A wearable system/device capable to track key COVID-19 symptoms is presented. Off-the-shelf hardware and software components as simple sensors, general purpose microcontroller, and gadget like mobile devices and peripheries are used to detect and monitor body temperature, heart rate, respiration rate and other vital signs, which are important to alert patients and remote medical staff about unusual symptoms correlated to COVID-19 or similar diseases. The basic idea about measuring principle, system integration, digital signal processing and networking is presented and accompanied with preliminary testing results. The principle is not just simple and low cost, based on the components we use every day, but very immune to noise and artifacts.

This paper addresses the impact of feedback information and facilitation on a decision-making process supported by a system dynamics model. We conducted the Solomon Four-Group Experiment under four conditions: (a 1 ) interrupted individual determination of a strategy supported by a simulation model and a facilitator; (a 2 ) interrupted individual determination of a strategy supported by a simulation model and a facilitator plus group information feedback (GIF); (a 3 ) continuous individual determination of a strategy supported by a simulation model; and (a 4 ) continuous individual determination of a strategy supported by a simulation model plus GIF. The observed variables were criteria function (CF), frequency of simulation runs (FSR), and insight into GIF. The hypotheses that a simulation model supports individual learning and additional GIF contributes to faster learning were confirmed. The importance of the facilitator and structure of feedback information was demonstrated and a model explaining learning in the decision process was developed. expensive and dangerous, Sterman and other authors argue that system dynamics (SD) and simulation are essential for effective learning (Forrester, 1961;Checkland, 1994;Checkland and Haynes, 1994;Sterman, 1994). Warren and Langley (1999) even state that the ideal of learning organizations can be approached with the application of SD models. Single-loop learning, by definition, can be efficiently supported by a simulation model, where an individual explores the model and approaches the goal through a tryobserve-adjust cycle. Since this type of learning is restricted to an individual, it is called individual learning, and the feedback information provided by the simulation model individual information feedback. In contrast, with double-loop learning the sharing of ideas (and reflection upon them) can change an individual's mental models, visions and beliefs. In theory, double-loop learning can be found on an individual level, but empirical evidence suggests it is usually associated with a group setting (Škraba et al., 2007). We called the information concerning the work of the group the group information feedback (GIF).Learning at an individual level, supported by simulation, was explored in the experiments of Gopinath and Sawyer (1999), Morecroft (2004), andJennings (2002). The results of all three experiments agree that simulation is effective in decision support. The results of Langley and Morecroft (2004) suggest that structure feedback positively influences an understanding of the problem and the time for task completion. According to Edmondson (1999), team learning is characterized by asking questions, seeking feedback, experimenting, and reflecting on the results. Wang (1997) describes the decision-making process as a complex process involving the systematic processing of knowledge needed for appropriate decision making that should minimize the possibility of making a mistake. It is, in fact, a learning process that should provide sufficient knowledge for efficient d...

The methodology of an integrated simulation system application for decision-making support in enterprises is described in this article. The use of visual interac tive modelling and animation can help users better understand simulation results, especially those who are not experts. Decision-makers are motivated by the aniniation while seeking better solutions for pro duction problems. The method was tested in a medium- sized company in order to improve operational plan ning and re-engineering. The real process was recorded on video, digitized, and implemented on a PC. Anima tion of the simulated process, synchronized with video of the real process, was projected in a room to achieve model validation and participant confidence. Opera tional production plans were simulated using current demands as the basis. Application of discrete event simulation in reengineering is also presented. The article shows how to develop a model, prepare sce narios and evaluate alternatives. Two criteria were evaluated for the alternative: the expected value and the value of multiple criteria evaluation, which was examined with the AHP method. Model validation and its use for alternative selection is also discussed.

This article addresses the influence of feedback information on the group decision process supported by the application of system dynamics models. An experimental system enabling the active cooperation of decision subjects was developed, and this is user friendly as far as the visualization and transparency of the simulation results are concerned. A model of the business system was applied to the experiment with decision groups. The experiment considered the task of strategy determination with an explicitly defined multicriteria function, which was defined in such a way as to increase the level of experimental control. The experiment was conducted under two experimental conditions: determination of the strategy with the application of a system dynamics model without group interaction; and determination of the strategy with the application of a formal model with subject interaction supported by group feedback information. The subjects were senior university students, of whom 95 participated in the experiment. The hypothesis that the model application and group feedback information positively influence convergence of the decision process and contribute to higher criteria function values was confirmed.

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