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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Sliding window process. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Assembly error angle of a single spline. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Form for machine recognition. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

RMSE versus architectural parameters. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Kalman process. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Attention mechanism. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

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

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

Summary of correlations in previous studies. by Dan Kuang (136225)

“…Furthermore, the infection rates of SARS-CoV-2, as estimated by the model, ranged from 0.012% (P5-P95: 0.004% - 0.020%) at the lowest baseline to 3.27% (P5-P95: 1.23% - 5.69%) at the peak of the epidemic, with 15.1% (P5-P95: 5.65% - 26.2%) of individuals infected during the epidemic wave between March 4th and June 15th. Additionally, we did not observe any COVID-19 outbreaks or cluster infections at the Chengdu 2023 FISU World University Games village, and there was no significant difference in the concentrations of SARS-CoV-2 in athletes before and after check-in at the village.…”

The characteristics of nine WWTPs. by Dan Kuang (136225)

“…Furthermore, the infection rates of SARS-CoV-2, as estimated by the model, ranged from 0.012% (P5-P95: 0.004% - 0.020%) at the lowest baseline to 3.27% (P5-P95: 1.23% - 5.69%) at the peak of the epidemic, with 15.1% (P5-P95: 5.65% - 26.2%) of individuals infected during the epidemic wave between March 4th and June 15th. Additionally, we did not observe any COVID-19 outbreaks or cluster infections at the Chengdu 2023 FISU World University Games village, and there was no significant difference in the concentrations of SARS-CoV-2 in athletes before and after check-in at the village.…”

Orsay virus infection does not modulate autophagic flux at 48hpi. by Gowri Kalugotla (10292196)

“…Each point represents 200 animals, and mean + /- standard error is shown. Statistical significance was determined by Mann-Whitney test (**** = p < 0.0001). …”

Changes in the mRNA integrity of ATP synthase subunits in maize seed embryos stored under different conditions. by Junming Zhang (5446787)

Supplementary file 1_The impact of enterprise digital transformation on employees’ intrinsic motivation: the mediating role of justice perceptions.docx by Xiaorui Chang (22804850)

“…The research results, derived from path analysis and moderating effect analysis methods, indicate that enterprise digital transformation has an motivational effect on employees’ intrinsic motivation, and employees’ distributive justice, procedural justice, and interactional justice perceptions all play significant mediating roles, while information processing job characteristics show significantly different moderating effects on different motivational paths: When information processing are low, the distributive justice path loses its motivational effect, while the motivational effect of the interactional justice path significantly decreases when information processing are high, and the moderating effect on the procedural justice path is always insignificant. …”

Micelle and Inverse Micelle Structure Driven Viscoelasticity and Phase Separation in 2‑Isobutoxyethanol–Water Mixtures: Insights from All-Atom Simulations by Mayank Dixit (7879364)

“…Structural analyses, including radial distribution functions (RDFs) and potentials of mean force (PMFs), reveal that hydrogen bonds between IBE-water are significantly stronger than those in IBE–IBE and water–water pairs, as evidenced by higher RDF peak intensities, deeper contact minima, and slower hydrogen-bond autocorrelation decay. …”

Micelle and Inverse Micelle Structure Driven Viscoelasticity and Phase Separation in 2‑Isobutoxyethanol–Water Mixtures: Insights from All-Atom Simulations by Mayank Dixit (7879364)

“…Structural analyses, including radial distribution functions (RDFs) and potentials of mean force (PMFs), reveal that hydrogen bonds between IBE-water are significantly stronger than those in IBE–IBE and water–water pairs, as evidenced by higher RDF peak intensities, deeper contact minima, and slower hydrogen-bond autocorrelation decay. …”