Mem-based pangenome indexing for k-mer queries by Stephen Hwang, Nathaniel K. Brown, Omar Y. Ahmed, Katharine M. Jenike, Sam Kovaka, Michael C. Schatz, Ben Langmead

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Problems of optimizing the energy consumption of households in the tasks of improving the energy efficiency of the housing sector by G. G. Grebenuk, S. M. Nikishov, A. A. Krygin, L. A. Sereda

“…It was shown that when  constructing such a technique, the primary question is the data that  the user can provide. The minimum composition of input data was  determined, according to which the necessary algorithms for  optimizing energy consumption were designed. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Given the instability and high volatility of wind power generation, this study proposes a short-term wind power prediction method based on BWO‒VMD and TCN‒BiGRU to improve the accuracy of wind power prediction and better support the energy transition under the “dual carbon” strategy.MethodsA short-term wind power generation prediction model based on the beluga whale optimization (BWO) algorithm, variational mode de-composition (VMD), temporal convolutional network (TCN), and bidirectional gated recurrent unit (BiGRU) was carefully proposed to improve the prediction accuracy of wind power generation, particularly considering its inherent instability and high volatility. …”
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Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery by Qing Ye, Yaxin Sun, Yaxin Sun, Yaxin Sun

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Development of Adaptive Testing Method Based on Neurotechnologies by E. V. Chumakova, D. G. Korneev, M. S. Gasparian

“…Traditionally, training was carried out for a large number of epochs; graphs of dependences of accuracy on the number of epochs for a different number of neurons in the hidden layer were experimentally obtained.Results. …”
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Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data by He Bing, Xu Zhifeng, Xu Yangjie, Hu Jinxing, Ma Zhanwu

“…The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.…”
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Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios by Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo, Zihao Wang

“…Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. …”
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Simulation of current output during chrome plating of parts for hardening and car parts restoration by A. N. Kotomchin, Yu. V. Shtefan, V. A. Zorin

“…To conduct the research, the necessary equipment for obtaining electroplating coatings, developed a new electrolyte composition for obtaining high-quality high-performance chrome precipitation, as well as the Statistica 13.0 program, which allowed us to reduce the time for calculations and building the necessary graphs, was used.Results. …”
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Hybrid subnet-based node failure recovery formal procedure in wireless sensor and actor networks by Hamra Afzaal, Nazir Ahmad Zafar, Fahad Alhumaidan

“…The algorithm is hybrid as pre-failure planning and post-failure recovery is assumed for the critical nodes. …”
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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

“…To this end, this article proposes a joint data and knowledge‐driven disturbance localisation method. A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. …”
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Describing the performance of U.S. hospitals by applying big data analytics. by Nicholas S Downing, Alexander Cloninger, Arjun K Venkatesh, Angela Hsieh, Elizabeth E Drye, Ronald R Coifman, Harlan M Krumholz

“…Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.…”
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Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles by Fatma S. Alrayes, Nazir Ahmad, Asma Alshuhail, Menwa Alshammeri, Ali Alqazzaz, Hassan Alkhiri, Jehad Saad Alqurni, Yahia Said

“…Nevertheless, owing to camera motion and composite environments, it is problematic to identify the UAV; conventional models frequently miss UAV detection and make false alarms. …”
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