2,920 research outputs found

    Value Discount of Business Groups Surrounding the Asia Financial Crisis: Evidence from Korean Chaebols

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    Asian Financial Crisis, Business Group, Chaebol, Diversification, Firm Value

    Frequency Characteristics of Double-Walled Carbon Nanotube Resonator with Different Length

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    In this paper, we have conducted classical molecular dynamics simulations for DWCNTs of various wall lengths to investigate their use as ultrahigh frequency nano-mechanical resonators. We sought to determine the variations in the frequency of these resonators according to changes in the DWCNT wall lengths. For a double-walled carbon nanotube resonator with a shorter inner nanotube, the shorter inner nanotube can be considered to be a flexible core, and thus, the length influences the fundamental frequency. In this paper, we analyze the variation in frequency of ultra-high frequency nano-mechnical resonators constructed from DWCNTs with different wall lengths

    Regulatory gaps between LNG carriers and LNG fuelled ships

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    Given a number of marine vessels treating the liquefied natural gas either as cargo or fuel, this paper examined the regulatory gaps of two different international Codes – the InternationalCodeof the Construction and Equipment of Ships Carrying Liquefied Gases in Bulk and the International Code of Safety for Ships Using Gases or Other Low-flashpoint Fuels – from the regulatory standpoint. Results of the gap analysis have identified and discussed the key areas encountered with regulatory discrepancies or ambiguities that might interrupt the proper design, construction and operation of LNG carrier and LNG fuelled ship. A systematic investigation and harmonisation process across the Codes was proposed to mitigate the potential issues that may arise from the discordant regulations. Also, the International Maritime Organization was suggested to take proactive action to improve such dissonances while a general insight into the importance of filling those gaps was provided for rule-makers and stakeholders

    Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

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    In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the KK most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the KK locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques

    Clinical Manifestation of Odontogenic Sinusitis

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