• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.321-355
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• 1993

This paper focuses on the geographical pattern of financial activities in the Korean urban system during 1975-1990, based on the assumption that financial activities can reveal control points in Korea's urban economy. In terms of spatial evolution of financial insitutions, different locational characteristics are revealed among different types of financial institutions, implying the role of urban hierarchy. Financial resources are highly concentrated in the capital region, Seoul and Kyonggi Province. Both centralization trends into the large metropolitan cities and relative declines of medium and small cities within the Korean urban system, have been experienced over the study period. Financial activities sustain relatively stable hierarchical structure in the urban hierarchy. Regarding the financial flows, dominant flow zones centered on major metropolitan cities are identified, clearly showing a prominant role of Seoul in financial flows in the entire urban system.

• Korean Journal of Ecology and Environment
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• v.52 no.3
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• pp.210-220
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• 2019

Population growth and the increase of energy consumption due to civilization caused global warming. Temperature on the Earth rose about $0.7^{\circ}C$ for the last 100 years, the rate is accelerated since 2000. Temperature is a factor, which determines physiological action, growth and development, survival, etc. of the plant together with light intensity and precipitation. Therefore, it is expected that global warming would affect broadly geographic distribution of the plant as well as structure and function ecosystem. In order to understand the effect of global warming on the ecosystem, a study about the effect of temperature rise on germination and growth in the plant is required necessarily. This study was carried out to investigate the effects of experimental warming on the germination and growth of two oak species(Quercus mongolica and Q. serrata) in temperature gradient chamber(TGC). This study was conducted in control, medium warming treatment($+1.7^{\circ}C$; Tm), and high warming treatment ($+3.2^{\circ}C$; Th) conditions. The final germination percentage, mean germination time and germination rate of two oak species increased by the warming treatment, and the increase in Q. serrata was higher than that in Q. mongolica. Root collar diameter, seedling height, leaf dry weight, stem dry weight, root dry weight, and total biomass were the highest in Tm treatment. Butthey were not significantly different in the Th treatment. In the Th treatment, Q. serrata had significantly higher H/D ratio, S/R ratio, and low root mass ratio (RMR) compared with control plot. Q. mongolica had lower RMR and higher S/R ratio in the Tm and Th treatments compared with control plot. Therefore, growth of Q. mongolica are expected to be more vulnerable to warming than that of Q. serrata. The main findings of this study, species-specific responses to experimental warming, could be applied to predict ecosystem changes from global warming. From the result of this study, we could deduce that temperature rise would increase germination of Q. serrata and Q. mongolica and consequently contribute to increase establishment rate in the early growth stage of the plants. But we have to consider diverse variables to understand properly the effects that global warming influences germination in natural condition. Treatment of global warming in the medium level increased the growth and the biomass of both Q. serrata and Q. mongolica. But the result of treatment in the high level showed different aspects. In particular, Q. mongolica, which grows in cooler zones of higher elevation on mountains or northward in latitude, responded more sensitively. Synthesized the results mentioned above, continuous global warming would function in stable establishment of both plants unfavorably. Compared the responses of both sample plants on temperature rise, Q. serrata increased germination rate more than Q. mongolica and Q. mongolica responded more sensitively than Q. serrata in biomass allocation with the increase of temperature. It was estimated that these results would due to a difference of microclimate originated from the spatial distribution of both plants.

• 한국노년학
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• v.39 no.4
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• pp.921-939
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• 2019

According to the social selectivity theory (SST), despite the disadvantages of life conditions, older adults experience less negative emotions because they regulate their emotions by avoiding negative stimuli or situations. Based on the SST, this study attempted to find out whether older adults are better able to regulate negative emotions than young adults in the Ultimatum Game (UG). In an UG, if the proposer proposes to distribute a portion of the money to the responder, the responder must decide whether to accept or reject it. If the responder accepts the offer, the proposer and the responder can each have their own share as proposed, but if s/he reject the offer, both get nothing. Thus, if the responder considers own economic benefits, it is a more reasonable decision to accept the unfair offer no matter how low, than to reject it. To accept an unfair offer, the responder must regulate the anger felt at the proposer. If older adults could regulate anger better than young adults, they would be less likely to reject the unfair offer than young adults. Fifty-seven olders and 60 university students participated in this study. Both the older and young adults accepted most of the fair offers. In contrast, older adults accepted unfair offers at a significantly higher rate than young adults. In addition, compared to young adults, older adults reported anger less frequently at the unfair offers. Accepting unfair offers was negatively correlated with anger report, but positively correlated with the emotion regulation measured by ERQ. The ERQ score was negatively correlated with anger report. Emotion regulation partially mediated the relationship between the age groups and acceptance of unfair offers. The present results showed that older adults accepted the unfair offers at a higher rate than young adults because they could regulate the negative emotions felt at the unfair offer better than young adults. This study provided new evidence for the claim that improving emotional regulation is a major developmental change in adulthood.

• 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.