Univariate linear regression analysis of scales. by Xiangyu Wang (341093)

Schema of the experimental procedure. by Marcel F. Hinss (14675457)

“…While learning effects may have negated the impact of mental fatigue across the 5 experimental blocks, a significant decrease in performance was observed within blocks. …”

Structure of a trial. by Marcel F. Hinss (14675457)

“…While learning effects may have negated the impact of mental fatigue across the 5 experimental blocks, a significant decrease in performance was observed within blocks. …”

Comparison of the cohesion ranges of different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Relationships between cohesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Hardness increase across different IDDSI levels and food categories. by Muxi Chen (9294270)

Subjects:

Relationships between adhesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Comparison of adhesiveness ranges for different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Cohesiveness changes across IDDSI levels and food categories. by Muxi Chen (9294270)

Subjects:

Relationships between hardness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Adhesiveness changes across different IDDSI levels and food categories. by Muxi Chen (9294270)

Subjects:

Statistical analysis of adhesiveness, hardness, and cohesiveness across IDDSI levels. by Muxi Chen (9294270)

Subjects:

Comparison of hardness ranges for different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Subgroup analysis of AIP and depression. by Siyi Deng (12887198)

“…When AIP > 0.60, premenopausal women had an increased risk of depression, while postmenopausal women had a decreased risk of depression.</p><p>Conclusions</p><p>This study demonstrates that elevated AIP levels are significantly associated with an increased risk of depression in both premenopausal and postmenopausal women. …”

Fig 6 - by Teagan McMahon (20108036)

The proportional change in maximum height for the five study groups. by Teagan McMahon (20108036)

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.…”

Structure diagram of ensemble 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. …”

Fitting formula 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. …”

Test plan. 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. …”