Bayesian deconvolution for computational cognitive modeling of fMRI data by Jonathon R. Howlett, Katia M. Harlé, Alan N. Simmons, Charles T. Taylor

“…A central goal of cognitive neuroscience is to make inferences about underlying cognitive processes from observable data. …”
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Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps by Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu, Shuo Hu, Tammie L. S. Benzinger, Jie Lu, Yun Zhou

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NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations by Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack Jr., Mary L. Hinkle

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Authenticity Recognition of Rice Varieties Based on Polarization Imaging Technology by Jie FU, Yue WU

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Development of Assemblr Edu Learning Media based on Augmented Reality to Improve Elementary School Students’ Mathematics Problem Solving Ability by Erina Fadilasari, Nursiwi Nugraheni

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Urban Recreational Space Heat-Emotion Distribution and Matching Pattern Based on Machine Learning: A Case Study of Guangzhou City by Wang Fuyuan, Zhang Zhiyu, Xie Yuanjing, Yang Xinyi, Sun Miao

“…Based on machine learning techniques, it discerns recreational emotions and synergizes Geographic Information System (GIS) spatial analysis with heat-emotion matching analysis. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…InSAR techniques allow for high-precision measurements of surface deformation across large areas, making them invaluable in understanding and modeling subsidence patterns. When combined with machine learning approaches, these technologies offer even greater potential for developing predictive models and susceptibility maps, enabling proactive risk management. …”
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Extracting individual trajectories from text by fusing large language models with diverse knowledge by Le Liu, Tao Pei, Zidong Fang, Xiaorui Yan, Chenglong Zheng, Xi Wang, Ci Song, Wenfei Luan, Jie Chen

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A radiomics approach for predicting gait freezing in Parkinson’s disease based on resting-state functional magnetic resonance imaging indices: A cross-sectional study by Miaoran Guo, Hu Liu, Long Gao, Hongmei Yu, Yan Ren, Yingmei Li, Huaguang Yang, Chenghao Cao, Guoguang Fan

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Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake by Wei Si, Zhixiong Chen, Chi Yung Jim, Ngai Weng Chan, Mou Leong Tan, Bingbing Liu, Dong Liu, Lifei Wei, Shaoyong Wang, Fei Zhang

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Development of Peer-Based Academic Supervision Model in Increase Skills Teaching The 21st Century in Elementary Schools by Retno Pramasari, Bambang Ismanto, Sophia Tri Satyawati, Ji Suk Young, Ester Uru Hida

“…This study aims to develop a peer-based academic supervision model to enhance 21st-century teaching skills in elementary schools. …”
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IoT‐Based Elderly Health Monitoring System Using Firebase Cloud Computing by Adhan Efendi, Muhammad Imam Ammarullah, Indra Griha Tofik Isa, Meli Puspita Sari, Jasmine Nurul Izza, Yohanes Sinung Nugroho, Hamid Nasrullah, Denny Alfian

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Differentiation between bipolar disorder and major depressive disorder based on AMPA receptor distribution by Sakiko Tsugawa, Sakiko Tsugawa, Yuichi Kimura, Junichi Chikazoe, Hiroki Abe, Tetsu Arisawa, Mai Hatano, Waki Nakajima, Hiroyuki Uchida, Tomoyuki Miyazaki, Tomoyuki Miyazaki, Yuuki Takada, Akane Sano, Kotaro Nakano, Tsuyoshi Eiro, Akira Suda, Takeshi Asami, Akitoyo Hishimoto, Hideaki Tani, Nobuhiro Nagai, Teruki Koizumi, Shinichiro Nakajima, Shunya Kurokawa, Yohei Ohtani, Kie Takahashi, Yuhei Kikuchi, Taisuke Yatomi, Taisuke Yatomi, Ryo Mitoma, Shunsuke Tamura, Shunsuke Tamura, Shingo Baba, Osamu Togao, Yoji Hirano, Yoji Hirano, Yoji Hirano, Hirotaka Kosaka, Hidehiko Okazawa, Masaru Mimura, Takuya Takahashi, Takuya Takahashi

“…This study aimed to develop a machine learning-based method to differentiate bipolar disorder from major depressive disorder using AMPAR density. …”
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T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging by Changjin Yuan, Bin Wang, Hong Wang, Fang Wang, Xiangze Li, Ya’nan Zhen

“…Multi-layer machine learning-based diagnostic models were developed by leveraging motif-based feature and deep learning-based feature extraction using ProteinBERT from the 100 most abundant CDR3 sequences per sample. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

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Intraoperative circulation predict prolonged length of stay after head and neck free flap reconstruction: a retrospective study based on machine learning by Zhongqi Liu, Zhongqi Liu, Jinbei Wen, Yingzhen Chen, Yingzhen Chen, Bin Zhou, Minghui Cao, Minghui Cao, Mingyan Guo, Mingyan Guo

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CT-based radiomics analysis for predicting EGFR and ALK mutational status in non‑small cell lung cancer by Ruimin He, Tengxiang Li, Li Sun, Peng Lu, Dan Sun, Rongrong Zhou, Dangchi Li, Xiaohua Yang, Zijian Zhang

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Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns by Qikai Tang, Chenfeng Ma, Hu Li, Jiaheng Xie, Weiqi Bian, Qingyu Lu, Zeyu Wan, Wei Wu

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Multi-User Opportunistic Spectrum Access for Cognitive Radio Networks Based on Multi-Head Self-Attention and Multi-Agent Deep Reinforcement Learning by Weiwei Bai, Guoqiang Zheng, Weibing Xia, Yu Mu, Yujun Xue

“…First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. …”
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DEVELOPING PERFORMANCE ASSESSMENT INSTRUMENT FOR MEASURING SCIENTIFIC ATTITUDES by Debi Shinta, Dadan Rosana

“…This study was aimed at developing an instrument of performance assessment based on the 7E learning cycle model and finding out the result of students’ scientific attitude measurement using the instrument. …”
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