QUANTIFICATION OF HYDRAULIC CHARACTERISTICS OF SWELLING MATERIAL by Boris Kavur, Jasmin Jug, Biljana Kovačević Zelić, Ivan Vrkljan

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Response Surface Method for Material Uncertainty Quantification of Infrastructures by Mohammad Amin Hariri-Ardebili, S. Mahdi Seyed-Kolbadi, Mohammad Noori

“…Results confirm the high accuracy of proposed technique in material uncertainty quantification.…”
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Optimized Allocation of Flood Control Emergency Materials Based on Loss Quantification by Wei Wang, Yunqing Wang, Li Huang, Yue Song

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A Systems Perspective on Material Stocks Research: From Quantification to Sustainability by Tiejun Dai, Zhongchun Yue, Xufeng Zhang, Yuanying Chi

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Brief Review of Vibrothermography and Optical Thermography for Defect Quantification in CFRP Material by Zulham Hidayat, Nicolas P. Avdelidis, Henrique Fernandes

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Comparison and reliability assessment of CO2 quantification methods for carbonated cementitious materials by Katsuya Namiki, Kazuya Yamashita, Kazuto Tabara, Suguru Noda

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Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches by Thomas Most

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Combined nanoindentation and SEM imaging for phase quantification and creep prediction of cementitious materials by Imane Bekrine, Benoit Hilloulin, Ahmed Loukili

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Detection and quantification of beef and pork materials in meat products by duplex droplet digital PCR. by Yicun Cai, Yuping He, Rong Lv, Hongchao Chen, Qiang Wang, Liangwen Pan

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Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction by Geng-Fu He, Pin Zhang, Zhen-Yu Yin

“…Despite the merits of flexible tree structure and formulable expression, random forest (RF) and evolutionary polynomial regression (EPR) cannot contribute to reliability-based design due to absent uncertainty quantification (UQ), thus hampering broader applications. …”
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Protocol for low-cost quantification of microplastics through electrochemical impedance spectroscopy from aqueous matrices by Almeenu Rasheed, Sovik Das

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Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials by Cosimo Ricci, Agata Gadaleta, Annamaria Gerardino, Angelo Didonna, Giuseppe Ferrara, Francesca Romana Bertani

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Quantification of capillary pores and large pores with narrow throats in concrete materials by the hysteresis characteristics of water vapor by Lianjuan Miao, Ming Wen, Songsong He, Chujie Jiao, Xinchun Guan

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Material decomposition approaches for monosodium urate (MSU) quantification in gouty arthritis: a (bio)phantom study by Torsten Diekhoff, Sydney Alexandra Schmolke, Karim Khayata, Jürgen Mews, Maximilian Kotlyarov

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Edge-guided slicing and inference (EGSI) with feature aggregation network for quantification of large defective temporary construction materials by Njoroge James Mugo, Akbar Ali, Song Jinwoo, Soonwook Kwon

“…While manual quantification of defective materials is time-consuming, especially for batches exceeding 200 materials, computer vision techniques offer a more efficient alternative. …”
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Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification by C. Bean, M. Calvat, Y. Nie, R.L. Black, N. Velisavljevic, D. Anjaria, M.A. Charpagne, J.C. Stinville

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Quantification of Sensitization in Aluminum–Magnesium Alloys Through Frequency-Dependent Ultrasonic Attenuation by Songwei Wang, Haiying Huang

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Tribologically induced nanostructural evolution of carbon materials: A new perspective by Guilherme Oliveira Neves, Nicolás Araya, Diego Berti Salvaro, Thiago de Souza Lamim, Renan Oss Giacomelli, Cristiano Binder, Aloisio Nelmo Klein, José Daniel Biasoli de Mello

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Impact of axisymmetric deformation on MR elastography of a nonlinear tissue-mimicking material and implications in peri-tumour stiffness quantification. by Marco Fiorito, Daniel Fovargue, Adela Capilnasiu, Myrianthi Hadjicharalambous, David Nordsletten, Ralph Sinkus, Jack Lee

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Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode by Masashi KISHIMOTO, Yodai MATSUI, Hiroshi IWAI

“…The constructed DCNN model represents a significant step towards surrogate modeling for structure quantification of energy materials.…”
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