Ranking position of feature importance for each cluster in the post-vaccination period. by Jéssica Villar (21453137)

Ranking position of feature importance for each cluster in the pre-vaccination period. by Jéssica Villar (21453137)

Optical Properties of Phenylthiolate-Capped CdS Nanoparticles by Eimear Madden (20541322)

“…We find that for each excited state, the intraligand excitation contribution is dominant and that bright excited states, corresponding to the clear peaks in the absorption spectra, have a larger ligand-to-metal charge-transfer contribution. …”

Fisher information vs. frequency. by Sina Tootoonian (12024799)

“…Positive coefficients indicate information increases with frequency, negative coefficients that it decreases. Centres of orange markers indicate the integral length scale (<i>L</i>_<i>U</i>, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0297754#sec016" target="_blank">Methods</a> Sec 5.2.8) in the vertical direction (orthogonal to the mean flow) of the vertical velocity field, computed at the midline where the odour sources were located (‘origin’, <i>L</i>_<i>U</i> = 0.85<i>ϕ</i>) and at the probe location (‘probe’, <i>L</i>_<i>U</i> = 2.6<i>ϕ</i>). …”

SHAP Values per cluster in the pre-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)

SHAP Values per cluster in the post-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)

S1 Dataset - by Riham Saud Alhazmy (19697677)

“…</p><p>Results</p><p>The mean HbA1c level decreased from 8.61 ± 1.70 to 7.92 ± 1.60 after implementing the WhatsApp group instructions; the values showed a significant difference (t-value = 5.107 and <i>P</i>-value < 0.001). …”

Particepant recruitment flowchart. by Riham Saud Alhazmy (19697677)

“…</p><p>Results</p><p>The mean HbA1c level decreased from 8.61 ± 1.70 to 7.92 ± 1.60 after implementing the WhatsApp group instructions; the values showed a significant difference (t-value = 5.107 and <i>P</i>-value < 0.001). …”

Segmentation regularizes the effect of outliers. by Ole Bialas (21416664)

“…However, the linear encoder models commonly used in such studies assume that the underlying data are stationary, varying to a fixed degree around a constant mean. …”

Generative simulation framework. by Ole Bialas (21416664)

“…However, the linear encoder models commonly used in such studies assume that the underlying data are stationary, varying to a fixed degree around a constant mean. …”

Simulation results. by Ole Bialas (21416664)

“…However, the linear encoder models commonly used in such studies assume that the underlying data are stationary, varying to a fixed degree around a constant mean. …”

Sequential viral infection modulates the intestinal bacterial microbiome. by Yuhao Li (1559569)

Parallel bond model. by Yanan Sun (316649)

“…<div><p>Freeze-thaw erosion is a common hazard in cold region engineering, which is capable of generating a large number of microcracks inside the rock mass. …”

Meso-parameters of numerical simulation. by Yanan Sun (316649)

“…<div><p>Freeze-thaw erosion is a common hazard in cold region engineering, which is capable of generating a large number of microcracks inside the rock mass. …”

The simulated freeze-thaw cycle process. by Yanan Sun (316649)

“…<div><p>Freeze-thaw erosion is a common hazard in cold region engineering, which is capable of generating a large number of microcracks inside the rock mass. …”

Deterministic limit of the stochastic model. by Jack Stein (21162036)

Sequential viral exposure creates an inflammatory host environment. by Yuhao Li (1559569)

PBMC T cell subsets vary between mock- and sequentially-infected mice. by Yuhao Li (1559569)

Overexpression of Upd3 in larval muscle inhibits growth and disrupts pupal development. by Madhulika Rai (4352803)

Sequential viral exposure modulates antibody levels and B cell lineages in tissues. by Yuhao Li (1559569)