Hyper Spectral Camera ANalyzer (HyperSCAN) by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao, Jann-Yenq Liu

“…After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. …”
Get full text

Revealing the early ice flow patterns with historical Declassified Intelligence Satellite Photographs back to 1960s by Shujie Wang, Hongxing Liu, Bailang Yu, Guoqing Zhou, Xiao Cheng

Get full text

Regional Pathways to Internationalization: The Role of Erasmus+ in European HEIs by Eleni Georgoudaki, Spyridon Stavropoulos, Dimitris Skuras

“…Employing hotspot analysis and a two-level random intercept model, this research analyses spatial patterns and the influences of regional characteristics and institutional variables on Erasmus mobility rates. …”
Get full text

Low-Carbon-Economic Collaborative Optimal Dispatching of Microgrid Considering Electricity-Hydrogen Integration by Lingling TAN, Wei TANG, Dongqing CHU, Zihan YU, Xingquan JI, Yumin ZHANG

“…In the low-carbon demand response model, with the saved carbon emission costs by reduced carbon emissions of microgrid users as an incentive signal, a mapping relationship between energy flow and carbon emission flow of microgrid is established to realize the transfer and allocation of carbon emission responsibilities and fine evaluation of carbon emission characteristics of microgrid operations, so as to establish a low-carbon DR model which guides users to participate in carbon emission reduction strategies according to the spatial and temporal differences of load carbon emission intensity, and to deeply explore the synergy between low-carbon and economy of source-load of microgrid. …”
Get full text

Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods by Jiani Wang, Yunqi Wang, Manyi Li, Zihan Qi, Cheng Li, Haimei Qi, Xiaoming Zhang

“…In order to assess the impact of various negative sample collection strategies on the prediction accuracy of the landslide susceptibility assessment (LSA) model, and to investigate the effectiveness of landslide susceptibility zoning methods. …”
Get full text

Mapping potential water repellency of Danish topsoil by Lucas Carvalho Gomes, Peter Lystbæk Weber, Cecilie Hermansen, Anne-Cathrine Storgaard Danielsen, Sebastian Gutierrez, Deividas Mikstas, Charles Pesch, Mogens Humlekrog Greve, Per Moldrup, David A. Robinson, Lis Wollesen de Jonge

“…This study presents a comprehensive survey of SWR in Denmark and its predicted spatial distribution, using approximately 7,500 samples. …”
Get full text

Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala, Chinta Anuradha, Patnala S. R. Chandra Murty, S. Rajeswari, N. Rajeswaran, M. Murugappan, Muhammad E. H. Chowdhury

Get full text

Enhancing Sea Ice Concentration Resolution in a Northern Sea Route Strait Using a Generative Adversarial Network by M. L. Rocha, A. H. Lynch, K. J. Bergen

“…However, typical climate model spatial resolutions limit the capacity to represent Arctic straits accurately. …”
Get full text

A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms by Pu Wang, Shenhua Yang, Guoquan Chen, Weijun Wang, Zeyang Huang, Yuanliang Jiang

“…The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors.…”
Get full text

Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…Our methodology leverages Gray-Level Co-occurrence Matrix (GLCM) features to quantify tissue texture, constructs a Sparse Cosine Similarity Matrix (SCSM) to model spatial relationships, and employs DeepWalk embeddings to capture topological patterns. …”
Get full text

Effect of protrusion structure on the performance of an advanced hydrodynamic cavitation reactor: An entropy-based analysis by Gaoju Xia, Sivakumar Manickam, Jingwei Li, Zhiqiang Yin, Wenlong Wang, Xun Sun

Get full text

Responsiveness to the Effect of Fluoxetine in Male and Female Rats Exposed to Single Prolonged Stress: A Behavioral, Biochemical, Molecular and Histological Study by Reza Eshaghi-Gorji, Sareh Rashidi, Sakineh Shafia, Fereshteh Talebpour Amiri, Mansoureh Mirzae, Moslem Mohammadi

“…Conclusion: We observed that male and female rats with PTSD, show a reduction of the levels of serum IGF-1, impaired spatial memory in a recognition location memory task and enhanced apoptotic-related factors expression in the hippocampus, and decreased hippocampal dendritic branches. …”
Get full text

Changing grizzly bear space use and functional connectivity in response to human disturbance in the southern Canadian Rocky Mountains by Eric C. Palm, Clayton D. Apps, Tal Avgar, Melanie Dickie, Bruce N. McLellan, Joseph M. Northrup, Michael A. Sawaya, Julie W. Turner, Jesse Whittington, Erin L. Landguth, Katherine A. Zeller, Clayton T. Lamb

“…Our study builds upon existing work simulating animal space use from fitted iSSFs by incorporating individual‐level variation into population‐level simulations and by fitting functional responses that help capture broad‐scale variation in behavior and improve model transferability to new areas. Our results provide insights into grizzly bear movement and connectivity in an area of high conservation importance, and our predictive maps can be used to directly inform transboundary management actions and conservation planning.…”
Get full text

Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) by Karem Saad, Amjad Kallel, Fabio Castaldi, Thouraya Sahli Chahed

“…These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. …”
Get full text

Normative structural connectome constrains spreading transient brain activity in generalized epilepsy by Jie Xia, Siqi Yang, Jiao Li, Yao Meng, Jinpeng Niu, Huafu Chen, Zhiqiang Zhang, Wei Liao

“…The collective abnormality of structurally connected neighbors significantly predicted regional activity abnormality, indicating that white matter network architecture constrains aberrant activity patterns. …”
Get full text

CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. …”
Get full text

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization by Venkata Nagaraju Thatha, M. Ganesh Karthik, Venu Gopal Gaddam, D. Pramodh Krishna, S. Venkataramana, Kranthi Kumar Lella, Udayaraju Pamula

Get full text

The Paradox of Urban Travel Behavior on Public Transport Use: A Bibliometric and Critical Theoretical Review by Kaamah Abdallah Faisal, Kombe Wilbard Jackson, Sakijege Tumpale

Get full text

Long-Term (2015–2024) Daily PM_2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang, Siwei Li

Get full text

A Three-Level Meta-Frontier Framework with Machine Learning Projections for Carbon Emission Efficiency Analysis: Heterogeneity Decomposition and Policy Implications by Xiaoxia Zhu, Tongyue Feng, Yuhe Shen, Ning Zhang, Xu Guo

“…Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. …”
Get full text