Advances in sensing and communication technologies have enabled a tight integration between computational and physical elements that is known as cyber-physical systems (CPS). This integration has been also applied to medical devices and healthcare systems and referred to as medical CPS (MCPS). This paper presents development of patient models and algorithms for a MCPS to control blood glucose level (BGL) in type 1 diabetic rats, which is known as artificial pancreas system (APS). An APS consists of continuous glucose monitor (CGM), insulin infusion pump, and control algorithm that makes autonomous decisions about insulin injection to maintain BGL within a normal range. As the control method, we adopted model predictive control (MPC) due to its robustness, flexibility, and use of constraints. Cyber-physical interactions in APS occur by physiological feedback (BGL read by CGM) to the controller, and developing MPC requires identification of such a model of glucose-insulin interactions to predict BGL and take an appropriate action accordingly. In this study, a physiological patient model of diabetic rats is developed and fitted to the data collected from a subject. Using the patient model, we train an artificial neural network (ANN) that predicts BGL based on time-series input data. Considering BGL predicted by the ANN, the NN-MPC controls insulin injection so that BGL can be maintained within the normal range. A simulation study showed the NN-MPC yielded a good performance in a simulated environment. The study showed the potential of the proposed approach for developing a fully closed-loop MCPS for BGL control.
This guide includes lists and links for recommended databases and journals with articles on computer science research and news. Also has helpful information for citation and writing with emphasis on the science disciplines.
This guide includes lists and links for recommended databases and journals with articles on computer science research and news. Also has helpful information for citation and writing with emphasis on the science disciplines. Resources for computer languages, programs and software
Aquatic agricultural systems (AAS) are diverse production and livelihood systems where families cultivate a range of crops, raise livestock, farm or catch fish, gather fruits and other tree crops, and harness natural resources such as timber, reeds and wildlife. This chapter focuses on the drivers influencing the evolution of the complex, multi-functional systems using the case of aquatic agricultural food systems in Southern Africa. It illustrates that multi-dimensional research, development and policy options are required to realize transformations in agri-food systems. Foresight exercises encourage stakeholders and experts to explore future changes by qualitatively and quantitatively analyzing plausible future developments and challenges. Research organizations are engaged in a dynamic dialogue with communities to assess their needs and develop technologies and management practices that are profitable and sustainable. The strongest and least dependent drivers were around policies and institutional arrangements related to trade, land access and tenure, and water access.
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