Analysis of peer effects. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

Predictors in ordinal regression model for GDS. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Classification of hand grip strength. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Rating scale for functional severity [28]. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

Regression model coefficients. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

ICOPE screening positive participant’s responses. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

WHO BMI classification for adults. by Shane Naidoo (20148021)

“…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…”

The flow of the participants from enrollment to analysis. by Soheila Qanbari (20455173)

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Correlation between treatment-induced changes in lumbar motor control and N150 amplitude. by Soheila Qanbari (20455173)

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Sensory-evoked potentials of N80 and N150. by Soheila Qanbari (20455173)

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The mean and standard deviation of outcome measures. by Soheila Qanbari (20455173)

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The demographical and clinical characteristics of subjects at baseline. by Soheila Qanbari (20455173)

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Electrode montage and electric field distribution simulated with SimNIBS software. by Soheila Qanbari (20455173)

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Bar chart of state-wise annual temperature shift. by MD. Nahid Hassan Nishan (22264861)

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Robust Ordinary Least Squares (OLS) regression models examining the influence of environmental and demographic factors on the age at menarche in India for the years 1992 and 2019. by MD. Nahid Hassan Nishan (22264861)

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Demographic characteristics of the participants. by MD. Nahid Hassan Nishan (22264861)

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Bar chart of state-wise annual specific humidity shift. by MD. Nahid Hassan Nishan (22264861)

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Bar chart showing the mean age at first menstruation across Indian states for the years 1992–93 and 2019–21. by MD. Nahid Hassan Nishan (22264861)

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Stratification of cortisol and prolactin concentration by gravidity in peripheral blood and umbilical blood at delivery. by Francois Kiemde (5369657)

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Cortisol and prolactin concentration in non-pregnant women. by Francois Kiemde (5369657)

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