Survey-based research methodology is commonly used in various disciplines, ranging from social sciences to healthcare. However, it is difficult to provide real-world experience of survey sampling methodologies to students and novice researchers. In this paper, the researchers proposed the development of a virtual learning environment based on agent-based modeling to help learn about different aspects and challenges of survey-based research.
Hybrid Simulation modeling has been grabbing researchers’ attention lately. This article reports on a preliminary review of the literature and investigates the prevalence and utilization of Hybrid Simulation in healthcare. Findings show that combining Discrete-Event Simulation and System Dynamics is the most common approach to developing HS models in healthcare. However, the popularity of combining Agent-Based Simulation with others is on the rise.
In many curricula and degree programs, simulation courses are not required, but these tools and techniques could be beneficial to students preparing for a variety of careers. The current paper describes two examples of simulation education embedded into broader course topics from a development perspective. The examples, from courses generally described in this paper as Cloud Computing and Big Data, offer a recommended approach for exposing students to practical uses of simulation. In the first example, we use simulation techniques to develop a web service emulation response database within a cloud computing environment for software testing. In the second example, simulation techniques provide an approach to generate data sets for learning data analytics techniques.
The capability of modeling real-world system operations has turned simulation into an indispensable problem-solving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose.
The paper first discusses the importance of discrete event simulation (DES) in the business school curriculum. It next notes how small Macintosh lap tops have become increasingly popular among business students. We next discuss what DES software is available on the Mac, first directly, then indirectly by running DES software for Windows in some way on the Mac. Noting that there is not much simple DES software on the Mac, but yet a great demand for such software from many business students, we turn to the transfer of one pedagogical software system, aGPSS, from Windows to the Mac. We here first give a brief historic background of aGPSS. Next we discuss some of the problems encountered when transferring aGPSS to the Mac. The paper ends with a brief discussion of some pedagogical aspects of using aGPSS on the Mac in the teaching of basic management science.
With the advances in the information and computing technologies, the ways the manufacturing enterprise systems are being managed are changing. More integration and adoption of the system perspective push further towards a more flattened enterprise. This, in addition to the varying levels of aggregation and details and the presence of the continuous and discrete types of behavior, created serious challenges for the use of the existing simulation tools for simulating the modern manufacturing enterprise system. The commonly used discrete event simulation (DES) techniques face difficulties in modeling such integrated systems due to increased model complexity, the lack of data at the aggregate management levels, and the unsuitability of DES to model the financial sectors of the enterprise. System dynamics (SD) has been effective in providing the needs of top management levels but unsuccessful in offering the needed granularity at the detailed operational levels of the manufacturing system. On the other hand the existing hybrid continuous-discrete tools are based on certain assumptions that do not fit the requirements of the common decision making situations in the business systems.
The stochastic Argus Invasive Species Spread Model (AISSM) is constructed using an Agent-Based Modeling (ABM) approach with cellular automata (CA) to account for spatial relationships and changes in those relationships over time. The model was constructed to support a wide range of geographical locations; however, this paper focuses on its application in the state of California. A timeseries of daily historical weather observations on a 6- kilometer grid was obtained for six weather variables important to insect and disease development. Weather conditions were then simulated using the K- nearest neighbor (K-nn) regional weather generator. The weather simulations were summarized into a monthly time-step and coupled with satellite land cover imagery to identify a habitat quality for each simulated month. This information was combined with the introduction of invasive species in the AnyLogic™ modeling environment. The spread of invasive species is driven by the habitat quality layer, which regulates its dispersal rate.
Research in biogerontology has largely focused on the complex relationship between mitochondrial dysfunction and biological aging. In particular, the mitochondrial free radical theory of aging (MFRTA) has been well accepted. However, this theory has been challenged by recent studies showing minimal increases in reactive oxygen species (ROS) as not entirely deleterious in nature, and even beneficial under the appropriate cellular circumstances. To assess these significant and nonintuitive observations in the context of a functional system, we have taken an in silico approach to expand the focus of the MFRTA by including other key mitochondrial stress response pathways, as they have been observed in the nematode Caenorhabditis elegans. These include the mitochondrial unfolded protein response (UPRmt), mitochondrial biogenesis and autophagy dynamics, the relevant DAF-16 and SKN-1 axes, and NAD+-dependent deacetylase activities. To integrate these pathways, we have developed a multilevel hybrid-modeling paradigm, containing agent-based elements among stochastic system-dynamics environments of logically derived ordinary differential equations, to simulate aging mitochondrial phenotypes within a population of energetically demanding cells.
Due to their favorable electrical and optical properties, quantum dots (QDs) nanoparticles have found numerous applications including nanomedicine. However, there have been concerns about their potential environmental impacts. The objective of this study is to develop an agent-based simulation model for predicting the diffusion dynamics and concentration of toxic materials released from QDs. Reaction kinetics is used to model the stability of surface capping agent particularly due to oxidation process.
Sea lice, Lepeophtheirus salmonis, are ectoparasites of farmed and wild salmonids. Infestations can result in significant morbidity and mortality of hosts in addition to being costly to control. Integrated PEST management programmes have been developed to manage infestations, and in some salmon farming areas, these programmes include the use of wrasse. To explore at what densities wrasse should be stocked in order to meet specific control targets, an individual-based model was built to simulate sea lice infestation patterns on a representative salmonid host. It was found that the wrasse can effectively control sea lice, and the densities of wrasse needed for effective control depend upon the source of the infestation and the targeted level of control.