Deep learning guided high-throughput virtual screening for in vitro antibody maturation by Gong Chen, Liu Hongde

“…Antibody affinity maturation is a crucial step in therapeutic antibody discovery. In this study, we present a virtual screening pipeline that integrates protein docking with deep learning-based structural prediction to identify antibody mutants with enhanced binding affinity for the antigen sST2. …”
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Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network by Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU

“…The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services.The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications.The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research.Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location.By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources.Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined.Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results.Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented.…”
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Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning by Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer, Albert P. Bartók

“…Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.…”
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Optimizing Well Selection in Hydraulic Fracturing Using Advanced Machine Learning Approaches by Hai T. Nguyen, Tarek Al-Arbi Ganat, Tu V. Truong

“…To address this limitation, machine learning models are applied to improve decision-making accuracy. …”
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Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS by Junyan Xu, Kaiyuan Zhu, Jieling Cai, Can Kockan, Natnatee Dokmai, Hyunghoon Cho, David P. Woodruff, S. Cenk Sahinalp

“…To address this, methods like SkSES use sketching for genome-wide association studies (GWAS) across distributed datasets while maintaining privacy. Here, we present a learning-augmented version of SkSES for more accurate identification of significant SNPs. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…This study presents a methodology for developing models that replicate the complex pop-up attack maneuver in air combat operations, using flight data from a Brazilian Air Force pilot in a 6-degree-of-freedom flight simulator. …”
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Estimating electricity consumption at city-level through advanced machine learning methods by Arpad Gellert, Lorena-Maria Olaru, Adrian Florea, Ileana-Ioana Cofaru, Ugo Fiore, Francesco Palmieri

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Influence of Service-Learning on Motivation, Prosociality, and Importance of Physical Education on Adolescents’ Students by Augusto Hoyo-Guillot, María Luisa Santos-Pastor, Eeva-Maria Hooli, Pedro Jesús Ruiz-Montero

“…Social changes have significantly impacted the educational system at various levels, for example, through legislative reforms, and have consequently guided the teaching–learning process. One of the emerging methodologies is Service-Learning (SL), which directly affects student competence and promotes habits related to physical activity and health. …”
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Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

“…In addition, an integrated data infrastructure platform is presented for the digitalization and control of the electrochemical CO2 reduction reactor. …”
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Detecting signs of depression on social media: A machine learning analysis and evaluation by Phi Ta, Nha Tran, Hung Nguyen, Hien D. Nguyen

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Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis by Abed Alrahman Chouaib, Hsin-Fang Chang, Omnia M. Khamis, Nadia Alawar, Santiago Echeverry, Lucie Demeersseman, Sofia Elizarova, James A. Daniel, Qinghai Tian, Peter Lipp, Eugenio F. Fornasiero, Salvatore Valitutti, Sebastian Barg, Constantin Pape, Ali H. Shaib, Ute Becherer

“…Each module uses distinct techniques, including deep learning, allowing the detection of rare events often missed by humans at a speed estimated to be approximately 60 times faster than manual analysis. …”
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Bankruptcy Prediction Using First-Order Autonomous Learning Multi-Model Classifier by Amine Sabek, Jakub Horák, Hussam Musa, Amélia Ferreira da Silva

“…Recent developments in the field of machine learning, and particularly autonomous learning classifiers, present a potential proposed alternative. …”
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Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications by Duarte Coelho, Ana Madureira, Ivo Pereira, Ramiro Gonçalves, Susana Nicola, Inês César, Daniel Alves de Oliveira

“…This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. …”
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Research on Predicting Mine Earthquakes Based on Deep Learning Time-Series Methods by Xiufeng Zhang, Wei Li, Yang Chen, Junpeng Zou, Hangrui Zhang, Hao Wang, Chaohong Shi, Shaopeng Yan, Quan Zhang

“…To more accurately monitor and predict mine earthquakes and thereby reduce the potential risk they pose, this paper presents a study on the inversion and localization of seismic sources of mine earthquakes and a study on the prediction of mine earthquakes based on the deep learning method. …”
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Using Transformers and Reinforcement Learning for the Team Orienteering Problem Under Dynamic Conditions by Antoni Guerrero, Marc Escoto, Majsa Ammouriova, Yangchongyi Men, Angel A. Juan

“…This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. …”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…The determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. …”
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A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis by Lili Qian, Xu Zhang, Xianguang Ma, Peng Xue, Xingying Tang, Xiang Li, Shuang Wang

“…This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. …”
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BenthicNet: A global compilation of seafloor images for deep learning applications by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg

“…Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. …”
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A Novel Multisupervised Coupled Metric Learning for Low-Resolution Face Matching by Guofeng Zou, Guixia Fu, Xiang Peng

“…This paper presents a new multisupervised coupled metric learning (MS-CML) method for low-resolution face image matching. …”
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Monitoring protected areas by integrating machine learning, remote sensing and citizen science by Thijs L. van derPlas, David G. Alexander, Michael J. O. Pocock

“…Machine Learning (ML) methods can address these challenges: geospatial foundation models for RS data can compress large data volumes, ML de‐biasing techniques can improve CS data quality, deep learning and multimodal ML can help to integrate RS and CS data, and transfer learning can fine‐tune models to local priorities. …”
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