An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…Non-linear, coupled partial differential equations are transformed into ordinary differential equations with similarity scaling to characterize heat transfer and fluid flow, which are then numerically solved using the modified finite difference method (the Keller-Box method). …”
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MATHEMATICAL MODELING OF THE INFLUENCE OF RELAXATION PROCESSES ON THE TEMPERATURE FIELD IN AN ELASTIC HALF-SPACE by I. A. Dzhemesyuk, S. G. Gorbunov

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Quantifying the Extent of Damage in a Moment-resisting Frame Caused by Ground Motions using Normalized Discrete Wavelet Coefficients by Seyyed Meisam Aghajanzadeh, Mohammadreza Mashayekhi

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Overlapping community-based fair influence maximization under a multi-transformation optimization algorithm by Chunfeng Jiang, Zegang Niu, Jingru Qu, Yulan Zhao, Amin Rezaeipanah

“…Also, selecting the most suitable transformation in advance is challenging, as different transformations lead to varying search behaviors for evaluating influence spread. …”
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Wavelet analysis of geomagnetically induced currents during the strong geomagnetic storms by Aksenovich Tatyana, Bilin Vladislav, Sakharov Yaroslav, Selivanov Vasiliy

“…In order to analyze the currents a wavelet transform was chosen, since this method allows to define not only the frequency composition but also changes in spectral characteristics over time, which is significant in the study of GIC. …”
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MSTNet: a multi-stage progressive network with local–global transformer fusion for image restoration by Ruyu Liu, Lin Wang, Jie He, Jiajia Wang, Jianhua Zhang, Xiufeng Liu, Chaochao Wang, Haoyu Zhang, Sheng Dai

“…In the medical field, image restoration techniques can significantly improve the quality of endoscopic images, helping doctors make more accurate diagnoses and providing higher-quality data support for computer vision-assisted detection. Existing methods for image restoration mainly use convolutional neural networks (CNNs) or Transformer models, which have different advantages and limitations in capturing spatial and channel information of the image. …”
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A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data by Longfei Cui, Xinyu Niu, Haizhong Qian, Xiao Wang, Junkui Xu

“…The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. …”
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Detection of Transformer Faults: AI-Supported Machine Learning Application in Sweep Frequency Response Analysis by Hakan Çuhadaroğlu, Yılmaz Uyaroğlu

“…In this study, we discussed how the increasing demand for electrical energy results in higher loads on transformers, creating the need for more effective testing and maintenance methods. …”
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A case study on entropy-aware block-based linear transforms for lossless image compression by Borut Žalik, David Podgorelec, Ivana Kolingerová, Damjan Strnad, Štefan Kohek

“…Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. …”
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Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver by Qingzhao Tan, Aiying Yang, Peng Guo, Zhao Zhao

“…Conventional fast Fourier transform based frequency offset estimation (FFT-FOE) algorithm is suitable for QPSK and 8/16/64QAM signals. …”
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Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction by Sumin Ha, Dongmin Bang, Sun Kim

“…For variability of substructures, we used three fragmentation methods such as BRICS, Bemis-Murcko scaffolds, and RDKit Functional Groups to formulate fragment-level graphs so that diverse substructures can be used to identify toxicity for different organs. …”
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Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration by Xinxin Cui, Yuee Zhou, Caihong Wei, Guodong Suo, Fengqing Jin, Jianlan Yang

“…Secondly, the Swin-Transformer module is combined with the convolution iterative strategy, and each layer of the decoder is carefully designed according to the semantic information characteristics of different decoding layers. …”
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High‐frequency content within the QRS complex can predict ventricular tachyarrhythmias in hypertrophic cardiomyopathy by Takeshi Tsutsumi, Jun Yokomachi, Takafumi Nakajima, Kentaro Minami, Nami Takano, Kuniaki Iwasawa, Gaku Oguri, Shigeru Toyoda, Toshiaki Nakajima

“…We measured bipolar X, Y, and Z leads and calculated the frequency power using continuous wavelet transform (CWT). We compared frequency powers, ranging from 15 to 250 Hz, between patients with HCM with and without L‐VAs. …”
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Transformer With Regularized Dual Modal Meta Metric Learning for Attribute-Image Person Re-Identification by Xianri Xu, Rongxian Xu

“…In this paper, we propose a regularized dual modal meta metric learning (RDM3L) method for AIPR, which employs meta-learning training methods to enhance the transformer’s capacity to acquire latent knowledge. …”
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Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes by P. J. Soto Vega, D. Lobo Torres, G. X. Andrade-Miranda, G. A. O. P. da Costa, R. Q. Feitosa

“…Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have become popular for change detection tasks, including the deforestation mapping application. …”
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A Latent Multi-Scale Residual Transformer Approach for Cross-Modal Medical Image Synthesis by Xinmiao Zhu, Yang Li

“…This module consists of two layers of residual convolutional blocks and transformer blocks of different scales, where the transformer blocks assist the convolutional blocks in capturing contextual features, and lower-level blocks support higher-level blocks in learning high-dimensional global information. …”
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A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data by Yinghao Tan, Junfeng Wu, Yihang Liu, Shiyu Shen, Xia Xu, Bin Pan

“…SA-Fit introduces a lightweight Transformer-based spatiotemporal analysis network to encode spatiotemporal information, which can integrate the interaction information between different coordinates in the data. …”
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CCTNet: CNN and Cross-Shaped Transformer Hybrid Network for Remote Sensing Image Semantic Segmentation by Honglin Wu, Zhaobin Zeng, Peng Huang, Xinyu Yu, Min Zhang

“…Furthermore, a simplified and efficient feature aggregation module is leveraged to gradually aggregate local and global information at different stages. Extensive comparison experiments on the ISPRS Vaihingen and Potsdam datasets reveal that our method obtains superior performance compared with state-of-the-art lightweight methods.…”
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A novel multimodel medical image fusion framework with edge enhancement and cross-scale transformer by Fei Luo, Daoqi Wu, Luis Rojas Pino, Weichao Ding

“…However, existing MMIF methods often struggle to preserve sharp edges and maintain high contrast, both of which are critical for accurate diagnosis and treatment planning. …”
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TRANSFORMATION OF VALUES OF THE HIGH TECHNOLOGY PROJECTS FROM A VUCA TO A BANI ENVIRONMENT MODEL by Sergiy Bushuyev, Kateryna Piliuhina, Elams Chetin

“…Tasks to be solved: to analyze the transformation from the VUCA model to the BANI model, to present values and their differentiation as the basis for survival in the new world order, to develop a method for assessing project risks in the BANI environment. …”
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