• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• English Language & Literature Teaching
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• v.11 no.2
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• pp.149-170
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• 2005

The purpose of this paper is to make a sentence systemic within the category of structural grammar for the modality in which a speaker expresses his attitude. It is the priority of a language to communicate meaning. By eliminating the theoretical description of traditional grammar, this paper also aims to illustrate the concepts of nine modal verbs through a systemic network. The concept of modality includes both the epistemic and the deontic characteristics of modality. Epistemic modality is associated with either knowledge or belief on the part of a speaker who gives his own judgments about the state of affairs, events, or actions. However, deontic modality is related to either the possibility or the necessity of acts that a speaker performs to give permission or fulfill an obligation. In conclusion, all the subsystems are described within the framework of the systemic network, with the intention of including all the potential options of the semantic functions available in a situation.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.35-48
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• 2015

We analyze the systemic risk based on the information flows using the variance decomposition, DebtRank methods, and the Industry Sector Indices during 2001. 01 to 2015. 08. Using the KOSPI stock market as our setting, we find that (i) the systemic risk calculated by information flows of variance decompositions method shows strong positive relations with the market volatility, (ii) the magnitude of systemic risk measured from the information flows network by DebtRank method increases after the subprime financial crisis.

• Knowledge Management Research
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• v.12 no.2
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• pp.69-80
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• 2011

This paper reports on a structural view of a knowledge network comprised of clubs and organisationsexpressly concerned with cultural activities in a regional Australian city. Social network analysis showed an uneven distribution of power, influence, and prominence in the network. The network structure consisted of two modules of vertices clustered around particular categories of creative arts and these modules were linked most frequently by several organisations acting as communication hubs and boundary spanners. The implications of the findings include 'network weaving' for improving the network structure and developing a systemic approach for exploring the structures of social action that form community-based knowledge networks.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.192-198
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• 2023

Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.

• English Language & Literature Teaching
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• v.8 no.1
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• pp.223-239
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• 2002

This paper aims to describe the theme of English clause in terms of systemic grammar. For this I analyze the three subaereas of subject theme and the four subareas of nonsubject theme in the form of features. Each of the seven feature sets of the seven thematic subareas is described in the systemic model. Finally All of the subsystems are described in the framework of the system network in order to show the potential of options of thematic English clause available in a certain situation.

• Clinical and Experimental Pediatrics
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• v.64 no.5
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• pp.208-222
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• 2021

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

• IMMUNE NETWORK
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• v.14 no.3
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• pp.138-148
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• 2014

MicroRNAs (miRNAs) are endogenous small RNA molecules best known for their function in post-transcriptional gene regulation. Immunologically, miRNA regulates the differentiation and function of immune cells and its malfunction contributes to the development of various autoimmune diseases including systemic lupus erythematosus (SLE). Over the last decade, accumulating researches provide evidence for the connection between dysregulated miRNA network and autoimmunity. Interruption of miRNA biogenesis machinery contributes to the abnormal T and B cell development and particularly a reduced suppressive function of regulatory T cells, leading to systemic autoimmune diseases. Additionally, multiple factors under autoimmune conditions interfere with miRNA generation via key miRNA processing enzymes, thus further skewing the miRNA expression profile. Indeed, several independent miRNA profiling studies reported significant differences between SLE patients and healthy controls. Despite the lack of a consistent expression pattern on individual dysregulated miRNAs in SLE among these studies, the aberrant expression of distinct groups of miRNAs causes overlapping functional outcomes including perturbed type I interferon signalling cascade, DNA hypomethylation and hyperactivation of T and B cells. The impact of specific miRNA-mediated regulation on function of major immune cells in lupus is also discussed. Although research on the clinical application of miRNAs is still immature, through an integrated approach with advances in next generation sequencing, novel tools in bioinformatics database analysis and new in vitro and in vivo models for functional evaluation, the diagnostic and therapeutic potentials of miRNAs may bring to fruition in the future.

• IMMUNE NETWORK
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• v.19 no.1
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• pp.1.1-1.13
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• 2019

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease characterized by production of autoantibodies to various nuclear antigens and overexpression of genes regulated by IFN-I called IFN signature. Genetic studies on SLE patients and mutational analyses of mouse models demonstrate crucial roles of nucleic acid (NA) sensors in development of SLE. Although NA sensors are involved in induction of antimicrobial immune responses by recognizing microbial NAs, recognition of self NAs by NA sensors induces production of autoantibodies to NAs in B cells and production of IFN-I in plasmacytoid dendritic cells. Among various NA sensors, the endosomal RNA sensor TLR7 plays an essential role in development of SLE at least in mouse models. CD72 is an inhibitory B cell co-receptor containing an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic region and a C-type lectin like-domain (CTLD) in the extracellular region. CD72 is known to regulate development of SLE because CD72 polymorphisms associate with SLE in both human and mice and CD72^-/- mice develop relatively severe lupus-like disease. CD72 specifically recognizes the RNA-containing endogenous TLR7 ligand Sm/RNP by its extracellular CTLD, and inhibits B cell responses to Sm/RNP by ITIM-mediated signal inhibition. These findings indicate that CD72 inhibits development of SLE by suppressing TLR7-dependent B cell response to self NAs. CD72 is thus involved in discrimination of self-NAs from microbial NAs by specifically suppressing autoimmune responses to self-NAs.