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The apps for drawing are present in our children's life. Nevertheless, little is known about the impact of mobile technology on the freehand drawing educational activity. There are few works which are contextualised within short periods of time, with teachers who are not theirs and, in some cases, outside the children's classroom. In this paper, we are focussed on the use of technology on freehand drawing activity. Thus, we have compared the graphics produced by 4- and 5-year-old children with paper&crayon in comparison with those with tablet-finger. Children made the drawings during a planned free-drawing activity, in their ordinary classrooms, with their teachers and during five sessions. Assessment of drawings has evidenced tablet feasibility for making graphics. Nevertheless, with the passing of time, quality of graphics (tablet-finger vs paper&crayons), are nearly matched, demonstrating the low impact level technology has on this activity. In addition, if drawings are analysed specifically according to ages, results have shown that both groups have to develop adaptation strategies of visual perceptual skills and fine motor skills for the touch screen in order to obtain the same quality in the drawings made on both support types. [ABSTRACT FROM AUTHOR]

Background: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. Methods: First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. Results: The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. Conclusions: Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics. [ABSTRACT FROM AUTHOR]