THE USE OF A FUZZY LOGIC SET-BASED DESIGN TOOL TO EVALUATE VARYING COMPLEXITIES OF LATE-STAGE DESIGN CHANGES

Advanced design methods, such as set-based design (SBD), can provide a structured approach to evaluating the design space in order to make accurate and informed decisions towards reaching a globally optimal design. The set-based communications required to appropriately implement SBD are counter-intuitive to the point-based communications of a typical design process. The use of a hybrid agent fuzzy logic design tool can help to facilitate the SBD process by ensuring the use of set-based communication of design variables. The design tool uses automation of certain aspects such as data collection and analysis while still allowing for input from human designers. One important advantage of using SBD is the ability to delay decisions until later in the design process when more information is known. This paper focuses on the robustness of the SBD process and its ability to handle late-stage design changes of varying complexity. Multiple SBD experiments instituting design changes of varying magnitude late in the design process were conducted using a hybrid agent fuzzy logic SBD tool. A simplified planing craft design was utilized for the experiments. Conclusions regarding the robustness of the SBD process under late-stage design changes were determined and outlined using information gathered by the SBD tool.

Dynamics of fibril collagen remodeling by tumor cells using individual cell-based mathematical modeling

Many solid tumors are characterized by dense extracellular matrix (ECM) composed of various ECM fibril proteins that provide structural support and biological context for the residing cells. The growing tumor cell colonies are capable of remodeling the ECM structure in tumor immediate vicinity to form specific microenvironmental niches. The changes in fibril patterns of the collagen (one of the ECM proteins) surrounding the tumor can be visualized experimentally using both histology and fluorescent imaging. In particular, three diverse tumor associated collagen signatures (TACS) were identified and related to tumor behavior, such as benign growth or invasion. Here we will use an off-lattice hybrid agent-based model (MultiCell-LF) to identify the rules of cell-ECM interactions that guide the development of various patterns of alignment of the ECM fibrils.

IMPROVED PARTICLE SWAM OPTIMIZATION FOR CROWD SIMULATION USING HYBRID AGENT REINFORCEMENT LEARNING ALGORITHM

In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, the models simulating individual cells’ behavior are needed. Here, we provided a link between the average data value recorded for radiologic image voxels and the cellular and vascular architecture of the tissue that fills these voxels. Using hybrid agent-based modeling, we generated a set of tissue morphologies capable of reproducing tissue oxygenation levels observed in radiologic images. We applied this approach to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies showed that intravascular changes in oxygen supply can reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, large magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in biologically feasible manner. Additionally, we developed a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications this approach can be used to generate a set of tissues representative for radiology images and to simulate tumor response to various anti-cancer treatments on the tissue-scale level.Authors SummaryLow levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells which are resistant to chemo-, radio- and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuation, however they operate on a macro-scale voxel level. To examine the causes of rapid oxygen fluctuations on the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that could be responsible for these cyclic effects in tissue oxygenation: variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This will also provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average voxel values. In the future applications, this approach can be used to generate a set of tissues representative of radiology images and to simulate tumor response to various anticancer treatments on the cell-scale level.

Reducing Handoffs and Improving Patient Flow in the ED

Over 145 million people visit US Emergency Departments annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated healthcare settings. In particular, handoffs, the transfer of patient care from one physician to another during shift transition are a common source of errors resulting from workflow interruptions and high cognitive workload. This research focuses on developing a hybrid agent-based discrete event simulation model to identify physician shifts that minimize handoffs without affecting other performance metrics. By providing overlapping shift schedules as well as implementing policies that restrict physicians from signing up a new patient during the last hour of the shift, we observed that handoffs and patient time in the emergency department could be reduced by as much as 42% and 17%, respectively.