Design optimization of inner and outer‐rotor PMSGs for X‐ROTOR wind turbines by Reza Yazdanpanah, Seyed Abolfazl Mortazavizadeh, Mohammad Salehian, David Campos‐Gaona, Olimpo Anaya‐Lara

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Development of a simulation model of energy-saving system based on CCGT-110 and ABСM for energy and thermodynamic analysis by L. V. Galimova, D. Z. Bayramov

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Drivers of Structural and Functional Resilience Following Extreme Fires in Boreal Forests of Northeast China by Jianyu Yao, Xiaoyang Kong, Lei Fang, Zhaohan Huo, Yanbo Peng, Zile Han, Shilong Ren, Jinyue Chen, Xinfeng Wang, Qiao Wang

“…Understanding how environmental factors regulate the resilience of key structural and functional parameters is critical for sustaining and enhancing ecosystem services under global change. …”
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Prediction of postoperative vault after implantable collamer lens implantation with deep learning by Dong-Qing Yuan, Fu-Nan Tang, Ying Wang, Hui Zhang, Wei-Wei Zhang, Liu-Wei Gu, Qing-Huai Liu

“…First, feature selection was performed on patients' preoperative examination parameters to identify those most closely related to postoperative vault and incorporate them into the machine learning model. …”
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Learning Discrete-Time Uncertain Nonlinear Systems With Probabilistic Safety and Stability Constraints by Iman Salehi, Tyler Taplin, Ashwin P. Dani

“…The controlled dynamic model of a discrete-time system with a zero-mean Gaussian process noise is approximated using an Extreme Learning Machine (ELM) whose parameters are learned subject to chance constraints derived using a discrete-time control barrier function and discrete-time control Lyapunov function in the presence of the ELM reconstruction error. …”
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Normative isokinetic knee strength values and prediction models in non-athletic Chinese adults by Kun Liu, Lulu Yin, Ye Zhang, Gongliang Liu, Ping Fang, Yanhong Ma, Lihua Huang

“…Two-way analysis of variance was utilised to analyse the characteristics and differences of these parameters amongst different age groups and genders. …”
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Multi-Parametric MRI Combined with Radiomics for the Evaluation of Lymphovascular Space Invasion in Cervical Cancer by Huanhuan Wang, Jie Meng, Guoqiang Dong, Lijing Zhu, Zhengyang Zhou, Yuan Jiang, Li Zhu

“…Conclusions: The diagnostic efficacy of radiomics is superior to conventional MRI parameters and clinical parameters. The radiomics-based machine learning model can help improve accuracy for the preoperative evaluation of LVSI in cervical cancer.…”
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Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review by Shahid Gulzar Padder, Jayesh Ambulkar, Atul Banotra, Sudhakar Modem, Sidharth Maheshwari, Kolleboyina Jayaramulu, Chinmoy Kundu

“…External battery parameters such as voltage, current, time, and temperature (V.C.T.T) and internal battery parameters such as impedance and ultrasonic data are the principal constituents of the Data-driven approaches. …”
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Prediction of Spectral Response for Explosion Separation Based on DeepONet by Xiaoqi Chen, Zhanlong Qu, Yuxi Wang, Zihao Chen, Ganchao Chen, Xiao Kang, Ying Li

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Bayesian Neural Networks With Robust Feature Interpretation for Enhanced Compressive Strength Prediction of Ultra‐High‐Performance Concrete by Tao Zhang, Ji Hao, Jie Zhang, Lina Du, Yunhao Zhang, Wenbin Jiao

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Research progress of industrial organic solid waste gasification technology by WANG Xu, LUO Zhenxing, XIA Zhipeng, GUO Qianqian, HU Yanjun*

“…Finally, we have sorted out the focuses in the field of industrial organic solid waste gasification in recent years and proposed future research directions, which include constructing a complete synergistic reaction pathway with the help of modern in-situ characterization techniques, analyzing the mechanism of multi-reaction coupling in the process of co-gasification by combining density functional theory calculations and theoretical modelling, optimizing the process parameters of the industrial organic solid waste gasification by using machine learning, and conducting life cycle analysis of the co-gasification of industrial organic solid waste.…”
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Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques by Mohammed Benaafi, Waleed M. Hamanah, Ebrahim Al-Wajih

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Prediction model for chemical explosion consequences via multimodal feature fusion by Yilin Wang, Beibei Wang, Yichen Zhang, Jiquan Zhang, Yijie Song, Shuang-Hua Yang

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Revolutionizing Holy-Basil Cultivation With AI-Enabled Hydroponics System by G. Lakshmi Priya, Chanthini Baskar, Sanket Sandeep Deshmane, C. Adithya, Souranil Das

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Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging by Yoshihiro Obata, Dilworth Y. Parkinson, Daniël M. Pelt, Claire Acevedo

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Combined clinical and MRI-based radiomics model for predicting acute hematologic toxicity in gynecologic cancer radiotherapy by Lumeng Luo, Lumeng Luo, Jiahao Wang, Jiahao Wang, Hongling Xie, Hongling Xie, Bingxin Chen, Bingxin Chen, Hui Wang, Hui Wang, Qiu Tang, Qiu Tang

“…We developed and validated a predictive model integrating clinical parameters and radiomic features, evaluating five machine learning approaches. …”
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An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy by Lijue Liu, Yuan Zang, Huimu Zheng, Siya Li, Yu Song, Xue Feng, Xiyuan Zhang, Yaoxu Li, Lulu Cao, Guanglin Zhou, Tingting Dong, Qi Huang, Teng Pan, Jinhai Deng, Danling Cheng

“…First, a deep neural network automatically measured the biometric parameters from ultrasound video to ensure the consistency and accuracy of the measurements, then a machine learning model predicted EPL risk to support doctors making decisions. …”
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Investigation of the Regularities of the Influence of Meteorological Factors on Avalanches in Eastern Kazakhstan by Marzhan Rakhymberdina, Natalya Denissova, Yerkebulan Bekishev, Gulzhan Daumova, Milan Konečný, Zhanna Assylkhanova, Azamat Kapasov

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Design and Transmission Analysis of Compound Connecting Rod Type Pineapple Picking Device by Xiaofei Liu, Xinghua Gao, Yabo Wei

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Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura by Guo Q, Xia S, Ding Y, Liu F

“…We compared laboratory parameters across these groups to identify significant differences associated with GIS complications. …”
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