Short-term results of treatment of non-epithelial gastro-intestinal tumors complicated by bleeding by S. G. Shapovalyants, A. I. Mikhalev, Ye. D. Fedorov, R. V. Plakhov, L. M. Mikhaleva, Z. V. Galkova, Ye. V. Ivanova, A. E. Sergeyenko

“…According to suggested diagnostic algorithm detailed characteristic of patients with bleeding tumor was given, the optimal method of surgical intervention was chosen. …”
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MAARS: Multiagent Actor–Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing by Ducsun Lim, Inwhee Joe

“…This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. …”
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THE USE OF CORRELATION ANALYSIS OF TWO-DIMENSION STOCHASTIC PROCESS FOR HELICOPTER MAINTENANCE WORKABILITY ESTIMATION by Y. M. Chinyuchin, A. S. Solov’ev

“…The new calculated program complex which main task is to find the optimal algorithm of stochastic helicopter de- sign and assembling in terms of maintenance efficiency is described. …”
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Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation. by Mouncef El Marghichi, Abdelilah Hilali, Abdelkhalek Chellakhi, Mohamed Makhad, Azeddine Loulijat, Najib El Ouanjli, Abdelhak Essounaini, Vikash Kumar Saini, Ameena Saad Al-Sumaiti

“…This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. …”
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FlyPhoneDB2: A computational framework for analyzing cell-cell communication in Drosophila scRNA-seq data integrating AlphaFold-multimer predictions by Mujeeb Qadiri, Ying Liu, Ah-Ram Kim, Myeonghoon Han, Eric Zhou, Austin Veal, Tzu-Chiao Lu, Hongjie Li, Yanhui Hu, Norbert Perrimon

“…However, the utility of FlyPhoneDB was limited by the relatively small number of available L-R pairs.Here, we present FlyPhoneDB2, a major upgrade featuring a significantly expanded knowledgebase that includes a greater number of L-R pairs, incorporating annotations from mammalian species and structural predictions from AlphaFold-Multimer. In addition, the algorithm has been optimized for improved performance and more effective noise filtering. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. Additionally, a Deformable Agent Attention (DAA) mechanism is designed to optimize attention weights through sparse queries, effectively improving detection accuracy for small targets and enhancing inference speed in complex scenarios. …”
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A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

“…In addition, the drop rate of some baseline algorithms with 50 users can achieve 62.5% for critical tasks, while the proposed TOMAC-PPO only has 5.5%.…”
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KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics by Jiaqi Yin, Ruidan Luo, Xiao Chen, Linhui Zhao, Hong Yuan, Guang Yang

“…Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. …”
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An Industrial Internet Security Assessment Model Based on a Selectable Confidence Rule Base by Qingqing Yang, Shiming Li, Yuhe Wang, Guoxing Li, Yanbin Yuan

“…Then, in combination with the Selection covariance matrix adaptive evolution strategy (S-CMA-ES) algorithm, a parameter optimization method for the BRB-s model is designed, which expands the selective constraints on expert knowledge. …”
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Tabu Search for Multi-Objective Hub Location Planning: A Case Study of the Northern Rail Line, Thailand by Nguyen Dang Nha, Pham Van Nam, Chanta Sunarin, Sangsawang Ornurai

“…This approach allows the model to handle large-scale problems within a reasonable time frame while generating Pareto fronts. The results demonstrate the effectiveness of multi-objective optimization in supporting rail hub location planning.…”
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Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network by Xiaofei Lu, Shouwang Li

“…To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. …”
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Research on Credit Risk Prevention in Supply Chain Finance Driven by Blockchain by Lin Kejing

“…Distributed ledger technology is utilized through blockchain to ensure the transparency and traceability of information, while smart contracts optimize the credit execution and supervision process, playing a key role in preventing and controlling credit risks. …”
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Quantum circuit unoptimization by Yusei Mori, Hideaki Hakoshima, Kyohei Sudo, Toshio Mori, Kosuke Mitarai, Keisuke Fujii

“…In this paper, we define and construct a quantum algorithmic primitive called quantum circuit unoptimization, which makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence, i.e., the inverse operation of circuit optimization. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…Channel pruning further reduces the decoder’s parameters, decreasing model size and computational requirements while maintaining precision. The enhanced algorithm achieved a mean average precision (mAP) of 89.4% ( $$\hbox {mAP}_{50}$$ ) and 78.3% ( $$\hbox {mAP}_{50:95}$$ ), while the number of model parameters was reduced to 9.9 M, the model size was reduced to 40.1 MB, and the number of floating point operations per second (FLOPs) was reduced to 31.2 G. …”
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Efficient Context-Preserving Encoding and Decoding of Compositional Structures Using Sparse Binary Representations by Roman Malits, Avi Mendelson

“…In addition, the proposed algorithms allow to optimize the amount of compute and memory needed to achieve the desired encoding/decoding performance.…”
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NLP-Driven Analysis of Pneumothorax Incidence Following Central Venous Catheter Procedures: A Data-Driven Re-Evaluation of Routine Imaging in Value-Based Medicine by Martin Breitwieser, Vanessa Moore, Teresa Wiesner, Florian Wichlas, Christian Deininger

“…<b>Methods:</b> We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists’ reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. …”
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An improved lightweight method based on EfficientNet for birdsong recognition by Haolun He, Hui Luo

“…Finally, we employ the Adam optimization algorithm to improve network convergence speed. …”
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Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection by Jessica Fernandes Lopes, Sylvio Barbon Junior, Leonimer Flávio de Melo

“…The results indicate that this framework preserves high accuracy while significantly reducing time requirements compared to Grid Search and Genetic Algorithm optimization.…”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…Empirical evidence demonstrates that this framework not only accurately captures the diverse characteristics of different data components but also significantly outperforms traditional benchmark models in predictive accuracy. By optimizing the GRU model with the grey wolf optimizer (GWO) algorithm, the framework enhances both prediction stability and adaptability, while the nonlinear integration approach effectively mitigates error accumulation. …”
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