Advanced Engineering Mathematics

Potter, Merle C.; Goldberg, Jack; Aboufadel, Edward

doi:10.1007/978-3-030-17068-4

Cited by 7 publications

(6 citation statements)

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“…Based on the wave equation for longitudinal waves [13] and properties of wave transmissions in the air at temperature 20 °C, air humidity 20-40% [14], assuming that all electrical energy convert into ultrasonic energy and transmit to air without loss, the magnitude of vibration (ξ) at the end of Ra is determined by the following equation:…”

Section: A Design Of Ultrasonic Transducermentioning

confidence: 99%

[RETRACTED] An Approach to Optimize the Design of Ultrasonic Transducer for Food Dehydration

Hay

Ngoc-Phuong²,

Nguyen³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Ultrasound-assisted air-drying is considered a useful method to reduce drying time and ensure some agricultural food products' quality. In this study, a practical design method of an Ultrasonic Transducer (UT), which is in the form of a circular stepped-plate, was developed by using Particle Swarm Optimization (PSO) algorithm and Finite Element Analysis (FEA). The numerical calculation is carried out for two different geometric dimensions of the stepped-plate. Based on the obtained results, the smaller UT dimensions were chosen, and its prototypes were fabricated. The prototype's measured resonance frequency is 19.927 kHz compared to calculations getting an error of 0.073 kHz (0.37%). Also, to evaluate the effects of ultrasound on a drying process, the UT is integrated into a heat-pump drying machine for drying foods. The experiments were carried out on Codonopsis javanica using heatpump drying method at air temperature 45 ± 0.5 °C, air humidity 18-20 %, air velocity 0.5 ± 0.1 m/s with and without ultrasonic intensity while keeping all other conditions and parameters constant. The results showed that the total drying time was reduced in the range of 25% to 42% depending on ultrasonic intensity, compared to heat-pump drying without ultrasonic intensity. The color of the dried products is also considered to testify the quality of the product when using UT in drying processes. The obtained results prove the effectiveness of the proposed approach in design of the UT for food dehydration.

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Section: A Design Of Ultrasonic Transducermentioning

confidence: 99%

[RETRACTED] An Approach to Optimize the Design of Ultrasonic Transducer for Food Dehydration

Hay

Ngoc-Phuong²,

Nguyen³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Even with the same MRI scanner and identical scanning parameters, variations in the MRI intensity cannot be avoided, and these may affect the process of feature extraction. To minimize the MRI intensity variations, we normalized the intensity of the image using the following formula (where x indicates the original intensity; f(x) indicates the normalized intensity; μ refers to the mean value; σ indicates the variance; s is an optional scaling, by default, it is set to 1) [ 14 ]. …”

Section: Methodsmentioning

confidence: 99%

Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images

Liu

Cui³

et al. 2019

BMC Med Imaging

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BackgroundTo evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH).MethodsThis study enrolled 369 consecutive patients with 446 lesions (a total of 222 HCCs and 224 HHs). A training set was constituted by randomly selecting 80% of the samples and the remaining samples were used to test. On magnetic resonance (MR) images of HCC and HH obtained with in-phase, out-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, we outlined the target lesions and extracted 1029 radiomics features, which were classified as first-, second-, higher-order statistics and shape features. Then, the variance threshold, select k best, and least absolute shrinkage and selection operator algorithms were explored for dimensionality reduction of the features. We used four classifiers (decision tree, random forest, K nearest neighbours, and logistic regression) to identify HCC and HH on the basis of radiomics features. Two abdominal radiologists also performed the conventional qualitative analysis for classification of HCC and HH. Diagnostic performances of radiomics and radiologists were evaluated by receiver operating characteristic (ROC) analysis.ResultsValuable radiomics features for building a radiomics signature were extracted from in-phase (n = 22), out-phase (n = 24), T2WI (n = 34) and DWI (n = 24) sequences. In comparison, the logistic regression classifier showed better predictive ability by combining four sequences. In the training set, the area under the ROC curve (AUC) was 0.86 (sensitivity: 0.76; specificity: 0.78), and in the testing set, the AUC was 0.89 (sensitivity: 0.822; specificity: 0.714). The diagnostic performance for the optimal radiomics-based combined model was significantly higher than that for the less experienced radiologist (2-years experience) (AUC = 0.702, p < 0.05), and had no statistic difference with the experienced radiologist (10-years experience) (AUC = 0.908, p>0.05).ConclusionsWe developed and validated a radiomics signature as an adjunct tool to distinguish HCC and HH by combining in-phase, out-phase, T2W, and DW MR images, which outperformed the less experienced radiologist (2-years experience), and was nearly equal to the experienced radiologist (10-years experience).

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“…We used three publicly-available GBM datasets obtained from The Cancer Genome Atlas (TCGA)(Dataset ID:HT_HG-U133A) 10 , Chinese Glioma Genome Atlas (CGGA) (Dataset ID: mRNAseq_325) 11 , and Ivy Glioblastoma Atlas Project (IvyGAP) 12 .We performed z-score normalization 13 for all the samples and genes in the data preprocessing. We have conducted three case studies with GBM datasets for (1) Survival analysis, (2) sub-type identification, and (3) IDH1 mutation analysis.…”

Section: Resultsmentioning

confidence: 99%

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation

Saghapour,

Yue,

Sharma

et al. 2024

Preprint

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This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

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Advanced Engineering Mathematics

Cited by 7 publications

References 0 publications

[RETRACTED] An Approach to Optimize the Design of Ultrasonic Transducer for Food Dehydration

[RETRACTED] An Approach to Optimize the Design of Ultrasonic Transducer for Food Dehydration

Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation

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