Article
Engineering, Mechanical
Yan Qiao, Wei Wei, Mohamed Arabi, Wei Xu, Eihab M. Abdel-Rahman
Summary: This paper proposes an alternative method for stochastic analysis of nonlinear dynamic systems to analyze the response of electrostatic MEMS bifurcation sensors to various noises. The study found that additive noise has a greater impact on sensor response than multiplicative noise. PDFs are used to investigate stochastic switching and variations in sensor states.
NONLINEAR DYNAMICS
(2022)
Article
Mechanics
Zhouyu Hu, Yanling Yang, Congqing Zhang, Qiubao Wang
Summary: In this paper, the stability of a class of stochastic dynamical systems with fast time-varying periodic delays is studied, and a new definition of bifurcation is proposed. The effectiveness and reasonability of the methods presented in this paper are verified through numerical simulations.
INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS
(2023)
Article
Physics, Multidisciplinary
Qiubao Wang, Zhouyu Hu, Yanling Yang, Congqing Zhang, Zikun Han
Summary: This paper investigates the effects of different memory effects on the evolution of species population densities. A stochastic logistic model driven by non-Gaussian noise, with discrete and distributed time-delay, is considered. The results show a change in the population system from a single steady-state to a bi-periodic oscillation to a single steady-state again as memory intensity increased from weak to strong.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Mathematics, Applied
Chen Jin, Zhongkui Sun, Wei Xu
Summary: This paper introduces a novel stochastic bifurcation and a discrimination method for stochastic dynamical systems. By defining the extremely possible response and stochastic extremum bifurcation, a convenient method for discriminating stochastic bifurcation is proposed. The validity of the method is demonstrated using the Van der Pol oscillator as an example.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Automation & Control Systems
Zhaorong Zhang, Juanjuan Xu, Xun Li
Summary: This paper investigates a discrete-time stochastic control problem with linear quadratic criteria over an infinite-time horizon. The focus is on control systems whose system matrices are associated with random parameters involving unknown statistical properties. A distributed stochastic approximation algorithm is designed to solve the Riccati equation and obtain the optimal controller for stabilizing the system. Convergence analysis is provided.
Article
Mathematics, Applied
Yue Zhang, Xiju Wu
Summary: In this paper, a stochastic SEIR epidemic model with alertness and distributed delay is studied. The dynamic behavior of the model is analyzed and an integral sliding mode controller is designed. Numerical simulations verify the correctness of the theoretical analysis and the effectiveness of the controller.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Engineering, Mechanical
Yan Qiao, Mohamed Arabi, Wei Xu, Hongxia Zhang, Eihab M. Abdel-Rahman
Summary: In this work, intrinsic noise sources in electrostatic MEMS were investigated and quantified, with a focus on their impact on the mass sensitivity of bifurcation-based inertial sensors. Experiments were conducted to measure the power spectral densities of mechanical-thermal noise and electric-thermal noise, and a stochastic model of the sensor was presented. The model showed that thermal noise caused an uncertainty of 2 Hz in the bifurcation frequency, leading to stochastic switching between stable orbits.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Mathematics, Interdisciplinary Applications
Hao Dai, Zikun Han, Qiubao Wang
Summary: In this paper, a stochastic time-delay mathematical model of the feedback system of glucose-insulin endocrine regulation with noise effects is proposed. The hopf bifurcation of the system is obtained with the delay as the parameter. Considering the influence of time delay and noise, the random bifurcation of the system is obtained, and the occurrence of bursting phenomenon is found more likely in a noise environment.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaofeng Zhang, Rong Yuan
Summary: This paper investigates the stochastic bifurcation of a stochastic logistic model with distributed delay, using the intrinsic growth rate of species as the bifurcation parameter. The exact expression of the joint density function of the system is obtained by deriving the corresponding Fokker-Planck equation.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Mathematics, Applied
Lautaro Cilenti, Maria Cameron, Balakumar Balachandran
Summary: This study develops a methodology for finding the most probable escape paths and estimating transition rates in arrays of coupled nonlinear oscillators under small noise limit. It applies the action plot method, large deviation theory, optimal control theory, and Floquet theory to compute and visualize the escape paths between stable vibrational modes in arrays of up to five oscillators. The study also discusses the dependence of the quasipotential barrier on system parameters.
Article
Automation & Control Systems
Bo Wang, Yu-Ping Tian
Summary: This paper addresses reference-frame-free formation control for multiagent systems, proposing robust algorithms and control laws to achieve desired formations. Simulation results validate the correctness and effectiveness of the proposed methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Zikun Han, Qiubao Wang, Hao Wu, Zhouyu Hu
Summary: This study focuses on the behavior of the Myc/E2F/miR-17-92 network under Gaussian white noise. The system exhibits bifurcation and a coexistence pattern of high and low protein concentrations. The research methods include stochastic center manifold theory, normal form theory, and stochastic averaging method.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2022)
Article
Engineering, Mechanical
Ya-Hui Sun, Yong-Ge Yang, Wei Xu
Summary: This paper investigates the stochastic P-bifurcation of a fractionally damped oscillator under additive and multiplicative Gaussian white noise. The critical conditions for stochastic bifurcation induced by system parameters are presented based on the change in the number of extreme points of the probability density function. Numerical results demonstrate the effectiveness of the proposed approach, showing detailed analysis for stochastic P-bifurcations under additive and multiplicative noise conditions.
