Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…Results show under a 50% probability of upstream data loss, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula>-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. …”
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Methodology for Determining the Effective Thickness of the Cemented Layer of Steel by S. G. Sandomirski, A. L. Val’ko, S. P. Rudenko

“…The technique provides a significant reduction in the influence of the structural banding of the metal and the inevitable error in measuring hardness on the result of determining the hef . …”
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The Neural Correlates and Behavioral Impact of Peripheral Noise Electrical Stimulation on Motor Learning by Li-Wei Chou, Man-Wai Kou, Hui-Min Lee, Felipe Fregni, Vincent Chen, Chung-Lan Kao

“…The differences (force error) between the actual and the targeted force were calculated, and motor learning was achieved by reducing the force error to a plateau. …”
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Prediction of Automotive Wire Harness Aging Based on CNN-biLSTM-Attention by Kun Xia, Qi Zhu, Qingqing Yuan, Jingxia Wang

“…The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance.…”
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Demonstration of Measurement-Enhanced State Preparation and Erasure Conversion in a Molecular Tweezer Array by Connor M. Holland, Yukai Lu, Samuel J. Li, Callum L. Welsh, Lawrence W. Cheuk

“…For these applications, the reduction and mitigation of errors remain major challenges. …”
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An Image and State Information-Based PINN with Attention Mechanisms for the Rapid Prediction of Aircraft Aerodynamic Characteristics by Yiduo Kan, Xiangdong Liu, Haikuo Liu

“…Extensive experiments validate the effectiveness of our model for rapid aircraft aerodynamic parameter prediction, achieving a significant reduction in prediction error that improves performance by 29.25% in RMSE and 37.99% in MRE compared to existing methods. …”
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WDM-PON Free Space Optical (FSO) System Utilizing LDPC Decoding for Enhanced Cellular C-RAN Fronthaul Networks by Dokhyl AlQahtani, Fady El-Nahal

“…Our system transmits 20 Gbps, 16-QAM intensity-modulated orthogonal frequency-division multiplexing (OFDM) signals, achieving a substantial reduction in bit error rate (BER). Numerical results show that the proposed WDM-PON-FSO architecture, augmented with LDPC decoding, maintains reliable transmission over 2 km under strong turbulence conditions.…”
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Efficient Learning of Long-Range and Equivariant Quantum Systems by Štěpán Šmíd, Roberto Bondesan

“…For interactions decaying as a power law with exponent greater than twice the dimension of the system, we recover the same efficient logarithmic scaling with respect to the number of qubits, but the dependence on the error worsens to exponential. Further, we show that learning algorithms equivariant under the automorphism group of the interaction hypergraph achieve a sample complexity reduction, leading in particular to a constant number of samples for learning sums of local observables in systems with periodic boundary conditions. …”
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Post-acceleration internal enhancement technology for streak camera and its nonlinear intensity correction by GU Li, YANG Fang, YANG Qinlao

“…The dynamic range error is 7.7% compared with the uncorrected one. …”
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A Hybrid GARCH and Deep Learning Method for Volatility Prediction by Hailabe T. Araya, Jane Aduda, Tesfahun Berhane

“…The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. …”
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Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia. by Rick A Adams, Laurent U Perrinet, Karl Friston

“…Furthermore, we show that a single deficit in the postsynaptic gain of prediction error units (encoding the precision of posterior beliefs) can account for several features of smooth pursuit in schizophrenia: namely, a reduction in motor gain and anticipatory eye movements during visual occlusion, a paradoxical improvement in tracking unpredicted deviations from target trajectories and a failure to recognise and exploit regularities in the periodic motion of visual targets. …”
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Hygroscopicity of ‘sucupira-branca’ (Pterodon emarginatus Vogel) fruits by Daniel E. C. de Oliveira, Osvaldo Resende, Lílian M. Costa, Glicélia P. Silva, Juliana de F. Sales

“…The models Chung-Pfost, Copace, Modified Halsey, Oswin Modified and Sigma Copace obtained high coefficient of determination (R2) and low chi-square (χ2), relative mean error (P) and estimated mean error (SE), and the Copace model was selected to represent the desorption isotherms. …”
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An Adaptive Learning Time Series Forecasting Model Based on Decoder Framework by Jianlong Hao, Qiwei Sun

“…Experiments carried out on multiple datasets indicate that the time series adaptive learning model based on the decoder achieved an overall reduction of 2.6% in MSE (Mean Squared Error) loss and 1.8% in MAE (Mean Absolute Error) loss when compared with the most advanced Transformer-based time series forecasting model.…”
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Observable-driven speed-ups in quantum simulations by Wenjun Yu, Jue Xu, Qi Zhao

“…For short-time simulations, we deliberately design and tailor product formulas to achieve size-independent errors for local and certain global observables. In arbitrary-time simulations, we reveal that Pauli-summation structured observables generally reduce average errors with a typically quadratic error reduction. …”
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Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control by Chien-Chih Wang

“…The results indicate that DDPFF consistently outperformed conventional ARIMA and analogous forecasting methodologies, yielding an average reduction of 35.7% in mean absolute error and a 41.8% enhancement in residual stability across all examined cases. …”
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Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm by G. Ayappan, S. Anila

“…The proposed approach demonstrates superior error reduction, highlighting its potential for effective COVID-19 detection.…”
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Development of Credibility Enhancement Method of Oil Products Metering at the Fuel Storage Depots by A. G. Godnev

“…The proposed automatic method of reservoir calibration making use of fuel dispenser under conditions of routine operation of fuel storage depot contributes to better accuracy and credibility of calibration, reduction in its timing and corresponding costs. Application of such method and equipment allows to interpret unmanageable systematic error of the fuel dispenser as a random one and (with more statistics available) to reduce resulting error of fuel dispenser from ±0.25 % practically up to до ±0.05 %.…”
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Remaining useful life prediction of Lithium-ion batteries based on data preprocessing and CNN-LSSVR algorithm by Ti Dong, Yiming Sun, Jia Liu, Qiang Gao, Chunrong Zhao, Wenjiong Cao

“…Compared with other traditional algorithms, the proposed RUL prediction method can reduce the mean absolute error and root mean square error by at least 37% and 61%, respectively, and has better stability. …”
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Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China by Jiarui Zhang, Zhong Sun, Qi Deng, Yidan Yu, Xingyue Dian, Juan Luo, Thilakavathy Karuppiah, Narcisse Joseph, Guozhong He

“…Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. …”
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Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures by Eslam Deef-Allah, Magdy Abdelrahman

“…The nonlinear models achieved a 69% reduction in the root mean square error (RMSE) for rutting, a 37% reduction in the RMSE for fatigue cracking, and a 21% reduction in the RMSE for thermal cracking. …”
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