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http://dx.doi.org/10.2337/ds15-0045 | DOI Listing |
Sensors (Basel)
January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
With the rapid development of IoT technology, sensors are widely used for monitoring environmental parameters. The data collected by sensors needs to be stored, and distributed storage systems provide an excellent platform to handle this vast amount of data. To enhance data reliability and reduce storage costs, erasure coding technology can be employed within distributed storage systems.
View Article and Find Full Text PDFFoods
January 2025
Electrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand.
This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Military Medical Psychology, Air Force Medical University, Chinese People's Liberation Army (PLA), 169 West Changle Road, Xi'an, 710032, Shaanxi, China.
Background: Internet addiction has emerged as a significant mental health issue among university students. The study aimed to compare the network structures of Internet addiction and mental health symptoms among university students in China and Malawi, which provide insights into culturally sensitive prevention and intervention strategies.
Methods: Network analysis was used on two datasets: Malawi (n = 688) and China (n = 975) using the Internet Addiction Test and the Self-Reporting Questionnaire.
J Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, US.
Background: Most cancer survivors have multiple cardiovascular risk factors, increasing their risk of poor cardiovascular and cancer outcomes. The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association's Life's Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors.
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