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In this paper, the POI data of 736 Cainiao stations in Nanjing is taken as the research sample. With the help of ArcGIS software, the standard deviation ellipse, spatial autocorrelation, average nearest neighbor, cold and hot spot analysis, nuclear density estimation, and other spatial analysis models are used to quantitatively characterize its business mode, spatial distribution characteristics, and equilibrium. Based on DNN, the spatial agglomeration characteristics and distribution directions of the Cainiao station in Nanjing were sorted out, the cold spots and hot spots of the spatial layout were identified, and the spatial differentiation rules and agglomeration patterns were revealed. Finally, the geographically weighted regression analysis model is used to analyze the influencing factors of the spatial agglomeration of the Cainiao station in Nanjing. The research found that Firstly, the proportion of Nanjing Cainiao station operating mode adopting the exclusive mode is 59.1%, the proportion adopting the concurrent operation mode is 33.7%, and the rest adopting the joint operation mode of cooperation with other logistics enterprises. Secondly, Nanjing Cainiao Station gathers in the central city area, forming a “central hot spot.” The urban fringe area does not form a “peripheral cold spot area,” and the whole presents a “1 + 4” five-core agglomeration model across the river. Thirdly, Regional GDP, population density, and the number of convenience stores/supermarkets are the main factors affecting the spatial agglomeration of the Cainiao station in Nanjing.

Background Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications. Results The 178 signature positions in 11 viral proteins were firstly screened as features by the scores of five amino acid factors and their random forest rankings. The Supporting Vector Machine algorithm achieved well performance. The most important amino acid factor (Factor 5) and the minimal range of signature positions (63 amino acid residues) were also explored. Moreover, human-origin avian influenza viruses with three or four genome segments from human virus had pandemic risk with high probability. Conclusion Using machine learning methods, the present paper scores the amino acid mutations and predicts pandemic risk with well performance. Although long evolution distances between avian and human viruses suggest that avian influenza virus in nature still need time to fix among human host, it should be notable that there are high pandemic risks for H7N9 and H9N2 avian viruses.

The existing treatment modalities for skin injuries mainly include dressings, negative-pressure wound treatment, autologous skin grafting, and high-pressure wound treatment. All of these therapies have limitations such as high time cost, the inability to remove inactivated tissue in a timely manner, surgical debridement, and oxygen toxicity. Mesenchymal stem cells have a unique self-renewal ability and wide differentiation potential, and they are one of the most promising stem cell types in cell therapy and have great application prospects in the field of regenerative medicine. Collagen exerts structural roles by promoting the molecular structure, shape, and mechanical properties of cells, and adding it to cell cultures can also promote cell proliferation and shorten the cell doubling time. The effects of collagen on MSCs were examined using Giemsa staining, EdU staining, and growth curves. Mice were subjected to allogeneic experiments and autologous experiments to reduce individual differences; all animals were separated into four groups. Neonatal skin sections were detected by HE staining, Masson staining, immunohistochemical staining, and immunofluorescence staining. We found that the MSCs pretreated with collagen accelerated the healing of skin wounds in mice and canines by promoting epidermal layer repair, collagen deposition, hair follicle angiogenesis, and an inflammatory response. Collagen promotes the secretion of the chemokines and growth factors associated with skin healing by MSCs, which positively influences skin healing. This study supports the treatment of skin injuries with MSCs cultured in medium with collagen added.

With the advancement of urban construction, more and more distribution network lines are changed from overhead lines to cable lines. Currently, the distribution network cable connection generally adopts the European or American cable elbow. If the temperature changes inside the cable elbow can be monitored in time, it will help to find the hidden fault of the cable elbow in time and improve the safe operation level of the power grid. In this paper, the online temperature measurement device of distribution network cable is discussed and researched, and a contact-type inductive wireless temperature measurement sensor suitable for the elbow joint of distribution network cable is designed. The sensor not only can accurately measure temperature, but also has the characteristics of a convenient communication network, safe and reliable information, and strong insulation ability, which is suitable for large-scale promotion and application.

With the improvement of the automation level of the power system, the number of operation and maintenance personnel continues to decrease, but the use of switchgear is quite large. Since the switchgear is a metal armoured device with a closed shell, the hot spots inside the switchgear cannot be detected in time during operation. Effective temperature measurement and the safety of equipment cannot be effectively guaranteed. In order to avoid the aging caused by the excessive current load of the switch cabinet, poor contact of cable joints and contacts, or long-term operation, which will cause heat in the place, forming a vicious circle and eventually leading to insulation breakdown or fire accidents, Establish a wireless temperature measurement system for switch cabinets to monitor the temperature of the parts where the equipment is prone to overheat, and issue an alarm message when the temperature rises abnormally. At present, the manual inspection of switch cabinets in the power supply system cannot effectively and timely detect fire hazards, so the problem of online monitoring of switch cabinet temperature is particularly prominent. Using the Internet of Things sensing technology and communication technology, build an Internet of Things-based electrical connection point temperature online monitoring system, the all-weather real-time monitoring of the connection parts of high-voltage electrical equipment can effectively reduce the equipment failure rate, promote the condition maintenance of power equipment, and ensure the safe and economical operation of the power system.

