• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.91-105
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• 2021

The current increasing size of container vessels affects the container port's situation. The containerization has changed the inter-modal handling process, which brought more flexibility and comfortableness in the shipping industry sector. Thus, it is very crucial to analyze the efficiency of container ports in the regional sphere. Such kind of efficiency analysis provide a powerful management tool for port operators and shipping managers in the Mediterranean market, and it also helps to form an information for planning new regional and national port operations. This paper aims to analyze the ports' technical efficiency of Mediterranean major container ports. It is conducted to establish the model of port performance and efficiency through the empirical test of the various factors. Regarding to the panel data collected from the 48 DMUs (decision making units), this study attempts to provide the empirical basis of the port efficiency relative to another factors in the total port performance. Due to the complexity of the various activities carried out at container ports, the study focuses only on the technical efficiency at the level of the Mediterranean container port. Unlike the practice of cross-sectional data analysis, originally established by Charnes et al. (1985), the panel data in DEA window analysis applications are used. The main focus of this study is the relative technical efficiency of 12 container ports from 7 countries in the Mediterranean market. The selection of ports under study is based on their high handling capability and rankings in World Top 100 (Containerization International, 2018).

• Acta Via Serica
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• v.3 no.2
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• pp.49-72
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• 2018

The extensive maritime trade network between the Harappan and Mesopotamian civilizations as early as the $3^{rd}$ millennium BCE is a testimony to the long maritime trade history of India. From the dawn of the historical epoch, the maritime trade network of India expanded extensively. The findings of a large number of coins, pottery, amphorae and other materials from Italy and various other European countries, west Asia, China, Korea, Africa, the Arabian Peninsula, Sri Lanka, Southeast Asia and Far-East countries in India, particularly in the coastal regions, are a testimony to the dynamic maritime trade of India with other countries in the early period. Similarly, pottery, sculptures, inscriptions and other materials of Indian origin are also found in those countries. The depiction of different types of ships on the coins, paintings, sculptures, seals and sealing, exhibit the variety of vessels used for navigation and other purposes in the early period. The over 7500 km-long coastline of India is well known for its seaports located at river mouths or outlets to the sea. The Periplus Maris Erythraei, Ptolemy, and Indian literary sources mention many seaports on the western coast of India. Interestingly, archaeological investigations in many of these port towns have yielded material evidence exhibiting their dominant role in transoceanic trade and commerce with many countries in the early period. This paper discusses in detail all the major ancient seaports on the western coast of India and their maritime trade activities. At the outset, the paper briefly deals with the Harappan's maritime network, their seaports and the type of ships of that period. Following this, the maritime trade network of India during the historical period with various countries in the east and west, the traces of Indian influence and materials abroad and foreign materials found in India, the products exported from India, the trade winds and navigational devices and the depiction of ships on the coins, paintings, and sculptures of the period are discussed in detail. After briefly highlighting the coastline of India and its favourable nature for safe anchorage of ships and the strategic position of the seaports of western India, an extensive account of the major ancient seaports of western India like Barygaza, Ashtacampra, Gundigar, Kammoni, Khambhat, Bardaxema, Suparaka, Calliena, Semylla, Sanjan, Naura, Tyndis, Muziris, Nelcynda and other seaports, and their maritime trade activities are given based on archaeological excavations and explorations, literature, epigraphy, foreign accounts, and numismatic evidence.

• Journal of Life Science
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• v.19 no.2
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• pp.234-240
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• 2009

We tried to analyze the HPV prevalence and HPV genotypes of sexually high-risk women living in Busan, the biggest seaport of South Korea. Six hundred sixty women engaging in high-risk occupations participated in this study. The prevalence of HPV infection and HPV genotyping were determined with $MyGene^{(R)}$ HPVDNA chip, which consisted of 16 high-risk HPV genotypes (oncogenic genotypes) and 8 low-risk HPV genotypes. The overall prevalence of HPV infection in this study population was 39.1% (258/660) and the 20's showed the highest prevalence of HPV infection (51.5%). The dominant HPV genotypes including single or multiple HPV-infected women were resulted in HPV-16 (15.9%), -53 (10.2%), -58 (7.7%), -18 (5.2%) in case of high-risk HPV genotype and HPV-70 (10.4%), -6 (4.1%), -11 (2.0%) in case of low-risk HPV genotypes. Remarkably, the proportion of women infected with high-risk HPV genotypes (62.0%) was almost four times higher than those of women infected with low-risk HPV genotypes (14.7%) and high/low-risk HPV genotypes (12.0%). Among the 258 HPV-infected women, single infection was 175, double infection 66, triple infection 12, quadruple infection 4, quintuple infection 1, respectively. Our finding suggests that the introduction and development of effective HPV vaccines should consider the current status of HPV genotypic infection in South Korean women.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.17-52
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• 2018

