Automation of aeronautical information processing based on multi-agent technologies by D. E. Eshmuradov, T. D. Elmuradov, N. M. Turayeva

“…In the research process, a formal definition of the multi-agent system of automatic aeronautical information processing is presented, which involves a set of agents, environment of agent functioning, a set of permissible relations between agents, description of rules for forming a network of agents, a set of individual and joint actions, communication interactions, behavior and actions strategies, a possibility of system evolution. …”
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An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity Estimation by S. Du, H. Huang, K. Shen, Z. Liu, S. Tang

“…While approaches like Geographically Weighted Regression (GWR) capture local variations, they fall short of uncovering global patterns and tracking the continuous evolution of spatial heterogeneity. Motivated by this limitation, we propose a novel perspective—that is, simultaneously modeling common features across different locations alongside spatial differences using deep neural networks. …”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…This paper develops LeakInv-CUNet, a novel attention-guided GPR inversion framework, to enable refined imaging of leakage plumes and their temporal-spatial evolution. To enhance network training, extensive GPR datasets are generated by augmenting simulated data and experimentally measured data, accounting for variations in injection orientation, plume dynamics, and subsurface media properties. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024 by Muhammad Junaid Akram, Muhammad Junaid Akram, Asad Nawaz, Asad Nawaz, Yuan Yuxing, Yuan Yuxing, Jinpeng Zhang, Jinpeng Zhang, Huang Haixin, Huang Haixin, Lingjuan Liu, Lingjuan Liu, Xu Qian, Jie Tian, Jie Tian

“…Global collaboration networks underscored strong partnerships but highlighted disparities in contributions from low-income regions.ConclusionThis analysis highlights the dynamic evolution of cardiomyopathy research, emphasizing the critical role of ML and AI in advancing diagnostics and therapeutic strategies. …”
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New ichthyofaunal microfossils from the Late Permian at Chaotian, Sichuan, Southwest China by Li Jiachun, Sun Zuoyu, Gilles Cuny, Zhao Siyan, Yao Mingtao, Song Lijun

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An open flame and smoke detection dataset for deep learning in remote sensing based fire detection by Ming Wang, Peng Yue, Liangcun Jiang, Dayu Yu, Tianyu Tuo, Jian Li

“…Taking the advantage of rapid advancements in remote sensing technology and artificial intelligence, we introduce the Flame And Smoke Detection Dataset (FASDD), a pioneering collection comprising over 120,000 heterogeneous images covering various fire scenarios, aiming to promote the evolution of fire detection models. FASDD serves as a challenging benchmark for object detection tasks and features three sub-datasets: FASDD_CV, FASDD_UAV, and FASDD_RS. …”
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The glacial paleolandscapes of Southern Africa: the legacy of the Late Paleozoic Ice Age by P. Dietrich, P. Dietrich, P. Dietrich, F. Guillocheau, G. A. Douillet, N. P. Griffis, G. Baby, D. P. Le Héron, L. Barrier, M. Mathian, I. P. Montañez, C. Robin, T. Gyomlai, T. Gyomlai, C. Kettler, A. Hofmann

“…These valleys may have later facilitated ice flow from highlands to lowlands, although the extent and configuration of such features remain speculative.</p> <p>The exhumed pre-LPIA landforms may in some cases be taken for pediments, pediplains, and pedivalleys and interpreted as recording the topographic evolution of Southern Africa after the dislocation of Gondwana during the Mesozoic. …”
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Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar by CHEN Jun, ZHANG Bo, ZHUANG Xingyue, SONG Zhishu, ZHENG Jun

“…The integration of GPR with borehole imaging and acoustic emission data can provide a multi-scale monitoring system, offering a more comprehensive view of the fracture network from micro-crack initiation to macro-scale propagation. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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DIGITAL ECOSYSTEMS AS A DRIVER OF BUSINESS LANDSCAPE TRANSFORMATION IN THE MODERN ECONOMY by Ganna Kolomiyets, Vladуslav Korol, Dmуtro Bilіanskiy

“… The article defines the features of the ecosystem business model, reveals the importance and role of digital ecosystems in the evolution of the digital economy and sustainable development. …”
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Synthesizing the Academic Administrators&rsquo; Competencies: Developing of a Model by Homa Rahmani, Asadollah Khadivi

“…Research shows that competent management can influence the development and evolution of the university and implement the planned changes effectively (Shimoni, 2017). …”
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Multi-omics integrated analysis reveals the molecular mechanism of tail fat deposition differences in sheep with different tail types by Wannian Wang, Zhixv Pang, Siying Zhang, Pengkun Yang, Yangyang Pan, Liying Qiao, Kaijie Yang, Jianhua Liu, Ruizhen Wang, Wenzhong Liu

“…Abstract Background The accumulation of tail fat in sheep is a manifestation of adaptive evolution to the environment. Sheep with different tail types show significant differences in physiological functions and tail fat deposition. …”
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Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo, Congzheng Han

“…The model introduces the residual neural network (ResNet) and the convolutional block attention module (CBAM) to integrate multi-scale features into the U-Net encoder–decoder architecture, enhancing its ability to capture the spatiotemporal evolution of precipitation systems. …”
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Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction by Bo Li, Zhongfa Zhou, Tianjun Wu, Jiancheng Luo

“…The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. …”
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AI-Powered Spectral Imaging for Virtual Pathology Staining by Adam Soker, Maya Almagor, Sabine Mai, Yuval Garini

“…Unstained human biopsy samples are scanned, and a Pix2Pix-based neural network generates realistic H&E-equivalent images. …”
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Expression of cell-wall related genes is highly variable and correlates with sepal morphology by Hartasánchez, Diego A., Kiss, Annamaria, Battu, Virginie, Soraru, Charline, Delgado-Vaquera, Abigail, Massinon, Florian, Brasó-Vives, Marina, Mollier, Corentin, Martin-Magniette, Marie-Laure, Boudaoud, Arezki, Monéger, Françoise

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Automated Detection of Galactic Rings from Sloan Digital Sky Survey Images by Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K. Aniyan, Sudhanshu Barway, Harish Kumar

“…—carry information about their structure, origin, and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environments in which the galaxies are located, and their interactions with other galaxies. …”
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A Closer Look at the Origin of LINER Emission in Later-type Galaxies and Its Connection to Evolved Stars with a Machine Learning Classification Scheme by Ahmad Nemer, Ivan Yu. Katkov, Joseph D. Gelfand, Changhyun Cho

“…We show in this work that the neural-network-based encoder was able identify LINER sources from the stellar continuum alone. …”
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A high-gain THz microstrip patch antenna designed for IoT and 6G communications with predicted efficiency using machine learning approaches by Md Sharif Ahammed, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Narinderjit Singh Sawaran Singh, Md. Ashraful Haque

“…This study highlights the potential of integrating THz technology with machine learning to enhance antenna design, presenting a novel framework for the evolution of future wireless networks with improved performance and energy efficiency.…”
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