A Review on Detection of Breast Cancer Cells by Using Various Techniques

Kandubothula, Vanaja; Uppada, Rajyalakshmi; Nandan, Durgesh

doi:10.1007/978-981-15-4032-5_73

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…When dealing with MEA material structure optimization modeling in the future, reliability design should be integrated into the simulation process to improve the life of PEMFCs. (3) With the rapid development of computer technology, ML technology is widely used in the fields of industrial inspection and measurement [130][131][132], medical diagnosis [133,134], life sciences [135,136] and so on. For example, AlphaFold2 constructed a protein structure prediction model through ML; it was able to predict the properties of proteins from gene sequences, obtaining 98.5% of the human protein structure [135].…”

Section: Discussionmentioning

confidence: 99%

Review: Modeling and Simulation of Membrane Electrode Material Structure for Proton Exchange Membrane Fuel Cells

Chen

Liu

et al. 2022

Coatings

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Hydrogen energy is recognized as the clean energy with the most development potential, and hydrogen fuel cell technology is considered the ultimate solution utilizing hydrogen energy. The proton exchange membrane fuel cell (PEMFC) has the merits of high energy efficiency, high energy density, low operating temperature, is clean, and affords environmental protection. Improving the structure of each functional layer could play a significant role in improving PEMFC performance. In addition, membrane electrode assemblies (MEAs) are the core components of a PEMFC, and their structure includes three main parts, namely, the gas diffusion layer (GDL), catalytic layer (CL), and proton exchange membrane (PEM). Therefore, this review focuses on progress in the modeling and simulation of the material structure in MEAs. First, the GDL simulation models are critically reviewed, including two-phase calculation models and microscopic simulation models. Second, CL microstructure models are comprehensively evaluated, involving power density enhancement, catalyst loading distribution, electrochemical reaction and its performance optimization. Third, the PEM simulation model, relating to molecular dynamics (MD) simulation techniques, 3D numerical techniques, and multiphysics simulation, are reviewed. Finally, the three aspects of similarity, individuality, and complementarity of these simulation models are discussed, and necessary outlooks, including the current limitations and challenges, are suggested, providing a reference for low-cost, high-performing PEMFC membrane electrodes for the future.

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Section: Discussionmentioning

confidence: 99%

Review: Modeling and Simulation of Membrane Electrode Material Structure for Proton Exchange Membrane Fuel Cells

Chen

Liu

et al. 2022

Coatings

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“…These algorithms will be improved as they are applied, and it is believed that future artificial intelligence will also produce more new algorithms that the optimization algorithm will apply to the EDM, such as selfish herds optimization, bald eagle search, etc. (4) With the fast advancement of technology, machine learning (ML) has found widespread use in a variety of industries, including industrial testing [121][122][123], medical diagnostics [124,125], life sciences [126,127], and renewable energy [128][129][130]. AlphaFold2, for instance, created a protein structure prediction model using ML, which can predict the properties of proteins based on gene sequences and achieve 98.5% of the structure of human proteins [126].…”

Section: Outlooksmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

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Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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