Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers for intelligent decisions, thereby providing objective information to monitor and control factors of automatic aquaculture machines and maximize farm production. This paper analyzes the requirements of a digital transformation infrastructure consisting of five-layered digital twins using extensive literature reviews. Thus, the results help realize our goal of providing efficient management and remote monitoring of aquaculture farms. The system embeds cloud-based digital twins using machine learning and computer vision, together with sensors and artificial intelligence-based Internet of Things (AIoT) technologies, to monitor fish feeding behavior, disease, and growth. However, few discussions in the literature concerning the functionality of a cost-effective digital twin architecture for aquaculture transformation are available. Therefore, this study uses the modified analytical hierarchical analysis to define the user requirements and the strategies for deploying digital twins to achieve the goal of intelligent fish farm management. Based on the requirement analysis, the constructed prototype of the cloud-based digital twin system effectively improves the efficiency of traditional fish farm management.

PT-peptide is derived from the anti-lipopolysaccharide factor of the swimming crab Portunus trituberculatus. The peptide, consisting of 34 amino acids, contains a lipopolysaccharide binding domain. In this study, we investigated the effect of PT-peptide encapsulated in raw milk-derived extracellular vesicles (EVs), designated as EVs-PT peptide, on immune regulation. The results showed that raw milk-derived EVs efficaciously delivered the PT-peptide into monocytes and elevated immune activity, including reactive oxygen species level, superoxide anion production, and phagocytosis. PT-peptide and EVs-PT peptide also elevated the secretion of cytokines, such as interferon-γ, interleukin-6, and tumor necrosis factor-α in human monocytic THP-1 cells. These results suggest that the PT-peptide could be developed as an immune stimulator.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

We studied the effects of salinity on the egg fertilization, hatching, and tadpole growth and survival rates of the Thailand frog (Rana tigerina Dubois, 1981), an introduced species that is commonly cultured in the Mekong Delta, Vietnam. Salinity levels of 2, 6, and 4% significantly affected the fertilization and hatching rates of the eggs, as well as the tadpole survival rates from the newly hatched to yolk sac exhaustion stages, respectively . Notably, during the stage from yolk sac exhaustion to juvenile tadpole, a salinity of 3% resulted in an increase in the final weight of the tadpoles throughout the stage and a significant increase in Phase 1 (the tadpole stage from yolk sac exhaustion to the onset of foreleg emergence) and Phase 2 (the tadpole stage from the onset of foreleg emergence to the onset of hind leg emergence); however, it did not show a significant effect on tadpole survival rates in Phases 1 and 2 of this stage . A salinity level of up to 5% showed no significant effect on the growth performance but caused a significant reduction in the survival rates of the tadpoles during this stage. We recommend that the artificial reproduction process of Thai frogs should be performed using salinities of <2%, with salinities of ≤2 and 3% being suitable for rearing tadpoles from the newly hatched to yolk sac exhaustion stage and from the yolk sac exhaustion to juvenile stage, respectively.