Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Test instrument. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Basic physical parameters of red clay. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

BP neural network structure diagram. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

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

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model prediction error analysis index. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Fitting curve parameter table. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”

Timeline of the respiratory panel showing wastewater influenza A and B virus, RSV A and B detections, alongside national surveillance weekly positivity rates, at (a-d) site 4 (univ... by Jay C. Bullen (10232606)

Participants’ endorsement of actual and mythical cancer risk factors. (a) Endorsement of actual causes of cancer. (b) Endorsement of mythical causes of cancer. by Senay Yildirim-Kahriman (22693464)

Kolmogorov-Smirnov (KS) test for the different parameter value pairs at a 95% confidence level. We tested the null hypothesis for all parameters: The RMSEs obtained for one pair of... by Cyrine Chenaoui (22177098)

Results of multidimensional scaling. The population structure of the 9902 genome sequences using a multidimensional scaling (MDS) method. by Ke Li (106849)

COVID19 effect on essential services. by Admas Abera (11821659)

“…The difference in the trends of services before and during COVID-19 was compared using linear-by-linear tests and the difference of magnitude across the indicators was compared using Autoregressive Integrated Moving Average (ARIMA) interrupted time series analysis at a 5% significance level.…”

ESR spectra (a) and UV-Vis absorption spectra (b) of ClO_{2_cx} (red), ASC (black) and CAW (green). Absorption spectra were normalized at 360 nm. by Hiroyuki Kawata (4002656)

Calibration results for the equation expressing of the relationship between parameter <i>a</i> and the crossing angle <i>θ</i>. by Xiaocheng Gao (19812705)

Data Sheet 1_Non-linear association between the dietary index for gut microbiota and the atherogenic index of plasma: insights from a cross-sectional study.docx by Tian-Ding Liu (21659201)

“…Restricted cubic spline (RCS) analysis identified a significant non-linear dose-response relationship (P for non-linearity = 0.018) with a threshold at DI-GM = 3.467. …”

<b>When more isn’t better: Sperm competition decreases fertilization success and motile sperm in two sea urchin species</b> by Luisa Kumpitsch (20874095)

“…<p dir="ltr">Abstract of the paper "<b>When more isn’t better: Sperm competition decreases fertilization success and motile sperm in two sea urchin species":</b></p><p dir="ltr">Fertilization is a fundamental process where sperm-egg fusion is essential to maintain life of most metazoans. …”