Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET by Hyder Abbas, Shen Bing Ren, Muhammad Asim, Syeda Iqra Hassan, Ahmed A. Abd El-Latif

“…Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. …”
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First-Arrival Tomography for Mountain Tunnel Hazard Assessment Using Unmanned Aerial Vehicle Seismic Source and Enhanced by Supervirtual Interferometry by Jun Zhang, Rongyi Qian, Zhenning Ma, Xiaoqiong Lei, Jianyu Ling, Xu Liu, Guibin Zhang

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Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting by Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan

“…However, the existing methods tend to inadequately weigh precipitation intensity features by only implicitly learning and modeling these features within the spatial distribution. …”
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Detecting Transit Deserts Through a Blend of Machine Learning (ML) Approaches, Including Decision Trees (DTs), Logistic Regression (LR), and Random Forest (RF) in Lucknow by Alok Tiwari

“…Employing a 100 × 100 m spatial grid data, the models classify transit accessibility based on economic status, trip frequency, population density, and service access. …”
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Geographic and socioeconomic variation of sodium and potassium intake in Italy: results from the MINISAL-GIRCSI programme by Simona Giampaoli, Francesco P Cappuccio, Chen Ji, Luigi Palmieri, Diego Vanuzzo, Chiara Donfrancesco, Renato Ippolito, Pasquale Strazzullo

“…Objectives To assess geographic and socioeconomic gradients in sodium and potassium intake in Italy.Setting Cross-sectional survey in Italy.Participants 3857 men and women, aged 39–79 years, randomly sampled in 20 regions (as part of a National cardiovascular survey of 8714 men and women).Primary outcome measures Participants’ dietary sodium and potassium intakes were measured by 24 h urinary sodium and potassium excretions. 2 indicators measured socioeconomic status: education and occupation. Bayesian geoadditive models were used to assess spatial and socioeconomic patterns of sodium and potassium intakes accounting for sociodemographic, anthropometric and behavioural confounders.Results There was a significant north-south pattern of sodium excretion in Italy. …”
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Computational principles of neural adaptation for binaural signal integration. by Timo Oess, Marc O Ernst, Heiko Neumann

“…The circuit incorporates an adaptation mechanism operating at the synaptic level based on local inhibitory feedback signals. The model's predictive power is demonstrated in various simulations replicating physiological data. …”
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CapSurv: Capsule Network for Survival Analysis With Whole Slide Pathological Images by Bo Tang, Ao Li, Bin Li, Minghui Wang

“…The results illustrate the proposed CapSurv model has the ability to improve the performance of the prediction by comparing with state-of-the-art survival models.…”
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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…Here we aim to enhance the accuracy and spatial resolution of SSN retrievals by developing improved regression and machine learning models, enabling the generation of global daily ∼ 8 km SSN products from satellite and model data. …”
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Pearl River Delta: Scales, Times, Domains by Liang Xiong

“…Applied in the PRD, the knowledge of the development of the urban landscape had been inventoried, synthesized, and presented in its own spatial-temporal model using maps. Three types of processes (landscape formation, infrastructure extension, and urbanization) were identified according to their speeds. …”
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EFCNet for small object detection in remote sensing images by Yutong Wang, Zhensong Li, Shiliang Zhu, Xiaotan Wei

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Multi-Timescale Fault Interactions and Earthquakes: A Review by Mian Liu

“…Earthquake models are based on elastic rebound, which predicts cyclic stress accumulation and release on fault planes. …”
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Attention-based multi-scale convolution and conformer for EEG-based depression detection by Ze Yan, Ze Yan, Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng

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The effects of seasonal human mobility and Aedes aegypti habitat suitability on Zika virus epidemic severity in Colombia. by Brandon Lieberthal, Brian Allan, Sandra De Urioste-Stone, Andrew Mackay, Aiman Soliman, Shaowen Wang, Allison M Gardner

“…Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. …”
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Spatiotemporal Dynamics and Driving Mechanisms of Ecosystem Services in the Beijing–Tianjin–Hebei Urban Agglomeration: Implications for Sustainable Land Use Planning by Shuanqging Sheng, Jinchuan Huang

“…This study unveils the spatial heterogeneity of ecosystem services and their driving mechanisms in the context of urbanization, offering valuable insights to inform regional ecological conservation and sustainable development policies.…”
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Enhancing Road Scene Segmentation With an Optimized DeepLabV3+ by Zhe Ren, Libao Wang, Tianming Song, Yihang Li, Jian Zhang, Fengfeng Zhao

“…Semantic segmentation, as a dense predictive task, is inevitably affected by various external factor, making common road image semantic segmentation models unable to meet dual demands of high accuracy and real-time performance in unstructured road scenarios. …”
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Analysis and Optimization of Soil Nail Parameters in a Deep Foundation Pit of Silty Clay Based on Response Surface Methodology by FAN Cheng, WANG Zi-yuan, QI Peng-fei, ZHANG Hong-bin, ZHANG Tiao-tiao

“…In the combined effect of support parameters, the soil nail length and spacing have the greatest impact on the safety factor. The predictive model established by the response surface method can provide the best optimization scheme. …”
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Artificial intelligence and machine learning to enhance critical mineral deposit discovery by Rhys S. Davies, McLean Trott, Jaakko Georgi, Alexander Farrar

“…Furthermore, subjective modelling approaches, such as Delphi methods, can complement ML by incorporating expert judgment to overcome predictive limitations. …”
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Extended homogeneous field correction method based on oblique projection in OPM-MEG by Fulong Wang, Fuzhi Cao, Yujie Ma, Ruochen Zhao, Ruonan Wang, Nan An, Min Xiang, Dawei Wang, Xiaolin Ning

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Binocular Vision-Based Target Detection Algorithm by Huiguo Zhang

“…The former can effectively balance the positive and negative samples to avoid over-learning of easy-to-classify samples during the training process; the latter successfully solves the problem of the failure of the detection method when there is no intersection between the prediction frame and the real detection frame, which significantly improves the robustness of the model. …”
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