Shipping and port industries are undergoing rapid environmental changes because of the reorganization of carrier alliances, enlargement of ships, and an increase in global uncertainty. Thus, the sustainable operation of container terminals requires a new assessment of port efficiency and measures to enhance efficient operation. Hence, we classified 21 global terminal operators (GTOs) into stevedore, carrier, and hybrid GTOs based on their operation characteristics and derived a sustainable container terminal operation method using data envelopment analysis efficiency and Malmquist productivity index analysis. The results showed that stevedore GTOs exhibited improved efficiency when the terminal infrastructure was expanded. However, the returns to scale and technical change factors in the productivity change trend decreased. Meanwhile, the objective of carrier GTOs is cost reduction, unlike stevedore and hybrid GTOs, which focus on generating profits. Consequently, carrier GTOs were the most inefficient with little intention to improve efficiency. A systematic efficiency improvement strategy through the acquisition of a terminal share was effective for hybrid GTOs. However, similar to stevedore GTOs, investment in technical change was insufficient for hybrid GTOs. The efficiency analysis we conducted for each operation characteristic is expected to provide useful basic data for establishing efficiency improvement strategies for every GTO.
Respiratory rate is an important biomarker that indicates changes in the clinical condition of critically ill patients, so a surveillance tool that can accurately monitor the changing respiratory rate in real time is needed. Through investigating various pairs of machine learning models, we proposed new machine learning model for real-time respiratory rate estimation using photoplethysmogram. New photoplethysmogram-driven respiratory rate dataset(StMary) was collected from surgical intensive care unit of a tertiary referral hospital, using photoplethysmogram signal collector. For 50patients and 50healthy volunteers, 2-minute photoplethysmogram was collected for each subject twice. To evaluate the respiratory rate of subject, it was inputted into the deep neural network model we built, and dataset was splitted into training, validation, testing dataset, then 4-fold cross validation was exploited. Our deep neural network model trained with StMary and two public datasets(BIDMC and CapnoBase) individually, or selectively merged dataset had shown a low error rate in respiration rate measurements. Our model trained with StMary showed low mean absolute error score(1.0273±0.8965), and trained with 3 datasets(CapnoBase, BIDMC and StMary) showed a lower error rate(1.7359±1.6724) than the model trained with CapnoBase and BIDMC(1.9480±1.6751). We could verify the performance of model evaluating respiratory rate from photoplethysmogram, and our dataset could contribute as the clinical research data that supports artificial intelligence models evaluating respiratory rate and surveillance tools to test whether their monitoring function works properly.
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