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We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.

How do you get students to engage in a historical episode or era? How do you bring the immediacy and contingency of history to life? Michael A. Barnhart shares the secret to his award-winning success in the classroom with Can You Beat Churchill?, which encourages role-playing for immersive teaching and learning. Combating the declining enrollment in humanities classes, this innovative approach reminds us how critical learning skills are transmitted to students: by reactivating their curiosity and problem-solving abilities. Barnhart provides advice and procedures, both for the use of off-the-shelf commercial simulations and for the instructor who wishes to custom design a simulation from scratch. These reenactments allow students to step into the past, requiring them to think and act in ways historical figures might have. Students must make crucial or dramatic decisions, though these decisions need not align with the historical record. In doing so, they learn, through action and strategic consideration, the impact of real individuals and groups of people on the course of history. There is a quiet revolution underway in how history is taught to undergraduates. Can You Beat Churchill? hopes to make it a noisy one.

Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment.\" It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings. · Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems· Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems· Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling* Ample end-of-chapter problems and full Solutions Manual* Includes CD with sample ARENA modeling programs

This article discusses the problem of finding the optimal design using SolidWorks simulation. A number of solutions to the problem using simulation are proposed. Flexible and rigid computational models for solving the problem are described; the disadvantages and advantages of the plow’s body are indicated when calculating automated SolidWorks modeling programs.

This simulation is designed for critical care transport providers but can be easily adapted for the inpatient setting. It is applicable to an interdisciplinary team including nurses, respiratory therapists, medical students, emergency medicine residents, and emergency medicine attendings. Cardiogenic shock carries an incredibly high burden of morbidity and mortality. Acute myocardial infarction accounts for 81% of cardiogenic shock patients and is a common indication for transfer to a tertiary care facility.1 Hypotension due to cardiogenic shock is often refractory to volume resuscitation and often requires pharmacologic intervention. Additionally, the resultant end organ dysfunction frequently requires advanced ventilatory support.1-6 This simulation aims to educate critical care transport providers on the best practices for management of the cardiogenic shock patients requiring resuscitation and intubation prior to transport. By the end of this simulation session, learners will be able to: 1) recognize the need for intubation in an unstable patient in cardiogenic shock who requires transport, 2) appropriately titrate bi-Level non-invasive ventilatory support (BiPAP) to optimize oxygenation and ventilation in preparation for intubation, 3) choose appropriate vasoactive medications to support the hemodynamics of a patient in cardiogenic shock, 4) perform rapid sequence intubation using appropriate induction and paralytic agents and dosing for a patient in cardiogenic shock, 5) choose appropriate initial lung-protective ventilator settings, and 6) implement an adequate analgesia and sedation plan for transport of an intubated patient in cardiogenic shock. This session was conducted using high-fidelity simulation, allowing learners to manage a patient in cardiogenic shock and respiratory distress requiring intubation. Each session was followed by a debriefing and discussion. Qualitative feedback provided by participants during the discussion session was utilized to adjust the simulation between each session. In addition, participants were surveyed using a five-point Likert scale (strongly disagree to strongly agree) on if the simulation met their professional and educational needs, its efficacy and appropriateness for Level, and whether it would change future practice. A total of 36 learners, including 20 physicians and 16 nurses, participated in the simulation over a total of nine sessions. Twenty out of the thirty-six participants completed the survey (both RNs and MDs) and 100% responded \"strongly agree\" to all four prompts (top response out of a five Likert scale). Feedback provided by participants was used after each session to adjust the simulation. Changes implemented included the addition of a nurse confederate, greater emphasis on management and titration of non-invasive ventilation for optimal preoxygenation, and initiation of post intubation sedation and analgesia. Cardiogenic shock is a common cause of mortality, often requires transport, and is particularly challenging to manage. This simulation was overall effective at educating learners on the resuscitation of cardiogenic shock, including appropriate use of vasopressors and ventilatory support. Cardiogenic shock, hypoxic respiratory failure, vasopressor management, airway management, intubation, non-invasive positive pressure ventilation management, ventilatory management, emergency medicine, critical care transport medicine.

