Search

Research on next-point-of-interest (POI) recommendation has become a new focus in the field of POI recommendation in recent years. The goal of POI recommendation tasks is to predict a user's future movement trajectory based on their current state and historical behavioral information. Recent studies have shown the effectiveness of neural network-based next-POI recommendation engines. However, most existing models only consider the correlation between consecutive visits, neglecting the complex dependencies of the POIs in the area and category features, as well as the processing of unstructured time series. This paper presents a new Attention-Based Spatial–Temporal Point-of-Interest-Group (AST-PG) model for POI recommendation. The model consists of a spatial module and a temporal module combined with each other by a multiple-attention mechanism. The spatial module in the proposed model groups the POIs based on geographic and category features, while the temporal module develops a uniform-length time trajectory vector for the unstructured temporal features. Comprehensive experimental results on two real datasets demonstrate that the proposed model of this study is superior to the state-of-the-art POI recommendation models in terms of performance. [ABSTRACT FROM AUTHOR]

Immunogenic cell death (ICD), a dying state of the cells, encompasses the changes in the conformations of cell surface and the release of damage‐associated molecular patterns, which could initiate an adaptive immune response by stimulating the dendritic cells to present antigens to T cells. Advancements in biomaterials, nanomedicine, and micro‐ and nano‐technologies have facilitated the development of effective ICD inducers, but the potential toxicity of these vesicles encountered in drug delivery via intravenous administration hampers their further application. As alternatives, the local drug delivery systems have gained emerging attention due to their ability to prolong the retention of high payloads at the lesions, sequester drugs from harsh environments, overcome biological barriers to exert optimal efficacy, and minimize potential side effects to guarantee bio‐safety. Herein, a brief overview of the local drug delivery techniques used for ICD inducers is provided, explaining how these techniques broaden, alter, and enhance the therapeutic capability while circumventing systemic toxicity at the same time. The historical context and prominent examples of the local administration of ICD inducers are introduced. The complexities, potential pitfalls, and opportunities for local drug delivery techniques in cancer immunotherapy are also discussed. [ABSTRACT FROM AUTHOR]

In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has become an inevitable problem in the process of urban development, bringing hazards and hidden dangers to citizens' travel and urban development. The management of traffic congestion first lies in the accurate completion of the identification of road traffic status and the need to predict road congestion in the city, so as to improve the use rate of urban infrastructure road facilities and better alleviate road congestion. In this study, a deep spatial and temporal network model (DSGCN) for predicting traffic congestion status is proposed. First, our study divides the traffic network into grids, where each grid represents a different independent region. In this paper, the centroids of the grid regions are abstracted as nodes, and the dynamic correlations between the nodes are expressed in the form of adjacency matrix. Then, Graph Convolutional Neural Network is used to capture the spatial correlation between regions and a two-layer long and short-term feature model (DSTM) is used to capture the temporal correlation between regions. Finally, the DSGCN outperforms other baseline models and has higher accuracy for traffic congestion prediction as demonstrated by experiments on real PeMS datasets. [ABSTRACT FROM AUTHOR]