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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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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Thermal radiation and diffusion effects on MHD sisko fluid flow over a nonlinearly stretchable porous sheet by V. Adilakshmi, Ali Akgül, G. Venkata Ramana Reddy, Murad Khan Hassani

“…For example, elevating the Sisko parameter from 1.0 to 2.0 propels the velocity profile upward by approximately 20%, while the temperature and concentration profiles witness reductions of 10% and 8%, respectively. Moreover, escalating values of the power-law exponent and the nonlinear stretching coefficient contribute to a decline in velocity, temperature, and concentration. …”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Machine Learning (ML) plays a key role in time-series applications because it analyzes observed data and predicts future values. The effectiveness of ML models in time-series forecasting is reduced by the occurrence of Concept Drift (CD). …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…PET/CT reclassified 13 patients from possible to definite IE, demonstrating an overall sensitivity of 83.3%, specificity of 93.7%, positive predictive value (PPV) of 83.3%, and negative predictive value (NPV) of 93.7%. …”
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Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study by Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński, Marek Macko, Tomasz Bednarek, Krzysztof Tyburek

“…By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. …”
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

“…The nomogram constructed based on 8 genes with diagnostic value had good predictive performance.…”
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Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks by Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener

“…Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. …”
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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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MAST Kinases’ Function and Regulation: Insights from Structural Modeling and Disease Mutations by Michael C. Lemke, Nithin R. Avala, Michael T. Rader, Stefan R. Hargett, Daniel S. Lank, Brandon D. Seltzer, Thurl E. Harris

“…We also estimate the functional consequences of disease point mutations on protein stability by integrating predictive algorithms and AlphaFold. <b>Results</b>: Higher-order organisms often have multiple MASTs and a single MASTL kinase. …”
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Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were then utilized to build predictive models using logistic regression (LR) and eight ML algorithms. …”
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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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