The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease by Dan Li, Samuel Iddi, Paul S. Aisen, Wesley K. Thompson, Michael C. Donohue, Alzheimer's Disease Neuroimaging Initiative

“…The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained. …”
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STUDY ON THE EFFECT OF PREPREG PLACEMENT TIME ON THE INTERLAMINAR SHEAR STRENGTH OF ARAMID COMPOSITE MATERIAL (MT) by JIN Bo, ZHANG AiHua, SHANG FuLin

“…According to the experimental data, the regression model of the process parameters on the ILSS was established, and the reliability and validity of the regression model were verified by the residual error, the comparison between the predicted value and the actual value. …”
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Evaluation of concordance between measurement techniques using graphical methods and regression models with an application by Doğukan Özen

“…<b>Aim: </b>The aim of this study was to introduce the graphical (Bland Altman and Mountain Plot) and methodogical (Passing- Bablok and Deming Regression analysis) methods which are used to evaluate the rational and absolute agreement between two measurement techniques with the help of an application data set.…”
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Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting by Michalis Patsiarikas, George Papageorgiou, Christos Tjortjis

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Three-dimensional surface deformation field monitoring and influencing factors analysis in mountainous areas based on SBAS-INSAR technology (Tianjin, China) by Jun Shang, Mingyang Wang, Xin Wang, Mengyao Yang, Yifan Wu, Wangze Du

“…The comprehensive analysis of land subsidence in northern Tianjin’s mountainous region revealed multi-directional surface deformation characteristics, with validation against GNSS Kriging interpolation data demonstrating root mean square errors of 5.74 mm and 5.09 mm in vertical and east–west directions, respectively. …”
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HOW DO THE MACROECONOMIC DETERMINANTS UNDERPIN THE CAPITAL MARKET DEVELOPMENT IN NORTH MACEDONIA? by Tatjana SPASESKA, Ilija HRISTOSKI

“…The empirical study is based on a time-series data analysis based on relevant secondary data sources, based on the utilization of the Johansen Test of Cointegration and the development of a corresponding Vector Error Correction Model (VECM) to estimate the relationship, the impact, the magnitude, and the significance of the determinants that support, and influence the liquidity of the stock market in North Macedonia during the period from 2008:Q1 to 2021:Q4. …”

Constitutive models for SPA-H weathering steel under coupled process and atmospheric corrosion by Weizhong Nie, Linfeng Lu, Hao Peng, Jichen Zhang

“…Error analysis showed that the model based on average corrosion depth achieved higher accuracy than that based on mass loss rate. …”
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Optimal Design of Welded Structure Using SVM by Sebghatullah Jueyendah, Carlos Humberto Martins

“…Moreover, Support Vector Machine with Radial Basis Function Kernel (SVM-RBF) is trained using the optimal design condition data. The values of the root mean square error of SVM-RBF in the prediction of the cost function are 0.00137 (correlation coefficient of R2=0.9999) and 0.217 (R2=0.9656) for training and testing, respectively. …”
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Impact of floor plans on visibility among healthcare members in inpatient care units: Employing agent-based simulation and social network analysis by Hao Yuan, Ying Zhou

“…This stems from the lack of methods to assess VHM in inpatient care units, where healthcare members' movements continuously alter their relative positions and visual relationships.This study proposes a novel method integrating agent-based simulation and social network analysis to assess VHM. The method first collects behavioral data from healthcare members to execute agent-based simulation. …”
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Assessing vitamin D's impact on pregnancy success: a predictive model for assisted reproductive technology outcomes by Songwei Jiang, Zushun Chen, Liuming Li

“…ObjectiveTo investigate the correlation between vitamin D levels and clinical pregnancy rates in infertile women undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) procedures and to assess the utility of vitamin D levels in developing a predictive model for assisted reproductive technology (ART) outcomes.MethodsA total of 188 infertile patients receiving their initial IVF or ICSI treatment at our reproductive center between June 2020 and July 2021 were selected for data collection. Vitamin D levels and other relevant ART-related factors were used to construct a predictive model.ResultThe multivariate regression analysis revealed that several independent variables significantly impacted ART pregnancy outcomes, including infertility age, vitamin D level, reproductive anti-Müllerian hormone, antral follicle count, Gn dose, daily endometrial thickness after human chorionic gonadotropin (HCG) administration, and number of retrieved eggs. …”
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Assessing the Accuracy and Reliability of the Monitored Augmented Rehabilitation System for Measuring Shoulder and Elbow Range of Motion by Samuel T. Lauman, Lindsey J. Patton, Pauline Chen, Shreya Ravi, Stephen J. Kimatian, Sarah E. Rebstock

“…Measurement reliability was evaluated using intraclass correlation coefficients (ICC3k), and accuracy was evaluated using root mean squared error (RMSE) analysis. MARS demonstrated excellent reliability with an average ICC3k of 0.993 and met the predefined accuracy threshold (RMSE ≤ 8°) for most movements, with the exception of sagittal elbow flexion. …”
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Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing by Saman Abdanan Mehdizadeh, Allan Lincoln Rodrigues Siriani, Danilo Florentino Pereira

“…The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. …”
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Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach by Mahadee Al Mobin

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Modeling and Simulation of Insulin Pump Set Failures Based on the Power Flow Method by Weijie Wang, Dinghui Guo, Long Quan, Shiwei Liu

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Denoising of acoustic emission signals from rock failure processes through ICEEMDAN combined with multiple criteria and wavelet transform by Tao Wang, Weiwei Ye, Liyuan Liu, Zhihui Zhao, Wei Huang

“…The ICEEMDAN algorithm is used to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) and employs cluster analysis to determine data thresholds based on their characteristics. …”
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An Alternative Type II Claims Pareto Model for Reliability and Bimodal Risk Analysis: Copulas, Properties, Mathematical Modeling, and Actuarial Case Study by Atef F. Hashem, Haitham M. Yousof, M. A. Abdelkawy, M. A. Abd Elgawad, Abdisalam Hassan Muse, Mohamed G. Khalil

“…As for the statistical analysis of risks considering bimodal insurance claims data, an integrated case study was presented in which we used some important and effective indicators in measuring and analyzing risks considering the new probability distribution. …”
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Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery by Yakun Zhang, Mengxin Guan, Libo Wang, Xiahua Cui, Yafei Wang, Peng Li, Shaukat Ali, Fu Zhang

“…The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R₅₅₂,R₅₅₅), RSI(R₅₃₇,R₅₇₃), and DSI(R₅₄₀,R₅₅₅), was optimal, with R² of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R² between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth.…”
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The Impact of Competitive and Collaborative Environments on Vocational Students’ Competitive Attitudes, Task Motivation, and Adaptability: A Multilevel Structural Equation Modeling... by Cheng Ma, Bo-Ching Chen

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Integrating a Hive Triangle Pattern with Subpixel Analysis for Noncontact Measurement of Structural Dynamic Response by Using a Novel Image Processing Scheme by Yung-Chi Lu, Shih-Lin Hung, Tzu-Hsuan Lin

“…This work presents a digital image processing approach with a unique hive triangle pattern by integrating subpixel analysis for noncontact measurement of structural dynamic response data. …”
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Evaluating the Accuracy of BMP280 and BME280 Sensors for Sea Level in a Coastal Environment: A Field Study at Tanjung Siambang Pier by Hollanda Arief Kusuma, Yuliani Yuliani, Tonny Suhendra, Dhanushka Devendra, Dwi Eny Djoko Setyono

“…Comparing air pressure data from the BMP280, BME280, and BMKG sensors showed that the BMP280 sensor had a higher accuracy than the BME280 sensor. …”
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