Results 61 to 70 of about 401,816 (284)

Integrative Analyses Identify a cGAS‐STING Pathway‐Driven Signature With Context‐Dependent Roles in Systemic Lupus Erythematosus

open access: yesAdvanced Science, EarlyView.
Zhang et al. identify M7core, a critical cGAS‐STING pathway‐driven gene signature that is activated in most lupus patients’ blood and links to lupus disease severity, lymphopenia, and lupus nephritis. They further reveal the diagnostic and pathogenic characteristics of M7core and emphasize the importance of assessing pathway activity before initiating ...
Lele Zhang   +13 more
wiley   +1 more source

State assessment of protective devices based on data of information processing system

open access: yesThe Journal of Engineering, 2019
At present, the evaluation of relay status maintenance depends on a large amount of manpower. However, the data sources of relay protection devices and fault recorder information system (as known as the information processing system) have reached the ...
Yuanbo Ye   +4 more
doaj   +1 more source

Transient Probability Functions: A Sample Path Approach [PDF]

open access: yesDiscrete Mathematics & Theoretical Computer Science, 2003
A new approach is used to determine the transient probability functions of Markov processes. This new solution method is a sample path counting approach and uses dual processes and randomization.
Michael L. Green   +4 more
doaj   +1 more source

Variational Perturbation Theory for Markov Processes

open access: yes, 2002
We develop a convergent variational perturbation theory for conditional probability densities of Markov processes. The power of the theory is illustrated by applying it to the diffusion of a particle in an anharmonic potential.Comment: Author Information
Axel Pelster   +11 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

open access: yesAdvanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu   +8 more
wiley   +1 more source

APPLICATION OF MARKOV PROCESSES AS EFFICIENT INSTRUMENT FOR MAN-AGEMENT DECISION MAKING ON THE BASIS OF ENTERPRISE BUSINESS MODEL-ING

open access: yesВестник Донского государственного технического университета, 2018
The issues concerning the potential application of Markov processes theory for efficient management decision making based on the production enterprise business modeling are considered.
Evgeniya R. Khabibullina
doaj  

A Markov chain-based model for wind power prediction in congested electrical grids

open access: yesThe Journal of Engineering, 2019
The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with ...
Fabrizio De Caro   +2 more
doaj   +1 more source

Debugging of Markov Decision Processes (MDPs) Models [PDF]

open access: yesElectronic Proceedings in Theoretical Computer Science, 2016
In model checking, a counterexample is considered as a valuable tool for debugging. In Probabilistic Model Checking (PMC), counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and
Hichem Debbi
doaj   +1 more source

Probabilistic Opacity for Markov Decision Processes

open access: yes, 2014
Opacity is a generic security property, that has been defined on (non probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer,
Bérard, Béatrice   +2 more
core   +3 more sources

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

open access: yesAdvanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong   +9 more
wiley   +1 more source

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