A cellular automata approach to simulate the diffusion of antibiotic residues on the surface of a river

Nguyen, Nguyen Hong; Nguyen, Khoa Duc; Nguyen, Linh Thuy Thi; Nguyen, Tri Q.; Huynh, Hiep Xuan

doi:10.1145/3310986.3311013

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…It is also important to note that using different subsets of data for training and validation resulted in variation in model performance (varying R 2 and RMSE). This was because of the non-uniformity of data, causing varying features of data subsets, which can be minimized by using a larger and more complete database [24,38].…”

Section: Data Processing Model Training and Validationmentioning

confidence: 99%

“…In recent years, machine learning (ML) has emerged as a preferrable and effective approach, not only to predict various geotechnical issues, but also to assess the interaction between different features [20][21][22]. Past ML applications, including the artificial neural network (ANN) and other advanced algorithms for pile foundation, have mainly focused on prediction of pile bearing capacity and settlement as highlighted in recent review studies, e.g., Baghbani et al (2022) and Nguyen et al (2023b) [23,24]. For example, many studies [25,26] employed shear parameters of soil from laboratory and field tests, including SPT and CPT data, to build ML models for predicting bearing capacity and settlement of piles, while the basic settlement-loading curves have also been used [20,27].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

Nguyen,

Le,

Huynh

et al. 2024

Geotechnics

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Understanding the role that settlement can have on the base resistance of piles is a crucial matter in the design and safety control of deep foundations under various buildings and infrastructure, especially for long to super-long piles (60–90 m length) in soft soil. This paper presents a novel assessment of this issue by applying explainable machine learning (ML) techniques to a robust database (1131 datapoints) of fully instrumented pile tests across 37 real-life projects in the Mekong Delta. The analysis of data based on conventional methods shows distinct responses of long piles to rising settlement, as compared to short piles. The base resistance can rapidly develop at a small settlement threshold (0.015–0.03% of pile’s length) and contribute up to 50–55% of the total bearing capacity in short piles, but it slowly rises over a wide range of settlement to only 20–25% in long piles due to considerable loss of settlement impact over the depth. Furthermore, by leveraging the advantages of ML methods, the results significantly enhance our understanding of the settlement–base resistance relationship through explainable computations. The ML-based prediction method is compared with popular practice codes for pile foundations, further attesting to the high accuracy and reliability of the newly established model.

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Section: Data Processing Model Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

Nguyen,

Le,

Huynh

et al. 2024

Geotechnics

View full text Add to dashboard Cite

show abstract

“…( 7) has been applied to unbalanced measures to find developmental trajectories of cells [42]. Another application of UOT is robust optimal transport in cases when data are corrupted with outliers [3] or when mini-batch samples [20,32] are biased representations of the data distribution. Similar to the OT, solving the UOT again could be done through its dual form [5,11,46]…”

Section: Unbalanced Optimal Transportmentioning

confidence: 99%

“…Let π * be the optimal transport map of the OT problem Eq. (32). Then, the marginal distribution of π * is µ and ν.…”

Section: Criteria For Choosing ψmentioning

confidence: 99%

Optimization of essential oil yield from Vietnamese green pepper (Piper nigrum) using hydro-distillation method

Dao

Tran

Nhan

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In this study, the essential oil of green pepper collected from Vung Tau City, Vietnam was recovered by hydro-distillation technique, giving the yield of approximately 0.75% yield on optimal conditions. Single factor investigation showed that optimal conditions of the extraction process consisted of water and material ratio of 5:1 mL/g, extraction time of 180 min, flow rate of 2.5 mL/min, size of material of puree, time for soaking of 3 hours. Nineteen components were identified in the obtained Piper nigrum essential oils using GC-MS. The main components of essential oils were Caryophyllene, 3-Carene, β-Pinene, α-Pinene, Sabinene, and Elemene.

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A cellular automata approach to simulate the diffusion of antibiotic residues on the surface of a river

Cited by 2 publications

References 3 publications

Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

Optimization of essential oil yield from Vietnamese green pepper (Piper nigrum) using hydro-distillation method

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