Maritime piracy incidents present significant threats to maritime security, resulting in material damages and jeopardizing the safety of crews. Despite the scope of the issue, existing research has not adequately explored the diverse risks and theoretical implications involved. To fill that gap, this paper aims to develop a comprehensive framework for analyzing global piracy incidents. The framework assesses risk levels and identifies patterns from spatial, temporal, and spatio-temporal dimensions, which facilitates the development of informed anti-piracy policy decisions. Firstly, the paper introduces a novel risk assessment mechanism for piracy incidents and constructs a dataset encompassing 3,716 recorded incidents from 2010 to 2021. Secondly, this study has developed a visualization and analysis framework capable of examining piracy incidents through the identification of clusters, outliers, and hot spots. Thirdly, a number of experiments are conducted on the constructed dataset to scrutinize current spatial-temporal patterns of piracy accidents. In experiments, we analyze the current trends in piracy incidents on temporal, spatial, and spatio-temporal dimensions to provide a detailed examination of piracy incidents. The paper contributes new understandings of piracy distribution and patterns, thereby enhancing the effectiveness of anti-piracy measures.
Global climate change, which is largely attributed to human activity, is one of the foremost challenges of the 21st century. In recent times, there have been notable alterations in the Earth's climate, resulting in profound impacts on ecosystems and biodiversity. These alterations are caused by greenhouse gas, such as carbon dioxide, methane, and nitrous oxide. Greenhouse gas emissions are caused by practices such as deforestation, industrial operations, and the combustion of fossil fuels in vehicles, vessels, aircraft, and manufacturing facilities. The maritime and aviation industry is currently responsible for approximately 6% of global greenhouse gas emissions. Due to logistical and economic constraints, these industries are heavily reliant on liquid fuels, making direct electrification options unavailable for large parts of these sectors. As a result, these sectors are considered ‘hard to abate’. Understanding the future climate mitigation challenges associated with the maritime and aviation sectors is crucial in shaping effective policy measures, avoiding stranded assets, and preserving the chance to meet Paris Agreement-compatible emission reduction pathways.
This thesis identifies three main challenges and proposes modelling approaches to address them when modelling decarbonization pathways for the aviation and maritime sectors. From these challenges, research gaps have been identified that this PhD thesis aims to fill. Three models have been developed for the thesis: a maritime optimization model, a maritime demand model, and an aviation demand model. The modelling landscape and methodology vary across models, ranging from econometrics and data science to mathematical optimization.
To overcome the challenges and fill in the research gaps, three corresponding modelling approaches have been successfully applied:
1. Developing a holistic decarbonization modelling landscape. This includes life-cycle representations of technology costs and emissions, the upscaling of bottleneck technologies, the availability of sustainable biomass, and consideration of competing demand from other industries, as well as representations of policy levers such as carbon pricing or improvements to fuel efficiency.
2. Developing demand models that interpret the underlying scenario narrative consistently (SSP framework).
3. Improving the representation of technological learning for low-carbon technologies in energy system models.
The findings acquired by applying these three modelling approaches are valuable for energy modellers, climate scientists, and policymakers and offer unique insights into the inherent system dynamics associated with decarbonization of hard-to-abate sectors. Utilizing this modelling landscape reveals that current decarbonization efforts for hard-to-abate sectors are insufficient.
Sensing data from vessel operations are of great importance in reflecting operational performance and facilitating proper decision-making. In this paper, statistical analyses of vessel operational data are first conducted to compare manual noon reports and autolog data from sensors. Then, new indicators to identify data aberrations are proposed, which are the errors between the reported values from operational data and the expected values of different parameters based on baseline models and relevant sailing conditions. A method to detect aberrations based on the new indicators in terms of the reported power is then investigated, as there are two independent measured power values. In this method, a sliding window that moves forward along time is implemented, and the coefficient of variation (CV) is calculated for comparison. Case studies are carried out to detect aberrations in autolog and noon data from a commercial vessel using the new indicator. An analysis to explore the source of the deviation is also conducted, aiming to find the most reliable value in operations. The method is shown to be effective for practical use in detecting aberrations, having been initially tested on both autolog and noon report from four different commercial vessels in 14 vessel years. Approximately one triggered period per vessel per year with a conclusive deviation source is diagnosed by the proposed method. The investigation of this research will facilitate a better evaluation of operational performance, which is beneficial to both the vessel operators and crew.
Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called HSI-ShipDetectionNet that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle and FAIR1M marine ship detection datasets and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of detection performance while being lightweight and suitable for deployment on resource-limited platforms.
The International Maritime Organization employs technical and operational indicators to assess ship energy efficiency. Weather conditions significantly impact ship fuel consumption during voyages, necessitating the consideration of this influence in energy efficiency calculations. This study aims to design models for estimating the impact of weather components on fuel consumption and develop correction factors to cope with the weather effect on the fuel consumption of container ships for different sea states. Using model-based machine learning, the study analyzes noon reports and hindcasted weather data from two sister container ships. It quantifies weather-induced fuel consumption across various sea states, ranging from 2% to 20%, with an average of 7%–13% depending on the model used. Correction factors specific to each sea state are derived, and different approaches for their integration into energy efficiency indicators are proposed. This study advocates tailored weather correction factors for energy efficiency metrics tied to specific sea states, emphasizing the need for standardized weather impact assessments. Prior to any formal policy application, future work is needed to address the limitations of the present study and extend this approach to various ship types and sizes and different geographical regions.
Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.
Unmanned surface vehicles (USVs) are increasingly appealing for gathering metocean data, including directional sea spectra. This paper presents new developments towards estimating the response amplitude operators (RAOs) of surface vehicles equipped with inertial sensors. The novel approach undertakes the data-driven estimation of vehicle models of the wave-induced heave, roll, and pitch motion dynamics, as required to perform subsequent seakeeping computations. Specifically, a genetic algorithm executes the calibration of available closed-form RAOs for a simplified geometry. The algorithm makes a population of model-fitting parameters evolve towards minimising discrepancies between the predicted and measured response spectra in stationary operational conditions. Trust in the model is eventually increased by screening and merging the best-fitting solutions. Resulting response predictions using high-resolution spectral wave data for the AutoNaut USV demonstrate satisfactory accuracy and robustness in heave and pitch but a worse fidelity in roll, thereby motivating follow-up studies to improve the estimation of roll RAOs.
Ship engines are subject to a very demanding work environment, where maximum availability is a must. In this project we look at different operational variables of a marine engine from large cargo ships, with the aim of detecting and trending damage onset on different engine sub-components. This information can be used by owners to expedite O&M interventions and maximize ship availability.
Purpose
The purpose of this paper is to investigate the impact of cloud computing (CC) on supply chain management (SCM).
Design/methodology/approach
The paper is conceptual and based on a literature review and conceptual analysis.
Findings
Today, digital technology is the primary enabler of supply chain (SC) competitiveness. CC capabilities support competitive SC challenges through structural flexibility and responsiveness. An Internet platform based on CC and a digital ecosystem can serve as “information cross-docking” between SC stakeholders. In this way, the SC model is transformed from a traditional, linear model to a platform model with the simultaneous cooperation of all partners. Platform-based SCs will be a milestone in the evolution of SCM – here conceptualised as Supply Chain 3.0.
Research limitations/implications
Currently, SCs managed holistically in cyberspace are rare in practice, and therefore empirical evidence on how digital technologies impact SC competitiveness is required in future research.
Practical implications
This research generates insights that can help managers understand and develop the next generation of SCM with the use of CC, a modern and commonly available Information and Communication Technologies (ICT) tool.
Originality/value
The paper presents a conceptual basis of how CC enables structural flexibility of SCs through easy, real-time resource and capacity reconfiguration. CC not only reduces cost and increases flexibility but also offers an effective solution for disruptive new business models with the potential to revolutionise current SCM thinking.