Geospatial measurement of urban sprawl and land transformation using multi-temporal datasets: A case study of Sonipat-Kundli urban agglomeration by Preeti Verma, Ravinder Jangra, S.P. Kaushik

“…Following India’s economic liberalization, rapid economic growth is driving urban sprawl in mid-sized cities, altering land use patterns from the core to the periphery of the city. …”
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Destripe Any Scale: Effective Stripe Removal Via Multiscale Decomposition Using the Luojia3-02 Stripe-Noise Dataset by Ru Chen, Mi Wang, Yingdong Pi, Ru Wang, Tao Peng, Fan Yang, Rongfan Dai

“…We first introduce the Luojia3-02 stripe-noise dataset, constructed from Luojia3-02 (Wuhan-1) satellite imagery, including stripe-noise patterns across multiple spatial scales. Building upon this dataset, we propose a multilevel perceptual stripe-noise-removal network. …”
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Genome-Wide Methylation Sequencing to Identify DNA Methylation Markers for Early-stage Hepatocellular Carcinoma in Liver and Blood by Siyu Fu, Ruben G. Boers, Joachim B. Boers, Pam E. van der Meeren, Jean Helmijr, Vanja de Weerd, Michail Doukas, Maurice Jansen, Bettina E. Hansen, Roeland F. de Wilde, Dave Sprengers, Joost Gribnau, Saskia M. Wilting, José D. Debes, Andre Boonstra

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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…This highlights CNN’s superior ability to capture complex patterns in wind speed dynamics over time. Concurrently, the analysis of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>E</mi><mi>V</mi><msub><mi>D</mi><mi>r</mi></msub></mrow></semantics></math></inline-formula> across various order statistics identified <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>E</mi><mi>V</mi><msub><mi>D</mi><mrow><mi>r</mi><mo>=</mo><mn>2</mn></mrow></msub></mrow></semantics></math></inline-formula> as the optimal model, supported by its favourable evaluation metrics in terms of Akaike information criteria (AIC) and Bayesian information criteria (BIC). …”
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SRCPAR&#x2014;Spike Response-Based Congestion Prediction for Adaptive Routing for 2D NoCs by Rajendra Singh, Manoj Kumar Bohra, Ashish Sharma, Sourabh Singh Verma, Devershi Pallavi Bhatt, Amit Kumar Bairwa, Masoud Daneshtalab

“…It leverages spike information alongside the local congestion data to accurately predict network congestion. Performance is evaluated using metrics such as average latency and throughput. …”
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A real-time predictive postural control system with temperature feedback by Yaoyu Duan, Huimin Jiao, Dangxiao Wang, Xingwei Guo

“…An Long Short-Term Memory (LSTM) neural network with a sliding window method is incorporated to predict the posture patterns of the next state. …”
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Multi-omics analysis reveals genetic architecture and local adaptation of coumarins metabolites in Populus by Wenke Zhang, Zhuoying Jin, Rui Huang, Weixiong Huang, Lianzheng Li, Yuling He, Jiaxuan Zhou, Chongde Tian, Liang Xiao, Peng Li, Mingyang Quan, Deqiang Zhang, Qingzhang Du

“…Furthermore, we constructed a genetic network of seven candidate genes that coordinately regulate coumarins biosynthesis, revealing the multiple regulatory patterns affecting coumarins accumulation in Populus tomentosa. …”
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Deep Learning–Based Prediction of Glaucoma Severity and Progression Using Imo/TEMPO Screening Program by Kei Sano, MD, PhD, Euido Nishijima, MD, PhD, Shunsuke Sumi, MD, PhD, Takahiko Noro, MD, PhD, Shumpei Ogawa, MD, PhD, Yuka Igari, MD, Aiko Iwase, MD, PhD, Tadashi Nakano, MD, PhD

