This study describes the steps for developing a course and its structure in the Virtual Learning Environment Moodle. It consisted of an application of nursing content offered in an online course included in an international workshop directed to a group of nursing students from a bachelor and a teaching diploma program in Brazil and Portugal. Distinct stages were identified during the study: planning, construction and transformation of content as well as availability of such content to students. The interactive activities and context were developed by professors with the participation of the technical team. The specific procedures and roles performed by professors, specialists, students and technicians are presented. The results of the development and offering of the online course appointed some aspects to be improved in the work process such as the format of content and use of tools.
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http://dx.doi.org/10.1590/s0034-71672012000400016 | DOI Listing |
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Sci Rep
January 2025
Department of Psychology, Faculty of Psychology and Sport Science, Justus Liebig University, Otto-Behaghel-Str. 10F, 35394, Gießen, Germany.
Adapting movements to rapidly changing conditions is fundamental for interacting with our dynamic environment. This adaptability relies on internal models that predict and evaluate sensory outcomes to adjust motor commands. Even infants anticipate object properties for efficient grasping, suggesting the use of internal models.
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January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
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January 2025
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.
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January 2025
Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh.
The transportation industry contributes significantly to climate change through carbon dioxide ( ) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government's official open data portal, we explored the impact of various vehicle attributes on emissions.
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