Natural Course of Electrocardiographic Features in Arrhythmogenic Right Ventricular Cardiomyopathy and Their Relation to Ventricular Arrhythmic Events by Anneli Svensson, Henrik Kjaerulf Jensen, Machteld J. Boonstra, Marianne Tétreault‐Langlois, Pia Dahlberg, Henning Bundgaard, Alex Hørby Christensen, Rebecca T. Rylance, Jesper H. Svendsen, Julia Cadrin‐Tourigny, Anneline S. J. M. te Riele, Pyotr G. Platonov

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Genome-wide characterization of the GmLUX binding preferences and its epigenic features in the soybean genome by Lv Tianxiao, Hong Xiao, Jingxing Wang, Ling Zhang, Tian Fan, Mingkun Huang, Chang-En Tian, Hua Yang, Yufang Hu

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Research on deep learning model for stock prediction by integrating frequency domain and time series features by Wenjie Sun, Jianhua Mei, Shengrui Liu, Chunhong Yuan, Jiaxuan Zhao

“…The StockMixer with ATFNet model proposed in this paper integrates both time-domain and frequency-domain features. By fusing information from both domains, the deep neural network significantly improves prediction accuracy and reliability. …”
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An evolutionary Bi-LSTM-DQN framework for enhanced recognition and classification in rural information management by Taiping Deng, Xi He, Jiao Li, Feifei Ye, Jingyang Tang

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Pangenome analysis of transposable element insertion polymorphisms reveals features underlying cold tolerance in rice by Yongqing Qian, Zuwen Zhou, Tianmin Ouyang, Dongao Li, Ru Li, Ping Gan, Renfei Qiao, Yingying Tan, Mingchao Qian, Liezhao Liu, Jiana Li, Kun Lu, Jijing Luo, Ling-Ling Chen, Jia-Ming Song

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Echoes From the Void: Detecting DNS Tunneling With Blackhole Features in Encrypted Scenarios With High Accuracy by Wafa S. Alorainy

“…The new approach introduces six new behavioral features concerning retry and domain-switching behavior in combination with eleven traditional DNS metrics. …”
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Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area by Xiang Li, Fa Zhang, Ziyi Liu, Yao Wei, Runlong Dai, Zhiyue Qiu, Yuxin Gu, Hong Yuan

“…This study establishes a national database comprising 1018 HSR station area samples across China in 2020, integrating built environment characteristics, HSR network topology, ecological considerations, and socioeconomic indicators. …”
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HASSDE-NAS: Heuristic–Adaptive Spectral–Spatial Neural Architecture Search with Dynamic Cell Evolution for Hyperspectral Water Body Identification by Feng Chen, Baishun Su, Zongpu Jia

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An Evolutionary Algorithm of the Regional Collaborative Innovation Based on Complex Network by Kun Wang, Duoyong Sun

“…The two main conceptions of evolution, “graph with dynamic features” and “network evolution,” have been provided in advance. …”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…First, we designed a spatial attention encoder-decoder to comprehensively explore spatial representations for extracting and reconstructing spatial features. Then, we designed a multi-scale spatiotemporal evolution module to obtain the spatiotemporal evolution patterns of weather using inter- and intra-frame computations. …”
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Annealing dynamics of regular rotor networks: Universality and its breakdown by András Grabarits, Gaetano Sammartino, Adolfo del Campo

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Generating surrogate temporal networks from mesoscale building blocks by Giulia Cencetti, Alain Barrat

“…As empirical datasets most often present complex features and interplays between structure and temporal evolution, creating surrogate data is however a challenging task, in particular for data describing time-resolved interactions between agents. …”
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Integrating quantitative knowledge into a qualitative gene regulatory network. by Jérémie Bourdon, Damien Eveillard, Anne Siegel

“…Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. …”
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An Event–Link Network Model Based on Representation in P-Space by Wenjun Zhang, Xiangna Chen, Weibing Deng

“…Moreover, the network’s growth and evolution can be flexibly adjusted by modifying the model parameters.…”
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MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation by Huantong Geng, Han Zhao, Zhanpeng Shi, Fangli Wu, Liangchao Geng, Kefei Ma

“…Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. …”
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An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection by Xinya Ding, Xuan Peng, Yanguang Xue, Liang Zhang, Tianying Wang, Yunpeng Zhang

“…An intelligent adapter module that performs adaptive feature fusion, automatically weighting and combining multi-source meteorological inputs (including temperature, wind fields, and humidity data) to maximize their synergistic effects while minimizing feature conflicts; the utilized network achieves an average improvement of over 4% across various metrics. 2. …”
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Legacy Vegetation and Drainage Features Influence Sediment Dynamics and Tidal Wetland Recovery After Managed Dyke Realignment by Samantha Crowell, Megan Elliott, Kailey Nichols, Danika van Proosdij, Emma Poirier, Jennie Graham, Tony Bowron, Jeremy Lundholm

“…We combined measurements of sediment flux and accretion, digital surface and drainage network models, and vegetation mapping to understand the effects of legacy features on geomorphological evolution and restoration trajectory at a Bay of Fundy MR site. …”
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Insights Into Spatiotemporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations From the TexNet Catalog (2017–2022) by Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis

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Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias

“…AE signals are first transformed into the frequency domain, where significant frequency components are extracted and used as inputs to an autoencoder network. The autoencoder model reduces the dimensionality of the data while preserving essential features, enabling unsupervised clustering to identify distinct damage states. …”
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Deep Convolutional Neural Networks in Medical Image Analysis: A Review by Ibomoiye Domor Mienye, Theo G. Swart, George Obaido, Matt Jordan, Philip Ilono

“…Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex medical imaging datasets. …”
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