Machine Learning Accurate Exchange–Correlation Potentials for Reducing Delocalization Error in Density Functional Theory by Yuan Zhuang, Yonghao Gu, Beini Zhang, Jiang Wu, Guanhua Chen

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Functional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk assessment by Flavia Indrio, Elio Masciari, Flavia Marchese, Matteo Rinaldi, Gianfranco Maffei, Ilaria Gangai, Assunta Grillo, Roberta De Benedetto, Enea Vincenzo Napolitano, Isadora Beghetti, Luigi Corvaglia, Antonio Di Mauro, Arianna Aceti

“…Background: Functional Gastrointestinal Disorders (FGIDs) can pose a great burden on affected children, their families, and the healthcare system. …”
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Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants by Shan Peng, Yukun Zhao, Xinyi Yao, Huilin Yin, Bei Ma, Ke Liu, Gang Li, Yang Cao

Subjects: “…machine learning…”
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Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning by Etienne Combrisson, Ruggero Basanisi, Matteo Neri, Guillaume Auzias, Giovanni Petri, Daniele Marinazzo, Stefano Panzeri, Andrea Brovelli

“…However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. …”
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SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction. by Shugang Zhang, Yuntong Li, Wenjian Ma, Qing Cai, Jing Qin, Xiangpeng Bi, Huasen Jiang, Xiaoyu Huang, Zhiqiang Wei

“…To date, there have been over 200 million known proteins, but only 0.2% of them have well-annotated functional terms. By measuring the contacts among residues, proteins can be described as graphs so that the graph leaning approaches can be applied to learn protein representations. …”
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Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact by Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton

“…Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.…”
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The Impact of Functional Asymmetry of the Cerebral Hemispheres in Students of a Physics and Mathematics Lyceum on the Learning Outcomes by Oksana Ikkert, Tetiana Korol, Kateryna Hlazunova, Iryna Tsinkevych

“…This indicates that the type of interhemispheric asymmetry is not a factor that causes learning difficulties or vice versa. The article summarizes the necessity of considering the individual psychophysiological characteristics of students in the educational process, particularly their functional asymmetry of the cerebral hemispheres. …”
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information by Wenkang Wang, Yunyan Shuai, Min Zeng, Wei Fan, Min Li

“…In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. …”
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Ramp Metering for a Distant Downstream Bottleneck Using Reinforcement Learning with Value Function Approximation by Yue Zhou, Kaan Ozbay, Pushkin Kachroo, Fan Zuo

“…The mechanism and development of the approximate value function and how learning of its parameters is integrated into the Q-learning process are well explained. …”
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The Effectiveness of Self-instruction training on Executive Functions and Cognitive Inhibition of Students with Special Learning Disorder by Mahmood Jabarzadeh Chaharbrod, Mansoor Beyrami, Asghar Nokhestin Goldost, Tooraj HAshemi

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Functional polymorphism of the mu-opioid receptor gene (OPRM1) influences reinforcement learning in humans. by Mary R Lee, Courtney L Gallen, Xiaochu Zhang, Colin A Hodgkinson, David Goldman, Elliot A Stein, Christina S Barr

“…Previous reports on the functional effects (i.e., gain or loss of function), and phenotypic outcomes (e.g., changes in addiction vulnerability and stress response) of a commonly occurring functional single nucleotide polymorphism (SNP) of the mu-opioid receptor (OPRM1 A118G) have been inconsistent. …”
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Rectified Tangent Activation (RTA): A Novel Activation Function for Enhanced Deep Learning Performance by Gaurav Kumar Pandey, Sumit Srivastava

Subjects: “…Deep learning…”
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Application of Machine Learning for Identifying Factors Associated with Renal Function Impairment in Gouty Arthritis Patients by Osman Cüre, Fatih Bal

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Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application by Chao Li, Ji Chen, Mengshi Dong, Hao Yan, Feng Chen, Ning Mao, Shuai Wang, Xiaozhu Liu, Yanqing Tang, Fei Wang, Jie Qin

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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models by Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui

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Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning by Yanan Zhang, Jizhou Zhang, Sijia Han

“…In this paper, we propose the Local Implicit Spatial-spectral Function (LISSF), which learns a local continuous representation of high spatial resolution hyperspectral images (HR-HSI) from the discrete inputs. …”
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Learning control system of lifting machine motors by V. I. Zimovets, A. S. Chirva, O. I. Marishchenko

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A machine learning model for automated contact tracing during disease outbreaks by Zeyad Aklah, Amean Al-Safi, Marwa H. Abdali, Khalid Al-jabery

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Bionic Compass Method Based on Atmospheric Polarization Optimization in Non-Ideal Clear Condition by Yuyang Li, Xia Wang, Min Zhang, Ruiqiang Li, Qiyang Sun

“…This paper proposes a bionic navigation method based on atmospheric polarization optimization to improve heading accuracy under non-ideal clear conditions. A signal model under non-ideal clear conditions was firstly established to introduce disturbances of aerosols and other particles into the raw signal function acquired by a camera. …”
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Assessment of dental, skeletal, and soft tissue changes following mandibular advancement with Invisalign in skeletal Class II by Hassan Al Subaie, Ghassan Alturki, Fahad Alsulaimani, Salma Ghoneim, Hosam Baeshen

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