Numerical study of magnetic nanoparticles injection into a brain tumor considering the effects of injection volume and location on the termination of cancerous cells.

Lately, magnetic nanoparticle (MNP) hyperthermia gained much attention because of its therapeutic efficiency. It is challenging to predict all the treatment parameters during the actual therapeutic environment. Hence, the numerical approaches can be utilized to optimize various parameters of interest.

Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning.

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs).

Numerical Investigation of Ferrofluid Preparation during In-Vitro Culture of Cancer Therapy for Magnetic Nanoparticle Hyperthermia.

Recently, studies of magnetic nanoparticle (MNP) hyperthermia have attracted significant attention because of the severity of this cancer therapy for culture. Accurate temperature evaluation is one of the key challenges of MNP hyperthermia. Hence, numerical studies play a crucial role in evaluating the thermal behavior of ferrofluids.

Parametric Investigations of Magnetic Nanoparticles Hyperthermia in Ferrofluid using Finite Element Analysis.

Recently, magnetic nanoparticles (MNPs) based hyperthermia therapy has gained much attention due to its therapeutic potential in biomedical applications. This necessitates the development of numerical models that can reliably predict the temporal and spatial changes of temperature during the therapy. The objective of this study is to develop a comprehensive numerical model for quantitatively estimating temperature distribution in the ferrofluid system.

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM).

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time.

A review on numerical modeling for magnetic nanoparticle hyperthermia: Progress and challenges.

Recent progress in nanotechnology has advanced the development of magnetic nanoparticle (MNP) hyperthermia as a potential therapeutic platform for treating diseases. Due to the challenges in reliably predicting the spatiotemporal distribution of temperature in the living tissue during the therapy of MNP hyperthermia, critical for ensuring the safety as well as efficacy of the therapy, the development of effective and reliable numerical models is warranted. This article provides a comprehensive review on the various mathematical methods for determining specific loss power (SLP), a parameter used to quantify the heat generation capability of MNPs, as well as bio-heat models for predicting heat transfer phenomena and temperature distribution in living tissue upon the application of MNP hyperthermia.