CDMRNet: multimodal meta-adaptive reasoning network with dynamic causal modeling and co-evolution of quantum states by Shengwei Wang, Keda Chen, Mengduo Yu, Pengjiao Zhao, Hui Duan

“…To evaluate its performance, the study conducts extensive experiments across three datasets: Visual Genome, MIMIC-CXR, and nuScenes. CDMRNet achieves 89.7% accuracy on Visual Genome, improves F1 score to 84.1%, and shows 3.9% performance drop only under modal absence, significantly outperforming state-of-the-art models. …”
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Stretchable optoelectronic synapses with ultraviolet to near-infrared perception for retina-inspired computing and vision-adaptive sensing by Linfeng Lan, Bo Huang, Yaping Li, Churou Wang, Jiayi Pan, Jiale Huang, Baozhong Chen, Qi zhou, Longzhen Qiu, Yafei Ding, Qing Wan, Zhong Ji, Yuan Li, Junbiao Peng, Yong Cao

“…Abstract Stretchable optoelectronic synapses are attractive for intelligent perception, neuromorphic computation and visual adaptation. Here, we demonstrate a highly stretchable organic optoelectronic synaptic transistor (s-OOST) with a transconductance up to 86 mS that can simultaneously accept modulation of electrical pulses and multi-wavelength light signals (from ultraviolet to near-infrared). …”
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CORRECTION OF POISTURE DISORDERS AMONG HARD OF HEARING ATHLETES BY MEANS OF ADAPTIVE PHYSICAL TRAINING IN THE TRAINING PROCESS by Zoya Sapronova, Arsen Abdullayev

“…Together with the doctors of Stavropol Regional Center of Physical Therapy and Sports Medicine we have carried out visual, anthroposcopic, palpation and anthropometric surveys with analysis of complaints and anamnesis data of hard of hearing athletes involved in track andfield athletics at the age of 14-15 years old. …”
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Learning Developmental Age From 3D Infant Kinetics Using Adaptive Graph Neural Networks by Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Tuiskula, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

“…These data are modeled using adaptive graph convolutional networks (AAGCNs), able to capture the spatio-temporal dependencies in infant movements. …”
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Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway by Malcolm G Dunlop, Yasuko Maeda, Janice Miller, Frances Gunn, Farhat VN Din

“…Objectives A customised data management system was required for a rapidly implemented COVID-19-adapted colorectal cancer pathway in order to mitigate the risks of delayed and missed diagnoses during the pandemic. …”
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A Fault Feature Extraction Method for Rolling Bearing Based on Pulse Adaptive Time-Frequency Transform by Jinbao Yao, Baoping Tang, Jie Zhao

“…Aiming at overcoming the shortcoming, a pulse adaptive time-frequency transform method is proposed to extract the fault features of the damaged rolling bearing. …”
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Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials by Bo Hu, Jun Xie, Huanqing Zhang, Junjie Liu, Hu Wang

“…This study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. …”
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Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification by Jiannan Chen, Chenju Yang, Rao Wei, Changchun Hua, Dianrui Mu, Fuchun Sun

“…In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. …”
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Unsupervised domain adaptation multi-level adversarial network for semantic segmentation based on multi-modal features by Zeyu WANG, Shuhui BU, Wei HUANG, Yuanpan ZHENG, Qinggang WU, Huawen CHANG, Xu ZHANG

“…In order to solve the problem of the distribution differences of visual, spatial, and semantic features between domains in domain adaptation, an unsupervised domain adaptation multi-level adversarial network for semantic segmentation based on multi-modal features was proposed.Firstly, an attentive fusion semantic segmentation network with three-layer structure was designed to learn the above three types of features from the source domain and target domain, respectively.Secondly, a self-supervised learning method jointing distribution confidence and semantic confidence was introduced into the single-level adversarial learning, so as to achieve the distribution alignment of more target domain pixels in the process of minimizing the distribution distance of the learnt features between domains.Finally, three adversarial branches and three adaptive sub-networks were jointly optimized by the multi-level adversarial learning method based on multi-modal features, which could effectively learn the invariant representation between domains for the features extracted from each sub-network.The experimental results show that compared with existing state-of-the-art methods, on the datasets of GTA5 to Cityscapes, SYNTHIA to Cityscapes, and SUN-RGBD to NYUD-v2 the proposed network achieves the best mean intersection over union of 62.2%, 66.9%, and 59.7%, respectively.…”
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The Turkish version of the brief Michigan hand outcomes questionnaire: cross-cultural adaptation, validity, and reliability testing by Halil İbrahim Ergen, Sedat Yiğit, Tuba Maden, Mehmet Vakıf Keskinbıçkı

