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A random process approach to study the performance of engineering students in professional institutions

AIP Conference Proceedings 2020年第1期2270卷

作者： A. Ezhilarasi R. Marudhachalam S. Selvanayaki R. Tamilselvi P. Devaki 1Department of Mathematics Kumaraguru College of Technology Coimbatore Tamil Nadu India 2Department of Electronics and Communication Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamil Nadu India 3Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India

In this paper, an analysis of the performance of engineering students of Professional College in Coimbatore District has been done using the concept of Absorbing Markov Chain. The study has been carried out to assess various performance attributes of the learners of engineering stream. The outcomes of four programmes offered by an engineering College for eight semesters in four years have been studied and it has been attempted to arrive out the mean value of the time used-up by the students during a semester, total semesters taken to get the graduation on completion and the probability of a student getting graduated from the institution or withdrawn from the course.

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Fuzzy based de-noising underwater images by Hybrid filters

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AIP Conference Proceedings 2020年第1期2270卷

作者： A. Ezhilarasi R. Tamilselvi S. Selvanayaki P. Devaki 1Department of Mathematics Kumaraguru College of Technology Coimbatore Tamil Nadu India 3Department of Electronics and Communication Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamil Nadu India 2Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India

In recent years, the underwater image processing has significantly improved due to the technological development. The real challenge is that the poor contrast and noise because of absorption of light and scattering in the oceanic environment inherently disturb these underwater images. Hybrid filters depending on fuzzy for de-noising underwater images is proposed and analyzed clearly. The proposed algorithm is compared with various existing de- noising algorithms such as Gaussian median filter, TMED and median filter. The PSNR and NMSE of each algorithm has been measured and analyzed, which compares the ability to de-noising.

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Transfer learning of ultrasonic guided waves using autoencoders: A preliminary study

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AIP Conference Proceedings 2019年第1期2102卷

作者： K. Supreet Alguri Joel B. Harley 1Department of Electrical and Computer Engineering University of Utah USA 2Department of Electrical and Computer Engineering University of Florida USA

In recent years, the use of scanning laser Doppler vibrometery and full wavefield acquisition has grown to aid the char- acterization of ultrasonic waves and the detection of structural defects. Yet, these methods require a considerable amount of time to acquire full wavefield data. Therefore, there is a significant need to reduce acquisition time. In this preliminary work, we present a transfer learning approach for reducing the number of sampled measurements necessary. Our method utilizes numerical simula- tions, combined with a small number of spatially sampled random measurements from an experimental structure, to reconstruct full wavefield data. Specifically, we use an autoencoder neural network to learn low-dimensional representations of wave propagation from numerical simulations. We then input a few experimental measurements into the neural network to reconstruct full wave- field data. To demonstrate the ability of our framework, we show our initial success in three scenarios. We show reconstruction accuracies of 86% with one-fourth of the total measurements.

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Polar sparse wavenumber analysis for guided wave reconstruction

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AIP Conference Proceedings 2019年第1期2102卷

作者： Soroosh Sabeti Joel B. Harley 1Department of Electrical and Computer Engineering University of Utah USA 2Department of Electrical and Computer Engineering University of Florida USA

In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in determining the state of the structure under inspection. Effective analysis of guided waves, however, is encumbered by their intricate nature. This intricacy is further aggravated in structures with anisotropic characteristics. Moreover, the data acquisition process can be costly and time-consuming. Therefore, it is significant to achieve behavior prediction of guided waves from limited measurements. To make this possible, compressive sensing based methodologies and predictive models have been presented in the literature. Specifically in prior work, a two-dimensional sparse wavenumber analysis (2D-SWA) framework was introduced to model anisotropic wave propagation. In this paper, we present a similar framework whereby a sparser representation of guided waves can be obtained by incorporating information of the measurements in polar coordinates. We implement this method, which we refer to as polar sparse wavenumber analysis (PSWA), on a simulated wavefield propagating in a composite material and demonstrate how it is capable of accurately reconstructing the entire wavefield from a few spatial measurements.

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Directional weighted hybrid median based fuzzy filter for de-noising random valued impulse noise

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AIP Conference Proceedings 2020年第1期2270卷

作者： R. Marudhachalam S. Selvanayaki R. Tamilselvi P. Devaki 1Department of Mathematics Kumaraguru College of Technology Coimbatore Tamil Nadu India 2Department of Electronics and Communication Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamil Nadu India 3Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu. India

This paper proposes directional weighed hybrid median based fuzzy filter for de-noising random valued impulse noise from digital images. The two-step process namely, noise detection through fuzzy noise detection process followed by directional weighed fuzzy hybrid median (DWFHM) filtering for de-noising random valued impulse noise is the proposed technique studied in this paper. The application of DWFHM filter to remove random valued impulse noise yields improved result over existing methods based on PSNR (peak signal to noise ratio) values and RMSD (root mean square deviation) values.

