Clinical case of a patient with pulmonary capillary hemangiomatosis: rapid progression or lost time? by E. A. Devetyarova, T. V. Martynyuk, A. A. Dyuzhikov, E. V. Paschenko, A. V. Dyuzhikova

“…The article describes a clinical case of a 37-year-old patient with pulmonary capillary hemangiomatosis of functional class IV according to the WHO classification with difficulties of diagnostic search and features of PAH-specific therapy.Pulmonary arterial hypertension - group 1 in the clinical classification is represented by several forms of pathology, including very rare diseases such as pulmonary veno-occlusive disease and pulmonary capillary hemangiomatosis.The difficulties of diagnostic search consist in the absence of specific symptoms, a variety of interstitial or focal changes according to spiral computed tomography, and the final diagnosis can be made only after performing a lung biopsy, which is associated with a high risk of possible complications. …”
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PONet: A Compact RGB-IR Fusion Network for Vehicle Detection on OrangePi AIpro by Junyu Huang, Jialing Lian, Fangyu Cao, Jiawei Chen, Renbo Luo, Jinxin Yang, Qian Shi

“…PONet incorporates Polarized Self-Attention to improve feature adaptability and representation with minimal computational overhead. …”
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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis by Jaskaran Singh Walia, Kavietha Haridass, L. K. Pavithra

“…We investigate multiple architectures, including YOLOv8n, YOLOv7, YOLOv6s, YOLOv5s, Faster R-CNN, and Mask R-CNN, analyzing key parameters such as mean average precision (mAP), inference speed, and computational efficiency. …”
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Methodology and Algorithm of Multicomponent Analysis of Positron Annihilation Spectra for Nanostructured Functional Materials by H.I. Klym, A.I. Ivanusa, Yu.M. Kostiv, D.O. Chalyy, T.I. Tkachuk, R.B. Dunets, I.I. Vasylchyshyn

“…Features of mathematical description of annihilation spectra was demonstrated. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…Conventional approaches primarily rely on the computation of fire indices based on weather conditions. …”
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Identification and authentication of additively manufactured components using their microstructural fingerprint by Kanhaiya Gupta, Konstantin Poka, Alexander Ulbricht, Anja Waske

“…This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. …”
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AHP-based multi-criteria analysis of multi-cloud data management techniques by Anton Caceres, Larysa Globa

“…Their main advantages, disadvantages, and features of use are given. The research tasks are formalizing the problem, defining cost, performance, security, and implementation effort parameters for each approach, and developing a multi-criteria decision analysis (MCDA) model using the Analytical Hierarchy Process (AHP) method. …”
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Evaluation of Patients’ Received Doses in Chest CT Scan Protocols for COVID-19 Diagnosis by Sadegh Shurche, Tinoosh Almasi, Maryam Tima, Nima Rostampour

“… Purpose: This study aims to investigate and compare the doses received by Corona Virus Disease (Covid-19) patients on Computed Tomography (CT) scans by changing the scan parameters to diagnose the disease and evaluate its course and effects. …”
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Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services by Abdusomad Nur, Yonas Muanenda

“…Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. …”
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Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification by Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah, Yousef A. Alduraywish, Alishba Tahir, Haixia Long

“…The novel methodology was divided into two main stages: feature extraction and classification. Firstly, SqueezeNet enables efficient feature extraction from segmented fundus images with minimal computational complexity, ensuring that critical retinal features are captured effectively. …”
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Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate by Shiyang Lv, Xiangying Ran, Mengsheng Xia, Yehong Zhang, Ting Pang, Xuezhi Zhou, Zongya Zhao, Yi Yu, Zhixian Gao

“…Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). …”
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Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications by Lih-Jen Kau, Chin-Kun Tseng, Ming-Xian Lee

“…With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. …”
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GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning by Siwei Wei, Xiangyuan Xu, Dewen Liu, Chunzhi Wang, Lingyu Yan, Wangyu Wu

“…Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. …”
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Efficient Packaging Line Object Counting by Cross-Frame Association With Wavelet Convolutions and Trajectory Compensation by Longxuan Wei, Yutao Zhu, Yufeng Li, Ming Qian, Xiang Zuo, Boan Chen, Shiyu Liang, Zhouhan Lin, Junchi Yan

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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…Furthermore, with the increase of feature complexity and computation amount, the algorithm processing speed will be reduced. …”
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Investigation of the profilogram structure of microstrip microwave modules manufactured using additive 3D-printing technology by D. S. Vorunichev, M. S. Kostin

“…The topological and radiophysical features of the additively formed upper and lower surface layers of experimental samples of boards of strip modules were studied. …”
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Gait stability prediction through synthetic time-series and vision-based data by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Ciaran O. Cathain, Vitor B. Nascimento, Thiago B. Rodrigues

“…SHAP analysis revealed that the synthetic data approach enhanced feature attribution alignment with established principles, particularly for step width, BMI, and fall history.DiscussionTherefore, our results show that: (1) synthetic data accurately replicated gait parameters with high fidelity; (2) synthetic data-trained models outperformed real data-trained models in MoS prediction; and (3) step width, BMI, and fall history were the most significant predictors of MoS in older adults. …”
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Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data by Lorena Díaz-González, Yael Sharim Toribio-Colin, Julio César Pérez-Sansalvador, Noureddine Lakouari

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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…Specifically, YOLOLS integrates GhostConv to generate feature maps through stepwise convolution, reducing computational redundancy while preserving representational capacity. …”
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Integrating high dimensional quadratic regression with penalties based predictive modeling for hydro power plants accurate tariff prediction by Ritesh Dash, Anupa Sinha, Abinash Mahapatro, Bhabasis Mohapatra, Binod Kumar Sahu

“…Compared to benchmarking models, the proposed approach consistently achieves the lowest forecast error. Computational complexity is minimized with 100 parameters and training time of 2 s. …”
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