An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale by Xinyi Shu, Chenlei Ye, Zongxue Xu, Ruting Liao, Pengyue Song, Silong Zhang

“…By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. …”
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Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone by Lidao Xu, Yongxin Yang, Qinghua Tan, Hongping Tan, Yifan Wang, Zeliang Hou, Jingxian Shen, Rihui Li, Yuxi Luo, Lizhang Zeng, Qiang Guo, Xuchu Weng, Jiuxing Liang

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Full‐Depth Reconstruction of Long‐Term Meridional Overturning Circulation Variability From Satellite‐Measurable Quantities via Machine Learning by Huaiyu Wei, Kaushik Srinivasan, Andrew L. Stewart, Aviv Solodoch, Georgy E. Manucharyan, Andrew McC. Hogg

“…Using a neural network interpretation technique, we identify ocean bottom pressure near the western boundary and along dense‐water export pathways as the dominant input features for MOC reconstruction. …”
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Remote Sensing Fine Estimation Model of PM_2.5 Concentration Based on Improved Long Short-Term Memory Network: A Case Study on Beijing–Tianjin–Hebei Urban Agglomeration i... by Yiye Ji, Yanjun Wang, Cheng Wang, Xuchao Tang, Mengru Song

“…Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). …”
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Simulating Intraday Electricity Consumption with ForGAN by Ralf Korn, Laurena Ramadani

“…Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German electricity consumption data. …”
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2D Object Detection: A Survey by Emanuele Malagoli, Luca Di Persio

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Recovering 3D Basin Basement Relief Using High-Precision Magnetic Data Through Particle Swarm Optimization and Back Propagation Algorithm by Shen Yan, Xinjun Zhang, Zhongda Shang, Kai Wang, Yixin Ma

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Prediction of the Next Solar Rotation Synoptic Maps Using an Artificial Intelligence–based Surface Flux Transport Model by Hyun-Jin Jeong, Mingyu Jeon, Daeil Kim, Youngjae Kim, Ji-Hye Baek, Yong-Jae Moon, Seonghwan Choi

“…Our model successfully generates magnetic features, such as the diffusion of solar active regions and the motions of supergranules. …”
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Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk Hα Images by GaoFei Zhu, GangHua Lin, Xiao Yang, Cheng Zeng

“…Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. …”
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A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis by Amal G. Diab, El-Sayed M. El-Kenawy, Nihal F. F. Areed, Hanan M. Amer, Mervat El-Seddek

“…The numeral of extracted features was reduced for identifying the most appropriate feature attributes for the disease. …”
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UNDERSTANDING THE KNOWLEDGE ECOSYSTEM: CORE AND FORMS by Iryna Kalenyuk, Antonina Djakona, Yevgen Panchenko

“…The authors employ a system-structural analysis to examine the evolution of conceptualisations of the terms "ecosystem" and "knowledge ecosystem". …”
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Neural-Driven heuristic for strip packing trained with Black-Box optimization by Mariusz Kaleta, Tomasz Śliwiński

“…Instead of relying on static heuristic rules, our approach adapts to the characteristics of each problem instance, enabling more efficient and effective packing strategies. To train the neural network, we employ the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-ofthe- art derivative-free optimization method. …”
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Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images by Qinggang Wu, Yonglei Wu, Yang Li, Wei Huang

“…This article presents an improved YOLOv5s network-based technique for remote sensing object recognition to overcome these difficulties. …”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. …”
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Triplane generator-based NeRF-GAN framework for single-view ship reconstruction by Tao Liu, Shiqi Geng, Yucheng Fu, Zhengling Lei, Yuchi Huo, Xiaocai Zhang, Fang Wang, Bing Han, Mei Sha, Zhongdai Wu

“…Subsequently, a novel generator and mask module are engineered to produce optimized feature outputs. Plus, discriminator and encoder networks, coupled with a tailored loss function, are formulated to direct model optimization. …”
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Deep Neural Emulation of the Supermassive Black Hole Binary Population by Nima Laal, Stephen R. Taylor, Luke Zoltan Kelley, Joseph Simon, Kayhan Gültekin, David Wright, Bence Bécsy, J. Andrew Casey-Clyde, Siyuan Chen, Alexander Cingoranelli, Daniel J. D’Orazio, Emiko C. Gardiner, William G. Lamb, Cayenne Matt, Magdalena S. Siwek, Jeremy M. Wachter

“…Previously, Gaussian processes (GPs) and dense neural networks have been used to make such a connection by being built as conditional emulators; their input is some selected evolution or environmental SMBH binary parameters and their output is the emulated mean and standard deviation of the GWB strain ensemble distribution over many Universes. …”
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A Deep Learning-Driven Black-Box Benchmark Generation Method via Exploratory Landscape Analysis by Haoming Liang, Fuqing Zhao, Tianpeng Xu, Jianlin Zhang

“…Once the feature criteria are met, the resulting topological map point is used to train a neural network to produce a surrogate function that retains the desired landscape characteristics. …”
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Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…Galaxy–galaxy mergers are crucial in galaxy evolution, but the tidal features around galaxies are often faint, making it difficult to identify interacting or merging galaxies. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…The research papers selected for this review focus on research utilizing TOF MRA as the imaging modality and emphasize computer-aided detection through both traditional and deep learning techniques, with a particular emphasis on Convolutional Neural Networks (CNNs). CNNs have proven to be a crucial component in improving the accuracy and efficiency of aneurysm detection by automatically learning features from raw imaging data, bypassing the need for manual feature extraction. …”
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Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring by Jiangfeng Li, Jiahao Qin, Kaimin Kang, Mingzhi Liang, Kunpeng Liu, Xiaohua Ding

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