Alpha test. by Yuanquan Lu (17125093)

“…What’s more, public services supply, physical capital, financial capital, and human capital affect the subjective poverty of urban and rural residents at different significance levels. It means that the formation of subjective poverty results from the superposition of multiple factors.…”

Descriptive statistics of variables. by Yuanquan Lu (17125093)

“…What’s more, public services supply, physical capital, financial capital, and human capital affect the subjective poverty of urban and rural residents at different significance levels. It means that the formation of subjective poverty results from the superposition of multiple factors.…”

Effects of CGA on renal biochemical markers. by Liping Bao (2936715)

“…The results show that the level of serum creatinine (Scr), blood urea nitrogen (BUN), and urinary protein excretion in diabetic rats were significantly decreased after CGA intervention. CGA administration can active the Nrf2 pathway and inhibit NLRP3 inflammasome activation. …”

S1 Raw data - by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

S2 Raw data - by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

Summary of HSS and LSI during the LIST. by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

Lick latency across trial types. by Alice Berners-Lee (15354255)

Irradiation effect on <i>D. melanogaster</i> male sterility. by Flavia Cerasti (21655934)

Supplementary Material for: Gut Microbiome Composition and Its Association with Sleep in Major Psychiatric Disorders by Mairinger M. (15351103)

IRE1 depletion increases chromosome instability. by Iratxe Zuazo-Gaztelu (21071939)

Piriform neurons respond to odor onset. by Alice Berners-Lee (15354255)

“…<p>(<b>a</b>) Proportion of all piriform neurons that significantly respond to odor onset by either increasing (odor-on up; green) or decreasing (odor-on down; yellow) their firing rate. …”

Supporting data for "The Mediating and Moderating Factors in the Association between Tooth Loss and Cognitive Decline" by Huimin Chen (12992361)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test instrument. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”