ACTA MECHANICA SINICA
(2021)
Article
Multidisciplinary Sciences
Lies De Keer, Mariya Edeleva, Freddy L. Figueira, Pablo Reyes, Dagmar R. D'hooge, Paul H. M. Van Steenberge
Summary: This article introduces population balance modeling and its applications in modern science. It discusses the methods and guidelines for handling input and output distributed data when using stochastic solvers. Polymerization and polymer modification case studies are used to demonstrate the importance of rediscretization and output distribution smoothing. The article also highlights the promising kernel-smoothing method for handling nonsymmetric distributions and provides guidelines for correctly interpreting the distribution tail.
ADVANCED THEORY AND SIMULATIONS
(2022)
Article
Multidisciplinary Sciences
Guobo Wang, Lifeng Ma
Summary: In this study, a fractional main drive system of a rolling mill with Gaussian white noise is developed, taking into account random factors. The potential deterministic bifurcation is investigated through linearized stability analysis, showing that the fractional order changes the system behavior. The stochastic response is obtained using equivalent transformation and stochastic averaging methods, revealing a symmetric distribution. The findings contribute to understanding the intrinsic mechanisms of vibration abatement.
Article
Neurosciences
Bahar Moezzi, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Nicolangelo Iannella, Michael C. Ridding, Jochen Triesch
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Article
Neurosciences
Bahar Moezzi, Latha Madhuri Pratti, Brenton Hordacre, Lynton Graetz, Carolyn Berryman, Louise Lavrencic, Michael C. Ridding, Hannah Ad Keage, Mark D. McDonnell, Mitchell R. Goldsworthy
Article
Environmental Sciences
Eriita G. Jones, Sebastien Wong, Anthony Milton, Joseph Sclauzero, Holly Whittenbury, Mark D. McDonnell
Article
Oncology
Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon J. Tumes, Guillermo A. Gomez
Summary: The study utilized a deep convolutional neural network to segment different tumor regions in glioblastoma, revealing gene signatures correlated with survival and driven by different cell types in various tumor microenvironments. Results indicated interactions between microglia/pericytes/monocytes and tumor cells in specific regions contributing to poor patient survival.
BRITISH JOURNAL OF CANCER
(2021)
Review
Medicine, General & Internal
Chris Boyd, Greg Brown, Timothy Kleinig, Joseph Dawson, Mark D. McDonnell, Mark Jenkinson, Eva Bezak
Summary: Research into machine learning for clinical vascular analysis faces challenges such as limited accessibility to diverse patient imaging datasets and lack of transparency in specific methods. Current ML techniques in image segmentation, disease risk prediction, and pathology quantitation have shown sensitivities and specificities over 70%, but inconsistencies in methodology and reporting of results hinder inter-model comparisons.
Article
Biology
Namrata Nath, Sang-Heon Lee, Mark D. McDonnell, Ivan Lee
Summary: This research explores algorithms that enhance word vectors and compares their performance with Bio + Clinical BERT vectors. The results show that Word2vec vectors augmented with information from the UMLS ontology exhibit the best correlation with human-annotated lists, while Bio + Clinical BERT performs better at NER tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Multidisciplinary Sciences
Iqbal Madakkatel, Ang Zhou, Mark D. McDonnell, Elina Hypponen
Summary: This study introduced a machine learning pipeline for risk factor discovery in biomedical databases, utilizing GBDT and SHAP values for model building and feature selection, followed by Cox models for confounder adjustment and validation. Results showed that a majority of health-related risk factors were accurately identified, while potential bias due to confounding factors was also observed.
SCIENTIFIC REPORTS
(2021)
Article
Oncology
Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Sieber, Michelle Palmieri, Stefan Kommoss, Sara Brucker, Mark D. McDonnell, Peter Hoffmann
Summary: The study demonstrated the potential of mass spectrometry-based imaging combined with machine learning in accurately distinguishing colorectal tumors and predicting lymph node metastasis in endometrial cancer, thus providing important guidance for cancer treatment.
Article
Astronomy & Astrophysics
Mark D. McDonnell, Eriita Jones, Megan E. Schwamb, K-Michael Aye, Ganna Portyankina, Candice J. Hansen
Summary: Dark deposits in the Martian south polar region are formed by explosive jets of carbon dioxide gas during springtime. Machine learning methods, specifically deep Convolutional Neural Networks (CNNs), were used to automatically identify these seasonal features in satellite imagery. The CNNs showed better performance in predicting the area and boundaries of seasonal deposits compared to ISODATA clustering, but were not effective in predicting the source point and directions of seasonal fans.
Review
Computer Science, Information Systems
Mark C. C. McKenzie, Mark D. D. McDonnell
Summary: This review paper focuses on the progression of value based Reinforcement Learning in the last five years, highlighting the minimal changes to the base algorithm. It suggests a new focus area for value based Reinforcement Learning research.
Meeting Abstract
Oncology
Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon Tumes, Guillermo A. Gomez
CLINICAL CANCER RESEARCH
(2021)
Proceedings Paper
Acoustics
Mark D. McDonnell, Wei Gao
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
(2020)
Review
Health Care Sciences & Services
Amin Zadeh Shirazi, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca, Guillermo A. Gomez
JOURNAL OF PERSONALIZED MEDICINE
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Mark D. McDonnell, Bahar Moezzi, Russell S. A. Brinkworth
2019 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Victor Stamatescu, Mark D. McDonnell
2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
(2018)