Very large floating structures (VLFS) have attracted the attention of architects, city planners, and engineers because they provide an exciting and environmentally friendly solution for land creation from the sea as opposed to the traditional land reclamation method. The applications of VLFS as floating piers, floating hotels, floating fuel storage facilities, floating stadia, floating bridges, floating airports, and even floating cities have triggered extensive research studies in the past two decades. The VLFS technology has developed considerably and there are many innovative methods proposed to minimize the hydroelastic motion, improve the mooring system and structural integrity of the VLFS. This keynote paper summarizes the applications, research and development of VLFS over the past two decades

Presented herein is a literature review on the design and performance of antimotion structures/devices such as breakwaters, submerged plates, oscillating water column breakwaters, air-cushion, auxiliary attachments, and mechanical joints for mitigating the hydroelastic response of very large floating structures (VLFS) under wave action. Shapes of VLFS that could minimize the hydrodynamic response of the structure are also discussed. The analytical, numerical, and experimental methods used in studying the effect of these antimotion structures/devices toward reducing the hydroelastic responses of VLFS are also reviewed.

Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the largest accumulation of marine litter that often goes undetected. Plastic debris makes up the majority of ocean litter and is a known source of microplastics in the ocean. This paper focuses on the detection of ocean plastic using neural network models. Two neural network models will be trained, i.e., YOLACT and the Mask R-CNN, for the instance segmentation of underwater litter in images. The models are trained on the TrashCAN dataset, using pre-trained model weights trained using COCO. The trained neural network could achieve a mean average precision ($mAP$) of 0.377 and 0.365 for the Mask R-CNN and YOLACT, respectively. The lightweight nature of YOLACT allows it to detect images at up to six times the speed of the Mask R-CNN, while only making a comparatively smaller trade-off in terms of performance. This allows for two separate applications: YOLACT for the collection of litter using autonomous underwater vehicles (AUVs) and the Mask R-CNN for surveying litter distribution.

The cost of renewable energy technologies such as wind and solar is falling significantly over the decade and this can have a large influence on the efforts to reach sustainability. With the shipping industry contributing to a whopping 3.3% in global CO₂ emissions, the International Maritime Organization has adopted short-term measures to reduce the carbon intensity of all ships by 50% by 2050. One of the means to achieve this ambitious target is the utilisation of propulsion systems powered by sustainable energy. This review paper summarises the current state of the adoption of renewable energy and alternative fuels used for ship propulsion. Special focus is given to the means of these alternative energies in achieving the United Nations Sustainable Development Goals, in particular Goal 7 (Affordable and Clean Energy), Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). A state-of-the-art for various ships powered by renewable energy and alternative fuels is investigated and their technologies for mitigating carbon emissions are described. The cost for each technology found in the literature is summarised and the pros and cons of each technology are studied.