AHerfReLU: A Novel Adaptive Activation Function Enhancing Deep Neural Network Performance by Abaid Ullah, Muhammad Imran, Muhammad Abdul Basit, Madeeha Tahir, Jihad Younis

“…In deep learning, the choice of activation function plays a vital role in enhancing model performance. …”
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Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to... by Yuan Zhang, Yanting Li, Yang Li, Lin Zhao, Yongkui Yang

“…Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. …”
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Mindful Eating: A Deep Insight Into Fructose Metabolism and Its Effects on Appetite Regulation and Brain Function by Gabriela Vanessa Flores Monar, Camila Sanchez Cruz, Ernesto Calderon Martinez

“…Fructose, a common sweetener in modern diets, has profound effects on both metabolism and brain function, primarily due to its distinct metabolic pathways. …”
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Deephive: A Reinforcement Learning Approach for Automated Discovery of Swarm-Based Optimization Policies by Eloghosa Ikponmwoba, Opeoluwa Owoyele

“…We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. …”
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Deep functional measurements of Fragile X syndrome human neurons reveal multiparametric electrophysiological disease phenotype by James J. Fink, Nathaniel Delaney-Busch, Ryan Dawes, Evanthia Nanou, Christopher Folts, Karthiayani Harikrishnan, Chris Hempel, Hansini Upadhyay, Trinh Nguyen, Himali Shroff, David Stoppel, Steven J. Ryan, Jane Jacques, Jennifer Grooms, Elizabeth Berry-Kravis, Mark F. Bear, Luis A. Williams, David Gerber, Mark Bunnage, Brinley Furey, Graham T. Dempsey

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Functional ultrasound imaging and prewhitening analysis reveal MK-801-induced disruption of brain network connectivity by Erik Hakopian, Argishti E. Stepanian, Shan Zhong, Shan Zhong, Kofi A. Agyeman, Kofi A. Agyeman, Nancy Zepeda, Kevin Wu, Charles Liu, Charles Liu, Charles Liu, Charles Liu, Darrin J. Lee, Darrin J. Lee, Darrin J. Lee, Darrin J. Lee, Vassilios Christopoulos, Vassilios Christopoulos, Vassilios Christopoulos, Vassilios Christopoulos

“…BackgroundDisruption of N-methyl-D-aspartate receptor (NMDAR) activity within the septohippocampal network - a critical circuit that includes the hippocampus, medial prefrontal cortex (mPFC) and other nuclei - is believed to contribute to learning and memory impairments. Although animal models using the NMDAR antagonist Dizocilpine (MK-801) replicate cognitive deficits associated with memory and learning disorders, the direct effects of MK-801 on brain network connectivity have not been well characterized.ObjectiveThis study aims to explore the effects of MK-801 on brain network connectivity using functional ultrasound imaging (fUSI) and apply time series analysis methods to mitigate potential statistical confounds in functional connectivity assessments.MethodsfUSI was employed to assess changes in cerebral blood volume (CBV) and network connectivity in MK-801-treated mice. …”
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Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome by András Ecker, Daniela Egas Santander, Marwan Abdellah, Jorge Blanco Alonso, Sirio Bolaños-Puchet, Giuseppe Chindemi, Dhuruva Priyan Gowri Mariyappan, James B Isbister, James King, Pramod Kumbhar, Ioannis Magkanaris, Eilif B Muller, Michael W Reimann

“…Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.…”
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Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells by Senhao Ren, Wenqiang Tang, Chao Ma, Li Hou, Xiaodong Chen, Jiashan Lin, Jie Yang, Yun Yang, Xiao Huo, Guoxin Li, Daowei Zhang

“…With the development of deep learning, several automated methods based on deep learning have been proposed to analyze the specific working conditions of pumping wells from dynamometer cards. …”
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Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach by Himanshu Choudhary, Arishi Orra, Kartik Sahoo, Manoj Thakur

“…In this study, we attempt to develop a risk-adjusted deep reinforcement learning (RA-DRL) approach leveraging three DRL agents trained using distinct reward functions, namely, log returns, differential Sharpe ratio, and maximum drawdown to develop a unified policy that incorporates the essence of these individual agents. …”
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Evaluating self-assistance during functional reach with a passive hydrostatic exoskeleton under artificial impairment by Julia Manczurowsky, Henry Mayne, David Nguyen, Meghan Kenney, John Peter Whitney, Christopher J. Hasson

“…However, most evidence comes from studies involving tasks with limited coordinative demands. In a functional task like reaching for and lifting an object, learning to generate coordinated assistive forces with an external device may pose bilateral sensorimotor challenges that limit motor learning in the impaired limb. …”
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Organ-system-based subclassification of preeclampsia using machine learning predicts pregnancy outcomes by Yanhong Xu, Yizheng Zu, Xiaosi Lu, Yiping Wang, Jiaying Zheng, Xia Xu, Jianying Yan

“…It remains unclear whether machine learning can identify organ-system-based subclasses of PE. …”
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Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

“…Three methods were used for screening 100+ clinical indicators, and five machine learning algorithms were used to develop and evaluate diagnostic models based on the most relevant indicators.ResultsMultivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”
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Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning by Lili Zhan

“…Then a semisupervised learning classifier based on Laplace regular least squares regression model is designed to use the relative support difference function as the decision method and optimize the function. …”
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Adaptive Focal Loss for Keypoint-Based Deep Learning Detectors Addressing Class Imbalance by Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin

“…Keypoint-based deep learning detectors have proven highly effective in object detection tasks by predicting specific keypoints to determine object classification and location. …”
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Prediction of Gait Neurodegenerative Diseases by Variational Mode Decomposition Using Machine Learning Algorithms by P. Visvanathan, P.M. Durai Raj Vincent

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Perspective of application of nonlinear stimulation therapy in the treatment of traumatic brain injuries and maintenance of cognitive functions in the elderly by M. V. Zueva

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Multi‐function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network by Lihong Wang, Kai Xie

“…Abstract In modern electronic warfare, multi‐function radar work mode recognition is increasingly crucial. …”
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G-KAN: Graph Kolmogorov-Arnold Network for Node Classification Using Contrastive Learning by Lining Yuan

“…G-KAN utilizes the Kolmogorov-Arnold Network (KAN) to dynamically learn activation functions and applies contrastive loss to implicitly extract node features. …”
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Simulation of the of the DeepLabv3 neural network learning process for the agricultural fields segmentation by A. F. Rogachev, I. S. Belousov

“…Approximations of the learning curve by the modified Johnson function are obtained by the methods of least squares and least modules.Result. …”
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Probiotic supplementation prevents stress-impaired spatial learning and enhances the effects of environmental enrichment by Cassandra M. Flynn, Lara M. Blackburn, Qi Yuan

“…Probiotics can improve cognitive functions, including learning and memory, by modulating the gut microbiota, reducing inflammation, and producing neuroactive substances. …”
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