The target audience for the key learning objectives of this Left-Ventricular Assist Device (LVAD) simulation are emergency medicine residents. Other team members such as attendings, nurses, pharmacists, and technicians could potentially be integrated. Left ventricular assist devices (LVADs) are common bridge therapy for patients suffering from severe heart failure to cardiac transplant or destination therapy for non-transplant candidates.1 Emergency medicine physicians must be prepared for a variety of device complications that may result in an acute care presentation, such as drive-line infections, suction events, arrhythmias, and cardiac arrest with device failure. In a review investigating ED presentations for patients with LVADs, device-specific complaints were among the fewest, with the most common presentations involving bleeding, infection, and arrythmias.2 The present case involves a suction event that is precipitated by a gastrointestinal (GI) bleed, which has an incidence of 30% for LVAD patients.3 This case was developed for a technology failure-themed resident simulation competition during the Western Society for Academic Emergency Medicine (SEAM) conference held on April 1, 2022. By the end of this simulation session, learners will be able to: 1) assess the hemodynamics of an LVAD patient by using a Doppler to determine mean arterial pressure, 2) Manage an arrhythmia in an LVAD patient with a suction event by addressing preload, 3) Identify and treat the source of hypovolemia (a massive lower gastrointestinal hemorrhage), 4) Perform clear closed-loop communication with other team members. This high-fidelity simulation case aims to train emergency medicine residents on recognition and management of an LVAD suction event, a rare but serious presentation encountered in the emergency department. This simulation can be successfully implemented either , in an immersive simulation center, or off-site. This case could be represented by lower fidelity mannequins without the capabilities to provide learner tactile feedback of hemodynamics or airway, with a separate monitor device such as SimMon to display vital signs and digital media to demonstrate needed clinical images. The audio file of the low-flow alarm can be accessed and played by any device with internet access. The simulation benefits from embedded simulation participants to act as the bedside nurse and wife to provide history. This simulation included debriefing focused on a critical action checklist. A working group of two simulation-trained faculty, a simulation fellow, and three senior emergency medicine residents chose and developed the simulation case. Two simulation-trained faculty implemented the pilot case series to gather feedback on performance against the critical action checklist. One simulation-trained faculty then facilitated two additional sessions, again evaluating performance on the critical actions as well as content of the debrief discussion. That data was used to iteratively edit the presentation and dynamics of the case in preparation for the SIMposium case competition. During March 2022, in a three-case pilot series, a total of 15 residents (five EM PGY4, four EM PGY3, five EM PGY2, one off-service PGY1) and two medical students (MS3) participated in the simulation case. Participant reactions were overwhelmingly positive, particularly from senior residents. The final version of the SIMposium case was held for a team of four emergency medicine residents from an alternate institution, all critical actions were met, and a discussion point arose regarding the reversal of anticoagulation in LVAD patients with acute GI bleed. Overall, this simulation was well received, effective, and easy to implement and translate to immersive, , or offsite locations for the training of emergency medicine residents on the management of a high acuity, low-frequency event of LVAD device complication. Each debrief stimulated an excellent discussion regarding the general management of LVAD patients regarding initial assessment, arrhythmia, and distinguishing pathologies from device alarms. Our main takeaway from this simulation was the power of a case involving a critical and high acuity patient with LVAD which stimulated residents to engage in more robust discussions during debriefing, leading to broader clinical learning. simulation, simulation competition, LVAD, left ventricular assist device.

People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.

The bootstrap is a versatile technique that relies on data-driven simulations to make statistical inferences. When combined with robust estimators, the bootstrap can afford much more powerful and flexible inferences than is possible with standard approaches such as T-tests on means. In this tutorial, we use detailed illustrations of bootstrap simulations to give readers an intuition of what the bootstrap does and how it can be applied to solve many practical problems, such as building confidence intervals for many aspects of the data. In particular, we illustrate how to build confidence intervals for measures of location, including measures of central tendency, in the one-sample case, for two independent and two dependent groups. We also demonstrate how to compare correlation coefficients using the bootstrap and to perform simulations to determine if the bootstrap is fit for purpose for a particular application. Our approach is to suggest and motivate what could be done in a situation, with an understanding that various options are valid, though they may help answer different questions about a dataset. The tutorial also addresses two widespread misconceptions about the bootstrap: that it makes no assumptions about the data, and that it leads to robust inferences on its own. The tutorial focuses on detailed graphical descriptions, with data and code available online to reproduce the figures and analyses in the article (OSF: https://osf.io/8b4t5/; GitHub: https://github.com/GRousselet/bootstrap).

Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.