Evolutionary dynamics on a regular networked structured and unstructured multi‐population
Abstract In this paper, we study collective decision‐making in a multi‐population framework, where groups of individuals represent whole populations that interact by means of a regular network. Each group consists of a number of players and every player can choose between two options.
Wouter Baar +2 more
wiley +1 more source
An ant colony genetic fusion routing algorithm based on soft define network
Abstract Aiming at the problem that there are many paths in data forwarding in soft define network (SDN) network, and the optimal path is difficult to find, combined with the advantages of ant colony algorithm and Genetic algorithm (GA), a routing control strategy based on the ant colony genetic fusion algorithm is proposed.
Kaixin Zhao, Yong Wei, Yang Zhang
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
SiOx‐Based Probabilistic Bits Enabling Invertible Logic Gate for Cryptographic Applications
To enable lightweight hardware encryption and decryption, a Ti/SiOx/Ti threshold switching device is engineered to generate controllable stochastic oscillations. By tuning the input voltage, the device produces a programmable spike probability governed by intrinsic switching dynamics, enabling probabilistic bits that construct an invertible ...
Jihyun Kim, Hyeonsik Choi, Jiyong Woo
wiley +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Optimal model‐based design of experiments for parameter precision: Supercritical extraction case
Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
wiley +1 more source
Turbulence, representations, and trace-preserving actions
We establish criteria for turbulence in certain spaces of C*-algebra representations and apply this to the problem of nonclassifiability by countable structures for group actions on a standard atomless probability space (X,\mu) and on the hyperfinite ...
Kerr, David, Li, Hanfeng, Pichot, Mikael
core +2 more sources
A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control
Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao +2 more
wiley +1 more source
Continuous Family of Invariant Subspaces for R-diagonal Operators
We show that every R-diagonal operator x has a continuous family of invariant subspaces relative to the von Neumann algebra generated by x. This allows us to find the Brown measure of x and to find a new conceptual proof that Voiculescu's S-transform is ...
Sniady, Piotr, Speicher, Roland
core +2 more sources

