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The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of "Circumjacent Monitoring-Blind Area Inference". In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843783 | PMC |
http://dx.doi.org/10.3390/ijerph16203788 | DOI Listing |
Neural Netw
September 2025
School of Computer Science, South China Normal University, Guangzhou, 510631, Guangdong, China; School of Artificial Intelligence, South China Normal University, Foshan, 528225, Guangdong, China. Electronic address:
Data-Free Knowledge Distillation (DFKD) have achieved significant breakthroughs, enabling the effective transfer of knowledge from teacher neural networks to student neural networks without reliance on original data. However, a significant challenge faced by existing methods that attempt to generate samples from random noise is that the noise lacks meaningful information, such as class-specific semantic information. Consequently, the absence of meaningful information makes it difficult for the generator to map this noise to the ground-truth data distribution, resulting in the generation of low-quality training samples.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation, Southeast University, Nanjing, 210096, China; Advanced Ocean Institute of Southeast University Nantong, Nantong, 226010, China. Electronic address:
Unmanned Aerial Vehicle (UAV) tracking requires accurate target localization from aerial top-down perspectives while operating under the computational constraints of aerial platforms. Current mainstream UAV trackers, constrained by the limited resources, predominantly employ lightweight Convolutional Neural Network (CNN) extractor, coupled with an appearance-based fusion mechanism. The absence of comprehensive target perception significantly constrains the balance between tracking accuracy and computational efficiency.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Smart Manufacturing, Industrial Perception and Intelligent Manufacturing Equipment Engineering Research Center of Jiangsu Province, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, China.
In the field of quality control, metal surface defect detection is an important yet challenging task. Although YOLO models perform well in most object detection scenarios, metal surface images under operational conditions often exhibit coexisting high-frequency noise components and spectral aliasing background textures, and defect targets typically exhibit characteristics such as small scale, weak contrast, and multi-class coexistence, posing challenges for automatic defect detection systems. To address this, we introduce concepts including wavelet decomposition, cross-attention, and U-shaped dilated convolution into the YOLO framework, proposing the YOLOv11-WBD model to enhance feature representation capability and semantic mining effectiveness.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
The rapid advancement of single-cell sequencing technology has generated vast amounts of multi-omics data, presenting unprecedented opportunities for single-cell multi-omics clustering analysis. However, existing single-cell clustering algorithms focus on extracting shared representations, overlooking the interactions and correlations among cells. This oversight inevitably leads to biased or confounded cell clustering results.
View Article and Find Full Text PDFDiabetologia
September 2025
Department of Diabetology and Internal Medicine, Medical University of Warsaw, Warsaw, Poland.
This review article, developed by the EASD Global Council, addresses the growing global challenges in diabetes research and care, highlighting the rising prevalence of diabetes, the increasing complexity of its management and the need for a coordinated international response. With regard to research, disparities in funding and infrastructure between high-income countries and low- and middle-income countries (LMICs) are discussed. The under-representation of LMIC populations in clinical trials, challenges in conducting large-scale research projects, and the ethical and legal complexities of artificial intelligence integration are also considered as specific issues.
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