of the preference relation to judge the linguistic judgment matrix whether has a satisfactory consistency. The method is utilized if there is one and only one element in the $ n - 1 $ st power of the preference relation, and the element 1 is not on the main diagonal. Then the linguistic judgment matrix has a satisfactory consistency. If there are illogical judgments, the decision schemes that form a 3-loop can be identified and expressed through the second-order sub-matrix of the preference relation matrix. The feasibility of this theory can be verified through examples. The corresponding schemes for illogical judgments are represented in spatial coordinate system.</p> </abstract>

Application of improved RBF neural network algorithm in hierarchical management of enterprise

Abstract The grade division of enterprises is conducive to their needs of green development, and the continuous strengthening of computer innovation technology provides an excellent platform for grading the regional management of enterprises. Owing to the differences in energy consumption, labour, land use area, GDP, etc., most of the data collected by the enterprises are unstable, and the data with a small number of samples in categories cannot be ignored. Therefore, based on the related data of basic development in a development area, Guangdong Province within the past 15 years, in this paper, according to the theory of hierarchical management in enterprise, four factors, such as land use, personnel, energy consumption and regional GDP, are used as the relevant attributes, and the grades of enterprises in this region are managed and divided. In addition, the structure of RBF neural network algorithm optimised by similarity relation matrix and the accuracy of enterprise classification under different neural network algorithms are compared. The results show that the hierarchical management of enterprises based on the improved RBF neural network algorithm has high efficiency and accuracy, which is of great significance to the green development of enterprises.

The application of gray system-variable fuzzy sets coupling theory on the susceptibility assessment of debris flow hazards

The occurrence of debris flow hazards has significant destructive effects people’s safety and the safety of their property, so the risk assessment of debris flow hazards has great significance. The maximum amount of debris flow at one time (X1), the reserves of loose substance (X2), the watershed area (X3), the length of the ditch (X4), the relative height difference (X5), the gradient of side slope (X6), the longitudinal slope of main ditch (X7), the length ratio of replenishment section (X8), the daily maximum rainfall (X9), and Faults developed in the watershed (X10) are selected as the evaluation index at first. Then the gray system-variable fuzzy sets coupling model is introduced; secondly, the relative membership degree matrix about the fuzzy variable sets is established, and the weight coefficients considering the uncertainty of assessment indices are determined based on the entropy weight method; finally, the susceptibility level of debris flow hazards is determined using the mean ranking feature value. The conclusion is drawn that the results obtained based on the coupling technique are consistent with the current specification; its accuracy arrives at 83%, and the method is feasible for the susceptibility level assessment of debris flow hazards. Compared to the traditional variable fuzzy sets model, it avoids the uncertainty of magnitude in the standard analysis, it can ensure the objectivity and accuracy of the calculation result, and its calculation process is simple and efficient. In total, the findings of the proposed model provide an alternate way to assess the susceptibility level of debris flow hazards and improve the evaluation accuracy in the future.

Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing.

Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.

Fuzzy self-consistent clustering ensemble

The subject of clustering ensemble (CE) has emerged as a pivotal area of exploration within unsupervised learning, primarily due to its remarkable capacity to integrate multiple base clusterings. However, the conventional CE framework exhibits limitations in effectively handling fuzzy relations and ensuring robustness. This study introduces an innovative and robust fuzzy self-consistent clustering ensemble (FSCE) model. Departing from customary approaches to processing base clustering outcomes, the FSCE model considers the scalable dummy variable representation of base clustering results as a novel feature attributes intrinsic to the original dataset. Subsequently, a γ-fuzzy operator ε+ is formulated, enabling the adjustment of coupling strength contingent upon the uncertainties inherent in practical problems. Leveraging this operator, the model reappraises the fuzzy relationships among objects, thereby engendering a corresponding relation matrix. This matrix profoundly enhances the model's capacity to contend with uncertain relationships. The resultant relation matrix assumes a pivotal role as the primary input in the consensus function, culminating in the derivation of the ensemble outcome. Moreover, the FSCE model deviates from the conventional CE paradigm by introducing a reallocation strategy for fuzzy objects within ensemble outcome. Through comprehensive experimentation, we substantiate that the introduction of γ-fuzzy operator ε+ offers a viable novel approach to enhancing both the overall performance and the fuzzy interpretability of the CE model. The FSCE model distinctly excels in consensus formation and robustness when juxtaposed against eight archetypal and state-of-the-art clustering models.

A Vague Rough Set Approach for Failure Mode and Effect Analysis in Medical Accident

Identifying the risk factor is a key to success for the Failure model. This paper presents a new FMEA (Failure Mode and Effect Analysis) in medical accident for the hospital management by using vague rough matrix information captured through vague rough set. Using β-product and β-complement product into fuzzy relation matrix and vague set to find the new lower and upper approximation of vague rough set. This method will provide an easy methodology to find the weight of risk factor. Using an example we validate our proposed approach.

Document-level relation extraction with three channels

Relation extraction aims to identify the relation between two entities. Early works on relation extraction are performed within a sentence. Numerous entity pairs with relations are distributed across sentences, so the task of document-level relation extraction is proposed. The current works perform well on document-level relation extraction through graph neural network, transformer, etc. However, these works extract relation features from one perspective, resulting in limited extraction performance of the model. To break through the limitation, this paper proposes the DRETC model consisting of three channels. Channel one first constructs the mention matrix and then extracts the relation features between entities. Channel two performs sequence inference from mention to entity before generating the relation matrix. Moreover, the bridge method is proposed to introduce information in channel one that is beneficial to relation extraction. Channel three directly generates entity representations through text sequences to make up for the absence of text information related to the entity. The three channels extract relation features from three different perspectives, and the combination leads to better extraction performance. Experimental results show that the proposed DRETC model outperforms previous strong baseline models with one channel on the public dataset DocRED. Further analysis demonstrates that the combination of multiple channels has a better relation extraction performance than a single channel.