Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach

Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime big data, which takes a significant part in port efficiency and maritime logistics enhancement. This study proposes a systematic and dynamic approach of predicting berth stay for tanker terminals. The approach covers three innovative aspects: 1) Data source employed is multi-faceted, including cargo operation data from tanker terminals, time-series data from automatic identification system (AIS), etc. 2) The process of berth stay is decomposed into multiple blocks according to data analysis and information extraction innovatively, and practical operation scenarios are also developed accordingly. 3) The predictive models of berth stay are developed on the basis of prior data analysis and information extraction under two methods, including regression and decomposed distribution. The models are evaluated under four dynamic scenarios with certain designated cargoes among two different terminals. The evaluation results show that the proposed approach can predict berth stay with the accuracy up to 98.81% validated by historical baselines, and also demonstrate the proposed approach has dynamic capability of predicting berth stay among the scenarios. The model may be potentially applied for short-term pilot-booking or scheduling optimizations within a reasonable time frame for advancement of port intelligence and logistics efficiency

RiskVis: Supply chain visualization with risk management and real-time monitoring

Agency for Science, Technology and Research (A*STAR

Social media for supply chain risk management

Agency for Science, Technology and Research (A*STAR

Glucose inhibits cardiac muscle maturation through nucleotide biosynthesis.

The heart switches its energy substrate from glucose to fatty acids at birth, and maternal hyperglycemia is associated with congenital heart disease. However, little is known about how blood glucose impacts heart formation. Using a chemically defined human pluripotent stem-cell-derived cardiomyocyte differentiation system, we found that high glucose inhibits the maturation of cardiomyocytes at genetic, structural, metabolic, electrophysiological, and biomechanical levels by promoting nucleotide biosynthesis through the pentose phosphate pathway. Blood glucose level in embryos is stable in utero during normal pregnancy, but glucose uptake by fetal cardiac tissue is drastically reduced in late gestational stages. In a murine model of diabetic pregnancy, fetal hearts showed cardiomyopathy with increased mitotic activity and decreased maturity. These data suggest that high glucose suppresses cardiac maturation, providing a possible mechanistic basis for congenital heart disease in diabetic pregnancy

Effects of fear factors in disease propagation

Upon an outbreak of a dangerous infectious disease, people generally tend to reduce their contacts with others in fear of getting infected. Such typical actions apparently help slow down the spreading of infection. Thanks to today's broad public media coverage, the fear factor may even contribute to prevent an outbreak from happening. We are motivated to study such effects by adopting a complex network approach. Firstly we evaluate the simple case where connections between individuals are randomly removed due to fear factor. Then we consider a different case where each individual keeps at least a few connections after contact reduction. Such a case is arguably more realistic since people may choose to keep a few social contacts, e.g., with their family members and closest friends, at any cost. Finally a study is conducted on the case where connection removals are carried out dynamically while the infection is spreading out. Analytical and simulation results show that the fear factor may not easily prevent an epidemic outbreak from happening in scale-free networks. However, it significantly reduces the fraction of the nodes ever getting infected during the outbreak.Comment: 19 pages, 10 figure

Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza

10.1371/journal.pone.0032203PLoS ONE7

Comparability of Results from Pair and Classical Model Formulations for Different Sexually Transmitted Infections

The “classical model” for sexually transmitted infections treats partnerships as instantaneous events summarized by partner change rates, while individual-based and pair models explicitly account for time within partnerships and gaps between partnerships. We compared predictions from the classical and pair models over a range of partnership and gap combinations. While the former predicted similar or marginally higher prevalence at the shortest partnership lengths, the latter predicted self-sustaining transmission for gonorrhoea (GC) and Chlamydia (CT) over much broader partnership and gap combinations. Predictions on the critical level of condom use (Cc) required to prevent transmission also differed substantially when using the same parameters. When calibrated to give the same disease prevalence as the pair model by adjusting the infectious duration for GC and CT, and by adjusting transmission probabilities for HIV, the classical model then predicted much higher Cc values for GC and CT, while Cc predictions for HIV were fairly close. In conclusion, the two approaches give different predictions over potentially important combinations of partnership and gap lengths. Assuming that it is more correct to explicitly model partnerships and gaps, then pair or individual-based models may be needed for GC and CT since model calibration does not resolve the differences

Data mining using radial basis function neural networks

There are many tools for data mining. Neural network is important in data mining due to its intuitional characteristics superior to other methods. The principles of neural networks are derived from the thinking pattern of the human brain. A neural network learns patterns form noisy data for generalization. In our work, the radial basis function network is employed.Doctor of Philosophy (EEE