Search

In the downlink communication, it is currently challenging for ground users to cope with the uncertain interference from aerial intelligent jammers. The cooperation and competition between ground users and unmanned aerial vehicle (UAV) jammers leads to a Markov game problem of anti-UAV jamming. Therefore, a model-free method is adopted based on multi-agent reinforcement learning (MARL) to handle the Markov game. However, the benchmark MARL strategies suffer from dimension explosion and local optimal convergence. To solve these issues, a novel event-triggered multi-agent proximal policy optimization algorithm with Beta strategy (ETMAPPO) is proposed in this paper, which aims to reduce the dimension of information transmission and improve the efficiency of policy convergence. In this event-triggering mechanism, agents can learn to obtain appropriate observation in different moment, thereby reducing the transmission of valueless information. Beta operator is used to optimize the action search. It expands the search scope of policy space. Ablation simulations show that the proposed strategy achieves better global benefits with fewer dimension of information than benchmark algorithms. In addition, the convergence performance verifies that the well-trained ETMAPPO has the capability to achieve stable jamming strategies and stable anti-jamming strategies. This approximately constitutes the Nash equilibrium of the anti-jamming Markov game. [ABSTRACT FROM AUTHOR]

• Intelligent recommendation systems are developed for chronic healthcare systems. • GCFYA (with attention) and GCFNA (without attention) performs better than traditional methods. • Recommendation accuracy is better in public and local hospitals datasets. With the rapid advancement of modern medical technology and the increasing demand for a higher quality of life there is an emergent requirement for personalized healthcare services. This is particularly pertinent in the sphere of pharmacological recommendations, where the necessity to provide patients with optimal and efficacious medication regimens is paramount. Traditional methodologies in this domain are increasingly seen as insufficient for the needs of contemporary medicine, prompting a shift towards more sophisticated technologies and algorithms. In this study, we addressed this pressing need by developing GCF++ i.e., two graph-based collaborative filtering methods, GCFYA (with attention) and GCFNA (without attention). These methods hold significant promise in revolutionizing how drug recommendations are made, ensuring that patients receive precise and trustworthy medication suggestions tailored to their unique needs and scenarios. To evaluate and compare these algorithms, we introduced three robust metrics: Precision, RMSE (Root Mean Square Error), and Recall. Precision value for GCF-YA is 88 % for hospital dataset, while 85 % for public dataset, similarly, GCF-NA is 77 % for hospital dataset while 78 % for public dataset which is much higher than other traditional methods. Furthermore, as algorithm models become increasingly intricate, transparency and interpretability have gained paramount importance. In response, we incorporated two model interpretation tools, SHAP and LIME, to demystify the decision-making processes behind these algorithms. These tools not only provide clear insights into the basis of recommendation results for both users and developers but also enhance patients' trust and satisfaction with the recommendation system. This study represents a significant step forward in the pursuit of personalized, transparent, and effective healthcare solutions. [ABSTRACT FROM AUTHOR]

As the population and industries continue to gather in cities, such cities face acute shocks caused by various natural disasters and high pressure caused by human interference. The means of accurately assessing and improving the level of urban resilience (UR) to cope with disturbances and shocks, enhance regional resilience, and improve sustainable urban development has become a hot topic. In this study, we constructed an UR assessment framework using "social-economic-institutional-ecological-engineering" to explore the dynamic evolution of spatial and temporal patterns in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, to reveal the main factors influencing the evolution of this spatial pattern and the strength and interaction mechanisms among such influencing factors and to dynamically simulate the spatial and temporal evolution characteristics in the next 16 years. The results show that from 2000 to 2019, the difference in UR level in the GBA is obvious. Hong Kong and Macao are obviously ahead of other cities, and the comprehensive resilience level of Guangzhou and Shenzhen cities in the mainland is developing rapidly. The difference in per capita consumption capacity, industrial structure optimization, foreign trade vitality, infrastructure support and other factors are the main causes of the spatial differentiation of UR in the GBA. In the future, the UR level of the GBA urban agglomeration will show an overall upwards trend. The three development poles of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao have obvious advantages in spatial distribution. The results of the study can enrich regional UR research, and also provide theoretical references to facilitate the high-quality development of the area. [Display omitted] • We constructed a "social-economic-institutional-ecological-engineering" urban resilience assessment framework. • The paper developed an evaluation system for the urban resilience to reveal its spatial and temporal evolution patterns. • The main factors influencing the evolution of the spatial patterns of urban resilience is investigated. • Our results provide a basis for the development of comprehensive management framework for urban resilient. [ABSTRACT FROM AUTHOR]

Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral remote sensing technology. Improving classification performance is still a significant problem, however, as a result of the high dimensionality and redundancy of hyperspectral image sets (HSIs), as well as the presence of class imbalance in hyperspectral datasets. In the past few years, convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs struggle to achieve good accuracy in low samples, while GCNs have a huge computational cost. To resolve these issues, this paper proposes a Multi-Feature Fusion of 3D-CNN and Graph Attention Network MFFCG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in improving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. We further develop two optimized GAT models named GAT1 and GAT2, which are used with different layers of 3D-CNN. Algorithms after merging with 3D-CNN are named MFFCG-1 and MFFCG-2, which produce better classification results then other developed methods. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classification time. [ABSTRACT FROM AUTHOR]

