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goChem: A Composable Library for Multi-Scale Computational Chemistry Data Analysis.
J Comput Chem
January 2025
Departmento de Química, Facultad de Ciencias, Universidad de Tarapacá, Arica, Chile.
Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data.
View Article and Find Full Text PDFDistinct Transmission of Left- and Right-Handed Magnon Modes in Compensated Ferrimagnet/Antiferromagnet Structures.
Adv Mater
January 2025
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
The chirality of magnons, exhibiting left- and right-handed polarizations analogous to the counterparts of spin-up and spin-down, has emerged as a promising paradigm for information processing. However, the potential of this paradigm is constrained by the controllable excitation and transmission of chiral magnons. Here, the magnon transmission is explored in the GdFeO/NiO/Pt structures.
View Article and Find Full Text PDFAn Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks.
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
View Article and Find Full Text PDFCTHNet: A CNN-Transformer Hybrid Network for Landslide Identification in Loess Plateau Regions Using High-Resolution Remote Sensing Images.
Sensors (Basel)
January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
View Article and Find Full Text PDFMonoSeg: An Infrared UAV Perspective Vehicle Instance Segmentation Model with Strong Adaptability and Integrity.
Sensors (Basel)
January 2025
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430000, China.
Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance.
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