Realistic Simulation Model Research Articles

Bacteria as Cloud Condensation Nuclei (CCN) in the Atmosphere

Bacteria activation and cloud condensation nuclei (CCN) formation have been studied in the atmosphere using the classical theory of heterogeneous nucleation. Simulations were performed for the binary system of sulfuric acid/water using laboratory-determined contact angles. Realistic model simulations were performed at different atmospheric heights for a set of 140 different bacteria. Model simulations showed that bacteria activation is a potentially favorable process in the atmosphere which may be enhanced at lower temperatures. CCN formation from bacteria nuclei is dependent on ambient atmospheric conditions (temperature, relative humidity), bacteria size, and sulfuric acid concentration. Furthermore, a critical parameter for the determination of bacteria activation is the value of the intermolecular potential between the bacteria’s surface and the critical cluster formed at their surface. In the classical nucleation theory, this is parameterized with the contact angle between substrate and critical cluster. Therefore, the dataset of laboratory values for the contact angle of water on different bacteria substrates needs to be enriched for realistic simulations of bacteria activation in the atmosphere.

Feasibility Study of Hydration Monitoring Using Microwaves–Part 1: A Model of Microwave Property Changes With Dehydration

Dehydration is a prevalent condition that can have profound health consequences. If detected early, it can often be treated by oral fluid replacement (drinking or eating). However, existing assessment techniques lack the accuracy and/or convenience for ongoing monitoring, motivating the development of novel methods. We propose using low-power microwave measurements (2–12 GHz) at the extremities to monitor human hydration, relying on the strong relationship between dielectric properties of tissues and water content. Electromagnetic simulations of realistic models are used to explore changes in microwave signals transmitted through the forearm to changes in hydration. Tissue properties are adjusted according to expected changes in water content, and average dielectric properties are estimated from signals transmitted through the arm by ultrawideband antennas placed in contact with the tissues. A causal relationship between weight loss due to water loss and dielectric permittivity is found in human simulation models. Little relationship is found with conductivity. The theoretical groundwork for hydration assessment with microwaves is developed through a model which relates changes in total body water content with changes in microwave properties at the extremities. This model could be useful for monitoring hydration in at-risk populations such as older adults and athletes.

MetaSPARSim: a 16S rRNA gene sequencing count data simulator

BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Enhanced GEANT4 Monte Carlo simulations of the space radiation effects on the International Space Station and Apollo missions using high-performance computing environment

A significant challenge to current and future manned and/or unmanned space missions is due to deep space radiation. An improvement in more realistic (more accurate) simulation models in predicting the effects of radiation within the spacecraft is required, especially to better predict dose to astronauts, energy deposition within sensitive electronics, and effectiveness of radiation shielding for long-term space missions. The International Space Station provides an invaluable resource for long-term measurements of the radiation environment in Low Earth Orbit (LEO); however, the only manned missions with dosimetry data available beyond LEO are the Apollo missions. Thus the physiological effects and dosimetry for deep space missions are not well understood in planning extended missions. GEANT4, a Monte Carlo method, represents a powerful physics simulation tool to assess the effects of radiation transport through spacecraft. The newest version of GEANT4 supports multithreading and Message Passing Interface (MPI) allowing for much faster distributive processing of simulations, using a high-performance computing environment. This paper introduces a new simulation model and application using GEANT4 that greatly reduces computational time to hours instead of weeks without any post simulation processing based on high-performance computing. This paper also introduces a new set of GEANT4 computational detectors for calculating dose distribution, besides the historically used International Commission of Radiation Units simulation spheres. The computational detectors include a thermoluminescent detector, tissue equivalent proportional counter, and human phantom, along with additional new scorers to calculate dose equivalence based on the International Commission of Radiation Protection standards. This study presents GEANT4 simulations of the dose deposition for the International Space Station and the Apollo 11 and 14 missions, which replicate well the dose measurements during these missions. The simulations of both Apollo missions show consistent doses from galactic cosmic rays and radiation belts with a small variation in dose distribution across the Apollo capsule. The greatest contributor to radiation dose for both Apollo missions in the simulations came from galactic cosmic rays. Simulations of historical solar particle events during an Apollo missions show a solar particle event would not be fatal and below mission limits. These GEANT4 models also provides the values of the dose deposition and dose equivalent for various organs within a human phantom in the International Space Station and Apollo command module, which are developed for the first time using this GEANT4 based application.

Ab Initio Cyclic Voltammetry on Cu(111), Cu(100) and Cu(110) in Acidic, Neutral and Alkaline Solutions.

