Relative abundance of microbiota in colonic content at the genus level (n =  5). by Houkang Ding (20968091)

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Baseline and Post-Treatment Serum Levels of NT-proBNP in Each Investigational Group. by Káryta Suely Macêdo Martins (22404155)

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Variables obtained from the SF-36 quality of life questionnaire in each experimental group. by Káryta Suely Macêdo Martins (22404155)

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OPLS-DA score plot and volcano plot in the control and diarrhea groups. by Houkang Ding (20968091)

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Potential metabolites in the colonic tissue related with diarrhea induced by FSE. by Houkang Ding (20968091)

Subjects:

Line graph showing the values of myocardial perfusion defects in the study groups. p = ANOVA Mixed Models. by Káryta Suely Macêdo Martins (22404155)

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Outcomes in DHI score, dizziness and unsteadiness at baseline and at three- and 15-month follow-ups for each intervention group, and within-group effects. by Anneli Peolsson (7867601)

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Heatmap and pathway analysis of the potential metabolites in colon tissue related with diarrhea. by Houkang Ding (20968091)

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Three-Tier Hierarchical Porous Structure with Ultrafast Capillary Transport for Flexible Electronics Cooling by Peilin Cui (18126360)

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Three-Tier Hierarchical Porous Structure with Ultrafast Capillary Transport for Flexible Electronics Cooling by Peilin Cui (18126360)

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Antimicrobial Activity of Glycyrrhizinic Acid Is pH-Dependent by Mathieu Joos (20331128)

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Three-Tier Hierarchical Porous Structure with Ultrafast Capillary Transport for Flexible Electronics Cooling by Peilin Cui (18126360)

Subjects:

Basic characteristics included in the study. by Juan Gu (288925)

“…A fixed-effects model was applied if P > 0.1 and I² ≤ 50%; otherwise, a random-effects model was used to account for significant heterogeneity Publication bias was assessed using funnel plots and Egger’s test. …”

Publication bias of the risk of all groups. by Juan Gu (288925)

“…A fixed-effects model was applied if P > 0.1 and I² ≤ 50%; otherwise, a random-effects model was used to account for significant heterogeneity Publication bias was assessed using funnel plots and Egger’s test. …”

Study selection process. by Juan Gu (288925)

“…A fixed-effects model was applied if P > 0.1 and I² ≤ 50%; otherwise, a random-effects model was used to account for significant heterogeneity Publication bias was assessed using funnel plots and Egger’s test. …”

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. …”