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Physical Layer Security Enhancement in IRS-Assisted Interweave CIoV Networks: A Heterogeneous Multi-Agent Mamba RainbowDQN Method. [PDF]
Sensors (Basel)Lin R, Xie S, Chen W, Xu T.
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Bridge, Reverse Bridge, and Their Control. [PDF]
Entropy (Basel)Baldassarri A, Puglisi A.
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The integrated information Φ of an integrate and fire network. [PDF]
PLoS Comput BiolDanilczuk M, Pokropski M, Suffczynski P.
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Analysis of Cost-Effectiveness of Biologic Therapies in Axial Spondyloarthritis Based on Real-World Data. [PDF]
Clinicoecon Outcomes ResAl-Saleh J +4 more
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The evolution of the theory of non-homogeneous Markov systems
Applied Stochastic Models and Data Analysis, 1997The article presents a review of the evolution of the theory of non-homogeneous Markov systems (NHMS). At the beginning the author gives a definition of the stochastic NHMS and considers some applications of the theory to manpower planning, ecological modelling and social mobility processes.
P -C G Vassiliou
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Stochastic control in non‐ homogeneous markov systems
International Journal of Computer Mathematics, 1984We study the problem of maintainability of the structure in a non‐homogeneous Markov ystem (NHMS) by input control. The set of maintainable structures is given as the convex hull of n‐points in Rn where n is the number of states of the NHMS. We also study the problem of attainability in a NHMS by input control and an algorithm is provided for the ...
N Tsantas
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The perturbed non-homogeneous Markov system in continuous time
Applied Stochastic Models and Data Analysis, 1997The authors consider the continuous-time non-homogeneous Markov system (NHMS). Let \(S=\{1,\ldots,k\}\) denote the state space of the system, \(E[N(t)]=(E[N_{1}(t)],\ldots,E[N_{k}(t)])\) be the expected structure of the system at time \(t\), \(T(t)\) be the total number of members of the system, \(R(t)=(r_{ij}(t))_{i,j\in S}\) be the matrix of ...
P -C G Vassiliou
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Fuzzy Non-Homogeneous Markov Systems
Applied Intelligence, 2002The non-homogeneous Markov systems (NHMS) are generalized for the case when input-output signals are fuzzy. The main theorems for classical NHMS theory are reconsidered and proved for fuzzy case. In my opinion, the presented theory is very similar to the artificial intelligence theory described by means of neural nets.
M. A. Symeonaki +2 more
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