Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
This research proposes a method to hedge freight rate risk in shipping markets under model uncertainty.
problem Managing freight risk in shipping markets under model uncertainty.
method The approach uses Wasserstein barycenter for modeling freight rates dynamics and optimal hedging strategy selection.
result The proposed method provides robust hedging strategies even in high noise cases.
YOLOv3 detects ships in real-time with high accuracy.
problem Real-time target detection in maritime scenarios.
method YOLOv3 model trained on a large dataset of marine vessels.
result Average Precision up to 96% for IoU of 0.5.
Machine learning predicts ship performance changes over time.
problem Estimating ship hydrodynamic performance over time.
method Machine learning methods (NL-PCR, NL-PLSR, probabilistic ANN) calibrated with in-service data.
result Probabilistic ANN model performs best in predicting ship performance changes.
Paper uses DBSCAN variation to detect ship anomalies.
problem Detecting anomalous ship behavior.
method Variation of DBSCAN algorithm applied to AIS data.
result Alternative anomaly metric is more statistically informative.
Transformer learns shipping costs more accurately than traditional methods.
problem Inaccurate shipping cost estimates lead to poor financial decisions.
method Proposes Rate Card Transformer (RCT) using self-attention to encode shipping information.
result Cost predictions made by RCT have 28.82% less error compared to GBDT models.
The paper improves marine buoy placement to detect ships robustly against disruptions.
problem Detecting fishing vessels in the presence of natural and man-made disruptions.
method Formulated as a clustering problem, used dropout k-means and k-median to improve buoy placement robustness.
result Improved ship detection probability with dropout k-means compared to classic methods.
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
problem How disruptions to shipping traffic at chokepoints affect global economy
method Empirically calibrated full-scale agent-based model of global commercial shipping fleet
result Rerouting changes arrival losses under chokepoint closures
Deep learning automates biofouling detection in ship hull images.
problem Automating biofouling assessment from ship hull images.
method Deep learning model trained on expert-annotated images.
result Deep learning model agrees with expert annotations.
A hybrid ML method improves ship response predictions across different sea conditions.
problem Improving accuracy and generalizability of ML methods for ship response predictions.
method A hybrid machine learning method that corrects forces in a low-fidelity equation of motion.
result The hybrid method offers improved prediction accuracy and generalizability compared to benchmarks.
Study reveals trade dynamics in dry bulk shipping networks, highlighting their randomness and periodic changes.
problem Understanding the randomness and periodic changes in dry bulk shipping networks.
method Analysis of micro-level trade flow data from 2015 to 2023, focusing on grain, coal, and iron ore networks.
result Dry bulk shipping networks exhibit small-world phenomena and periodic life cycles, influenced by importing ports and global events.
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential threat to ac…
This paper studies the market phenomenon of non-convergence between futures and spot prices in the grains market. We postulate that the positive basis observed at maturity stems from the futures holder's timing options to exercise the shipping certificate delivery item and subsequently liquidate the physical grain. In …
Study of dynamic competition between old and new technologies using a nonlinear map.
problem Dynamic competition between old and new technologies in market share.
method Simulations with three key parameters: scientific-technological, purely technological, and economic.
result Different outcomes in terms of technology prevalence based on interaction of key parameters.
Image compression techniques reveal network structure for shipping box optimization.
problem Designing and planning shipping box networks between nodes.
method Image transformations and Bayesian reinforcement learning.
result Learned network structure can recommend future connectivity.
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely …
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Crypto markets show negative spillovers between chains, not positive co-movements.
problem Negative spillovers in crypto asset returns across different blockchains.
method On-chain data from multiple blockchains (Ethereum, Solana, Binance, Arbitrum, Avalanche) analyzed over 2022-2025.
result Surges on one chain often coincide with declines on others, especially during attention shocks.
We generalize the Zermelo navigation problem and its solution on Riemannian manifolds (M,h) admitting a space dependence of a ship's own speed ∣u(x)∣h≤1 in the presence of a perturbation W determined by a mild velocity vector field ∣W(x)∣h<∣u(x)∣h, with application of Finsler metric of Randers type.
