Review
Automation & Control Systems
Yumei Zhou, Yuru Guo, Chang Liu, Hui Peng, Hongxia Rao
Summary: This paper investigates synchronisation for Markovian master-slave neural networks by adopting an event-triggered impulsive transmission strategy and designing a corresponding controller. Information transmission only occurs at discontinuous instants determined by a state-dependent event-triggered condition and a predesigned forced impulse interval, to cope with the communication channel bandwidth constraint. Synchronization for Markovian master-slave NNs is guaranteed by a sufficient condition, and the controller gains are designed using the obtained results. A numerical simulation is conducted to demonstrate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Management
Zhenyu Cui, J. Lars Kirkby, Duy Nguyen
Summary: A novel Monte Carlo simulation method based on CTMC approximation is proposed for two-dimensional stochastic differential equation systems, particularly for simulating asset prices under general stochastic local volatility models in finance. The simulation algorithm shows advantages in flexible boundary condition treatment and efficient simulation scheme, providing high accuracy and efficiency in numerical examples.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Physics, Mathematical
Yuji Hirono, Ryo Hanai
Summary: This paper introduces a novel method for computing stationary distributions of stochastic chemical reaction systems based on second quantization. It is shown that when the rate equation for a reaction network admits a complex-balanced steady-state solution, the corresponding stochastic reaction system has a stationary distribution with a product form of Poisson distributions. The study further explores the transformation of the time-evolution operator of the chemical master equation under the action of a squeeze operator, which leads to a different reaction network with a stationary distribution that can be obtained analytically.
JOURNAL OF STATISTICAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
Tabea Waizmann, Luca Bortolussi, Andrea Vandin, Mirco Tribastone
Summary: Stochastic reaction networks are models describing species interactions, with the master equation providing evolution of probability distribution and the deterministic rate equation offering a macroscopic approximation. The finite-state expansion method mediates between microscopic and macroscopic interpretations, translating networks into expanded systems for improved accuracy.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Martina Prugger, Lukas Einkemmer, Carlos F. Lopez
Summary: Solving the chemical master equation is crucial for understanding biological and chemical systems. However, directly solving it faces the curse of dimensionality. A low-rank approach based on partitioning the network into biologically relevant subsets is proposed to tackle this issue, successfully simulating large-scale biological networks.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Statistics & Probability
Boqian Zhang, Vinayak Rao
Summary: Markov jump processes (MJPs) are continuous-time stochastic processes commonly used in various disciplines. Inference is typically done through Markov chain Monte Carlo (MCMC), which can suffer from poor mixing when sampling unknown parameters. This work proposes a new algorithm to address this issue and demonstrates superior performance compared to existing methods in experiments.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Engineering, Industrial
Keivan Tafakkori, Fariborz Jolai, Reza Tavakkoli-Moghaddam
Summary: This paper presents decentralized capacity planning models for different types of supply chain entities, aiming to enhance their resilience. Novel resilience metrics are developed to measure the proximity of capacities to disruptions, and optimization models are used to select business continuity plans that maximize resilience and cost-efficiency. Uncertainties associated with recovery time and disruptions are addressed using a robust-stochastic optimization method, and disruption scenarios are simulated using a discrete-time Markov chain. Computational tests confirm the robustness, validity, and generality of the proposed models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Mathematics, Applied
Hyukpyo Hong, Bryan S. Hernandez, Jinsu Kim, Jae Kyoung Kim
Summary: The long-term behaviors of biochemical systems can be described by steady states in deterministic models and stationary distributions in stochastic models. Analytic solutions for obtaining these behaviors are limited to certain cases, but can be easily obtained when the networks have special topologies called weak reversibility and zero deficiency. We develop a computational package, TOWARDZ, that automatically identifies networks with these topologies, allowing for the analysis of biochemical systems.
SIAM JOURNAL ON APPLIED MATHEMATICS
(2023)
Article
Multidisciplinary Sciences
Augustinas Sukys, Kaan Ocal, Ramon Grima
Summary: The Chemical Master Equation provides an accurate description of stochastic biochemical reaction networks, but it is analytically intractable for most practical systems. This article proposes a neural network-based approach, called Nessie, to approximate the solutions of the CME using a small number of stochastic simulations. The method demonstrates the ability of simple neural networks to capture complex distributions in parameter space, enabling faster computationally intensive tasks.
Article
Mathematics
Thomas Spanninger, Beda Buchel, Francesco Corman
Summary: Train delays are a major inconvenience for passengers and railway operations. This study introduces an advanced Markov chain model to predict train delays using historical train operation data. By using process time deviations instead of absolute delays, and relaxing the stationarity assumptions for transition probabilities, our model achieves a prediction accuracy gain of 56% compared to state-of-the-art models based on absolute delays.