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Efficient computation offloading using grey wolf optimization algorithm

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AIP Conference Proceedings 2019年第1期2061卷

作者： Parmeet Kaur Shikha Mehta 1Department of Computer Science Engineering Jaypee Institute of Information Technology Noida India

The widely available smart-phones as well as other mobile devices can be utilized as valuable compute resources with the support of the powerful and elastic paradigm of cloud computing. This is possible by performing computations of a limited scale on mobile devices and migrating or offloading complex services to the cloud. Use of mobile devices for computations will enable a plethora of applications to execute on mobile devices alone without dependence on static infrastructure. However, this entails the cost of transfer of computations to the cloud along with the cost of cloud computing resources. This paper formulates computation offloading as an optimization problem and utilizes nature-inspired approach of Grey Wolf Algorithm (GWO) to achieve near-optimal solutions. Results precisely depict that although the best cost solutions are attained by the brute force technique but the number of computations is significantly higher as compared to Grey Wolf Algorithm. Moreover with the small increase in number of tasks, there is exponential increase in number of computations. Considering these tradeoffs, its more appropriate to use nature-inspired algorithms for computation offloading in a mobile cloud computing environment.

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Magnetic Tracking: A Facile Magnetic System for Tracking of Medical Devices (Adv. Mater. Technol. 6/2021)

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Advanced Materials Technologies 2021年第6期6卷

作者： Liam Swanepoel Nouf Alsharif Alexander Przybysz Pieter Fourie Pierre Goussard Mohammad Asadullah Khan Abdullah Almansouri Jurgen Kosel Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Makkah 23955-6900 Saudi Arabia Department of Paediatrics and Child Health Stellenbosch University Stellenbosch Western Cape 7600 South Africa Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield S1 3JD UK Department of Electrical and Electronic Engineering University of Jeddah Jeddah Makkah 21959 Saudi Arabia Department of Sensor Systems Silicon Austria Labs Villach 9524 Austria

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Biodiesel synthesis from used cooking oil using calcium oxide (CaO) catalyst from chicken bones

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AIP Conference Proceedings 2020年第1期2248卷

作者： S. Miskah T. Aprianti M. Agustien Y. Utama M. Said 1Department of Chemical Engineering Sriwijaya University South Sumatera Indonesia 2Department of Chemical Engineering The University of Western Australia Perth Australia 3Department of Civil Engineering Sriwijaya University South Sumatera Indonesia 4Department of Information Systems and Computer Science Sriwijaya University South Sumatera Indonesia

Biodiesel is an alternative fuel derived from plant oil, animal fat or used oil through esterification with alcohol where it can be used without modifying the engine of a diesel machine. Used cooking oil is categorized as a waste that pollutes the environment, which can be the cause of some diseases such as cancer, coronary heart disease, stroke, and also hypertention. Calcium oxide (CaO) is an alkali metal oxide that can be used as a heterogeneous catalyst in biodiesel synthesis, its raw materials can be sourced from waste material such as chicken bones. The conversion of calcium to CaO is conducted through thermal decomposition of calcium carbonate (CaCO3) from chicken bones heated at a high temperature. This study utilizes used cooking oil as the raw material and CaO as the catalyst. The purpose of this study was to determine the effect of the amount of catalyst in the biodiesel synthesis from used cooking oil and also the effect of temperature variation on chicken bone heating as the material of the catalyst. This study was carried out by transesterification and calcination procedures with temperature variations of 700, 750, 800, 850 and 900°C, as well as the variations in the amount of CaO as the catalyst of 2, 3, 4, 5, and 6 % wt. This study shows that the most favorable calcination temperature for the chicken bones is at 750°C and the percentage of CaO as the catalyst is 2%.

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Combining traditional analysis and machine learning to predict early, middle, and long-term recurrence of intrahepatic cholangiocarcinoma

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European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2025年第9期51卷 110141页

作者： Ruoyu Zhang Zengshuai Wang Min Yang Bo Chen Mei Liu Minhua Zheng Peter Xiaoping Liu Liming Wang Department of Hepatobiliary Surgery National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College No. 17 Panjiayuan Nanli Area Chaoyang District Beijing 100021 China. School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China. National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences and Peking Union Medical College 10073 Beijing China. State Key Laboratory of Molecular Oncology Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College No. 17 Panjiayan Nanli Chaoyang District Beijing China. Laboratory of Cell and Molecular Biology & State Key Laboratory of Molecular Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China. Electronic address: mhzheng@***. School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China Department of Systems and Computer Engineering Carleton University Ottawa ON K1S 5B6 Canada. Electronic address: xpliu@sce.carleton.ca. Department of Hepatobiliary Surgery National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College No. 17 Panjiayuan Nanli Area Chaoyang District Beijing 100021 China. Electronic address: stewen_wang@***.

