• Informatization Policy
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• v.27 no.3
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• pp.39-55
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• 2020

This study was designed to analyze the relationship among the motivations for the use of digital devices, attitudes toward the use of digital devices and performance resulting from the use of digital devices in middle-aged and elderly groups by using a path analysis. For an empirical analysis, the 2018 Survey on the Digital Divide by the National Information Society Agency was referenced, analyzing the data of 1,664 people, selected through listwise deletion from total 2,303 people aged 55 or over. A close examination of how measuring variables reflect potential variables (motivations for, attitudes toward and performance resulting from the use of digital devices) showed a significance of 0.001, which suggests that each individual measuring variable does reflect the potential variables. Evaluation of the pathway factors of the structural model from the motivations for the use of digital devices to the attitudes toward the use of digital devices (β=0.847) and from the attitudes toward the use of digital devices to the performance resulting from the use of digital devices (β= 0.745) revealed significant statistical effects. The results of this study suggest that the motivations for the use of digital devices does not directly enhance the digital device use performance, but that it can improve the attitudes toward the use of digital devices, which can in turn increase the performance resulting from the use of digital devices. The analysis holds significance that it has presented practical and policy implications.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.507-515
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• 2024

Microbial forensics is a scientific discipline for analyzing evidence related to biological crimes by identifying the origin of microorganisms. Multiple locus variable number tandem repeat analysis(MLVA) is one of the microbiological analysis methods used to specify subtypes within a species based on the number of tandem repeat in the genome, and advances in next generation sequencing(NGS) technology have enabled in silico anlysis of full-length whole genome sequences. In this paper, we analyzed unknown samples provided by Robert Koch Institute(RKI) through The United Nations Secretary-General's Mechanism(UNSGM)'s external quality assessment exercise(EQAE) project, which we officially participated in 2023. We confirmed that the 3 unknown samples were B. anthracis through nucleic acid isolation and genetic sequence analysis studies. MLVA results on 32 loci of B. anthracis were analysed by using genome sequences obtained from NGS(NextSeq and MinION) and Sanger sequencing. The MLVA typing using short-reads based NGS platform(NextSeq) showed a high probability of causing assembly error when a size of the tandem repeats was grater than 200 bp, while long-reads based NGS platform(MinION) showed higher accuracy than NextSeq, although insertion and deletion was observed. We also showed hybrid assembly can correct most indel error caused by MinION. Based on the MLVA results, genetic identification was performed compared to the 2,975 published MLVA databases of B. anthracis, and MLVA results of 10 strains were identical with 3 unkonwn samples. As a result of whole genome alignment of the 10 strains and 3 unknown samples, all samples were identified as B. anthracis strain A4564 which is associated with injectional anthrax isolates in heroin users.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.11-20
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• 2017

Recently, developments in the various fields of cloud computing technology has been utilized. Whereas the demand for cloud computing services is increasing, security threats are also increasing in the cloud computing environments. Especially, in case when the hosts interconnected in the cloud environments are infected and propagated through the attacks by malware. It can have an effect on the resource of other hosts and other security threats such as personal information can be spreaded and data deletion. Therefore, the study of malware analysis to respond these security threats has been proceeded actively. This paper proposes a type of attack clustering method of Zeus botnet using the k-means clustering algorithm for malware analysis that occurs in the cloud environments. By clustering the malicious activity by a type of the Zeus botnet occurred in the cloud environments. it is possible to determine whether it is a malware or not. In the future, it sets a goal of responding to an attack of the new type of Zeus botnet that may occur in the cloud environments.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.971-977
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• 2015

