Methods for semantic analysis of the state of the process of functioning of a system for detecting, preventing and eliminating the consequences of computer attacks by V. O. Shablya, S. A. Konovalenko, E. O. Orlov

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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. …”
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Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis by Alessandro Grecucci, Miriam Langerbeck, Richard Bakiaj, Parisa Ahmadi Ghomroudi, Davide Rivolta, Xiaoping Yi, Xiaoping Yi, Xiaoping Yi, Irene Messina, Irene Messina

“…We also expect these networks to be associated with psychological symptoms experienced by borderline patients such as impulsivity and anger issues, as well as lack of self-control and neuroticism among others.MethodAn unsupervised machine learning method known as Group-ICA, was applied to resting state fMRI images of 200 individuals to predict BPT from the temporal variability of independent macro networks.ResultsResults indicated abnormal dynamic functional connectivity inside the SN including areas implicated in emotional reactivity and sensitivity, and in a network that partially overlaps with the DMN, including regions involved in social cognition and mind reading. …”
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Hygiene and didactic criteria of adaptation to the initial stage of training in higher education establishments. by N. P. Grebnyak, S. A. Schudro, R. A. Fedorchenko

Subjects: “…functional system…”
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Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation by N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov

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Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode by Zimovets V. I., Shamatrin S. V., Olada D. E., Kalashnykova N. I.

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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS):... by Yuen Zhong, Yu Bao, Hong Cheng, Chaoqun Liu, Shengzhu Huang, Hualong Qiu, Honglin Huang, Jiajun Ren, Hailiu Jin, Caitong He, Long Tian, Yu Zhang, Bangzhu Luo, Tao Liang, Mujun Li, Zengnan Mo, Longman Li, Xiaobo Yang

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Double Sigmoid Activation Function for Fault Detection in Wind Turbine Generator using Artificial Neural Network by NOOR FAZLIANA FADZAIL, Samila Mat Zali, Ernie Che Mid

Subjects: “…activation function…”
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Beyond meta-studies: Learnings from a large multi-site primary dataset on non-tangible benefits of nature-based solutions by Martina Viti, Jacob Ladenburg, Roland Löwe, Hjalte J.D. Sørup, Ursula S. McKnight, Karsten Arnbjerg-Nielsen

“…These insights indicate that decision-makers and NBS-planners can learn more broadly from past experiences from a variety of (cultural) contexts, which can assist in delivering more targeted and multi-functional NBS.…”
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Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study by Romen Samuel Wabina, Panu Looareesuwan, Suphachoke Sonsilphong, Htun Teza, Wanchana Ponthongmak, Gareth McKay, John Attia, Anuchate Pattanateepapon, Anupol Panitchote, Ammarin Thakkinstian

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Motor learning leverages coordinated low-frequency cortico-basal ganglia activity to optimize motor preparation in humans with Parkinson’s disease by Kara N. Presbrey, Kara N. Presbrey, Kara N. Presbrey, Thomas A. Wozny, Thomas A. Wozny, Kenneth H. Louie, Kenneth H. Louie, Simon Little, Simon Little, Philip A. Starr, Philip A. Starr, Reza Abbasi-Asl, Reza Abbasi-Asl, Doris D. Wang, Doris D. Wang

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Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance by Harshada Mhetre, Sagar Pande, Babita Singla, Pavan Hiremath, Samriddh Sahu, Sarvesh Sorte, Ketan Kotecha, Nithesh Naik

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A Rapid Cortical Learning Process Supporting Students’ Knowledge Construction During Real Classroom Teaching by Xiaodan Feng, Xinran Xu, Zhaonan Meng, Jiahao Jiang, Miao Pei, Yonghe Zheng, Chunming Lu

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Optimal Weighting Factors Design for Model Predictive Current Controller for Enhanced Dynamic Performance of PMSM Employing Deep Reinforcement Learning by Muhammad Usama, Amine Salaje, Thomas Chevet, Nicolas Langlois

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Gallic acid alleviates ferroptosis by negatively regulating APOC3 and improves nerve function deficit caused by traumatic brain injury by Yu Liu, Xiaojia Fu, Jing Li, Jianqiang Guo, Zongren Zhao, Jinyu Zheng

“…The effect of Gallic acid on the improvement of neurological function was studied by Nissl staining and FJB staining. …”
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Comparative neuropsychological analysis of audio-verbal memory features in first-formers with different learning success by G. F. Khodakovskaya

Subjects: “…higher mental functions…”
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A Three-Level Meta-Frontier Framework with Machine Learning Projections for Carbon Emission Efficiency Analysis: Heterogeneity Decomposition and Policy Implications by Xiaoxia Zhu, Tongyue Feng, Yuhe Shen, Ning Zhang, Xu Guo

“…Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. …”
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Les machines y voient-elles quelque chose ? by Denis Bonnay

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SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang, Zongze Zhao

Subjects: “…deep learning…”
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Deep-CABPred: Deep learning model for predicting functional chlorophyll a-b binding proteins in trait-based plant ecology using hybrid embedding with semi-normalized temporal convolutional networks by Farman Ali, Raed Alsini, Tamim Alkhalifah, Fahad Alturise, Wajdi Alghamdi, Majdi Khalid

“…Identifying these proteins is vital for understanding ecological function and productivity, but traditional experimental methods are laborious. …”
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