Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

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Improving Narrative Writing Skills of Madrasah Tsanawiyah Students Through a Group Inquiry Learning Model Based on Religious Character Values by Mimi Mulyani, Sukron Mazid, Farikah Farikah, Dwi Riyanti, Atsani Wulansari

“…This research analyzes the effectiveness of the group inquiry learning model based on religious character values in enhancing narrative writing skills, specifically in the form of diaries. …”
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A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery by Su-Zhen Yang, Ming-Hui Peng, Quan Lin, Shi-Wei Guan, Kai-Lun Zhang, Hai-Bo Yu

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Evaluating INSTALL as an instructional model to enhance EFL pre-service teachers’ TPACK: a case of three Indonesian universities by Maulana Mualim, Margana Margana, Agus Widyantoro, Hoang Huu Nguyen, Khristianto Khristianto, Faisal Faisal, Nisa Roiyasa

“…PurposeThis study investigates the effects of an instructional model incorporating inquiry-based, technology saturated, and flipped learning (INSTALL) on EFL pre-service teachers’ TPACK in Indonesia and finds out their responses about it.Design/approach/methodThis is an explanatory mixed method research where quantitative data were further explained by qualitative data. …”
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Earthquake monitoring and high-resolution velocity tomography for the central Longmenshan fault zone by a temporary dense seismic array by ShaoBo Yang, HaiJiang Zhang, MaoMao Wang, Ji Gao, Shuaijun Wang, BaoJin Liu, XiWei Xu

“…Considering the large quantity of observed seismic data, the data processing workflow consisted of deep learning-based automatic earthquake detection, phase arrival picking, and association. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

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Differentiated Learning Model in Inclusive Education to Strengthen The Pancasila Students Profile by Yari Dwikurnaningsih, Krismiyati Krismiyati, Krisma Widi Wardani

“…The data analysis technique used in this research is descriptive analysis, combining qualitative and quantitative approaches. Based on the results of the development, the model is named the Pancasila Differentiated Learning Model, which contains model description and its syntax consisting of 5 stages; understanding the student, preparing the options, providing the options to the students, observing & supporting, and reflecting. …”
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An evidenced-based, farmer-focused revival of traditional cereals by Ruth DeFries, Ruth DeFries, E. D. I. Oliver King, Mansi Monga, Harini Nagendra, Amrita Neelakantan

“…Based on our observations from over 2,000 households surveys in the central Indian Highlands, we propose three principles to guide the collection of evidence for an ecologically-, nutritionally-, and socially-secure revival: (1) maintain diversity of millet species and varieties to preserve genetic resources in a rapidly changing climate; (2) ensure equitable access to technology, inputs, information and markets for all farmers; and (3) enable farmers to balance the trade-off between income from selling and nutritional benefits from consuming millets. …”
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Motion Patterns Learning in Crowded Scenes by KANG Yong-xin, YUAN Li-ying

“…To address the motion patterns learning task in crowded scenes, we propose a novel Distance Dependent Chinese Restaurant Franchise (DDCRF) mixture model, which is a hierarchical nonparametric Bayesian model based on dependencies DDCRF can learn the latent patterns accurately by introducing the distance information which is dependent of the observations of data points. …”
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Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal by Arnab Singh, Dibas Shrestha, Kaman Ghimire, Sangya Mishra, Darwin Rana, Sunil Acharya

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An interpretable physics-informed deep learning model for estimating multiple air pollutants by Binjie Chen, Jiacong Hu, Yumiao Wang, Tian Feng, Weiwei Sun, Zunlei Feng, Gang Yang, Huiqiong Wang

“…Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. …”
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A machine learning model for automated contact tracing during disease outbreaks by Zeyad Aklah, Amean Al-Safi, Marwa H. Abdali, Khalid Al-jabery

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Elucidating thermal phenomena of non-Newtonian experimental data based copper-alumina-ethylene glycol hybrid nanofluid in a cubic enclosure with central heated plate by machine lea... by Md. Mamun Molla, Md Farhad Hasan, Md. Mahadul Islam

“…Finally, a cross-validation performance analysis was conducted using a machine learning model and good accuracy was obtained. Some of the key findings suggested that due to the existence of the flat heat radiator, the fluid flow and temperature profiles were distributed from the central position but the upper side of the cavity mostly experienced the greater rate of fluid and heat transfer. …”
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Robust-PFedproto: robust federated prototype learning based on personalized layers by XU Mingdi, LI Zhengxiao, WANG Zihang, JIN Chaoyang

“…To address data heterogeneity, the PFedproto framework was proposed, incorporating personalized layers based on prototype learning. These layers, positioned before each client’s decision layer, were optimized to enhance client model adaptation to local datasets and improve localized task prediction accuracy. …”
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Student intention to use MUVEs: An integrated model of technology acceptance and learning engagement theories by Feng Zhang, Gege Li, Heng Luo

“…Based on this research gap, this study established and tested a structural equation model (SEM) to explore the sophisticated interwoven relationships between students’ technology acceptance, learning engagement, learning satisfaction, and their intention to use MUVEs. …”
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Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning by Miao Jia, Jie Hu, Zhongliang Yang, Weijie Liu, Jin Qi, Bin Chen

“…First, we created a dataset of 26,240 Dunhuang murals for model training. Second, we built an image restoration model based on U-net to automatically restore murals. …”
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DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline by Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu

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Refining Water Body Extraction by Remote Sensing With Deep Learning Models: Exploring Different Band Combinations by Ziheng Feng, Fei Zhang, Xu Ma, Chi Yung Jim, Di Wu, Dong Liu, Saheed Adeyinka Oke, Weiwei Wang, Lifei Wei, Songlin Nie, Zhihui Li, Haowei Xu

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Deep learning-based surface deformation tracking with interferometric fringes: A case study in Taiwan by Shih-Teng Chang, Shih-Yuan Lin, Yu-Ching Lin

“…Although Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) provides detailed displacement measurements, its application in continuous monitoring remains constrained by high computational demands and complex data processing, often interrupting observation continuity. To address these challenges, this study proposes a deep learning-based method that processes wrapped Differential InSAR (D-InSAR) interferograms to directly detect surface deformation patterns. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…This enhanced performance in practical detection surpasses that of the original YOLOv5 model. The number of observed particle chains increases following optimization by integrating the improved YOLOv5 model with the grayscale subtraction technique for detecting particle motion. …”
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