• BMB Reports
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• v.57 no.6
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• pp.281-286
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• 2024

Adult hippocampal neurogenesis plays a pivotal role in maintaining cognitive brain function. However, this process diminishes with age, particularly in patients with neurodegenerative disorders. While small, non-coding microRNAs (miRNAs) are crucial for hippocampal neural stem (HCN) cell maintenance, their involvement in neurodegenerative disorders remains unclear. This study aimed to elucidate the mechanisms through which miRNAs regulate HCN cell death and their potential involvement in neurodegenerative disorders. We performed a comprehensive microarray-based analysis to investigate changes in miRNA expression in insulin-deprived HCN cells as an in vitro model for cognitive impairment. miR-150-3p, miR-323-5p, and miR-370-3p, which increased significantly over time following insulin withdrawal, induced pronounced mitochondrial fission and dysfunction, ultimately leading to HCN cell death. These miRNAs collectively targeted the mitochondrial fusion protein OPA1, with miR-150-3p also targeting MFN2. Data-driven analyses of the hippocampi and brains of human subjects revealed significant reductions in OPA1 and MFN2 in patients with Alzheimer's disease (AD). Our results indicate that miR-150-3p, miR-323-5p, and miR-370-3p contribute to deficits in hippocampal neurogenesis by modulating mitochondrial dynamics. Our findings provide novel insight into the intricate connections between miRNA and mitochondrial dynamics, shedding light on their potential involvement in conditions characterized by deficits in hippocampal neurogenesis, such as AD.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.159-167
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• 2024

This study analyzed the survey data of 162 rescuers working in G Special Self-Governing Province to find out the effect of the transactional and transformational leadership of 119 rescuer leaders on life satisfaction. As a result of analysis through multiple regression analysis, it was found that the life satisfaction of 119 rescuers was greatly influenced by the leadership style of their leaders. It has been confirmed that transformational and transactional leadership are not independent of each other, and that the appropriate balance of these two leadership approaches has a positive effect on the life satisfaction and organizational life of the rescuers. Therefore, the 119 rescuers' team leaders should create an environment in which transformational and transactional leadership can be balanced. Specifically, transformational leadership should be exercised through individualized consideration, intellectual stimulation, and inspirational motivation, while at the same time improving transactional leadership by establishing a reward system based on performance. Through this, it is expected that the quality of rescue service at the site will be improved by increasing the life satisfaction of 119 rescuers and laying the foundation for them to demonstrate their potential capabilities. The findings of this study provide practical implications for improving the quality of life and organizational performance of 119 rescuers.

• Knowledge Management Research
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• v.25 no.2
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• pp.219-243
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• 2024

This study investigates the impact of privacy concerns, perceived utility, and awareness of the right to personal data self-determination on the effective use and expansion of MyData services, which are critical to the data economy. Integrating the value-based adoption model with privacy calculus theory, the research examines how perceived utility, privacy concerns, trust, and personal innovativeness influence perceived value, perceived privacy, and the intention to provide personal information. Data collected from an online survey of 442 MyData service users and prospective users were analyzed using PLS-SEM and Bootstrapping methods via SmartPLS 4. The results indicate that perceived utility positively affects the intention to provide personal information, while privacy concerns have a negative impact. Trust and personal innovativeness positively influence the intention to adopt MyData services, and the awareness of personal data self-determination rights moderates these intentions. The findings underscore the importance of developing beneficial services that mitigate users' privacy concerns and build trust for the successful implementation of MyData services. Additionally, the study highlights the need for education and awareness campaigns to enhance understanding of the right to personal data self-determination.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.835-840
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• 2023

RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Industry Promotion Research
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• v.9 no.1
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• pp.89-101
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• 2024

The purpose of this study is to examine the relationship between parental/parental attachment and career indecision among college students, and to examine the goodness of fit and influence of variables in a model that assumes that socially imposed perfectionism has an influence on the relationship between parental/parental attachment and career indecision. It's about verification. For this purpose, an online survey was conducted by randomly sampling 250 college students attending 4-year institutions across the country, and data analysis was conducted using a three-stage regression method using SPSS Win 25.0. The analysis results are as follows. First, psychological attachment to parents appears to have a negative effect on career indecision, confirming that the more a stable attachment relationship with parents is formed, the less difficulties in career decision-making. Second, the mediating effect of socially imposed perfectionism was confirmed in the relationship between psychological attachment to parents and career indecision. This shows that the more stable the psychological attachment to the father and mother is formed, the lower the level of socially imposed perfectionism and career indecision. Based on these research results, implications for career counseling practice and follow-up research were discussed.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.9-18
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• 2023

Climate change affects the safety of river revetments, especially those associated with external flooding. Research on slope reinforcement has been actively conducted to enhance revetment safety. Recently, technologies for producing embankment blocks using recycled materials have been developed. However, it is essential to analyze the impact of block shapes on the flow characteristics of exclusion zones for revetment safety. Therefore, this study investigates the influence of revetment block shapes on the hydraulic characteristics of revetment surfaces through 3D numerical simulations. Three block shapes were proposed, and numerical analyses were performed by installing the blocks in an idealized river channel. FLOW-3D was used for the 3D numerical simulations, and the variations in maximum flow velocity, bed velocity beneath the revetment, and maximum shear stress were analyzed based on the shapes of the revetment blocks. The results indicate that for irregularly sized and spaced revetment blocks, such as the natural stone-type vegetation block (Block A), when connected to the revetment in an irregular manner, the changes in flow velocity in the revetment installation zone are more significant than those for Blocks B and C. It is anticipated that considering the topographical characteristics of rivers in the future will enable the design of revetment blocks with practical applicability in the field.

• Information Systems Review
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• v.21 no.1
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• pp.91-111
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• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.