Abstract
Ammonia nitrogen, as a “deadly killer” in aquaculture, poses a fatal threat to fish due to its accumulated concentration. Fish usually exhibit different behavior characteristics before and after being subjected to ammonia nitrogen stress. However, the traditional behavior analysis methods have the disadvantages of easy detachment and interfere with the experimental results. To address this, we propose multiple object detection and tracking models under different conditions to enable both qualitative and quantitative analysis of ammonia nitrogen stress responses in fish. By integrating high-resolution cameras and deep learning algorithms, these methods automatically analyzes behavioral indicators (e.g., swimming distance, velocity, spatial distribution, etc) correlated with ammonia exposure. Experiments were conducted on bass, sturgeon, and carp under controlled ammonia nitrogen concentrations, with video data processed using convolutional neural networks (CNNs) for feature extraction. Furthermore, by optimizing camera configurations (quantity and placement), we developed a 3D reconstruction method for fish locomotion to enable more precise behavioral analysis. Results demonstrated over 90% accuracy in fish detection, significantly outperforming traditional manual observation. The methods provide a cost-effective, scalable solution for early stress detection, aiding precision aquaculture management. This work highlights the potential of computer vision in transforming water quality monitoring and improving fish welfare.

Abstract
Swine wastewater is a major environmental concern due to its high concentrations of nutrients and
organic matter, which can contribute to eutrophication and oxygen depletion in aquatic ecosystems.
Traditional treatment approaches, such as microalgae-based systems, are limited by low nutrient
removal efficiency and high freshwater demand. This study presents the first successful cultivation
of the red seaweed Agardhiella subulata in diluted swine and aquaculture wastewater,
demonstrating its strong potential as a sustainable bioremediation tool for nutrient-rich effluents.
Under salinity conditions of 27–34 ppt and ammonia concentrations up to 600 μM, A. subulata
removed up to 93% of ammonium (NH₄⁺) and 68% of phosphate (PO₄³⁻) within 24 hours. It also
significantly improved water quality by raising dissolved oxygen levels. Moreover, the seaweed
exhibited an exceptional carbon sequestration capacity, capturing approximately 52.5 t-CO₂ ha⁻¹
yr⁻¹—five times greater than the average annual carbon uptake by global forests. These results
highlight the feasibility of integrating A. subulata into aquaculture or coastal wastewater treatment
systems as a nature-based solution to mitigate nutrient pollution while enhancing ecosystem
services. By reducing reliance on freshwater, lowering greenhouse gas emissions, and enabling
nutrient recycling, A. subulata cultivation offers a dual benefit for sustainable aquaculture and
environmental protection. Looking ahead, the incorporation of red seaweed bioremediation into
circular aquaculture practices—especially near livestock farming zones—can play a key role in
closing nutrient loops, reducing pollution loads, and promoting blue carbon strategies in coastal
regions.

Abstract
Blue mussel production today is wide-spread through Europe and is mainly produced for food. They provide important ecosystem services such as nutrient recycling, habitat provision, and water quality improvement. Small mussels in the Baltic Sea are small by nature due to the low salinity and have had no commercial market but still provide these valuable ecosystem services (Holbach et al., 2020; Vaher et al. 2024), and provide an opportunity for sustainable protein production and circular nutrients. Small mussels are also a side stream from traditional commercial food-mussel production, and valorising them provides an opportunity for stronger business models for mussel farmers also in higher salinity areas. Projects and studies have also shown blue mussel production could be subject for carbon- and or nutrient credit schemes (BalticBlueGrowth, 2019; Lindahl et al., 2005; van den Burg, 2022). The Baltic MUPPETS project is an investment in a blue bioeconomy that aims to deliver economically and environmentally viable businesses with a positive socio-economic impact. The project sets out to create an entirely new value chain based on small blue mussels, enabling a new circular economy in the Baltic Sea region and supporting mussel farmers throughout Europe to develop, diversify and scale their existing businesses. Ultimately, Baltic MUPPETS aims to bend today’s linear nutrient economy, by creating a profitable pet-feed market from recycled nutrients. Looking at three business case studies, and product manufacturing from the full mussel value chain, innovative sustainable products and business models are developed.

Abstract

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT‑based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi‑criteria decision‑making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision‑making related to authentication issues in IoHT systems