“…Methods: This study was conducted in accordance with Beaton et al.’s Guidelines for the Process of Cross-Cultural Adaptation of SelfReport Measures. A total of 54 patients with various hand and wrist problems were included in the study. …”
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Retinal Phosphenes Induced by Transorbital Electrical Stimulation: Influence of Light Adaptation, Electrode Montage, and View Direction by Alexander Hunold, Daniela Ortega, Stefanie Freitag, Dietmar Link, Andrea Antal, Sascha Klee, Jens Haueisen

“…Our results indicated the lowest phosphene thresholds under light adaptation. Cumulative charts of phosphenes visualized the location of phosphene hot spots and their focality for the investigated directions of view and electrode montages. …”
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Power Transmission Corridors Wildfire Detection for Multi-Scale Fusion and Adaptive Texture Learning Based on Transformers by Yicen Liu, Xi Liu, Zhikun Zheng, Shimin Luo, Yongjun Xiao, Shuxian Wang, Xiaojiang Liu, Zhenhong Sun

“…To address this, we propose a unified wildfire detection framework incorporating two key modules: the Multi-scale Adaptive Texture Encoding (MATE) module and the Dynamic Feature Pyramid Integration (DFPI) module. …”
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Psychological adaptation and recovery in youth with sarcoma: a qualitative study with practical implications for clinical care and research by Rachel M Taylor, Craig Gerrand, Ana Martins, Maria Onasanya, Rachael Windsor, Urska Kosir, Lucy Bowes

“…The qualitative nature of our study sheds light on meaningful connections between various factors and their role in one’s psychological adaptation to sarcoma. We devised a visual matrix to illustrate how risk and protective factors in adaptation vary between and within individuals.Conclusions This study demonstrates that young people with sarcoma report an array of both positive and negative factors related to their illness experience. …”
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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

“…Traditional diagnostic methods, including visual examination and PCR tests, face limitations such as misdiagnoses, high costs, and unavailability in resource-limited areas. …”
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Cross-modal learning with multi-modal model for video action recognition based on adaptive weight training by Qingguo Zhou, Yufeng Hou, Rui Zhou, Yan Li, JinQiang Wang, Zhen Wu, Hung-Wei Li, Tien-Hsiung Weng

“…In this study, an effective Vision-Language action recognition adaptation was implemented based on few-shot examples spanning different modalities. …”
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Duck Egg Crack Detection Using an Adaptive CNN Ensemble with Multi-Light Channels and Image Processing by Vasutorn Chaowalittawin, Woranidtha Krungseanmuang, Posathip Sathaporn, Boonchana Purahong

“…Therefore, this paper presents duck egg crack detection using an adaptive convolutional neural network (CNN) model ensemble with multi-light channels. …”
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Drought adaptation index (DAI) based on BLUP as a selection approach for drought-resilient switchgrass germplasm by Shiva Om Makaju, Shiva Om Makaju, Shiva Om Makaju, Hari Bahadur Chhetri, Chanaka Roshan Abeyratne, Mirko Pavicic, Hari Poudel, Jazib Ali Irfan, Jazib Ali Irfan, Anita Giabardo, Katrien M. Devos, Katrien M. Devos, Katrien M. Devos, Daniel Jacobson, Ali Mekki Missaoui, Ali Mekki Missaoui, Ali Mekki Missaoui

“…BLUP-estimated biomass yields were used to calculate the DAI, which enabled classification of genotypes into four adaptation groups: very well-adapted, well-adapted, adapted, and unadapted. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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The Adaptive Characteristics of Cholesterol and Bile Acid Metabolism in Nile Tilapia Fed a High-Fat Diet by Rui-Xin Li, Yi-Fan Qian, Wen-Hao Zhou, Jun-Xian Wang, Yan-Yu Zhang, Yuan Luo, Fang Qiao, Li-Qiao Chen, Mei-Ling Zhang, Zhen-Yu Du

“…Visually healthy Nile tilapia fingerlings (average weight 3.50±0.05 g) were randomly distributed into four treatments (4-week control diet or HFD and 8-week control diet or HFD). …”
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Neural adaptations to temporal cues degradation in early blind: insights from envelope and fine structure vocoding by Hyo Jung Choi, Hyo Jung Choi, Jeong-Sug Kyong, Jeong-Sug Kyong, Jong Ho Won, Hyun Joon Shim, Hyun Joon Shim

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