Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.…”
Get full text

Contribution of relative humidity on aerosol optical depth role in influencing the regionalization pattern of precipitation over West Africa by Ayomide Victor Arowolo, Ayodeji Festus Ologun, Precious Ebiendele, Ayodeji Oluleye

“…Understanding the complex interactions between aerosol optical depth (AOD), relative humidity, and precipitation is essential for improving climate and weather prediction models, particularly in regions like West Africa, which are vulnerable to climate variability. …”
Get full text

Flood Hazard Assessment Using Weather Radar Data in Athens, Greece by Apollon Bournas, Evangelos Baltas

“…This study highlights the significance of incorporating radar precipitation and real-time soil moisture assessments to improve flood prediction accuracy and provide valuable flood risk assessments.…”
Get full text

A Case Study of Mass Transport during the East-West Oscillation of the Asian Summer Monsoon Anticyclone by Jiali Luo, Jiayao Song, Hongying Tian, Lei Liu, Xinlei Liang

Get full text

On the division of things into immovable and movable by Nikolić Dušan Ž.

“…In this context, it should be noted that apart from the GPS, there are also precise digital programs and models that enable predictive land mapping (digital land surface models for predictive mapping). …”
Get full text

Spatiotemporal patterns and environmental determinants of wildlife-vehicle collisions in Banke National Park, Nepal by Narayan Prasad Koju, K. C. Anish, Keshav Dodhari, Pratikshya Giri, Miriam Lee, Sudip Pokhrel, Asmina Ghimire, Lila Nyaichyai, Kenneth Otieno Onditi, Xuelong Jiang, Randall C. Kyes

“…The methodological approach integrated field surveys, spatial analyses using kernel density estimation, and statistical modelling to pinpoint collision hotspots and elucidate contributing factors. …”
Get full text

A Novel Monitoring Method of Wind-Induced Vibration and Stability of Long-Span Bridges Based on Permanent Scatterer Interferometric Synthetic Aperture Radar Technology by Jiayue Ma, Xiaojun Xue, Guoliang Zhi, Haoyang Zheng, Hanqing Zhu

“…Experimental results demonstrate that this method offers precise, real-time monitoring of wind-resistant stability. By leveraging the spatial accuracy and long-term monitoring capability of PS-InSAR, along with the time-series forecasting strength of ARMA models, the method enables data-driven analysis of bridge vibrations. …”
Get full text

Seasonal Early Warning of Impacts of Harmful Algal Blooms on Farmed Shellfish in Coastal Waters of Scotland by O. Stoner, T. Economou, A. R. Brown

“…Through a comprehensive yearly prediction experiment, we demonstrate considerable skill in predicting the impact of unseen HAB seasons at a regional level.…”
Get full text

MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging by Yusheng Wu, Qiang Lin, Yang He, XianWu Zeng, Yongchun Cao, ZhengXing Man, Caihong Liu, Yusheng Hao, Zhengqi Cai, Jinshui Ji, Xiaodi Huang

Get full text

Spatiotemporal Impacts of Urban Structure and Socioeconomic Factors on Carbon Dioxide Emissions: A Case Study of the Yangtze River Delta Urban Agglomeration by Zhechen Zhou, Su Xu, Jun Wang, Haitao Shen

“…The results show the following: (a) The spatial differentiation of urban structures between cities was obvious, and 4 landscape indices showed significant spatial autocorrelation. …”
Get full text

Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project by Kaleem Mehmood, Shoaib Ahmad Anees, Sultan Muhammad, Fahad Shahzad, Qijing Liu, Waseem Razzaq Khan, Mansour Shrahili, Mohammad Javed Ansari, Timothy Dube

“…A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. …”
Get full text

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

Get full text

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. …”
Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text