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This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.

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DNA vaccines must induce a greater immune response to be effective in the biomedical industry. Therefore, we tested the trafficking trait of the bovine herpesvirus 1 (BHV-1) protein VP22 (BVP22) fused to an antigen and applied this unique trait to genetic immunization. DNA immunization with BVP22-antigen stimulates immune responses superior to that of standard DNA immunization. Mice were injected intramuscularly with gene constructs expressing the antigen yellow fluorescent protein (YFP), YFP fused to BVP22, or YFP fused to BHV-1 tegument protein VP16 (BVP16). The results revealed a significantly enhanced YFP antibody response with BVP22-YFP DNA immunization compared with either YFP or BVP16-YFP gene immunization. Notably, the BVP22-YFP DNA construct induced a stronger T helper 1 (Th1) response, based on IFN-gamma and IL-4 cytokine levels, and IgG2a/IgG1 ratios. Furthermore, BVP22-YFP genetic immunization induced a greater cytotoxic T lymphocyte response. The genetic adjuvant properties of BVP22 can make DNA vaccines much more effective clinically.

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Summary This paper presents a mixed‐integer model predictive controller for walking. In the proposed scheme, mixed‐integer quadratic programs (MIQP) are solved online to simultaneously decide center of mass jerks, footsteps positions, durations, and rotations while respecting actuation, geometry, and contact constraints. Most walking controllers require preplanned footstep rotations to avoid dealing with the nonlinearity introduced by foot rotation decision. The main contribution of this work is an optimization formulation where feet rotations are automatically planned to attain a reference speed rotation. Finally, simulation results are shown to present and discuss the capabilities of the proposed formulation.

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Predictive Control formulations can be designed with nominal asymptotic stability guarantees, provided that the associated optimization problem is feasible at each sampling time. However, model-plant mismatches, external perturbations or faults may cause the optimization to become infeasible. Such a problem motivates the development of techniques aimed at recovering feasibility without violating hard physical constraints imposed by the nature of the plant. This paper investigates the possible advantages of employing a policy of setpoint management to circumvent infeasibility problems in a Predictive Control framework. The investigation is mainly concerned with robustness of the controller regarding actuator faults that can be modelled as a change in the allowed excursion of the control signal. The results obtained with the proposed setpoint management technique are compared to the default solution provided by the adopted computational toolbox in case of infeasibility. An application involving a nonlinear simulation model of a laboratory helicopter with three degrees of freedom is presented.