WXX2000野战医疗箱组下层简易固定装置的研制

目的：以医疗箱ZT-4-1为例研制WXX2000野战医疗箱组下层简易固定装置。方法：根据医疗箱ZT-4-1的装箱指导原则,量取医疗箱下层空间、玻璃安瓿等相关尺寸,进行设计布局。选取适当工具与材料,分步骤制作外围固定木箱和泡沫填充物。结果：下层简易固定装置使放置物品固定可靠,运输安全。结论：下层简易固定装置固定效果肯定,值得推广。     

新型组合式功能配套医疗箱的研制

一、引言医疗箱是各级医疗救治单位平战时展开工作的基本装备。以前,我军大多数医疗救治单位使用优质红松木医疗箱。然而,国内红松木来源日趋紧张,影响了医疗箱的成批生产和装备。外军医疗箱的主要原材料多取之于民,常用主要材料有木材、薄钢板、铝合金板、增强     

新型海军医疗箱组的研制

1概述为提高海上救治能力,方便医务人员携带现行药材供应标准规定的各类药材,以及必要的卫生军需用品,针对海上特殊环境和展开救治工作的需要,研制了新型海军医疗箱组。它是在继承传统医疗箱沿用多年的形式的基础上,结合现代工艺条件研制而成的。既保留了传统医疗箱取用方便、内容物暴露充分、可快速方便展开成医疗橱和医疗台使用的优点,又克服了传统医疗箱使用的木材、纤维板、玻璃钢、薄钢板和铝合金材料存在的变形、开裂、生虫和抗冲击能力弱等方面的问题。它适合在舰船、码头、岛屿等地域使用。另外,考虑到部队的实际应用,还配套设计了…     

野战医疗箱组内装物缓冲固定

论述了野战医疗箱组的装箱原则、工艺及内装物缓冲材料设计及固定方法，为野战部队规范装箱提供参考。     

玻璃钢医疗箱

为海上机动医疗队研制的医疗箱,采用玻璃钢材料作箱体,具有重量轻,比强度高,耐腐蚀。是一套组合式、功能配套的医疗箱。     

新型医疗箱材料应用研究

医疗箱是医疗救治单位开展医疗救治工作的基本装备,然而生产医疗箱用的红松木,国内供需矛盾日益突出,来源日趋减少。因此研究开发红松木的代用材料是非常紧迫、重要的。本文的目的,就是讨论用新型纤维板代替红松木生产医疗箱的可能性。     

研制护理器材补给箱组的必要性和基本设计思路

20世纪90年代以来,“模块化”技术开始兴起,医疗箱由于其装备统一、便于供应、展收迅速、使用方便等优点,在外军得到广泛的应用。“九五”期间,我军在总结医疗箱研制经验的基础上,积极吸取外军医疗箱优点,结合我国和我军实际,研制成功我军新一代医疗箱,为护理器材补给箱组的研制提供了一种优质箱体。目前,开展护理器材补给箱组研制的条件已经成熟。护理器材补给箱组的研制依据,是总后卫生部颁发的《师、旅、团、营救护所野战卫生装备整装建设指南》、《军队医院机动卫勤分队建设暂行规定》、《战伤救治规则》和1999年8月下发的《军队师以下…     

野战医疗箱旋转模塑工艺条件下着色研究

目的：研究新型野战医疗箱旋转模塑工艺条件下的着色。方法：通过配色试验。旋转模塑试验，力学试验和仪器分析考查了医疗箱在高温条件下制造时，不同品种着色剂和不同着色工艺条件对制品的着色效果和理化性能的影响。结果：制品的色泽达到相关国家军用标准，且着色使制品性能略有提高，结论：所选着色剂配方和工艺条件适合新型野战医疗箱的着色。     

医疗箱在军队卫生装备现代化建设中的地位和作用

历史上曾经作为卫勤保障的重要物质手段的医疗箱,在今后卫生装备现代化的进程中,特别是一些高技术的发展已在医疗器械上采用的今天,它还有没有进一步研究的必要?本文作了分析和论述,并对我军医疗箱的研制和发展提出了建议。     

