TrainNet for locking state recognition of side door of railway freight car by Ning Xing, Cai Kangcheng, Zhou Shumin, Lan Xiangui, Lai Yihui

“…The model weight file size of new algorithm is 60.2MB with 19.5% reduction compared to the baseline. …”
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Multi-scenario analysis of green water resource efficiency under carbon emission constraints in the Chengdu-Chongqing urban agglomeration, China: A system dynamics approach by Keyao Yu, Zhigang Li

“…Five scenarios are then set up: baseline development, economic priority, green innovation, water-saving and emission reduction, and comprehensive development. These scenarios are used to simulate and predict different development paths and evaluate the impact of various key indicators on GWRE. …”
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microRNA-497-5p-based screening identifies a novel synthetic lethal-type interaction via PKMYT1 and WEE1 in pleural mesothelioma by Nathanael Pruett, Sierra Wilferd, Anand Singh, Agnes Y. Choi, Shivani Dixit, Vivek Singh, Charlize Nguyen, Olivia Lin, David S. Schrump, Christopher L. Plaisier, Chuong D. Hoang

“…Surprisingly, multiple identified targets were not predicted by in silico algorithms. Using patient samples, cell lines, murine xenograft models, and our localized nanoparticle miRNA delivery platform, we validated miR-497-5p anti-tumor mechanisms, which consisted of pro-apoptotic and anti-cell-cycle effects. …”
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Predictors of community-based health insurance enrollment among reproductive-age women in Ethiopia based on the EDHS 2019 dataset: a study using SHAP analysis technique, 2024 by Sisay Yitayih Kassie, Solomon Abuhay Abebe, Mekdes Wondirad, Samrawit Fantaw Muket, Ayantu Melke, Alex Ayenew Chereka, Adamu Ambachew Shibabaw, Abiy Tasew Dubale, Yitayish Damtie, Habtamu Setegn Ngusie, Agmasie Damtew Walle

“…Eight machine learning algorithm classifiers were applied to a total weighted sample of 9,013 reproductive-age women and evaluated using performance metrics to predict community-based health insurance enrollment with Python 3.12.2 software, utilizing the Anaconda extension. …”
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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…The variable combination population analysis–iteratively retains informative variables algorithm was iterated to optimize effective features. …”
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JAK2 Inhibitors and Emerging Therapies in Graft-Versus-Host Disease: Current Perspectives and Future Directions by Behzad Amoozgar, Ayrton Bangolo, Abdifitah Mohamed, Charlene Mansour, Daniel Elias, Christina Cho, Siddhartha Reddy

“…Recent advances in biomarker development, such as the MAGIC Algorithm Probability (MAP), are enabling early risk stratification and response prediction. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Three machine learning (ML) models, multilayer perceptron (MLP), support vector regressor (SVR), and light gradient boosting machine (LightGBM), were assessed for their output estimation accuracy and compared to the CCD prediction model. The computational framework utilized experimental variables structured by CCD software as input metadata. …”
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Multi-Omics Profiling Reveals Glycerolipid Metabolism-Associated Molecular Subtypes and Identifies ALDH2 as a Prognostic Biomarker in Pancreatic Cancer by Jifeng Liu, Shurong Ma, Dawei Deng, Yao Yang, Junchen Li, Yunshu Zhang, Peiyuan Yin, Dong Shang

“…These findings establish a novel avenue for studying prognostic prediction and precision medicine in PC patients.…”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Compared with the YOLOv8n algorithm, the model size is significantly reduced, with the parameter count at only 2.9M and the computational load at 7.0G, representing reductions of 21.6% and 15.6%, respectively. …”
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LSTM‐based real‐time stress detection using PPG signals on raspberry Pi by Amin Rostami, Koorosh Motaman, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari

“…Additionally, the authors’ optimised model exhibits substantial reductions in both size and latency while maintaining high accuracy. …”
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LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints by Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, Alvaro Javier Prado Romo

“…In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. …”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path by Allan Dos S. Braga, Hugo A. O. Da Cruz, Leslye E. C. Eras, Jasmine P. L. Araujo, Miercio C. A. Neto, Diego K. N. Silva, Gervasio P. S. Cavalcante

“…This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…The most common study objectives were to predict the need for critical care and life‐saving interventions (29%), assist in triage (22%), and predict survival (20%). …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. …”
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Cancer epigenetics: Moving forward. by Angela Nebbioso, Francesco Paolo Tambaro, Carmela Dell'Aversana, Lucia Altucci

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Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma by Jiafen Xie, Xiaoming Wang

“…Prognostic aggrephagy-related genes (AGGRGs) were identified through univariate Cox and LASSO regression analyses, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups based on the median risk score. …”
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Development of a three-species gut microbiome diagnostic model for acute pancreatitis and its association with systemic inflammation: a prospective cross-sectional study by Yuanyuan Gou, Long Yao, Wenli Yang, Qian Chen, Yuetao Wen, Jie Cao

“…Notably, Veillonella parvula and Fusobacterium periodonticum abundances correlated positively with white blood cell count (r = 0.40, p = 0.018) and procalcitonin (r = 0.41, p = 0.015). Functional prediction revealed elevated microbial pathways linked to cell membrane transport and infectious disease risk in AP. …”
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Mapping the air temperature in China from time-normalized MODIS land surface temperature data via zone-based stacking ensemble models by Yan Xin, Yongming Xu, Xudong Tong, Yaping Mo, Yonghong Liu, Shanyou Zhu

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AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis by Irfan Ahmed, Wei Zhang, Pikting Cheung, Vardhan Basnet, Zulfiqar Ali, May PY Tse, Fraser Hill, Tom Tak Lam Chan, Haibo Hu, Xinyue Li, Condon Lau

“…In total, the geometrical features of 8.48 million segmented cells (4.24 million pairs) were translated into a tabular format and paired based on the Euclidean cell matching algorithm. This approach facilitated the prediction of cell labels, achieving sensitivity and specificity of 0.98 and 0.97 (0.94 and 0.99), respectively for CD3 (PAX5). …”
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Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy by Hassan H. Alhassan

“…Among all models, the Random Forest (RF) algorithm had the best prediction accuracy, with a value of 0.6832 on the test set and 0.7432 on the training set, and was employed to screen the target library of 11,032 phytochemicals. …”
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