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Budak M, Günal E, Kılıç M, Çelik İ, Sırrı M, Acir N. Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:53253-53274. [PMID: 36853536 DOI: 10.1007/s11356-023-26064-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha -1), 6.64 (Mg C ha-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.
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Affiliation(s)
- Mesut Budak
- Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Siirt University, Siirt, Turkey
| | | | - Miraç Kılıç
- Department of Plant and Animal Production, Vocational School of Kahta, Adiyaman University, Adiyaman, Turkey.
| | - İsmail Çelik
- Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Cukurova University, Adana, Turkey
| | - Mesut Sırrı
- Department of Plant and Animal Production, Vocational School of Kurtalan, Siirt University, Siirt, Turkey
| | - Nurullah Acir
- Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Kirsehir Ahi Evran University, Kirsehir, Turkey
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Keutzer L, You H, Farnoud A, Nyberg J, Wicha SG, Maher-Edwards G, Vlasakakis G, Moghaddam GK, Svensson EM, Menden MP, Simonsson USH. Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin. Pharmaceutics 2022; 14:pharmaceutics14081530. [PMID: 35893785 PMCID: PMC9330804 DOI: 10.3390/pharmaceutics14081530] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 01/27/2023] Open
Abstract
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0–24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.
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Affiliation(s)
- Lina Keutzer
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
| | - Huifang You
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
| | - Ali Farnoud
- Computational Health Center, Helmholtz Munich, 85764 Neuherberg, Germany; (A.F.); (M.P.M.)
| | - Joakim Nyberg
- Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden; (J.N.); (E.M.S.)
| | - Sebastian G. Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, 20146 Hamburg, Germany;
| | - Gareth Maher-Edwards
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
| | - Georgios Vlasakakis
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
| | - Gita Khalili Moghaddam
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Elin M. Svensson
- Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden; (J.N.); (E.M.S.)
- Department of Pharmacy, Radboud Institute of Health Sciences, Radboud University Medical Center, 6525 EZ Nijmegen, The Netherlands
| | - Michael P. Menden
- Computational Health Center, Helmholtz Munich, 85764 Neuherberg, Germany; (A.F.); (M.P.M.)
- Department of Biology, Ludwig-Maximilian University Munich, 82152 Planegg-Martinsried, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany
| | - Ulrika S. H. Simonsson
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
- Correspondence:
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Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance. MATERIALS 2022; 15:ma15051707. [PMID: 35268949 PMCID: PMC8911380 DOI: 10.3390/ma15051707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/16/2022] [Accepted: 01/26/2022] [Indexed: 11/17/2022]
Abstract
Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between milling surface topography parameters and wear resistance. Firstly, this paper took the surface after high-speed milling as the research object, established the residual height model of the milled surface based on static machining parameters, and analyzed the relationship between the residual height of the surface and the machining parameters. Secondly, a high-speed milling experiment was designed to explore the influence law of processing parameters on surface topography and analyzed the influence law of processing parameters on specific topography parameters; Finally, a friction and wear experiment was designed. Based on the BP neural network, the wear resistance of the milled surface in terms of wear amount and friction coefficient was predicted. Through experimental verification, the maximum error of the prediction model was 16.39%, and the minimum was 6.18%.
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Alajmi MS, Almeshal AM. Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization. MATERIALS 2021; 14:ma14143773. [PMID: 34300691 PMCID: PMC8304365 DOI: 10.3390/ma14143773] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/25/2021] [Accepted: 07/02/2021] [Indexed: 11/16/2022]
Abstract
Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.
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Affiliation(s)
- Mahdi S. Alajmi
- Department of Manufacturing Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait
- Correspondence:
| | - Abdullah M. Almeshal
- Department of Electronics Engineering Technology, College of Technological Studies, P.A.A.E.T., P.O. Box 42325, Shuwaikh 70654, Kuwait;
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