Zhengchun Liu

A new approach for improving exchange-spring magnets

It is demonstrated here that an already ideal exchange–spring magnet can be further improved by intermixing the interface. This is counter-intuitive to the general expectation that optimal exchange–spring magnet behavior requires an ideal, atomically coherent soft–hard interface. Epitaxial Sm–Co/Fe thin-film exchange–spring bilayers are thermally processed, by annealing or high-temperature deposition, to induce interdiffusion. With increasing processing temperature, the hysteresis loop becomes more single-phase-like, yet the magnetization remains fully reversible. The interface is characterized via synchrotron x-ray scattering and electron microscopy elemental mapping. The magnetization behavior is modeled by assuming a graded interface where the material parameters vary continuously. The simulations produce demagnetization curves similar to experimental observations.

Quantitative Evaluation of Decision Effects in the Management of Emergency Department Problems

Abstract Due to the complexity and crucial role of an Emergency Department(ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulating the emergency department, the objective of this research is to design a simulator, in order to better understand the bottleneck of ED performance and provide ability to predict such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. This agent-based simulator provides the advantage of knowing the behavior of an ED system from the micro-level interactions among its components. The model was built in collaboration with healthcare staff in a typical ED and has been implemented and verified in a Netlogo modeling environment. Case studies are provided to present some capabilities of the simulator in quantitive analysis ED behavior and supporting decision making. Because of the complexity of the system, high performance computing technology was used to increase the number of studied scenarios and reduce execution time.

A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity

A method to calibrate an emergency department model with incomplete data.Simulation-based optimization for model parameter calibration.An accurate emergency department simulator. To tackle the problem of efficiently managing increasingly complex systems, simulation models have been widely used. This is because simulation is safer, less expensive, and faster than field implementation and experimenting. To achieve high fidelity and credibility in conducting prediction and exploration of the actual system with simulation models, a rigorous calibration and validation procedure should firstly be applied. However, one of the key issues in calibration is the acquisition of valid source information from the target system. The aim of this study is to develop a systematic method to automatically calibrate a general emergency department model with incomplete data. The simulation-based optimization was used to search for the best value of model parameters. Then we present a case study to particularly demonstrate the way to calibrate an agent-based model of an emergency department with real data scarcity. The case study indicates that the proposed method appears to be capable of properly calibrating and validating the simulation model with incomplete data.

Simulating the micro-level behavior of emergency department for macro-level features prediction

Emergency departments are currently facing major pressures due to rising demand caused by population growth, aging and high expectations of service quality. With changes continuing to challenge healthcare systems, developing solutions and formulating policies require a good understanding of the complex and dynamic nature of the relevant systems. However, as a typically complex system, it is hard to grasp the non-linear association between macro-level features and micro-level behavior for a systematic understanding. Instead of describing all the potential causes of this complex issue, in this paper we present a layer-based application framework to discover knowledge of an emergency department system through simulating micro-level behaviors of its components to facilitate a systematic understanding. Finally, case studies are used to demonstrate the potential use of the proposed approach. Results show that the proposed framework can significantly rtheeflect the non-linear association between micro-level behavior and macro-level features.

Model of Collaborative UAV Swarm Toward Coordination and Control Mechanisms Study

In recent years, thanks to the low cost of deploying, maintaining an Unmanned Aerial Vehicle (UAV) system and the possibility to operating them in areas inaccessible or dangerous for human pilots, UAVs have attracted much research attention both in the military field and civilian application. In order to deal with more sophisticated tasks, such as searching survival points, multiple target monitoring and tracking, the application of UAV swarms is forseen. This requires more complex control, communication and coordination mechanisms. However, these mechanisms are difficult to test and analyze under flight dynamic conditions. These multi- UAV scenarios are by their nature well suited to be modeled and simulated as multi-agent systems. The first step of modeling an multi-agent system is to construct the model of agent, namely accurate model to represent its behavior, constraints and uncertainties of UAVs. In this paper we introduce our approach to model an UAV as an agent in terms of multi-agent system principle. Construction of the model to satisfy the need for a simulation environment that researchers can use to evaluate and analyze swarm control mechanisms. Simulations results of a case study is provided to demonstrate one potential use of this approach.