Graph Neural Networks (GNNs) serve as a powerful framework for representation learning on graph-structured data, capturing the information of nodes by recursively aggregating and transforming the neighboring nodes’ representations. Topology in graph plays an important role in learning graph representations and impacts the performance of GNNs. However, current methods fail to adequately integrate topological information into graph representation learning. To better leverage topological information and enhance representation capabilities, we propose the Graph Topology Attention Networks (GTAT). Specifically, GTAT first extracts topology features from the graph’s structure and encodes them into topology representations. Then, the representations of node and topology are fed into cross attention GNN layers for interaction. This integration allows the model to dynamically adjust the influence of node features and topological information, thus improving the expressiveness of nodes. Experimental results on various graph benchmark datasets demonstrate GTAT outperforms recent state-of-the-art methods. Further analysis reveals GTAT’s capability to mitigate the over-smoothing issue, and its increased robustness against noisy data.

The coronavirus pandemic has seriously affected public health and social order. Prediction methods based on machine learning can identify the infectivity phenotype and pandemic risk of coronavirus. Currently, six types of coronaviruses that infect humans have been discovered, with significant differences in viral genome sequences. Continuous genetic variation of the virus will lead to reduced performance of machine learning models and potential learning forgetting. To solve this challenge, we propose an incremental learning and knowledge distillation framework (ILKD). First, we employ Dna2Vec to extract virus features and encode the virus sequence into virus feature vector. Second, we use hierarchical clustering to continuously identify new coronavirus groups. Third, ILKD employ a combined strategy of incremental learning and knowledge distillation to transform the Back Propagation (BP) neural network to continuously learn and predict the phenotypes of human-to-human coronavirus infection. Experimental results show that ILKD can effectively alleviate the learning forgetting phenomenon. Further analysis reveals ILKD has better performance than other incremental learning models, and has important public health application value.

Infectious diseases have long been a shaping force in human history, necessitating a comprehensive understanding of their dynamics. This study introduces a co-evolution model that integrates both epidemiological and evolutionary dynamics. Utilizing a system of differential equations, the model represents the interactions among susceptible, infected, and recovered populations for both ancestral and evolved viral strains. Methodologically rigorous, the model’s existence and uniqueness have been verified, and it accommodates both deterministic and stochastic cases. A myriad of graphical techniques have been employed to elucidate the model’s dynamics. Beyond its theoretical contributions, this model serves as a critical instrument for public health strategy, particularly predicting future outbreaks in scenarios where viral mutations compromise existing interventions.

Objectives To measure the myocardial extracellular volume (ECV) in patients with heart failure with preserved ejection fraction (HFpEF) using dual-energy computed tomography with late iodine enhancement (LIE-DECT) and to evaluate the relationship between ECV and risk of HFpEF and cardiac structure and function. Methods A total of 112 consecutive patients with HFpEF and 80 consecutive subjects without heart disease (control group) who underwent LIE-DECT were included. All patients were divided into ischaemic and non-ischaemic groups according to the LIE patterns detected using iodine maps. The ischaemic scar burden was calculated in the ischaemic HFpEF group. Iodine maps and haematocrit were used to measure ECV in the non-ischaemic HFpEF group and remote ECV of the non-scarred myocardium in the ischaemic HFpEF group, respectively. Cardiac structural and functional variables were collected. Results ECV in patients with non-ischaemic HFpEF ( n  = 77) and remote ECV in patients with ischaemic HFpEF ( n  = 35) were significantly higher than those in control subjects ( p  < 0.001). Multivariate logistic regression analysis revealed that after adjusting for age, sex, body mass index, smoking, and drinking, a higher ECV/remote ECV was still associated with non-ischaemic HFpEF and ischaemic HFpEF ( p  < 0.001). A positive correlation was established between ECV and cardiac structural and functional variables ( p  < 0.05) in all participants. Subgroup analysis showed that ECV/remote ECV and ischaemic scar burden positively correlated with heart failure classification in the HFpEF subgroup ( p  < 0.05). Conclusion ECV/remote ECV elevation was significantly associated with non-ischaemic and ischaemic HFpEF. Remote ECV and LIE may have synergistic effects in the risk assessment of ischaemic HFpEF. Key Points • ECV/remote ECV elevation is associated not only with non-ischaemic HFpEF but also with ischaemic HFpEF. • ECV/remote ECV and ischaemic scar burden are correlated with cardiac structure and function .

In recent years, the digital transformation of power grids has become a new trend in the development of power grid enterprises. As a key link, the digitalization and intellectualization of substations have been included in the development plans of major power grid companies. This paper introduces a construction scheme for smart substations. By deploying edge Internet of Things agents, the construction scheme from online monitoring of electrical quantities of electric power equipment to data edge aggregation and data application is realized, which provides an effective solution for the construction of smart substations.