Article
Computer Science, Artificial Intelligence
Lucas R. C. de Farias, Aluizio F. R. Araujo
Summary: This paper introduces a MOEA/D-UR algorithm based on decomposition, which utilizes a metric to detect improvements and a procedure to increase diversity in the objective space. Experimental results suggest that MOEA/D-UR is more effective in handling real-world problems and multi-objective scenarios compared to other algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Mechanical
Jinwoo Im, Calogero B. Rizzo, Felipe P. J. de Barros, Sami F. Masri
Summary: This study introduces a general methodology based on genetic programming for identifying nonlinear multi-physics systems, aiming to discover governing equations solely from measured data. By constructing candidate models with expression trees and evaluating model fitness through evolutionary processes, the methodology demonstrates applicability to distinct physical systems with varying output errors. The proposed approach offers physical insights into governing complex, nonlinear, multi-physics systems.
NONLINEAR DYNAMICS
(2021)
Article
Computer Science, Artificial Intelligence
Qian Zhang, Jie Lu, Yaochu Jin
Summary: Recommender systems use artificial intelligence to provide personalized services, improve prediction accuracy, and solve data sparsity and cold start issues. This paper discusses how AI can effectively enhance technological development in recommender systems and reviews current research problems and new directions in this field. It also examines the use of various AI techniques, such as fuzzy techniques, transfer learning, genetic algorithms, neural networks, and deep learning, in improving recommender systems.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Mechanical
Miaomiao Lin, Changming Cheng, GuanZhen Zhang, Baoxuan Zhao, Zhike Peng, Guang Meng
Summary: This paper proposes a novel identification method based on a joint optimization approach to identify Bouc-Wen hysteretic systems. By introducing Duhamel's integral and a jointly optimized objective function, the calibration of system parameters and internal hysteretic force is improved. Simulation and experimental studies confirm the effectiveness of this method.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Yingxu Wang, Ming Hou, Konstantinos N. Plataniotis, Sam Kwong, Henry Leung, Edward Tunstel, Imre J. Rudas, Ljiljana Trajkovic
Summary: This paper explores the intelligent and mathematical foundations of autonomous systems, focusing on the structural and behavioral properties that constitute their intelligent power, as well as the evolution of system intelligence from reflexive to cognitive intelligence.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Biochemistry & Molecular Biology
Ritika Kabra, Shailza Singh
Summary: Worldwide researchers and companies are exploring various strategies, including vaccine development, drug repurposing, and AI-based drug discovery, to combat COVID-19 pandemic. The use of computational biology and machine-learning algorithms is being considered for their potential to provide fast and accurate outcomes in this crisis. Furthermore, the development of evolutionary peptides through machine-learning algorithms shows promise in providing cross-protection against diverse Covid-19 variants.
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE
(2021)
Review
Biochemistry & Molecular Biology
Zhidong Chen, Xinpei Wang, Xu Chen, Juyang Huang, Chenglin Wang, Junqing Wang, Zhe Wang
Summary: Therapeutic proteins, especially antibodies, have gained increasing attention in human medicine; however, their clinical application is hindered by multiple challenges in developability. Conventional optimization methods are time-consuming and inefficient, calling for the integration of advanced computational strategies to accelerate therapeutic protein design. This article compares the differences between therapeutic proteins and small molecules in terms of developability and provides an overview of computational approaches for their design and optimization.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Review
Environmental Sciences
Rajnish Kumar, Garima Yadav, Mohammed Kuddus, Ghulam Md Ashraf, Rachana Singh
Summary: The metagenomics approach revolutionized the study of genetic information from uncultured microbes and complex microbial communities. In silico research allowed for a better understanding of protein interactions, drug design, and microbial evolution. Artificial intelligence, particularly machine learning and deep learning, has enabled the analysis and utilization of large datasets generated from nucleic acid sequencing and proteomics, leading to breakthroughs in microbiology research.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Automation & Control Systems
Han Li, Peishu Wu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi
Summary: This paper reviews the use of mathematical tools to enhance LFIA performance, and proposes a novel taxonomy. It also presents the outlook of developing POCT in conjunction with other state-of-the-art techniques, and highlights the importance of applying computational intelligence methods in boosting POCT development.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Acoustics
Miaomiao Lin, Bing Sun, Changming Cheng, Baoxuan Zhao, Zhike Peng, Guang Meng
Summary: A novel method based on alternating state-parameter identification is proposed in this paper to identify Bouc-Wen hysteretic systems. The hysteretic force is reconstructed using harmonic balance method based on steady-state harmonic response, while the Duhamel's integral is used to represent the identification problem and reduce the effect of noise. The effectiveness of the method is validated through numerical simulation and dynamic experiment.
JOURNAL OF SOUND AND VIBRATION
(2022)
Review
Immunology
Denis A. Mogilenko, Alexey Sergushichev, Maxim N. Artyomov
Summary: Immunometabolism has become a new interdisciplinary field of research in recent years, providing important insights into the regulation of immune responses. Traditional approaches and new technologies, such as spatially resolved metabolic imaging and computational algorithms, have helped us understand the complexity of immunometabolic regulation. This review discusses recent studies and technological developments that aim to capture the interplay between immune responses and metabolism.
ANNUAL REVIEW OF IMMUNOLOGY
(2023)
Article
Nanoscience & Nanotechnology
Ana F. Cunha, Andre F. V. Matias, Cristovao S. Dias, Mariana B. Oliveira, Nuno A. M. Araujo, Joao F. Mano
Summary: Studying the interactions between living adherent cells and mechanically stable materials allows us to understand and utilize physiological phenomena mediated by cell-extracellular communication. This study investigates the effect of microparticle diameter on cell attachment and movement, and proposes a model to explain the biological mechanisms of cell adhesion, providing insights for addressing healthcare challenges.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Nanoscience & Nanotechnology
Ana F. Cunha, Andre F. V. Matias, Cristovao S. Dias, Mariana B. Oliveira, Nuno A. M. Araujo, Joao F. Mano
Summary: Studying the interactions between living cells and mechanically stable materials helps us understand physiological phenomena and potentially develop innovative applications. By examining how cells interact with objects of different stability, we can uncover biological responses and address healthcare challenges.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Computer Science, Theory & Methods
Christoph Jansen, Bjoern Lindequist, Klaus Strohmenger, Daniel Romberg, Tobias Kuester, Nick Weiss, Michael Franz, Lars Ole Schwen, Theodore Evans, Andre Homeyer, Norman Zerbe
Summary: Automated image analysis and AI are increasingly common in digital pathology software. The EMPAlA Consortium is developing an open and decentralized platform for AI apps to be integrated with clinical IT infrastructures, reducing integration efforts for vendors and providing pathologists access to advanced AI tools.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Review
Engineering, Environmental
Shabnam Ahmadi, Abbas Rezae, Soumya Ghosh, Alhadji Malloum, Artur Banach
Summary: Bioelectrochemical systems (BESs) are green technology suitable for pollutant treatments, which can stimulate microbes metabolism and increase electron transfer, achieving more than 90% removal of wastewater emerging pollutants (EPs). However, little attention has been paid to computational approaches and kinetics studies of EPs remediation in BESs.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2023)