Estimated impact of the 9/11 terrorism attacks on authority-virtue word salience in the U.S. by Daniel Hart (440152)

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Phrases used in moral foundation dictionaries to code authority and loyalty. by Daniel Hart (440152)

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Salience of authority-virtue phrases by year. by Daniel Hart (440152)

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Salience of vice phrases by year in fiction published in English. by Daniel Hart (440152)

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Salience of loyalty-virtue phrases by year. by Daniel Hart (440152)

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Salience of loyalty-vice phrases by year. by Daniel Hart (440152)

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Salience for sanctity-virtue and sanctity-vice phrases by country. by Daniel Hart (440152)

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Frequency of the word ‘Terrorism’ in books published in the United States, Great Britain, and in Spanish-speaking countries, by year. by Daniel Hart (440152)

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Salience for care-virtue and care-vice phrases by country. by Daniel Hart (440152)

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Salience of virtue phrases by year in fiction published in English. by Daniel Hart (440152)

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Correlations among time series for authority and loyalty salience for three corpora and GDP. by Daniel Hart (440152)

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Salience of vice and virtue by year in U.S. by Daniel Hart (440152)

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Values of <i>g</i>(<i>a</i>_<i>i</i>) satisfying Eq (2) for each action rule in <i>A</i> by Hitoshi Yamamoto (738861)

Exposure to high temperatures decreases the lifespan of <i>Strongyloides</i> spp. FLFs. by Ben T. Gregory (20442502)

DHE reports decreased cholesterol uptake by ∆<i>mceG</i> Mtb. by Yue Chen (73753)

The values for <i>k</i>_cat and <i>K</i>_M are sourced from [19]. by J. Roadnight Sheehan (20141887)

Schematic for measuring <i>D</i> and <i>D</i>′ values. by Hong Zhang (25820)

“…A recommended median area factor of 1.43 is therefore proposed. The measured <i>I</i>_s decreases following a power-law trend as <i>β</i> and <i>D</i> increase, with weathering reducing the sensitivity of <i>I</i>_s to <i>β</i> but not significantly altering its sensitivity to <i>D</i>. …”

All TPM values. by Hyuk Sung Yoon (20208672)

Modelled policy outcomes, with posterior values for individual nodes ranked from 1-5 across the parties - Green, Reform UK, Liberal Democrat, Conservative, and Labour, where 1 is the lowest rank and 5 is the highest. The parties are ranked for each policy node in the network (e.g., income tax), based on the direction of change (“performance indicator”) stated in the first column (e.g., lowest, highest, most, improvement). For example, a value of 5 for Green tax means that green tax is lowest under Reform UK’s policies, while a value of 1 means that green tax is highest under the Green Party’s policies. Larger text in bold and italic font indicates that the directional change (i.e., increase or decrease) of a node under a party’s policies is consistent with the direction indicated, whereas black text indicates an opposite directional change (e.g., income tax is predicted to decrease under Reform UK in alignment with the performance indicator, and increase under Green and Labour). by Richard Stafford (174326)

“…Larger text in bold and italic font indicates that the directional change (i.e., increase or decrease) of a node under a party’s policies is consistent with the direction indicated, whereas black text indicates an opposite directional change (e.g., income tax is predicted to decrease under Reform UK in alignment with the performance indicator, and increase under Green and Labour).…”

The results of linear mixed model. by Soheila Qanbari (20455173)

“…The tDCS group demonstrated a significant decrease in AMT of MF and TrA/IO muscles (P < 0.05) and an increase in N80 amplitude (P = 0.027), with no significant changes in the control group. …”