Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than two million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to selfisolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

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This work considers the problem of designing a control law to yield a desired closed-loop response subject to input constraints and plant-model mismatch. To this end, a two-tiered model predictive controller is designed that first computes the best response of a certain desired form, and then computes the control action to achieve the prescribed closed–loop response. The key idea is first illustrated in the absence of plant–model mismatch for a prescribed first- and second-order response of the controlled variables, and shown using a simulation example. Subsequently, a formulation is presented to handle plant–model mismatch. Finally, the proposed formulation is applied to a chemical process example.

The integration of different stakeholders’ perspectives when planning large-scale infrastructure projects such as power transmission lines is becoming increasingly important in the public debate. Partly conflicting interests of stakeholders should be taken into account in order to allow for best possible routing of new lines. Particularly when transmission lines which are bridging large distances are considered, externalities within this complex setting include social, ecological, economical and technical dimensions. An optimal routing of lines may help address different issues, such as public resistance. Models for the investigation of these large-area impacts for optimal route formation often only cover small regions or lack the georeferenced data necessary to quantify different criteria. We develop an open-source approach which allows for transparent and replicable route determination, tracing, and assessment covering the whole of Europe. Therefore, we provide several friction layers with high spatial resolution. Each layer represents a criterion affecting the routing of a power line. Together with the start and end point of a construction project, this allows for creating accumulated cost rasters for various relationships between the weightings of the perspectives which are relevant during line infrastructure routing processes. The present work explains the underlying methods of data collection, processing, and algorithms of data preparation, route generation, and assessment. Subsequently, this approach is verified with two case studies of HVDC transmission lines which are currently in the planning stages. All processed datasets and applied scripts described in this paper are open-access and made publicly available. Hence, this should support the current project routing debate by providing more transparency and by improving stakeholder involvement.

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