Multi-stage deep learning artifact reduction for parallel-beam computed tomography by Jiayang Shi, Daniël M. Pelt, K. Joost Batenburg

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A systematic review of economic evaluations of population-based sodium reduction interventions. by Silvia F Hope, Jacqui Webster, Kathy Trieu, Arti Pillay, Merina Ieremia, Colin Bell, Wendy Snowdon, Bruce Neal, Marj Moodie

“…<h4>Objective</h4>To summarise evidence describing the cost-effectiveness of population-based interventions targeting sodium reduction.<h4>Methods</h4>A systematic search of published and grey literature databases and websites was conducted using specified key words. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. …”
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Early reduction in gut microbiota diversity in critically ill patients is associated with mortality by Hannah Wozniak, Nadia Gaïa, Vladimir Lazarevic, Christophe Le Terrier, Tal Sarah Beckmann, Eleonora Balzani, Martin Urner, Jérôme Pugin, Jacques Schrenzel, Claudia-Paula Heidegger, Gut Microbiota working group

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Procedural Volume and Outcomes After Septal Reduction Therapies in Hypertrophic Obstructive Cardiomyopathy by Nirav Patel, Naman S. Shetty, Mokshad Gaonkar, Abdulla Shahid, Girija P. Divekar, Akhil Pampana, Nehal Vekariya, Peng Li, Mustafa I. Ahmed, Garima Arora, Pankaj Arora

“…Background Septal myectomy and alcohol septal ablation (ASA) are septal reduction therapies for patients with symptomatic obstructive hypertrophic cardiomyopathy. …”
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A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electrici... by Xinfu Pang, Wen Jia, Haibo Li, Qingzhong Gao, Wei Liu

“…Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. …”
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Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks by Nicolas Sibuet, Sebastian Ares de Parga, Jose Raul Bravo, Riccardo Rossi

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Two-Stage Combined Model for Short-Term Electricity Forecasting in Ports by Wentao Song, Xiaohua Cao, Hanrui Jiang, Zejun Li, Ruobin Gao

“…This study introduces a novel approach that transcends the limitations of single prediction models by employing a Binary Fusion Weight Determination Method (BFWDM) to optimize and integrate three distinct prediction models: Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM), Multi-Quantile Recurrent Neural Network (MQ-RNN), and Deep Factors. …”
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Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice by Mahya Hajihassanpour, Georges Kesserwani, Per Pettersson, Vasilis Bellos

“…Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. …”
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Viscosity reduction of extra heavy crude oil by magnetite nanoparticle-based ferrofluids by Juan E Aristizábal-Fontal, Farid B Cortés, Camilo A Franco

“…Fe 3 O 4 nanoparticles were synthesized by coprecipitation method. The effect of the concentration of nanoparticles in the viscosity reduction degree was determined for dosages between 0 and 50,000 mg/L. …”
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Complexity reduction by symmetry: Uncovering the minimal regulatory network for logical computation in bacteria. by Luis A Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A Makse

“…Symmetry principles play an important role in geometry, and physics, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. …”
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Efficient spectral data reduction for accurate iodine quantification in multi-energy CT by Olivia F. Sandvold, Roland Proksa, Heiner Daerr, Amy E. Perkins, Kevin M. Brown, Thomas Koehler, Ravindra M. Manjeshwar, Peter B. Noël

“…Abstract This study proposes a spectral data reduction method for multi-channel computed tomography (CT) that optimizes material decomposition accuracy while minimizing data complexity. …”
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Load Modeling and CVR Analysis for Residential Appliances and Distribution Network&#x2014;A Review by Mithila Seva Bala Sundaram, Tan Chia Kwang, Jeyraj A L Selvaraj Selvaraj, Ab Halim Abu Bakar

“…It explores both component-based and measurement-based load modeling methods, including static and dynamic models, with a special emphasis on the ZIP load model. …”
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Developing and validating a risk assessment method for noise-induced hearing loss in workers by Milad Abbasi, Saeid Yazdanirad, Ali Ahmadi

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Construction of a Prediction Model for Energy Consumption in Urban Rail Transit Operations Using a Bottom–Up Approach by Boyu Chen, Ye Lin

“…The factors were grouped based on the scale of the urban rail transit network, and planned indicators were screened using stepwise regression and machine learning eigenvalue methods. Predictive models were then constructed using these planned indicators through multiple linear regression and random forest regression. …”
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Subsystem-Based Fault Detection in Robotics via <italic>L2</italic> Norm and Random Forest Models by Elham Abdullah Alamoudi

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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Hyperparameter optimization using Bayesian methods enhances predictive performance. Results show Gaussian process regression and artificial neural networks excel in accuracy, though all models’ performance declines with increased soiling. …”
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Analysis of heart rhythm variability by linear and non-linear dynamics methods by Vuksanović Vesna, Gal Vera

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NSGT: A Plasma Position Prediction Method for the HL-2A Tokamak Device Based on Transformer by Ling Zhou, Bin Yang, Xiangcheng Lin, Kaihao Zhu, Ziran Lan, Xiangwen Li

“…The experimental results show that the NSGT model has a significant reduction in the root - mean - square error of prediction compared with the LSTM method, effectively supporting the real - time adjustment of the closed - loop control system. …”
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Study on the Shear Lag Calculation Method for Damaged Box Girder by Yulong Zhang, Junguang Wang, Xiaoguang Wu, Jiahao Yin, Yuanxu Shi

“…Shear lag stresses increase significantly in cracked concrete box girders; however, most existing models assume intact sections and are, therefore, unsuitable for rapid field diagnosis. …”
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