Antifungal efficacy of thyme essential oil: a multi-model approach to growth inhibition by Ricardo H. Hernández-Figueroa, Emma Mani-López, Aurelio López-Malo, Enrique Palou, Teresa Soledad Cid-Pérez, Guadalupe Virginia Nevárez-Moorillón, Raúl Avila-Sosa

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Prediction and design of thermostable proteins with a desired melting temperature by Purva Tijare, Nishant Kumar, Gajendra P. S. Raghava

“…We created a user-friendly web server, and a Python package for predicting and designing thermostable proteins is available at https://webs.iiitd.edu.in/raghava/pptstab , https://github.com/raghavagps/pptstab .…”
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Development and validation of a nomogram model for predicting postoperative delirium in elderly patients with oral cancer: a retrospective study by Chen Ying, Liu Xiaona, Zhang Aili, Wang Zengxiang, Wu Ying, Pu Yu, Zhang Hongbo, Wang Danni, Jiang Meiping, Dai Hongyuan

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External Validation of 3 Prediction Models for Mortality in Out‐of‐Hospital Cardiac Arrest Survivors: A Retrospective Multicenter Study by Chun‐Hsiang Huang, Edward Pei‐Chuan Huang, Cheng‐Yi Fan, Yi‐Chien Kuo, Chien‐Tai Huang, Ching‐Yu Chen, Chi‐Hsin Chen, Yi‐Ju Ho, Chun‐Ju Lien, Yun‐Chang Chen, Sih‐Shiang Huang, Tzu‐Hsueh Wang, Wen‐Chu Chiang, Wei‐Tien Chang, Chien‐Hua Huang, Chih‐Wei Sung

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Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques by Rebin Abdulkareem Hamaamin, Omar Mohammed Amin Ali, Shahab Wahhab Kareemc

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Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications by João P. Castanheira, Beltran N. Arribas, Rui Melicio, Paulo Gordo, André R. R. Silva

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MambaLLM: Integrating Macro-Index and Micro-Stock Data for Enhanced Stock Price Prediction by Jin Yan, Yuling Huang

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Prediction of vaccination coverage levels in populations with increasing levels of heterogeneity by Fan Bai

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Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers by Palawat Busaranuvong, Emmanuel Agu, Deepak Kumar, Shefalika Gautam, Reza Saadati Fard, Bengisu Tulu, Diane Strong

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Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises by Aleksandr V. Grachev

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Ensemble machine learning for predicting academic performance in STEM education by Aklilu Mandefro Messele

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Prediction of subway shield construction parameters based on multi-layer perceptron by LI Wenqian, WU Yunhuan, WU Jingye, CHEN Zhihuai, XIE Senlin, HU Anfeng

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Identification of key programmed cell death genes for predicting prognosis and treatment sensitivity in colorectal cancer by Jian-ying Ma, Jian-ying Ma, Yi-xian Wang, Zhen-yu Zhao, Zhen-yu Xiong, Zi-long Zhang, Jun Cai, Jia-wei Guo

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A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes by Cosmina-Mihaela Rosca, Adrian Stancu

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Efficient Machine Learning Models for Solar Radiation Prediction Using Ensemble Techniques: A Case Study in Low-Rainfall Arid Climates by Jimmy Aurelio Rosales Huamani, Uwe Rojas Villanueva, Christian Leonardo Rosales Ventocilla, Jose Luis Castillo Sequera, Jose Manuel Gomez Pulido

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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

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Evaluating Prediction Performance: A Simulation Study Comparing Penalized and Classical Variable Selection Methods in Low-Dimensional Data by Edwin Kipruto, Willi Sauerbrei

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Predicting Short-Range Weather in Tropical Regions Using Random Forest Classifier by Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung

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HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors by Viet-Khoa Tran-Nguyen, Ulrick Fineddie Randriharimanamizara, Olivier Taboureau

“…It also demonstrated superior screening power compared to virtual screening schemes that used existing scoring functions. This model, named “HERGAI,” along with relevant input/output data and user-friendly source code, is available in our GitHub repository ( https://github.com/vktrannguyen/HERGAI ) and can be used to predict drug-induced hERG blockade, even on large data sets.…”
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An Empirical Correlation to Predict the Ignition Delay Time for Some Hydrocarbon Fuels by F. S. Shariatmadar, Sh. Ghanbari Pakdehi, M. A. Zarei

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