Article
Mathematics, Applied
Yasuyuki Suzuki, Keigo Togame, Akihiro Nakamura, Taishin Nomura
Summary: In this study, a comprehensive numerical recipe was developed to simulate switched-type Fokker-Planck equations for switched-type hybrid stochastic delay differential equations with unstable subsystems. The dynamics of the model were analyzed through a Markov chain model, which was validated by comparing it with Monte Carlo-based dynamics of the model.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Environmental Studies
Madhukar Chhimwal, Saurabh Agrawal, Girish Kumar
Summary: The study aims to explore the circularity potential of non-ferrous materials, specifically Aluminium, in developing countries using the Indian Aluminium industry as a case study. The Markov Chain method is used to analyze the material flow and it reveals that a significant amount of materials end up in landfills after completing their average useful life, with low recycling rates.
Article
Biochemical Research Methods
Vincent Wagner, Nicole Radde
Summary: The Chemical Master Equation (CME) is a set of linear differential equations used to describe the probability distribution evolution of chemical reaction systems. However, it is limited to small systems due to the rapid increase in dimension with the number of molecules. Moment-based approaches are commonly used to address this limitation, but their reliability in predicting the existence of fat-tailed distributions in the CME's solution is questionable. This study investigates the performance of two moment-estimation methods for reaction systems with fat-tailed equilibrium distributions.
Article
Computer Science, Artificial Intelligence
Qian Cui, Lulu Li, Jinde Cao
Summary: This paper investigates the stability of inertial delayed neural networks with stochastic delayed impulses, considering stochastic impulsive intensity and density, and impulsive delays simultaneously. The model is converted to a first order differential equation with stochastic delayed impulsive effect, and stability criteria are presented using matrix measure approach and stochastic analysis techniques. Numerical examples are provided to illustrate the theoretical results.
Article
Statistics & Probability
Miguel Biron-Lattes, Alexandre Bouchard-Cote, Trevor Campbell
Summary: This article introduces a method to improve the efficiency of Bayesian inference for Continuous-Time Markov chains (CTMCs). The method avoids the computation of exact matrix exponentials and utilizes doubly-monotone matrix exponential approximations to construct unbiased and nonnegative estimates of likelihood. Experimental results show that this approach yields more efficient posterior inference for various CTMCs.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Biology
Kunal Bhattacharya, Shikha Mahato, Satyendra Deka, Nongmaithem Randhoni Chanu, Amit Kumar Shrivastava, Pukar Khanal
Summary: Chemoresistance, a major challenge in cancer treatment, is associated with the cellular glutathione-related detoxification system. A study has identified GSTP1 enzyme as critical in the inactivation of anticancer drugs and suggests the need for GSTP1 inhibitors to combat chemoresistance. Through molecular docking and simulations, the study found that quercetin 7-O-beta-D-glucoside showed promise as a potential candidate for addressing chemoresistance in cancer patients.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Manwi Shankar, Majji Sai Sudha Rani, Priyanka Gopi, P. Arsha, Prateek Pandya
Summary: This study investigates the interaction between the food dye BBY and the serum protein BSA. The results show that BBY binds to a specific site on BSA through hydrophobic interactions, affecting the structural stability of the protein. These findings enhance our understanding of the molecular-level interactions between BBY and BSA.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Chi Zhang, Qian Gao, Ming Li, Tianfei Yu
Summary: In this study, we propose a graph neural network-based autoencoder model, AGraphSAGE, that effectively predicts protein-protein interactions across diverse biological species by integrating gene ontology.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kangjie Wu, Liqian Xu, Xinxiang Li, Youhua Zhang, Zhenyu Yue, Yujia Gao, Yiqiong Chen
Summary: Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) and big data analysis, with wide application range. This paper proposes an improved neural network method for NER of rice genes and phenotypes, which can learn semantic information in the context without feature engineering. Experimental results show that the proposed model outperforms other models.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Suman Hait, Sudip Kundu
Summary: Interactions between amino acids in proteins are crucial for stability and structural integrity. Thermophiles have more and more stable interactions to survive in extreme environments. Different types of interactions are enriched in different structural regions.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kountay Dwivedi, Ankit Rajpal, Sheetal Rajpal, Virendra Kumar, Manoj Agarwal, Naveen Kumar
Summary: This study aims to identify biomarkers for non-small cell lung cancer (NSCLC) using copy number variation (CNV) data. A novel deep learning architecture, XL1R-Net, is proposed to improve the classification accuracy for NSCLC subtyping. Twenty NSCLC-relevant biomarkers are uncovered using explainable AI (XAI)-based feature identification. The results show that the identified biomarkers have high classification performance and clinical relevance. Additionally, twelve of the biomarkers are potentially druggable and eighteen of them have a high probability of predicting NSCLC patients' survival likelihood according to the Drug-Gene Interaction Database and the K-M Plotter tool, respectively. This research suggests that investigating these seven novel biomarkers can contribute to NSCLC therapy, and the integration of multiomics data and other sources will help better understand NSCLC heterogeneity.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Pengli Lu, Wenqi Zhang, Jinkai Wu
Summary: Researchers have developed a computational method, AMPCDA, to predict circRNA-disease associations using predefined metapaths, achieving high predictive accuracy. This method effectively combines node embeddings with higher-order neighborhood representations and provides valuable guidance for revealing new disease mechanisms in biological research.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
