Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Effective connectivity analysis. by Akke Mats Houben (21572855)

Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory by Drippy Stone (21218843)

Subjects:

<b>Force-Position-Speed Planning and Roughness rediction for Robotic Polishing</b> by Ma Haohao (19780875)

“…The improved dung beetle optimization algorithm, back propagation neural network, finite element analysis and response surface method provide a strong guarantee for the selection of robotic polishing process parameters. …”

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process by Alexis Boulin (20659921)

“…A data-driven selection method for the tuning parameter is also proposed. …”

Data Sheet 1_Extraction of exact symbolic stationary probability formulas for Markov chains with finite space with application to production lines. Part I: description of methodolo... by Konstantinos S. Boulas (21709982)

“…</p>Results<p>A general algorithm that commences with the Markov chain transition matrix as an input element and forms the state transition diagram. …”

Video 1_Extraction of exact symbolic stationary probability formulas for Markov chains with finite space with application to production lines. Part I: description of methodology.mp... by Konstantinos S. Boulas (21709982)

“…</p>Results<p>A general algorithm that commences with the Markov chain transition matrix as an input element and forms the state transition diagram. …”

Multi-Task Learning for Gaussian Graphical Regressions with High Dimensional Covariates by Jingfei Zhang (580874)

“…We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. …”

Practical implementation of an End-to-end methodology for SPC of 3-D part geometry: A case study by Yulin An (833223)

“…The approach is based on monitoring the spectrum of the Laplace–Beltrami (LB) operator of each scanned part estimated using finite element methods (FEM). The spectrum of the LB operator is an intrinsic summary of the geometry of a part, independent of the ambient space. …”

DataSheet1_Enhancing slope stability prediction through integrated PCA-SSA-SVM modeling: a case study of LongLian expressway.docx by Jianxin Huang (11944014)

“…Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. …”

Stress and frequency optimization of prismatic sandwich beams with structural joints: Improvements through accelerated topology optimization by Shengyu Yan (21450735)

“…To address computational demands, accelerated linear finite element (FE) solvers and eigensolvers are employed, specifically adapted for density-based TO to enhance efficiency and maintain accuracy. …”

Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

Data Sheet 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.pdf by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

supporting data for PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children" by Yuliang Wang (9151616)

“…Analyses use standardized mean differences (Hedges g) under random-effects models, stratified by comparator type (medicine, active, sham, passive) and, where applicable, contrasted across protocol families (customised algorithm, SCP, SMR, TBR).</p><p dir="ltr">The supporting dataset contains the <b>raw arm-level descriptive statistics</b> required to compute effect sizes: per study, outcome, and timepoint it lists group means, standard deviations, and sample sizes for neurofeedback and control arms, along with rater, comparator category, protocol type, and outcome direction coding (so higher values consistently reflect the intended construct). …”

Table 1_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 12_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 8_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Table 7_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”

Image 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…In this study, we employed bioinformatics methods to identify eight TaAkPHY genes in the Aikang58 wheat variety. …”