The purpose of this paper is to measure the clustering change and analyze empirical results. Additionally, by using k-means, hierarchical, and mixed models on Asian container ports over the period 2006-2015, the study aims to form a cluster comprising Busan, Incheon, and Gwangyang ports. The models consider the number of cranes, depth, birth length, and total area as inputs and container twenty-foot equivalent units(TEU) as output. Following are the main empirical results. First, ranking order according to the increasing ratio during the 10 years analysis shows that the value for average linkage(AL), mixed ward, rule of thumb(RT)& elbow, ward, and mixed AL are 42.04% up, 35.01% up, 30.47%up, and 23.65% up, respectively. Second, according to the RT and elbow models, the three Korean ports can be clustered with Asian ports in the following manner: Busan Port(Hong Kong, Guangzhou, Qingdao, and Singapore), Incheon Port(Tokyo, Nagoya, Osaka, Manila, and Bangkok), and Gwangyang Port(Gungzhou, Ningbo, Qingdao, and Kasiung). Third, optimal clustering numbers are as follows: AL(6), Mixed Ward(5), RT&elbow(4), Ward(5), and Mixed AL(6). Fourth, empirical clustering results match with those of questionnaire-Busan Port(80%), Incheon Port(17%), and Gwangyang Port(50%). The policy implication is that related parties of Korean seaports should introduce port improvement plans like the benchmarking of clustered seaports.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.29-58
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• 2016

The purpose of this paper is to show the clustering trend and the comparison of empirical results and is to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the bootstrapped DEA(Data Envelopment Analysis) and game Cross-efficiency models for 38 Asian ports during the period 2003-2013 with 4 input variables(birth length, depth, total area, and number of cranes) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, bootstrapped DEA efficiency of SW and LT is 0.7660, 0.7341 respectively. Clustering results of the bootstrapped DEA analysis show that 3 Korean ports [ Busan (6.46%), Incheon (3.92%), and Gwangyang (2.78%)] can increase the efficiency in the SW model, but the LT model shows clustering values of -1.86%, -0.124%, and 2.11% for Busan, Gwangyang, and Incheon respectively. Second, the game cross-efficiency model suggests that Korean ports should be clustered with Hong Kong, Shanghi, Guangzhou, Ningbo, Port Klang, Singapore, Kaosiung, Keelong, and Bangkok ports. This clustering enhances the efficiency of Gwangyang by 0.131%, and decreases that of Busan by-1.08%, and that of Incheon by -0.009%. Third, the efficiency ranking comparison between the two models using the Wilcoxon Signed-rank Test was matched with the average level of SW (72.83 %) and LT (68.91%). The policy implication of this paper is that Korean port policy planners should introduce the bootstrapped DEA, and game cross-efficiency models when clustering is needed among Asian ports for enhancing the efficiency of inputs and outputs. Also, the results of SWOT(Strength, Weakness, Opportunity, and Threat) analysis among the clustering ports should be considered.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.139-164
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• 2009

The purpose of this paper is to investigate the value chain efficiency of Korean port investment by using the newly developed multi-year and multi-stage value chain efficiency model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 14 years(1994-2007) for 20 Korean seaports by using two kinds of DEA models. Empirical main results are as follows: First, Model 1 shows that the ranking order of multi-stage value chain efficiency is Stage 2, Stage 3-1, Stage 1, and Stage 3-2. And according to the value chain average efficiency scores, ranking order is stages 2, 1, 3-1, and 3-2. In Model 2, 3(Incheon, Mogpo, and Jeju) out of 9 ports show the ranking order of Stages 2, 3-2, 3-1, and 1. And value chain average efficiency scores rank in order of Stages 2, 3-2, 3-1, and 1. Second, the difference among the value chain efficiency scores of each stage comes from the efficiency deterioration of all ports except Stages 2 and 1 in Model 1. In Model 2, value chain efficiency scores among the Stages 3-1, 3-2 compared to Stage 1 were deteriorated. The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the multi-year, multi-stage value chain efficiency method for deciding the port investment amount and evaluating the effect of port investment after considering the empirical results of this paper carefully.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.31-70
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• 2021

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Hierarchical clustering(single, complete, average, and centroid), Silhouette, and 2SCE[the Second Stage(Type II) cross-efficiency] matrix clustering models on Asian container ports over the period 2009-2018. The models have chosen number of cranes, depth, birth length, and total area as inputs and container TEU as output. The main empirical results are as follows. First, ranking order according to the efficiency increasing ratio during the 10 years analysis shows Silhouette(0.4052 up), Hierarchical clustering(0.3097 up), and 2SCE(0.1057 up). Second, according to empirical verification of the Silhouette and 2SCE models, 3 Korean ports should be clustered with ports like Busan Port[ Dubai, Hong Kong, and Tanjung Priok], and Incheon Port and Gwangyang Port are required to cluster with most ports. Third, in terms of the ASEAN, it would be good to cluster like Busan (Singapore), Incheon Port (Tanjung Priok, Tanjung Perak, Manila, Tanjung Pelpas, Leam Chanbang, and Bangkok), and Gwangyang Port(Tanjung Priok, Tanjung Perak, Port Kang, Tanjung Pelpas, Leam Chanbang, and Bangkok). Third, Wilcoxon's signed-ranks test of models shows that all P values are significant at an average level of 0.852. It means that the average efficiency figures and ranking orders of the models are matched each other. The policy implication is that port policy makers and port operation managers should select benchmarking ports by introducing the models used in this study into the clustering of ports, compare and analyze the port development and operation plans of their ports, and introduce and implement the parts which required benchmarking quickly.