Range analysis data. by Lei Zhu (16642)

“…Finally, sensitivity analysis was performed using range analysis and variance analysis to determine the sensitivity of factors. 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. …”

Peak stress of orthogonal experimental group. by Lei Zhu (16642)

“…Finally, sensitivity analysis was performed using range analysis and variance analysis to determine the sensitivity of factors. 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. …”

FLAC3D code. by Lei Zhu (16642)

“…Finally, sensitivity analysis was performed using range analysis and variance analysis to determine the sensitivity of factors. 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. …”

Establishment of Numerical Model. by Lei Zhu (16642)

“…Finally, sensitivity analysis was performed using range analysis and variance analysis to determine the sensitivity of factors. 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. …”

PsyCap and its four components. by Yin Ru SHI (19762198)

“…Pre-, post-, and follow-up surveys were distributed before, after, and one week after the training. ANOVA results suggested that teachers showed a significant increase in their PsyCap scores after completing the training, although the training effect slightly decreased after one week. …”

S1 Data - by Yin Ru SHI (19762198)

“…Pre-, post-, and follow-up surveys were distributed before, after, and one week after the training. ANOVA results suggested that teachers showed a significant increase in their PsyCap scores after completing the training, although the training effect slightly decreased after one week. …”

Land-use change in Quetta from (a) 1992, (b) 2002, (c) 2013 and (d) 2022. by Naveed Ahmed (2433958)

“…It is evident from the results that barren land in Quetta increased in 2022, whereas build-up area significantly decreased, as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0317342#pone.0317342.g006" target="_blank">Fig 6</a>. …”

Improved optimization results. by Yanchun Li (248761)

“…Additionally, the variation in node utilization rates significantly decreased from 30.1% to 12.25%, markedly enhancing resource scheduling efficiency and overall balance within the supply chain.…”

Original optimization results. by Yanchun Li (248761)

“…Additionally, the variation in node utilization rates significantly decreased from 30.1% to 12.25%, markedly enhancing resource scheduling efficiency and overall balance within the supply chain.…”

Base-case results. by Mingxi Xie (19812594)

“…Clinical findings have showed that deprescribing service significantly decreased inappropriate PPIs utilization. …”

Phage–host adsorption results. by Rokaiya Nurani Shatadru (22656316)

Lectin microarray analysis of T/F and chronic HIV-1. by Yiying Zhang (198443)

Correlation analysis of lectin microarray data. by Yiying Zhang (198443)

Modulating the Coordination Environment of Cu-Embedded Mo<i>X</i>₂ (<i>X</i> = S, Se, and Te) Monolayers for Electrocatalytic Reduction of CO₂ to CH<sub>4</su... by Thamarainathan Doulassiramane (17382128)

“…Among these three SACs, Cu-MoTe₂ SAC is the most promising catalyst for reducing CO₂ to CH₄, with as low as <i>U</i>_<i>L</i> of −0.34 V vs RHE. Our results demonstrate that the local coordination environment in SACs has a significant impact on the catalytic activity of CO₂RR.…”

Evidence of the Giant Barocaloric Effect in the PVA-Slime System by Molecular Dynamics Simulations by Richard Javier Caraballo-Vivas (22113727)

“…As a result, this contributes significantly to the change in internal energy and, consequently, to the barocaloric effect. …”

Evidence of the Giant Barocaloric Effect in the PVA-Slime System by Molecular Dynamics Simulations by Richard Javier Caraballo-Vivas (22113727)

“…As a result, this contributes significantly to the change in internal energy and, consequently, to the barocaloric effect. …”

Architecture of Swin-T model. by Wen-Qing Huang (5258126)

“…Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. …”

Token merging module. by Wen-Qing Huang (5258126)

“…Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. …”