Recent Advances in Data Analysis and Artificial Intelligence for Degradation and Asymmetric Mechanisms
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 31 July 2024 | Viewed by 310
Special Issue Editors
Interests: data analysis; machine learning; degradation modeling; Bayesian estimation and control
Special Issue Information
Dear Colleagues,
This Issue aims to highlight the latest advancements in the data analysis and artificial intelligence (AI) techniques used to understand degradation and asymmetric mechanisms, with applications in engineering, medical, and biology fields. Degradation and asymmetry are prevalent phenomena in various domains, and comprehending their underlying mechanisms is crucial for developing effective solutions. In engineering systems, the asymmetry of degradation and monitoring poses challenges in accurately monitoring system conditions. In the biological field, cell polarization and symmetry breaking are ubiquitous in cellular processes such as cell division, embryonic development, cell differentiation, chemotaxis and cell signaling, during which the establishment of asymmetry is essential for guaranteeing proper cell function. The visualization and analysis of biological objects can be frustrating and laborious due to weak signal, high signal-to-background ratio, data diversity (e.g., image data, genomic data, protein structure) and data complexity. In medical fields, clinical and diagnostic data have high volume and are highly variable. The application of data analysis and machine learning can help researchers to analyze and characterize degradation and asymmetry mechanisms, therefore simplifying data processing. For example, machine learning can be used to analyze asymmetric diagnostic results to differentiate health and disease conditions for the purpose of assisting in medical diagnosis.
This Special Issue will provide researchers with a platform with which to present novel findings, methodologies, and applications that leverage data analysis and AI to address the challenges associated with degradation and asymmetry in engineering, medical, and biology fields. Topics of interest include, but are not limited to, degradation analysis, aging modeling, asymmetric cell division, neural asymmetry, data fusion techniques, reliability estimation, and explainable AI for interpreting degradation and asymmetry.
This Special Issue welcomes original research articles, reviews, and methodological papers that contribute to the understanding and application of data analysis and AI in the study of degradation and asymmetric mechanisms.
Dr. Chaoqun Duan
Dr. Ting Gong
Guest Editors
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
- data analysis
- artificial intelligent
- degradation modeling
- degradation estimation
- reliability estimation
- asymmetry mechanisms
