The increase of driver information and infotainment systems includes also interaction technologies like speech interaction that minimize visual-manual demand and put the focus to cognitive demand. The question is whether this could lead to distraction effects and decreased traffic safety. This study presents an evaluation method for cognitive demand based on different detection paradigms in a dual task setting. A listening and a backward counting task are realized on three difficulty levels as simulations of cognitively loading secondary tasks and investigated using a visual versus a tactile detection paradigm. The results show that both detection paradigms are able to discriminate the task levels and that subjects successfully apply compensation strategies in the dual task setting especially during the listening task.
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http://dx.doi.org/10.3233/WOR-2012-0786-4919 | DOI Listing |
Ind Health
January 2025
Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Malaysia.
Low back pain (LBP) is a commonly encountered medical disorder in Malaysia's primary care setting, though establishing a direct connection between LBP and the workplace environment in adults is challenging. This case presents a clinic nurse who developed LBP due to a prolapsed intervertebral disc and her clinical management from an Occupational Health Doctor perspective. Her occupational management involved a walk-through survey at an urban hospital, which identified bone marrow aspiration as her most physically demanding task.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
February 2025
Department of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Background: Cognitive training (CT) has been one of the important non-pharmaceutical interventions that could delay cognitive decline. Currently, no definite CT methods are available. Furthermore, little attention has been paid to the effect of CT on mood and instrumental activities of daily living (IADL).
View Article and Find Full Text PDFInt J Pharm
January 2025
Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FABRX Artificial Intelligence, Carretera de Escairón, 14, Currelos (O Saviñao) CP 27543, Spain; FABRX Ltd., Henwood House, Henwood, Ashford, Kent TN24 8DH, UK; Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK. Electronic address:
Compounding medications in pharmacies is a common practice for patients with prescriptions that are not available commercially, but it is a laborious and error-prone task. The incorporation of emerging technologies to prepare personalised medication, such as 3D printing, has been delayed in smaller pharmacies due to concerns about potential workflow disruptions and learning curves associated with novel technologies. This study examines the use in a community pharmacy of a pharmaceutical 3D printer to auto-fill capsules and blisters using semisolid extrusion, incorporating an integrated quality control system.
View Article and Find Full Text PDFInt J Med Inform
January 2025
Oulu Advanced Research on Service and Information Systems, University of Oulu, Linnanmaa campus, Pentti Kaiteran katu 1 90570 Oulu, Finland. Electronic address:
Background: Studies have demonstrated that interventions targeting weight loss and body mass index (BMI) reduction can be successful, although the specific factors that influence their effectiveness are still unclear. Behavior change support systems (BCSS) are an approach that aims to help users in their efforts to modify their behavior. A useful tool for assessing BCSS is the Persuasive Systems Design model (PSD), where different features and postulates can be employed.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, China. Electronic address:
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.
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