• Journal of Dental Anesthesia and Pain Medicine
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• v.24 no.1
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• pp.47-56
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• 2024

Background: Among the various pain-related diseases that can be encountered at the clinic, there is a neuropathic pain that is difficult to treat. Numerous methods have been proposed to treat neuropathic pain, such as taking medication, nerve block with lidocaine, or neurolysis with alcohol or phenol. Recently, a method of perineural injection using dextrose instead of lidocaine was proposed. This study was designed to compare the effects of perineural injection therapy (PIT) with buffered 5% dextrose or 0.5% lidocaine on neuropathic pain. Methods: The data were collected from the database of pain clinic from August 1st, 2019 to December 31st, 2022 without any personal information. The inclusion criteria were patients diagnosed with postherpetic neuralgia (PHN), trigeminal neuralgia (TN), complex regional pain syndrome (CRPS), or peripheral neuropathy (PN), and patients who had undergone PIT with buffered 5% dextrose (Dextrose group) or 0.5% lidocaine (Lidocaine group) for pain control. The data of patients, namely sex, age, and pain score (numerical rating scale, NRS) were collected before PIT. The data of NRS, side effects, and satisfaction grade (excellent, good, fair, or poor) were collected one week after each of the four PIT, and two weeks after the last PIT. Results: Overall, 112 subjects were enrolled. The Dextrose group included 89 and Lidocaine group included 23 patients. Because the number of patients in the Lidocaine group was too small to allow statistical analysis, the trend in Lidocaine group was just observed in each disease. There were no significant side effects except for a few bruise cases on the site of injection in all groups. The NRS in most Dextrose groups except CRPS were reduced significantly; however, the Lidocaine group showed a trend of pain reduction only in PHN. The Dextrose group except CRPS showed increased satisfaction two weeks after the final PIT. Conclusion: From the results, it is suggested that PIT with buffered 5% dextrose may have a good effect for neuropathic pain without any side effect except for patients with CRPS. This may offer a window into a new tool that practitioners can employ in their quest to help patients with neuropathic pain.

• Journal of the Korean Chemical Society
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• v.68 no.3
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• pp.151-159
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• 2024

Indian spices are well known for their numerous health benefits, flavour, taste, and colour. Recent Advancements in chemical technology have led to better extraction and identification of bioactive molecules (phytochemicals) from spices. The therapeutic effects of spices against diabetes, cardiac problems, and various cancers has been well established. The present in silico study aims to investigate the binding affinity of 29 phytochemicals from 11 Indian spices with two prominent proteins, BCL3 and CXCL10 involved in invasiveness and bone metastasis of breast cancer. The three-dimensional structures of 29 phytochemicals were extracted from PubChem database. Protein Data Bank was used to retrieve the 3D structures of BCL3 and CXCL10 proteins. The drug-likeness and other properties of compounds were analysed by ADME and Lipinski rule of five (RO5). All computational simulations were carried out using Autodock 4.0 on Windows platform. The proteins were set to be rigid and compounds were kept free to rotate. In-silico study demonstrated a strong complex formation (positive binding constants and negative binding energy ΔG) between all phytochemicals and target proteins. However, piperine and sesamolin demonstrated high binding constants with BCL3 (50.681 × 10³ mol^-1, 137.76 × 10³ mol^-1) and CXCL10 (98.71 × 10³ mol^-1, 861.7 × 10³ mol^-1), respectively. The potential of these two phytochemicals as a drug candidate was highlighted by their binding energy of -6.5 kcal mol^-1, -7.1 kcal mol^-1 with BCL3 and -6.9 kcal mol^-1, -8.2 kcal mol^-1 with CXCL10, respectively coupled with their favourable drug likeliness and pharmacokinetics properties. These findings underscore the potential of piperine and sesamolin as drug candidates for inhibiting invasiveness and regulating breast cancer metastasis. However, further validation through in vitro and in vivo studies is necessary to confirm the in silico results and evaluate their clinical potential.

• (The)Study of the Eastern Classic
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• no.70
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• pp.303-345
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• 2018

In this article, how the Bangmok(榜目) documents, which are essentially lists of successful passers for the civil competitive examination system of the $Chos{\breve{o}}n$ dynasty, when rendered into digitalized formats, could serve as source of information, which would not only lets us know the $Chos{\breve{o}}n$ individuals' social backgrounds and bloodlines but also enables us to understand the intricate nature that the Yangban network had, will be discussed. In digitalized humanity studies, the Bangmok materials, literally a list of leading elites of the $Chos{\breve{o}}n$ period, constitute a very interesting and important source of information. Based upon these materials, we can see how the society -as well as the Yangban community- was like. Currently, all data inside these Bangmok lists are rendered in XML(eXtensible Makrup Language) format and are being served through DBMS(Database Management System), so anyone who would want to examine the statistics could freely do so. Also, by connecting the data in these Bangmok materials with data from genealogy records, we could identify an individual's marital relationship, home town, and political affiliation, and therefore create a complex narrative that would be effective in describing that individual's life in particular. This is a graphic database, which shows-when Bangmok data is punched in-successful passers as individual nodes, and displays blood and marital relations in a very visible way. Clicking upon the nodes would provide you with access to all kinds of relationships formed among more than 90 thousand successful passers, and even the overall marital network, once the genealogical data is input. In Korea, since 2005 and through now, the task of digitalizing data from the Civil exam Bangmok(Mun-gwa Bangmok), Military exam Bangmok (Mu-gwa Bangmok), the "Sa-ma" Bangmok and "Jab-gwa" Bangmok materials, has been completed. They can be accessed through a website(http://people.aks.ac.kr/index.aks) which has information on numerous famous past Korean individuals. With this kind of source of information, we are now able to extract professional Jung-in figures from these lists. However, meaningful and practical studies using this data are yet to be announced. This article would like to remind everyone that this information should be used as a window through which we could see not only the lives of individuals, but also the society.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Korean Business Review
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• v.17
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• pp.53-81
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• 2004

