Utility of quantitative measurement of T 2 using restriction spectrum imaging for detection of clinically significant prostate cancer by Mariluz Rojo Domingo, Christopher C. Conlin, Roshan Karunamuni, Courtney Ollison, Madison T. Baxter, Karoline Kallis, Deondre D. Do, Yuze Song, Joshua Kuperman, Ahmed S. Shabaik, Michael E. Hahn, Paul M. Murphy, Rebecca Rakow-Penner, Anders M. Dale, Tyler M. Seibert

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Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning by Maëliss Jallais, Marco Palombo

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Training Nursing Skills: A Quantitative Study of Nursing Students’ Experiences before and after Clinical Practice by Elisabeth Solvik, Solveig Struksnes

“…Sample. The population of 160 first-year students was randomly divided into two classes. …”
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Domain Adaptation Using the Replay Buffer: Adaptive Sampling Using Domain-Specific Classifier by Seokmin Kim, Youngbae Hwang

“…This approach enhances the quality of the training set, ensuring the model focuses on high-quality, domain-relevant data. …”
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Quantitative characterization of damage characteristics of coal using evaluation parameters considering the spatial structural characteristics of fractures by Jin Yan, Xiaoming Ni, Erlei Su, Ruize Niu

“…Combined with uniaxial compression tests, a mathematical model for coal damage factors based on fracture spatial structure parameters was established, and the influence of fracture parameters on the mechanical properties of coal damage was discussed. …”
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Study on the Quantitative Assessment of Impact Damage of Yellow Peaches Using the Combined Hyperspectral Technology and Mechanical Parameters by Feng Zhang, Bin Li, Hai Yin, Jiping Zou, Aiguo Ouyang

“…Then, the raw spectra were preprocessed by three spectral pretreatment methods, which are standard normal variate (SNV), multiplicative scatter correction (MSC), and SG smoothing, respectively, and the feature wavelengths were selected by the competitive adaptive reweighted sampling (CARS), and the quantitative relationships between spectra and mechanical parameters were successfully modeled based on partial least squares regression (PLSR). …”
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Multiparametric magnetic resonance imaging of experimental chronic kidney disease: A quantitative correlation study with histology. by Gunnar Schley, Jutta Jordan, Stephan Ellmann, Seymour Rosen, Kai-Uwe Eckardt, Michael Uder, Carsten Willam, Tobias Bäuerle

“…<h4>Materials and methods</h4>We evaluated excised kidneys in a murine adenine-induced nephropathy model for CKD by MRI and correlated quantitative MRI parameters (T1, T2, and T2* relaxation times, apparent diffusion coefficient and fractional anisotropy) with histological hallmarks of progressive CKD, including renal fibrosis, inflammation, and microvascular rarefaction. …”
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A Guideline for the Evolution of Quantitative Accounting Research in Iran: Shifting from Local to International Scientific Patterns by Fakhroddin MohammadRezaei

“…The aim is to develop a quantitative paper that meets global scientific standards. …”
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A Quantitative Chemometric Study of Pharmaceutical Tablet Formulations Using Multi-Spectroscopic Fibre Optic Probes by Peter J. G. Remoto, Keith C. Gordon, Sara J. Fraser-Miller

“…This study evaluated the quantitative performances of Probe A and Probe B using model pharmaceutical tablets. …”
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Study Quality in Quantitative L2 Research: A Path Analysis on the Perceptions of Iranian Published Authors by Parviz Ajideh, Mohammad Zohrabi, Akbar Jahanbakhsh

“…Moreover, using Structural Equation Modeling (SEM), it was revealed that a path of perception exists which starts from sampling issues and going through design and statistical to reporting practices. …”
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PETROGRAPHY AND APPLICATION OF THE RIETVELD METHOD TO THE QUANTITATIVE ANALYSIS OF PHASES OF NATURAL CLINKER GENERATED BY COAL SPONTANEOUS COMBUSTION by Pinilla A. Jesús Andelfo, Ramos G Miguel A., Ríos R Carlos Alberto, Candela H Sol Ángel, Quintero D John Alexander, Henao M José Antonio, Carreño Angélica María

