Dynamic evolution and network structure characteristics of the wellbeing eco-efficiency of cities in the Yellow River Basin, China by Meixia Wang, Qingyun Zheng, Li Liu

“…Clarifying its spatial and temporal dynamic evolution and network structure characteristics is crucial for promoting the ecological protection and high-quality development in the entire Yellow River Basin (YRB). …”
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Multilevel Feature Cross-Fusion-Based High-Resolution Remote Sensing Wetland Landscape Classification and Landscape Pattern Evolution Analysis by Sijia Sun, Biao Wang, Zhenghao Jiang, Ziyan Li, Sheng Xu, Chengrong Pan, Jun Qin, Yanlan Wu, Peng Zhang

“…To address these issues, this study proposes the multilevel feature cross-fusion wetland landscape classification network (MFCFNet), which combines the global modeling capability of Swin Transformer with the local detail-capturing ability of convolutional neural networks (CNNs), facilitating discerning intraclass consistency and interclass differences. …”
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Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

“…This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. …”
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Signatures of Varying Climate on Geomorphic and Topologic Characteristics of Channel Networks by Aysan H. Bavojdan, Sevil Ranjbar, Dingbao Wang, Arvind Singh

“…Abstract Channel networks are important landscape features that transport water, sediment, and nutrients. …”
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A complex network perspective on spatiotemporal evolution of extreme precipitation over the middle and lower reaches of the Yangtze river by Ziyi Hu, Aixia Feng, Changgui Gu, Peng Zhao, Qiguang Wang

“…In this study, we aimed to analyze the spatiotemporal evolution characteristics of extreme precipitation based on visible graph network and state transition networks at different percentile thresholds, and to analyze and compare with the topology, network type, anomalous year, trend of the change stage, key modes, and the future evolution trend. …”
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Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network by Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen

“…Secondly, establish a financial flow data forecasting framework using MVE²-STFN. Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. …”
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Evolution of Bluetooth Technology: BLE in the IoT Ecosystem by Grigorios Koulouras, Stylianos Katsoulis, Fotios Zantalis

“…It examines the current state of BLE, including its applications, challenges, limitations, and recent advancements in areas such as security, power management, and mesh networking. The recent release of Bluetooth Low Energy version 6.0 by the Bluetooth Special Interest Group (SIG) highlights the technology’s ongoing evolution and growing importance within the IoT. …”
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Advancing Engineering Solutions with Protozoa-Based Differential Evolution: A Hybrid Optimization Approach by Hussam N. Fakhouri, Faten Hamad, Abdelraouf Ishtaiwi, Amjad Hudaib, Niveen Halalsheh, Sandi N. Fakhouri

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Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis by Chaojun Li, Kai Ma, Shengrong Li, Xiangshui Meng, Ran Wang, Daoqiang Zhang, Qi Zhu

“…Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. …”
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Research on the policy inconsistency, network motifs and low carbon effects for municipal solid waste management by Bo Lv, Tianxu Cui, Daiheng Li, Weiyue Yao

“…This study combines the policy consistency formula, a four-node network motif evolution algorithm, and the Exponential Random Graph Model (ERGM), analyzing MSWM green network motifs with carbon emission data. …”
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Research on traffic representation in network anomaly detection by SUN Jianwen, ZHANG Bin, CHANG Heyu

“…Subsequently, the evolution of traffic representation in network anomaly detection was systematically reviewed, providing a comprehensive analysis of its forms, feature learning, and application in anomaly detection both globally and domestically. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. …”
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Dynamic facial expression recognition integrating spatiotemporal features by LIU Baobao, TAO Lu, YANG Jingjing, WANG Heying

“…To address the challenges of extracting key facial features and capturing the dynamic changes of expressions in natural environments, a network model based on keyframes, named three-dimensional resnet and attention mechanism with GRU (TDRAG) was proposed. …”
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Prediction of crystalline structure evolution during solidification of aluminum at different cooling rates using a hybrid neural network model by Rafi B. Dastagir, Shorup Chanda, Farsia K. Chowdhury, Shahereen Chowdhury, K. Arafat Rahman

“…By combining the temporal pattern descriptors of LSTMs with the feature extraction potential of convolutional neural networks (CNN), the hybrid Conv1D-LSTM model achieves higher accuracy in predicting crystal structural evolution curves, in contrast to the performance of standalone LSTM and CNN models. …”
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Evolutionary model of heterogeneous clustering wireless sensor networks based on local world theory by Xiu-wen FU, Wen-feng LI

“…Current research on the scale-free evolutionary model of wireless sensor networks (WSN) treated each network as a homogenous one and does not take into account the evolutionary characteristics of the network in realistic scenarios,thus leading to significant differences between homogenous networks and realistic ones.Therefore,based on local-world theory,a heterogonous evolution model of WSN in relation to cluster-structure,energy-sensitivity and dynamic behavior of WSN (e.g.…”
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Evolutionary model of heterogeneous clustering wireless sensor networks based on local world theory by Xiu-wen FU, Wen-feng LI

“…Current research on the scale-free evolutionary model of wireless sensor networks (WSN) treated each network as a homogenous one and does not take into account the evolutionary characteristics of the network in realistic scenarios,thus leading to significant differences between homogenous networks and realistic ones.Therefore,based on local-world theory,a heterogonous evolution model of WSN in relation to cluster-structure,energy-sensitivity and dynamic behavior of WSN (e.g.…”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…Then the topological features of the network are extracted according to the meta-paths between domain and disease nodes. …”
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The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. …”
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An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm by S. Abinayaa, S. S. Sridhar

“…To resolve these issues, we present a bio-inspired hybrid optimization framework where Differential Evolution (DE) is integrated with Grey Wolf Optimization (GWO) to improve the efficiency of feature selections. …”
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