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Sodium diformate (SDF) is an organic acid that modulates growth performance, health and immunological properties. However, nowadays there is limited information available about the effects of SDF on innate immune system, growth performance and immune‐related gene expression. Due to the lack of information about this supplement, this study aimed to evaluate the effect of SDF on the innate immune system, growth and immune‐related gene expression. Salmo trutta caspius fed control or diet supplemented with different levels (0.5, 1 and 1.5 g/Kg) of SDF for 8 weeks. The results showed that T3 improved significantly (p <.05) fish growth performance following 60 days of application, while T1 did not act (p <.05). The results showed that dietary administration of SDF significantly (p <.05) up‐regulated the expression of IGF‐1, FATP, γ‐GTP, IL‐1β and IL‐10. Also, elevated immune parameters were observed in S. t. caspius fed SDF supplemented the diet. The present results revealed beneficial effects of 1.5 g/Kg dietary SDF on immune response and growth up to 60 days. These results also highlighted the potential use of SDF (1.5 g/Kg) as additive in S. t. caspius diets but are not recommended in the long term when they culture in freshwater. [ABSTRACT FROM AUTHOR]

Wireless Sensor Actor Networks (WSANs) have contributed to the advancement of ubiquitous computing wherein time and energy considerations to perform the tasks of ubiquitous applications are critical. Therefore, real-timeliness and energy-awareness are amongst the grand challenges of WSANs. In this paper, we present a context-aware task distribution approach for assigning real-time tasks to cost-effective actors and for scheduling these tasks at actor level subject to minimizing the energy consumptions of actors and meeting the deadlines of tasks. The proposed approach comprises of three protocols, namely a Market-based Task Assignment Protocol (MaTAP), an Energy Calculation Protocol (ECaP), and a Context-aware Task Scheduling Protocol (CaTSP). We present the formal models of the proposed protocols using Timed Automata and prove their soundness to validate the correctness of the proposed approach. We show that our proposed approach is more efficient in terms of both the total remaining energies of actors and the average tasks completion time compared to stochastic approach. We also show that our approach guarantees the deadlines of all tasks. [ABSTRACT FROM AUTHOR]