“…Mean F1 score for total deviation (TD) and pattern deviation (PD) probability plot classification. …”
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The Role of Urinary Microbiome Analysis in the Diagnostic Approach and Management of Urinary Incontinence: A Systematic Review by Pouyan Shaker, Zohreh Roshani, Ehsan Timajchi, Zahra Sharifi, Sarah Nikzadfar Goli, Behrooz Broumand, Mohammad Ali Shafiee

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Investigation on potential bias factors in histopathology datasets by Farnaz Kheiri, Shahryar Rahnamayan, Masoud Makrehchi, Azam Asilian Bidgoli

“…Abstract Deep neural networks (DNNs) have demonstrated remarkable capabilities in medical applications, including digital pathology, where they excel at analyzing complex patterns in medical images to assist in accurate disease diagnosis and prognosis. …”
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W-Band Transverse Slotted Frequency Scanning Antenna for 6G Wireless Communication and Space Applications by Hurrem Ozpinar, Sinan Aksimsek, Nurhan Türker Tokan

“…Additionally, it exhibits pattern-reconfigurable capabilities, enabling main lobe beam steering between 5° and 68° with low side lobe levels. …”
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Incorporating Attention Mechanism Into CNN-BiGRU Classifier for HAR by Ohoud Nafea, Wadood Abdul, Ghulam Muhammad

“…The proposed methodology uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to extract the spatial and temporal features. …”
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A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices by Esam Mahdi, Carlos Martin-Barreiro, Xavier Cabezas

“…We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed Index. We evaluate the performance of our proposed model by comparing it with four other machine learning models, two are non-sequential feedforward models: radial basis function network (RBFN) and general regression neural network (GRNN), and two are bidirectional sequential memory-based models: bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU). …”
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Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal by Tao Wang, Zishuo Liu, Liyuan Liu

“…It effectively discerns diverse acoustic emission features through group convolution and evaluates their significance using the SE module. Compared to the traditional C3D neural network, this model greatly reduces complexity and time requirements while improving efficiency in distinguishing between various damage stages.…”
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Detecting phishing gangs via taint analysis on the Ethereum blockchain by Kangrui Huang, Weili Chen, Zibin Zheng

“…And we also summarize gang patterns and reveal money laundering in phishing activities. …”
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Assessing climate risks to Net Zero power system in Great Britain by Jaise Kuriakose, Eduardo A. Martínez Ceseña, Ruth Wood

“…Against this backdrop, this article evaluates selected 2050 Net Zero energy scenarios and assesses their impact on the vulnerability of Great Britain’s power transmission and distribution networks to climate change. …”
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Towards Geodetic Datum Modernization: A Comparative Study of GNSS Solutions in KGD2002 Using GAMIT/GLOBK and Bernese by Seung-Jun Lee, Hong-Sik Yun

“…Using a nationwide network of approximately 3000 unified geodetic control points (UGCPs), we analyze horizontal coordinate differences (ΔN, ΔE) to identify regional patterns and potential systematic biases. …”
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Bayesian optimized deep learning and ensemble classification approach for multiclass plant disease identification by Silpa Padmanabhuni, Pradeepini Gera

“…Bayesian optimization is used to identify and combine optimal activation functions, enhancing the network's capacity to learn complex disease patterns from tomato leaf images. …”
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NeuroTutor: Neural Decoding of Student Engagement During Virtual and Robotic Tutoring by Shahd Elgammal, Shaymaa Alsereidi, Mahra Almansoori, Shamma Alblooshi, Abdelhadi Hireche, Abdelkader Nasreddine Belkacem

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Assessing the prognostic and therapeutic value of cuproptosis-related genes in colon adenocarcinoma patients by Zhanhui Ye, Yixian Song, Mengqing Zhu, Fuying Zheng, Wenjie Qin, Wenjie Qin, Xue Li, Pei Wang, Zihua Li, Kequan Chen, Aimin Li

“…However, the particularly as the role of cuproptosis-related genes (CRGs) in COAD prognosis and therapy remains unclear.MethodsWe analyzed RNA sequencing data from The Cancer Genome Atlas for COAD, focusing on CRG expression patterns and their clinicopathological correlations. …”
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