Napa (Finland)

Many traditional and state-of-the-art ship routing methods rely on single-objective formulations, deterministic weather inputs, or fixed operational assumptions, which may lead to suboptimal or impractical routing decisions under realistic and uncertain marine environments. This study presents an ensemble-based multi-objective optimization framework for ship route planning under uncertain weather conditions. The framework integrates a neural network model, trained on real onboard ship performance data and tuned using Bayesian hyperparameter optimization, to predict fuel consumption based on ship speed and marine weather parameters. An ensemble of weather forecasts is assigned to route waypoints using a bootstrapping method, enabling the evaluation of multiple cost functions reflecting trade-offs between voyage time, fuel consumption, and safety. Four optimization objective strategies — ensemble mean, worst-case, risk-aware, and Conditional Value-at-Risk (CVaR) — are implemented within a Grey Wolf Optimizer (GWO) to derive optimal routes across various voyages. The results demonstrate notable variations in route performance based on the selected strategy. For example, the CVaR approach achieves a balance between robustness and efficiency, with voyage fuel consumption for the longest journey (Voyage 3) reaching 490,475 kg, while the worst-case strategy prioritizes risk-averse paths, resulting in the highest fuel usage at 505,308 kg. Conversely, the ensemble mean strategy offers the lowest average fuel consumption (474,078 kg) but may expose the voyage to higher uncertainty. Furthermore, the proposed GWO demonstrates high precision in schedule adherence, maintaining arrival time deviations within a 30-minute margin across all optimized voyages, thereby justifying its effectiveness in handling complex multi-objective constraints. The developed framework is applicable to real-time voyage optimization and can support ship operators in achieving fuel efficiency and safety under varying ocean conditions. • Ensemble weather data integrated into ship routing optimization. • Bayesian-optimized NN model predicts ship fuel consumption. • Four strategies compare trade-offs in fuel consumption, duration, and risk. • GWO-based optimization applies ensemble mean, worst-case, risk-aware, and CVaR objectives. • CVaR balances efficiency and risk, while worst-case ensures maximum safety.

Survivability of damaged ships, especially ro-ro/passenger (ropax) vessels, is of paramount interest. Nowadays, time-domain simulation of flooding and motions of damaged ships are more frequently performed to obtain a more realistic overview of the actual survivability in case of a flooding accident. An international benchmark study on simulation of flooding and motions of damaged ropax vessels was conducted within the EU Horizon 2020 project FLARE, using new dedicated model tests as a reference. The test cases include transient flooding in both calm water and in irregular beam seas, as well as gradual flooding and capsizing in beam seas. The studied damage case is a two-compartment collision damage, and the studied intact metacentric height values were lower than the statutory requirements to achieve also capsize cases. Numerical results were carefully compared against measurement data from the model tests. In transient flooding cases the capsize conditions were generally detected well by most codes. However, much variation was observed in the internal flooding and capsize mechanisms. For gradual flooding in beam seas, the results for capsize rate and time-to-capsize were characterized by significant variability among the codes. Results indicate that more research is needed to further improve the time-domain flooding simulation methods to correctly capture both transient flooding phenomena and motions of damaged ship in high waves.

Generally speaking, I think that the paper is of good quality. The work performed is clearly described and is original since I am not aware any other such experiments at full scale. Please find below some more detailed comments and questions to the authors:

A decision support system with damage stability analysis has been recognized as an important tool for passenger ships. Various software applications have been developed and taken into use over the years, without a direct link to any compelling requirement, set forth in the international regulatory framework. After the Costa Concordia accident, new regulations have been established, setting minimum requirements for a decision support system, as an extension to a loading computer. Yet, more advanced systems have been developed recently, aiming at providing valuable additional information on the predicted development of the stability of the damaged ship. This paper presents these alternative decision support systems with damage stability analysis methods for flooding emergencies on passenger ships. The technical background, usability, and usefulness of the various approaches are compared and discussed, taking into account the important statutory approval point of view. In addition, practical examples, including past accidents, are presented and discussed.

