Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Results The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R2 compared to the best-performing IDW variant (RS-IDW). …”
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Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method by Y. Zhu, Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, C. Geiß, S. Fatichi

“…Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. …”
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Free Iron Determination in Soil by Flame Atomic Absorption Spectrometry by Sui’an ZHANG, Zhongrui YANG, Yuyu DUAN, Kaiqi YANG, Zhijie HU, Chun YANG, Sha HOU

“…The detection limit (3σ) of this method is 0.05g/kg, the quantification limit is 0.20g/kg, the linear correlation coefficient of the standard curve is 0.9995, the precision (RSD, n=6) is 1.95%−3.76%, and the standard error is less than 5%, which meets the analysis requirements of soil investigation. …”
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Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand by R. Santana, R. Santana, R. Gorman, E. Lane, S. Moore, C. Bosserelle, G. Reeve, C. Rautenbach, C. Rautenbach

“…This variability was also evident in the Tm01 predictions, with notable improvements in bias reduction through model downscaling, particularly at Baring Head. …”
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A pilot study for assessing NAO humanoid robot assistance in shoulder rehabilitation by Alessandra Raso, Martina Pulcinelli, Emiliano Schena, Alfio Puglisi, Giovanni Pioggia, Arianna Carnevale, Umile Giuseppe Longo

“…Performance was evaluated by mean absolute error (MAE) for ROM and execution time, with smoothness assessed through Log Dimensionless Jerk analysis. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios.…”
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Enhanced power sharing and voltage regulation for islanded nano-satellite DC microgrids in spinning flight scenarios by Khalil Louassaa, Josep M. Guerrero, Baseem Khan, Muhammad Zain Yousaf, Mohamed Ali Zdiri, Liu Zhang, Rajkumar Sivanraju

“…Comprehensive validation through stability analysis, simulations, and experimental testing demonstrates superior performance versus conventional methods, with significant improvements in transient response speed, steady-state error reduction, and disturbance rejection capability. …”
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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

“…Our best result showed that a reduction of 97 % in collection events increases the MAE by only 6 % with the LSTM model, demonstrating that resource use optimisation can be achieved by slightly reducing the temporal resolution of data collection with marginal error increase. …”
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Explosion characteristics and overpressure prediction of hydrogen-doped natural gas under ambient turbulence conditions by Ranran Li, Zhongmo Xu, Mingzhi Li, Shuhong Li, Zhenyi Liu, Zihao Xiu, Qiqi Liu

“…The proposed model achieves a root mean square error of 0.140 kPa under various wind speed conditions, demonstrating good predictive accuracy.…”
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Electric Vehicle charging station load forecasting with an integrated DeepBoost approach by Joveria Siddiqui, Ubaid Ahmed, Adil Amin, Talal Alharbi, Abdulelah Alharbi, Imran Aziz, Ahsan Raza Khan, Anzar Mahmood

“…For the dataset of Adaptive Charging Networks (ACN), the Mean Absolute Error (MAE) of DeepBoost improves by 9.4%, 32.7% and 88% as compared to CatBoost, XgBoost and LSTM networks, respectively.…”
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A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments by Abdullah AlHayan, Jalal Al-Muhtadi

“…These results represent a substantial improvement over standalone deep learning models (standalone LSTM FNR = 0.302, FPR = 0.185) and compare favorably to state-of-the-art benchmarks reported in the literature, particularly in minimizing critical detection errors. The findings indicate that the proposed hybrid STL-LSTM framework presents a robust and viable solution for high-stakes IoT network security, effectively balancing high detection accuracy with exceptionally low error rates, making it well-suited for real-time deployment in protecting critical IoT infrastructure.…”
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Photospectral Data Obtaining with the Unmanned Aerial Spectrometry Vehicle by A. A. Lamaka, A. V. Gutarau, N. G. Shcherbakou, P. V. Ivuts

“…This led to the timing error standard deviation reduction from 142 ms to 15 ms. …”
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How Does Assimilating SMAP Soil Moisture Improve Characterization of the Terrestrial Water Cycle in an Integrated Land Surface‐Subsurface Model? by Haojin Zhao, Carsten Montzka, Johannes Keller, Fang Li, Harry Vereecken, Harrie‐Jan Hendricks Franssen

“…ET characterization shows a limited improvement with a highest ubRMSE reduction of 15% at the Rollesbroich1 site with the CLM‐ParFlow model. …”
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Validation of the scale compassion fatigue inventory in health professional Spanish-speaking: a cross-sectional study by Antonio Kobayashi-Gutiérrez, Blanca Miriam Torres-Mendoza, Bernardo Moreno-Jiménez, Rodrigo Vargas-Salomón, Jazmin Marquez-Pedroza, Rosa Martha Meda-Lara

“…The CFA showed good fit indices and psychometric values (Cronbach´s alpha = 0.87, Omega = 0.87, Comparative Fit Index = 0.99, Tucker Lewis = 0.99, root mean square error of approximation = 0.045, Standardized Root Mean Square Residual = 0.05). …”
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Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System by Cunliang Ye, Yunlong Wang, Yongfu Wang, Yan Liu

“…The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. …”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. …”
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Probabilistic Site Adaptation for High-Accuracy Solar Radiation Datasets in the Western Sichuan Plateau by Lianlian Ye, Mengqi Liu, Disong Fu, Hao Wu, Hongrong Shi, Chunlin Huang

“…The median ensemble (MED) method delivers optimal error reduction (RMSE: 163.97 W/m², nRMSE: 34.43%). …”
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Unraveling overestimated exposure risks through hourly ozone retrievals from next-generation geostationary satellites by Siwei Li, Ge Song, Jia Xing, Jiaxin Dong, Maolin Zhang, Chunying Fan, Shiyao Meng, Jie Yang, Lechao Dong, Wei Gong

“…Here, we utilize a next-generation geostationary satellite with ultraviolet capabilities to retrieve hourly O3 concentrations, achieving high accuracy (R2 = 0.94) and improving daily maximum 8-hour estimates, particularly in semi-urban areas (R2 + 0.10, error reduction >7 μg/m³). Our analysis reveals a 30% drop in O3-related health risks compared to traditional polar-orbit estimates, with the greatest impact in semi-urban and rural areas where satellite data plays an important role due to the lack of ground measurements. …”
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A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm) by Gawon Lee, Dong Hye Ye, Se-Hong Oh

“…PhyCHarm showed a greater reduction in volume differences after harmonization for gray and white matter than U-Net, Pix2Pix, CALAMITI, or HarmonizingFlows. …”
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