• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.37-46
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• 2013

Lane closure in freeway work zone due to maintenance and repair of freeway facilities results in abrupt change of traffic flow. Sudden change of traffic flow results strong interactions among vehicles, and reduces capacity compared to the ordinary traffic condition. Such capacity reduction is likely to cause congestion, traffic queues, and economic loss cost. This study aims to determine the base capacity for a short-term freeway work zone that can be used to establish a work zone schedule in advance without any traffic impact. First, the research collected detector data and schedule data of road works on all freeways in Korea. Second, the research determined 23 study sites finding the capacity values of work zone after matching two kinds of data. All study sites had varying characteristics regarding traffic flow being adjacent to work zone during road works. The capacity values were reviewed in terms of lane closure configuration, the types of work, and design speed. Finally, research proposed capacity values for a short-term freeway work zone with the design speeds of 100 kph, 120 kph and 1,700 pcphpl, 1,750 pcphpl, respectively.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.573-585
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• 2021

The banking sector is one of the most important sectors in Pakistan's struggling economy. Recent studies have recommended that suitable methods can be applied to predict bankruptcy. In this context, this work analyzes Pakistan's banking sector's financial status through the five-factor Altman Z-score model, which determines the probability of bankruptcy for an organization. Banking data has been collected through the Pakistan Stock Exchange (PSX) in the period 2013-2017. The Z-score assessment criteria is defined as: Z> 2.99 - "safe" zone; Z> 1.8 Z>2.98- "grey" zone; and Z <1.8 - "distress" zone. Results show good predictions for the local banking industry, while most foreign Pakistani banks were found bankrupt with the Z-score below 1.1. One of the financial risks investors face when investing in any company is the risk of bankruptcy. One of the most used models for predicting financial distress for any company is Altman's Z-score model. On the other hand, the Z-score analysis suggests that all banking establishments are not bankrupt because they have sufficient ability to control bankruptcy. At the same time, foreign banks failed financially and would not be able to be sustained in the future because they do not have the ability to pay the short-term and long-term debt.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.