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
We generalize the Zermelo navigation problem and its solution on Riemannian manifolds (M,h) admitting a space dependence of a ship's speed 0<∣u(x)∣h≤1 in the presence of a perturbation W~ determined by a strong velocity vector field satisfying ∣W~(x)∣h=∣u(x)∣h, with application of Finsler m…
In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practic…
We consider the Zermelo navigation problem on the ellipsoid of revolution (spheroid) in the presence of a perturbation W determined by a mild velocity vector field, ∣W∣<1, with application of Finsler metric of Randers type in the context of the corresponding optimal control represented by a time-efficient ship's he…
Optimizes package types for e-commerce to reduce damage and costs.
problem Sub-optimal package types lead to damaged shipments and high costs.
method Multi-stage approach that balances shipment and damage costs using a scalable algorithm.
result Significant cost savings of tens of millions of dollars achieved.
Deep learning improves nearshore bathymetry estimation from sparse data.
problem Estimating nearshore bathymetry from limited and often sparse data.
method Deep Neural Network (DNN) and Conditional Generative Adversarial Network (cGAN) for posterior estimates; Kriging for comparison.
result DNN-based methods outperform traditional Kriging in predicting nearshore bathymetry with sharp gradients.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
Paper proposes a deep learning model for understanding e-commerce addresses.
problem Challenges in parsing shipping addresses with no fixed format.
method Combines NLP techniques with pre-processing steps for addresses, uses RoBERTa for vector representations.
result RoBERTa model achieves 90% accuracy in sub-region classification for North and South Indian cities.
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes sev…
We generalize and study the Zermelo navigation problem on Hermitian manifolds in the presence of a perturbation W determined by a mild complex velocity vector field ∣∣W(z)∣∣h<∣∣u(z)∣∣h, with application of complex Finsler metric of complex Randers type. By admitting space-dependence of ship's relative speed $||u(…
In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning …
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize sm…
The central problem of strip theory is the calculation of potential flowaround 2D sections. One particular method of solutions to this problem is conformal mapping of the body section to the unit circle over which a solution of potential flow is available. Here, a new multiparameter conformal mapping method is presente…
Study uses social media to analyze COVID-19 impact.
problem Understanding the global impact of COVID-19.
method Machine learning and linguistic tools to analyze social media posts.
result Automatic detection of positive reports of COVID-19.
This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
Let Mn be a closed, connected n-manifold. Let $\mtm$ denote the Thom spectrum of its stable normal bundle. A well known theorem of Atiyah states that $\mtm$ is homotopy equivalent to the Spanier-Whitehead dual of M with a disjoint basepoint, M+. This dual can be viewed as the function spectrum, F(M,S), whe…
Provides a compendium of data sources for various applications.
problem Lack of comprehensive data sources for data science, machine learning, and AI.
method Compilation of diverse data sources across multiple application areas.
result A comprehensive list of data sources for data scientists and machine learning experts.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
problem Pervasive challenges in managing cyber risk, especially for capital allocation.
method Combines insurance frequency-severity models with cybersecurity cascade models for comprehensive cyber risk assessment. Facilitates informed capital allocation through a two-pillar framework.
result Demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies.
As the multi-view data grows in the real world, multi-view clus-tering has become a prominent technique in data mining, pattern recognition, and machine learning. How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge. Aiming at th…
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.
Image classification system identifies bumble bee species from images.
problem Manual identification of bumble bee species is time-consuming and requires expert knowledge.
method Transfer learning using Inception, VGG16, VGG19, and ResNet models.
result Inception and VGG classifiers achieved up to 23% accuracy for single species identification.
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Research identifies risks in selecting project managers for civil engineering projects.
problem Lack of awareness of project manager selection criteria and associated risks.
method Combined ANP-FMEA approach for risk analysis.
result ANP-FMEA model identifies more significant risks than traditional FMEA.
Deep learning improves portfolio management by optimizing asset weights.
problem Traditional portfolio managers are outperformed by deep learning models in trading.
method Proposes a deep reinforcement learning portfolio manager that allocates weights to assets.
result The proposed portfolio manager outperforms conventional managers in risk-adjusted returns.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
problem Exploring managers' characteristics and their impact on restructuring decisions.
method Empirical investigation using upper echelons theory and data from 342 managers of French firms.
result Managers with longer tenure and higher education levels prefer private restructuring over court involvement.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.