初期弾性率を組み入れた非圧縮超弾性モデルによる動脈組織の非線形の力学的挙動の特性評価：定式化と動脈硬化，動脈解離への適用

九州工業大学博士学位論文（要旨）学位記番号：生工博甲第382号 学位授与年月日：令和2年9月25

In-silico model development and validation of the L5-S1 spinal unit

AbstractThe L5-S1 segment of the spine is highly susceptible to injury, frequently causing low back pain. The segment has gained a lot of scientific interest, leading to many experimental works that can be found describing its biomechanical characteristics. But, there is a lack of work focusing on its computational studies, which can significantly aid its further studies. In the current study, a subject-specific single-segment finite element model of the L5-S1 unit was developed from a T2-mapped MRI scan. This study is mainly intended to probe the requirements for modelling the annulus of the disc and also attempts to understand the role of ligaments exclusive to the L5-S1 spinal unit to establish its validated finite element model. The annulus was represented by two different forms of hyperelastic material models (isotropic and anisotropic) for which the constants were determined from experimental data found in the literature. Their ability to impart the required characteristic was tested for the finite element model to mimic the experimental responses during sagittal and lateral moment loads. A comparison of results with the two material models is also discussed for other valuable parameters like contact pressure at the facets, maximum von-Mises stresses in the vertebrae, ligament strains, and midplane Tresca shear stresses of the annulus. The anisotropic Gasser-Ogden-Holzapfel (GOH) model was observed to deliver a response that consistently showed good compliance with the experimental response and hence, it is recommended for the computational studies of this segment

Optimization of material removal rate and surface characterization of wire electric discharge machined Ti-6Al-4V alloy by response surface method

Wire electric discharge machining (WEDM) is one of the foremost methods which has been utilized for machining hard-to-cut materials like Titanium alloys. However, there is a need to optimize their important operating parameters to achieve maximum material removal rate (MRR). The present paper investigates the effect of control factors like current, pulse on time (Ton), pulse off time (Toff) on MRR of machining of Ti-6Al-4V alloy. The study showed that, increase in current from 2 A to 6 A results in a significant increase in MRR by 93.27% and increase in Ton from 20 μs to 35 μs improved the MRR by 7.98%, beyond which there was no improvement of MRR. The increase in Toff showed a counterproductive effect. Increase in Toff from 10 μs to 30 μs showed an almost linear decrease in MRR by 52.77%. Morphological study of the machined surface showed that cut surface consists of recast layer on which microcracks were present, and revealed the presence of globules, ridge-structured formations of recast layers and voids. In addition, a regression model was developed to predict the MRR with respect to the control factors, which showed a good prediction with an R2 value of 99.67%

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models

Diabetic retinopathy in patients with diabetic foot syndrome in South India

Purpose: The purpose was to study the retinopathy status in diabetic patients with a risk of diabetic foot (DF) syndrome visiting a tertiary care hospital in South India. Methods: In this cross sectional study all patients with diabetes mellitus (DM) with a risk of DF syndrome, visiting a tertiary care hospital during the study period, underwent an ophthalmological evaluation for documentation of their retinopathy status. Results: One hundred and eighty-two patients diagnosed to have a risk profile for DF syndrome were included in the study. Their mean age was 59.28 years and 75.27% were males. The mean duration of Type 1 and Type 2 variants of DM was 14.9 years and 10.9 years, respectively. Of the 182 patients, 67.58% had retinopathy changes. Proliferative diabetic retinopathy (DR) constituted 17.88% of the total patients with retinopathy. An increased presence of retinopathy in patients with an increased risk grade of DF was found significant by the Chi-square test (P < 0.001). Conclusion: Our study found an increased presence of DR in a South Indian cohort with DF syndrome. The severity of retinopathy was greater in patients with higher grades of risk for DF. The establishment of an association between DR and DF syndrome will help in developing an integrated management strategy for these two debilitating consequences of diabetes