Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the CL at different angles. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the RHL at different angles. by Fei Li (143236)

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Design of the lamp beads in the virtual circular module (unit: cm). by Fei Li (143236)

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The maximum differences in positive illuminance (lx) and their distances to the origin (m) among various configurations at different angles. by Fei Li (143236)

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Schematic diagram of the spatial relationships between the module and the illuminated area. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the <i>x</i>-axis (d-f) of the RVL at different angles. by Fei Li (143236)

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The maximum illuminance values (lx) and distances to the origin (m) of various configurations at different angles. by Fei Li (143236)

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The differences in illumination (a-c) and their three-dimensional distributions (d-f) between RVL and RHL at different angles. by Fei Li (143236)

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Parameters of the rectangular LED module (unit: cm). by Fei Li (143236)

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The fitted illumination data of LED lamp at different angles. by Fei Li (143236)

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The differences in illumination (a-c) and their three-dimensional distributions (d-f) between RVL and CL at different angles. by Fei Li (143236)

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Significance analysis data. by Lei Zhu (16642)

“…The results indicate that: (1) The manifestation of mining pressure in gangue backfill mining is influenced by factors such as mining height and backfill collapse ratio; (2) Under the support of coal gangue, the concentrated stress in the coal seam significantly decreases, forming an arched shape according to the mining stages; (3) The range of plastic failure in the coal seam remains relatively stable under gangue backfill mining, with the plastic zone of the roof plate exhibiting a strip-like distribution; (4) Both range analysis and variance analysis revealed that the sensitivity ranking is backfill collapse ratio > mining height > elastic modulus. …”

Flow chart of data screening and analysis. by Yan She (8639796)

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Stress concentration factor value in the numerical samples with different shapes and sizes. by Yongfeng Zhu (7361045)

B2 decreases glycolytic intermediates in cells. by Craig Eyster (392633)

RTA408 significantly reduces RGCs loss in glaucoma. by Hongmei Qian (538647)

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HPD 1466 decreases empty capsid accumulation in cells replicating HBV. by Daniel P. Bradley (10306893)

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. …”