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The maritime industry has adopted the concept of clustering to promote the growth of related maritime sectors. Based on the theoretical development of maritime cluster evolution in the current research literature, components and functions of maritime clusters are observed to have changed over time. However, very few empirical studies have been conducted on maritime cluster evolution that reflect the diverse components and their interactions within a cluster. Particularly, there is insufficient literature that systematically studies the relationship between ports and other maritime sectors, though the port is deemed to play an important role among sectors in maritime cluster development. This paper aims to fill these research gaps by analysing two cases – London and Hong Kong. London is considered as an international maritime service centre, while Hong Kong is en route to be an international maritime service centre, with the latter cluster possesses a supportive port while the former does not. Grounded on the symbiosis theory, this paper examines the evolution of maritime clusters empirically through investigating the interactions between a port and other sectors within a maritime cluster with the Lotka-Volterra model. Empirical results show that advanced maritime services, namely marine insurance and shipbroking, benefit from port development in London. However, these maritime services sectors are in pure competition with the port sector in Hong Kong. The research provides reference for policy makers on the dynamic development path of maritime clusters in practice. [ABSTRACT FROM AUTHOR]

Harmful Algal Blooms (HAB) are damaging to ecosystem functions and pose challenges to environmental and fisheries management. The key to HAB management and understanding the complex algal growth dynamics is the development of robust systems for real-time monitoring of algae populations and species. Previous algae classification studies mainly rely on the combination of an in-situ imaging flow cytometer and an off-site lab-based algae classification model such as Random Forest (RF) for the analysis of high-throughput images. An on-site AI algae monitoring system on top of an edge AI chip embedded with the proposed Algal Morphology Deep Neural Network (AMDNN) model is developed to achieve real-time algae species classification and HAB prediction. Based on a detailed examination of real-world algae images, dataset augmentation is first performed: consisting of orientation, flipping, blurring, and Resizing with Aspect ratio Preserved (RAP). The dataset augmentation is shown to significantly improve classification performance which is superior to that of the competitive RF model. And the attention heatmaps show that for relatively regular-shaped algal species (e.g., Vicicitus), the model weights the color and texture information heavily; while the shape-related features are more important for complex-shaped algae (e.g., Chaetoceros). The AMDNN is tested on a dataset of 11,250 algae images containing the 25 most common HAB classes in Hong Kong subtropical waters with 99.87% test accuracy. Based on the fast and accurate algae classification, the AI-chip-based on-site system is applied to a one-month dataset in February 2020; the predicted trends of total cell counts and targeted HAB species counts are in good agreement with observations. The proposed edge AI algae monitoring system provides a platform for the development of practical HAB early warning systems that can effectively support environmental risk and fisheries management. [Display omitted] • A new low-cost platform for HAB early warning system for environmental management. • Low-cost edge AI chip eliminates transfer delay, enables real-time algae monitoring. • Data augmentation generalizes the model for better real-world algae classification. • Heatmap shows learned features for each species by model parameters visualization. • The on-site system predicts HAB based on real-world data classification and analysis. [ABSTRACT FROM AUTHOR]