Measuring Tanker Market Future Risk with the use of FORESIM

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of the tanker market risk. This is the first attempt to express future tanker market risk in relation to current market fundamentals. We follow a system's analysis seeking for internal and external parameters affecting risk. Therefore we apply dynamic features in risk measurement taking into account all Tanker market characteristics and potential excitations from non-systemic parameters as well as their contribution to freight level formulation and fluctuation. In this way we are able to measure the behavior of future market risk as long as twelve months ahead with very encouraging results. The output information is therefore useful in all aspects of risk analysis and decision making in shipping markets

Decarbonizing Ports for a Sustainable Future: Challenges and Strategies

Ports are critical nodes in global trade and logistics, yet they are significant contributors to greenhouse gas emissions and local air pollution, presenting challenges for achieving sustainable development. This article explores the multifaceted efforts required to decarbonize ports, focusing on the integration of renewable energy, adoption of alternative fuels, investment in infrastructure modernization, efficiency improvement, and leveraging digital technologies. Drawing on case studies and a review of contemporary research, the paper identifies key strategies such as the implementation of shore-side power systems, predictive scheduling using artificial intelligence, and the development of port-specific microgrids. Despite technological advancements, barriers such as high capital costs, stakeholder misalignment, and fragmented policy frameworks hinder progress. The findings underscore the importance of international collaboration, regulatory alignment, and public-private partnerships to overcome these challenges. By synthesizing lessons from successful implementations worldwide, this paper provides actionable insights into decarbonizing ports while highlighting the environmental, economic, and social benefits of such transformations. Ultimately, this work argues for a systemic, collaborative approach to achieving sustainable maritime operations and advancing global decarbonization goals

Level Crossing Probabilities for Multipath Acoustic Processes with Bimodal Spectra

A general method of evaluating upcrossing probabilities for a class of random processes consisting of two narrow_band signals is presented. One of the two significant frequencies of the corresponding bimodal spectra is assumed to be dominant. The method approximates the maxima of these processes by the corresponding values of the envelope processes. It is also assumed that the discrete processes of the maxima are Markov. The results have several applications. Two prominent examples are detection problems of multipath partially saturated processes in underwater acoustics and the problem of the structural reliability of marine diesel engine shafting systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86229/1/Perakis11.pd

Social impact assessment of biofuel production for maritime and aviation sectors: a case study of a pilot biorefinery project

This work presents a comprehensive Social Life Cycle Assessment (S-LCA) and Social Cost-Benefit Analysis (S-CBA) conducted as part of a research project, studying biofuel production for the maritime and aviation sectors, from various types of non-food waste biomasses. The inclusion of social considerations complements and expands on the environmental and economic ones. The importance of social group criteria was determined through expert questionnaires, leading to the identification of social impacts groups and social criteria from stakeholders across participating countries. The results successfully identified and quantified social impacts, and align with those reported in similar cases in relevant literature. Social Cost-Benefits, monetarizing social factors, demonstrated several social benefits, including reduction in Greenhouse Gas Emissions. However, it also highlighted social costs, such as Economic Costs associated with the initial investment. The study revealed critical social hotspots within the impact categories, making significant strides in understanding the social impacts of biofuel production, providing valuable insights for decision-makers, and contributing to the broader goal of sustainable and socially responsible biofuel production

A convergence between cultural heritage and shipbuilding science

In the contemporary context, the demand for wooden vessels remains prevalent in the domains of fisheries, tourism and contemporary sailing. This necessitates a convergence of traditional shipbuilding techniques and contemporary naval engi-neering. In this regard, there is a growing imperative for the scientific documenta-tion of traditional shipbuilding practices, utilising modern techniques such as three-dimensional documentation and representation. Concurrently, contemporary naval engineering contributes to the design of lines plan. This advanced docu-mentation facilitates the estimation of hydrodynamic resistance through the utili-sation of modern calculation methodologies. The integration of contemporary na-val engineering, material science and engineering optimisation of materials' inter-ventions and maintenance with traditional shipbuilding art in an arsenal has mod-ernised, reproduced and made viable the construction of a traditional boat. Inno-vative practices must be adopted to ensure the revival of the arsenals and the fu-sion of traditional shipbuilding and naval engineering, leading to the wooden ships that Aegean, Mediterranean and the world need. The aim of this paper is to present a comprehensive action plan for the cultural, digital and scientific docu-mentation of traditional wooden shipbuilding art. The complete, comprehensive and multidimensional documentation constitutes the basic foundation of the sus-tainable development and promotion of the Greek shipbuilding tradition. The proposed plan is not confined to the collection, recording and study of the tradi-tional vessels currently being preserved; rather, it employs a comprehensive and multifaceted approach, with targeted actions, seeking the recognition of traditional shipbuilding as an integral part of the cultural heritage and its preservation and transmission to future generations

Financial risk assessment in shipping : a holistic machine learning based methodology

Corporate financial distress (FD) prediction models are of great importance to all stakeholders, including regulators and banks, who rely on acceptable estimates of default risk, for both individual borrowers and bank loan portfolios. Whilst this subject has been covered extensively in finance research, its application to international shipping companies has been limited while the focus has mainly been on the application of traditional linear modelling, using sparse, cross-sectional financial statement data. Insufficient attention has been paid to the noisy and incomplete nature of shipping company financial statement information. This study contributes to the literature through the design, development and testing of a novel holistic machine learning methodology which integrates predictor evaluation and missing data analysis into the distress prediction process. The model was validated using a longitudinal dataset of over 5000 company year-end financial statements combined with macroeconomic and market predictors. We applied this methodology first for individual company level distress prediction before testing the models’ ability to provide accurate confidence intervals by backtesting conditional value-at-risk estimations of the distress rates for bank portfolios. We conclude that, by adopting a holistic approach, our methodology can enhance financial monitoring of company loans and bank loan portfolios thereby providing a practical “early warning system” for financial distress

A survey of shipping finance research: setting the future research agenda

Financing shipping related investment projects has always been a focal area of debate and research within the international maritime industry since access to funding can determine the competitiveness of a capital-intensive business as well as its success or failure under adverse market conditions. This paper provides, for the first time, a comprehensive and structured survey of all published research in the area of shipping finance and investment. The review spans approximately four decades (1979-2018) of empirical evidence, including 162 studies published in 48 scholarly journals, complemented with select books and book chapters. The study provides a bibliometric analysis and comprehensive synthesis of existing research offering an invaluable source of information for both the academic community and business practice, shaping the future research agenda in shipping finance and investment

A reference architecture for cloud-edge meta-operating systems enabling cross-domain, data-intensive, ML-assisted applications: architectural overview and key concepts

Future data-intensive intelligent applications are required to traverse across the cloudto-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.For UPC authors: this research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100.Peer ReviewedPostprint (published version

Spot Charter Rate Forecast for Liquefied Natural Gas Carriers

Recent maritime legislation demands the transformation of the transportation sector to greener and more energy efficient. Liquified natural gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have focused on the prediction of the LNG price; however, no previous work has been carried out on the forecast of the spot charter rate of LNG carrier ships, an important factor for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the general regression neural network presented a stable overall performance for forecasting periods of 2, 4 and 6 months ahead