The current work provides a mathematical second order perturbed singular delay differential model (SO-PSDDM) by using the standard form of the Lane-Emden model. The inclusive structures based on the delay terms, singular-point and perturbation factor and shape forms of the SO-PSDDM are provided. The novel form of the SO-PSDDM is numerically solved by using the procedures of artificial neural networks (ANNs) along with the optimization measures based on the swarming procedures (PSO) and interior-point algorithm (IPA). An error function is optimized through the swarming PSO procedure along with the IPA to solve the SO-PSDDM. The precision, substantiation and validation are observed for three problems of the SO-PSDDM. The exactness of the novel SO-PSDDM is observed by comparing the obtained and exact solutions. The reliability, stability and convergence of the proposed stochastic algorithms are observed for 30 independent trials to solve the novel SO-PSDDM.
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http://dx.doi.org/10.1038/s41598-022-26566-4 | DOI Listing |
Neural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
View Article and Find Full Text PDFPharmaceutics
December 2024
Department of Pharmacy, College of Pharmacy, Ajou University, Suwon 16499, Republic of Korea.
Background/objectives: Aronia extract or its active compounds, especially anthocyanin, have shown potential for Alzheimer's disease (AD)-related pathologies, including neuroinflammation, fibrillogenesis of amyloid beta (Aβ), and cognitive impairment. However, there was still concern about their structural instability in vivo and in vitro. To solve the instability of anthocyanins, we combined aronia bioactive factions (ABFs) and alginic acid via electrostatic molecular interactions and created an ABF-alginic acid nanocomplex (AANCP).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece.
This paper addresses the complex problem of multi-goal robot navigation, framed as an NP-hard traveling salesman problem (TSP), in environments with both static and dynamic obstacles. The proposed approach integrates a novel path planning algorithm based on the Bump-Surface concept to optimize the shortest collision-free path among static obstacles, while a Genetic Algorithm (GA) is employed to determine the optimal sequence of goal points. To manage static or dynamic obstacles, two fuzzy controllers are developed: one for real-time path tracking and another for dynamic obstacle avoidance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering, Myongji College, Seoul 03656, Republic of Korea.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.
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