Intratumor heterogeneity related signature for clinical outcome and immunotherapy advantages in lung adenocarcinoma by Yanhua Zuo, Li Lin, Libo Sun

“…Additionally, compared to the clinical stage and numerous other existing prediction models, a higher C-index was demonstrated in IRS. …”
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Spatial assessment of settlement consolidation potential: insights from Zhejiang Province, China by Qiushi Zhou, Wenze Yue, Mengmeng Li, Hongwei Hu, Leyi Zhang

“…Here, we evaluate settlement consolidation potential across rural land systems in Zhejiang Province, by leveraging machine learning models fed with reference data from completed consolidation projects and associated explanatory variables. …”
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Dataset on the long-term monitoring of foundation vertical deformations on medium-expansive soilMendeley Data by Hawkar Hashim Ibrahim, Rizgar Ali Hummadi

“…It is particularly useful in developing machine learning algorithms that can be used to predict foundation behavior in response to different environmental conditions, optimize foundation designs on expansive soils, and specifically predict foundation heave. …”
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How Can Business Analytics Enhance Decision-Making in Oil and Gas Surface Facilities? by Agung Prasetya, Meditya Wasesa, Yos Sunitiyoso

“…Predictive analytics enables proactive maintenance through machine learning, while prescriptive analytics optimizes operations using simulations and multi-objective decision models. …”
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CNN based method for classifying cervical cancer cells in pap smear images by Remita Austin, R. Parvathi

“…Several pre-trained convolutional neural network (CNN) models are used via transfer learning methods, hence predicting and evaluating the accurate classifier with the best optimal solution. …”
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Disassembly Plan Representation by Hypergraph by Abboy Verkuilen, Mirjam Zijderveld, Niels de Buck, Jenny Coenen

“…Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. …”
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Overview of Traffic Flow Forecasting Techniques by Annarita Carianni, Andrea Gemma

“…The study classifies forecasting methods into four categories: naïve techniques, parametric methods, simulation-based approaches, and nonparametric models such as machine learning and deep learning. …”
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Operational data analytics for failure prediction and availability improvement in gas turbine power plants by Ahiamadu Jonathan Okirie, Ebigenibo Genuine Saturday, Mathew Izuchukwu Gift, Dickens Ewe

“…The study recommends that future research should focus on refining predictive maintenance models through machine learning and AI-based analytics to further improve turbine efficiency and operational resilience. …”
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Directional migration of recirculating lymphocytes through lymph nodes via random walks. by Niclas Thomas, Lenka Matejovicova, Wichat Srikusalanukul, John Shawe-Taylor, Benny Chain

“…We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node insilico. …”
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A Survey of Autonomous Robotic Ultrasound Scanning Systems by Khushboo Munir, Abdullah F. Al-Battal, Ammar Alsheghri, Harald Becher, Michelle Noga, Kumaradevan Punithakumar

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Multiscale Materials Imaging and Spectroscopy for Battery Materials by Youngwoo Choi, Gumin Kang, Seonghyun Kim, Yoonhan Cho, Jaewhan Oh, Dongho Kim, Jacob Choe, Jong Min Yuk, Pyuck‐Pa Choi, Yongsoo Yang, Sung‐Yoon Chung, Chi Won Ahn, Jongwoo Lim, Seungbum Hong

“…Moreover, the integration of machine learning accelerates data processing, enabling multiscale correlations and predictive modeling. …”
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EFLOP: a sparsity-aware metric for evaluating computational cost in spiking and non-spiking neural networks by Simon Narduzzi, Friedemann Zenke, Shih-Chii Liu, L Andrea Dunbar

“…Applying weight sparsity-aware training to both SNNs and ANNs, we achieve up to 8.9× reduction in EFLOPs for gated recurrent unit models and 3.6× for LIF models by sparsifying weights by 80 $\%$ , without sacrificing accuracy on the Spiking Heidelberg Digits and Spiking Speech Command datasets. …”
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Advances in the Diagnosis of Urinary Tract Infection: A Narrative Review by Juan Victor Ariel Franco, Nicolás Meza

“…POCT innovations, such as lateral flow immunoassays, enzymatic-based rapid tests, and novel biosensors, facilitate prompt bedside diagnosis, although specificity challenges remain. Meanwhile, AI and machine learning models demonstrate significant potential for risk stratification, prediction of infection, and improving antibiotics prescription practices yet face barriers related to validation, practical integration, and clinical acceptability. …”
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Review of impression creep test: a small-scale testing method for evaluation of creep properties of materials by Naveena, M. D. Mathew, Shin-Ichi Komazaki

“…Significant research efforts are ongoing to optimize aspects such as specimen and machine design, testing protocols, data interpretation methods, and constitutive modeling, while also striving to establish correlations between IC-derived parameters and those obtained from conventional creep tests. …”
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Research on Turning Deformation After Heat Treatment to Aviation Gears with Thin Webs Considering Multi-physics Coupling Effects by Tang Cheng, Tang Zhongwei, Tao Qi, Tang Jinyuan

“…Based on this model, the effects of different process parameters, routing and clamping methods on the deformation in the heat treatment and turning process of the aviation gear with thin webs are analyzed, and the main factors leading to the deformation of parts are defined and optimized.…”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The paper also addresses challenges associated with the use of unmanned aerial vehicles, such as data quality limitations, the complexity of processing large volumes of images, and the need for the development of more advanced models. The paper proposes solutions to these issues, including algorithm optimization and improved data preprocessing techniques. …”
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Human AI collaboration for unsupervised categorization of live surgical feedback by Rafal Kocielnik, Cherine H. Yang, Runzhuo Ma, Steven Y. Cen, Elyssa Y. Wong, Timothy N. Chu, J. Everett Knudsen, Peter Wager, John Heard, Umar Ghaffar, Anima Anandkumar, Andrew J. Hung

“…We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts. …”
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Data driven design of ultra high performance concrete prospects and application by Bryan K. Aylas-Paredes, Taihao Han, Advaith Neithalath, Jie Huang, Ashutosh Goel, Aditya Kumar, Narayanan Neithalath

“…This advancement facilitates optimized material design and performance prediction while reducing the experimental workload required to inform ML models. …”
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Structural health monitoring based on three-dimensional point cloud technology: A systematic review by Yanzong Zhang, Guibo Nie, Duozhi Wang

“…It outlines the entire process, from data acquisition to modeling and visualization, and compares six core methods: geometric morphology analysis, multi-temporal differential analysis, feature extraction, mapping, machine learning, and deep learning. …”
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An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection by Chengsheng Yuan, Zhenyu Xu, Xinting Li, Zhili Zhou, Junhao Huang, Ping Guo

“…Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. …”
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