INTRODUCTION:Intrahepatic cholangiocarcinoma (ICC) is a rare and highly aggressive cancer. Few patients are eligible for radical surgery, and most face the high risk of recurrence. METHODS:We developed early-, middle- and long-term (1-, 2-, and 3-year) ICC disease-free survival (DFS) prediction models using traditional Logistic analysis combined with machine learning (ML) and systematically compared the performance of traditional analysis and MLs. RESULTS:275, 256, and 238 ICC patients under radical surgery were included in the 1-, 2-, and 3-year DFS groups respectively. Five-fold cross-validation results demonstrated that both traditional Logistics and ML models exhibited remarkable robustness. MLs outperformed traditional Logistic models for DFS prediction across the AUC, accuracy and F1-scores. Specifically, the average AUC of training cohorts for the ML models were 0.878, 0.897 and 0.917 in 3 groups, compared to 0.657 (P < 0.001), 0.817 (P = 0.05), and 0.798 (P = 0.005) in traditional models. The average AUCs of testing cohorts for ML models were 0.831, 0.768, 0.803 in ML models in 3 groups, compared to 0.619 (P < 0.001), 0.719 (P = 0.008), 0.698 (P < 0.001) in traditional models. SHAP analysis identified lymph node metastasis played significant role in all-round recurrence, T stage and neural invasion had strong correction with middle and long-term recurrence in ICC patients. CONCLUSION:Models with high predictive efficiency across early, middle, and long-term recurrence have been successfully built. ML models outperformed Logistic models for DFS prediction in ICC patients. This study suggests new possibilities for advancing statistical analysis software, such as SPSS and Stata, through ML integration.

关键词： Intrahepatic cholangiocarcinoma Machine learning Recurrence Retrospective study Traditional analysis

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HIV and COVID-19 co-infection: A mathematical model and optimal control

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Informatics in Medicine Unlocked 2022年 31卷 100978页

作者： Ringa, N. Diagne, M.L. Rwezaura, H. Omame, A. Tchoumi, S.Y. Tchuenche, J.M. Data and Analytic Services British Columbia Centre for Disease Control 655 W 12th Ave Vancouver BC V5Z 4R4 Canada School of Population and Public Health University of British Columbia 2329 West Mall Vancouver BC V6T 1Z4 Canada Département de Mathématiques UFR des Sciences et Technologies Université de Thiès Thiès BP 967 Senegal Mathematics Department University of Dar es Salaam P.O. Box 35062 Dar es Salaam Tanzania Department of Mathematics Federal University of Technology Owerri Nigeria Abdus Salam School of Mathematical Sciences Government College University Lahore Pakistan Department of Mathematics and Computer Sciences ENSAI University of Ngaoundéré P.O. Box 455 Ngaoundéré Cameroon School of Computer Science and Applied Mathematics University of the Witwatersrand Private Bag 3 Wits 2050 Johannesburg South Africa School of Computational and Communication Sciences and Engineering Nelson Mandela African Institution of Science and Technology P.O. Box 447 Arusha Tanzania

A new mathematical model for COVID-19 and HIV/AIDS is considered to assess the impact of COVID-19 on HIV dynamics and vice-versa. Investigating the epidemiologic synergy between COVID-19 and HIV is important. The dynamics of the full model is driven by that of its sub-models;therefore, basic analysis of the two sub-models;HIV-only and COVID-19 only is carried out. The basic reproduction number is computed and used to prove local and global asymptotic stability of the sub-models’ disease-free and endemic equilibria. Using the fmincon function in the Optimization Toolbox of MATLAB, the model is fitted to real COVID-19 data set from South Africa. The impact of intervention measures, namely, COVID-19 and HIV prevention interventions and COVID-19 treatment are incorporated into the model using time-dependent controls. It is observed that HIV prevention measures can significantly reduce the burden of co-infections with COVID-19, while effective treatment of COVID-19 could reduce co-infections with opportunistic infections such as HIV/AIDS. In particular, the COVID-19 only prevention strategy averted about 10,500 new co-infection cases, with similar number also averted by the HIV-only prevention control. © 2022 The Author(s)

关键词： Basic reproduction number Co-infection COVID-19 HIV Optimal control

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