• Journal of Contemporary Eastern Asia
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• v.17 no.2
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• pp.237-253
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• 2018

In order to understand China's internet governance, this paper examined 1,931 Internet policies of China by bibliometric techniques. Specifically, the bibliometric techniques include simple document counting, co-word analysis, collaboration network analysis and citation analysis. The findings include: (1) China's Internet legislations mainly emphasized e-commerce and Internet governance, and, to some extent, neglected personal data protection; (2) China's Internet is under intensive multiple regulatory controls by central government. A large number of government agencies are involved in Internet policy-making. The Propaganda Department of the Central Committee of the Communist Party of China and the State Information Leading Group of the State Council, enforced fewer policy documents, but occupy higher positions in the Internet governance hierarchy; (3) China's Internet legislation system is primarily composed of industry-specific administrative rules, rather than laws or administrative regulations. Nevertheless, laws and administrative regulations received significantly more citations owing to their superior force. This paper also discussed current gaps in China's internet governance and how the country's internet policies are situated in the broader global context.

• Journal of Microbiology and Biotechnology
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• v.21 no.4
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• pp.438-447
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• 2011

Deinococcus radiodurans is extremely resistant to various genotoxic conditions and chemicals. In this study, we characterized the effect of a sublethal concentration (100 ${\mu}M$) of cadmium (Cd) on D. radiodurans using a whole-genome DNA microarray. Time-course global gene expression profiling showed that 1,505 genes out of 3,116 total ORFs were differentially expressed more than 2-fold in response to Cd treatment for at least one timepoint. The majority of the upregulated genes are related to iron uptake, cysteine biosynthesis, protein disulfide stress, and various types of DNA repair systems. The enhanced upregulation of genes involved in cysteine biosynthesis and disulfide stress indicate that Cd has a high affinity for sulfur compounds. Provocation of iron deficiency and growth resumption of Cd-treated cells by iron supplementation also indicates that CdS forms in iron-sulfur-containing proteins such as the [Fe-S] cluster. Induction of base excision, mismatch, and recombinational repair systems indicates that various types of DNA damage, especially base excision, were enhanced by Cd. Exposure to sublethal Cd stress reduces the growth rate, and many of the downregulated genes are related to cell growth, including biosynthesis of cell membrane, translation, and transcription. The differential expression of 52 regulatory genes suggests a dynamic operation of complex regulatory networks by Cd-induced stress. These results demonstrate the effect of Cd exposure on D. radiodurans and how the related genes are expressed by this stress.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.4981-4985
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• 2015

Background: Colorectal cancer (CRC) is a leading cause of morbidity and mortality worldwide. Targeting autophagic cell death is emerging as a novel strategy in cancer chemotherapy. Aldose reductase (AR) catalyzes the rate limiting step of the polyol pathway of glucose metabolism; besides reducing glucose to sorbitol, AR reduces lipid peroxidation-derived aldehydes and their glutathione conjugates. A complex interplay between autophagic cell death and/or survival may in turn govern tumor metastasis. This exploratory study aimed to investigate the potential role of AR inhibition using a novel inhibitor Fidarestat in the regulation of autophagy in CRC cells. Materials and Methods: For glucose depletion (GD), HT-29 and SW480 CRC cells were rinsed with glucose-free RPMI-1640, followed by incubation in GD medium +/- Fidarestat ($10{\mu}M$). Proteins were extracted by a RIPA-method followed by Western blotting ($35-50{\mu}g$ of protein; n=3). Results: Autophagic regulatory markers, primarily, microtubule associated protein light chain (LC) 3, autophagy-related gene (ATG) 5, ATG 7 and Beclin-1 were expressed in CRC cells; glyceraldehyde-3 phosphate dehydrogenase (GAPDH) was used as an internal reference. LC3 II (14 kDa) expression was relatively high compared to LC3A/B I levels in both CRC cell lines, suggesting occurrence of autophagy. Expression of non-autophagic markers, high mobility group box (HMG)-1 and Bcl-2, was comparatively low. Conclusions: GD +/- ARI induced autophagy in HT-29 and SW-480 cells, thereby implicating Fidarestat as a promising therapeutic agent for colorectal cancer; future studies with more potent ARIs are warranted to fully dissect the molecular regulatory networks for autophagy in colorectal carcinoma.

