Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…The developed sigmoid (SG) activation function-based ELM (SG-ELM) shows performance improvement over sine (SI) function-based ELM (SI-ELM) model and PS-SVR model with an improvement of 79.25 % and 89.4 % using root mean square error (RMSE) performance measuring parameter. …”
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Functional outcome improvement and surgical time reduction in a single‐surgeon consecutive case series of hip arthroscopy for femoroacetabular impingement: A minimum 5 years follow... by Enrico Tassinari, Filippo Caternicchia, Mattia D. Rosa, Francesco Castagnini, Edoardo Angeletti, Valentina Fantoni, Irene Tampieri, Andrea Burla, Stefano Zaffagnini

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Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…Given CMI’s robust and consistent predictive capability for stroke outcomes, we developed a machine learning-derived nomogram incorporating five key predictors: age, CMI, hypertension status, high-sensitivity C-reactive protein (hsCRP) and renal function (measured as serum creatinine). …”
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Anatomical heterogeneity in low-grade and high-grade gliomas: A multiscale perspective by Shengpeng Liang, Nuo Dong, Yumin Chen, Yang Yang, Haibing Xu

“…Finally, we compared 42 white matter fibers extracted using XTRACT with 39 functional brain connectivity networks from the multi-subject dictionary learning (MSDL) atlas, revealing significant associations among the frontal aslant tract (FAT) and the intraparietal sulcus (IPS). …”
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Collaborative carbon emission peak actions in urban agglomerations: multi-agent reinforcement learning analysis of the urban agglomerations of Beijing-Tianjin-Hebei and Yangtze Riv... by Bingbing Zhao, Min Deng, Wei-Zhen Lu, Kaiqi Chen

“…The socioeconomic actions of cities within an urban agglomeration are coordinated using the QMIX algorithm, which employs value function factorization to integrate local decisions and feedback on reduced emissions. …”
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A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

“…Supervised learning is widely used in pattern recognition and classification due to its strong ability to enhance data accuracy. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…Second, in order to establish accurate keypoints correspondence in the matching phase, hard-mean loss function is proposed to optimize GLFE-Net, which jointly utilizes hard negative samples and remaining negative samples to guide descriptors in learning more discriminant features to better distinguish positive and negative samples in SAR and optical images. …”
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Development of a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease: A secondary analysis of cross-sectional national survey by Sudarshan Pant, Hyung Jeong Yang, Sehyun Cho, EuiJeong Ryu, Ja Yun Choi

“…Objective This study aims to develop and validate a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease (COPD) using data from a national survey. …”
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Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…The proposed method utilizes deep learning models to maximize signal processing and suppress peak-power variations, while ensuring signal integrity. …”
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Comparative analysis of early outcomes of the first 150 cases of posterior approach robotic‐assisted radical prostatectomy and identification of the learning curve: A single‐surgeo... by Li June Tay, Henry Y. C. Pan, Leigh James Spurling, Philip Dundee

“…Oncological and functional outcomes are excellent throughout when compared with published literature. …”
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Selection of Failure Data in Software Reliability Modeling Based on RVM by Xiaoming Yang, Jungang Lou, Zhangguo Shen, Wenjun Hu

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Selection of Failure Data in Software Reliability Modeling Based on RVM by Xiaoming Yang, Jungang Lou, Zhangguo Shen, Wenjun Hu

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Provably secure quantum resistance two-server password-authenticated key exchange protocol by Anqi YIN, Yuanbo GUO, Ding WANG, Tongzhou QU, Lin CHEN

“…Aiming at the problem that the lattice-based single-sever password-authenticated key exchange (PAKE) protocols are not resistant to server compromise attack, while the existing lattice-based multi-server PAKE protocols are inefficient and incompatible with two-server scenarios.The first lattice-based two-party smooth projective hash function (SPHF) was proposed by utilizing the learning with errors (LWE), which was indistinguishability under adaptive chosen-ciphertext attack(IND-CCA2) secure.The parameters of the based public key encryption (PKE) scheme were also identified.On this basis, pertinent two-server PAKE protocols from lattices were designed countering both passive and active attackers.The two quantum resistance protocols were able to achieve password-only settings and the expensive cryptographic primitives were not used, including signature/verification, fully homomorphic encryption and secret sharing.The utilization of zero knowledge proofs were avoided by the protocol under the passive attackers.In the standard model, rigorous security proofs were provided for the two proposed protocols.Experimental results show that the proposed SPHF and PAKE protocols exhibit higher execution efficiency.…”
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Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis by Cong Hu, Heli Li, Jinru Cui, Yunjie Li, Feiyan Zhang, Hao Li, Xiaoping Luo, Yan Hao

“…This study aimed to identify critical genes linking BBB function and neuroinflammatory activation, with the ultimate goal of evaluating potential therapeutic targets. …”
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Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI by Jiaqi Shi, Hongyu Wang, Haiyan Gou, Yan Chen, Jia He, Youyang Qu, Xinya Wei, Mingyue Fan, Yanlong Wang, Yanmei Zhu, Yulan Zhu

“…Resting - state functional magnetic resonance imaging (fMRI) scans were carried out on all patients and 86 of them underwent simultaneous electroencephalogram (EEG) and electromyogram (EMG) examinations. …”
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Constructing a Glioblastoma Prognostic Model Related to Fatty Acid Metabolism Using Machine Learning and Identifying F13A1 as a Potential Target by Yushu Liu, Hui Deng, Ping Song, Mengxian Zhang

“…However, the characterization and function of FAM-related genes in glioblastoma (GBM) have not been fully explored. …”
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Development of executive mathematics problem-solving self-assessment questionnaire for upper primary school students by Supaluk Chaiarwut, Parama Kwangmuang, Sanit Srikoon

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“Where” and “What” Do Adolescent Athletes Learn When It Comes to Food Literacy Compared With Adolescents that Do Not Play Sports? A Gender-Based Thematic Analysis by Alysha L Deslippe, Coralie Bergeron, Olivia Y Wu, Kimberley J Hernandez, Emilie Comtois-Rousseau, Tamara R Cohen

“…Results: Functional competencies were learned most and often through social media. …”
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Black-box and white-box machine learning tools to estimate the frost formation condition during cryogenic CO2 capture from natural gas blends by Farag M.A. Altalbawy, Fadhel F. Sead, Dharmesh Sur, Anupam Yadav, José Gerardo León Chimbolema, Suhas Ballal, Abhayveer Singh, Anita Devi, Kamal Kant Joshi, Nizomiddin Juraev, Hossein Mahabadi Asl

“…This study deals with the development of machine learning models for predicting the FFT of CO2 in natural gas blends. …”
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Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region by Beijing XIE, Heng LI, Zheng LUAN, Zhen LEI, Xiaoxu LI, Zhuo LI

“…Furthermore, the WIOU boundary regression loss function is employed to increase the model's focus on common quality samples, addressing issues such as regression errors caused by imbalanced training samples. …”
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