Gene expression omnibus datasets. by Xinyi Xia (7516694)

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Fig 2 - by Xinyi Xia (7516694)

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Representative experimental samples. by Weisheng Zhao (3746023)

“…Hygroscopic experiments were conducted using a temperature and humidity chamber, with quartz sand as non-clay mineral control groups, and strength experiments were performed on reconstituted shale samples with varying water content. Results from the hygroscopic experiments showed that the equilibrium water content (EWC) of high clay-content shale decreases with lower humidity and higher temperature. …”

Mechanical testing apparatus and samples. by Weisheng Zhao (3746023)

“…Hygroscopic experiments were conducted using a temperature and humidity chamber, with quartz sand as non-clay mineral control groups, and strength experiments were performed on reconstituted shale samples with varying water content. Results from the hygroscopic experiments showed that the equilibrium water content (EWC) of high clay-content shale decreases with lower humidity and higher temperature. …”

Relationship between UCS, E, and water content. by Weisheng Zhao (3746023)

“…Hygroscopic experiments were conducted using a temperature and humidity chamber, with quartz sand as non-clay mineral control groups, and strength experiments were performed on reconstituted shale samples with varying water content. Results from the hygroscopic experiments showed that the equilibrium water content (EWC) of high clay-content shale decreases with lower humidity and higher temperature. …”

Hygroscopic experimental device. by Weisheng Zhao (3746023)

“…Hygroscopic experiments were conducted using a temperature and humidity chamber, with quartz sand as non-clay mineral control groups, and strength experiments were performed on reconstituted shale samples with varying water content. Results from the hygroscopic experiments showed that the equilibrium water content (EWC) of high clay-content shale decreases with lower humidity and higher temperature. …”

Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS¹ by Ensemble Averaging by Guillaume Laurent Erny (21000926)

“…However, the complexity and variability in the data can introduce significant errors, impacting the reliability of the results. …”

Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS¹ by Ensemble Averaging by Guillaume Laurent Erny (21000926)

“…However, the complexity and variability in the data can introduce significant errors, impacting the reliability of the results. …”

Source data - by Desheng Xie (21439869)

“…The results indicate that: 1) Vegetation cover in the affected regions decreased from 0.43 to 0.38 between 2017 and 2024, with Fire Zone 1 showing minimal recovery and Fire Zone 3 exhibiting moderate improvement, though the overall low vegetation area expanded due to ongoing fires; 2) The Remote Sensing Ecological Index (RSEI) declined from 0.41 to 0.38, with poor-rated areas increasing significantly, particularly in Fire Zone 3, reflecting the ongoing degradation of ecological conditions caused by both fire activity and climate factors; 3) The Flat Spectral Shape Index (FSSI) showed stability, but fluctuations in the areas with moderate and high probability of exposed coal, especially in Fire Zone 3, pointed to the expanding fire footprint and worsening ecological degradation; 4) Surface subsidence varied considerably, with Fire Zone 3 experiencing the most severe subsidence, indicating unstable geological conditions exacerbated by fire activity. …”

(a) Calorific value of biogas (MJ) as a function of temperature for the integrated and control systems. (b) Total gas volume (mL) produced at varying temperatures for the integrate... by Ning Zuo (17295415)

Jitter plot illustrating the distribution of three racial groups across varying rural percentages. by Michael Bradfield (3540005)

MaxEnt-generated wheat suitability maps (Esri ArcGIS Pro 2024) with classification predicted for different period of time (the wheat distribution is given between brackets). by Khalifa M. Al-Kindi (3719854)

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The variables selected for the model predicting the geographical distribution of wheat. by Khalifa M. Al-Kindi (3719854)

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The association between wheat distribution and bioclimatic factors for 1970–2020. by Khalifa M. Al-Kindi (3719854)

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Wheat distribution and bioclimatic factors for 2021–2040. Panels a–e show the associations between wheat distribution and key climatic parameters: temperature seasonality, average... by Khalifa M. Al-Kindi (3719854)

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Schematic representation of the sequential steps in the analytical process. by Khalifa M. Al-Kindi (3719854)

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a. The percentage of temporal wheat cultivation suitability showing the land distribution across five categories, very high, high, moderate, low and very low suitability, from 1970... by Khalifa M. Al-Kindi (3719854)

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S1 File - Climate change and its impact on wheat distribution in semi-arid ecosystems: A case study from the Sultanate of Oman by Khalifa M. Al-Kindi (3719854)

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Locations within the study area featuring the elevation map and wheat data used to train and test the model (Esri ArcGIS Pro 2024). by Khalifa M. Al-Kindi (3719854)

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Model assessment showing the AUC outcomes based on historical and projected bioclimatic variables (Esri ArcGIS Pro 2024). by Khalifa M. Al-Kindi (3719854)

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