My home blood pressure monitor shows irregular heartbeat

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Any process consists of several tasks, and there may be precedence constraints among them.

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In our paper we consider a special case, where the precedence constraints form short disjoint directed paths. This model occurs frequently in practice, but as far as we know it is considered very rarely in the literature. The goal is to find a good resource allocation schedule to minimize the makespan.

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The problem is known to be strongly NP-hard, and such hard problems are often solved by heuristic methods. We found only one paper which is closely related to our topic, this paper proposes the heuristic method HH. We propose a new heuristic called QLM which is inspired by reinforcement learning methods from the area of machine learning.

As we did not find appropriate benchmark problems for the investigated model.

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We note that a heuristic method can give a "near optimal" solution very fast while an exact solver provides the optimal solution, but it may need a huge amount of time to find it. In our computational evaluation we experienced that our heuristic is more effective than HH and finds the optimal solution in many cases and very fast.

We have developed a method by which we can construct a fault tree, considering human properties.

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It can be explored by analyzing the factors that lead to the critical major event, errors indicated by humans and their possible combinations. The goal is to assign an activity to the person with the best qualities among the set of the available people. We introduce a decision supporting tool and its application for the my home blood pressure monitor shows irregular heartbeat choice of a human resource in case of process scheduling.

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Abstract: In case of process scheduling any process consists of tasks, and there can be precedence constraints among them. We assign the tasks to unrelated machines so that the makespan is minimized. In our earlier work we proposed a metaheuristic algorithm called QLM Q-Learning Motivated based on a reinforcement learning technique.

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The QLM algorithm uses several parameters discount rate, learning rate, temperature for simulated annealing and iteration number. Currently these parameters are set manually and the goal is to determine the appropriate parameter values automatically. Neumann Kollokvium konferencia-kiadványa.

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The purpose of this pilot study was to determine the usability, utility, and feasibility of a website created to increase engagement and improve the quality of the preoperative education that patients having hip and knee arthroplasty surgery receive. Materials and Methods: A website was created, and its appearance was designed with evidence-based "menu-driven" drop-downs to make the screen options age-appropriate to the patient population; the content was supported with video and PDFs of educational material, the same or similar to the usual education provided to patients.

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The patient-specific outcomes included qualitative data regarding patient knowledge, satisfaction, utilities, and usability. These objectives were assessed based on the perceived health website usability questionnaire online survey.

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Eighty patients who met inclusion criteria were recruited, ranging in age from 40 to 65 years old. Among them, The patients were randomly assigned to the paper only or website education cohorts in a ratio. However, only 19 from each cohort participated in the survey questionnaire.

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Results and Conclusions: We hypothesized that findings would show that patients receiving web-based education would feel more knowledgeable about their procedure, have less anxiety, and greater satisfaction with the addition of the website content; and that nurses would report that a website could conserve nursing time and resources.

The study revealed no statistically significant differences between the cohorts, with an Alpha level set at 0. However, survey results showed that patients using the website rated self-perceived increase in knowledge, and their satisfaction in the time to find and review the information was higher than that of the paper-only cohort. The nursing survey revealed that website education improved workflow, efficiency, and patient education.

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Social skills therapy has been shown to be helpful. Over the past several years, computer-assisted and robot-assisted therapies have been infiltrating the social skills teaching environment.

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Rapid progress in the field of technology, especially in the robotics area, offers tremendous possibilities for innovation and treatment or even education for individuals with ASD. This paper's purpose is to drive awareness of these innovative interventions in order to support the social lives of children with ASD. The aims of the paper are identifying 1 the types of Information Technology platforms that are being evaluated in computer and robot-assisted therapies for children with ASD; 2 the various disciplines or professions studying and utilizing these computer and robot-assisted social skill therapies; 3 the outcomes being evaluated in each trial; and 4 if results demonstrate benefits to children with autism.

Search terms incorporated the subject intersection of autism, and computer or robot-assisted therapy. Results were mined for pediatric populations only and study designs establishing controlled comparisons.

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