In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore). 

Accident data-driven human fatigue analysis in maritime transport using machine learning

Determining factors affecting second-hand ship value: linkages and implications for the shipbuilding industry

PurposeThe accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.Design/methodology/approachThis paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.FindingsThe results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.Originality/valueThis paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.

Pricing the second-hand dry bulk vessel through stacking ensemble with add-on plain feedforward neural networks

With the rapid advancements in science and technology, the manufacturing capacity of sailboats has significantly improved, leading to an expansion in the second-hand sailboat market. However, the absence of a comprehensive pricing mechanism for second-hand sailboats remains a challenge. This study aims to address this pricing issue for sailing vessels by establishing a robust mathematical model. The initial step in this investigation involved preprocessing all provided data. As a preliminary example, a one-way analysis of variance revealed a substantial influence of the sailboat brand on pricing. Consequently, a K-means clustering analysis was conducted, resulting in the categorization of sailboats into 20 distinct classes. To streamline subsequent analyses, the most numerous variants within each class were selected as representative samples. Further, relevant data on length overallLOA), length at waterlineLWL), and other numerical indicators were extracted. Similarly, for effective quantification of all textual data, this research opted to supplement geographic information such as northern latitudes and GDP. Subsequently, a multiple linear regression analysis was executed on the entire dataset, yielding the final pricing formula as the model. This methodology was applied to catamarans as well. Additionally, this paper explores the potential impact of geographical factors on pricing by examining various regions. Parameters including coastline length, GDP, wind and wave conditions, weather, and sea transport were selected for quantification. These factors were further assessed through principal component analysis to derive composite factors, whose weights were determined using the TOPSIS entropy weight method. These weights signify the degree of influence on pricing. To investigate regional effects, the contribution of geographical factors to sailboat pricing was assessed. Ultimately, the variables deemed to have significant practical implications were identified. 

Unlocking Second-hand Sailboat Prices: A Data-Driven Approach

This study investigates the determinants of the rate of return on shipping sale-leaseback (SLB) transactions. Since the credit crunch in the shipping industry after the 2008 crisis, SLB has emerged as an alternative funding source. However, little is known about the use of SLBs in the shipping industry. Therefore, this study analyzes 62 shipping SLB transactions occurring between 2017–2022. Examining 23 factors related to financial characteristics, firm- and vessel-specific aspects, and shipping market indices, this study finds that the SLB rate of return is negatively associated with the operating margin of a shipping firm, and this result is robust even when controlling for variations in the key interest rate. In addition, with winsorized values of the financial ratios that fluctuate during the sample period, it is also found that a lower rate of return is required for vessels with time-charters. The findings of this study provide shipping companies with valuable implications for understanding the cost of debt financing. 

The determinants of the rate of return on shipping sale-leasebacks*

We investigate the efficacy of the Extreme Gradient boosting (XGBoost) machine learning technique in desktop vessel valuation and compare it to benchmark models consisting of a LASSO regression, a ...

Second-hand vessel valuation: an extreme gradient boosting approach

Second-hand vessel valuation : a generalized additive model and extreme gradient boosting approach

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Julius Jørgensen

Author Tools

Chat about Author

Papers

Second-hand vessel valuation: an extreme gradient boosting approach

Second-hand vessel valuation : a generalized additive model and extreme gradient boosting approach