Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

Failure Samples and Fitting Results. by Dongyan Wang (195482)

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”

Region division of traditional SVPWM algorithm. by Jun Li (6494)

Small area control of the conventional algorithm. by Jun Li (6494)

General overview of the total model. by Xueliang Guo (4797057)

Study design flow chart. by Müge Yalçin (16486563)

FixbbGCN benchmark results. by Jack B. Maguire (5123183)

Free vibration analysis and optimal design of adhesively bonded double-strap joints by using artificial neural networks by Qi Guo (133147)

“…The effects of the adhesive material properties and joint geometrical parameters on the joint dynamic characteristics were investigated in detail using the trained ANNs. The optimum design problem is defined as a multi-objective optimization problem considering maximizing the first natural frequency and corresponding loss factor while minimizing the total structural weight. …”

Optimal contrast analysis with heterogeneous variances and budget concerns by Show-Li Jan (3538697)

“…Optimal allocation procedures for the Welch-Satterthwaite tests of standardized and unstandardized contrasts are presented to minimize the total sample size with the designated ratios, to meet a desirable power level for the least cost, and to attain the maximum power performance under a fixed cost. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Study flowchart. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Risk of bias graph. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”

Characteristics of included studies. by Kaiyu Zhang (198799)

“…Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. …”