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Type: ReviewPublisher: Springer

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Now showing 1 - 3 of 3
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    Review
    Citation - WoS: 29
    Citation - Scopus: 46
    Impact of Nanotechnology on Conventional and Artificial Intelligence-Based Biosensing Strategies for the Detection of Viruses
    (Springer, 2023) Ramalingam, Murugan; Jaisankar, Abinaya; Cheng, Lijia; Krishnan, Sasirekha; Lan, Liang; Hassan, Anwarul; Marrazza, Giovanna
    Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.
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    Review
    Citation - WoS: 11
    Citation - Scopus: 12
    The Importance of Media on Decision to Undergo Cosmetic Surgery Operation
    (Springer, 2022) Sonmez, Mehmet; Esiyok, Elif
    Background How media disseminates ideal beauty, and its effect on the decision-making process of cosmetic procedures are among the most discussed topics in the literature. This study aimed to investigate the effects of media on patients' decisions to undergo cosmetic surgery. Materials and Methods Between March and September 2021, 82 patients participated in this study and informed consent was obtained from all patients. A questionnaire containing three different parts was developed by a consultant plastic surgeon and a public relations and marketing specialist, according to the literature. All statistical analyses were performed using SPSS version 22.0. Results The majority of patients underwent rhinoplasty (31.7%), breast reduction (25.6%), and breast augmentation (12.2%). Some of the patients underwent two different operations (6%). The correlation analysis results showed that, there was a medium, positive correlation between wanting to be attractive and thinking that media is an important tool in the decision to undergo cosmetic surgery (r=.307, p<.01). Want to look like people on the media and compare themselves with those showing a positive and strong correlation (r=.640, p<.01). The photographs on the magazines affected the patients aged between 40-49 and 50-59 more (chi(2)(4) = 11,378, p<.05); however, the published news on the Internet affected the younger sample (30-39 and 21-29) more than the other age groups (chi(2)(4)= 11,808, p<.05). The participants aged 30-39 and 21-29 tend to compare themselves with people on the Internet. Conclusion The study concludes that media is not only important for disseminating beauty ideals but is also an important source during decision making. However, further studies with more participants and objective scales are needed to verify our results.
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    Review
    Citation - WoS: 4
    Citation - Scopus: 6
    A Systematic Review on Smart Waste Biomass Production Using Machine Learning and Deep Learning
    (Springer, 2023) Peng, Wei; Sadaghiani, Omid Karimi
    The utilization of waste materials, as an energy resources, requires four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the waste biomass production step. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in the surveying and collecting the applications of Machine Learning in the waste biomass. To fill this gap with the current work, the kinds and resources of waste biomass as well as the role of Machine Learning and Deep Learning in their development are reviewed. Moreover, the storage and transportation of the wastes are surveyed followed by the application of Machine Learning and Deep Learning in these areas. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the waste collecting quality and quality, improve the predictions, diminish the losses, as well as increase storage and transformation conditions.