Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem by Vladimir V. Vasilchikov

“…For the experiment, the author developed a special application in the C# language designed to generate various sets of initial data with specified parameters. The paper describes the features of the generated initial pairs of graphs, as well as the results obtained during the experiment.…”
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Development of a Novel Transonic Fan Casing Making Use of Rapid Prototyping and Additive Manufacturing by Andrew Cusator, Nicole L. Key

“…Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. …”
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PS-YOLO: A Lighter and Faster Network for UAV Object Detection by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang, Liang Zhao

“…This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s.…”
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Quantum Kernel Machine Learning With Continuous Variables by Laura J. Henderson, Rishi Goel, Sally Shrapnel

“…We derive a general closed form solution for all CV quantum kernels and show every such kernel can be expressed as the product of a Gaussian and an algebraic function of the parameters of the feature map. Furthermore, in the multi-mode case, we present quantification of a quantum-classical separation for all quantum kernels via a hierarchical notion of the “stellar rank" of the quantum kernel feature map. …”
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Frequency-resolved Time Lags due to X-Ray Disk Reprocessing in AGN by Christos Panagiotou, Iossif Papadakis, Erin Kara, Marios Papoutsis, Edward M. Cackett, Michal Dovčiak, Javier A. García, Elias Kammoun, Collin Lewin

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YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes by Guoxun Cong, Guoxun Cong, Xinghong Chen, Xinghong Chen, Zongyu Bing, Zongyu Bing, Wenhuan Liu, Wenhuan Liu, Xiangling Chen, Qun Wu, Zheng Guo, Zheng Guo, Yongqiang Zheng, Yongqiang Zheng

“…The YOLOv8-Scm model achieves improvements of 2.0% in mAP50 and 1.5% in Precision over the original YOLOv8n, with only a slight increase in computational parameters (0.182M). The model’s Recall rate decreases minimally by 0.01%. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

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Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation by Quan Li, Xinhua Xu, Jinjun Liu, Guangmin Li

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Enhancing thermal transport in chemically reacting nanoparticles using the energy source and Cattaneo-Christov heat flux model by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah Berrouk, Saeed Islam, Asim Aamir

“…The important and intriguing feature of this remarkable work is that, for all parameters examined, the heat transfer rate rises with minimal measurement of errors, consistent with the core objective of applying nanofluids to nanotechnology for their prospective implications.…”
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Development and validation of a CT-based multi-omics nomogram for predicting hospital discharge outcomes following mechanical thrombectomy by Feifan Liu, Jiayi Hong, Yuhan Chen, Huan Liu, Yue Wang, Lijian Su, Sheng Hu, Jingjing Fu

“…ObjectiveThis study aimed to develop a multi-omics nomogram that combines clinical parameters, radiomics, and deep transfer learning (DTL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict functional outcomes at discharge.MethodsThis study enrolled 246 patients with HIM who underwent MT. …”
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Security monitoring via sound analysis and voice identification with artificial intelligence by Balabanova Ivelina, Sidorova Kristina, Georgiev Georgi

“…Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. …”
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Non-Invasive Bioelectrical Characterization of Strawberry Peduncles for Post-Harvest Physiological Maturity Classification by Jonnel Alejandrino, Ronnie Concepcion, Elmer Dadios, Ryan Rhay Vicerra, Argel Bandala, Edwin Sybingco, Laurence Gan Lim, Raouf Naguib

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Eagle-YOLOv8: UAV Object Detection Inspired by the Eagle-Eye Vision System by Dianwei Wang, Zehao Gao, Jie Fang, Yuanqing Li, Zhijie Xu

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Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis by Ranjeet Bidwe, Sashikala Mishra, Simi Bajaj, Ketan Kotecha

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CMRNet: An Automatic Rapeseed Counting and Localization Method Based on the CNN-Mamba Hybrid Model by Jie Li, Chenbo Yang, Chengyong Zhu, Tao Qin, Jingmin Tu, Binhui Wang, Jian Yao, Jiangwei Qiao

“…The model synergizes local feature extraction via CNN with the global modeling strengths of the Mamba state space model, yielding semantically rich features while significantly enhancing computational efficiency and inference speed. …”
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Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations by Jin Zhao, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, Linzhe Zhang

“…Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. …”
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FEXGBIDS: Federated XGBoost-Based Intrusion Detection System for In-Vehicle Network by Jie Li, Yuanyuan Song, Ming-Gang Zheng, Shuo Zhang, Han Liang

“…Additionally, we employ BLS aggregate signatures to achieve efficient and verifiable parameter aggregation, and leverage the DDSketch distributed quantile estimation algorithm to optimize the feature bucketing process. …”
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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
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YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects by Ziheng Liang, Tingting Zhu, Guang Teng, Yajun Zhang, Zhe Gu

“…Firstly, the original C3k2 module and A2C2f module of YOLOv12 were replaced with RFEM in the backbone network to enhance feature extraction for small targets without increasing computational complexity. …”
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