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The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

Container line reliability improves for second consecutive quarter, Drewry reports.

The main target of DSP in respect of Contship Italia Group, is to address the IT strategies of the various companies to guarantee an harmonious development respecting the peculiarity and the autonomy of the single realities. In the last years DSP, using the acquired know-how in the IT problems of shipping, port management and intermodal transportation has enlarged is portfolio of activities offering to the market professional services in terminal operations processes and systems deployment and optimisiation. In 2007 DSP became partner of NAVIS (part of Cargotec Corporation) and certified its staff as SPARCS 3.7 and SPARCS N4 senior consultants, carrying out various international projects. DSP has recently developed for Contship Italia Group an innovative and flexible system for automatic invoicing (Fatteuro) for container and general cargo terminals interfaced with other systems in order to manage all the necessary information to calculate and register the invoices. It is currently is use at CICT (Cagliari), EGT(Tangier), LSCT(La Spezia) and at the General Cargo Terminal SPETER of La Spezia. DSP is also tightly linked with the new University of Applied Science of Southern Switzerland. In his team a professor of this university is leading analysis and design activities and most part of his personnel has a degree in Computer Science and where recruited there. This also gives the chance to DSP to participate to research projects on the transport and IT area and to remain always skilled with the newest technology

Container ships collide in Sydney storm.