Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

Subjects:

3D reconstruction of a control PFC PV interneuron using Neurolucida showing the soma in light blue, the dendrites in green, and the axon in light blue. by Sara Hijazi (21656615)

Factors associated with the hemoglobin level stratified by age group: Linear regression. by Alejandro Rojas Chaves (20614814)

VP16 protein abundance is decreased at low MOI in the ΔCTRL2 recombinant virus. by Pankaj Singh (116938)

Gender-specific incidence trends of HFMD in Guangzhou from 2013 to 2023. by Haipeng Luo (5322419)

Minimal inhibition concentration (%) of plant extracts or green synthesized SeNPs. Each sample was tested in triplicates. ND = not detected. by Pompido Chilala (21578182)

Multiple Linear Regression of the Effects of Work Resources and Prior Traumatic Events on Stress Specific to COVID-19. by Rachel G. Baskin (22127334)

Algorithms for the water indices used in this study. B3 (green), B4 (red), B5 (red edge 1), B7 (red edge 3). by Francisco Flores-de-Santiago (20391063)

Results of the generalized linear mixed model (GLMM) for correct responses in the speech recognition task. by Elena Bolt (20396190)

Distribution of the same ingredient across different IDDSI levels (Percentages and frequencies) and statistical significance analysis (Chi-square analysis). by Muxi Chen (9294270)

Subjects:

Linear regressions of pPOM and sPOM stable carbon and nitrogen isotope values versus collection date. by Nathan D. McTigue (10706460)

Lys823 in NLRP3 is essential for its K27-linked ubiquitination and autophagic degradation. by Huaji Jiang (16907514)

VANGL2 recruits MARCH8 to promote the K27-linked ubiquitination of NLRP3. by Huaji Jiang (16907514)

The injectable contraceptives depot medroxyprogesterone acetate and norethisterone enanthate substantially and differentially decrease testosterone and sex hormone binding globulin... by Chanel Avenant (409756)

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

Subjects:

Benchmark regression results. 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. …”

Heterogeneity test. 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. …”

S1 File - 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. …”

The robustness test. 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. …”

Mechanistic testing. 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. …”