Assessment of mass characteristics of a wheeled chassis when designing a preventive suspension system by Malinovsky M.P., Tarichko V.I., Chikina A.K.

“…The article presents a suspension system developed by the authors, the peculiarity of which is the preventive nature of the action, consisting in the construction of a predictive algorithm from the driver’s control actions on the controls, unlike most existing stabilization systems with a corrective nature of the action. …”
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Future Outdoor Safety Monitoring: Integrating Human Activity Recognition with the Internet of Physical–Virtual Things by Yu Chen, Jia Li, Erik Blasch, Qian Qu

“…Advanced HAR–IoPVT algorithms and predictive analytics would identify potential hazards, enabling timely interventions and reducing accidents. …”
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Identification of effective subdominant anti-HIV-1 CD8+ T cells within entire post-infection and post-vaccination immune responses. by Gemma Hancock, Hongbing Yang, Elisabeth Yorke, Emma Wainwright, Victoria Bourne, Alyse Frisbee, Tamika L Payne, Mark Berrong, Guido Ferrari, Denis Chopera, Tomas Hanke, Beatriz Mothe, Christian Brander, M Juliana McElrath, Andrew McMichael, Nilu Goonetilleke, Georgia D Tomaras, Nicole Frahm, Lucy Dorrell

“…These vulnerable and so-called "beneficial" regions were of low entropy overall, yet several were not predicted by stringent conservation algorithms. Consistent with this, stronger inhibition of clade-matched than mismatched viruses was observed in the majority of subjects, indicating better targeting of clade-specific than conserved epitopes. …”
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Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning by Tairan Hu, Zhaohui Fang

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.ResultsLeeches may influence cellular immunity by modulating immune receptor activity, particularly through the activation of RGS10, CAPS2, and OPA1, thereby impacting the pathology of Type 2 Diabetes Mellitus (T2DM).DiscussionHowever, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.…”
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Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: assessing the potential of statistical and machine learnin... by Christopher Coffey, Meike Reusken, Frans Cruijssen, Bertrand Melenberg, Cascha van Wanrooij

“…However, accurately forecasting future prevalence of cases remains challenging, with the application of predictive models being notably scarce. Addressing this gap, this paper aims to identify factors associated with Global Acute Malnutrition (GAM) and explores the potential of machine learning in predicting its prevalence using data from Somalia.Methods Survey data on GAM prevalence systematically collected in Somalia every 6 months at a district level from 2017 to 2021 were collated alongside a range of potential climatic, demographic, disease, environmental, conflict and food security-related factors over a matching time period. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Immune cell infiltration patterns were quantified via single-sample gene set enrichment analysis (ssGSEA). A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).ResultsA total of 271 DEGs were identified, primarily enriched in multiple biological pathways. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study suggested the potential of ToxCast bioassays and machine learning models in predicting DART potential, offering a promising approach to address data-gap in consumer product safety.…”
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Smart CAR-T Nanosymbionts: archetypes and proto-models by Juan C. Baena, Juan C. Baena, Juan Sebastián Victoria, Juan Sebastián Victoria, Alejandro Toro-Pedroza, Alejandro Toro-Pedroza, Cristian C. Aragón, Cristian C. Aragón, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Juan Esteban Garcia-Robledo, Juan Esteban Garcia-Robledo, David Torres, Lady J. Rios-Serna, Lady J. Rios-Serna, Ludwig Albornoz, Joaquin D. Rosales, Joaquin D. Rosales, Carlos A. Cañas, Carlos A. Cañas, Carlos A. Cañas, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Tania Fleitas, Ivan Laponogov, Alexandre Loukanov, Alexandre Loukanov, Alexandre Loukanov, Kirill Veselkov, Kirill Veselkov

“…At the same time, artificial intelligence (AI), with its powerful algorithms for data analysis and predictive modeling, is transforming how we design, evaluate, and monitor advanced therapies, including the optimization of manufacturing processes. …”
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Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays by Armando Arce, Fernando Arce, Enrique Stevens-Navarro, Ulises Pineda-Rico, Marco Cardenas-Juarez, Abel Garcia-Barrientos

“…Beam patterns are optimized using a genetic algorithm during the training phase in order to reduce sidelobes and achieve high directivity. …”
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Guidelines for releasing a variant effect predictor by Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer, Sushant Kumar, Kresten Lindorff-Larsen, David M. McCandlish, Rose Orenbuch, Courtney A. Shearer, Lara Muffley, Julia Foreman, Andrew M. Glazer, Ben Lehner, Debora S. Marks, Frederick P. Roth, Alan F. Rubin, Lea M. Starita, Joseph A. Marsh

“…Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. …”
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Federated Transfer Learning for IIoT Devices With Low Computing Power Based on Blockchain and Edge Computing by Peiying Zhang, Hao Sun, Jingyi Situ, Chunxiao Jiang, Dongliang Xie

“…The experimental results show that our algorithm can achieve high security and high training accuracy.…”
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Method for detecting cracks in retaining walls based on improved YOLOv5s by Yanhai Wang, Chenxin Guo, Guoyong Duan, Yuhao Zhang, Chao Yang, Huafeng Deng

“…The experimental results showed that the improved algorithm reduced the number of YOLOv5s parameters by 12% and increased the average precision by 1.5% compared to YOLOv5s using only the rotating prediction frames. …”
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Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”
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Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model by Shengli Wang, Xiaolong Guo, Tianle Sun, Lihui Xu, Jinfeng Zhu, Zhicai Li, Jinjiang Zhang

“…A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). …”
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Optimization strategy of UAV‐ARIS assisted vehicular communication system by Haitao Zhao, Zhuoran Xu, Yiyang Ni, Wenxue Sun, Hongbo Zhu, Yaxuan Liu, Zhaoying Mo

“…The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. …”
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INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

“…The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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Research on a Conflict Early Warning System Based on the Active Safety Concept by Bozhao Shen, Zhiqing Zhang, Hao Liu, Shiyun Li, Lei Zhao

“…Through a driving simulation experiment, the speed prediction model after implementation of the warning system was examined. …”
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Machine Learning G-Code Optimization by Héctor Lasluisa-Naranjo, David Rivas-Lalaleo, Joaquín Vaquero-López, Christian Cruz-Moposita

“…This research applies the K-means machine learning clustering algorithm to optimize G-code parameters such as extruder and heated bed temperature, deposition speed, and flow control. …”
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Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite by Tong Lu, Zhengqiang Li, Cheng Fan, Zhuo He, Xinran Jiang, Ying Zhang, Yuanyuan Gao, Yundong Xuan, Gerrit de Leeuw

“…The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. …”
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Control of Overfitting with Physics by Sergei V. Kozyrev, Ilya A. Lopatin, Alexander N. Pechen

“…An application of this analogy allows us to explain the selection of wide likelihood maxima and ab overfitting reduction for GANs.…”
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