Comparison of steroid hormone levels in rats in each group. by Shudan Liu (7049483)

Subjects:

Data Sheet 1_Previous COVID-19 infection significantly reduces elastase levels in newly diagnosed pulmonary tuberculosis patients.pdf by Nancy Hilda Joseph (21837314)

“…Introduction<p>Tuberculosis (TB) is considered a risk factor for severe COVID-19 disease and the quality of life of patients co-infected with COVID-19 and TB is significantly impacted due to the nature of these diseases. …”

The influencing factors of RLM. by Yi Yu (28902)

Cox model of LASSO model gene. by Wan-Jing Zhen (20427813)

Maps of alcohol-induced crude rates in the United States at the county level for 1999, 2021, 2024. by Tony Wong (779773)

Rifaximin and lactulose on inflammation in MHE rats. by Xueyan Lin (448667)

“…<p><b>(A)</b> Serum LPS levels in the portal vein of the MNS group was significantly higher than that of the C group, then obviously decreased after the intervention of rifaximin and lactulose. …”

Percentage distribution of maternal deaths by cause of death, PDHS 2007 and PMMS 2019. by Farid Midhet (17403945)

Subjects:

Maternal mortality ratio (MMR) by cause of death, per 100,000 live births, PDHS 2007 and PMMS 2019. by Farid Midhet (17403945)

Subjects:

Comparison of the association between maternal mortality and antenatal care and skilled birth attendance in the last pregnancy; Pakistan 2007 and 2019 (see notes 2, 3). by Farid Midhet (17403945)

Subjects:

MMR (maternal deaths per 100,000 live births) and 95% confidence limits by urban and rural areas, PDHS 2007 and PMMS 2019. by Farid Midhet (17403945)

Subjects:

Comparison of the association between maternal mortality in rural areas and distance to a hospital providing CEmONC and distance to public transport, Pakistan 2007 and 2019 (see no... by Farid Midhet (17403945)

Subjects:

MMR (maternal deaths per 100,000 live births) and 95% confidence limits, PDHS 2007 and PMMS 2019. by Farid Midhet (17403945)

Subjects:

SPSS data analysis files. by Farid Midhet (17403945)

Subjects:

Recruitment flow diagram of the current study. by Somayeh Momenyan (10111603)

“…Predictors of HRQoL included sociodemographic, psychological, medical, and trauma-related factors collected at baseline. We applied generalized additive mixed models to flexibly capture nonlinear changes in HRQoL over time, and piecewise latent growth curve model to analyze distinct linear phases of recovery across defined time intervals.…”

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

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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 formula parameter table. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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. …”

Test plan. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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 surface parameters. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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 generalisation validation error analysis. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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 prediction error analysis. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. 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. …”