Malignant glioblastoma multiforme (GBM) is the most malignant brain tumor and despite recent advances in diagnostics and treatment prognosis remains poor. In this retrospective study, we assessed the clinical and radiological parameters, as well as fluorescence in situ hybridization (FISH) of 1p19q deletion, in a series of cases. A total of 816 patients with GBM who received surgery and radiation between January 2010 and May 2014 were included in this study. Kaplan-Meier survival analysis and Cox regression analysis were used to find the factors independently influencing patient progression free survival (PFS) and overall survival (OS). Age at diagnosis, preoperative Karnofsky Performance Scale (KPS) score, KPS score change at 2 weeks after operation, neurological deficit symptoms, tumor resection extent, maximal tumor diameter, involvement of eloquent cortex or deep structure, involvement of brain lobe, Ki-67 and MMP9 expression level and adjuvant chemotherapy were statistically significant factors (p<0.05) for both PFS and OS in the univariate analysis. Cox proportional hazards modeling revealed that age ${\leq}50$ years, preoperative KPS score ${\geq}80$, KPS score change after operation ${\geq}0$, involvement of single frontal lobe, deep structure involvement, low Ki-67 and MMP9 expression and adjuvant chemotherapy were independent favorable factors (p<0.05) for patient clinical outcomes.

• KSBB Journal
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• v.17 no.4
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• pp.370-375
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• 2002

Isolation of a gene and determination of its expression pattern are essential in understanding its function. Among the genes localized in 12ql3, stSG3435 EST was chosen to study its expression pattern. The full-length CDNA was cloned by screening of human brain CDNA library and its sequence was determined by serial deletion followed by automated sequencing of the clones with overlapping fragments. The sequence analysis revealed that stSG 3435 CDNA displayed 100% identity to human MYGI and 86% identity to mouse melanocyte proliferation gene-1 (Gamm 1) originally identified from melanocyte, suggesting that MYGI determined by Northern blot analysis revealed the strongest expression in testes with ubiquitous expression in all the tissues tested. In order to investigate the cellular localization of its protein product, the green fluorescence protein gene was fused into the full-length coding sequence of MYGI, Transfection of the fusion construct followed by confocal microscopy resulted in the green fluorescence signal as a punctate state in cytoplasm indication that MYGI was localized in one of the cellular organelles.

• Phonetics and Speech Sciences
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• v.12 no.1
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• pp.51-63
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• 2020

Phonological awareness is a metalinguistic awareness ability of phonology and is known to predict language skills, such as reading and vocabulary skills. The purpose of this study was to investigate the relationship between phonological awareness, phonological processing, and language skills in 4- to 6-years-old typically developing (TD) children and children with language delay (LD). A total of 32 children (TD=18, LD=15) participated in this study. They performed a phonological awareness task consisting of counting, deletion, and discrimination at syllable level. Nonword Repetition, Digit Backward, Receptive & Expressive Vocabulary Test, and Grammaticality Judgment Task were performed to analyze the correlation between phonological awareness, phonological processing, and language ability. A multiple stepwise regression analysis was performed to examine the phonological awareness subtasks that predict language ability. In the TD group, the syllable categorization task significantly predicted the receptive vocabulary and the performance of the Grammaticality Judgment Task. The LD group showed that the syllable counting task significantly predicted the receptive vocabulary, the expressive vocabulary, and the performance of the Grammaticality Judgment Task. The results showed that the phonological awareness performance was significantly different between the two groups. Further, correlation analysis and regression analysis showed different results for each group. The result of the phonological awareness performance predicted the language ability of each group significantly, suggesting the importance of the meta-linguistic awareness ability of phonology.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.63-72
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• 2020

This study aims to develop a video-based smart utterance deep analyser (SUDA) application that analyzes semiautomatically the utterances that child and mother produce during interactions over time. SUDA runs on the platform of Android, iPhones, and tablet PCs, and allows video recording and uploading to server. In this device, user modes are divided into three modes: expert mode, general mode and manager mode. In the expert mode which is useful for speech and language evaluation, the subject's utterances are analyzed semi-automatically by measuring speech and language factors such as disfluency, morpheme, syllable, word, articulation rate and response time, etc. In the general mode, the outcome of utterance analysis is provided in a graph form, and the manger mode is accessed only to the administrator controlling the entire system, such as utterance analysis and video deletion. SUDA helps to reduce clinicians' and researchers' work burden by saving time for utterance analysis. It also helps parents to receive detailed information about speech and language development of their child easily. Further, this device will contribute to building a big longitudinal data enough to explore predictors of stuttering recovery and persistence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.50-55
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• 2019

