Starch–Glycerol-Based Hydrogel Memristors for Bio-Inspired Auditory Neuron Applications by Jiachu Xie, Yuehang Ju, Zhenwei Zhang, Dianzhong Wen, Lu Wang

“…Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…For each rock class, we applied regression techniques—such as random forest (RF), k-nearest neighbors (k-NN), extreme gradient boosting (XGB), and artificial neural networks (ANN)—to estimate the logarithm of capillary pressure. …”
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Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting by Mohini Yadav, Majid Jamil, Mohammad Rizwan, Richa Kapoor

“…RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. …”
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Development Trend of Information Technology and AdvancedNuclear Power Generation by Tao ZHOU, Haolei ZHANG, Yao YAO, Chunmei LIU

“…Quantum technology can enhance the core fuel function. Artificial intelligence machine data capture and neural networks learn to process and apply information more precisely. …”
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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

“…Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. …”
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Identifying and Diagnosing Lytic Cell Death Genes in Atherosclerosis Using Machine Learning and Bioinformatics by Zhang G, Ma R, Jin H, Zhang Q, Li W, Ding Y

“…Machine learning was used to screen characteristic LCDEGs, and an artificial neural network (ANN) model was developed. …”
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γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders by Zhong-Peng Dai, Qiang Wen, Ping Wu, Yan-Ni Zhang, Cai-Lian Fang, Meng-Yuan Dai, Hong-Liang Zhou, Huan Wang, Hao Tang, Si-Qi Zhang, Xiao-Kun Li, Jian-Song Ji, Liu-Xi Chu, Zhou-Guang Wang

“…By targeting γ oscillatory patterns and restoring healthy cross-frequency coupling, interventions may alleviate cognitive and behavioral symptoms linked to disrupted communication. This review examines clinical applications of γ neuromodulations, including enhancing cognitive function through 40 Hz multisensory stimulation in Alzheimer’s disease, improving motor function in Parkinson’s disease, controlling seizures in epilepsy, and modulating emotional dysfunctions in depression. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. …”
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Stochastic Disruption of Synchronization Patterns in Coupled Non-Identical Neurons by Irina A. Bashkirtseva, Lev B. Ryashko, Ivan N. Tsvetkov, Alexander N. Pisarchik

“…Our findings contribute to the understanding of stochastic synchronization phenomena in coupled neuronal systems and offer potential implications for neural dynamics in biological and artificial networks.…”
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Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region by Ngo Nghi Truyen Huynh, Pierre‐André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay‐Allemand, Pierre Javelle

“…This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. …”
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Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism by Ling Sun, Lingyan He, Hai-Hua Pan, Chang-Lin Zhai

“…Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. …”
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Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework by Niti Klinkaew, Attasit Wiangkham, Atthaphon Ariyarit, Prasert Aengchuan, Anupap Pumpuang, Sarunporn Sripratum, Atthaphon Maneedaeng, Jiraphon Srisertpol, Ekarong Sukjit

“…Using a single-cylinder diesel engine from POLAWAT ENGINE Company Limited, we evaluated different bio-hydrogenated diesel and biodiesel blends, optimizing their composition through the Reference Vector Guided Evolutionary Algorithm with a surrogate objective function via Generalized Regression Neural Networks. …”
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Numerical thermodynamic-economic study and machine learning-based optimization of an innovative biogas-driven integrated power plant combined with sustainable liquid CO2 and liquid... by Ruijia Yuan, Fan Shi, Azher M. Abed, Mohamed Shaban, Sarminah Samad, Ahmad Almadhor, Barno Abdullaeva, Mouloud Aoudia, Salem Alkhalaf, Samah G. Babiker

“…Hence, a machine learning algorithm is implemented using artificial neural networks combined with the NSGA-II method for multi-criteria optimization, focusing on exergy efficiency, net present value, and products' sum unit cost as objective functions. …”
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