Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting by Jenna Ritvanen, Bent Harnist, Miguel Aldana, Terhi Makinen, Seppo Pulkkinen

“…In the model, differences between consecutive rain rate fields in Lagrangian coordinates are fed into a U-Net-based CNN, known as RainNet, that was trained with the root-mean-squared-error loss function. This results in a better representation of rainfall temporal evolution compared to the RainNet and the extrapolation-based LINDA model that were used as reference models. …”
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A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia by Addis A. Alaminie, Sofie Annys, Jan Nyssen, Mark R. Jury, Giriraj Amarnath, Muluneh A. Mekonnen, Seifu A. Tilahun

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Predictive modelling of creep age forming parameters using artificial neural networks by Yo-Lun Yang, Cheng-Ling Tai, Wan-Ling Chen, Sundarakannan Rajendran, Vigneshwaran Shanmugam

“…The developed ANN demonstrated exceptional accuracy, achieving R2 values of 0.99 for both outputs with remarkably low error metrics: RMSE of 0.237 nm for precipitate radius and 1.405 MPa for yield strength, and MAPE values below 1% for all predictions. …”
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Evaluation of Low-Cost Multi-Spectral Sensors for Measuring Chlorophyll Levels Across Diverse Leaf Types by Prattana Lopin, Pichapob Nawsang, Srisangwan Laywisadkul, Kyle V. Lopin

“…All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.…”
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Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea by Congshuang Xie, Siqi Zhang, Zhenhua Zhang, Peng Chen, Delu Pan

“…We tested our method in the South China Sea, where it produced highly accurate results with a coefficient of variation (R2) value >0.93 and a root mean square error (RMSE) < 0.83 m when compared to actual measurements from an island in the area. …”
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The Effect of Hydraulic Partitioning on Prediction the Rate of Bed Load Transport in Gravel-bed Rivers using Support Vector Machine by Kiyoumars Roushangar, Mohammad Hosseini, Saman Shahnazi

“…In a second step, the data in different intervals are categorized according to the hydraulic and sediment characteristics using trial and error. Obtained results show that prediction of bed load transport with the median diameters of sediment particles (D50) ranging from 1 to 1.4 mm led to significant outcomes of NSE= 0.952, as well as flow condition in the intervals of 0.65 and 0.75 of Froude number generate better predictive ability with NSE= 0.925. …”
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Enhanced transport in non-newtonian nano-level fluid flow over a rotating disk under radiation and soret–dufour effects: A numerical insight by Abbas Khan, Hashim, Bandari Shankar, Suriya Uma Devi, Waseem Sikandar, Kamel Guedri, Bandar M. Fadhl, Wasim Jamshed, Moaz Al-Lehaibi

“…The accuracy of the numerical results is validated through residual error analysis and comparison with benchmark studies. …”
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Enhancing water management in Northern European lowland chalk streams: A parsimonious, high-resolution hydrological model using groundwater level as a proxy for baseflow by Thomas Homan, Nicholas J.K. Howden, Ruth Barden, Barbara Kasprzyk-Hordern, Jan Hofman

“…New hydrological insights for the region: Our results show that chalk stream dry-weather flows can be simulated accurately and parsimoniously at high-resolution (Nash-Sutcliffe efficiency = 0.97, mean relative error = 2.86 %, for a five-year period). We also show that spring discharges are the dominant form of flow accretion in all seasons and are critical to dilute sewage treatment inputs during the ecological growing season, whilst runoff and quick-flow pathways in the river valley corridor contribute a small proportion to annual flow accretion (< 5.2 %). …”
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Hyperspectral signature-band extraction and adaptation for sugar content prediction on Ziziphus mauritiana and Syzygium samarangense by Yung-Jhe Yan, Jen-Tzung Chien, Zi-Yin Hong, Wen-Li Lee, Kuo-Dung Chiou, Chi-Cho Huang, Mang Ou-Yang

“…For Ziziphus mauritiana, the 1D-CNN model trained solely on signature bands achieved an average mean absolute error (MAE) of 1.04 °Brix. By incorporating domain adaptation, the MAE was significantly reduced to 0.71 °Brix. …”
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A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory by Mengyuan Xu, Haoxuan Yang, Annan Hu, Lee Heng, Linyi Li, Ning Yao, Gang Liu

