Table 1_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 2_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 3_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 5_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 13_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 6_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 11_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 7_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p dir="ltr">- `canonical_prefix`: cleaned/standardized prefix used for constructing `sample_id` </p><p dir="ltr"> - `NOR`, `HBP`, `DIA`, `HD`, `CRC`, `LUN`, `SPAN`. …”

Table 2_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…Learners also reported an increased cognitive load and heightened stress levels when navigating these more complex, AI-driven activities.…”

Table 4_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…Learners also reported an increased cognitive load and heightened stress levels when navigating these more complex, AI-driven activities.…”

Table 3_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…Learners also reported an increased cognitive load and heightened stress levels when navigating these more complex, AI-driven activities.…”

Table 1_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…Learners also reported an increased cognitive load and heightened stress levels when navigating these more complex, AI-driven activities.…”

Numerical Simulation and Machine Learning-Driven Engineering of K₂GeI₆ Perovskite Solar Cells for High-Efficiency and Sustainable Photovoltaics by Md. Tarekuzzaman (21793271)

“…Further optimization was performed by analyzing the absorber thickness, intrinsic defect density, and the thickness and doping levels of the transport layers. The absorber layer was optimized with a thickness of 0.7 μm, an acceptor density (<i>N</i>_A) of 1 × 10¹⁸ cm^– 3, and a defect density (<i>N</i>_t) of 1 × 10¹⁵ cm^– 3. …”

Machine vision system for quantification of aortic and pulmonic valvuloplasty catheter compliance by Jiazhe Tang (17596080)

“…Upon ballon inflation, the defocused image is then refocused though passive focusing algorithms used to identify the best focal position. …”

Table 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.xlsx by Roberto Visalli (8152251)

“…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …”

Data Sheet 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.docx by Roberto Visalli (8152251)

“…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …”

Climate anomalies due to Cerrado native vegetation loss by Argemiro Leite-Filho (10283801)

“…Our methodology includes the following steps: (1) using machine learning algorithms to model climate variability across the Brazilian Cerrado, (2) evaluating model accuracy, (3) applying a detrending procedure to remove trends related to other factors than deforestation, and (4) validating the detrending process to ensure its reliability.…”