In middle-aged and older men, prostate cancer (PCa) is a common tumor disease with a mortality rate second only to lung cancer. The automatic and accurate segmentation of the prostate and tumor in magnetic resonance imaging (MRI) images can help doctors diagnose malignancies more efficiently. T2 weighted imaging (T2W) is now used in the majority of studies on prostate MRI image segmentation; however, diffusion-weighted imaging (DWI) is more valuable in the diagnosis of PCa. The morphological differences between the prostate and tumor regions are minimal, the tumor size is uncertain, the border between the tumor and surrounding tissue is hazy, and the categories separating normal regions from tumors are uneven. Consequently, it is challenging to segment prostate and tumor on DWI images. For the segmentation of prostate and tumor regions on DWI images, this study offers a dual attention-guided 3D convolutional neural network (3D DAG-Net). A visual attention method is built into the encoder step to obtain the features of various receptive fields and deliver more detailed contextual information. A multiscale attention technique is proposed at the decoder stage to fuse multiscale features to acquire finer global and local details. To resolve the class discrepancies between the prostate, tumor, and background regions in segmentation tasks, we propose a hybrid loss function for handling class imbalance. We tested the algorithm on DWI images of PCa obtained from a nearby hospital, demonstrating the uniqueness and effectiveness of the method. Dice similarity coefficient (DSC) values for prostate and tumor DWI segmentation were 92.28% and 88.73%, respectively. We present a unique dual-attention mechanism 3D segmentation network architecture for quantitative assessment of prostate and tumor volumes on DWI. The automatic segmentation results produced by our technology were highly correlated and consistent with expert manual segmentation findings. [ABSTRACT FROM AUTHOR]

Accurate and massive medical image annotation data is crucial for diagnosis, surgical planning, and deep learning in the development of medical images. However, creating large annotated datasets is challenging because labeling medical images is complicated, laborious, and time-consuming and requires expensive and professional medical skills. To significantly reduce the cost of labeling, an interactive image annotation framework based on composite geodesic distance is proposed, and medical images are labeled through segmentation. This framework uses Attention U-net to obtain initial segmentation based on adding user interaction to indicate incorrect segmentation. Another Attention U-net takes the user's interaction with the initial segmentation as input. It uses a composite geodesic distance transform to convert the user's interaction into constraints, giving accurate segmentation results. To further improve the labeling efficiency for large datasets, this paper validates the proposed framework against the segmentation background of a self-built prostate MRI image datasets. Experimental results show that the proposed method achieves higher accuracy in less interactive annotation and less time than traditional interactive annotation methods with better Dice and Jaccard results. This has important implications for improving medical diagnosis, surgical planning, and the development of deep-learning models in medical imaging. [ABSTRACT FROM AUTHOR]

As a result of rapid industrialization and urbanization, China is now facing a host of environmental problems that have serious health implications. Studies of air pollution's impact on human health are vital in many fields, including epidemiology, environmental science, and the social sciences. To ensure the effective growth of socioeconomic sectors, it is critical to investigate the effect of socioeconomic factors on primary air pollutant particulate matter (PM 2.5) and the driving mechanism. We conducted group-wise (i.,e. divide data in 5 different periods, D1 (2002–2006), D2 (2007–2011), D3 (2012–2016), D4 (2017–2021) and D5(2002–2021) spatial autocorrelation and spatial panel regression analyses of PM 2.5 emissions using panel data from 34 provincial-level administrative units in China from 2002 to 2021 to understand the factors influencing air pollutant emissions. This study adds to the literature by considering comprehensive features and spatial effects in the panel-data econometric framework of the different areas. The spatial features analysis reveals that pollutant emissions in these regions decreased during the study period, although socioeconomic and natural factors are essential sources of PM 2.5. PM 2.5 emissions also showed significant positive spatial autocorrelations. Several statistical tests were run to examine the spatial autocorrelation among the regions. The results of a random effect regression model and geometric weighted regression (GWR) revealed that both socioeconomic and natural factors were statistically significant for PM 2.5 , though to varying degrees depending on region type. Positive and statistically significant results were obtained for China when considering the impacts of urban population, urban green space, economic growth, and economic spending. China has a positive and significant link with the exploitation of energy and natural resources. In light of these findings, we have developed several ideas for addressing air pollution and improving environmental sustainability, such as increasing regional collaboration and reforming the economy. [ABSTRACT FROM AUTHOR]

Fraxinellone, a degraded limonoid, has been mainly isolated from some renewable plants in Meliaceae and Rutaceae. In a continuous effort to discover new natural products-based pesticides, two series of fraxinellone derivatives containing N -phenylpyrazole moiety were designed, synthesized and evaluated for their pesticidal activities against Mythimna separata Walker and Plutella xylostella Linnaeus. Two structures of compounds 7g and 8k were unambiguously determined by X-ray diffraction further. The bioassay showed that over half of the target compounds exhibited better insecticidal activity against M. separata than the precursor fraxinellone. Among all the target compounds, the compounds 7g – i and 8g – j exhibited more potent insecticidal activity than toosendanin, a commercial botanical pesticide. Furthermore, the compound 8g displayed more promising larvicidal activity with the LC 50 value of 0.31 μmol mL −1 than the toosendanin against P. xylostella . The structure–activity relationship (SAR) revealed that introduction of polyhalogenated phenylpyrazole ring on furyl-ring of fraxinellone could lead more potent compounds both against M. separata and P. xylostella than that of monohalogenated phenylpyrazole ring or electron-donating groups substituted phenylpyrazole ring. [ABSTRACT FROM AUTHOR]