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

How to Mask in Error Correction Code Transformer: Systematic and Double Masking

In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability. As deep learning's applicability has broadened across diverse domains, there is a growing research focus on neural network-based decoders that outperform traditional decoding algorithms. Among these neural decoders, Error Correction Code Transformer (ECCT) has achieved the state-of-the-art performance, outperforming other methods by large margins. To further enhance the performance of ECCT, we propose two novel methods. First, leveraging the systematic encoding technique of ECCs, we introduce a new masking matrix for ECCT, aiming to improve the performance and reduce the computational complexity. Second, we propose a novel transformer architecture of ECCT called a double-masked ECCT. This architecture employs two different mask matrices in a parallel manner to learn more diverse features of the relationship between codeword bits in the masked self-attention blocks. Extensive simulation results show that the proposed double-masked ECCT outperforms the conventional ECCT, achieving the state-of-the-art decoding performance with significant margins.Comment: 8 pages, 5 figure

Effect of GCSB-5, a Herbal Formulation, on Monosodium Iodoacetate-Induced Osteoarthritis in Rats

Therapeutic effects of GCSB-5 on osteoarthritis were measured by the amount of glycosaminoglycan in rabbit articular cartilage explants in vitro, in experimental osteoarthritis induced by intra-articular injection of monoiodoacetate in rats in vivo. GCSB-5 was orally administered for 28 days. In vitro, GCSB-5 inhibited proteoglycan degradation. GCSB-5 significantly suppressed the histological changes in monoiodoacetate-induced osteoarthritis. Matrix metalloproteinase (MMP) activity, as well as, the levels of serum tumor necrosis factor-α, cyclooxygenase-2, inducible nitric oxide synthase protein, and mRNA expressions were attenuated by GCSB-5, whereas the level of interleukin-10 was potentiated. By GCSB-5, the level of nuclear factor-κB p65 protein expression was significantly attenuated but, on the other hand, the level of inhibitor of κB-α protein expression was increased. These results indicate that GCSB-5 is a potential therapeutic agent for the protection of articular cartilage against progression of osteoarthritis through inhibition of MMPs activity, inflammatory mediators, and NF-κB activation

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression:Prediction Model Development Study

BACKGROUND: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. OBJECTIVE: This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. METHODS: This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets. RESULTS: In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months). CONCLUSIONS: We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.</p