Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems by Ammar M. Bahman, Emil Pradeep, Zafar Said, Prabhakar Sharma

“…When validated against an empirical correlation, the ML models exhibited a mean absolute error (MAE) of 0.17% for thermal conductivity and below 8% for viscosity. …”
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Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data by Yachao Li, Tingting Liu, Zemin Wang, Mohammed Shokr, Menglin Yuan, Qiangqiang Yuan, Shiyu Wu

“…The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K.…”
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Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites by Harshit Sharma, Gaurav Arora, Raj Kumar, Suman Debnath, Suchart Siengchin

“…The LDPE/CNT composite material demonstrates superior prediction accuracy, with a maximum error of just 1.61%, making it a better option for high-precision applications due to enhanced mechanical interactions and improved CNT dispersion.…”
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A Wide-Band Test Fixture for Analyzing Parasitic Effects of RF Passive SMD Components by Xin Cao, Zongxi Tang

“…The designed fixture is feasible and applicable for quick and accurate measurement of RF passive SMD components.…”
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Research on the Control of High-power Induction Motor for Ship Propulsion by LIU Yong, HU Xian, GAN Weiwei

“…Results of ground experiment and field operating verification show that using this algorithms, torque output error and harmonic current content of motor are less in the whole speed range under the rated torque operating condition; high dynamic acceleration requirements can be achieved in the power restrictions and other harsh operating conditions; the inverter system can fully meet the high-performance requirements for ship-propulsion applications.…”
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On efficient iterative schemes for finding all solutions of non-linear engineering problems by Mudassir Shams, Nasreen Kausar

“…Using a few engineering applications, we show that the order strategy surpasses the current approach in terms of residual error, stability, and consistency.…”
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Proficiency tests for soil analysis services via proximal sensing by Tiago R. Tavares, José A.M. Demattê, Abdul M. Mouazen, Eyal Ben-Dor, Adriano A. Anselmi, Victor Monseff de A. Campos, Budiman Minasny, Sérgio H. Godinho Silva, Sabine Chabrillat, Konstantinos Karyotis, Heitor Cantarella, Cristiano A. Andrade, Maurício R. Cherubin, Hudson W.P. de Carvalho, José Lavres

“…It also highlights the significance of error metrics for accurate interpretation. Establishing robust proficiency tests is crucial for ensuring reliable soil analysis services and promoting the adoption of best practices in this technology.…”
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Assessment and Prediction of Typhoon-Related Direct Economic Loss in Fujian Province Based on LightGBM by Zhang Zhixia, Yang Jian, Chen Sixiao, Lin Sen

“…This study evaluated predictive performance of the model using five indicators: the Pearson correlation coefficient (R), coefficient of determination (R2), mean squared error, mean absolute error, and median absolute error. …”
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Remote sensing algorithm for dissolved organic carbon in the Laptev Sea: Correction of photobleaching effect using spectral slope by Ruiwu Zhang, Ruru Deng, Jun Ying, Cong Lei, Jiayi Li, Yu Guo, Tongtong Zhao

“…The correction method is therefore most applicable to surface waters. Remote sensing results show that this method reduces DOC overestimation in coastal areas by 12 %, improving fitting accuracy and minimising error distribution. …”
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A Novel Capacitance Estimation Method of Modular Multilevel Converters for Motor Drives Using Recurrent Neural Networks with Long Short-Term Memory by Mehdi Syed Musadiq, Dong-Myung Lee

“…Accurate estimation of submodule capacitance in modular multilevel converters (MMCs) is essential for optimal performance and reliability, particularly in motor drive applications such as permanent magnet synchronous motor (PMSM) drives. …”
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Research on Multi-VSG Parallel Control Strategy Based on Sliding Mode Active Disturbance Rejection by Fanxing Rao, Yupeng Xiang, Shuai Weng, Huimin Xiong, Xiaopin Yang, Jizheng Zhang, Cui Wang, Yunchuan Ding

“…Concurrently, an integral sliding mode linear state error feedback (SM-LSEF) control law is formulated to rapidly adjust the frequency error control quantity, eliminating the reaching phase and accelerating the system’s response speed. …”
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YOLO Model-Based Eye Movement Detection During Closed-Eye State by Shigui Zhang, Junhui He, Yuanwen Zou

“…Our tracker achieved a mean distance error of 0.77 mm, with 90% of predicted eye position distances having an error of less than 1.67 mm, enabling real-time tracking at 30 frames per second. …”
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Performance Evaluation of Fixed-Point Continuous Monitoring Systems: Influence of Averaging Time in Complex Emission Environments by David Ball, Nathan Eichenlaub, Ali Lashgari

“…The data were collected as part of a novel single-blind controlled release study, where release sources and emission rates are not disclosed during the testing period. Several error metrics are defined and evaluated across a range of relevant averaging times, demonstrating that despite significant error variance in short-duration estimates, the low bias of the system results in substantially improved emission estimates when aggregated to longer timescales. …”
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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…Experiments are conducted on meteorological datasets from Seattle and Ukraine, and the results are verified using mean absolute error (MAE), root mean square error (RMSE), and bias evaluation indicators. …”
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Comparative analysis of ensemble learning techniques for enhanced fatigue life prediction by Sasan Farhadi, Samuele Tatullo, Francesco Ferrian

“…To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean absolute percentage (SMAPE), and Tweedie score. …”
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Impact of Data Balancing and Feature Engineering on Accident Severity Models by Fayez ALANAZI, Aminu SULEIMAN

“…Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Log Loss, Area under the Curve (AUC), and Area under the Precision-Recall Curve (AUCPR) are employed. …”
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Stuck-at Faults Tolerance and Recovery in MLP Neural Networks Using Imperfect Emerging CNFET Technology by An Qi Zhang, Amr M. S. Tosson, Dylan Ma, Rebecca Fang, Lan Wei

“…As these technologies are in their early development stages, their devices still suffer from high levels of defects and variations, thus unsuitable for nowadays general-purpose applications. On the other hand, applications with inherent error resilience and high-performance demands would suppress the impact of process imperfection and benefit from the performance boost. …”
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Estimation of bimodal soil-water characteristics curve under wetting process. by Nura Bello, Alfrendo Satyanaga, Nurly Gofar, Jong Kim

“…Given the significance of wetting-induced failures, the proposed model is highly relevant in engineering applications.…”
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Rapid in-air ultrasound holography measurement and camera-in-the-loop generation using thermography by Zak Morgan, Youngjun Cho, Sriram Subramanian

“…This method achieved a 1.7% error in pressure with a single point compared to 7.8% for a conventional algorithm, and a 3.6% error and 4.2% standard deviation for 16 points compared to 9.7% and 6.9%. …”
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Fibre tracing in biomedical images: An objective comparison between seven algorithms. by Youssef Arafat, Cristina Cuesta-Apausa, Esther Castellano, Constantino Carlos Reyes-Aldasoro

“…For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. …”
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