“…The results obtained in this investigation show that the Rietveld method can be used as a powerful tool in the quantitative analysis of phases in polycrystalline samples, which has been a traditional problem in geology.…”
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Development of the algal pigment-based salinity transfer function for lakes on the Tibetan Plateau and evaluation of the quantitative reconstruction by Xinyao Sun, Qi Lin, Chenliang Du, Shixin Huang, Ke Zhang, Ji Shen

“…Sedimentary pigment preservation and salinity range have been taken into consideration, and locally weighted weighted average (LWWA, R2 = 0.74, RMSEP = 0.99 g/L) and locally weighted weighted average partial least squares (LWWAPLS, R2 = 0.74, RMSEP = 0.95 g/L) were identified as the best performing models. The pigment-based transfer function was applied to quantitatively reconstruct the salinity of Haidingnuoer Lake salinity on the northeastern Tibetan Plateau over the past 150 years, and the reliability and robustness of the reconstruction was verified. …”
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A Study on the Factors Influencing Sunlight in Block Layout: A Case Study of Barcelona Sample by Yunan Zhang, Wenxuan Chen, Kaidi Zhu

“…By using the Ladybug plug-in to simulate the Cerda Barcelona neighborhood model, this study analyzes the quantitative impacts of different geographic latitudes and north–south angle changes on the daylight levels of streets, courtyards, building facades, and ground floor building elevations. …”
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Quantifying the Sensitivity of Targeted eDNA Surveys to Improve Detection of Invasive Cane Toads (Rhinella marina) by Ewen K. Lawler, Simon Clulow, Alejandro Trujillo‐González, Paul G. Nevill, Richard P. Duncan

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Quantitative kinetic rules for plastic strain-induced α - ω phase transformation in Zr under high pressure by Achyut Dhar, Valery I. Levitas, K. K. Pandey, Changyong Park, Maddury Somayazulu, Nenad Velisavljevic

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A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. by Julian Hecker, Dmitry Prokopenko, Matthew Moll, Sanghun Lee, Wonji Kim, Dandi Qiao, Kirsten Voorhies, Woori Kim, Stijn Vansteelandt, Brian D Hobbs, Michael H Cho, Edwin K Silverman, Sharon M Lutz, Dawn L DeMeo, Scott T Weiss, Christoph Lange

“…Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. …”
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Quantitative Analysis of Amorphous Form in Indomethacin by Near Infrared Spectroscopy Combined with Partial Least Squares Regression Analysis by Mingdi Liu, Rui Fu, Jichao Liu, Ping Song, Haichao Li, Weibing Dong, Zan Sun

“…Different pretreatment methods were employed to enhance the reconstructed spectra by highlighting relevant eigen information while eliminating invalid information caused by environmental factors or physical characteristics of samples. The most suitable PLSR model for quantitative analysis of A-INDO within the range of 0.0000–100.0000% <i>w</i>/<i>w</i>% was established, screened, and validated. …”
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Banked Human Milk and Quantitative Risk Assessment of Bacillus cereus Infection in Premature Infants: A Simulation Study by Antoine Lewin, Gilles Delage, France Bernier, Marc Germain

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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…In response to the problem of low accuracy in fault diagnosis of mine hoist main bearings under small samples, a VAE-WGAN based mine hoist main bearing sample augmentation model was constructed by fusing variational autoencoder and Wasserstein to generate adversarial networks. …”
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Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification by Yuefeng Zhao, Jingqi Sun, Nannan Hu, Chengmin Zai, Yanwei Han

“…Furthermore, a new Random-based Feature Recalibration Module (RFRM) is proposed to reassign the feature weights via random matrix, which fully explore feature weight relationships to guide the sample adaptation process. Besides, we design a joint loss function with combining the FSL loss and domain adaptive loss for further optimization model. …”
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