Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches by Imad Eddine Tibermacine, Samuele Russo, Francesco Citeroni, Giuseppe Mancini, Abdelaziz Rabehi, Amal H. Alharbi, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Christian Napoli, Christian Napoli, Christian Napoli

“…The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain–computer interfaces (BCIs) in clinical or consumer-grade settings. …”
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Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods by Huaguang Li, Ali B.M. Ali, Rasha Abed Hussein, Narinderjit Singh Sawaran Singh, Barno Abdullaeva, Zubair Ahmad, Soheil Salahshour, Mohammadreza Baghoolizadeh, Mostafa Pirmoradian

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Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction by Mbah O. M., Madueke C. I., Umunakwe R., Okafor C. O.

“…Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). …”
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Technical Condition Assessment and Modelling of Reed Valves in Vehicle Engine Intake Systems by Oleksandr Kravchenko, Alexander Khrulev, Juraj Gerlici, Oleksii Saraiev, Serhii Danets

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SPBView: An extendable data analysis and combined visualization tool for saccadic eye-movement, pupil size, and blink detection by Marcel Ritter, Alexandra Hoffmann, Nikolaus Rauch, Pierre Sachse, Atbin Djamshidian, Matthias Harders, Philipp Ellmerer

“…During the last 20 years, eye-tracking has become an important method for researchers in different fields like medicine, psychology, marketing, and even gaming. …”
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Experimental Demonstration of OFDM/OQAM Transmission for Visible Light Communications by Bangjiang Lin, Xuan Tang, Zabih Ghassemlooy, Xi Fang, Chun Lin, Yiwei Li, Shihao Zhang

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Validation of guiding needle placement using registered ultrasound imaging for gynecologic brachytherapy in a simulated pelvic Phantom by Jing Zeng, Jianguo Zhao, Jinlong Hao, Peisong Sun, Yuanjing Hu

“…Abstract Our previous research demonstrated that, under ideal conditions, high-precision image registration between real-time ultrasound (US) images and preoperative CT/MR images could be achieved using real-time US guidance for needle insertion. …”
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Enhanced load frequency regulation in microgrids with renewable energy sources and electric vehicles by Imran Khan, Suheel Abdullah Malik, Amil Daraz, Baitullah Bareer

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Effects of Different Numbers of Trials on Saccadometry Test Results by Aysenur Kucuk Ceyhan, Asya Fatma Men, Zahra Polat

“…The prosaccades, mean latency, velocity, directional error, and overall error (Wilcoxon signed ranks test; p > 0.05) and mean accuracy (paired samples t‐test; p > 0.05) did not differ between 100 and 60 trials. …”
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Development of a method for using an optical module to determine the biometric parameters of the udder by D. Yu. Pavkin, S. S. Yurochka

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Recurrent neural network consisting of FitzHugh–Nagumo systems: characteristics required for training by Semenova, Nadezhda Igorevna

“…The network was trained using gradient descent from different initial conditions. In the process of research, it was found that the use of standard recurrent network training characteristics such as mean squared error or mean absolute error was not applicable to this task, so an alternative method for computing the loss function was proposed. …”
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Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models by Saeed Khorram, Nima Jehbez

“…Analyses of the daily discharge results revealed a significant decrease in the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error from, respectively, 2.945 to 1.692, 5.176 to 3.215, and 16.323 to 12.952, as well as an increase in the R-Squared Correlation, Nash Sutcliffe Model Efficiency Coefficient and Kling–Gupta Efficiency from, respectively, 0.956 to 0.988, 0.96 to 0.987 and 0.972 to 0.987 in favour of the hybrid model compared to the single hydrodynamic model. …”
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A theory and data-driven method for rapid bottom hole pressure calculation in UGS by Yang Li, Haiwei Guo, Xianfeng Gong, Naixin Lu, Kairui Zhang

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Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm by Aseel Bennagi, Obaida AlHousrya, Daniel T. Cotfas, Petru A. Cotfas

“…PSA performance was assessed using root mean square error (RMSE), mean bias error (MBE), and absolute error (AE). …”
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A dual-purpose solar collector as a parallel flow heat exchanger: A novel mathematical framework by Mustafa Moayad Hasan, Krisztián Hriczó

“…To ensure the reliability and robustness of the suggested model, the obtained results were compared with experimental data from existing studies, focusing on two key metrics: relative percentage error and average relative percentage error. The analysis yielded a relative percentage error of 2.94% and an average relative percentage error of 1.3%. …”
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Optimizing compressive strength of foamed concrete using stepwise regression by Iman Kattoof Harith, Ehsan Elewy Salman, Mohammed L. Hussien, Ahmed Y. Mohammed, Wissam Nadir

“…The model achieved a high coefficient of determination (R2) of 97.59%, a low mean absolute error (MAE) of 1.45, and a low root mean squared error (RMSE) of 1.74. …”
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Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards by Andrea Szabó, János Tamás, Attila Nagy

“…The coefficient of determination (R²) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. …”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R²) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. …”
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A case study on thermal conductivity characteristics and prediction of rock and soil mass at a proposed ground source heat pump (GSHP) site by Yongjie Ma, Jingyong Wang, Fuhang Hu, Echuan Yan, Yu Zhang, Hao Deng, Xuefeng Gao, Jianguo Kang, Haoxin Shi, Xin Zhang, Jianqiao Zheng, Jixiang Guo

“…This model is used to supplement the layered thermal conductivity of the CY01 test hole. The research results can provide a new way to determine the thermal conductivity in SGE exploration.…”
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The mediating role of instrumental activities of daily living between cognitive function and depressive symptoms in community-dwelling older adults: the Arao study by Maiko Nagaoka, Katsuya Nagaoka, Naoto Kajitani, Seiji Yuki, Asuka Koyama, Kazuhiro Yoshiura, Kazuki Honda, Yusuke Miyagawa, Shuken Boku, Noboru Fujise, Minoru Takebayashi

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