An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments

Abstract Hospital based emergency departments (EDs) are highly integrated service units devoted primarily to handling the needs of patients arriving without prior appointment, and with uncertain conditions. In this context, analysis and management of patient flows play a key role in developing policies and decisions for overall performance improvement. However, patient flows in EDs are considered to be very complex because of the different pathways patients may take and the inherent uncertainty and variability of healthcare processes. The agent-based model provides a flexible platform for studying ED operations, as it predicts the system-level behavior from individual level interactions. In this way, policies such as staffing can be changed and the effect on system performance, such as waiting times and throughput, can be quantified. The overall goal of this study is to develop tools to better understand the complexity, evaluate policy and improve efficiencies of ED units. The main contribution of this paper includes: an agent-based model of ED, a flexible atomic data monitoring layer for agent state tracing, and a master/worker based framework for efficiently executing the model and analyzing simulation data. The presented model has been calibrated to imitate a real ED in Spain, the simulation results have proven the feasibility of using agent-based model to study ED system.

Cross-geography scientific data transferring trends and behavior

Wide area data transfers play an important role in many science applications but rely on expensive infrastructure that often delivers disappointing performance in practice. In response, we present a systematic examination of a large set of data transfer log data to characterize transfer characteristics, including the nature of the datasets transferred, achieved throughput, user behavior, and resource usage. This analysis yields new insights that can help design better data transfer tools, optimize networking and edge resources used for transfers, and improve the performance and experience for end users. Our analysis shows that (i) most of the datasets as well as individual files transferred are very small; (ii) data corruption is not negligible for large data transfers; and (iii) the data transfer nodes utilization is low. Insights gained from our analysis suggest directions for further analysis.

Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues

Scientific computing systems are becoming increasingly complex and indeed are close to reaching a critical limit in manageability when using current human-in-the-loop techniques. In order to address this problem, autonomic, goal-driven management actions based on machine learning must be applied end to end across the scientific computing landscape. Even though researchers proposed architectures and design choices for autonomic computing systems more than a decade ago, practical realization of such systems has been limited, especially in scientific computing environments. Growing interest and recent developments in machine learning have spurred proposals to apply machine learning for goal-based optimization of computing systems in an autonomous fashion. We review recent work that uses machine learning algorithms to improve computer system performance, identify gaps and open issues. We propose a hierarchical architecture that builds on the earlier proposals for autonomic computing systems to realize an autonomous science infrastructure.

Toward a smart data transfer node

Abstract Scientific computing systems are becoming significantly more complex, with distributed teams and complex workflows spanning resources from telescopes and light sources to fast networks and Internet of Things sensor systems. In such settings, no single, centralized administrative team and software stack can coordinate and manage all resources used by a single application. Indeed, we have reached a critical limit in manageability using current human-in-the-loop techniques. We therefore argue that resources must begin to respond automatically, adapting and tuning their behavior in response to observed properties of scientific workflows. Over time, machine learning methods can be used to identify effective strategies for autonomic, goal-driven management behaviors that can be applied end-to-end across the scientific computing landscape. Using the data transfer nodes that are widely deployed in modern research networks as an example, we explore the architecture, methods, and algorithms needed for a smart data transfer node to support future scientific computing systems that self-tune and self-manage.

Support managing population aging stress of emergency departments in a computational way

Abstract Old people usually have more complex health problems and use healthcare services more frequently than young people. It is obvious that the increasing old people both in number and proportion will challenge the emergency departments (ED). This paper firstly presents a way to quantitatively predict and explain this challenge by using simulation techniques. Then, we outline the capability of simulation for decision support to overcome this challenge. Specifically, we use simulation to predict and explain the impact of population aging over an ED. In which, a precise ED simulator which has been validated for a public hospital ED will be used to predict the behavior of an ED under population aging in the next 15 years. Our prediction shows that the stress of population aging to EDs can no longer be ignored and ED upgrade must be carefully planned. Based on this prediction, the cost and benefits of several upgrade proposals are evaluated.