Flexible Phase Synchronization for Wireless Optical Coherent Communication System With Adaptive Fractionally-Spaced Blind Equalization Combined With Adaptive Kalman Filter by ShuPeng Zhang, LiYing Tan, Jing Ma

“…The signal quality of the proposed scheme with constant parameter output can be improved 1-2dB in both mean square error (MSE) and symbol to error ratio (SER) compared to traditional equal gain combining (EGC) followed by Viterbi-Viterbi phase estimation (VVPE) and the proposed constant parameter scheme has more laser line-width options. …”
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Bayesian-Optimized Multi-Task Gaussian Process Regression With Composite Kernels for Soybean Oil Futures Forecasting by Hui-Dong Yin, Yi-Yang Li

“…Traditional econometric models and machine learning approaches often struggle with non-stationary data and uncertainty quantification, while existing Gaussian process applications in agricultural markets remain underexplored. …”
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VDMS: An Improved Vision Transformer-Based Model for PM_2.5 Concentration Prediction by Tong Zhao, Meixia Qu

“…Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM_2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 μg/m³, and a mean absolute error (MAE) of 3.23 μg/m³. …”
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Time-frequency analysis and autoencoder approach for network traffic anomaly detection by Ruchira Purohit, Satish Kumar, Sameer Sayyad, Ketan Kotecha

“…Further developments can be made in autoencoder architectures to achieve their full potential in large-scale systems. • The model is robust and scalable for real-time applications, achieving 95% detection accuracy by identifying 72 anomalies. • Obtained results indicate that the approach is feasible for deploying in practical cybersecurity applications.…”
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Power of quantum measurement in simulating unphysical operations by Xuanqiang Zhao, Lei Zhang, Benchi Zhao, Xin Wang

“…We demonstrate our method in two applications closely related to error mitigation and quantum machine learning, where it exhibits a favorable scaling. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…The results from the model development show that the model demonstrates reasonable precision, with a mean square error of 0.0588, root mean square error of 0.24, mean absolute error of 0.1057 and a coefficient of determination of 0.95. …”
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Estimation of Evaporation Rate Using Advanced Methods by Mohammed Falah Allawi, Uday Hatem Abdulhameed, Mohammed Freeh Sahab, Sadeq Oleiwi Sulaiman

“…Several statistical indicators have been used to evaluate the prediction results which are root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and correlation (R2) the prediction accuracy. …”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. …”
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Study of a Companion Trajectory Kinematics Analysis Method for the Five-Blade Rotor Swing Scraper Pump by Chong Wang, Shigong Zhang, Tiezhu Zhang, Hongxin Zhang, Minghao Li

“…Comparing the results of theoretical calculations and simulation reveals that the error in the scraper swing angle is 1.85%, the maximum error in the scraper angular velocity is 4.93%, and the maximum error in the scraper angular acceleration is 2.47%, confirming the accuracy of the kinematic analysis method. …”
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Cross-scale covariance for material property prediction by Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, Fei Zhou, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov

“…This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.…”
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Using multispectral information and machine learning to improve daytime fire radiative power estimation from METImage measurements by Yingxin Gu, Ivan Csiszar, Marina Tsidulko, Wei Guo

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Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains by Caterina Capponi, Andrea Menapace, Silvia Meniconi, Daniele Dalla Torre, Maurizio Tavelli, Maurizio Righetti, Bruno Brunone

“…The accuracy of leak localization is demonstrated using three different degrees of noise in terms of mean absolute error, ranging between 0.54 m and 2.1 m. This proposed hybrid approach shows prospects for in-field applications.…”
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A note on some new modifications of ridge estimators by Yasin Asar, Aşır Genç

“…According to both simulation results and applications, our new estimators have better performances in the sense of MSE in most of the situations. …”
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Numerical Modelling of Two-Phase Flow in a Gas Separator Using the Eulerian–Lagrangian Flow Model by S. Amzin, S. Norheim, B. Haugen, B. Rødland, H. Momeni

“…Gravity-driven separators are broadly used in various engineering applications to remove particulate matters from gaseous fluids to meet legislation demands. …”
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Integrated algorithm based on vectors in node localization for wireless sensor networks by WANG Yu-feng, WANG Yan

“…The algorithm and its complexity and validity had been approved through simulation,the results show that the localization error of DV-hop has been reduced by 75% using the algorithm, and it is also applicable to low-density networks.…”
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On-demand droplet formation at a T-junction: modelling and validation by Hongyu Zhao, William Mills, Andrew Glidle, Peng Liang, Bei Li, Jonathan M. Cooper, Huabing Yin

“…Abstract Droplet microfluidics have found increasing applications across many fields. While droplet generation at a T-junction is a common method, its reliance on trial-and-error operation imposes undesirable constraints on its performance and applicability. …”
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Electrotextile-Based Flexible Electromagnetic Skin for Wearables and Remote Monitoring by Rossella Rizzo, Giuseppe Ruello, R. Massa, Maxim Zhadobov, Giulia Sacco

“…Such phantoms may be used in a wide range of body-centric mmW applications, including remote sensing and medical applications.…”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…The work is therefore targeted at trying to address these challenges in the development of an efficient and reliable model that could be applicable in the fault detection for solar PV systems. …”
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Predicting patient visits at the psychiatric polyclinic of a public hospital in Bali, Indonesia: A forecasting approach using single exponential smoothing by Ni Made Ratih Comala Dewi Dewi, Putu Cintariasih, Ni Wayan Suryani, Luh Gde Nita Sri Wahyuningsih

“…Forecasting accuracy was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), with forecasts for 2024–2026 generated for monthly patient visits. …”
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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

“…Performance was measured using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Robustness tests introduced noise and missing data, while computational efficiency was assessed in terms of training time, inference speed, and memory usage. …”
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