ABSTRACTA new approach for analysing the level of survivability for a passenger ship in the event of a collision damage is presented. Monte Carlo simulation is used for generating a large number of damage cases. The probability distributions for damage extent and location are based on the current SOLAS regulation. Each case is calculated with time-domain flooding simulation, assuming quasi-static motions and calm sea, thus enabling evaluation of the righting lever curve during the flooding process. The ship survivability is analysed based on requirements for a sufficient reserve stability. The proposed method is demonstrated with a large passenger ship design. Based on the detailed investigation of the results, the effects of various design changes are studied. Furthermore, the impact of waves on the flooding process is studied. The presented method for survivability assessment is robust and especially suitable for comparing design variations, or ships with similar main dimensions.

Theoretical studies predict that the stability of the singlet state of metalla-N-heterocyclic carbenes (MNHCs) is strongly influenced not only by the substituents at the α-nitrogen atoms and the nature of ligands at the transition metal center but also by the substituents at the carbenic backbone. All the MNHCs were found to have a stable singlet ground state and the computed ΔES-T values for some of the MNHCs were found to be significantly large and lie within the range of experimentally known carbenes (31.0-84.0 kcal mol-1). Furthermore, they were found to have superior σ-donation ability to conventional NHCs. The calculated proton affinities, gallium pyramidalization, pKa and nucleophilicity index values for the MNHCs were found to be in good agreement with their calculated electron donation ability.

Tough smart starch modified hyperbranched polyurethane/reduced graphene oxide–silver–reduced carbon nanodot nanocomposites with self-healing and self-cleaning attributes under a sustainable energy source.

Detection of canines are well-known to be valuable in the location of contraband, such as explosives or narcotics. More recently, canines have been trained and utilized in the detection of concealed mass storage devices that might contain evidence of illegal activity such as child pornography. To lay the analytical foundation for this detection work, research was carried out to determine the volatile organic compounds associated with mass storage devices (MSD) that could be used by trained canines for detection. Headspace analysis of a variety of electronic devices was performed using solid phase microextraction (SPME) with gas chromatography/mass spectrometry (GC/MS). Analyses found several volatile compounds common to SIM and SD cards, as well as USB drives, including 2-propenenitrile, styrene, isophorone, hydroxycyclohexyl phenyl ketone, and 2-furanmethanol, tetrahydro. Results indicated that mass storage devices do have a characteristic odor profile making detection with minimal false alerts feasible for trained canines.

A simple, facile and one-pot route for preparing SnO2 nanoclusters embedded on a mesoporous Sn(iv) organophosphonate (MSnP) framework is described. Reaction of SnCl4·5H2O with a flexible tris-phosphonic acid, mesityl-1,3,5-tris(methylenephosphonic acid), in the presence of a surfactant under hydrothermal conditions produced the desired nanocomposite, SnO2@MSnP. Analytical, spectroscopic and microscopic studies establish that SnO2@MSnP composite is comprised of SnO2 nanoparticles of an average size of 5 nm evenly and abundantly dispersed over the MSnP framework. The mesoporous metal organophosphonate support significantly augments the catalytic efficacy and vapor sensitivity of SnO2 nanoparticles. The catalytic efficiency of SnO2@MSnP was tested for two acid-catalyzed reactions: deoximation reaction and esterification of fatty acids. SnO2@MSnP exhibits remarkable sensitivity towards ammonia and acetone vapors at near room temperature and under open atmospheric conditions. The present method represents an important step towards preparation of mesoporous metal organophosphonate supported metal oxide nanoclusters and hence offers easy access to functional metal oxide based nanocomposites.

Herein, we describe a waterborne polymer/carbon dot nanocomposite system as an efficient, resourceful and sustainable photocatalyst for para-selective hydroxylation of substituted aromatic compounds using H2O2 under UV light.