Since time-domain simulations of wave energy converters are computationally expensive, how can we analyse their dynamics and test wide ranges of design variables, without simplifying the physics involved? One possible solution is the use of General Polynomial Chaos (gPC). GPC provides computationally efficient surrogate models for partial differential equation based models, which are particularly useful for sensitivity analysis and uncertainty quantification. We demonstrate the application of gPC to study the dynamics of a wave energy converter in an operational sea-state, when there is uncertainty in the values of the stiffness and damping coefficient of the power take-off.
In this paper, we contribute to the literature on uncertainty and the drivers of social exchange. We explore the 2008 financial crisis and hand-collect unique data on more than 2,700 vessel chartering deals closed in the container shipping industry from 2000 to 2011. Our contribution is twofold. We challenge the literature by finding that low and high status players use different collaborative strategies under uncertainty: the high status players are more prone to coopetition and the low status ones reach out to external buyers. We also extend the literature on social exchange and uncertainty and introduce other constructs: strategic versatility and country-level long-term orientation of the suppliers that we study in our model. Our findings are relevant for policy and managerial decision-makers in the industry.
This paper presents an assessment of three methods used for sea state estimation via the wave buoy analogy, where measured ship responses are processed. The three methods all rely on Machine Learning exclusively but they have different output; Method 1 provides bulk parameters, Method 2 yields a point wave spectrum and the wave direction, while Method 3 gives the directional wave spectrum in non-parametric form. The assessment is made using full-scale data from an in-service container ship in cross-Atlantic service. Training and testing of the methods are made using data from a wave radar, and the three methods perform well. An uncertainty measure, equivalently, a trust level indicator, based on the variation between the post-processed outputs of the methods is proposed, and this facilitates determination of estimates with small errors; without knowing the ground truth.
Recent advances, especially in deep learning, allow to effectively detect ship targets in surveillance videos. However, the translation of these detections to the real-world locations of ships has not been sufficiently explored. The common approach in the literature is using a transformation matrix to convert a pixel to a real-world coordinate. However, this approach has three shortcomings: first, a set of reference point pairs has to be manually prepared to establish the matrix; second, the matrix always maps a pixel to the same real-world coordinate, ignoring that there is no one-to-one correspondence between discrete pixel coordinates and continuous real-world coordinates; third, this approach can only work with one camera. In light of this, we propose a technique PixelToRegion that explicitly takes into account the uncertainty in coordinate conversion by mapping each pixel to a spatial polygon. Next, we propose a new algorithm MCbSLE that can estimate ship locations using pixel sets from multiple cameras. The precision of location estimation by MCbSLE is enhanced through spatial intersection between polygons from different cameras. Experiments are conducted under 16 carefully designed multi-camera settings to evaluate MCbSLE wrt four factors: different ports, the number of cameras, the distance between cameras, and camera headings. Results on one-day ship trajectory data show that (1) an 79.8% accuracy in the number of coordinates can be achieved by MCbSLE when there are no more than 10 ships in camera views; (2) using multiple cameras can improve the precision of location estimation by one order of magnitude compared with using one camera.
The PermaGov Deliverable focuses on exploring the EU policy landscape within the context of the European Green Deal (EGD), structured around four regime complexes: marine life, marine plastics, marine energy, and maritime transport. These complexes provide a framework for analyzing the EU's approach to achieving the EGD's vision for sustainable marine governance. This report aims to offer a descriptive overview of marine EU policies relevant to the PermaGov project, focusing on policies identified as relevant to the overarching goals set forth in the EGD. It also considers relevant initiatives at global and regional levels.
The marine life regime sees the EU Biodiversity Strategy for 2030 as its overarching strategy, essential for the EGD's element of preserving and restoring ecosystems and biodiversity. Tackling the challenges of marine waste pollution, the marine plastics regime is guided by the EU Circular Economy Action Plan and the EU Action Plan: Towards Zero Pollution for Air, Water, and Soil, targeting the EGD's elements of a mobilizing industry for a clean and circular economy and a zero-pollution ambition for a toxic-free environment. The marine energy regime is shaped by the European Climate Law and the Offshore Renewable Energy Strategy, which are the overarching instruments that contribute to the EGD's elements of increasing the EU's climate ambition for 2030 and 2050 and ensuring the supply of clean, affordable, and secure energy. Lastly, the maritime transport regime sees the'Fit for 55'Package and the'Sustainable and Smart Mobility Strategy'as the two main instruments to achieve the EGD's elements of increasing the EU.
