Deterministic weighted networks have been widely used to model real-world complex systems. In this paper, we study the weighted iterated q-triangulation networks, which are generated by iteration operation F(⋅). We add q(q∈N) new nodes on each old edge and connect them with two endpoints of the old edge. At the same time, the newly linked edges are given weight factor r(0
Download full-text PDF
Source
http://dx.doi.org/10.1063/1.5120368 DOI Listing Publication Analysis
Top Keywords
JDS Commun
January 2025
TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium.
Previous studies have shown that milk citrate predicted by milk mid-infrared (MIR) spectra is strongly affected by a few genomic regions. This study aimed to explore the effect of weighted single-step GBLUP on the accuracy of genomic prediction (GP) for MIR-predicted milk citrate in early-lactation Holstein cows. A total of 134,517 test-day predicted milk citrate collected within the first 50 DIM on 52,198 Holstein cows from the first 5 parities were used.
View Article and Find Full Text PDFSci Rep
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
View Article and Find Full Text PDFSensors (Basel)
January 2025
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
With advancements in autonomous driving technology, the coupling of spatial paths and temporal speeds in complex scenarios becomes increasingly significant. Traditional sequential decoupling methods for trajectory planning are no longer sufficient, emphasizing the need for spatio-temporal joint trajectory planning. The Constrained Iterative LQR (CILQR), based on the Iterative LQR (ILQR) method, shows obvious potential but faces challenges in computational efficiency and scenario adaptability.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
To address the issue of low-elevation target height measurement in the Multiple Input Multiple Output (MIMO) radar, this paper proposes a height measurement method for meter-wave MIMO radar based on transmitted signals and receive filter design, integrating beamforming technology and cognitive processing methods. According to the characteristics of beamforming technology forming nulls at interference locations, we assume that the direct wave and reflected wave act as interference signals and hypothesize a direction for a hypothetical target. Then, the data received are processed to obtain the height of low-elevation-angle targets using a cognitive approach that jointly optimizes the transmitted signal and receive filter.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!