Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm by K. V. Dobrego, I. A. Koznacheev

“…The efficiency of the parameter optimization algorithm and the adequacy of the resulting model are shown. …”
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An Enhanced and Adaptive Algorithm for Secure Encryption of Data using Advanced Encryption by Mohanad S. Taha

“…The technique is applied on the plaintext of the AES algorithm. Thanks to this approach, breaking the encryption is extremely difficult, as you need the right key, though AES itself remains a simple system. …”
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Research on identification algorithm of oscillating current in DC incoming lines of metro by WANG Nanqing, QI Yu, XIN Lanyuan, SONG Jinchuan, TIAN Xingjun

“…The paper presents a fault diagnosis and analysis algorithm centered on variational mode decomposition (VMD), sample entropy, and TOPSIS, to address the challenge of frequent misoperations of relay protection systems triggered by oscillating current in the incoming lines of metro traction power supply systems. …”
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Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time by Kukuh Triyuliarno Hidayat, Riza Arifudin, Alamsyah Alamsyah

“…In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. …”
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A versatile framework for attitude tuning of beamlines at light source facilities by Peng-Cheng Li, Xiao-Xue Bi, Zhen Zhang, Xiao-Bao Deng, Chun Li, Li-Wen Wang, Gong-Fa Liu, Yi Zhang, Ai-Yu Zhou, Yu Liu

“…The tuning of a Raman spectrometer demonstrates more specialized use of the framework with customized optimization algorithms. With similar applications in mind, this framework is estimated to be capable of fulfilling most attitude-tuning needs. …”
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AN ALGORITHM FOR APPLYING STATISTICAL METHODS IN THE IDENTIFICATION OF SUBSPECIES AND HYBRIDS OF PANTHERA PARDUS by O. Chernova, T. T. Perfiova, A. Kiladze, P. Sorokin

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A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare by Yehia Ibrahim Alzoubi, Ahmet E. Topcu, Ersin Elbasi

“…AI algorithms and tools are categorized into three groups: explicit AI algorithms for sustainability for energy efficiency (e.g., Federated Learning, Hybrid Quantum-Classical Optimization, Modified Lempel-Ziv-Welch (mLZW)), traditional AI algorithms for sustainable healthcare (e.g., Bidirectional Long Short-Term Memory (Bi-LSTM), Backpropagation Neural Networks (BPNNs), Convolutional Neural Networks (CNNs)), and sustainable AI techniques (e.g., Adaptive Sampling, AutoML for Model Compression (AMC)) that support low-power computing (e.g., edge computing, neuromorphic hardware, adaptive sampling). …”
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Developing personalized algorithms for sensing mental health symptoms in daily life by Adela C. Timmons, Abdullah Aman Tutul, Kleanthis Avramidis, Jacqueline B. Duong, Kayla E. Carta, Sierra N. Walters, Grace A. Jumonville, Alyssa S. Carrasco, Gabrielle F. Freitag, Daniela N. Romero, Matthew W. Ahle, Jonathan S. Comer, Shrikanth S. Narayanan, Ishita P. Khurd, Theodora Chaspari

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Algorithms for the visual analysis of an environment by an autonomous mobile robot for area cleanup by M. E. Beliakov, S. A. K. Diane

“…The purpose of this work is to develop the underlying algorithmics for the vision system of robots executing area cleaning functions.Methods. …”
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Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography by Koji Tamai, Keiho Imanishi, Masaki Terakawa, Masato Uematsu, Minori Kato, Hiromitsu Toyoda, Akinobu Suzuki, Shinji Takahashi, Akito Yabu, Yuta Sawada, Masayoshi Iwamae, Yuto Kobayashi, Yuki Okamura, Hiroshi Taniwaki, Yuki Kinoshita, Masatoshi Hoshino, Hitoshi Tabuchi, Hiroaki Nakamura, Hidetomi Terai

“…Samples were divided into training (n = 200) and test (n = 30) datasets. …”
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Assessment of Soil Thermal Conductivity Based on BPNN Optimized by Genetic Algorithm by Chenyang Liu, Xinmin Hu, Ren Yao, Yalu Han, Yong Wang, Wentong He, Huabin Fan, Lizhi Du

“…Apparently, it has great meaning to accurately predict conductivity around a site through easily accessible parameters. In this paper, 40 samples are taken from 37 experimental points in Changchun, China, and the BPNN optimized by genetic algorithm (GA-BPNN) is used to evaluate the thermal conductivity by moisture content, porosity, and natural density of undisturbed soil. …”
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M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling by Xinyue Cui, Yuhao Xia, Minghua Hou, Xuanfeng Zhao, Suhui Wang, Guijun Zhang

“…Results To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. …”
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Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning by Moein Khajehnejad, Forough Habibollahi, Alon Loeffler, Aswin Paul, Adeel Razi, Brett J. Kagan

“…Through this, we introduce a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency.…”
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Untargeted metabolic analysis in serum samples reveals metabolic signature in children with congenital heart failure on enalapril therapy by N. J. L. Smeets, I. N. van Hoek, J. J. M. Jans, M. Dalinghaus, S. Laer, M. Bajcetic, M. Bajcetic, C. Male, S. N. de Wildt, S. N. de Wildt

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Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine by JIN Zhenzhen, HE Deqiang, MIAO Jian, XU Weichang

“…In view of above problems, a fault diagnosis method of train bogie bearing based on multi-scale sample entropy improved extreme learning machine is proposed. …”
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Representative Sample Size for Estimating Saturated Hydraulic Conductivity via Machine Learning: A Proof‐Of‐Concept Study by Amin Ahmadisharaf, Reza Nematirad, Sadra Sabouri, Yakov Pachepsky, Behzad Ghanbarian

“…Results showed that for all training sample sizes the number of samples was not enough for the training and cross‐validation curves to reach a plateau. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Given the scarcity, extreme values, and high volatility of the sample data, a Sequence Variational Autoencoder (SeqVAE) algorithm is employed to generate numerical weather prediction data and corresponding power samples. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…The samples were split into training, testing and holdout sets. …”
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Thermal Damage Characterization and Modeling in Granite Samples Subjected to Heat Treatment by Leveraging Machine Learning and Experimental Data by Gabit Sansyzbekov, Amoussou Coffi Adoko, Paul Mathews George

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GIRAFE v1: a global climate data record for precipitation accompanied by a daily sampling uncertainty by H. Konrad, R. Roca, A. Niedorf, S. Finkensieper, M. Schröder, S. Cloché, G. Panegrossi, P. Sanò, C. Kidd, C. Kidd, R. A. Jucá Oliveira, R. A. Jucá Oliveira, K. Fennig, T. Sikorski, M. Lemoine, R. Hollmann

“…The daily product is accompanied by a dedicated sampling uncertainty estimate based on decorrelation scales in space and time in infrared-based instantaneous precipitation fields. …”
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