“…A comprehensive assessment of the downscaled results using in-situ SM acquired from 264 International Soil Moisture Network (ISMN) sites densely distributed across the continental U.S. indicated that this downscaling approach had an overall high accuracy, with an average unbiased root mean square error (ubRMSE) of 0.048 m3/m3. In addition, the downscaled SM exhibited marked improvement in spatial details over the original SMAP SM maps, providing clearer land surface features. …”
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Advanced Optimization Methods for Nonlinear Backstepping Controllers for Quadrotor-Slung Load Systems by Muhammad Maaruf, Sulaiman S. Ahmad, Waleed M. Hamanah, Abdullah M. Baraean, Md Shafiul Alam, Mohammad A. Abido, Md Shafiullah

“…To achieve the stability mentioned, this article frames the task of finding the optimal parameters for the backstepping controller gains through an optimization problem where the minimization of the integral time squared error (ITSE) of the load position is considered as the objective function for the optimal design of the NBC parameters of the test system. …”
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Experimental study on the drag reduction performance of sodium alginate in saline solutions by Zhensong Cheng, Xin Zhang, Xiaodong Dai, Hengli Zhai, Xinwang Song, Xudong Wang, Liang Gao, Guoxin Zhang, Yuan Lu, Lei Li, Xiu Yan, Jianhua Zhang

“…By comparing the predicted results with the experimental outcomes, we found that the accuracy of the predictive model is high, with the error controlled within ± 20%. To provide a more intuitive understanding of the effect of salinity on the drag reduction performance of sodium alginate, this paper introduces the innovative concept of Drag Reduction Inhibition Rate (ε). …”
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Reinforcement learning-based assimilation of the WOFOST crop model by Haochong Chen, Xiangning Yuan, Jian Kang, Danni Yang, Tianyi Yang, Xiang Ao, Sien Li

“…The findings suggest that RL-based crop model assimilation can improve model accuracy and efficiency, with potential for practical applications in precision agriculture.…”
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Robust Band-Pass Filter-Based PLL-Less Approach for Three-Phase Nonsinusoidal Grid Conditions by Manish Kumar, Anant Kumar Verma, Claudio Burgos-Mellado, Raj Kumar Jarial, Ravinder Nath, Bhumaiah Jula, Diego Munoz-Carpintero, Catalina Gonzalez-Castano, Pedro Roncero-Sanchez

“…Furthermore, the phase angle and amplitude are adaptively estimated using an off-line error-resolving approach, which is derived from the transfer function of the proposed prefiltering solution. …”
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Control of multi-level quadratic DC-DC boost converter for photovoltaic systems using type-2 fuzzy logic technique-based MPPT approaches by Souheyb Mohammed Belhadj, Bouziane Meliani, Habib Benbouhenni, Sarra Zaidi, Z.M.S. Elbarbary, Mohammed M. Alammer

“…High-gain DC-DC converters like the TLQDC-DCBC are beneficial in PV applications as they boost low PV voltages to higher levels, thereby reducing power losses and improving overall efficiency. …”
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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale by Khadija Meghraoui, Teeradaj Racharak, Kenza Ait El Kadi, Saloua Bensiali, Imane Sebari

“…Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. …”
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A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners by Anna Chrbolková, Martin Štroner, Rudolf Urban, Ondřej Michal, Tomáš Křemen, Jaroslav Braun

“…Accuracy analysis included systematic and random error assessment, axis-specific displacement evaluation, and profile-based local accuracy measurements. …”
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Enhanced U-Net for Underwater Laser Range-Gated Image Restoration: Boosting Underwater Target Recognition by Peng Liu, Shuaibao Chen, Wei He, Jue Wang, Liangpei Chen, Yuguang Tan, Dong Luo, Wei Chen, Guohua Jiao

“…Built upon the U-Net architecture with added residual connections, our network combines a VGG16-based perceptual loss with Mean Squared Error (MSE) as the loss function, effectively capturing high-level semantic features while preserving critical target details during reconstruction. …”
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Optimizing Backbone Networks Through Hybrid–Modal Fusion: A New Strategy for Waste Classification by Houkui Zhou, Qifeng Ding, Chang Chen, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

“…Traditional manual methods are time-consuming, labor-intensive, costly, and error-prone, resulting in reduced accuracy. Deep learning has revolutionized this field. …”
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AI-Powered Dental Forensics in Transforming Age Estimation Techniques: A Narrative Review by Parul Khare, Kalyani Bhargava, M Siddharth, Deepak Bhargava, Anoushka Chauhan

“…This review highlights how combining AI with different imaging techniques can enhance accuracy, reduce human error and address population-specific variations in forensic age estimation.…”
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