• Smart Media Journal
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• v.9 no.2
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• pp.78-85
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• 2020

This study aims to clarify the impact of environmental issues on sustainable environmental management performance through the establishment of internal and external networks and environmental management activities. As a result of analyzing 163 final collected data of companies, social environmental issues did not have a significant effect on the internal sharing system, but they had a significant positive effect on both regulatory response and cooperation network establishment. In addition, the regulatory response to the environment has a significant effect on both the internal sharing system, cooperation network establishment, and environmental management activities. Lastly, environmental management activities have a significant impact on sustainable environmental management performance, indicating that environmental issues are leading to environmental management activities and performance by promoting sustainable growth of companies.

• Journal of Life Science
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• v.28 no.10
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• pp.1233-1243
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• 2018

Sterol regulatory-element binding proteins (SREBPs) are a family of transcription factors that regulate lipid homeostasis and metabolism by controlling the expression of enzymes required for endogenous cholesterol, fatty acid (FA), triacylglycerol, and phospholipid synthesis. The three SREBPs are encoded by two different genes. The SREBP1 gene gives rise to SREBP-1a and SREBP-1c, which are derived from utilization of alternate promoters that yield transcripts in which distinct first exons are spliced to a common second exon. SREBP-2 is derived from a separate gene. Additionally, SREBPs are implicated in numerous pathogenic processes, such as endoplasmic reticulum stress, inflammation, autophagy, and apoptosis. They also contribute to obesity, dyslipidemia, diabetes mellitus, and nonalcoholic fatty liver diseases. Genome-wide analyses have revealed that these versatile transcription factors act as important nodes of biological signaling networks. Changes in cell metabolism and growth are reciprocally linked through SREBPs. Anabolic and growth signaling pathways branch off and connect to multiple steps of SREBP activation and form complex regulatory networks. SREBPs are activated through the PI3K-Akt-mTOR pathway in these processes, but the molecular mechanism remains to be understood. This review aims to provide a comprehensive understanding of the role of SREBPs in physiology and pathophysiology at the cell, organ, and organism levels.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.187-198
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• 2005

In this paper, we propose the enhanced header compression scheme for the efficient data traffic transmission in Ipv6 networks. The bandwidth of wireless links and IP networks will probably be limited due to properties of the physical medium and regulatory limits on the use of frequencies for radio communications. That is major cause of user throughput reduction. Therefore, We discuss the IPHC(RFC2507) and ROHC(RFC3095) scheme. IPHC is simple header compression scheme and ROHC is enhanced header compression that have fast optimal recovery scheme. We have studied the enhanced header compression scheme in ROHC. We will show that indication of compression context values preventing from packet losses can provide the fast recovery of compression state. Computer simulations show that the proposed scheme has better performance than the previous one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1178-1188
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• 2002

Local Loop Unbundling(LLU) has been understood as the way of facilitate the competition on the access and the local telephone service market. There are major benefits of stimulating the competition in the local service, avoiding access network duplication and reducing in environmental disruption. However, LLU has several disadvantages such as removing incentives for building alternative access networks, undermining existing investment in alternative access networks, introducing new substantial costs to the industry and requiring prolonged and detailed regulatory intervention. The economic effects of LLU generally will be different according to the special situation of each countries and the object of LLU. In case of EU, most of countries have already introduced and implemented LLU. But EU can't expect the economic effects on LLU emerged in the early stage of introducing it and faced with dilemma. To be realized the successful implementation of LLU, I think, it is required to be reviewed about the main issues such as the problem of regulation, the reasonable level of LLU pricing and the technical & operational problems.

• Genomics & Informatics
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• v.2 no.4
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• pp.191-194
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• 2004

Exponentially increasing biopathway data in recent years provide us with means to elucidate the large-scale modular organization of the cell. Given the existing information on metabolic and regulatory networks, inferring biopathway information through scientific reasoning or data mining of large scale array data or proteomics data get great attention. Naturally, there is a need for a user-friendly system allowing the user to combine large and diverse pathway data sets from different resources. We built a data warehouse - BIOWAY - for analyzing and visualizing biological pathways, by integrating and customizing resources. We have collected many different types of data in regards to pathway information, including metabolic pathway data from KEGG/LIGAND, signaling pathway data from BIND, and protein information data from SWISS-PROT. In addition to providing general data retrieval mechanism, a successful user interface should provide convenient visualization mechanism since biological pathway data is difficult to conceptualize without graphical representations. Still, the visual interface in the previous systems, at best, uses static images only for the specific categorized pathways. Thus, it is difficult to cope with more complex pathways. In the BIOWAY system, all the pathway data can be displayed in computer generated graphical networks, rather than manually drawn image data. Furthermore, it is designed in such a way that all the pathway maps can be expanded or shrinked, by introducing the concept of super node. A subtle graphic layout algorithm has been applied to best display the pathway data.