Evolution analysis of content, teaching and examination modes with impact analysis on student satisfaction and learning effect

This paper presents the evolution of a master course taught simultaneously at multiple master specialisations. The analysis of the course structure, content, teaching and evaluation modes has been presented over several years, including both pre-pandemic and during Covid19 pandemic time. The challenges of dealing with a highly heterogeneous student group are explained and solutions that have been implemented over the years are analysed by means of their effect in student satisfaction scores and learning effect percentage. Copyright (C) 2022 The Authors.

Effect of Social Distancing for Office Landscape on the Ergonomic Illumination

In office buildings valuable energy is wasted if not properly regulated as a function of presence of humans and active demands for illumination levels. Effective and clever usage of the sunlight is essential for optimal use of energy resources in large office buildings. Additionally, productivity of the employees can be improved by maintaining a constant light intensity. In context of social distancing enforced onto landscape area structure and occupancy, they have effects in the illumination pattern and ergonomics. This paper presents the practical setup to mimic the illumination regulatory problem in landscape offices and the dynamic properties of such a system in the context of social distancing regulations. The light level control is performed with distributed predictive control, whereas a comparison is made among various situations. The original contribution of the paper is a fast, adaptive control algorithm, which can deal with changing context parameters;e.g. varying landscape office structures. Copyright (C) 2020 The Authors.

Perspectives on hybrid control of the anesthesia-hemodynamic system in the pandemic context

This paper provides a visionary perspective on human-machine collaboration in a medical cyber-physical system (MCPS) during the 2020 pandemic context. For the time being, medical specialists in the operating room (OR) or Intensive Care Units (ICU) face special responsibilities when the procedures involve a patient with an infectious disease (e.g., COVID-19) that can cause several complications. The added workload of the anesthesiologist can be diminished by the context-aware pervasive assistance in the decision- making process for maintaining the optimal anesthesia and hemodynamics of the patient. A self-aware control system, with feedback from patient's monitored parameters and several surgical/ICU contextual data, is able to adapt his action accordingly, increasing treatment accuracy. The three main parts of general anesthesia (neuromuscular blockade, hypnosis and analgesia) and the hemodynamics (cardiac output, blood pressure) are perused from a global objective viewpoint, while intersecting the anesthesiologist's action upon his request. The integrative anesthesiologist-in-the-loop cyber-physical system (CPS) is emerging as an intelligent solution for hybrid control of anesthesia's depth, instead of total autonomous closed-loop controllers. This paper aims to create awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations. Moreover, it proposes an opening horizon for multi-disciplinary research. Hence, the connection between clinical and engineering frameworks envisages significant patient safety. © 2021 IEEE.