Multi Criteria Decision on Selecting Optimal Ship Accident Rate for Port Risk Mitigation

AbstractLarge ports have potential catastrophic accidents by handling enormous amount of hazardous and dangerous materials which tend to increase the risk of port and the facilities in its vicinity. In the paper, we propose a mathematical method to identify the ship accident types affecting risk in port areas which propagate into death of people as a consequence of the accidents. We consider a multi criteria port risk problem and a goal programming modeling is constructed for calculating accident rates of each accident type. The obtained results can be employed by decision makers or port authorities in implementing the port risk mitigation measures or in designing (planning) future port construction.For the study, we use the accident data for the 12 domestic ports over the last 5 years from 2002 to 2007

Efficiency Analysis of Major Container Ports in Asia: Using DEA and Shannon’s Entropy

This paper attempts to evaluate performance (i.e. efficiency) of Asias container ports. Measurement of the ports performance is critical to increase the competitiveness of maritime transport, ultimately leading to one nations competitive advantages over other countries. Data Envelopment Analysis (DEA), which is a non-parametric method widely used for assessing efficiency of units which have similar characteristics, was selected to analyse the data. Due to the limitations of the DEA method producing diverse results according to different models, and to the complexities of choosing a specific model among several DEA models, Shannons Entropy was also employed. By including Shannons Entropy, the efficiency results calculated from each model were integrated in order to rank the ports. The results in this study will provide port managers with valuable information in order to understand the current status of Asias container ports in terms of their efficiency

Corporate Image and Reputation in the Shipping Industry in Four Asian Countries: Republic of Korea, China, Japan, and Thailand

This study aims to analyze the perceptions regarding shipping companies corporate image and reputation in Republic of Korea, China, Japan, and Thailand. For this study, the shipping industry is confined to the bulk and container shipping sectors to prevent confusion arising from the different sectors. An international questionnaire survey was administered in each country. The participants were asked to report their perceptions on eight indicators of corporate image and seven indicators of corporate reputation relating to the shipping companies. Descriptive analyses and a one-way between-groups analysis of variance (ANOVA) were conducted using SPSS 20. Findings show that there are significant differences in perceptions concerning corporate image and reputation among four countries. Some cases show significant differences in the analyses in line with demographic characteristics. While China shows the highest scores in most variables, Korea is revealed to have the lowest scores. The results indicate the need to develop programs for improving the external positive perceptions of the shipping companies, as well as to broaden the scope of marketing activities targeting the general public. This study is of critical importance as it discusses relatively ignored but important issues by conducting comparative research in four major Asian countries comprehensively, particularly targeting samples rarely considered in the empirical shipping-related studies despite their significance to academic development. Further research is required to demonstrate the effectiveness of the findings by applying the measures in different national contexts with a more diverse group of samples

Synthesis and Properties of Fluorinated Polyimides from Rigid and Twisted Bis(Trifluoromethyl)Benzidine for Flexible Electronics

Fluorinated polyimides were prepared from the twisted benzidine monomer containing two trifluoromethyl (CF3) groups on one aromatic ring. The diamine monomer having a rigid and nonplanar structure was polymerized with typical dianhydride monomers including BPDA, BTDA, ODPA, 6-FDA, and PMDA, to obtain the corresponding polyimides. Most polyimides are soluble in organic solvents due to their twisted chain structure and can be solution cast into flexible and tough films. These films have a UV-vis absorption cut-off wavelength at 354–398 nm and a light transparency of 34–90% at a wavelength of 550 nm. They also have tensile strengths of 92–145 MPa and coefficients of thermal expansion (CTE) of 6.8–63.1 ppm/°C. The polymers exhibited high thermal stability with 5% weight loss at temperatures ranging from 535 to 605°C in nitrogen and from 523 to 594°C in air, and high glass temperature (Tg) values in the range of 345–366°C. Interestingly, some of the soluble polyimides showed thermo-responsive behaviors in organic solvents presumably due to the multiple hydrogen bondings with unsymmetrically positioned two CF3 groups along the polymer chains

Boosting Learning for LDPC Codes to Improve the Error-Floor Performance

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates and deploying LDPC codes in scenarios demanding ultra-high reliability. In this work, we propose training methods for neural min-sum (NMS) decoders to eliminate the error-floor effect. First, by leveraging the boosting learning technique of ensemble networks, we divide the decoding network into two neural decoders and train the post decoder to be specialized for uncorrected words that the first decoder fails to correct. Secondly, to address the vanishing gradient issue in training, we introduce a block-wise training schedule that locally trains a block of weights while retraining the preceding block. Lastly, we show that assigning different weights to unsatisfied check nodes effectively lowers the error-floor with a minimal number of weights. By applying these training methods to standard LDPC codes, we achieve the best error-floor performance compared to other decoding methods. The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor .Comment: 17 pages, 10 figure