Test instrument. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

A prepubertal CD decreases fat mass accumulation and prevents lean mass loss in adulthood despite a chronic HFD in Sprague-Dawley male rats. by Ana Aguilar-Lozano (20614619)

Pathological changes in lung tissue of mice exposed to continuous inhalation of CS. by Yue He (385239)

<i>SLC2A2</i> is essential for liver differentiation in developing vertebrates by Yejin Kim (740789)

“…Data are represented as mean ± standard error of the mean (SEM). (G) Quantitative RT-PCR analysis was performed to evaluate the expression levels of igf1r. …”

Datasets used in the study. by Rajon Banik (12066099)

“…</p><p>Conclusion</p><p>The findings indicate a significant increase in the availability of health facilities offering modern family planning services in Bangladesh; however, a slight decline has been observed in their overall mean readiness score. …”

Pathways analysis of ERCC1 based on CWP metabolome and proteome. by Hao Deng (409186)

Procedure for attaching the eye drop bottle and instilling eye drops. by Yuka Kasai (21354922)

Subjects:

The video demonstrates how to use the eye drop aid. by Yuka Kasai (21354922)

Subjects:

Comparison of eye drop instillation success rates across different age groups. by Yuka Kasai (21354922)

Subjects:

Sociodemographic characteristics. by Hea Ree Park (10769827)

“…Increased circadian preference for eveningness and social jet lag were noted. A significant decrease in sleep duration and sleep efficiency, along with an increased prevalence of insomnia and daytime sleepiness, was noted with age- and sex-specific variations.…”

Study flow chart. by Hea Ree Park (10769827)

“…Increased circadian preference for eveningness and social jet lag were noted. A significant decrease in sleep duration and sleep efficiency, along with an increased prevalence of insomnia and daytime sleepiness, was noted with age- and sex-specific variations.…”

Table 1_Group B Streptococcus vaginal colonisation throughout pregnancy is associated with decreased Lactobacillus crispatus and increased Lactobacillus iners abundance in the vagi... by Toby I. Maidment (15296219)