Static mechanical performance of aluminum alloys 7075 and 2A12 for front uprights in racing formula cars by Xinqiao Ding, Lingyi Fang, Xia Liu

“…While traditional 7075 aluminum alloy uprights exhibit susceptibility to surface wear and corrosion potentially compromising stability, 2A12 aluminum alloy offers superior characteristics including low density, high specific strength, excellent wear/corrosion resistance, and enhanced machinability. Using CATIA, we developed 3D models of the front upright, analyzing the driving force, rolling resistance, and braking force. …”
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Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy by Xiaohu Ma, Alice Fischerauer, Sebastian Haacke, Gerhard Fischerauer

“…Principal component analysis was applied to the EIS data to extract relevant features as input data for machine learning models. These features, combined with water content and density, were used to train regression models based on fully connected neural networks to estimate the bentonite content in the mixtures. …”
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Symbolic Regression Method for Estimating Distance Between Two Coils of an Inductive Wireless Power Transfer System by Davide Milillo, Lorenzo Sabino, Rafiq Asghar, Francesco Riganti Fulginei

“…SR employs a genetic algorithm to identify the analytical formula that best represents the input–output relationship, combining the strengths of traditional machine learning and analytical modeling. The results, with prediction errors of less than 1%, indicate that SR not only enhances predictive accuracy but also provides insights into the underlying physical principles governing WPT systems. …”
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Automation of reversible steganographic coding with nonlinear discrete optimisation by Ching-Chun Chang

“…Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. …”
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Numerical Analysis of Damage in Composites: From Intra-Layer to Delamination and Data-Assisted Methods by Alireza Taherzadeh-Fard, Alejandro Cornejo, Sergio Jiménez, Lucia G. Barbu

“…This review highlights the strengths and limitations of each approach, underscoring the need for further advancements in computational efficiency, multiscale modeling, and integration with experimental data. The findings serve as a foundation for future research into optimizing damage prediction techniques to improve the reliability and durability of composite structures.…”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

“…Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. …”
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Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys by Germán Omar Barrionuevo, Iván La Fé-Perdomo, Jorge A. Ramos-Grez

“…This dataset offers a valuable resource for researchers to benchmark their results, better understand key factors influencing RD, and validate models or explore new machine-learning approaches tailored to L-PBF.…”
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Personalized prediction of gait freezing using dynamic mode decomposition by Zhiwen Fu, Congping Lin, Yiwei Zhang

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New directions in mapping the Earth’s surface with citizen science and generative AI by Linda See, Qingqing Chen, Andrew Crooks, Juan Carlos Laso Bayas, Dilek Fraisl, Steffen Fritz, Ivelina Georgieva, Gerid Hager, Martin Hofer, Myroslava Lesiv, Žiga Malek, Milutin Milenković, Inian Moorthy, Fernando Orduña-Cabrera, Katya Pérez-Guzmán, Dmitry Schepaschenko, Maria Shchepashchenko, Jan Steinhauser, Ian McCallum

“…Yet the accuracy of these maps is still limited by the lack of in situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…Furthermore, it explores existing AI models and their applicability in diagnosing refractive errors and strabismus. …”
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Applications of AI in Predicting Drug Responses for Type 2 Diabetes by Shilpa Garg, Robert Kitchen, Ramneek Gupta, Ewan Pearson

“…Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies.…”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Research on a virtual-real fusion experimental system for the cutting part of a boom-type roadheader. by Jianzhuo Zhang, Ce Chen, Xiaoyu Ding, Tao Wang, Chuanxu Wan, Wenliang Li

“…This system incorporates functions including digital modeling of coal-rock, simulation of mechanical properties of the cutting unit, and integration of virtual and physical experiments. …”
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Mechanical testing of miniature carbon fiber reinforced polymer (CFRP) samples under digital light microscopy by E. S. Statnik, Iu. A. Sadykova, E. N. Prokopev, A.I. Salimon, E. V. Nazarov, N. V. Turbin, A.M. Korsunsky

“…Mechanical testing machines must be specifically designed and optimized for each scale, combined with microscopy to observe the response under tension, bending, and compression. …”
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Artificial intelligence-driven metabolic engineering is applied to the development of active ingredients in Traditional Chinese Medicine by Zhang Guoqi, Wang Juan

“…In this paper, we introduce AI-driven machine learning algorithms in prediction models, and also review recent research progress on AI-assisted metabolic engineering design and production of high-value compounds, focusing on how to use AI methods to achieve directed evolution of strains.…”
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Solution of a flexible jobshop scheduling problem using an Estimation of Distribution Algorithm. by R. Pérez, S. Jöns, A. Hernández

“…The flexible jobshop scheduling problem permits the operation of each job to be processed by more than one machine. The configuration mentioned generally seeks to minimize the completion time of all jobs known in the literature as ‘makespan’. …”
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The role of phytoplankton in structuring global oceanic dissolved organic carbon pools by Zhe Lu, Guoming Qin, Lingling Zheng, Yanyan Zhang, Lincheng Huang, Jinge Zhou, Yongxin Liu, Mianhai Zheng, Enqing Hou, Lirong Song, Hongbin Liu, Nianzhi Jiao, Faming Wang

“…Furthermore, leveraging these optimized models, we generated a global marine DOC dataset and found that diatoms explained up to 63.8% of the variance in surface DOC. …”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Objectives: This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. …”
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The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation by Lakitha O. H. Wijeratne, Daniel Kiv, John Waczak, Prabuddha Dewage, Gokul Balagopal, Mazhar Iqbal, Adam Aker, Bharana Fernando, Matthew Lary, Vinu Sooriyaarachchi, Rittik Patra, Nora Desmond, Hannah Zabiepour, Darren Xi, Vardhan Agnihotri, Seth Lee, Chris Simmons, David J. Lary

“…This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. …”
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Research on Visualization of Cylindrical Gear Hobbing Cutting Force and the Influence of Cutting Parameter on Hobbing Cutting Force by Xiaoqing Tian, Xinrong Zheng, Ruofeng Chen, Jiang Han, Lian Xia

“…The influence of different cutting parameter on hobbing cutting force is analyzed quantitatively. The predictive model helps to optimize the design of the gear hobbing process to improve the quality and efficiency of the machining and reduce the cost.…”
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