Electrochemical reactions depend on the electrochemical interface between the electrode surfaces and the electrolytes. To control and advance electrochemical reactions there is a need to develop realistic simulation models of the electrochemical interface to understand the interface from an atomistic point-of-view. Here we present a method for obtaining thermodynamic realistic interface structures, a procedure we use to derive specific coverages and to obtain ab initio simulated cyclic voltammograms. As a case study, the method and procedure is applied in a matrix study of three Cu facets in three different electrolytes. The results have been validated by direct comparison to experimental cyclic voltammograms. The alkaline (NaOH) cyclic voltammograms are described by H* and OH*, while in neutral medium (KHCO3 ) the CO species are dominating and in acidic (KCl) the Cl* species prevail. An almost one-to-one mapping is observed from simulation to experiments giving an atomistic understanding of the interface structure of the Cu facets. Atomistic understanding of the interface at relevant eletrolyte conditions will further allow realistic modelling of electrochemical reactions of importance for future eletrocatalytic studies.

Calibration of Vehicle-Following Model Parameters Using Mixed Traffic Trajectory Data

A number of models for car following have been proposed for homogeneous traffic and some of these have been modified or adapted to represent mixed traffic conditions in India. Vehicle-following behavior under mixed traffic is both complex and challenging and cannot be adequately captured by conventional lane-based following models and their variants. For example, the behavior of a subject vehicle in a mixed traffic condition depends on the behavior of lead vehicle as well as the influence of neighboring vehicles. Most existing models are based on the longitudinal spacing and the relative speed of the lead and the subject vehicles. However, the vehicular interactions also depend on the lateral movements such as lateral spacing and lateral speed. Furthermore, the response of the subject vehicle also depends on the type of vehicles involved and their maneuvers in the surrounding space. This study aims to address some of these gaps in the existing vehicle-following models for mixed traffic. Mixed traffic trajectory data collected from the mid-block section of a six-lane divided urban arterial road in Chennai city were used for this study. From the data set, leader–follower vehicle pairs identified based on three different methods: influence area method, headway method, and video data tracking are compared, and the most suitable method is chosen for further analysis. Variation of driver behavior due to different factors such as follower’s speed, relative spacing, lateral position, vehicle types, and following behaviors were examined. Calibration and validation of the models were done for different leader–follower vehicle pairs. The results show that the model parameters vary with not only by subject vehicle type, but also by leader–follower pairs. In addition, there is a significant effect of factors such as lateral position of vehicles and types of following behaviors. This study will find application in developing more realistic mixed traffic simulation models by including these factors.

3D Reconstruction and Visual Simulation of Double-Flowered Plants Based on Laser Scanning

Flower plants have become a major difficulty in virtual plant research because of their rich external morphological structure and complex physiological processes. Computer vision simulation provides powerful tools for exploring powerful biological systems and operating laws. In this paper, Chrysanthemum and Chinese rose, double flowers as the symbolic flowers of Beijing, are chosen as the study subject. On the basis of maximizing the protection of flower growth structure, an effective method based on laser scanning for three-dimensional (3D) reconstruction and visual simulation of flower plants is proposed. This method uses laser technology to scan the sample and store it as point cloud data. After applying a series of image analysis and processing techniques such as splicing, denoising, repairing and color correction, the digital data optimized by the sample is obtained accurately and efficiently, and a highly realistic 3D simulation model of the plant is formed. The results of the research indicate that it is a convenient research method for the 3D reconstruction of flower plants and computer vision simulation of virtual plants. It also provides an effective way for in-depth study of scientific experiments and digital protection of rare and endangered plants.

Performance of an automated process model discovery – the logistics process of a manufacturing company

Abstract The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA

Buildings are responsible for more than 30% of the total energy consumption and an equally large amount of related greenhouse gas emissions. Improving the energy performance of buildings is a critical element of building energy conservation. Furthermore, renovating existing buildings’ envelopes and systems offers significant opportunities for reducing Life Cycle Cost (LCC) and minimizing negative environmental impacts. This approach can be considered as one of the key strategies for achieving sustainable development goals at a relatively low cost, especially when compared with the demolition and reconstruction of new buildings. One of the main methodological and technical issues of this approach is selecting a desirable renovation strategy among a wide range of available options.The main idea and motivation behind this study relies on trying to bridge the gap between Simulation-Based Multi-Objective Optimization (SBMO) and Artificial Neural Network (ANN). For a whole building simulation and optimization, current SBMOs often need thousands of simulation evaluations. Therefore, the optimization becomes unfeasible because of the computation time and complexity of the dependent parameters. To this end, one feasible technique to solve this problem is to implement surrogate models to computationally imitate expensive real building simulation models. The objective of the research focuses on developing a robust ANN to explore vast and complex data generated from the SBMO model. More specifically, this research aims to propose an accurate ANN to predict energy consumption using data from the SBMO model. The proposed model will potentially offer new venues to predict Total Energy Consumption (TEC), LCC, and Life Cycle Assessment (LCA) for different renovation scenarios, and select the optimum scenario. To illustrate the applicability of the model, a case study was developed and the accuracy of the proposed model was evaluated. Results show that models constructed using ANNs are considerably less time-consuming than the conventional Building Energy Model (BEM) while achieving acceptable accuracy.

