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  • Other Sources  (158)
  • 2020-2024  (158)
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  • 1
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    A Novel Intelligent Ant Colony System Based on Blockchain (2022)
    In:  Advances in Swarm Intelligence | Lecture Notes in Computer Science
    Details
    Publication Date: 2023-01-19
    Description: Swarm intelligence occurs when the collective behavior of low-level individuals and their local interactions form an overall pattern of uniform function. Incorporating swarm intelligence allows us to disregard global models when we explore collective cooperation systems that lack any central control. Blockchain is a key technology in the functioning of Bitcoin and combines network and cryptographic algorithms. A group of agents agrees on a particular status and records the protocol without controlling it. Blockchain and other distributed systems, such as ant colony systems, allow the building of “ants” that are more secure, flexible, and successful. We use the principle of blockchain technology and carry out ant colony research to solve three urgent problems. We use new security protocols, system implementations, and business models to generate ant swarm system scenarios. Finally we combine these two technologies to solve the problems of limitation and reduced future potential. Our work opens the door to new business models and approaches that allow ant colony technologies to be applied to a wide range of market applications.
    Language: English
    Type: info:eu-repo/semantics/bookPart
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  • 2
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    Robust fixed-time connectivity preserving consensus of nonlinear multi-agent systems with disturbance (2022)
    In:  International Journal of Robust and Nonlinear Control
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    Publication Date: 2023-01-06
    Description: This article studies the fixed-time tracking consensus of second-order nonlinear multi-agent systems with disturbance. To make the fixed-time tracking consensus, a consensus protocol based on the integral sliding mode surface is proposed, which can ensure the adjacent agents remain within a limited communication range in the communication process. By adopting Lyapunov stability theory and matrix theory, sufficient conditions for the fixed-time tracking consensus are given, and a bound of the settling time is obtained. Finally, a simulation example is presented to verify the potential correctness of the obtained results.
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  • 3
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    Music improves the therapeutic effects of bevacizumab in rats with glioblastoma: Modulation of drug distribution to the brain (2022)
    In:  Frontiers in Oncology
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    Publication Date: 2023-01-17
    Description: The development of new methods for modulation of drug distribution across to the brain is a crucial step in the effective therapies for glioblastoma (GBM). In our previous work, we discovered the phenomenon of music-induced opening of the blood-brain barrier (OBBB) in healthy rodents. In this pilot study on rats, we clearly demonstrate that music-induced BBB opening improves the therapeutic effects of bevacizumab (BZM) in rats with GBM via increasing BZM distribution to the brain along the cerebral vessels.
    Language: English
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  • 4
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    Data-Driven Discovery of Stochastic Differential Equations (2022)
    In:  Engineering
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    Publication Date: 2023-01-17
    Description: Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
    Language: English
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  • 5
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    Neural-Network-Based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case (2023)
    In:  IEEE Transactions on Cybernetics
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    Publication Date: 2023-02-03
    Description: This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach.
    Language: English
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  • 6
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    Black-Box Impedance Prediction of Grid-Tied VSCs Under Variable Operating Conditions (2022)
    In:  IEEE Access
    Details
    Publication Date: 2023-01-21
    Description: Impedance/admittance models (IM/AMs) have been widely used to analyze the small-signal stability of grid-tied power electronic devices, such as the voltage source converter (VSC). They can be either derived from theoretical analysis under white-box conditions, where all parameters and control structures are fully known, or measured based on experiments under black-box conditions, where the topology and parameters of the controllers are completely unknown. As the IM/AMs depend on specific operating conditions, it is highly desirable to develop fast algorithms for IM/AM prediction (or estimation) under the black-box and variable-operating-point conditions. This article extends the nearly-decoupled AM method for sequence AMs proposed recently by Liu et al to fit any unknown control structure, including not only grid-following VSC, but also grid-forming VSC. It is, therefore, referred to as the fully-decoupled IM (FDIM) method. Furthermore, a curve fitting method for the transfer function is proposed to expedite the algorithm, based on the information of a few disturbance frequencies only. Finally, the algorithm is verified by wide simulations and experiments under different situations, including the direct-drive wind turbine generator. The whole approach is expected to be broadly applicable to the stability analysis of power-electronic-based power systems under variable operating conditions.
    Language: English
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  • 7
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    Converting high-dimensional complex networks to lower-dimensional ones preserving synchronization features (2022)
    In:  EPL (Europhysics Letters)
    Details
    Publication Date: 2023-01-17
    Description: Studying the stability of synchronization of coupled oscillators is one of the prominent topics in network science. However, in most cases, the computational cost of complex network analysis is challenging because they consist of a large number of nodes. This study includes overcoming this obstacle by presenting a method for reducing the dimension of a large-scale network, while keeping the complete region of stable synchronization unchanged. To this aim, the first and last non-zero eigenvalues of the Laplacian matrix of a large network are preserved using the eigen-decomposition method and Gram-Schmidt orthogonalization. The method is only applicable to undirected networks and the result is a weighted undirected network with smaller size. The reduction method is studied in a large-scale a small-world network of Sprott-B oscillators. The results show that the trend of the synchronization error is well maintained after node reduction for different coupling schemes.
    Language: English
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  • 8
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    Equivalent synchronization patterns in chaotic jerk systems (2022)
    In:  EPL (Europhysics Letters)
    Details
    Publication Date: 2023-01-17
    Description: Jerk systems are some of the simplest dynamical systems that can exhibit chaotic dynamics. This paper investigates the synchronization of coupled jerk systems with coupling in single variables. We apply the well-known approach for synchronization analysis, the master stability function, which determines the stability of the synchronization manifold. It is shown that a jerk system in which the jerk equation is not dependent on the acceleration has similar master stability functions when coupled in velocity or acceleration variables. Therefore, the system has the same synchronization behavior in these two coupling configurations. Such an equivalence has not been reported in the literature.
    Language: English
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  • 9
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    Noise-induced artificial intelligence (2022)
    In:  Physical Review Research
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    Publication Date: 2023-01-17
    Description: We show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises.
    Language: English
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  • 10
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    Photodynamic Opening of the Blood–Brain Barrier and the Meningeal Lymphatic System: The New Niche in Immunotherapy for Brain Tumors (2022)
    In:  Pharmaceutics
    Details
    Publication Date: 2023-01-17
    Description: Photodynamic therapy (PDT) is a promising add-on therapy to the current standard of care for patients with glioblastoma (GBM). The traditional explanation of the anti-cancer PDT effects involves the PDT-induced generation of a singlet oxygen in the GBM cells, which causes tumor cell death and microvasculature collapse. Recently, new vascular mechanisms of PDT associated with opening of the blood–brain barrier (OBBB) and the activation of functions of the meningeal lymphatic vessels have been discovered. In this review, we highlight the emerging trends and future promises of immunotherapy for brain tumors and discuss PDT-OBBB as a new niche and an important informative platform for the development of innovative pharmacological strategies for the modulation of brain tumor immunity and the improvement of immunotherapy for GBM.
    Language: English
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