BP neural network architecture. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Peak strain and stress curves. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Coarse and fine aggregates used for testing. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Loading setup for cured samples. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Steel tube material property test. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Measurement point arrangement and loading device. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

STCDSRAC axial compressive stress–strain curves. by Wang Xiaoqian (10502222)

“…Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.…”

Nucleic acid sequences used in this study. by Gillian C. A. Taylor (3909568)

“…In one case (<i><i>Ncaph2</i></i>), a C-terminal auxin-inducible degron fusion strongly increased protein stability in some tissues but decreased it in others. Destabilisation resulted from tissue-specific ‘leakage’ of the auxin-inducible degron, which depended on TIR1 expression, and occurred selectively in the small intestine where basal concentrations of auxin/ indole-3-acetic acid can reach levels that are sufficient to trigger protein degradation in cultured cells. …”

Relatedness of <i>Legionella pneumophila</i> Refseq. by Fanny Mazzamurro (19845903)

“…The analysis of transformation rates in the light of phylogeny revealed they evolve by a mixture of frequent small changes and a few large quick jumps across 6 orders of magnitude. …”

Proportion of persistent genes in plasmids. by Fanny Mazzamurro (19845903)

“…The analysis of transformation rates in the light of phylogeny revealed they evolve by a mixture of frequent small changes and a few large quick jumps across 6 orders of magnitude. …”

SNAP-25 (1-197) binds to syntaxin-1A, and the interaction is enhanced by Ser187 phosphorylation. by Dilara Koc (22431017)

Rht1 functions in cell cycle progression and modulates fluconazole susceptibility. by Ci Fu (148006)

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

Domain organization of TSPANs and TBP55 from <i>Entamoeba histolytica</i>. by Han Jiang (195350)

The long-term <i>in vitro</i> surrogate bacterial viability of canine and feline FMT products frozen at -80°C for six months. by Nina K. Randolph (21030732)

The parameter space for the constant period (, <i>red surface</i>) with increasing degradation rates <i>k</i>₁, <i>k</i>₂, and <i>k</i>₃, and that... by Shingo Gibo (21762920)

“…<p>The phase of second-order frequency should decrease with increasing <i>k</i>₁, <i>k</i>₂, and <i>k</i>₃ outside the yellow surface (). …”

S1 Data - by Jan Willem Koten (17743224)

“…This was in particular the case when samples were small or connectome coordinates were randomly selected. …”

Connectivity statistics. by Jan Willem Koten (17743224)

“…This was in particular the case when samples were small or connectome coordinates were randomly selected. …”

High-Temperature Resistance, Lightweight, and Thermally Insulating Silica Aerogel via Doping Hollow Silica Nanoparticles by Mingyang Yang (1405321)

“…To increase the thermal stability of standard aerogels comprising small full-density SiO₂ nanoparticles (SFPs) (typically 2–15 nm in diameter), SiO₂ aerogels were doped with large hollow SiO₂ nanoparticles (LHPs) with diameters of 100–250 nm. …”

High-Temperature Resistance, Lightweight, and Thermally Insulating Silica Aerogel via Doping Hollow Silica Nanoparticles by Mingyang Yang (1405321)

“…To increase the thermal stability of standard aerogels comprising small full-density SiO₂ nanoparticles (SFPs) (typically 2–15 nm in diameter), SiO₂ aerogels were doped with large hollow SiO₂ nanoparticles (LHPs) with diameters of 100–250 nm. …”