Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model by Mahmood Heshmati, Sajad Hayati, Saeed Javanmiri, Mohammad Javadian

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Research on Soft-Sensing Method Based on Adam-FCNN Inversion in <i>Pichia pastoris</i> Fermentation by Bo Wang, Wenyu Ma, Hui Jiang, Shaowen Huang

“…To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in <i>Pichia pastoris</i> fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. …”
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Machine learning-driven prognostic prediction model for composite small cell lung cancer: identifying risk factors with network tools and validation using SEER data and external co... by Fei Li, Mengfan Zhao, Linlin Cao, Shuai Qie

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Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall survival in cervical cancer patients undergoing concurrent chemora... by Jinyu Shi, Lian Wang, Min Zhou, Shushan Ge, Shushan Ge, Bin Zhang, Jiangqin Han, Jihui Li, Shengming Deng, Shengming Deng

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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

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Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models by Jagadheshwar Balan, Shannon K. McDonnell, Zachary Fogarty, Nicholas B. Larson

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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation by Siqi Liao, Wanting Sun, Haoran Zheng, Qiyang Xu

“…As a kind of high-oxygen organic liquid produced during biomass pyrolysis, wood vinegar possesses significant industrial value due to its rich composition of acetic acid, phenols, and other bioactive compounds. …”
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Hospital-based innovation labs to accelerate digital health transformation: lessons learned from twenty types of experts by Eric R. Edelman, Joost G. E. Verbeek, Perry Hunen, Martijn de Groot, Nicole van Eldik, Marieke D. Spreeuwenberg

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A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone by Liangjun Li, Zhihao Ren, Cheng Tang, Shengning Lu, Yong Liang

“…Their behavioral rhythms respond sensitively to environmental fluctuations, especially variations in sediment composition and illumination. However, due to their small size and tidal-driven activity patterns, conventional behavior detection methods suffer from low efficiency and considerable observer bias, particularly under dark conditions where detection errors and omissions are prevalent. …”
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Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery by Charles J Abolt, Javier E Santos, Adam L Atchley, Lucas Wells, Daithi Martin, Russell A Parsons, Rodman R Linn

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Research on Early Diagnosis Methods for Broiler Chicken Diseases Based on Swarm Intelligence Optimization Algorithms and Random Forest by X Peng, C Chen, L Yu, X Kong, B Sun

“…Comparative analysis revealed that traditional PCA methods risk losing essential pathological features by disregarding nonlinear data relationships, whereas deep learning requires substantial computational resources and high-quality datasets. …”
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Metagenomics reveals unique gut mycobiome biomarkers in major depressive disorder - a non-invasive method by Xuan Wang, Di Cao, Wei Chen, Jiaxin Sun, Huimin Hu

“…Subsequently, we constructed a machine learning model using support vector machine-recursive feature elimination to search for potential fungal markers for MDD.ResultsOur findings indicated that the composition and beta diversity of intestinal fungal communities were significantly changed in MDD compared to the healthy controls. …”
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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

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Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction by Abhishek Kagalkar, Swapnil Dharaskar, Nitin Chaudhari, Vinay Vakharia, Rama Rao Karri

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The Impact of Research-Based Learning and Institutional Support on Student Research Productivity at Madrasah Aliyah Negeri in Jakarta, Indonesia by Farida Hanun, Onok Yayang Pamungkas, Suprapto Suprapto, Achmad Dudin, Wakhid Kozin, Lisa'diyah Ma'rifataini

“…This study used a mixed-method method with data collection instruments using questionnaires distributed to 490 research-based madrasah students in Jakarta, selected using the cluster random sampling technique. …”
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AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings by Rujun Li, Haotian Wang, Qiunan Yu, Jing Cai, Liangzhen Jiang, Ximei Luo, Quan Zou, Zhibin Lv

“…Traditional experimental methods for identifying antioxidant peptides are time consuming and costly, so effective machine learning models are increasingly being valued by researchers. …”
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The Impact of Students' Motivational Drive and Attitude toward Online Learning on Their Academic Engagement during the Emergency Situation by Audi Yundayani, Yatha Yuni, Fiki Alghadari

“…The structural equation modeling and Hayes' bootstrapping technique were employed to analyze the data further, which was collected through an internet-based poll. In addition, the Confirmatory Factor Analysis (CFA) method was employed to assess the reflective measurement models. …”
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Analysis of machine learning approaches for the interpretation of acoustic fields obtained by well noise data modelling by N. V. Mutovkin

“…Assessing the phase composition of the fluid in a well based analysis of the frequencies of the radial resonance modes excited by acoustic noise in the inflow zone is a promising method for interpreting the results of passive noise metering. …”
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Rapid Design of Efficient Mn3O4‐Based Photocatalysts by Machine Learning and Density Functional Theory Calculations by Haoxin Mai, Xuying Li, Tu C. Le, Salvy P. Russo, David A. Winkler, Dehong Chen, Rachel A. Caruso

“…Here, a comprehensive design strategy is presented for the fast development of efficient Al‐doped Mn3O4‐based photocatalysts, combining density functional theory (DFT), machine learning (ML), and laboratory experiments. …”
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Advancing ship automatic navigation strategy with prior knowledge and hierarchical penalty in irregular obstacles: a reinforcement learning approach to enhanced efficiency and safe... by Hao Zhang, Jiawen Li, Jiawen Li, Jiawen Li, Jiawen Li, Liang Cao, Liang Cao, Liang Cao, Shucan Wang, Ronghui Li, Ronghui Li, Ronghui Li

“…However, existing reinforcement learning (RL)–based autopilot methods still face challenges such as low learning efficiency, redundant invalid exploration, and limited obstacle avoidance capability. …”
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