• Journal of Pharmacopuncture
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• v.26 no.3
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• pp.247-256
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• 2023

Objectives: Enterococcus faecalis is a gram positive diplococci, highly versatile and a normal commensal of the gut microbiome. Resistance to vancomycin is a serious issue in various health-care setting exhibited by vancomycin resistant Enterococci (VRE) due to the alteration in the peptidoglycan synthesis pathway. This study is thus aimed to detect the VRE from the patients with root caries from the clinical isolates of E. faecalis and to evaluate the in-silico interactions between vanA and the Aegles marmelos bio-compounds. Methods: E. faecalis was phenotypically characterized from 20 root caries samples and the frequency of vanA and vanB genes was detected by polymerase chain reaction (PCR). Further crude methanolic extracts from the dried leaves of A. marmelos was assessed for its antimicrobial activity. This is followed by the selection of five A. marmelos bio-compounds for the computational approach towards the drug ligand interactions. Results: 12 strains (60%) of E. faecalis was identified from the root caries samples and vanA was detected from two strains (16%). Both the stains showed the presence of vanA and none of the strains possessed vanB. Crude extract of A. marmelos showed promising antibacterial activity against the VRE strains. In-silico analysis of the A. marmelos biocompounds revealed Imperatonin as the best compound with high docking energy (-8.11) and hydrogen bonds with < 140 TPSA (Topological polar surface area) and zero violations. Conclusion: The present study records the VRE strains among the root caries with imperatorin from A. marmelos as a promising drug candidate. However the study requires further experimentation and validation.

• Journal of Microbiology and Biotechnology
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• v.30 no.10
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• pp.1516-1524
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• 2020

Climate change is expected to affect not only availability and quality of water, the valuable resource of human life on Earth, but also ultimately public health issue. A six-year monitoring (total 20 times) of Escherichia coli O157, Salmonella enterica, Legionella pneumophila, Shigella sonnei, Campylobacter jejuni, and Vibrio cholerae was conducted at five raw water sampling sites including two lakes, Hyundo region (Geum River) and two locations near Water Intake Plants of Han River (Guui region) and Nakdong River (Moolgeum region). A total 100 samples of 40 L water were tested. Most of the targeted bacteria were found in 77% of the samples and at least one of the target bacteria was detected (65%). Among all the detected bacteria, E. coli O157 were the most prevalent with a detection frequency of 22%, while S. sonnei was the least prevalent with a detection frequency of 2%. Nearly all the bacteria (except for S. sonnei) were present in samples from Lake Soyang, Lake Juam, and the Moolgeum region in Nakdong River, while C. jejuni was detected in those from the Guui region in Han River. During the six-year sampling period, individual targeted noxious bacteria in water samples exhibited seasonal patterns in their occurrence that were different from the indicator bacteria levels in the water samples. The fact that they were detected in the five Korea's representative water environments make it necessary to establish the chemical and biological analysis for noxious bacteria and sophisticated management systems in response to climate change.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4461-4475
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• 2017

Recently, introduction of the hospital information system has remarkably improved the efficiency of health care services within hospitals. Due to improvement of the hospital information system, the issue of integration of medical information has emerged, and attempts to achieve it have been made. However, as a preceding step for integration of medical information, the problem of searching the same patient should be solved first, and studies on patient identification algorithm are required. As a typical case, similarity can be calculated through MPI (Master Patient Index) module, by comparing various fields such as patient's basic information and treatment information, etc. but it has many problems including the language system not suitable to Korean, estimation of an optimal weight by field, etc. This paper proposes a method searching the same patient using MRI information besides patient's field information as a supplementary method to increase the accuracy of matching algorithm such as MPI, etc. Unlike existing methods only using image information, upon identifying a patient, a highest weight was given to physical information of medical image and set as an unchangeable unique value, and as a result a high accuracy was detected. We aim to use the similarity measurement result as secondary measures in identifying a patient in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.1001-1004
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• 2018

As the industry generates a lot of data, it is increasingly dependent on the IoT platform. For this reason, the performance and anomaly detection of IoT platform is becoming an important factor. In this paper, we propose a system model of IoT platform that detects device anomaly without performance issue. The proposed system uses Micro Batch which calculates the data transmission cycle to provide Soft Real-time service. In the industry, it was difficult to collect abnormal data, so the Hotelling's $T^2$ model was applied to the data analysis experiment. And the Hotelling's $T^2$ model successfully detected anomalies.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.70.3-70.3
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• 2019

