• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.442-454
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• 2017

Biological weapon is manipulated and produced from microorganisms such as bacteria, virus, rickettsia, fungi etc. It is classified as one of the Weapons of Mass Destruction (WMD) along with chemical weapon and radiological weapon. Biological weapon has a number of operational advantages over the other WMDs including ease of development and production, low cost and possibility of covert dissemination. In this study we analyze the history of biological weapon's development and the existing biological threats. Then, we predict the social impact of biological attack based on the physical properties of biological agent and infection mechanisms. By analyzing the recognition, dispersion pattern of agents, characteristics of the diseases in the biological weapon related historical events such as Sverdlovsk anthrax accident, 2001 anthrax attack, we found out some of the facts that biological attack would not likely to be recognized rapidly, produce large number of the exposed, increase number of paients who suffed from severe respiratory illness. It would lead the public health and medical service providers to be struggled with hugh burden. Base on the facts that we found from this case study, we suggested the main capabilities of public health required to respond to bioterrorism event efficiently. Syndromic surveillance and other reporting system need to be operated effeciently so that any suspicious event should be detected promptly. the pathogen which suspected to be used should be identified through laboratory diagnostic system. It is critical for the public health agency to define potentially exposed population under close cooperation with law enforcement agencies. Lastly, massive prophylaxis should be provided rapidly to the people at need by operating human and material resources effeciently. If those capacities of public health are consistantly fortified we would be able to deal with threat of bioterrorism successfully.

• Cross-Cultural Studies
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• v.43
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• pp.289-326
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• 2016

The strategy of "soft power" in the foreign and internal policies of modern Russia is one of the important factors in the implementation of public policies, and the influence of soft power is increasingly becoming stronger and gaining new forms and methods of implementation. The Russian government exerts efforts to form a positive image of Russia in the international arena, in order to strengthen the country's competitiveness, based on active use of "soft power." Currently, Russian cultural policy is developing in two main directions. In the internal policy sphere, the Russian government emphasizes national unity and civic solidarity, and fosters a sense of patriotism and national pride. In the sphere of foreign policy, the Russian government is attempting to regain its status as a great power and to create a new image of Russia that is different from that of the former Soviet Russia. In this article, we examine and analyze various aspects of the hidden political mechanisms involved in mega-sporting events, in particular the Sochi Olympics, from the viewpoint of Russian internal and foreign policy. We address the major functions of mega-sporting events and their influence in the political realm. The political impact of mega-sports projects can even compensate for economic losses incurred during the preparation and hosting of the Olympic games. In this respect, we can define mega-sporting events as one of the main components of soft power; such events reflect the basic directions of internal and foreign policy in post-Soviet Russia, which are to form and promote an image of Russia using national branding. In order to fairly and objectively analyze the recognition and perception held by Russians of the significance of mega-sporting events, in this work, we carefully studied the results of various surveys conducted by the Russian research organization VCIOM (Russian Public Opinion Research Center) before and after Russia hosted the Winter Olympic games in Sochi (2014) and the Summer Olympic games in Kazan (2013). Furthermore, on the basis of the ranking of national brands by Simon Anholt (Anholt Nation Brands Index - NBI), and on the basis of the ranking of 100 national brands conducted by the British consulting company "Brand Finance" (Brand Finance Nation Brands 100), we minutely trace the development and qualitative change in Russia's image and the role of the mega-sporting projects. This article also examines the Kremlin's internal and foreign policies that were successfully carried out in practical terms. This study contributes to the understanding of the value of mega-sporting events from the point of view of cultural policy of the current ruling party of Russia. This standpoint allows us to outline the main directions of Russian cultural policy and to suggest perspectives on the branding strategy of modern Russia, including strategies related to consolidating Russia's position in the international arena.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.