Simulation-Based Optimization for the Operation of Toll Plaza at Car Park Exit with Mixed Types of Tollbooths and Waiting-Time-Dependent Service by Shanchuan Yu, Yuchuan Du, Jindong Wang, Yishun Li, Yong Zhu

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Stroke in children and adolescents: topical problems of pre-hospital diagnostics by Y. A. Khachaturov, I. O. Shchederkina, N. F. Plavunov, A. M. Sidorov, E. E. Petryaykina, I. P. Vytkovskaya, V. A. Kadyshev

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How math shapes the world of life science animation by Rafael Oliveira, Evellyn Araujo Dias, Evellyn Araujo Dias, Ricardo Santos, Ricardo Santos, Vinicius Cotta-De-Almeida, José Aguiar Coelho Nt, José Aguiar Coelho Nt, José Aguiar Coelho Nt, Luiz Anastácio Alves

“…With the advances in technology, it is noticeable the educational potential of animation in the field of cell biology, physiology and other basic life science disciplines, revolutionizing the learning process in science. …”
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Sampling calorimeter to measure the photon’s incident angle by Lim GeiYoub, Abe Ryota, Ahn JungKeun, Ji Su-Jeong, Kim Chong, Kim Eun-Joo, Kim Junlee, Kim YoungJun, Kim YoungJun, Kotera Katsushige, Lim Sanghoon, Naito Taichi, Park JeongWoo, Shiomi Koji, Tajima Yasuhisa, Yang Seongbae, Yoshida Hiroshi

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Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles by Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim

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Adaptive Receiver-Window Adjustment for Delay Reduction in LTE Networks by N. D. Adesh, A. Renuka

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Peculiarities of early diagnosis and tactics of treatment in an acute pancreatitis in pregnant women by Ya. P. Feleshtynskyi, О. V. Golyanovskyi, Т. P. Pavliv

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ADAPTIVE VISION AI by V. Vodyanitskyi, V. Yuskovych-Zhukovska

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The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK by Solomon White, Tiago Silva, Laurent O. Amoudry, Evangelos Spyrakos, Adrien Martin, Adrien Martin, Encarni Medina-Lopez

“…This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. …”
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Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance by Jian Feng, Zhe Chen, Gaoming Wang, Yu Yao, Xuewen Min, Jing Luo, Kai Xie

“…The prognostic signature, comprising nine CRPGs, accurately predicted 1-, 2-, and 3-year overall survival. TNNC1 was identified as a crucial tumor suppressor gene and potential drug target. …”
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AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding by Nhan Duc Thanh Nguyen, Huy Phan, Simon Geirnaert, Kaare Mikkelsen, Preben Kidmose

“…First, the algorithm predicts representations of the attended speech signal envelopes; second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. …”
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Selection of a Complex of Informative Features for Assessing the Internal Characteristics of Eggs for Consumption by Toncho Kolev, Mariya Georgieva-Nikolova, Miglena Kazakova, Danail Bonchev, Hristo Lukanov, Zlatin Zlatev

“…These results highlight the feature stability and predictability variation across different types of eggs and their manufacturers.…”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…<italic>Results:</italic> The classification accuracy where true class matches with the most probable class predicted by the model (Top-1 accuracy) is 83.8&#x0025;, while the accuracy where true class matches with any one of the 5 most probable classes predicted by the model (Top-5 accuracy) is 97.6&#x0025;, for the food classification subsystem. …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Results 157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. …”
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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…Genes with diagnostic value were identified using multiple machine learning algorithms to construct EC prediction models and evaluate their performance. …”
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Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach by Mingyue Luo, Hui Li, Wanbo Luo, Hewei Li, Jianan Li

“…Moreover, trajectory prediction at longer distances also poses significant challenges for such trajectory planning methods. …”
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Automated Calibration of SWMM for Improved Stormwater Model Development and Application by Hossein Ahmadi, Durelle Scott, David J. Sample, Mina Shahed Behrouz

“…Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. …”
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Accuracy is not enough: a heterogeneous ensemble model versus FGSM attack by Reham A. Elsheikh, M. A. Mohamed, Ahmed Mohamed Abou-Taleb, Mohamed Maher Ata

“…Abstract In this paper, based on facial landmark approaches, the possible vulnerability of ensemble algorithms to the FGSM attack has been assessed using three commonly used models: convolutional neural network-based antialiasing (A_CNN), Xc_Deep2-based DeepLab v2, and SqueezeNet (Squ_Net)-based Fire modules. …”
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Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia by Fan Lu, Ni Zeng, Xiang Xiao, Xingxing Wang, Han Gong, Houkang Lei

“…The function of 5 hub genes was analyzed and the interaction between drugs and hub genes was predicted. A total of 5 small molecule drugs were predicted, namely benzbromarone, 9,10-phenanthrenequinone, chembl312032, insulin and aldesleukin. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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