An Optimal Reconfiguration Strategy of Microgrid Cluster Based on Vulnerability Assessment by LIU Haocheng, DING Xiying

“…Firstly, using the simulation operation data of microgrid group under multiple working conditions, the operation state data matrix is constructed based on the measurable variables of each node of the synchronous system, and it is converted into a large number of pictures containing fault information, which is used to train and check the fault identification and classification network based on capsule neural network. …”
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Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction by Yannis Schmutz, Noemi Imfeld, Stefan Brönnimann, Erik Graf

“…WeRec3D is trained and validated in a self‐supervised manner using ERA5's surface temperature and pressure data over Europe. …”
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Parameter estimation for allometric trophic network models: A variational Bayesian inverse problem approach by Maria Tirronen, Anna Kuparinen

“…We represent the model as a Bayesian neural network, which combines an artificial neural network with Bayesian inference, using a surrogate for the posterior distribution of model parameters, and train this model by evolutionary optimization to avoid potentially costly computation of the gradient with respect to the model parameters. …”
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Predictive Framework for Sustainable Engineering through Machine Learning and Cross-Sector Collaboration by Choudhary Abhik, Adhikari Upasana, Roy Dipankar, Gupta Subir, Roy Priyanka, Bhaduri Aparna

“…Still, collaboration across the different sectors of AIG (academia, industry, and government) is poorly integrated due to operational silos and structures lacking a centralized data-driven approach. This research proposes a new methodology based on ensemble machine learning, assessing, and predicting the outcomes of engineering projects with real-time open-source data focused on sustainability. …”
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The effect of gamification on brand co-creation in app-based businesses: investigating the mediating role of consumer engagement and the moderating role of hedonic and innovativene... by Rayhane Rezagholizade, Zahra Sadeqi-Arani, Esmail Mazroui

“…To collect data, standard and researcher-made questionnaires were used. …”
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Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process by Yajing Zheng, Dekun Zhang

“…Purpose – The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times. …”
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Prediction and validation of nanowire proteins in Oleidesulfovibrio alaskensis G20 using machine learning and feature engineering by Dheeraj Raya, Vincent Peta, Alain Bomgni, Shiva Aryal, Tuyen Duc Do, Kalimuthu Jawaharraj, David R. Salem, Venkataramana Gadhamshetty, Saurabh Sudha Dhiman, Etienne Z. Gnimpieba

“…These findings enable data-driven decisions for biomedical and biotechnological applications. …”
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An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity Estimation by S. Du, H. Huang, K. Shen, Z. Liu, S. Tang

“…The approach is validated by predicting vegetation gross primary productivity (GPP) using global climate and land cover data from 2001 to 2020. Trained on 50 million samples and tested on 2.8 million, the proposed model achieves an RMSE of 0.836, outperforming LightGBM (1.063) and TabNet (0.944). …”
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Transfer Learning for Facial Expression Recognition by Rajesh Kumar, Giacomo Corvisieri, Tullio Flavio Fici, Syed Ibrar Hussain, Domenico Tegolo, Cesare Valenti

“…We present a methodology based on transfer learning with the pre-trained models VGG-19 and ResNet-152, and we highlight dataset-specific preprocessing techniques that include resizing images to 124 × 124 pixels, augmenting the data and selectively freezing layers to enhance the robustness of the model. …”
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Prediction of Remaining Useful Life of Operating Mechanism Driven by Deep Feature in Classification Model by Xilei Dong, Fuping Zhang, Yunyang Ye, Huan Zhang

“…For a given sample, with the principal component analysis method, the extracted deep features from the trained network can be further optimized. Finally, with the assistance of linear fitting of a few sample data, the prediction of the remaining service life of the operating mechanism can be achieved. …”
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A framework for predicting zoonotic hosts using pseudo-absences: the case of Echinococcus multilocularis by Andrea Simoncini, Dimitri Giunchi, Marta Marcucci, Alessandro Massolo

