Thermal features prediction in asphalt pavements using ANFIS-based regression by Mohammad Ali Khasawneh, Hiren Mewada, Ahmad Ali Khasawneh, Ansam Adnan Sawalha

“…Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). …”
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A high-precision 1 × 15 infrared temperature measurement linear array based on thermopile sensors by Jindong Bai, Wenhang Yang, Shouzheng Zhu, Haijun Jin, Yuchen Zhang, Ke Jin, Xiaoshuai Liu, Chunlai Li, Jianyu Wang, Hongxing Qi, Shijie Liu

“…On the FPGA control board, a multiparameter temperature compensation algorithm is used to address intrinsic temperature differences and consistency errors among the sensors. Compared with the traditional two-point calibration method, the temperature measurement accuracy of the proposed method reaches 26 mK in the temperature range of 293–303 K, the maximum repeatability error of the sensor is less than 5.5 mK, and the non-uniformity error between 15 sensors is less than 11.9 mK. …”
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Self-Weighted Quantile Estimation for Drift Coefficients of Ornstein–Uhlenbeck Processes with Jumps and Its Application to Statistical Arbitrage by Yuping Song, Ruiqiu Chen, Chunchun Cai, Yuetong Zhang, Min Zhu

“…The estimation performance is evaluated using metrics such as mean, standard deviation, and mean squared error (MSE). The simulation results show that the self-weighted quantile estimator proposed in this paper performs well across different metrics, such as 8.21% and 8.15% reduction of MSE at the 0.9 quantile for drift parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula> compared with the traditional quantile estimator. …”
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Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID by Yan Li, Miao Qian, Daojing Dai, Weitao Wu, Le Liu, Haonan Zhou, Zhong Xiang

“…The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). …”
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Assessing doming mitigation strategies for enhanced inspection of masonry retaining walls with SfM as a cost-effective 3D imaging solution by Maxwell Wondolowski, Alexandra Hain, Sarira Motaref, Michael Grilliot

“…SfM models not initially exhibiting doming displayed an average of 5.8 mm (0.23 in) root mean square error (RMSE) when compared to TLS baselines. The errors in SfM models that exhibited doming were improved with the addition of ground control points, resulting in a 46% reduction in error. …”
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A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models by Athanasios Donas, George Galanis, Ioannis Pytharoulis, Ioannis Th. Famelis

“…The derived results demonstrate a significant reduction in systematic error, as the bias decreased by up to 88% for 10-meter wind speed and 58% for 2-meter air temperature. …”
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O-Arm Imaging With Real-Time Control for Organ Motion Tracking: A Feasibility Study by Ashkan Ghorbanian, Mobin Salehi, Mohammad Sajad Sokout, Borhan Beigzadeh

“…The tracking accuracy of the applied control system can maintain errors within 1mm for the X- and Y-axis, and 1.5mm for the Z-axis after two respiration cycles for an ideal model of the respiration; such error for the Z-axis is about 2mm for actual respiration data. …”
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Delivering Dual Polarization-Division-Multiplexing Millimeter-Wave Signals at W-Band by One Pair of Antennas by Yifan Chen, Jiangnan Xiao, Ze Dong

“…For simplification of network architecture, apart from being deployed as multiplexer&#x002F;de-multiplexer, two ortho-mode transducer devices are used to be a pair of panel antennas at both the transmitter and the receiver sides in the scheme of the short-haul wireless link employing PDM-16QAM signal. The bit error ratio (BER) is less than the new-generation forward-error-correction (eFEC) threshold of 2&#x00A0;&#x00D7;&#x00A0;10^&#x2212;2 with CMA algorithm for equalization.…”
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Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues by Lamyae El Youbi El Idrissi, Ismail Akharraz, Aziza El Ouaazizi, Abdelaziz Ahaitouf

“…Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.…”
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The Association of Peroxisome Proliferator-Activated Receptor δ and Additional Gene-Gene Interaction with C-Reactive Protein in Chinese Population by Xiao-Ying Ding, Hao-Zheng Yuan, Ru Gu, Yan-Feng Gao, Xiao-Gang Liu, Ya Gao

“…Generalized multifactor dimensionality reduction (GMDR) was employed to analyze the interaction. …”
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Mechanistic Impacts of a Scale‐Aware Convection Scheme on Typhoon Intensity: Diagnostics From Minimum Sea‐Level Pressure by Yanjie Liu, Xiaocong Wang, Yimin Liu, Hao Miao, Xuesong Zhu, Wei Huang, Yaxin Zhao, Kai Wang

“…The results of four typhoon cases show the scale‐aware CPS generally reduces the track error by about 15 km and the intensity error by about 10% compared to the default CPS. …”
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Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

“…The model was trained using cutting parameters as inputs and evaluated with performance metrics such as mean absolute error (<i>MAE</i>), mean squared error (<i>MSE</i>), and the coefficient of determination (<i>R</i>²). …”
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Sustainability in motion: Investigation of automated gravimetric sample preparation in industrial liquid chromatography by Mo Legelli, Marcel Vranceanu, Michaela Wirtz, Stefan Lamotte

“…In the context of dilution series, this can be achieved by the use of low-volume dispensing tools, which usually have a higher relative instrument error, resulting in a less accurate overall method. …”
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Rudraksh: A compact and lightweight post-quantum key-encapsulation mechanism by Suparna Kundu, Archisman Ghosh, Angshuman Karmakar, Shreyas Sen, Ingrid Verbauwhede

“…We have done a scrupulous and extensive analysis and evaluation of different design elements, such as polynomial size, field modulus structure, reduction algorithm, and secret and error distribution of an LWE-based KEM. …”
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Atropine (0.05%) for rapid progressive childhood myopia (ARM study) by Rohit Saxena, Vinay Gupta, Himani Thakur, Rebika Dhiman, Thirumurthy Velpandian, Swati Phuljhele, Namrata Sharma

“…Methods: This prospective interventional single-arm clinical trial included children aged between 6–12 years, spherical equivalent refractive (SER) error between − 2 and − 6D, and having documented myopia progression of >0.75D in the preceding year. …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…Assessment metrics, such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), emphasize the MLP model’s exceptional performance. …”
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Machine learning techniques in monitoring and controlling friction stir welding process: a critical review by Bhardwaj Kulkarni, Saurabh Tayde, Yashwant Chapke, Swapnil Vyavahare, Avinash Badadhe

“…By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. …”
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Rapid analysis of temperature fields in electronic enclosures based on the finite difference thermal resistance network method by Xiaoyue Zhang, Yinmo Xie, Bing Liu, Yingze Meng, Kewei Sun, Guangsheng Wu, Jianyu Tan

“…The results indicate that this model not only accurately represents the temperature field but also controls the maximum relative error within 5 %, achieving a 99.67 % reduction in calculation time. …”
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Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting by Yang Lin, Hanxian Fang, Die Duan, Ding Yang, Hongtao Huang, Chao Xiao, Ganming Ren

“…Our evaluation, based on test set data from 2015 to 2022, demonstrate that the PCA‐Informer model outperforms the IRI‐2016, standalone Informer, and PCA‐enhanced Long Short‐Term Memory (PCA‐LSTM) models in terms of accuracy with root mean squared error (RMSE) of 2.60 TECU and mean relative error (MRE) of 14.1%, and stability for predicting TEC maps for the subsequent 2 days. …”
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Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…Experimental results demonstrate that the framework achieves significant channel estimation accuracy and robustness across several public datasets and real test scenarios, with the channel estimation error markedly smaller than that of traditional least squares (LS) and linear minimum mean square error (LMMSE) methods. …”
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