Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning by Yuanhao Xu, Kairong Lin, Caihong Hu, Xiaohong Chen, Jingwen Zhang, Mingzhong Xiao, Chong‐Yu Xu

“…The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. …”
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Calibration of Electron Microscopes Through Deep Learning and Bayesian Optimization by Jilles S. van Hulst, Roy A. C. van Zuijlen, Narges Javaheri, Maurits Diephuis, Duarte J. Antunes, W. P. M. H. Heemels

“…Specifically, we perform transfer learning on an adapted ResNet18 architecture using a large data set of simulated Ronchigrams. …”
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Estimation of soil properties using Hyperspectral imaging and Machine learning by Eirini Chlouveraki, Nikolaos Katsenios, Aspasia Efthimiadou, Erato Lazarou, Kalliopi Kounani, Eleni Papakonstantinou, Dimitrios Vlachakis, Aikaterini Kasimati, Ioannis Zafeiriou, Borja Espejo-Garcia, Spyros Fountas

“…Hyperspectral sensors generate vast arrays of spectral bands, offering unprecedented opportunities to estimate soil properties quickly and cost-effectively when integrated into the appropriate machine learning (ML) pipeline. However, the high dimensionality and collinearity inherent to these spectra pose challenges for precise property detection, often leading to poor generalization. …”
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Improving Satellite Imagery Masking Using Multitask and Transfer Learning by Rangel Daroya, Luisa Vieira Lucchese, Travis Simmons, Punwath Prum, Tamlin Pavelsky, John Gardner, Colin J. Gleason, Subhransu Maji

“…Our model leverages multitask learning to improve accuracy while sharing computation across tasks for added efficiency. …”
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Machine learning-guided field site selection for river classification by Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

“…This study developed a general and practical field site selection framework by incorporating machine learning in a human-in-the-loop manner. This framework includes three steps: (1) initial field site selection via machine learning from prior datasets, (2) selected field site accessibility evaluation and observation, and (3) additional field site decision and selection via an iterative learning process. …”
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Designing for Engagement in Primary Health Education Through Digital Game-Based Learning: Cross-National Behavioral Evidence from the iLearn4Health Platform by Evgenia Gkintoni, Emmanuella Magriplis, Fedra Vantaraki, Charitini-Maria Skoulidi, Panagiotis Anastassopoulos, Alexandra Cornea, Begoña Inchaurraga, Jaione Santurtun, Ainhoa de la Cruz Mancha, George Giorgakis, Kleri Kouppas, Stella Timotheou, Maria Jose Moreno Juan, Miren Muñagorri, Marta Harasiuk, Alfredo Garmendia Lopez, Efi Skoulidi, Apostolos Vantarakis

“…This study evaluates design effectiveness in Digital Game-Based Learning (DGBL) for primary health education through systematic teacher assessment of the iLearn4Health platform. …”
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Multi‐stage image inpainting using improved partial convolutions by Cheng Li, Dan Xu, Hao Zhang

“…Abstract In recent years, deep learning models have dramatically influenced image inpainting. …”
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Model of Dynamic Mechanical Parameters and Vibration Analysis in Hot Rolling by Ming Zhang, Zihao Huang, Yanbo Yang

“…To address the problem of the limited prediction of mill vibration due to small sample data in non-steady states, we propose to predict dynamic mechanical parameters (rolling force and rolling torque) based on the TCN-LSTM step strategy transfer model. The prediction accuracies of the TCN-LSTM step strategy transfer model under 1000 sets of training data reach 95.8% and 93.2%, respectively, and at the same time, it saves the time of training and regulation of the deep learning model and can better meet the needs of online prediction. …”
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Learning how to explore spiritual aspects in encounters with patients with chronic pain: a pre-test post-test trial on the effectiveness of a web-based learning intervention by Felix Michael Schmitz, Ann-Lea Buzzi, Beate Gabriele Brem, Kai Philipp Schnabel, Joana Berger-Estilita, Fredy-Michel Roten, Simon Peng-Keller, Sissel Guttormsen

“…In the intervention phase, the students completed the 45-minute learning module on a personal computer. The module presented InSpiRe-related content as text and step-by-step video demonstrations, including hints that denote critical actions. …”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…The K-MGCMLD model is divided into four main steps. The first step is the construction of a heterogeneous graph. …”
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Time Series Anomaly Detection Using Signal Processing and Deep Learning by Jana Backhus, Aniruddha Rajendra Rao, Chandrasekar Venkatraman, Chetan Gupta

“…In this paper, we propose a two-step approach for time series anomaly detection that combines signal processing techniques with deep learning methods. …”
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A Machine Learning Approach to Volume Tracking in Multiphase Flow Simulations by Aaron Mak, Mehdi Raessi

“…Bypassing the computationally expensive steps of interface reconstruction and flux calculation, the proposed ML approach performs volume advection in a single step, directly predicting the volume fractions at the next time step. …”
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CANNABIDIOL IMPROVES LEARNING AND MEMORY AND EXERTS ANXIOLYTIC EFFECT IN EXPERIMENTAL STUDY by Maria Georgieva-Kotetarova, Ilin Kandilarov, Hristina Zlatanova, Natalia Vilmosh, Nikolay Yanchev, Delian Delev, Ilia Kostadinov

“…Memory and learning were evaluated using step-down passive avoidance test. …”
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Near real-time online reinforcement learning with synchronous or asynchronous updates by Mircea-Bogdan Radac, Darius-Pavel Chirla

“…In this paper, we propose a solution for addressing a major limitation of the existing RL schemes when it comes to interleaving the environment interaction step with the learning step. Leveraging the neural network approximation complexity with the real-time learning capability is one of several reasons for which RL has not been adopted more in practical control systems. …”
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Enhancing freight train delay prediction with simulation‐assisted machine learning by Niloofar Minbashi, Jiaxi Zhao, C. Tyler Dick, Markus Bohlin

“…This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. …”
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Bayesian variable selection with graphical structure learning: Applications in integrative genomics. by Suprateek Kundu, Yichen Cheng, Minsuk Shin, Ganiraju Manyam, Bani K Mallick, Veerabhadran Baladandayuthapani

“…Our framework provides a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers.…”
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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…All analyses were supported by step-by-step code to ensure reproducibility. Findings. …”
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Graph-based reinforcement learning for software-defined networking traffic engineering by Jingwen Lu, Chaowei Tang, Wenyu Ma, Wenjuan Xing

“…This paper proposes GRL-TE (Graph-based Reinforcement Learning for Traffic Engineering), a novel framework that achieves near-optimal performance while maintaining computational efficiency across diverse network scales. …”
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Hypertensive disorders in pregnancy and the general practitioner: A vignette and lessons to learn by J. Moodley

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Fault Diagnosis Method of Power Electronic Converter Based on Broad Learning by Ran Han, Rongjie Wang, Guangmiao Zeng

“…The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. …”
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