“…Identifying the host range of zoonotic parasites is challenging due to limited data and sampling biases. In particular, while more information exists for susceptible hosts, data on resistant species is extremely scant. …”
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Models for the Analysis of the Structural Capacity of Railway Bridges in Peru in Accordance with the American Railway Engineering and Maintenance-of-Way Association Standard by Juan Zapata, Doris Esenarro

“…These data were used in order to be able to warn of certain technical aspects that would influence the recommendations for a locomotive replacement project in which new units had different load distributions between the axles, which would make it necessary to review the tracks and bridges of the same in order to determine if they would be able to withstand the new forces safely, as well as to reinforce structural elements according to the material and the structural condition, and finally, to assess the variation in the increase in train speed in some road corridors to achieve a better FRA (Federal Railway Administration) classification of Class 3, where the presence of structures dating back to the last century has been verified as well (1851–1856–1908). …”
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Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability by Waqas Ali, Mariam Sabir

“…Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. …”
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HOLESOM: Constraining the Properties of Slowly Accreting Massive Black Holes with Self-organizing Maps by Valentina La Torre, Fabio Pacucci

“…We present HOLESOM (HOLESOM is publicly available at: http://github.com/valentinalatorre/holesom ), a machine learning-powered tool based on the self-organizing maps (SOMs) algorithm, specifically designed to identify slowly accreting MBHs using sparse photometric data. Trained on a comprehensive set of ∼20,000 spectral energy distributions, HOLESOM can (i) determine if the photometry of a source is consistent with slowly accreting MBHs and (ii) estimate its black hole mass and Eddington ratio, including uncertainties. …”
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Prediction of Atmospheric Bioaerosol Number Concentration Based on PKO–AGA–SVM Fusion Algorithm and Fluorescence Lidar Telemetry by Zhimin Rao, Yicheng Li, Jiandong Mao, Hu Zhao, Xin Gong

“…In order to realize early warning prediction of the distribution characteristics of atmospheric bioaerosol content, this paper proposes using fluorescence lidar as a technical means to establish a prediction model of atmospheric bioaerosol concentration by obtaining the observation data set of bioaerosol concentration, combining it with the data set of atmospheric environmental parameters related to bioaerosol content, and utilizing the fusion algorithm PKO–AGA–SVM. …”
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Uncertainty Estimation in Cardio Landmark Detection and Heart Disease Diagnosis on Chest X-Ray Images by Dmitry Lvov, Ivan Stebakov, Alexei Kornaev, Ilya Pershin, Tamerlan Mustafaev, Danil Afonchikov, Ramil Kuleev, Iskander Bariev, Bulat Ibragimov

“…The proposed models were trained to predict the mean and log variance of a normal distribution for each input image, with the variance value estimating the uncertainty of the prediction. …”
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Enhancing diabetic retinopathy classification accuracy through dual-attention mechanism in deep learning by Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, Hazrat Ali

“…Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalised treatment. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalisation of deep learning models trained for DR classification. …”
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Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference. by Johannes C J Heyn, Miguel Atienza Juanatey, Martin Falcke, Joachim O Rädler

“…The trained neural network in turn is used to infer the probability distribution of a limited number of model parameters that correspond to the experimental trajectories. …”
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Mapping dominant plant communities in the degraded Zoige swamp using Sentinel-1/2 imagery and its implications for vegetation restoration by Guoying Zhang, Chuanpeng Zhao, Mingming Jia, Rong Zhang, Hou Jiang, Zongming Wang

“…Through satellite remote sensing, existing studies have obtained data on the distribution and changes in area of degraded Zoige swamp; however, the detailed identification of constituent plant communities has not been conducted. …”
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The Cognitive Correlates of Obsessive-Compulsive Disorder: The Predictive Role of Looming Maladaptive Style, Executive Function, Thought-Action Fusion and Metacognition by Maryam Koosheshi, Rahim Yousefi, Ladan Vaghef

“…Methodology This study has a theoretical aim and employs a descriptive-correlational approach for data collection. The statistical population includes all students from Azarbaijan Shahid Madani University during the 2019-2020 academic year. …”
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