Using Calibration Transfer Strategy to Update Hyperspectral Model for Quantitating Soluble Solid Content of Blueberry Across Different Batches by Biao Chen, Xuhuang Huang, Shenwen Tan, Guangjun Qiu, Huaiyin Lin, Xuejun Yue, Junzhi Chen, Wenshan Zhong, Xuantian Li, Le Zhang

“…Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). …”
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Feasibility of energy dispersive X-ray fluorescence for quantitative prediction of elemental profile in untreated bovine colostrum by D. Martini, M. De Marchi, A. Goi, M. Pozza, G. Niero

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Fish diversity assessment and semi-quantitative biomass estimation through metabarcoding of environmental DNA by Yassine Kasmi, Ismael Núñez-Riboni, Tina Blancke, Benita Möckel, Matthias Bernreuther, Christoph Stransky, Reinhold Hanel

“…Modelling of trawl outputs as a function of read counts and sampling depth yielded up to 70% correlation between the model and the observed data for common dab.The outcomes of this study again highlight the potential of eDNA for marine biodiversity monitoring, not only for presence/absence assignments of species, but also for biomass estimates, with a high degree of reliability as compared to reference methods. …”
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The impact of fractional cover distribution in training samples on the accuracy of fractional cover estimation: a model-based evaluation by Rujia Wang, Chen Shi

“…In machine learning-based fractional cover estimation, the fractional cover distribution in training samples critically influences model construction and, consequently the accuracy of the estimations. …”
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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

“…The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). …”
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Food Grade Synthesis of Hetero-Coupled Biflavones and 3D-Quantitative Structure–Activity Relationship (QSAR) Modeling of Antioxidant Activity by Hongling Zheng, Xin Yang, Qiuyu Zhang, Joanne Yi Hui Toy, Dejian Huang

“…In addition, we evaluated the antioxidant potential of these biflavones using a DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay and the DPPH value ranges between 0.75 to 1.82 mM of Trolox/mM of sample across the 28 synthesized dimers. Additionally, a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was conducted to identify structural features associated with enhanced antioxidant activity. …”
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Optimizing interpolation and quantitative morphometry analysis suitability to soil erodibility modeling for soil erosion risk mapping in a complex topography by Solomon Temidayo Owolabi, Johanes A. Belle

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Measuring community resilience of flash floods using a multilevel interpretive structure model: Quantification and time-varying responses by Ming Zhong, Feng Ling, Weichen Zhong, Qian Zhang

“…This study carries out a research on disaster resilience and time-varying effects from the perspective of flash flood, and proposes a multidisciplinary integrated approach: (1) A multilevel interpretive structure model of the influencing factors of community resilience based on coupling decision mathematical model was constructed to analyze the differential influencing factors of community resilience; (2) The information diffusion method was used to quantitatively analyze and rank the community resilience of flash flood disasters; (3) Rand Index was used to quantitatively estimate the magnitude of community resilience and analyze the characteristics of its time-varying effect curve. …”
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Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning by Yuanyuan Wu, Cai Liu, Jinge Huang, Fang Wang

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Can Family Farmers Thrive in Commodity Markets? Quantitative Evidence on the Heterogeneity in Long Agribusiness Supply Chains by Thiago de Carvalho Verano, Gabriel da Silva Medina, João Ricardo de Oliveira Júnior

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Parameter estimation and uncertainty quantification for an epidemic model by Alex Capaldi, Samuel Behrend, Benjamin Berman, Jason Smith, Justin Wright, Alun L. Lloyd

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Impact of NWP Model Configuration and Training Sample Characteristics on Random Forest‐Based Day‐1 Convective Outlook Guidance by Aaron Johnson, Xuguang Wang

“…However, within the 2019/2021 data set, training on cases from a different year than the year being forecast also leads to statistically significant degradations of skill, apparently at least in part due to the different sample climate between 2019 and 2021. For this particular NWP (Numerical Weather Prediction) model configuration change, the consistency in sample climate between training and forecast cases is at least as important as consistency in model configuration. …”
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Rapid Classification and Quantitative Prediction of Aflatoxin B₁ Content and Colony Counts in Nutmeg Based on Electronic Nose by Ruiqi Yang, Keyao Zhu, Yuanyu Zhao, Xingyu Guo, Yushi Wang, Jiayu Wang, Huiqin Zou, Yonghong Yan

“…Subsequently, electronic nose (E-nose) was employed to analyze the odor of nutmeg and was combined with six machine learning algorithms to establish a classification model for samples with different degrees of mold. …”
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Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model by Banglong Pan, Hongwei Cheng, Shuhua Du, Hanming Yu, Shaoru Feng, Yi Tang, Juan Du, Huaming Xie

“…Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. …”
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Evaluating image-derived input functions for cerebral [18F]MC225 PET studies by Giordana Salvi de Souza, Giordana Salvi de Souza, Pascalle Mossel, Pascalle Mossel, Joost F. Somsen, Laura Providência, Anna L. Bartels, Antoon T. M. Willemsen, Rudi A. J. O. Dierckx, Cristiane R. G. Furini, Cristiane R. G. Furini, Adriaan A. Lammertsma, Charalampos Tsoumpas, Gert Luurtsema

“…Kinetic modelling of brain PET data is crucial for estimating quantitative biological parameters, traditionally requiring arterial sampling. …”
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Quantitative measures of recent and lifetime agricultural pesticide use are associated with increased pesticide concentrations in house dust by Shuai Xie, Jonathan N. Hofmann, Joshua N. Sampson, Pabitra R. Josse, Jessica M. Madrigal, Vicky C. Chang, Nicole C. Deziel, Gabriella Andreotti, Alexander P. Keil, Mary H. Ward, Laura E. Beane Freeman, Melissa C. Friesen

“…Objective: Elevated pesticide concentrations have been found in dust from homes with residents who use agricultural pesticides, but few studies have compared these concentrations to quantitative measures of their use. We evaluated household pesticide dust concentrations in relation to quantitative, active ingredient-specific metrics of agricultural pesticide use in the Biomarkers of Exposure and Effect in Agriculture Study. …”
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Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments by Joshua M. Rosera, Graham W. Lederer, John H. Schuenemeyer

“…We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. …”
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Linkage analysis of a model quantitative trait in humans: finger ridge count shows significant multivariate linkage to 5q14.1. by Sarah E Medland, Danuta Z Loesch, Bogdan Mdzewski, Gu Zhu, Grant W Montgomery, Nicholas G Martin

“…The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. …”
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Psychology's Questionable Research Fundamentals (QRFs): Key problems in quantitative psychology and psychological measurement beyond Questionable Research Practices (QRPs) by Jana Uher, Jan Ketil Arnulf, Jan Ketil Arnulf, Paul T. Barrett, Moritz Heene, Jörg-Henrik Heine, Jack Martin, Lucas B. Mazur, Lucas B. Mazur, Marek McGann, Robert J. Mislevy, Craig Speelman, Aaro Toomela, Ron Weber, Ron Weber

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Accelerator mass spectrometry for quantitative in vivo tracing by John S. Vogel

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