Abstract

With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as Python tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools). Comment: Astronomy & Computing Accepted, 25 pages, 20 figure

Abstract

Long term projections of cargo throughput are indispensable for port development plans. Although commodity flow projections are useful for governments, port authorities, terminal operating companies and port users, scientific research on commodity flow projections for ports is limited. Existing studies generally use econometric models that assume stable relationships between growth of port throughput and such variables as GDP growth and trade growth. This paper presents a method that was developed as part of the Port Vision 2030 project of Port of Rotterdam Authority. The method combines a model with expert judgement and commodity specific research. This combination enables incorporating disruptions of past growth patterns. The contribution of this paper is the description of this method, its application to the volumes in 2030 of all major commodities handled in the Hamburg – Le Havre range, with four different scenarios, while most studies deal with a few commodities in one port, and often for a shorter period and with less scenarios. The results show that in all scenarios, total throughput is expected to rise, although in three scenarios not as fast as in the previous two decades. Furthermore, intermediates and container flows are expected to continue to grow, while throughput of raw materials (iron ore, crude oil) may decline.