Wavefront Sensor for the Determination of Nanostructured Surface Defects by V.V. Buchenko, A.A. Goloborodko, V.V. Lendel, O.S. Oberemok

“…The theoretical analysis and computer simulation of the wavefront sensor operation to detect the spatial distribution of the refractive index are performed. …”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

“…New modules such as the Omni-Directional Convolution Channel Prior Convolutional Attention Efficient Layer Aggregation Network (OA-ELAN), Spatial Pyramid Pooling Channel Shuffling and Pixel-level Convolution Bilat-eral-branch Transformer (SPPCSPCBiFormer), and Ghost-Shuffle Convolution Enhanced Layer Aggregation Network-High performance (G-ELAN-H) are central to its design, which reduce the computational burden and enhance the accuracy in detecting small targets and capturing local features and crucial information. …”
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Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region by J. Zhao, J. Guo, X. Zheng

“…A detailed examination of the 21 July 2023 case reveals that the FH_RD experiment generally exhibits more skillful storm forecasts in terms of areal coverage, storm mode, and orientation, benefiting from refined mesoscale wind analysis. Particularly, in the RD experiment, RWP data assimilation notably reduces wind errors and improves the representation of mesoscale atmospheric features near the Taihang Mountains upstream of Beijing, crucial for convective initiation (CI). …”
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Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring by Giuseppe Marano, Sara Rossi, Ester Maria Marzo, Alice Ronsisvalle, Laura Artuso, Gianandrea Traversi, Antonio Pallotti, Francesco Bove, Carla Piano, Anna Rita Bentivoglio, Gabriele Sani, Marianna Mazza

“…By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation.…”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…However, the methods previously used for genomic and proteomic data analysis techniques are plagued by issues of high dimensionality, imbalanced class distribution and feature redundancy, which work against the accurate predictability and generalizability of such methods. …”
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Regularities of the Natural Evolution of the Proliferative Process in Diabetic Retinopathy. The Concept of Remission of Proliferative Diabetic Retinopathy by S. V. Sdobnikova

“…The data analyzed in this review indicate that an important feature of the natural evolution of diabetic retinopathy (DR) is the possibility of reverse development of its main signs, including newly formed vessels. …”
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Predicting police and military violence: evidence from Colombia and Mexico using machine learning models by Juan David Gelvez

“…Results show high accuracy, specificity, and sensitivity in predicting police and military violence. In addition, feature-importance analysis highlights the most influential variables in the models’ predictions. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. …”
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Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approachResearch in context by Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff

“…Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008–2019) and ECG-View II (775,535 patients, 1994–2013). …”
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Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias

“…The present study introduces a novel approach that integrates frequency-domain analysis using the fast Fourier transform (FFT) with deep learning techniques for more accurate and proactive damage detection. …”
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Enhancement Processing of High-Resolution Spaceborne SAR Wake Based on Equivalent Multi-Channel Technology by Lei Yu, Yuting Liu, Xiaofei Xi, Pengbo Wang

“…Ship wake detection plays a crucial role in compensating for target detection failures caused by defocusing or displacement in SAR images due to vessel motion. …”
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Circulating Tumor Cells in Early Breast Cancer: A 10-Year Follow-Up Update by Tania Rossi, Emanuela Scarpi, Roberta Maltoni

“…This study provides a 10-year follow-up analysis of disease-free survival (DFS) in a previously described cohort of early-stage BC patients, aiming to evaluate the long-term prognostic significance of CTC detection. …”
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Understanding socialbot behavior on end hosts by Yukun He, Qiang Li, Jian Cao, Yuede Ji, Dong Guo

“…These features can be used by someone to design approaches to identifying socialbots on a host; (3) our proposed detection approach can effectively distinguish between a socialbot and a benign application on end hosts.…”
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Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters by Mehrnaz Saghab Torbati, Ahmad Zandbagleh, Mohammad Reza Daliri, Amirmasoud Ahmadi, Reza Rostami, Reza Kazemi

“…XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. …”
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Year-Round Acoustic Presence of Beaked Whales (Ziphiidae) Far Offshore off Australia’s Northwest Shelf by Evgenii Sidenko, Iain Parnum, Alexander Gavrilov, Robert McCauley, Christine Erbe

“…The detection workflow included: (1) the extraction of impulsive signals from passive acoustic recordings based on an auto-regression model, (2) the calculation of a set of features of extracted signals, and (3) binary signal classification based on these features. …”
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Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model by Wenjing Wang, Qingbiao Zhang, Shimei Fan, Yuyin Wang, Xingyan Le, Min Ai, Chunqi Du, Junbang Feng, Chuanming Li

“…The 3D segmentation of the tumour was manually delineated by two radiologists who were unaware of the pathological results and KRAS gene detection outcomes. The PyRadiomics package in Python was used to extract 2,264 radiomic features from each ROI. …”
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ONE TYPICAL EXTREMUM IN ELECTRICAL PROBLEMS by V. I. Goroshko, T. E. Shukovskaya

“…The aim of this work is to attract attention of teachers, scientific personnel, engineers and students to one peculiarity of extremum seeking in different electrical problems. This feature lies in the fact that in many parts of electrical engineering extremum seeking comes to analysis one and the same mathematical structure (T-structure), but differences lie only in many symbols (designation).In one problems this structure appear in finale, the most simple form, but in others – T-structure is “veiled”, and as a rule  we need  elementary algebraic transformation to detect it.Taking into account high frequency of this structure appearing in electrical problems, in the first part of article the authors  carried out the investigation of extremum characteristics of T-structure and show the results in easy algorithms. …”
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OA−ICOS−Based Oxygen and Carbon Dioxide Sensors for Field Applications in Gas Reflux Chicken Coops by Weijia Li, Guanyu Lin, Jianing Wang, Jifeng Li, Yulai Sun, Depu Yao, Xiaogang Yan, Zhibin Ban

“…Utilizing Allan variance analysis, the sensor’s detection limits for O₂ and CO₂ were ascertained to be 2.9 ppm and 7.4 ppb, respectively. …”
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The Optical Signature of a Bottom Boundary Layer Ventilation Event in the Northern Gulf of Mexico's Hypoxic Zone by Jason K. Jolliff, Ewa Jarosz, Sherwin Ladner, Travis Smith, Stephanie Anderson, James Dykes

“…Abstract Oceanographic observations in the northern Gulf of Mexico captured an unusual combination of atmospheric forcing and shelf frontal dynamics that brought about the surface ventilation of sediment‐laden bottom waters. Colorimetric analysis of in situ optical data indicates a specific turquoise‐white color signature for turbid and low‐oxygen bottom waters that match a coincident shelf‐wide feature detected in Moderate Resolution Imaging Spectroradiometer satellite true color images. …”
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PigFRIS: A Three-Stage Pipeline for Fence Occlusion Segmentation, GAN-Based Pig Face Inpainting, and Efficient Pig Face Recognition by Ruihan Ma, Seyeon Chung, Sangcheol Kim, Hyongsuk Kim

“…In doing so, our system detects occlusions, reconstructs obscured regions, and emphasizes key facial features, thereby improving overall performance. …”
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