IMU data and video synchronization. by Saravanan Manoharan (21273304)

“…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …”

Confusion matrix-punch classification. by Saravanan Manoharan (21273304)

“…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …”

Demographics of the enrolled patients. by Yuka Kasai (21354922)

“…The success rate in the seated position without the eye drop aid was 71.6%, and this rate decreased with increasing age; with the eye drop aid, the success rate improved significantly to 97.8%. …”

Overhead view of the eye drop aid. by Yuka Kasai (21354922)

“…The success rate in the seated position without the eye drop aid was 71.6%, and this rate decreased with increasing age; with the eye drop aid, the success rate improved significantly to 97.8%. …”

Data. by Michael C. Payne (2664379)

“…Using a generalized estimating equation model that accounted for age, sex, bednet use, and sentinel site, we found no significant difference in the effectiveness of the two treatment approaches (p = 0.845 for Mf and p = 0.332 for CFA). …”

Comparison of absolute and relative errors. by Lahoucine Tadoummant (21647670)

“…A significant reduction in both error types is observed, with the relative error |<i>X</i>_<i>r</i>| decreasing from approximately 10⁻¹ to 10⁻⁸. …”

Rate of convergence for relative errors. by Lahoucine Tadoummant (21647670)

“…A significant reduction in both error types is observed, with the relative error |<i>X</i>_<i>r</i>| decreasing from approximately 10⁻¹ to 10⁻⁸. …”

Ultrafine Particulate Matter Exacerbates the Risk of Delayed Neural Differentiation: Modulation Role of METTL3-Mediated m⁶A Modification by Rui Wang (52434)

“…By employing <i>N</i>6-methyladenosine (m⁶A) methylated RNA immunoprecipitation sequencing and bioinformatics, we identified <i>Zic1</i> as a key target of PM_0.1-induced developmental disturbances. …”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for ether extract intake (g/d) as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein cal... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... by Shahryar Kargar (18880600)

GSRS was positively associated with T cell exhaustion (TEX) and predicted a reduced likelihood of response to immunotherapy. by Yu Zhang (12946)

Fig 1B raw image. by Rachel K. Meade (22216529)

“…From a Ugandan household contact study, we identify significant associations between <i>CTSZ</i> variants and TB disease severity. …”

S1A Fig raw image. by Rachel K. Meade (22216529)

“…From a Ugandan household contact study, we identify significant associations between <i>CTSZ</i> variants and TB disease severity. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”