Scientific Publications

Emily Hague & Lauren McWhinnie To cite this article: Emily Hague & Lauren McWhinnie (09 Nov 2023): The Arctic Circle Assembly: navigating the Arctic, The Polar Journal, DOI: 10.1080/2154896X.2023.2276569 To link to this article: https://doi.org/10.1080/215489...

This paper, written by Sanja Bauk, Rasim Mujalovic, Nexhat Kapidani, Zdravko Paladin, gives an overview of blockchain in maritime, with emphasis on (1) TradeLens blockchain based maritime ecosystem, which was in operation from 2018 since 2023, and (2) Blockshipping decentralized platform for containers’ smart booking and releasing. More precisely, a conceptual framework of blockchain in maritime is presented, including its benefits, concerns, key players, events, and documents in shipping and port management. Principles of smart contracts and crypto-currency payment mechanisms are described at a rather high level of abstraction regarding their complexity. Furthermore, Blockshipping global shared container platform and associated crypto-currency payment mechanisms are presented, along with the potential for increasing efficiency, reducing costs, and environmental impacts through enhancing rational containers’ shipments.

Dynamic accurate predictions of Arctic sea ice, ocean, atmosphere, and ecosystem are necessary for safe and efficient Arctic maritime transportation; however a related technical roadmap has not yet been established. In this paper, we propose a management system for trans-Arctic maritime transportation supported by near real-time streaming data from air-space-ground-sea integrated monitoring networks and high spatio-temporal sea ice modeling. As the core algorithm of integrated monitoring networks, a long short-term memory (LSTM) neural network is embedded to improve Arctic sea ice mapping algorithms. Since the LSTM is localized in time and space, it can make full use of streaming data characteristics. The sea ice–related parameters from satellite remote sensing raw data are used as the input of the LSTM, while streaming data from shipborne radar networks and/or buoy measurements are used as training datasets to enhance the accuracy and resolution of environmental streaming data from outputs of LSTM. Due to large size of streaming data, the proposed management system of trans-Arctic shipping should be built on a cloud distribution platform using existing wireless communications networks among vessels and ports. Our management system will be used by the ongoing European Commission Horizon 2020 Programme “ePIcenter.” Keywords Arctic climate change . Arctic routes . Streaming data . Management system of trans-Arctic shipping

Digital technologies are fundamentally changing the development of economies and societies, affecting transport and logistics. Data and data-driven innovations are at the centre of this transformation. At the same time, the role of government and research organisations in the development of the physical internet is not sufficiently clear. This investigation has been performed to research the current limitations of cloud-based big data repositories, business attitudes toward sharing the data and identify priority areas for governmental and public research institutions. The study revealed the interest and desire of companies to cooperate while identifying the essential conditions for cooperation - data security, maintaining competitiveness, the mechanism of data preparation and provision, and possible pricing.

The article was prepared using research output received implementing the ePIcenter project funded by the European Union program HORIZON 2020. A brief description of the project is presented. The paper presents theoretical research regarding essential data requirements that may be important for improving logistics processes. After identifying the main data requirements, scientific research is presented. Analysis of valid legal acts to determine the regulatory criteria and aspects of the relevance and availability of data is performed. Essential areas of data exchange, their importance and trends are identified, taking into account business and governmental organisations. Trends and possible development perspectives are presented. Keywords: Logistics, ePIcenter, BIG DATA, regulation, management, technology

The paper is based on the research project ePIcenter (www.ePIcenterproject.eu) supported by the EU HORIZON 2020 Programme. ePIcenter connects thirty-six partners: port authorities, logistic service providers, manufacturers, academic institutions, and technology partners. The main goal is to develop and test AI driven logistic software solutions, apply new technologies and methodologies to increase the efficiency of global supply chains and reduce their environmental impact. One of the significant aspect the project focusses on is optimisation, using AI, digitalisation, automation and innovations in freight transport and handling technologies. Finally, modelling powerful solutions to enable resilient, efficient and environment friendly supply chains. Knowledge sharing is one of powerful tool for researchers, policymakers, service providers and other stakeholders to develop a holistic and comprehensive common knowledge base. First, a theoretical framework had been developed through identification and review knowledge available from previous projects funded through the European Commission as well as other international funding projects. Second, lessons learned and success stories from previous EC-funded projects and other international research programmes were reviewed and provided in the first year of project implementation. Keywords: projects, transportation, supply chain, innovation, AI, IoT, big data

Many shipping companies tackle the challenge of potentially replacing conventional trucks with electric ones in economically developed countries. The aim of this work is to describe the principle of scaling models for analysing the operation of a fleet of vehicles, when the decision to use a particular type of model is made considering the accuracy and completeness of the initial data, as well as the goals of modelling. At the end of the report, information is provided on the TraPodSim simulation system developed by the authors, which is based on a multi-agent simulation model created using AnyLogic software. The paper considers modelling methods aimed at assessing the physical indicators of the transportation process. Various aspects of using three types of mathematical models are discussed: a) analytical deterministic models, b) analytical models using the Monte Carlo method and c) simulation models.