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. …”

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. …”

Table 1_Inflammatory parameters mediates the relationship between dietary index for gut microbiota and frailty in middle-aged and older adults in the United States: findings from a... by Qijiang Yang (21096683)

“…RCS showed that the risk of frailty decreased linearly with increasing DI-GM levels. Mediation analysis indicated significant effects for leukocyte count, neutrophil count, NLR, and SIRI, with mediation proportions of 5.7, 7.9, 4.4, and 5.5%, respectively (all p < 0.001).…”

Bird’s own song is similarly represented by NCM neurons when sung or played back a) Schematic of behavioral contexts. b-e) Effect of behavioral context on response properties of ne... by Daniel J. Pollak (14593065)

Glucagon-like peptide 1 receptor agonists modestly reduced low-density lipoprotein cholesterol and total cholesterol levels independent of weight reduction: a meta-analysis and met... by Frederick Berro Rivera (12679463)

“…Data extraction and quality assessment were performed, and outcomes were analyzed using a random-effects model to calculate weighted mean differences (MDs) in milligrams per deciliter (mg/dl) and 95% confidence intervals (CIs). …”

Results for the EPIC bowel subscale and the EORTC QLQ PR25 bowel symptoms scale. by John N. Staffurth (20636565)

“…A statistically significant (6 months versus baseline, P = 0.005; 12 months versus baseline, P = 0.013) increase occurs in bowel QoL (i.e., a reduction in symptoms). …”

The threshold effect analysis of the BRI on HGS. by Zhihao Wei (10909679)

“…Before this threshold, for every unit increase in BRI, HGS increases significantly (β = 2.19, 95% CI = 1.66, 2.72).…”

Heterotrophic cell growth and dry cell yield of <i>Rhodovulum sulfidophilum</i> in artificial seawater (ASW) based growth medium of different salinities, wherein 100% corresponds t... by Shamitha Rao Morey-Yagi (19779981)

Swimming endurance training reduces T-wave amplitude and area in <i>Mib1</i>^<i>flox</i><i>;Tnnt2</i>^<i>Cre</i> mice. by Vítor S. Fernandes (21262991)

Table1_Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and runni... by YanXiu Quan (19645810)

“…Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.…”