Occupational Exposure to Lead Fume Among Automobile Welders by Omolbanin Motamedrezaei, Hamed Lotfi, Farnaz Jahani, Gholam Reza Sharifzadeh, Farough Mohammadian, Fereydoon Laal

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Modeling Process of Forming Flat Surface Tool for Processing Conical Parts by A. S. Kozeruk, R. O. Dias Gonzalez, M. I. Filonova

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MACROECONOMIC DETERMINANTS OF MIGRATION: AN EMPIRICAL ANALYSIS FOR TURKIYE by Osman Cenk Kanca

“…In addition, dynamic least squares (DOLS) and fully adjusted least squares (FMOLS) estimation methods were used to determine long run coefficients. …”
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Optical properties of silicon carbide thin films deposited by atomic substitution on porous silicon by Valerii Kidalov, Lukas Hertling, Roman Redko, Volodymyr Dzhagan, Sergii Mamykin, Andrey Revenko, Marc Assmann, Alena Dyadenchuk, Vitalii Kidalov, Dietrich R T Zahn

“…Spectroscopic ellipsometry in the range of 0.6–5.1 eV was performed in order to determine the refractive index (n), extinction coefficient (k), and bandgap (Eg) of the SiC layer. …”
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Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples by Fubin Zhu, Changda Zhu, Zihan Fang, Wenhao Lu, Jianjun Pan

“…Soil texture is one of the most important physical properties of soil and plays a crucial role in determining its suitability for crop cultivation. Currently, supervised classification machine learning methods are most commonly used in digital soil mapping. …”
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Design and parameter optimization of portable tree transplanting machine by Zhang Jingping, Zhu Jianxi, Sun Teng

“…Second, in order to increase the machine's working performance, a response surface method was considered to optimize the working parameter of the portable tree transplanting machine. …”
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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…To quantify the degrees of land suitability for the target crops, a parametric approach based on the square root method was employed. Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. …”
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Adaptive Variational Modal Decomposition–Dual Attention Mechanism Parallel Residual Network: A Tool Lifetime Prediction Method Based on Adaptive Noise Reduction by Jing Kang, Taiyong Wang, Yi Li, Ye Wei, Yaomin Zhang, Ying Tian

“…The experimental results show that, compared with the original ResNet model, the proposed AVMD-DAMResNet model improves the coefficient of determination (R²) by 5.8%, reduces the root mean square error (RMSE) by 31.2%, and decreases the mean absolute percentage error (MAPE) by 31.4%. …”
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Post-heparin LPL activity measurement using VLDL as a substrate: a new robust method for routine assessment of plasma triglyceride lipolysis defects. by Mathilde Di Filippo, Christophe Marçais, Sybil Charrière, Oriane Marmontel, Martine Broyer, Mireille Delay, Micheline Merlin, Axel Nollace, René Valéro, Michel Lagarde, Valérie Pruneta-Deloche, Philippe Moulin, Agnès Sassolas

“…<h4>Background</h4>Determination of lipoprotein lipase (LPL) activity is important for hyperchylomicronemia diagnosis, but remains both unreliable and cumbersome with current methods. …”
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Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams by ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

“…Experimental results demonstrate that the proposed IDBO-DHKELM model achieves high decision-making accuracy, with a root mean square error (RMSE) of 0.143, a mean absolute error (MAE) of 0.119, and a coefficient of determination (R2) of 0.971.…”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…Experimental results show that the coefficient of determination (R²) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. …”
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Multi-Objective Vibration Control of a Vehicle-Track-Bridge Coupled System Using Tuned Inerter Dampers Based on the FE-SEA Hybrid Method by Xingxing Hu, Qingsong Feng, Min Yang, Jian Liu

“…Short-wavelength track irregularities are introduced as high-frequency excitation, and the accuracy and efficiency of this method are validated by comparison with the traditional finite element method (FEM). …”
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Estimation of Forest Canopy Cover by Combining ICESat-2&#x002F;ATLAS Data and Geostatistical Method&#x002F;Co-Kriging by Jinge Yu, Hongyan Lai, Li Xu, Shaolong Luo, Wenwu Zhou, Hanyue Song, Lei Xi, Qingtai Shu

“…The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) &#x003D; 0.09 with a coefficient of determination (R²) &#x003D; 0.91. …”
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ALGORITHM OF AIRCRAFT FLIGHT DATA PROCESSING IN REAL-TIME by Elkhan SABZIEV

“…The calculation of the data bundling coefficients by the least-squares method is proposed based on the data related to loads, orientation angles and speed recorded in the black box during the flight. …”
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A Calibration Algorithm for Microelectromechanical Inertial Sensors by Nguyen Trong Yen, Nguyen Quoc Khanh, Ha Manh Thang

“…A calibration algorithm was developed based on the least squares method.Results. An algorithm for determining the calibration parameters of sensors regardless of the initial alignment of the sensor sensitive axes relative to a reference system was developed. …”
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Same data, different results? Machine learning approaches in bioacoustics by Kaja Wierucka, Derek Murphy, Stuart K. Watson, Nikola Falk, Claudia Fichtel, Julian León, Stephan T. Leu, Peter M. Kappeler, Elodie F. Briefer, Marta B. Manser, Nikhil Phaniraj, Marina Scheumann, Judith M. Burkart

“…We investigated the impact of using different feature extraction (spectro‐temporal measurements, linear and Mel‐frequency cepstral coefficients (MFCC), as well as highly comparative time‐series analysis) and classification methods (discriminant function analysis, neural networks, random forests (RF), and support vector machines) on the consistency of caller identity classification accuracy across 16 mammalian datasets. …”
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Development and Validation of a Method for the Analysis of Multiple Pesticides in Fishery Products Using Gas Chromatography with Micro-Electron Capture Detection and Gas Chromatogr... by Myungheon Kim, Mihyun Cho, Changkyo Seo, Jaebin Im, Changhyeon Park, Yoonmi Lee, Mi-Ra Jo, Yong-Sun Moon, Moo-Hyeog Im

“…Matrix-matched calibration curves exhibited linearity with coefficients of determination exceeding 0.99 for all target analytes. …”
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Assessment of greenness, blueness, and whiteness profiles of a validated HPLC-DAD method for quantitation of Donepezil HCl and Curcumin in their laboratory prepared co-formulated n... by Mennah M. Abd Elwahab, Samar Saad, Zeinab A. Sheribah, Nahed El-Enany

“…In the present study, a sensitive eco-friendly HPLC-DAD method was carried out and validated to allow concurrent determination of Donepezil HCl (DPZ) and Curcumin (CUR) in their pure form and laboratory made nano-liposome formulation. …”
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Bioaccumulation coefficient and concentration of essential and non-essential heavy metals in rice grains: a case study in Ranau valley, Sabah, Malaysia by Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim, Tukimat Lihan, Zulfahmi Ali Rahman

“…The concentration of heavy metals was determined by the wet digestion method and was measured using Inductively Coupled Plasma Mass Spectrometry. …”
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Quantification of onion (Allium cepa L.) evapotranspiration and crop coefficient via weighable lysimeter under semi-arid climate of Melkasa, Ethiopia by Nigusie Kebede, Mekonen Ayana, Boja Mekonnen

“…The results indicated that the pooled seasonal crop evapotranspiration was 460.27 mm, while the reference evapotranspiration for the same period was 509.18 mm. The average crop coefficient values were determined to be 0.68, 0.89, 1.03, and 0.86 for the initial, developmental, mid-, and late-season stages, respectively. …”
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