Prioritizing Hepatitis C Treatment in U.S. Prisons

Hepatitis C virus (HCV) prevalence in prison systems is about 10 times higher than in the community. As such, prison systems offer a unique opportunity to control the HCV epidemic. New HCV-treatment drugs are very effective, but providing treatment to all inmates is prohibitively expensive unless prices fall. Current practice is to prioritize treatment based on disease severity and puts less emphasis on other factors such as the remaining sentence length and injection drug use behavior. In “Prioritizing Hepatitis C Treatment in U.S. Prisons,” T. Ayer, C. Zhang, A. Bonifonte, A. Spaulding, and J. Chhatwal analyze optimal approaches for treatment prioritization under resource constraints by developing a restless bandit modeling framework. They present an easy-to-implement closed-form index policy to support hepatitis C treatment prioritization decisions in U.S. prisons. They also test their proposed policy using a detailed, realistic agent-based simulation model and shed light on several controversial health policy decisions related to hepatitis C treatment prioritization.

Optimising yaw control at wind farm level

The wind turbines in a wind farm must turn to face the wind to achieve maximum power production. Until now, each turbine achieves this individually: the nacelle-mounted wind vane measures the yaw misalignment, from which the turbine controller decides when to make a yaw correction by using a combination of heavy filtering and counters or dead-band hysteresis. In a wind farm context, each turbine could make use of information from its neighbours, and therefore, in principle, it should be possible to achieve better yaw control by using a centralised farm-level yaw control algorithm which uses input measurements from all the turbines, and then tells each turbine how to yaw. “Better” yaw control will always be a compromise between maximising energy production and minimising yaw actuator duty. Using a realistic dynamic simulation model of a wind farm which uses actual measured wind data as input, different possible yaw strategies are tested, and compared in terms of energy production and yaw actuator duty. The results indicate that centralised yaw control is likely to achieve a better compromise. There also is much current interest in the use of wake steering, where yaw setpoints are optimised to steer the wakes of some turbines away from others. Centralised yaw control may offer a better way to implement these setpoints, rather than sending yaw offsets to be acted on by the normal turbine yaw controllers with their own filtering, dead-band logic, etc.

A Transferable, Multi-Resolution Coarse-Grained Model for Amorphous Silica Nanoparticles.

Despite the ubiquity of nanoparticles in modern materials research, computational scientists are often forced to choose between simulations featuring detailed models of only a few nanoparticles or simplified models with many nanoparticles. Herein, we present a coarse-grained model for amorphous silica nanoparticles with parameters derived via potential matching to atomistic nanoparticle data, thus enabling large-scale simulations of realistic models of silica nanoparticles. Interaction parameters are optimized to match a range of nanoparticle diameters in order to increase transferability with nanoparticle size. Analytical functions are determined such that interaction parameters can be obtained for nanoparticles with arbitrary coarse-grained fidelity. The procedure is shown to be extensible to the derivation of cross-interaction parameters between coarse-grained nanoparticles and other moieties and validated for systems of grafted nanoparticles. The optimization procedure used is available as an open-source Python package and should be readily extensible to models of non-silica nanoparticles.

Low-Cost 3D Scanning in a Smart Learning Factory

With the increased focus over the recent years on digitalizing the factory, 3D scanning has become more and more popular. Acquiring a point cloud of a given factory has shown several benefits, such as better documentation, realistic simulation models, collision detection of materials, visualization of factory development, and more. This paper investigates the testing of a developed low‐cost 3D scanner in a Smart Learning Factory based on parameters identified in literature. Furthermore, the paper compares the developed solution to a commercially available solution. This comparison indicates possible application areas for the developed low-cost solution. The 3D scanner is based on the Microsoft Kinect and a developed hardware platform combined with custom software for acquiring 360° point clouds. A discussion on the acquired results, as well as future works on the developed solution finalizes the paper.

Reducing Acoustic Inhomogeneity Based on Speed of Sound Autofocus in Microwave Induced Thermoacoustic Tomography.

