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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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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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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Enhanced Next Generation Millimeter-Wave Multicarrier System with Generalized Frequency Division Multiplexing by Hidekazu Shimodaira, Joongheon Kim, Ali S. Sadri

“…This paper studies the performance improvements in terms of PAPR reduction for GFDM. Based on the performance results, the optimal numbers of subcarriers and subsymbols are calculated for PAPR reduction while minimizing the Bit Error Rate (BER) performance degradation. …”
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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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Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach by Wenhai LU, Lixia SONG, Huawei XIONG, Zhigao CHEN, Yan LU, Yanpeng CHENG, Zhen ZHANG

“…Model performance was evaluated using four metrics: mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (SMAPE). …”
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Finite Element Dynamic Modeling of Smart Structures and Adaptive Backstepping Control by Zhipeng Xie, Dachang Zhu, Zhenzhang Liu, Yun Long, Fangyi Li

“…Simulation and experimental results demonstrate that the proposed method can effectively handle the nonlinearity and modeling errors of smart structures, achieving high-precision trajectory tracking and verifying the accuracy of the dynamic model as well as the robustness of the controller.…”
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Dynamic Force Identification for Beamlike Structures Using an Improved Dynamic Stiffness Method by S.L. Chen, M. Géradin

“…Because the technique partly bypasses the processes of modal parameter extraction, global matrix inversion, and model reduction, it can eliminate many of the approximations and errors that may be introduced during these processes. …”
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Image Guidance in Radiation Therapy: Techniques and Applications by Shikha Goyal, Tejinder Kataria

“…However, delivering “high precision radiotherapy” without periodic image guidance would do more harm than treating large volumes to compensate for setup errors. In the present review, we discuss the concept of image guidance in radiotherapy, the current techniques available, and their expected benefits and pitfalls.…”
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Overview of diffusion boriding problems in industrial applications by Željko Stojanović, Sanja Stanisavljev

“…The adverse effects of brittleness and formation of cracks that lead to fracture and peeling of the boric layer were considered, the problems of choosing high process temperatures which can be harmful for the base material, but also other errors which to be avoided in order to achieve the optimum characteristics of the boride layers. …”
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Provide a Solution Based on Teacher and Student Learning Algorithm to Reduce Regression Test Cases by Mahmood Deypir, Amirhossein Mohammadpour

“…The aim of selecting test items is to choose a subset that has the potential to detect errors due to changes within the software. In other words, the purposes of test selection methods is to reduce the number of test cases after changing the code and focus on identifying the modified parts of the program. …”
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Data Quality Improvement Method for Power Equipment Condition Based on Stacked Denoising Autoencoders Improved by Particle Swarm Optimization by JI Rong, HOU Huijuan, SHENG Gehao, ZHANG Lijing, SHU Bo, JIANG Xiuchen

“…However, equipment failures and personnel errors result in dirty data, having a negative effect on data quality and subsequent analysis results. …”
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An Adaptive CNN-Based Approach for Improving SWOT-Derived Sea-Level Observations Using Drifter Velocities by Sarah Asdar, Bruno Buongiorno Nardelli

“…The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. …”
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