Currently, there are many management issues in emergency department, such as long time waiting caused by unreasonable procedure, the unbalance utilization of doctors and nurses and so on. This study's aim is to evaluate resource allocation for optimal use of medical workers for adjusted registration procedure in West China Hospital of Sichuan University. The author used data he collected to construct a stimulation model of registration procedure for West China Hospital. Through analyzing the parameters after operating the model, the model's efficiency can be evaluated. Then the author designed new registration procedure based on the deficiencies of the old model and used compute-assisting method to find the optimal allocation of resources. The original emergency department registration process needs 104 hours nurses' working, and the cost of new process is 96 hours. It is 8 hours working hours less compared to the original process after we optimized the procedure. According to the analysis of waiting time for each stage, both average waiting times in preview triage area and registration office were reduced in new procedure, especially in the peak period during the day (8:00 to 16:00). This thesis makes simulation modeling about the registering system in the Emergency Department of West China Hospital and designs the new registering procedure. In the end, the resource utilization rate and the patients' waiting time of the new procedure are more satisfying than the primary one.
We presented a study method for gene regulatory network using graph theory. In this paper, we proposed the dynamics equations of regulatory network, and studied the measurement of regulatory influence of regulatory network. We also studied the modular approach of studying regulatory network and proposed a modular algorithm to do lossless decomposition of regulatory networks. Finally, an illustrative example was provided, and its results suggested that graph theory is an effective way for the analysis of regulatory network.
The large knowledge base system partition problem is known to be NP-complete. In this paper, we first present a genetic algorithm for solving the partitioning problem of knowledge base based on fuzzy cognitive map. In the methodology, we utilize the feature of fuzzy cognitive map to construct partition rules and use GA to partition the knowledge base. Finally, an illustrative example is provided, and its results suggest that the method is capable of partitioning fuzzy cognitive map.
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ABSTRACT The enhancement of plant growth by soil fertilization and microbial inoculation involves different mechanisms, particularly by altering the phyllosphere microbiome. This study investigated how nitrogen (N) fertilization, Pseudomonas fluorescens strain R124 inoculation and their combined effects influence the growth of different‐aged Salix matsudana cuttings by modulating N dynamics within the phyllosphere microbiome. Results showed that P. fluorescens inoculation was significantly more effective than N fertilization alone, enhancing biomass, plant nutrient uptake, soil nutrient content and root development by 90.51%, 18.18%, 72.74% and 126.20%, respectively. Crucially, the inoculation notably shifted the beta‐diversity of the phyllosphere microbial community, with K‐strategy fungi enhancing plant N fixation and subsequent plant growth. Cuttings from middle‐aged forests displayed more robust growth than those from young‐aged, associated with a varied impact on phyllosphere fungi, notably increasing the relative abundance of Myriangiales in young (76.37%) and Capnodiales in middle‐aged cuttings (42.37%), which improve phyllosphere stability and plant health. These findings highlight the effectiveness of microbial inoculation over N fertilization in promoting plant growth and provide valuable insights for the sustainable management of willow plantations at different stages of development.
Background Pancreatic cancer is renowned for its elevated incidence and mortality rates on a global scale. The disease burden of pancreatic cancer is anticipated to increase, particularly in Asia, due to its vast and rapidly aging population. Methods Data from the Global Burden of Disease 2019 were analyzed for pancreatic cancer burden across 52 countries in Asia, including the incidence, mortality, and disability-adjusted life years (DALY) for pancreatic cancer, with a focus on risk factors such as high body mass index (BMI), elevated fasting plasma glucose, and smoking. We applied the Estimated Annual Percentage Change, the Age–Period–Cohort model, and decomposition analysis to evaluate incidence trends and effects. Results From 1990 to 2019, both incidence and mortality rates of pancreatic cancer in Asia significantly increased, with an average annual standardized incidence rate change of 1.73%. Males consistently exhibited higher rates than females, with smoking as a key risk factor. Central Asia reported the highest rates, and South Asia the lowest. The incidence rose with age, peaking in those aged 70~74. The disease burden increased in all age groups, particularly in populations aged 55 and above, representing 84.41% of total cases in 2019, up from 79.01% in 1990. Pancreatic cancer ranked the fifth in incidence among six major gastrointestinal tumors but presented a significant growth rate of mortality and DALY. Conclusion With the growing, aging population in Asia, the pancreatic cancer burden is projected to escalate, bringing a significant public health challenge. Hence, comprehensive public health strategies emphasizing early detection, risk modification, and optimized treatment of pancreatic cancer are imperative.
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