Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System by Patrick Laloyaux, Thorsten Kurth, Peter Dominik Dueben, David Hall

“…Abstract Model bias is one of the main obstacles to improved accuracy and reliability in numerical weather prediction conducted with state‐of‐the‐art atmospheric models. …”
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Novel integrated computational AMP discovery approaches highlight diversity in the helminth AMP repertoire. by Allister Irvine, Darrin McKenzie, Ciaran J McCoy, Robert L J Graham, Ciaren Graham, Sharon A Huws, Louise E Atkinson, Angela Mousley

“…This study highlights limitations in the homology-based approaches, used to identify putative nematode AMPs, for the characterisation of flatworm AMPs, and reveals that innovative algorithmic AMP prediction approaches provide an alternative strategy for novel helminth AMP discovery. …”
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Numerical Simulation of Basic Properties of Full-Steel Slag Aggregate Concrete by Zhu Bian, Yuan Fang, Feng Yu, Xuliang Wang, Guosheng Xiang

“…In this study, the bit-level table of orthogonal test is adopted as the coding form of Genetic Algorithm (GA), and a Back Propagation (BP) neural network prediction model of the basic properties of full steel slag aggregate concrete (FSSAC) is established, and the experiment data are validated with good agreement. …”
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Estimation of drought-induced forest decline of Scots pine in the Navarrese Pyrenees (Spain) by Marina Rodes-Blanco, Paloma Ruiz-Benito, Miguel A. Zavala, Inmaculada Aguado, Mariano García

“…Identifying and predicting areas affected by drought-induced forest decline using remote sensing is key for monitoring forest-responses to drought, as well as for forest conservation and management. …”
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Data Balancing Techniques Using the PCA-KMeans and ADASYN for Possible Stroke Disease Cases by Uung Ungkawa, Muhammad Avilla Rafi

“…This condition can cause the minority class prediction to be wrong because the model tends to predict the majority class. …”
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Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004–2023) by Jian-cheng Lou, Xiao-fen Yu, Jian-jun Ying, Da-qiao Song, Wen-hua Xiong

“…The most notable research hotspots currently are the optimal selection of neural imaging markers and the most suitable machine learning algorithm models.…”
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Electricity pinch analysis method for flexibility supply-demand matching in power systems by Yaling Mao, Tiejiang Yuan, Xueqin Tian, Yue Teng

“…First, the net-load profile is decomposed by successive variational mode decomposition (SVMD) optimized with the Red-billed Blue Magpie Optimization (RBMO) algorithm to construct a flexibility demand model. …”
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Optimizing solar PV systems using fuzzy logic for Climate-Resilient Healthcare infrastructure in Kyrgyzstan by Nivine Guler, Zied Ben Hazem, Ali Gunes

“…Also, the proposed scheme showed 3% prediction error when checked with the PVWatts calculator. …”
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Cross-Domain Fake Review Detection via Orthogonal Counterfactual Representations by Richa Gupta, Vinita Jindal, Indu Kashyap

“…Working of the proposed COAT is divided in three phases: 1) two-step masking to remove source-specific cues, 2) counterfactual generation using BERT, followed by orthogonal alignment via Procrustes algorithm, and 3) training and prediction using a Bi-LSTM classifier in a zero-shot setting. …”
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A Statistical Optimization Method for Sound Speed Profiles Inversion in the South China Sea Based on Acoustic Stability Pre-Clustering by Zixuan Zhang, Ke Qu, Zhanglong Li

“…Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. …”
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Equatorial convection controls boreal summer intraseasonal oscillations in the present and future climates by Aditya Kottapalli, P. N. Vinayachandran

“…The study implies that with a correct representation of BSISO’s equatorial convection, the prediction of BSISO and extreme rainfall associated with BSISOs becomes more reliable.…”
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A robust, deep learning-based analysis of time-domain signals for NMR spectroscopy by Kyungdoe Han, Eunhee Kim, Kyoung-Seok Ryu, Donghan Lee

“…As a proof of concept, we present the resulting spectra, along with peak lists predicted by supplying only FID input to the deep learning algorithm. …”
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Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility by Oguz Kagan Isik, Ivan Petrunin, Antonios Tsourdos

“…The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. …”
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Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter by Aleksander Suti, Gianpietro Di Rito, Giuseppe Mattei

“…The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.…”
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Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice by G. Colas, V. Masson, F. Bouttier, L. Bouilloud, L. Pavan, V. Karsisto

“…We have developed an ice content prediction to account for the freezing and melting of the water content on the surface. …”
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Integrated OVMD-BiGRU-SMAC Framework for Forecasting Construction Accidents in the Kingdom of Saudi Arabia by Badr T. Alsulami, Afaq Khattak

“…These IMFs are individually forecasted using Bidirectional Gated Recurrent Unit (BiGRU) models, which are capable of learning sequential patterns in both temporal directions. To enhance the predictive accuracy, the hyperparameters of each BiGRU model are optimized using the Sequential Model-based Algorithm Configuration (SMAC) technique. …”
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Fluid flow characteristics estimation of a new integrated bifluid/airbased photovoltaic thermal system utilizing a hybrid optimization method by Ghassan A. Bilal, Abdullateef A. Jadallah, Omayma M. Abdulmajeed, Müslüm Arıcı

“…Furthermore, combining two techniques gives the best predictions compared to actual data. The results of the comparison showed a satisfactory correspondence between the predicted and the experimental data. …”
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A Comparative Study of Three-Dimensional Flow Based, Geometric, and Empirical Tortuosity Models in Carbonate and Sandstone Reservoirs by Benedicta Loveni Melkisedek, Yoevita Emeliana, Irwan Ary Dharmawan

“…This comprehensive comparison underscores the importance of incorporating multiple pore-scale parameters for robust tortuosity prediction, improving the understanding of flow behavior in heterogeneous reservoir rocks.…”
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An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease by Steffi Rajan, Suresh Ponnan

“…The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). The present study enhances prediction functions by extracting MGD-related features and complicated relationships from the DED dataset. …”
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A Method for Developing a Digital Terrain Model of the Coastal Zone Based on Topobathymetric Data from Remote Sensors by Mariusz Specht, Marta Wiśniewska

“…They were processed using the “Depth Prediction” plug-in based on the Support Vector Regression (SVR) algorithm, which was implemented in the QGIS software as part of the INNOBAT project. …”
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