Differential colour-ring loss among Dalmatian Pelican (Pelecanus crispus) colonies and its consequence on survival estimates by Anastasios Bounas, Giorgos Catsadorakis, Dionyssia Hatzilacou, Theodoros Naziridis, Jocelyn Champagnon, Alain J. Crivelli

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Regionalization Analysis of Environmental Drivers of CONUS Grazing Land Biomass by Jisung Geba Chang, Feng Gao, Martha C. Anderson, Richard Cirone, Haoteng Zhao

“…Investigating the performance of several machine learning approaches in reproducing RAP biomass, the random forest model performed best, with a mean absolute error of 373 lb/acre and a coefficient of determination (<italic>R</italic>²) of 0.66. …”
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Research on Remote Sensing Quantitative Inversion of Oil Spills and Emulsions Using Fusion of Optical and Thermal Characteristics by Zongchen Jiang, Jie Zhang, Yi Ma, Xingpeng Mao

“…By employing the oil&#x2013;water brightness temperature difference polar coordinate thermal model, the absolute thickness of the oil-in-water emulsions was inverted with an average relative error below 12.7&#x0025;. When applied to airborne and satellite remote sensing images of actual oil spill incidents, the OQIM model exhibited significant inversion potential and generalization capabilities in practical applications, offering crucial methodological support for emergency responses to marine oil spills.…”
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Study and Verification of a Physical Simulation System for Formation Pressure Testing while Drilling by Tianshou Ma, Nian Peng, Ping Chen, Chunhe Yang, Xingming Wang, Xiong Han

“…The tendency of pressure change is nearly the same for both the present and the previous systems, and the pressure curve of the present system is much smoother and better than that of the previous system. The relative error of explaining formation pressure is less than 1% and 4% for the present and the previous systems, respectively. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…MBAUNet also effectively distinguished aquatic vegetation, turbid waters, clouds, and low-density CyanoHABs, maintaining a low error rate of 5.8% across varied environments. When applied to other lakes, MBAUNet consistently delivered over 90% precision. …”
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Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization by T.D. Dongale, K.G. Kharade, N.B. Mullani, G.M. Naik, R.K. Kamat

“…We measure the performance of the ANN model with regard to mean square error (MSE) and the correlation coefficient between expected output and output provided by the network. …”
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A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning by Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber

“…At national scale, predicted phenology resulted in a reasonable precision of R2 > 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. …”
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System Performance Optimization of Frequency-Swept Pump-Based Rectangular Brillouin Optical Filter by M. Shi, L. Yi, W. Wei, G. Pu, W. Hu

“…We evaluate the system performance of the signal, which is fed into the SBS filter based on frequency-swept pump with different sweep periods ranging from 0.1&#x00A0; to 100&#x00A0;<italic>&#x03BC;</italic>s, in terms of eye diagrams and bit-error rate (BER) after 12.5&#x00A0; and 25&#x00A0;km of fiber transmission, respectively. …”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…With a mean absolute percentage error (MAPE) below 0.707%, structural similarity (SSIM) exceeding 0.924, and peak signal-to-noise ratio (PSNR) above 45.416, the attention-guided leakage imaging framework exhibits high efficiency and accuracy, showcasing its potential applications in early leakage warning and pipeline time-lapse monitoring.…”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…A multi-layer neural network emerged as the top predictor of pure tone averages, achieving a mean absolute error of just 3.05 dB. Feature analysis identified age, gender, blood pressure and waist circumference as key associated factors. …”
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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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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…The results demonstrate that our framework significantly improves prediction accuracy, with a mean absolute error more than 30% lower than other methods. Further analysis demonstrated the robustness of DGL-STFA across various battery life stages, including early, mid, and end-of-life phases. …”
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Development of lesion and organ negative cast modelling technique for quality assurance and optimization of nuclear medicine images by Roberto Fedrigo, Robin J. N. Coope, Guillaume Chaussé, Ingrid Bloise, Claire Gowdy, François Bénard, Arman Rahmim, Carlos F. Uribe

“…Results Mean absolute error (MAE) for tumour volume between the original template and casted models is 13.8%, indicating that the method is reasonably accurate. …”
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Low Complexity Unquantized Forward Stack Decoding Algorithm for Spinal Codes in Measurement While Drilling Communication by Xiaoyang Yu, Lei Liang, Ke Jiang, Tianwei Chen

“…In simulated MWD environment, the proposed system achieved an average bit error rate (BER) of <inline-formula> <tex-math notation="LaTeX">$5.58\times 10^{-5}$ </tex-math></inline-formula>, with BER consistently below 0.1% across all intervals. …”
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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 was evaluated using the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and normalized-root-mean square error (NRMSE) for the Quantitative Maps Generator, and using SSIM, PSNR, and volumetric analysis for the Harmonization network, respectively. …”
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Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT by Emanuele Trucco, Joanna M Wardlaw, Wenwen Li, Grant Mair, Amos Storkey, Alessandro Fontanella, Antreas Antoniou, Eleanor Platt, Paul Armitage

“…Ultimately, this should lead to more robust and widely applicable methods.…”
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Near-infrared spectroscopy analysis to predict urinary allantoin in dairy cows by Leonardo A.C. Ribeiro, Guilherme L. Menezes, Tiago Bresolin, Sebastian I. Arriola Apelo, Joao R.R. Dórea

“…The partial least squares regression model achieved an R2 of 0.55, a concordance correlation coefficient of 0.73, and a root mean squared error of prediction (RMSEP) of 3.63 mmol/L to predict allantoin concentration from the spectra data set without preprocessing. …”
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Predicting carbohydrate quality in a global database of packaged foods by Eric Antoine Scuccimarra, Alexandre Arnaud, Marie Tassy, Marie Tassy, Kim-Anne Lê, Fabio Mainardi

“…The overall mean absolute error on the test set was 0.96 g/100 g of product. …”
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Vertical Stratification Increases the Capacity of Morphological Traits to Predict Trophic Position in Neotropical Ants by Jésica Vieira, Karen C. Neves, Lino A. Zuanon, Heloise Gibb, Alan N. Andersen, Heraldo L. Vasconcelos

“…Predictive capacity increased substantially when data from different traits were combined in multiple regression models, especially when separate equations were derived for the arboreal and ground faunas. The estimation error of these models was below 15% for 70% of the species, with the most informative traits being femur length, petiole length, and head width for ground species and eye length, mandible length, and Weber's length for arboreal species. …”
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Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

“…The models were evaluated using several statistical performance parameters, scatter plots, residual error curves, and eight statistical performance metrics to ensure predictive accuracy and reliability. …”
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