Determination of bladder blood flow in LETO and OLETF rats. by Takafumi Kabuto (14797727)

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This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

a-d. Difference in (a) probing depth; (b) clinical attachment level; (c) bleeding on probing, and (d) plaque index values between baseline and six months after treatment using syst... by Iva Milinkovic (22331710)

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Effect of follicular fluid (FF) supplementation and time of maturation on development of embryos to the blastocyst stage. by Verónica M. Negrón-Pérez (20691412)

Effect of follicular fluid (FF) supplementation during the maturation period on cumulus cell expansion. by Verónica M. Negrón-Pérez (20691412)

Information of QTL interval and lead SNP significantly associated with meat color in fast-growing white-feathered chickens. by Jiahong Sun (6497603)

Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and Experimental Validation by Xiaoyu Shen (4715430)

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Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Adhesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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MXene/Bi₂O₃ Nanocomposites as Supercapacitors for Portable Electronic Devices by Nanasaheb M. Shinde (8560572)

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S1 File - by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for ClinicalBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Student model architecture. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Configuration of the LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for DistilBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

ROC curve for LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Sample Posts from the ADHD dataset. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Top-level overview for ADHD classification study. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”