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)

a. Positive ion mode significant difference metabolite expression difference factor analysis (A) mild PTOA group VS severe PTOA group (B) mild PTOA group VS normal group (C) Severe... by Peng-fei Han (694841)

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. …”

Overexpression of TNFAIP9 alleviated the renal fibrosis caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 remitted the renal inflammation caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 suppressed inflammation in mouse peritoneal macrophages caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 inhibited the TGF-β-induced fibrosis in HK-2 cells. by Ying Chen (9697)

TNFAIP9 expression was increased in mice with chronic kidney disease (CKD) induced by 5/6 nephrectomy (Nx). by Ying Chen (9697)

Overexpression of TNFAIP9 counteracted the renal damage caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

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. …”

Overexpression of TNFAIP9 inhibited the TNF-α-induced inflammation in HK-2 cells. by Ying Chen (9697)