Transcending Borders: Yearning to Belong and Homeland Reinterpretation in Diasporic Spaces among Malaysians in the United Kingdom by Helena Francis Granger

“…Findings reveal that Malaysians in the UK negotiate a convoluted relationship with their home country, experiencing both a yearning for their roots and challenges of assimilation. …”
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Action recognition using part and attention enhanced feature fusion by Danfeng Zhuang, Min Jiang, Lei Wang, Mohamed Sanim Akremi, Hedi Tabia

“…First, we construct center-of-gravity dynamics within semantic body parts to enhance motion representation and capture dynamic trends. …”
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Exploratory development of human–machine interaction strategies for post-stroke upper-limb rehabilitation by Kang Xia, Xue-Dong Chang, Chong-Shuai Liu, Yu-Hang Yan, Han Sun, Yi-Min Wang, Xin-Wei Wang

“…The active patient-in-charge strategy incorporates data acquisition matrices and a new deep learning model, which is developed based on Convolutional Neural Network (CNN) and Transformer structure, aims to capture subtle motion intentions. …”
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Performance evaluation and correction of Al2O3 and YSZ-doped In2O3/In2O3 multilayer heterogeneous thin-film thermocouples up to 1850 °C by Meng Wang, Zhongkai Zhang, Jiaming Lei, Le Li, Bo Li, Zhaojun Liu, Yong Xia, Dan Liu, Bian Tian, Weixuan Jing

“…To improve the repeatability of the TFTCs without affecting their thermoelectric outputs, a convolutional neural network-long short-term memory network (CNN-LSTM)-attention neural network is implemented to mitigate the repeatability errors, achieving a high repeatability of 99.53%. …”
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Hybrid transformer-CNN network-driven optical-scanning undersampling for photoacoustic remote sensing microscopy by Yihan Pi, Jijing Chen, Kaixuan Ding, Tongyan Zhang, Hao Zhang, Bingxue Zhang, Junhao Guo, Zhen Tian, Jiao Li

“…Imaging speed is critical for photoacoustic microscopy as it affects the capability to capture dynamic biological processes and support real-time clinical applications. …”
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Proposal of a Viscous Model for Nonviscously Damped Beams Based on Fractional Derivatives by Mario Lázaro, Jose M. Molines-Cano, Ignacio Ferrer, Vicente Albero

“…Viscoelastic materials are widely used in structural dynamics for the control of the vibrations and energy dissipation. …”
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Historicizing National Socialism and Mehmet Genç by Ahmet Okumuş

“…National Socialism, more than just a political ideology, signifies one of the most convoluted historical events of the twentieth century. …”
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Community Detection Framework Using Deep Learning in Social Media Analysis by Ao Shen, Kam-Pui Chow

“…The proposed end-to-end community detection framework is the implementation of Graph Convolution Network and can display the social network topology, locate the core members of the community, and show the connections between users. …”
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Kolmogorov–Arnold–Enhanced Nonlinear Expansions for Fine-Grained Feature Amplification in Robust Near-Shore SAR Vessel Discrimination by Hankang Wang, Gaopeng Huang, Zhao Huang, Xingru Huang, Zhaoyang Xu, Zhiwen Zheng, Shanwei Liu, Shiqing Wei, Jin Liu, Xiaoshuai Zhang

“…Finally, a Detail Enhancement Detection Head is proposed to reduce computational overhead through shared convolutional layers, while enhancing local feature utilization via direction-sensitive convolutions and group normalization. …”
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A Smartphone-Based Non-Destructive Multimodal Deep Learning Approach Using pH-Sensitive Pitaya Peel Films for Real-Time Fish Freshness Detection by Yixuan Pan, Yujie Wang, Yuzhe Zhou, Jiacheng Zhou, Manxi Chen, Dongling Liu, Feier Li, Can Liu, Mingwan Zeng, Dongjing Jiang, Xiangyang Yuan, Hejun Wu

“…Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. …”
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Enhanced SOC estimation method for lithium-ion batteries using Bayesian-optimized TCN–LSTM neural networks by Taotao Hu, Xiting Zhu, Maokai Tian

