China medical waste disposal market size statistics and forecast, 2018 to 2029. by Jihui Hu (19812736)

Subjects:

The S/N ratio plot of the AH-NSAFSA in the Taguchi methodology. by Jihui Hu (19812736)

Subjects:

MAE versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³. by Limin Ma (556873)

Evaluation metrics graphs for different loss functions. by Huiying Zhang (200681)

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

Boxplots showcasing variability of additional key topological metrics obtained after 50 algorithm runs for the generation of a) chorionic vessels and b) villous vessels. by Diana C. de Oliveira (9220729)

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. by Yuxi Long (11024307)

“…The benchmarked algorithms include physics-based and deep learning algorithms from the famous AutoDock framework (scoring function of AutoDock4 implemented in the AutoDockFR package [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref068" target="_blank">68</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref077" target="_blank">77</a>], Vinardo [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref069" target="_blank">69</a>], GNINA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref070" target="_blank">70</a>]), empirical (AA-Score [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref071" target="_blank">71</a>]), knowledge-based (SMoG2016 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref072" target="_blank">72</a>]), and deep learning-based scoring functions (OnionNet [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref073" target="_blank">73</a>], PLANET [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref074" target="_blank">74</a>]). …”

Risk and density functions for standard relapse from remission condition to disease <i>b</i> condition (similar shapes for standard relapse to disease <i>a</i>). by Benoîte de Saporta (20454662)

Perturbations and their effects within networks. by Matthew Aguirre (9558032)

“…<b>(D)</b> KO effects as a function of network distance between two genes, and <b>(E)</b> within and across modules given by the generating algorithm. …”

Cell2Spatial enables versatile applications across various analytical domains. by Huamei Li (8815955)

Investigating the spatial architecture of the mouse kidney. by Huamei Li (8815955)

Performance assessment of Cell2Spatial in real ST data from various tissue types. by Huamei Li (8815955)

Hash function construct used in SKINNY-tk3-hash. by Sonal Arvind Barge (20454967)

“…IoT devices are secured by cryptographic algorithms, but their dependability is a major concern. …”

Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. by Huamei Li (8815955)

Functional enrichment analysis. by Junjie Su (368807)

Exploring the spatial architecture of the human dorsolateral prefrontal cortex (DPLFC). by Huamei Li (8815955)

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. by Dovilė Kurmanavičiūtė (21072549)

Application of Cell2Spatial to 10× Genomics Xenium mouse brain data. by Huamei Li (8815955)

Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions. by Huamei Li (8815955)

Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)