Introduction: Due to their importance in transmitting knowledge, teachers can play a crucial role in students' scientific literacy acquisition and motivation to respond to ongoing and future economic and societal challenges. However, to conduct this task effectively, teachers need to continuously improve their knowledge, and for that, a periodic update is mandatory for actualization of scientific knowledge and skills. This work is based on the outcomes of an educational study implemented with science teachers from Portuguese Basic and Secondary schools. We evaluated the effectiveness of a training activity consisting of lectures covering environmental and health sciences conducted by scientists/academic teachers.
Material And Methods: The outcomes of this educational study were evaluated using a survey with several questions about environmental and health scientific topics. Responses to the survey were analyzed before and after the implementation of the scientific lectures.
Results: Our results showed that Basic and Secondary schools teachers' knowledge was greatly improved after the lectures. The teachers under training felt that these scientific lectures have positively impacted their current knowledge and awareness on several up-to-date scientific topics, as well as their teaching methods.
Learning Outcomes: This study emphasizes the importance of continuing teacher education concerning knowledge and awareness about health and environmental education.
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http://dx.doi.org/10.3389/fpubh.2018.00041 | DOI Listing |
Sensors (Basel)
December 2024
Department of Automation, North China Electric Power University, Baoding 071003, China.
To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship.
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December 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Transformer is a powerful model widely used in artificial intelligence applications. It contains complex structures and has extremely high computational requirements that are not suitable for embedded intelligent sensors with limited computational resources. The binary quantization technology takes up less memory space and has a faster calculation speed; however, it is seldom studied for the lightweight transformer.
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December 2024
College of Information, Liaoning University, Shenyang 110036, China.
Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; training and inference processes consume significant computational resources. Thus, developing a lightweight and suitable fault diagnosis algorithm for small samples is particularly crucial.
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December 2024
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.
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