“…To address this, a novel Bayesian-optimized temporal convolution network (TCN)–long short-term memory (LSTM) network is proposed, combining temporal convolution network (TCN) and long short-term memory (LSTM) to enhance SOC estimation accuracy. …”
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A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation by Ankai Ying, Ankai Ying, Ankai Ying, Jinwang Lv, Jinwang Lv, Jinwang Lv, Junchen Huang, Junchen Huang, Junchen Huang, Tian Wang, Peixin Si, Peixin Si, Jiyu Zhang, Guokun Zuo, Guokun Zuo, Guokun Zuo, Guokun Zuo, Jialin Xu, Jialin Xu, Jialin Xu, Jialin Xu

“…This model combines a multi-scale spatial-temporal convolution module with a spatial-temporal-enhanced strategy, a convolutional auto-encoder for information reconstruction, and a long short-term memory with self-attention, enabling the comprehensive extraction and fusion of EEG features across fine-grained time-frequency variations and dynamic spatial-temporal patterns.ResultsThe proposed FN-SSIR achieved a classification accuracy of 86.7% ± 6.6% on our force variation MI dataset, and 78.4% ± 13.0% on the BCI Competition IV 2a dataset.DiscussionThese findings highlight the potential of this paradigm and algorithm for advancing MI-BCI systems in rehabilitation training based on dynamic force interactions.…”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…For the extraction of spatial features, by given an adjacency matrix and a feature matrix,graph convolutional network is used to reorganize the spatial distribution features of time series in the Fourier domain. …”
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Reconfigurable versatile integrated photonic computing chip by Yufei Wang, Kun Liao, Kuo Zhang, Zhuochen Du, Ze Wang, Bo Ni, Tianyu Xu, Shuai Feng, Yan Yang, Qi-Fan Yang, Quan Sun, Xiaoyong Hu, Qihuang Gong

“…We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks. As a proof of concept, we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform, demonstrating the realization of fully connected, convolutional, and recurrent neural network models within a unified structure. …”
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An Unsupervised Learning Method for Radio Interferometry Deconvolution by Lei Yu, Bin Liu, Cheng-Jin Jin, Ru-Rong Chen, Hong-Wei Xi, Bo Peng

“…Comparative analyses with state-of-the-art algorithms demonstrate the outstanding performance of our method, i.e., achieving a dynamic range nearly 45–100 times higher than that of multiscale CLEAN.…”
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Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study by Yanli Guo, Hongxia Zhang, Yang Guang, Wen He, Bin Ning, Chen Yin, Mingchang Zhao, Fang Nie, Pintong Huang, Rui-Fang Zhang, Qiang Yong, Jianjun Yuan, Yicheng Wang, Lijun Yuan, Litao Ruan, Tengfei Yu, Haiman Song, Yukang Zhang

“…To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.Results The AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).Conclusion DL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…While this study primarily targets static image datasets, preliminary evaluations on dynamic video frames suggest potential for real-time monitoring applications. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…It points out that current mapping methods, while effective, still struggle with dynamic changes in the orchard, such as variations of fruits and light conditions. …”
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A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Raja Rizal Azman Bin Raja Aman, Nadia Fareeda Muhammad Gowdh, Izzah Amirah Binti Mohd Ahir, Mohd Salahuddin Bin Kamaruddin, Khairul Azmi Abdul Kadir, Filippo Molinari, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…Three significant innovations are included in the suggested architecture: (1) a hybrid Dual Attention Mechanism that combines Dynamic Routing and Cross-Attention for improved region-specific feature discrimination; (2) a Multi-Scale Feature Extraction Module with parallel convolutional pathways that captures both contextual and fine-grained features; and (3) an Adaptive Random Vector Functional Link layer that significantly reduces training time while maintaining high classification performance. …”
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Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq. by Bingbing Lei, Xuanjun Zhu, Tao Zhou, Yuxi Zhang

“…Existing prediction models often overlook the cross-regional transmission dynamics of HFMD, limiting their applicability to single regions. …”
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