The aim of this study was to analyze the distribution of pathogenic bacteria in hospitalized patients in elderly care centers under the mode of integration of medical care and elderly care service, and explore the influencing factors to reduce the health care-associated infection rate of hospitalized patients.A total of 2597 inpatients admitted to elderly care centers from April 2018 to December 2019 were included in the study. The etiology characteristics of health care-associated infections (HCAI) was statistically analyzed, univariate analysis, and multivariate logistic regression analysis method were used to analyze the influencing factors of HCAI.A total of 98 of 2597 inpatients in the elderly care centers had HCAI, and the infection rate was 3.77%. The infection sites were mainly in the lower respiratory tract and urinary tract, accounting for 53.92% and 18.63%, respectively. A total of 53 pathogenic bacteria were isolated, 43 of which (81.13%) were Gram-negative, mainly Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae, which respectively accounted for 24.53, 16.98, and 13.21%. 9 (16.98%) strains were Gram-positive, mainly Staphylococcus aureus and Enterococcus faecium, respectively accounting for 7.55 and 5.66%. Only 1 patient (1.89%) had a fungal infection. Multivariate logistic regression analysis indicated that total hospitalization days, antibiotic agents used, days of central line catheter, use of urinary catheter and diabetes were independent risk factors of nosocomial infection in elderly care centers (P < .05).Many factors can lead to nosocomial infections in elderly care centers. Medical staff should take effective intervention measures according to the influencing factors to reduce the risk of infection in elderly care facilities.

With the rapid development of cross-border e-commerce, however, logistics has become a bottleneck in the development of cross-border electricity traders. The results of studies on cross-border e-commerce logistics are still less, and the relevant theoretical studies are not yet mature enough. As cross-border e-commerce occupies a share of foreign trade in foreign trade increases, so does their influence. In order to eliminate bottlenecks in the cross-border logistics of an electric enterprise, it is of great importance to systematically study the issues of synergy in the logistics of the supply chain of a cross-border electric enterprise and validate how cross-border traders and cross-border logistics work together using cross-border discussion based on the perspective of cross-border e-commerce ecosystem. At the same time, an analysis of the need for cross-border logistics collaboration electricity traders and cross-border logistics is being carried out, as well as an in-depth study of synergy mechanisms between cross-border electricity traders and cross-border logistics based on a cross-border ecosystem perspective. The empirical results show that cross-border logistics is available function service capability; cross-border logistics information sharing level, cross-border logistics resource optimization and allocation capability, and the opening level of cross-border logistics environment have different contributions to the impact on the efficiency of cross-border e-commerce logistics. Among them, the level of cross-border logistics information exchange has the most significant influence on the logistics efficiency of cross-border traders, followed by cross-border logistics functional services capabilities, again the level of openness of the cross-border logistics environment, and finally, the ability to optimally distribute cross-border logistics resources.

The optimization of the e-commerce logistics distribution path has always been an important research object in intelligent control. Based on the geographic information system (GIS) platform, this paper proposes an improved ant colony algorithm for the logistics distribution path optimization model based on the GIS platform. Firstly, ArcGIS software is used to solve the problem of complex networks and realize the selection and output of optional routes. Secondly, the basic ant colony algorithm is improved, and a dynamic path planning method combining global planning information and local planning is proposed. It mainly includes the improvement of the heuristic function and the improvement of the update method of pheromone. Finally, through simulation experiments and case validation experiments, it is concluded that the improved algorithm outperforms the traditional ant colony algorithm. In the case validation part, a local area of Beijing is selected to simulate a natural distribution environment, and the path optimization experiments are validated by building an actual road network model. The results show that the model can plan the optimal path for logistics distribution according to the road congestion. The data analysis shows that the route optimization model proposed in this paper can effectively reduce the distribution cost of enterprises, increase vehicle loading, and increase the profit and industry competitiveness of logistics enterprises.