The purpose of this study is to understand the realities and the subject of environmental management for small and medium-sized companies in Gwangwon area, through surveying the present status as to acquiring the certification of ISO14001, and to seek for a plan to facilitate environmental management. Given summarizing key results, those are as follows. First, while the number of companies in our country which acquired the certification of ISO14001, amounts to 1,215 businesses as of April of 2003, the number of small and medium-sized companies in Gwangwon area which obtained the certification of ISO14001 reached only 26 businesses, the lowest level among metropolitan municipalities. Second, for the reason that companies who didn't acquire the certification, strive not to receive the certification, it did present the point that' costs to be needed in acquiring and maintaining the certification are larger than practical benefit. Third, the biggest reason for either companies which did not acquire the certification of ISO14001 or companies which did (try to) acquire the certification of ISO1400, was, enhancement of a corporate image,' and the effect after a company who obtained the certification introduced the environmental management system, was also shown to be 'the improvement of a corporate image.' Fourth, many companies who acquired the certification of ISO1400 pointed out the response related to 'burden on document creation and costs' and 'lack of manpower' as problems when introducing the environmental management system. On the basis of major results of a study as the above, given presenting the subject and a plan for activating the environmental management of small and medium-sized companies in Gwangwon area, those are as follows. First, because most of companies who did not obtain the certification of ISO1400 have low recognition of ISO14001, it needs continuous and positive publicity, education and a training system. Second, it requires to carry out an educational program to nurture professional manpower due to lack of manpower relevant to environmental management, to expand payment of subsidies, to open exclusive-charge department and consulting contact, to have the relevant information be database and to develop software. Third, in order to make the certification obtained through inexpensive costs and simple procedures, it needs to positively consider the creation of public approval system for a small and medium-sized company, group approval system, industrial-complex approval system, and others.

• Journal of dental hygiene science
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• v.14 no.4
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• pp.468-476
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• 2014

The aim of this study was to examine whether there is an association between vitamin intakes and established periodontitis in Korean adult population. The 6,245 subjects aged over 19 years old, who participated in health survey, oral examination and nutrition survey were selected for this study from the database of the Fourth Korean National Health and Nutrition Examination Survey. Established periodontitis was defined as ${\geq}$ code 3 in community periodontal index. Vitamin intake was assessed with the food-frequency questionnaire. In analysis, participants were classified by quintile of vitamin intakes. We also considered covariates as socio-demographic characteristics, health-related behaviors including physical activities, systemic diseases and oral health-related behaviors. Multiple logistic regression was performed to assess the crude and adjusted associations. All analyses considered a complex sampling design using SAS 9.2. In crude analysis, less intake of vitamin A, retinol and vitamin B2 significantly increased the risk of periodontitis (vitamin A, odds ratio [OR] Q1=1.00, Q2=0.73, Q3=0.80, Q4=0.77, Q5=0.78; retinol, OR: Q1=1.00, Q2=0.86, Q3=0.73, Q4=0.62, Q5=0.55; vitamin B2, OR: Q1=1.00, Q2=0.70, Q3=0.63, Q4=0.67, Q5=0.68). However, after adjusting for socio-demographics, general and oral health status and behaviors, only vitamin B2 was significantly associated with established periodontitis (OR: Q1=1.00, Q2=0.72, Q3=0.73, Q4=0.76, Q5=0.84). An adequate vitamin B2 intake was significantly associated with a decreased risk of periodontitis. This finding shows that nutrient intake is slightly correlated with periodontitis in Korean adult population. Further studies are needed to understand this association between nutrients intake and periodontitis in more details.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.4
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• pp.526-534
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• 2010

The prevalence rate for chronic diseases such as obesity, diabetes, hypertension etc. caused by the increment of national income and the change of food life according to the globalization in Korea have been increased. Especially excess sodium intake may contribute to the development of hypertension, increasing cardiovascular disease risk. The objective of this study was to investigate sodium intake of nursery school meals in Gyeonggi-Do, and to construct database for lesser sodium intake policy. Survey consisted of 601 sample intakes of sodium in summer and in winter. A food weighed record method was used for measuring food intakes. Average intakes of ten children per nursery school were measured. The sodium contents of meals were analyzed by ICP-OES (inductively coupled plasma-optical emission spectrometer) after acid digestion by microwave. The sodium contents on food groups showed that sources (693 mg/100 g), grilled foods (689 mg/100 g) and kimchies (643 mg/100 g) had respectively higher sodium contents and the average sodium intake per meal was $582\pm204$ mg. The sodium contents of soups & hot soups and kimchies had 37.5% and 15.8% of total sodium intakes per meal, respectively. Sodium intakes per meal in summer and winter showed 572.3 mg and 592.3 mg, respectively. Regional ranking of sodium intakes showed the ascending order of apartment (514.3 mg/meal), rural region (540.5 mg/meal), multiplex house (635.9 mg/meal) and industrial complex (696.4 mg/ meal). A habit of excessive sodium intakes in childhood will threaten their health when they grow up to be adults; thus lesser intake of sodium per meal is needed for children in nursery school.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.