Results 51 to 60 of about 667,394 (343)

The class of semi-Markov accumulation processes [PDF]

AIP Conference Proceedings, 2018
In this paper, we introduce a new accumulation process, the Semi-Markov Accumulation Process (SMAP). This class of processes extends the framework of continuous-time Markov Additive Processes (MAPs) by allowing the underlying environmental component to be a semi-Markov process instead of a Markov process.
Jean-Marie, Alain, Vatamidou, Eleni
openaire   +2 more sources

$H_{\infty}$ Consensus of Linear Multi-Agent Systems With Semi-Markov Switching Network Topologies and Measurement Noises

IEEE Access, 2019
This paper investigates the H∞ consensus of linear multi-agent systems with semi-Markov switching network topologies and measurement noises. The information that each agent measures its neighbors's has multiplicative noises.
Meiyan Cong, Xiaowu Mu
doaj   +1 more source

Diffusion approximation for processes with Semi-Markov switches and applications in queueing models

, 1999
Chapter 5Stochastic processes with semi-Markov switches (or in semi-Markov environment) and general Switching processes are considered. In case of asymptotically ergodic environment functional Averaging Principle and Diffusion Approximation types ...
Anisimov, Vladimir V., Vladimir V. Anisimov   +1 more
core   +1 more source

Finite-Time Asynchronous Fault Detection Filter Design for Conic-Type Nonlinear Semi-Markovian Jump Systems

IEEE Access, 2021
In this work, the problem of finite-time asynchronous fault detection filter design is investigated for conic-type nonlinear semi-Markovian jump systems with time delay, missing measurements and randomly jumping fault signal.
V. Nithya, V. T. Suveetha, R. Sakthivel, Yong-Ki Ma   +3 more
doaj   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Theory and Inference for a Markov-Switching GARCH Model [PDF]

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity
Jeroen V.K. Rombouts, Arie Preminger, Luc Bauwens   +2 more
core   +6 more sources

Stress History Establishes a Transient Tolerant State That Shapes Antibiotic Survival Upon Resuscitation

Advanced Science, EarlyView.
High‐throughput single‐cell analysis of resuscitating bacteria reveals a starvation‐history‐dependent transiently tolerant subpopulation that survives β$\beta$‐lactam exposure by temporarily reducing growth. Distinct from classical persisters, these actively growing yet dynamically modulated cells dominate survival across clinically relevant antibiotic
Kieran Abbott, Georgeos Hardo, Ruizhe Li, Jack Bradley, Ashraf Zarkan, Somenath Bakshi   +5 more
wiley   +1 more source

Timed Comparisons of Semi-Markov Processes [PDF]

, 2018
Semi-Markov processes are Markovian processes in which the firing time of the transitions is modelled by probabilistic distributions over positive reals interpreted as the probability of firing a transition at a certain moment in time. In this paper we consider the trace-based semantics of semi-Markov processes, and investigate the question of how to ...
Mathias Ruggaard Pedersen, Nathanaël Fijalkow, Giorgio Bacci, Kim G. Larsen, Radu Mardare   +4 more
openaire   +2 more sources

Full‐Stack Architectures for Intelligent Brain‐Computer Interfaces

Advanced Science, EarlyView.
System‐level overview of brain–computer interfaces (BCIs), illustrating the integration of neural signal acquisition, wireless transmission, and adaptive decoding. Advanced electrode, tissue interfaces, energy‐efficient communication, and robust algorithms collectively enable stable signal quality, real‐time processing, and closed‐loop operation ...
Hee Kyu Lee, Hyun Bin Kim, Sang Uk Park, Janghoon Joo, Jinhong Min, Geumbee Lee, Joohoon Kang, Hyoyoung Jeong, Jae‐Young Yoo, Sang Min Won   +9 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source