Assembly process of machine recognition form. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Process of steel truss incremental launching. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

CGAN and AutoML stacking device. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Comprehensive prediction process of shape errors. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Shape error manual calculation process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Data Sheet 1_Temperature influences mood: evidence from 11 years of Baidu index data in Chinese provincial capitals.csv by Mengjiao Yin (10995604)

“…Conversely, a 1°C increase in DTR led to decreases of 30.35%, 31.19%, and 15.41% in these indices (p < 0.05). …”

U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Sliding window process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Original record form of error matrix. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Form for machine recognition. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus architectural parameters. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Kalman process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Attention mechanism. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Shape error measurement results statistics. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Path regulates glial proliferation via the mTOR-S6K pathway. by Qian Dong (414788)

“…Path downregulation and defects in BBB expansion consequently led to a decrease in brain AAs, such as Leu, which hinders brain tumor protein synthesis to slow down tumor growth. …”

Five years comparation of efficacy and safety after ICL-V4c implantation for high and super high myopia correction by Qi Wan (271320)

“…Compared to baseline, we observed a significant increase in IOP at the 1-week follow-up, which decreased significantly at the one-month visit. …”

Productive indicators comparing harvests 1 and 2. by Zeina El Sebaaly (11478409)

“…Mushroom numbers ranged between 13.0 and 29.5 at harvest 1 (H1) and between 9.5 and 26.5 at harvest 2 (H2), showing a significant decrease in H2 in comparison to H1 in all treatments. …”

Properties of initial substrates. by Zeina El Sebaaly (11478409)

“…Mushroom numbers ranged between 13.0 and 29.5 at harvest 1 (H1) and between 9.5 and 26.5 at harvest 2 (H2), showing a significant decrease in H2 in comparison to H1 in all treatments. …”

Supplementary Material for: Global, Regional, and National Burden of Neonatal Encephalopathy Due to Birth Asphyxia and Trauma, Its Attributable Risk Factors, 1990-2021: Results fro... by figshare admin karger (2628495)

“…Low SDI regions experienced a substantial 112.9% increase in NEBAT cases, while high-middle SDI regions witnessed a significant 34.8% decrease. …”

<b>Mass, Residency Duration, and Previous Experience Shape Post-Escalation but Not Pre-Escalation Outcomes in Experimental Territorial Contests of Atlantic Mudskippers (</b><b><i>P... by Michael Smith (21714377)

“…First-order Markov chain analysis tested the effects of previous fighting experience on outcome probabilities. A binomial generalized linear model with logit link estimated the probability of resident escalation as a time-dependent, predictor-driven decision process, while generalized linear models examined how predictors influenced the resident’s total assessment duration in escalated contests and the post-escalation contest duration. …”