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Symmetry
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Symmetry in Mechanical and Biomedical Mechanical Engineering II
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Symmetry in Mechanical and Biomedical Mechanical Engineering II

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 847

Special Issue Editor

Prof. Dr. Natalie Baddour

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada
Interests: wearable sensors; mHealth; signal processing; mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first Special Issue of Symmetry, titled “Recent Advances in Mechanical and Biomedical Mechanical Engineering”, it is my pleasure to be the Guest Editor for a second Special Issue.

Symmetries in real engineering systems can be used to facilitate their analysis or may be exploited in their design. Similarly, asymmetries can also be purposely exploited to achieve certain outcomes. Simple symmetries often give insight into complex systems. Mechanical and biomedical mechanical engineering systems are those of solid and fluid mechanics, materials, thermodynamics, dynamics, and controls and the many applications of these disciplines. There are many theories, applications and analyses of mechanical engineering systems that deliberately make use of symmetry or asymmetry.

This Special Issue invites researchers to submit original research papers related to mechanical or biomedical mechanical engineering in which theoretical or practical issues of symmetry are considered.

Prof. Dr. Natalie Baddour
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry
  • mechanical engineering
  • biomedical mechanical engineering

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Published Papers (2 papers)

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Research

19 pages, 5459 KiB  
Article
A CNN Approach for Emotion Recognition via EEG
by Aseel Mahmoud, Khalid Amin, Mohamad Mahmoud Al Rahhal, Wail S. Elkilani, Mohamed Lamine Mekhalfi and Mina Ibrahim
Symmetry 2023, 15(10), 1822; https://doi.org/10.3390/sym15101822 - 25 Sep 2023
Cited by 6 | Viewed by 2519
Abstract
Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature [...] Read more.
Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural networks (CNNs) for EEG emotion recognition. Unlike traditional methods, our CNN-based approach learns discriminative cues directly from raw EEG signals, bypassing the need for intricate feature engineering. This approach not only simplifies the preprocessing pipeline but also allows for the extraction of more informative features. We achieve state-of-the-art performance on benchmark emotion datasets, namely DEAP and SEED datasets, showcasing the superiority of our approach in capturing subtle emotional cues. In particular, accuracies of 96.32% and 92.54% were achieved on SEED and DEAP datasets, respectively. Further, our pipeline is robust against noise and artefact interference, enhancing its applicability in real-world scenarios. Full article
(This article belongs to the Special Issue Symmetry in Mechanical and Biomedical Mechanical Engineering II)
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25 pages, 9801 KiB  
Article
Development of a Smart Metered-Dose Inhaler for Asthma Based on Computational Fluid Dynamics
by Zhiguo Zhang and Maoning Wei
Symmetry 2023, 15(9), 1712; https://doi.org/10.3390/sym15091712 - 7 Sep 2023
Viewed by 1785
Abstract
Asthma is a common respiratory disease with symptoms such as repeated wheezing, shortness of breath, and coughing. However, currently, asthma cannot be cured but only controlled or relieved using medication. The metered-dose inhaler (MDI) is known to lead to high deposition fractions of [...] Read more.
Asthma is a common respiratory disease with symptoms such as repeated wheezing, shortness of breath, and coughing. However, currently, asthma cannot be cured but only controlled or relieved using medication. The metered-dose inhaler (MDI) is known to lead to high deposition fractions of drug particles in the mouth and throat, resulting in inadequate drug efficacy. Therefore, herein, the factors influencing the deposition fraction of asthma drugs in the mouth and throat regions were explored by computational fluid dynamics and a smart MDI for asthma was designed. The smart MDI was designed based on the obtained simulation results, which demonstrated that the deposition fraction gradually increased from 55.78% to 65.75% with an increase in the peak inspiratory flow rate at an angle of incidence of 0°. The deposition fraction first decreased and then increased as the angle of incidence increased at a constant peak inspiratory flow rate. The deposition fraction increased as the inspiration–press interval time increased at a constant angle of incidence and peak inspiratory flow rate. Meanwhile, performance analysis of the designed smart MDI indicated that the inhaler could effectively reduce the deposition fraction of drugs in the mouth and throat regions by 17% on average. Full article
(This article belongs to the Special Issue Symmetry in Mechanical and Biomedical Mechanical Engineering II)
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