热压成型聚乙烯碳酸钙低发泡板的研制

一、引言随着我国基本建设事业的发展,需用木材量迅速地增加,而我国六五计划中木材产量和上调量只分别增长0.5%,同时我军现行医疗箱仍以木质为主,鉴于上述原因,医疗箱的生产和装备受到一定影响。因此,研制廉价的代木材料制做医疗箱已成当前的重要课题。国外廉价的代木材料—钙塑料,最早由日本狮子油脂公司于1972年试制成功,商品名     

Selecting a Dynamic Simulation Modeling Method for Health Care Delivery Research—Part 2: Report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force

In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods—system dynamics, discrete-event simulation, and agent-based modeling. We review criteria for selecting the most suitable method depending on 1) the purpose—type of problem and research questions being investigated, 2) the object—scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic simulation modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing “volume, velocity and variety” and availability of “big data” to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.     

基于CAS的固体废物管理系统演化与调控模型

引入复杂适应系统理论和基于Agent的建模方法探索固体废物管理系统的动态演化进程。给出了基于Agent的固体废物管理系统演化仿真模型，该模型能正确描述废物管理系统的演化与发展趋势，对模型中涉及的Agent及其交互关系进行了设计。     

Applying Dynamic Simulation Modeling Methods in Health Care Delivery Research—The SIMULATE Checklist: Report of the ISPOR Simulation Modeling Emerging Good Practices Task Force

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.     

三维动态仿真技术在DNA合成仪研制中的应用

目的:为了提高多通道高通量DNA合成仪的设计开发效率,将三维动态仿真技术引入到研制过程,分析其应用方法和应用价值。方法:以DNA合成仪的关键部件合成舱为例,分析了三维计算机辅助设计流程,在三维实体建模基础上研究了动态优化分析和动态装配仿真方法,探讨三维动态仿真技术在DNA合成仪中的应用方法,展望动态仿真技术的潜在应用价值。结果:实现了DNA合成仪合成舱的动态优化分析和动态装配仿真,完善了三维计算机辅助设计技术的设计流程,为DNA合成仪等仪器设备的动态仿真提供参考方法。结论:应用动态仿真技术可提高DNA合成仪的研制效率,对设计方案制定及装配优化、设计优化、制造优化、成本优化有重要意义。     

心脏建模研究现状及发展趋势

心脏建模对于心脏疾病的诊断和治疗具有重要意义。随着生物医学技术、材料及计算机科学的发展．心脏建模仿真在心脏数据的获取、建模方法的研究以及临床应用方面提出了新的要求。该文从几何／解剖学、电生理学和机械动力学角度对心脏模型进行了介绍．并综合分析了心脏建模中存在的问题及今后的发展趋势。     

Modeling patient service centers with simulation and system dynamics

We report on the use of simulation modeling for redesigning phlebotomy and specimen collection centers (or patient service centers) at a medical diagnostic laboratory. Research was performed in an effort to improve patient service, in particular to reduce average waiting times as well as their variability. Discrete-event simulation modeling provided valuable input into new facility design decisions and showed the efficacy of pooling sources of variation, particularly patient demand and service times. Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of service centers eventually resulted in unanticipated performance problems. We show how a system dynamics model might have helped predict these implementation problems and suggest some ways to improve results.     

System dynamics modeling for public health: background and opportunities

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.     

Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling

ObjectivesThe objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods.MethodsGiven the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective.ResultsSome of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models.ConclusionThe economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.     

基于多Agent的医院复杂系统建模与仿真

目的：为提高医院的组织和管理水平，优化工作流程，提高医疗质量，降低医疗费用。方法：提出采用多Agent技术对医院进行复杂系统建模与仿真的方法，简单介绍了复杂系统理论及其建模思想．分析了医院的复杂系统特征及其业务流程模型。结果：概述了多Agent建模技术及其原则，最后给出了医院系统多Agent建模与仿真的步骤．介绍了Swarm仿真平台和Agent模型的细节设计。结论：指出了对医院系统的仿真研究需要用复杂系统理论的研究方法．即用基于多Agent的建模仿真方法来开展研究．     

Dynamic prediction in functional concurrent regression with an application to child growth

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject‐specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject‐specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques.