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. …”

Combined 3D-MRI showing the patellar cartilage superimposed on the femoral trochlear cartilage before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Femoral trochlear 3D-MRI before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Patellar 3D-MRI before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Comparison of simulation results. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Technical parameters of the shearer. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Simulation-related parameters. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Chain drive specification parameters. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Femoral tensile test data. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Group differences in perceptual bias scores. by Daniel E. Lidstone (21567523)

LEfSe Analysis. by Tejaswini Baral (14320736)

Analysis of cellular proliferation in βA3ΔG91 vs. WT mice lenses using BrdU assay. by Akosua K. Boateng (21672931)

Parameters of screws. by Pilan Jaipanya (12861176)

“…For these reasons, surgeons may avoid CPS insertion despite its benefit of greater biomechanical strength. Therefore, an improvement in the CPS design is needed to avoid this catastrophic complication.…”

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. …”