Understanding how the filamentary structure affects the formation of the prestellar cores and stars is a key issue to challenge. We use the Heterodyne Array Receiver Program (HARP) of the James Clerk Maxwell Telescope (JCMT) to obtain molecular line mapping data for two prestellar cores in different environment, L1544 in filamentary cloud and L694-2 in a small cloud isolated. Observing lines are $^{13}CO$ and $C^{18}O$ (3-2) line to find possible flow motions along the filament, $^{12}CO$ (3-2) to search for any radial accretion (or infalling motions) toward the cores of gas material from their surrounding regions, and $HCO^+$ (4-3) lines to find at which density and which region in the core gases start to be in gravitational collapse. In the 1st moment maps of $^{13}CO$ and $C^{18}O$, velocity gradient patterns implying the flow of material were found at the cores and its surrounding filamentary clouds. The infall asymmetry patterns of HCO+ and $^{13}CO$ line profiles were detected to be good enough to analyze the infalling motions toward the cores. We will report further analysis results on core formation in the filamentary cloud at this meeting.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.767-777
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• 2023

In modern society, user privacy is emerging as an important issue as closed-circuit television (CCTV) systems increase rapidly in various public and private spaces. If CCTV cameras monitor sensitive areas or personal spaces, they can infringe on personal privacy. Someone's behavior patterns, sensitive information, residence, etc. can be exposed, and if the image data collected from CCTV is not properly protected, there can be a risk of data leakage by hackers or illegal accessors. This paper presents an innovative approach to "machine learning based reversible chaotic masking method for user privacy protection in CCTV environment." The proposed method was developed to protect an individual's identity within CCTV images while maintaining the usefulness of the data for surveillance and analysis purposes. This method utilizes a two-step process for user privacy. First, machine learning models are trained to accurately detect and locate human subjects within the CCTV frame. This model is designed to identify individuals accurately and robustly by leveraging state-of-the-art object detection techniques. When an individual is detected, reversible chaos masking technology is applied. This masking technique uses chaos maps to create complex patterns to hide individual facial features and identifiable characteristics. Above all, the generated mask can be reversibly applied and removed, allowing authorized users to access the original unmasking image.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3117-3121
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• 2013

Objective: Previous epidemiologic studies demonstrated that obesity might associated with the risk of bladder cancer. However, many of the actual association findings remained conflicting. To better clarify and provide a comprehensive summary of the correlation between obesity and bladder cancer risk, we conducted a meta-analysis to summarize results of studies on the issue. Stratified analyses were also performed on potential variables and characteristics. Methods: Studies were identified by searching in PubMed and Wanfang databases, covering all the papers published from their inception to March 10, 2013. Summary relative risks (SRRs) with their corresponding 95% confidence intervals (CIs) were calculated by either random-effect or fixed-effect models. Results: A total of 11 cohort studies were included in our meta-analysis, which showed that obesity was associated with an increased risk for bladder cancer in all subjects (RR=1.10, 95% CI=1.06-1.16; p=0.215 for heterogeneity; $I^2$=24.0%). Among the 9 studies that controlled for cigarette smoking, the pooled RR was 1.09 (95% CI 1.01-1.17; p=0.131 for heterogeneity; $I^2$=35.9%). No significant publication bias was detected (p = 0.244 for Egger's regression asymmetry test). Conclusions: Our results support the conclusion that obesity is associated with the increased risk of bladder cancer. Further research is needed to generate a better understanding of the correlation and to provide more convincing evidence for clinical intervention in the prevention of bladder cancer.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Korean Journal of Veterinary Research
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• v.63 no.3
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• pp.21.1-21.6
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• 2023

The increasing prevalence of toxic substance-exposure in pets in South Korea endangers the health and safety of numerous companion animals, and has become a cause for concern. Notably, the annual incidence of forensic analysis in pets has increased by more than 150% in South Korea, mainly in populous regions such as Seoul, Incheon, and Gyeonggi. In response to this growing issue, veterinary forensic examinations were conducted on 549 dogs and cats from 2019 to 2022. This study revealed the presence of various toxic substances, including pesticides, insecticides, and drugs such as analgesics, anesthetics, antidepressants, and muscle relaxants, in pets. Among the 38 different toxins identified in pets, coumatetralyl, methomyl, terbufos, and buprofezin were the most frequently detected. In this study, toxic substances for pets were identified based on the "toxic agent list for humans," developed by the National Forensic Services, because no list of toxic agents for animals currently exists and data regarding potentially toxic substances for dogs and cats is limited. This is one of the limitations of this study, and necessitates the establishment of a toxic agent list for animals. Continued monitoring and research is also recommended to reveal the incidence, causes, and solutions of toxicity in animals.