Hard questions for soft skills: Carpentries community collaboration in Aotearoa & Australia gan eRNZ Admin (6438486)

“...We seek to strengthen our networks by asking the hard questions of how and why we foster community. ...”

A blocked staggered-level design for an experiment with two hard-to-change factors gan Peter Goos (805576)

“...The experiment was run in blocks and involved one quantitative hard-to-change factor, one two-level categorical hard-to-change factor, and three quantitative easy-to-change factors. ...”

Nuclear Translocation of hARD1 Contributes to Proper Cell Cycle Progression gan Ji-Hyeon Park (554738)

“...The high homology and widespread expression of ARD1 across multiple species and tissues signify that it serves a fundamental role in cells. Human ARD1 (hARD1) has been suggested to be involved in diverse biological processes, and its role in cell proliferation and cancer development has been recently drawing attention. ...”

Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model gan Min Li (12799)

“...<div><p>Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. ...”

Species matter for predicting the functioning of evolving microbial communities – An eco-evolutionary model gan Timothy G. Barraclough (7274534)

“...Yet predicting dynamics and functioning of these complex systems is hard, making interventions to enhance functioning harder still. ...”

Delete one edge on Karate network to change the consensus opinion—<i>β</i> = 1. gan Xi Chen (35903)

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For the Karate network. gan Xi Chen (35903)

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Influence of edge edition on consensus. gan Xi Chen (35903)

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Graphical illustration of Case 2 from Theorem 1 (i.e. <i>p</i> < 0 and <i>β</i> ≥ −1/<i>p</i>). gan Xi Chen (35903)

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Opinion formation on the star graph. gan Xi Chen (35903)

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Based on one ER model (<i>n</i> = 100, <i>ρ</i> = 0.0606), one WS model (<i>n</i> = 100, <i>K</i> = 6, <i>σ</i> = 0.2), and one BA model (<i>n</i> = 100, <i>M</i>₀ = 4,... gan Xi Chen (35903)

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For 1,000 random y(0) on Karate network. gan Xi Chen (35903)

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Based on three WS models with different rewiring probabilities (<i>n</i> = 100, <i>K</i> = 6, <i>σ</i> = 0.1, 0.3, 0.8). gan Xi Chen (35903)

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<i>β</i>^<i>P</i> for real-world twitter datasets. gan Xi Chen (35903)

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For 1,000 random y(0). gan Xi Chen (35903)

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For a random opinion vector y(0) with mean −0.0395, on 1,000 ER models with <i>n</i> = 100 and <i>ρ</i> = 0.4. gan Xi Chen (35903)

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For a random opinion vector y(0), on ER models with <i>n</i> = 100 and <i>ρ</i> ∈ (0,1]. gan Xi Chen (35903)

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<i>x</i>₁(<i>t</i> + 1) as a function of <i>x</i>_2,3,4,5(<i>t</i>). gan Xi Chen (35903)

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Add one edge on Karate network to change the consensus opinion—<i>β</i> = 1. gan Xi Chen (35903)

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Supplementary Figure 1 from The evolution and fate of diversity under hard and soft selection gan Patrick Chen (4343074)

“...Each point is the mean proportion of resistant individuals from evolved lines undergoing either soft or hard selection (n=12 for each treatment). ...”