Objective: To verify the reliability of vocal self-assessment tools in individuals with vocal complaints using a digital platform and in-person.
Methods: Data from 50 Brazilian adults with vocal complaints were collected. The participants answered the Voice Symptoms Scale (VoiSS), the Voice Handicap Index - 10 (VHI-10), and questions regarding personal information and their general health. The data collection occurred in two moments, within a period of 2-14 days, with time variation between participants, and always in the same collection order: 1. using a digital platform (Google Forms); 2. In-person. The data were analyzed descriptively and inferentially using the SPSS 25.0 software and the Wilcoxon Test.
Results: The self-perception of vocal symptoms in the Emotional factors for women presented a statistical difference between the digital platform and the in-person response (P = 0.029); higher scores were observed when answers were given in-person compared to using a digital platform.
Conclusion: Overall, the participants perceived their voice symptoms and voice handicap similarly when answers were given using a digital platform and in-person. The Emotion voice symptoms in women were the only exception, although remaining deviated; however, the perception of the symptoms may have been attenuated during the in-person care.
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http://dx.doi.org/10.1016/j.jvoice.2023.04.014 | DOI Listing |
Eur J Trauma Emerg Surg
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AO Research Institute Davos, Davos, Switzerland.
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January 2025
Príncipe Felipe Research Center, Polymer Therapeutics Lab., Valencia, 46012, Spain.
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January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
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December 2024
Department of Computer Science & Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks.
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December 2024
School of Engineering, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia.
Civil infrastructure assets' contribution to countries' economic growth is significantly increasing due to the rapid population growth and demands for public services. These civil infrastructures, including roads, bridges, railways, tunnels, dams, residential complexes, and commercial buildings, experience significant deterioration from the surrounding harsh environment. Traditional methods of visual inspection and non-destructive tests are generally undertaken to monitor and evaluate the structural health of the infrastructure.
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