Table 1_Effect of decreased suspended sediment content on chlorophyll-a in Dongting Lake, China.docx by Le Zhang (88249)

“…The findings showed that, from BIT to AIT, the area proportion of ultraoligotrophic state significantly decreased, while the area proportion of oligotrophic, mesotrophic, and eutrophic states significantly increased, with eutrophic state observed for the first time in 2017. …”

Changes in the number of cultural heritages over time. by Jie Li (15030)

Spatial dry and wet side fitting of the TS-NDVI characteristics for different time periods in the Santun River Irrigation Area: (a) 2006/05; (b) 2007/04; (c) 2008/10; (d) 2009/08;... by Yuxin Wei (11510974)

Average split and overall race times (HH:MM:SS). by Beat Knechtle (5504402)

“…For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.…”

Quantile regression results of male triathletes. by Junhui Zhao (626062)

“…For slower elite athletes, bike time is more critical. Temperature has a positive effect on race times, while altitude also shows a significant positive impact, with race times decreasing as altitude increases (up to 1,600 meters in this study’s dataset). …”

Descriptive statistics for categorical variables. by Junhui Zhao (626062)

“…For slower elite athletes, bike time is more critical. Temperature has a positive effect on race times, while altitude also shows a significant positive impact, with race times decreasing as altitude increases (up to 1,600 meters in this study’s dataset). …”

List of triathlon events analyzed. by Junhui Zhao (626062)

“…For slower elite athletes, bike time is more critical. Temperature has a positive effect on race times, while altitude also shows a significant positive impact, with race times decreasing as altitude increases (up to 1,600 meters in this study’s dataset). …”

Descriptive statistics for numerical variables. by Junhui Zhao (626062)

“…For slower elite athletes, bike time is more critical. Temperature has a positive effect on race times, while altitude also shows a significant positive impact, with race times decreasing as altitude increases (up to 1,600 meters in this study’s dataset). …”

Cultural heritage dataset. by Jie Li (15030)

“…The total number of cultural heritages shows an upward trend before the modern period, peaking in the Ming to Qing period. (2) The cultural heritage exhibits an overall aggregated spatial distribution, with varying patterns across different types. The Three-River Estuary is the high-density core area, with the number and density of cultural heritage decreasing as its distance increases. (3) Distribution characteristics of cultural heritage vary across different periods. …”

S1 Data - by Beat Knechtle (5504402)

“…For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.…”

S2 Data - by Beat Knechtle (5504402)

“…For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.…”

Men-to-women ratio across age groups. by Beat Knechtle (5504402)

“…For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.…”

Biotransformation Dynamics and Products of Cyanobacterial Secondary Metabolites in Surface Waters by Xuejian Wang (532695)

“…Increasing the initial cyanopeptide concentration lengthened the lag time and decreased their initial removal, supporting inhibitory effects of cyanopeptides toward enzymes involved in their own transformation. …”

Concentrations of C, N, and P in time change. by Shizhen Xu (4474321)

Data Sheet 1_The dynamics of lowland river sections of Danube and Tisza in the Carpathian basin.pdf by Imre M. Jánosi (7480598)

“…Almost all the water level and discharge records indicate a decadal decreasing trend for annual maximum values. The timing (day of the year) for annual maxima and minima is evaluated. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”