Troubled-Cell Indicator Based on Mean Value Property for Hybrid WENO Schemes

In this paper, we introduce a new type of troubled-cell indicator to improve hybrid weighted essentially non-oscillatory (WENO) schemes for solving the hyperbolic conservation laws. The hybrid WENO schemes selectively adopt the high-order linear upwind scheme or the WENO scheme to avoid the local characteristic decompositions and calculations of the nonlinear weights in smooth regions. Therefore, they can reduce computational cost while maintaining non-oscillatory properties in non-smooth regions. Reliable troubled-cell indicators are essential for efficient hybrid WENO methods. Most of troubled-cell indicators require proper parameters to detect discontinuities precisely, but it is very difficult to determine the parameters automatically. We develop a new troubled-cell indicator derived from the mean value theorem that does not require any variable parameters. Additionally, we investigate the characteristics of indicator variable; one of the conserved properties or the entropy is considered as indicator variable. Detailed numerical tests for 1D and 2D Euler equations are conducted to demonstrate the performance of the proposed indicator. The results with the proposed troubled-cell indicator are in good agreement with pure WENO schemes. Also the new indicator has advantages in the computational cost compared with the other indicators.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Integration from proteins to organs: the IUPS Physiome Project

The IUPS Physiome Project is an internationally collaborative open source project intended to provide a public domain framework for computational physiology, including the development of modeling standards, computational tools and web-accessible databases of models of structure and function at all spatial scales and across all organ systems. Here, we illustrate the application of this multi-scale modeling approach to three organ systems: the heart, the lungs and the musculo-skeletal system, and in each case we show how the organ level models incorporate tissue and cell-level physiology. Although the computational physiology framework presented here does not yet incorporate models of ageing processes, the model-based approach is certainly capable of describing ageing and disease-related processes both via parameter changes within the models of normal physiological processes and via models of additional processes added to the framework.     

金银花颗粒在旋风分离器中的流场数值模拟分析

目的 针对金银花颗粒在旋风分离器中的运动行为过程进行研究,考察不同设计参数和操作工艺条件下,颗粒在设备内部的运动过程变化规律。方法 基于仿真模拟技术,考察受不同设备长径比、入口进气雷诺数、颗粒质量流量3个因素影响的金银花颗粒在分离器内的运动规律,并以金银花颗粒得粉率为验证指标参数,对模型可靠性进行了分析。结果 通过仿真模拟得到了各个不同工艺条件下的金银花颗粒运动轨迹分布,计算出了对应设备的颗粒得粉率。提出金银花颗粒分离的最佳工艺建议为设备长径比5∶1,入口进气雷诺数3.23×10⁴,颗粒质量流量80.1 mg/s,得到的金银花颗粒得粉率为96.30%。结论 为旋风分离器内金银花颗粒的有关研究提供指导,同时改善旋风分离器在控制和设计上的不足。     

Prosopo-Thoracopagus Conjoined Twins and Other Cephalopagus-Thoracopagus Intermediates: Case Report and Review of the Literature

Prosopo-thoracopagus twins are united from the face down to the umbilicus, none with union in the brain but all with visceral anomalies intermediate between those of cephalopagus and thoracopagus. In a review of over 1200 cases of conjoined twins reported during the past 100 years, there were 14 that illustrate the continuum between cephalopagus and thoracopagus, including three that were united only from the cervical region to the umbilicus. Classic cephalopagus twins are joined from the top of the head to the umbilicus, sharing a single foregut as well as two relatively normal hearts, the “posterior” one often diminished. Typical thoracopagus, however, are conjoined only from the upper thorax to the umbilicus, each twin with a normal foregut but both sharing a single complex multiventricular heart. The intermediate cases shared either a single very abnormal heart or two hearts united by double aortic arches, and all except one had a single foregut. It is these cases intermediate between cephalopagus and thoracopagus which are the subject of this report. Received September 11, 1996; accepted December 16, 1996     

Legal and ethical issues in telemedicine and robotics.

Modern medical concerns with telemedicine and robotics practiced across national or other jurisdictional boundaries engage the historical, complex area of law called conflict of laws. An initial concern is whether a practitioner licensed only in jurisdiction A who treats a patient in jurisdiction B violates B's laws. Further concerns are whether a practitioner in A who violates a contract or treats a patient in B negligently incurs liability in B, A, or both, and, if treatment lawful in A is unlawful in B, whether the practitioner commits a crime. Judicial procedures are set by courts in which proceedings are initiated, but courts may decline jurisdiction due to inconvenience to parties. If courts accept jurisdiction, they may apply their own substantive legal rules, but may find that the rules of a conflicting jurisdiction should apply. Cross-border care should not change usual medical ethics, for instance on confidentiality, but may mitigate or aggravate migration of specialists.     

我国药品不良反应监测工作对开展医疗器械不良事件监测的启示

为探讨我国医疗器械不良事件监测工作开展模式,分析比较影响药品不良反应监测与医疗器械不良事件监测工作发展的关键因素。根据医疗器械不良事件监测工作的特点,从相关法律法规制订、组织机构与人员配备、技术体系建设等方面提出了医疗器械不良事件监测的工作思路。     

英国伦理委员会的现状与分析

伦理委员会的主要职责是保护药物临床试验受试者的权益、安全和健康.现对英国伦理委员会的法律、法规、申请及审评程序等进行归纳分析,旨在对规范我国伦理委员会的运作具有参考借鉴作用.     

基于中风病各期用药规律的证候动态变化规律分析

目的 本研究通过收集近十年国内外医学期刊中的中风病医案,挖掘中风病各期的用药规律.方法 收集中风病医案,建立数据库,采用频数分析的方法统计中风病各期用药特点,分析证候动态变化规律.结果 共收集医案292例,出现药物249味,用药总频数为3 692次,用药频率在25次以上的药物共有40味,总的用药频率达到69.47％,292例医案中能够具体分期的医案共有279例,用药总频率3 236次,其中,急性期169例,出现药物215味,恢复期68例,出现药物203味,后遗症期42例,出现药物185味.结论 中风病存在证候动态演化规律,痰瘀两种证候几乎贯穿整个病程,始终占有很高的比例,成为中风发病的主线.