Pre-Exercise Ingestion of Hydrogen-Rich Cold Water Enhances Endurance Performance and Lactate Response in Heat by Mariem Khlifi, Nidhal Jebabli, Nejmeddine Ouerghi, Fatma Hilal Yagin, Ashit Kumar Dutta, Reem Alwhaibi, Anissa Bouassida

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ARIMA Markov Model and Its Application of China’s Total Energy Consumption by Chingfei Luo, Chenzi Liu, Chen Huang, Meilan Qiu, Dewang Li

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Strategic vacancy engineering advances record-high ductile AgCu(Te, Se, S) thermoelectrics by Nan-Hai Li, Xiao-Lei Shi, Si-Qi Liu, Meng Li, Tian-Yi Cao, Min Zhang, Wan-Yu Lyu, Wei-Di Liu, Dong-Chen Qi, Zhi-Gang Chen

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MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment by Yingzhang Guo, Mingjin Zhan, Hanzeyu Xu, Xiao Li, Junjun Fang, Xingchen Zhou, Dan Lin, Wenhui Chen

“…To enhance the effectiveness of RSEILA in EEQ, we propose a modified RSEILA model (MRSEILA) implemented on the Google Earth Engine (GEE) platform, consisting of three components: (1) optimization of moving window sizes tailored to each target region; (2) automatic recognition and correction of PCA-induced eigenvector direction inconsistencies; and (3) refinement of PCA computation within each circular window to improve the accuracy of EEQ evaluations. …”
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Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques by Gi-Won Yoon, Segyeong Joo

“…This study explores the potential of transforming 1D signals into 2D Gramian Angular Field (GAF) images for improved classification of four ECG categories: Atrial Fibrillation (AFib), Left Ventricular Hypertrophy (LVH), Right Ventricular Hypertrophy (RVH), and Normal ECG. • The study employed GAF transformations to convert 1D ECG signals into 2D representations at three resolutions: 5000 × 5000, 512 × 512, and 256 × 256 pixels. • Segmentation methods were applied to enhance feature localization. • The ConvNext deep learning model, optimized for image classification, was used to evaluate the transformed ECG images, with performance assessed through accuracy, precision, recall, and F1-score metrics.The 512 × 512 resolution achieved the optimal balance between computational efficiency and accuracy. …”
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Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data by F. Leone, A. Caporali, A. Pascarella, C. Perciballi, O. Maddaluno, A. Basti, P. Belardinelli, L. Marzetti, G. Di Lorenzo, V. Betti

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Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices by Gabriel Kalweit, Gabriel Kalweit, Anusha Klett, Paula Silvestrini, Paula Silvestrini, Jens Rahnfeld, Jens Rahnfeld, Mehdi Naouar, Mehdi Naouar, Yannick Vogt, Yannick Vogt, Diana Infante, Diana Infante, Rebecca Berger, Jesús Duque-Afonso, Tanja Nicole Hartmann, Marie Follo, Marie Follo, Elitsa Bodurova-Spassova, Elitsa Bodurova-Spassova, Michael Lübbert, Michael Lübbert, Roland Mertelsmann, Roland Mertelsmann, Roland Mertelsmann, Joschka Boedecker, Joschka Boedecker, Joschka Boedecker, Evelyn Ullrich, Evelyn Ullrich, Evelyn Ullrich, Maria Kalweit, Maria Kalweit

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Compact ultra-wide band two element vivaldi non-uniform slot MIMO antenna for body-centric applications by Sahar Saleh, Tale Saeidi, Nick Timmons, Bader Alali, Faroq Razzaz, Ayman A. Althuwayb

“…This innovative design, which optimizes antenna positioning and incorporates ground plane modifications, achieves remarkable improvements in BW, which covers multiple bands, including WLAN (2.4–2.485 GHz), X-band (8–12 GHz), and part of Ku band (12–18 GHz). …”
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KT-Deblur: Kolmogorov–Arnold and Transformer Networks for Remote Sensing Image Deblurring by Baoyu Zhu, Zekun Li, Qunbo Lv, Zheng Tan, Kai Zhang

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A Progressive Spectral Correction and Spatial Compensation Network for Pansharpening by Rixian Liu, Hangyuan Lu, Biwei Chi, Yong Yang, Shuying Huang

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Widefield ultra-high-density optical breast tomography system supplementing x-ray mammography by Miguel Mireles, Edward Xu, Morris Vanegas, Ailis Muldoon, Rahul Ragunathan, Shijie Yan, Bin Deng, Jayne Cormier, Mansi Saksena, Stefan A. Carp, Qianqian Fang

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ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets by Ye Zheng, Daniel P. Caron, Ju Yeong Kim, Seong-Hwan Jun, Yuan Tian, Florian Mair, Kenneth D. Stuart, Peter A. Sims, Raphael Gottardo

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An improved energy saving clustering method for IWSN based on Gaussian mutation adaptive artificial fish swarm algorithm by Yeshen Lan, Chuchu Rao, Qike Cao, Bingyu Cao, Mingan Zhou, Bo Jin, Fengjiang Wang, Wei Chen

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Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis by Mostafa Alizade-Harakiyan, Amin Khodaei, Ali Yousefi, Hamed Zamani, Asghar Mesbahi

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Status and Development Prospects of Solar-Powered Unmanned Aerial Vehicles—A Literature Review by Krzysztof Sornek, Joanna Augustyn-Nadzieja, Izabella Rosikoń, Róża Łopusiewicz, Marta Łopusiewicz

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Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction by Jae sung Shin, Jun Ma, Mao Makara, Nak-Jun Sung, Seong Jun Choi, Sung yeup Kim, Min Hong

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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

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Agreement on site of latest electrical activation between right ventricular pacing and intrinsic conduction as target for left ventricular lead position in cardiac resynchronizatio... by Henrik Laurits Bjerre, MD, Jens Cosedis Nielsen, MD, PhD, DMSc, Anders Lehmann Dahl Pedersen, MD, Christian Gerdes, MD, PhD, Jens Kristensen, MD, PhD, Jesper Møller Jensen, MD, PhD, DMSc, Bjarne Linde Nørgaard, MD PhD, DMSc, Mads Brix Kronborg, MD, PhD, DMSc

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A survey of neural architecture search by Mingjie HE, Jie ZHANG, Shiguang SHAN

“…Recently,deep learning has achieved impressive success on various computer vision tasks.The neural architecture is usually a key factor which directly determines the performance of the deep learning algorithm.The automated neural architecture search methods have attracted more and more attentions in recent years.The neural architecture search is the automated process of seeking the optimal neural architecture for specific tasks.Currently,the neural architecture search methods have shown great potential in exploring high-performance and high-efficiency neural architectures.In this paper,a survey in this research field and categorize existing methods based on their performance estimation methods,search spaces and architecture search strategies were presented.Specifically,there were four performance estimation methods for computation cost reduction,two typical neural architecture search spaces and two types of search strategies based on discrete and continuous spaces respectively.Neural architecture search methods based on continuous space are becoming the trend of researches on neural architecture search.…”
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Transcriptome analysis of multiple tissues and identification of tissue-specific genes in Lueyang black-bone chicken by Pan Li, Yufei Yang, Bo Ning, Yingmin Tian, Ling Wang, Wenxian Zeng, Hongzhao Lu, Tao Zhang

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