Enhanced and shortened Mn 2+ emissions by Cu + co-doping in borosilicate glasses for W-LEDs

A novel pair of transition metal ions Cu+, Mn2+ is co-doped in borosilicate glasses. Both copper and manganese ions exist in lower valence states (Cu+, Mn2+) in the as-prepared glasses. Around 5-time enhanced Mn2+ emission under the UV excitation is observed, which, as demonstrated by excitation spectra and emission decay curves, is due to an energy transfer from Cu+ ions resulting in greatly increased absorption of Mn2+ ions in the UV region, and relaxation on doubly-forbidden transition of Mn2+ leading to the much shortened Mn2+ emission lifetime from millisecond to microsecond level. Besides, a composite white emission is generated by combining the blue-green part from Cu+ ions with the green-red part from Mn2+ ions and it can be effectively tuned from cold to warm by adjusting host glass composition and altering excitation wavelength. Relevant mechanisms are discussed

Seismic fragility analysis and index evaluation of concrete-filled steel tube column frame-core tube structures

To understand the effect of connection type on the seismic fragility of concrete-filled steel tube (CFST) column frame-core tube structures, typical structures with different numbers of storeys (15, 30, 45, and 60) and different types of beam-end connections (rigid connections, hinged connections, and rigid connections with outriggers) are designed. The probabilistic seismic demand models (PSDMs) of the structures are obtained using cloud analysis, and seismic fragility curves based on the mean period and the number of storeys are established. The results show that the differences in the seismic fragilities among the structures with the three types of connections can be effectively determined by the critical inter-storey drift ratio (IDR) and the storey ductility and that the determination method with the critical IDR is relatively simple. The use of beam-end hinges in the 15-storey structure results in an advantageous seismic performance, with a lower probability of exceeding the limit states, and the performance of the 60-storey structure with beam-end hinges is sensitive to spectral acceleration. The spectral acceleration and peak ground acceleration (PGA) can effectively describe the fragility curves based on the number of storeys and the mean period, respectively

A deep graph convolutional neural network architecture for graph classification.

Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to extract high-level features of nodes. There are two main reasons for this: 1) Overlaying too many graph convolution layers will lead to the problem of over-smoothing. 2) Graph convolution is a kind of localized filter, which is easily affected by local properties. To solve the above problems, we first propose a novel general framework for graph neural networks called Non-local Message Passing (NLMP). Under this framework, very deep graph convolutional networks can be flexibly designed, and the over-smoothing phenomenon can be suppressed very effectively. Second, we propose a new spatial graph convolution layer to extract node multiscale high-level node features. Finally, we design an end-to-end Deep Graph Convolutional Neural Network II (DGCNNII) model for graph classification task, which is up to 32 layers deep. And the effectiveness of our proposed method is demonstrated by quantifying the graph smoothness of each layer and ablation studies. Experiments on benchmark graph classification datasets show that DGCNNII outperforms a large number of shallow graph neural network baseline methods

Attention-based spatial–temporal multi-graph convolutional networks for casualty prediction of terrorist attacks

Abstract At present, terrorism has become an important factor affecting world peace and development. As the time series data of terrorist attacks usually show a high degree of spatial–temporal correlation, the spatial–temporal prediction of casualties in terrorist attacks is still a significant challenge in the field of counter-terrorism. Most of the existing terrorist attack prediction methods lack the ability to model the spatial–temporal dynamic correlation of the time series data of terrorist attacks, so they cannot yield satisfactory prediction results. In this paper, we propose a novel Attention-based spatial–temporal multi-graph convolutional network (AST-MGCN) for casualty prediction of terrorist attacks. Specifically, we construct the spatial adjacency graph and spatial diffusion graph based on the different social-spatial dynamic relationships of terrorist attacks and determine the multi-scale period of time series data of terrorist attacks by using wavelet transform to model the temporal trend, period and closeness properties of terrorist attacks. The AST-MGCN mainly consists of spatial multi-graph convolution for extracting social-spatial features in multi-views and temporal convolution for capturing the transition rules. In addition, we also use the spatial–temporal attention mechanism to effectively capture the most relevant spatial–temporal dynamic information. Experiments on public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines

Co-embedding of edges and nodes with deep graph convolutional neural networks

Abstract Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method

Awareness of and willingness to use oral pre-exposure prophylaxis for HIV prevention among HIV-serodiscordant heterosexual couples: a cross-sectional survey in Xinjiang, China.

OBJECTIVES: We aimed to investigate the awareness of and willingness to use oral pre-exposure prophylaxis (PrEP) for HIV prevention among HIV-negative partners in HIV-serodiscordant heterosexual couples in Xinjiang, China and determine factors that predict willingness to use oral PrEP. METHODS: Between November 2009 and December 2010, a cross-sectional survey was carried out among 351 HIV-negative partners in HIV-serodiscordant heterosexual couples from three cities in Xinjiang, China. Participants completed a self-administered questionnaire to assess their awareness of and willingness to use oral PrEP. Additionally, blood samples were collected to test for HIV infection. Univariate and multivariate logistic regression analyses were performed to identify predictors of willingness to use oral PrEP. RESULTS: Only 10 participants (2.8%) reported having heard of PrEP, and only two reported ever using PrEP. However, 297 (84.6%) reported that they were willing to use oral PrEP if it was proven to be both safe and effective. Results of multivariate analysis revealed the following independent predictors of willingness to use oral PrEP: monthly household income (adjusted odds ratio = 2.78, <1000 RMB vs. ≥ 1000 RMB, 95% confidence interval: 1.36-5.69), perceived likelihood of contracting HIV from HIV-positive partner (adjusted odds ratio = 2.63, likely vs. unlikely, 95% confidence interval: 1.12-6.19), and worrying about being discriminated against by others due to oral PrEP use (adjusted odds ratio = 9.43, No vs. Yes, 95% confidence interval: 3.78-23.50). CONCLUSIONS: Our results showed HIV-negative partners in HIV-serodiscordant heterosexual couples in China had low awareness of oral PrEP but high willingness to use oral PrEP for HIV prevention. Cost of oral PrEP should be taken into consideration in future PrEP prevention strategy. In addition, efforts should be made to reduce stigma attached to oral PrEP use, which may increase its acceptability among potential users

Molecular typing of Chinese Streptococcus pyogenes isolates

Streptococcus pyogenes causes human infections ranging from mild pharyngitis and impetigo to serious diseases including necrotizing fasciitis and streptococcal toxic shock syndrome. The objective of this study was to compare molecular emm typing and pulsed field gel electrophoresis (PFGE) with multiple-locus variable-number tandem-repeat analysis (MLVA) for genotyping of Chinese S.pyogenes isolates. Molecular emm typing and PFGE were performed using standard protocols. Seven variable number tandem repeat (VNTR) loci reported in a previous study were used to genotype 169. S. pyogenes geographically-diverse isolates from China isolated from a variety of disease syndromes. Multiple-locus variable-number tandem-repeat analysis provided greater discrimination between isolates when compared to emm typing and PFGE. Removal of a single VNTR locus (Spy2) reduced the sensitivity by only 0.7%, which suggests that Spy2 was not informative for the isolates screened. The results presented support the use of MLVA as a powerful epidemiological tool for genotyping S.pyogenes clinical isolates

Relationship between demographic characteristics and willingness to use oral PrEP.

<p>Note: A total of 349 participants provided responses for employment status and education.</p><p>Abbreviations: PrEP, pre-exposure prophylaxis; CI, confidence interval; OR, odds ratio; RMB, Renminbi.</p