We contribute to the identification of marine biodiversity status and changes in the coastal area of Southeast Greenland through consultation with holders of local and Indigenous knowledge (LEK/IK). Through in-depth interviews with coastal fishermen and hunters in the Ammassalik area, we explore a range of changes to known and new species in relation to ecosystem dynamics. Key observations include diminishing presence of polar cod (Boreogadus saida), new abundance of known fish species (Gadus morhua, Salvelinus alpinus, Reinhardtius hippoglossoides, Cyclopterus lumpus), inflow of new/rare species of whales, fish, and shellfish (Oncorhynchus gorbuscha, Lamna nasus, Paralithodes camtschaticus, Physeter macrocephalus, Globicephala melas, Megaptera novaeangliae, Phocoena phocoena), and increasing absence in the fjords of some local seal species (Cystophora cristata and Pusa hispida). Observed changes in local abundances are understood with reference to the physical changes in temperature, ocean currents, glacier melt, and snowfall. Changed dynamics in prey-predator relationships are observed to mediate the local presence of target species. Other environmental changes include an influx of new food items in food chains and increased seaweed growth. Our study confirms the relevance and timeliness of systematically incorporating local and Indigenous knowledge to enhance the understanding of coastal marine dynamics in the context of climate change and the geographical 'opening' of the East Greenlandic region.
In the present work, the determinants of port choice regarding container cargoes from specific hinterland regions are analyzed, based on an empirical study of Spain. Previous work has been extended by including novel explanatory variables for the market shares of ports in hinterland locations. Discrete choice theory is the methodological approach used here. More specifically, a nested logit model is proposed. As potential explanatory variables, the model includes maritime connectivity to specific overseas regions and intermodal connectivity of the port to specific hinterland locations. The empirical analysis is based on detailed Spanish customs data. The analysis shows that all variables hypothesized to influence the market share of a port in a specific hinterland region (i.e., road distance to the hinterland region, maritime distance, maritime connectivity of the port, and intermodal connectivity of the port) indeed influence significantly its market share, with the signs as expected. The findings add to the understanding of port competitiveness in specific regions with three conclusions: First, port hinterlands are relational, in the sense that they depend on the overseas origin or destination of the cargo; Second, the analysis suggests that ports that predominantly handle transhipment cargoes may have a “transhipment orientation,” which is an impediment for reaching hinterland markets; Third, intermodal connectivity is a determinant of the market share of a port in a certain hinterland region.
Continuous inspection and mapping of the seabed allows for monitoring the impact of anthropogenic activities on benthic ecosystems. Compared to traditional manual assessment methods which are impractical at scale, computer vision holds great potential for widespread and long-term monitoring.
We deploy an underwater remotely operated vehicle (ROV) in Jammer Bay, a heavily fished area in the Greater North Sea, and capture videos of the seabed for habitat classification. The collected JAMBO dataset is inherently ambiguous: water in the bay is typically turbid which degrades visibility and makes habitats more difficult to identify. To capture the uncertainties involved in manual visual inspection, we employ multiple annotators to classify the same set of images and analyze time spent per annotation, the extent to which annotators agree, and more.
We then evaluate the potential of vision foundation models (DINO, OpenCLIP, BioCLIP) for automating image-based benthic habitat classification. We find that despite ambiguity in the dataset, a well chosen pre-trained feature extractor with linear probing can match the performance of manual annotators when evaluated in known locations. However, generalization across time and place is an important challenge.
Using evidence from 25,250 records of vessels entering and clearing the rivers of the Chesapeake Bay, this article demonstrates that intercolonial trading captains and crews significantly reduced the number of days their vessels spent in port in Virginia between 1698 and 1766. This contraction reflected a quantifying ethos in shipping that emerged during the early age of sail as the result of mutually reinforcing legal requirements and management practices. Responding to these productivity pressures, captains embraced practices that limited sailors’ freedom and turned to enslaved sailors to guarantee their maritime labor force. Embracing unfreedom aided captains to realize the dispatch goals that helped guarantee their investors’ returns.
In this article, we develop a deep neural network model to estimate the wave added resistance. The required data to train the model is generated using strip theory calculations over a wide range of hull geometries and operational conditions. The model is efficient as it only requires the ship’s main particulars: length, beam, draft, block coefficient, and slenderness ratio. In addition, we present an application of this model in a vessel performance framework. This will be used for predicting propulsion power and analyzing the degree of biofouling on ships from the company Ultrabulk2. The study shows that the developed deep neural network model produces reliable results in predicting the added wave resistance coefficient in comparison to strip theory calculations. Also, the developed ship propulsion and biofouling analysis display satisfactory output for monitoring hull performance under actual ship operational conditions.