Recently Ransomware attacks are continuously increasing, and new Ransomware, which is difficult to detect just with a basic vaccine, continuously has its upward trend. Various solutions for Ransomware have been developed and applied. However, due to the disadvantages and limitations of existing solutions, damage caused by Ransomware has not been reduced. Ransomware is attacking various platforms no matter what platform it is, such as Windows, Linux, servers, IoT devices, and block chains. However, most existing solutions for Ransomware are difficult to apply to various platforms, and there is a limit that they are dependent on only some specific platforms while operating. This study analyzes the problems of existing Ransomware detection solutions and proposes the onboard module based Ransomware detection system; after the system defines the function of necessary elements through analyzing requirements that can actually reduce the damage caused by the Ransomware from the viewpoint of users, it supports various OS without pre-installation and is able to restore data even after being infected. We checked the feasibility of each function of the proposed system through the analysis of the existing technology and verified the suitability of the proposed techniques to meet the user's requirements through the questionnaire survey of a total of 264 users of personal and corporate PC users. As a result of statistical analysis of the questionnaire results, it was found that the score of intent to introduce the system was at 6.3 or more which appeared to be good, and the score of intent to change from existing solution to the proposed system was at 6.0 which appeared to be very high.

• Korean Journal of Veterinary Service
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• v.44 no.2
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• pp.73-83
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• 2021

Although many swine farms continuously vaccinated to sow to prevent Porcine epidemic diarrhea(PED), PED has occurred annually in swine herds in Jeonbuk province, Korea. In the present study, the small intestine and feces samples from 17 farms where severe watery diarrhea and death of newborn piglets occurred in 2019 were collected, amplified by RT-PCR and determined the complete nucleotide sequences of the spike (S) glycoprotein genes of nine Jeonbuk PEDV isolates. The spike (S) glycoprotein is an important determinant for molecular characterization and genetic relationship of PEDV. These nine complete S gene isolates were compared with other PEDV reference strains to identify the molecular diversity, phylogenetic relationships and antigenicity analysis. 9 field strains share 98.5~100% homologies with each other at the nucleotide sequence level and 97.3~100% homologies with each other at the amino acid level. The nine Jeonbuk PEDV isolates were classified into G2b group including a genetic specific signal, S-indels (insertion and deletion of S gene). In addition, comparisons the neutralizing epitopes of S gene between 9 field strains and domestic vaccine strains of Korea mutated 12-15 amino acids with SM-98-1 (G1a group) and mutated 0-3 amino acids with QIAP1401 (G2b group). Therefore, the development of G2b-based live vaccines will have to be expedited to ensure effective prevention of endemic PED in Korea. In addition, we will need to be prepared with periodic updates of preventive vaccines based on the PEDV variants for the re-emergence of a virulent strain.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.125-135
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• 2009

Logs from various security system can reveal the attack trials for accessing private data without authorization. The logs can be a kind of confidence deriving factors that a certain IP address is involved in the trial. This paper presents a rule-based expert system for derivation of privacy violation confidence using various security systems. Generally, security manager analyzes and synthesizes the log information from various security systems about a certain IP address to find the relevance with privacy violation cases. The security managers' knowledge handling various log information can be transformed into rules for automation of the log analysis and synthesis. Especially, the coverage of log analysis for personal information leakage is not too broad when we compare with the analysis of various intrusion trials. Thus, the number of rules that we should author is relatively small. In this paper, we have derived correlation among logs from IDS, Firewall and Webserver in the view point of privacy protection and implemented a rule-based expert system based on the derived correlation. Consequently, we defined a method for calculating the score which represents the relevance between IP address and privacy violation. The UI(User Interface) expert system has a capability of managing the rule set such as insertion, deletion and update.