Many works are using artificial intelligence to forecast postprandial blood glucose. However, the following questions arise: is it necessary to develop artificial intelligence techniques to predict blood glucose? How important is artificial intelligence for this purpose? This work gives some insights seeking the answer to these questions in the context of using postprandial blood glucose predictions to optimize the prandial insulin bolus. Considering the bolus optimization model proposed in this work, the error in the postprandial glycemia due to an inaccurate postprandial blood glucose prediction is in the same amount as the error made in the prediction. Therefore, more accurate postprandial blood glucose predictions lead to postprandial blood glucose values closer to the predefined blood glucose target defined for that patient. In this way, it is possible to conclude that artificial intelligence could have a relevant role in helping patients control their blood glucose levels. In particular, regarding non-controlled patients with high glucose variability.

The 3rdSymposium on Modelling and Simulation in Computer Sciences and Engineering was held in the14th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2016), Rhodes, Greece, 19-25 September 2016.

The 2nd Symposium on Modelling and Simulation in Computer Sciences and Engineering was held in the 13th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2015), Rhodes, Greece, 23-29 September 2015. (...)

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

The 4th Symposium on Modelling and Simulation in Computer Sciences and Engineering was held in the 16th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2018), Rhodes, Greece, 13-18 September 2018.

Patients with type 1 diabetes mellitus use intensive insulin therapy to suppress their insulin needs and avoid the adverse consequences of chronic hyperglycemia. Intensive insulin therapy consists of a combination of basal insulin and bolus insulin. While the basal insulin dose is periodically adjusted in collaboration with the healthcare team, patients have to estimate the bolus insulin dose by themselves, before each meal. To accurately estimate the bolus insulin dose, patients must know the carbohydrates content of each meal and their insulin to carbohydrate ratio. The insulin to carbohydrate ratio is initially calculated by experienced diabetologists using high-quality data. However, regarding the glucose complex metabolism, it varies over the day due to several factors. Consequently, daily, patients use approximate values to estimate their bolus insulin. Thus, depending on the error of the insulin to carbohydrate ratio estimates, the patient could experience hypo or hyperglycemic events. Therefore, to avoid the consequences of inaccurate bolus insulin and to improve the patient's glycemic control, this work presents an adaptive insulin bolus calculator that uses the patient's glycemic data to dynamically adjust the mealtime bolus and compensate for the adverse effects of inaccurate insulin to carbohydrate ratio estimates.

To mitigate the adverse consequences of chronic hyperglycemia, patients with type 1 diabetes mellitus must provide their bodies with insulin to control their blood glucose. In most cases, insulin therapy consists of a combination of basal insulin and bolus insulin, the so-called basal-bolus insulin therapy. To determine the bolus insulin, patients must know not only the carbohydrate content of each meal but also the values of the insulin-to-carbohydrate ratio and the insulin sensitivity factor. Although important, the blood glucose complex dynamics make determining these parameters a difficult and error-prone task, usually performed by experienced diabetologists using high-quality data. Moreover, the insulin-to-carbohydrate ratio and the insulin sensitivity factor vary over the day due to several factors. Thus, daily, patients use approximate values to determine their prandial bolus. In this paper, we propose an analytic method to find the safe maximum interval for the error in the estimates of the insulin-to-carbohydrate ratio and, therefore, avoid dysglycemia. Our study suggests that slimmer patients with smaller insulin-to-carbohydrate ratios need to be more careful when estimating it. Another significant finding of our work is that in such cases, having small meals reduces the adverse effect of inaccurate insulin-to-carbohydrate ratio estimates in the postprandial blood glucose.

The 5th Symposium on Modelling and Simulation in Computer Sciences and Engineering was held in the 17th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2019), Rhodes, Greece, 23-28 September 2019.

A survey about approaches developed around the controllability of control systems on time scales is presented. Certain conventional dynamic models are described in the two parallel areas: continuous and discrete times. The results obtained in these two theories produce similar conclusions, but require different techniques for solving them. Then, it is important to create a technique that unifies these different times. Created by Stefan Hilger, the language of time scales seems to be an ideal tool for this purpose. Since that creation, many classical results of controllability were extended to this new theory and applied in mixed time control systems. In this chapter is presented the evolution of these results for linear control systems.