Microwave induced thermoacoustic tomography is a newly developing non-invasive and non-ionizing modality. In practical applications, such as breast tumor detection and brain imaging, the acoustic properties in the tissue to be detected are usually unknown and spatially non-uniform, which results in distortion and blurring of the buried targets. In this paper, a reconstruction method based on speed of sound (SoS) autofocus is proposed to reduce the effect of acoustic inhomogeneity in different soft tissues. According to this method, the number of tissue types, which are referred to as clusters in this work, can be automatically determined by a decision graph. To distinguish the boundaries of different tissues, a Gaussian Mixture Model (GMM) is fitted to the obtained image data for soft clustering instead of traditional hard clustering. Through fixing the tissue centers which are characterized by corresponding data density peaks as the means of Gaussian parameters rather than choosing them randomly, adaptive and robust reconstruction performance can be guaranteed. After performing an iterative GMM optimization, the SoS autofocus is achieved. Image reconstructed by using the updated SoS distribution is with higher accuracy than that with homogeneous assumption. Compared with the existing similar methods, the proposed method strategy obviates the need of extra experiment costs, and possesses good robustness with respect to hard assignment model errors when the medium is relatively complex. Realistic breast model and brain model simulations combined with experiments of agar phantom and pig's brain are provided to demonstrate the effectiveness of the proposed method.

MUSCL scheme for Single Well Chemical Tracer Test simulation, design and interpretation

Our paper presents an improved numerical scheme to simulate Single Well Chemical Tracer Test (SWCTT) method. SWCTT is mainly applied to determine the residual oil saturation of reservoirs. It consists in injecting an aqueous slug of a primary tracer into the reservoir formation and displacing it at a certain distance from the well. This tracer is partly miscible with oil on the one hand, and generates in situ a secondary tracer on the other hand. As a consequence, a shift is observed between the primary and the secondary tracers arrival times when production is resumed. This time shift is used to evaluate the residual oil saturation. In our paper, we propose a numerical scheme based on a fractional time stepping technique to decouple the resolution of the phases mass conservation equations and the chemical tracers mole conservation equations. For the phases resolution, we use an implicit scheme to ensure stability and robustness. For the chemical tracers, we propose an explicit second-order scheme in time and in space via MUSCL technique to improve the tracers time-shift calculation. The proposed numerical method is implemented on a realistic simulation model consisting of a vertical well crossing a reservoir consisting of a stack of homogeneous layers. By reducing the numerical dispersion, the proposed scheme improves the accuracy of predicted concentration profiles, without significantly increasing the computation time. Finally, the advantages of using a second-order scheme for residual oil saturation assessment are discussed on the basis of a radial 1D mesh convergence study.

Dynamic Response Analysis of Geogrid Reinforced Embankment Supported by CFG Pile Structure During a High-Speed Train Operation

Abstract The performance of railroad structure has a tremendous influence on the safety and stable operation of high-speed trains. Strong vibrations and the degradation rate of the track are the main factors affecting the transport safety of a railroad built over a weak soil. Geogrid reinforced embankment supported by pile structure is a new efficient construction technique used to ensure the stability and enhance the performance of the railroad system; but only a few studies are oriented to its behavior under train operation. This paper investigates the dynamic response of geogrid reinforced embankment supported by cement fly-ash gravel pile structure during a high-speed train operation. The establishment of a realistic simulation model for railroad subjected to a moving train load, is an important first step towards the reliable design of geogrid reinforced embankment supported by pile structure. Thus, a 3D nonlinear FEM has been established to simulate the instrumented Harbin-Dalian railway test section. Each train carriage was modeled as a transient dynamic load through a user-defined Dload subroutine. The developed model was successfully validated by the dynamic response recorded from the field test section. The improvement of the railroad structure by the CFG piles and geogrids contributed significantly to the reduction of the vibration in the structure, which attenuates 1.2 times faster with the structure depth, even under overload conditions. Moreover, the phenomenon of resonance observed when the train reaches speeds of 100 and 260 km/h were annihilated. The analysis of the stress distribution within the embankment revealed that a dynamic arch is formed in the embankment at 2 m from the ground. The stress onto the pile was 16 times greater than that acted on the soil and the tensile stress developed in the geogrid was high at the piles edge below. In addition, the coupling effect of geogrid with various tensile strengths and the piles with different strength grades indicated that the combination of a high-strength pile and geogrid significantly reduces the displacement gap due to the variation of train speed. As a result, the vibrations of the track were almost constant during the train operation; thus, ensuring comfort to passengers and reducing the risk of derailment.

The role of scaffold reshaping and disassembly in dynamin driven membrane fission.

The large GTPase dynamin catalyzes membrane fission in eukaryotic cells, but despite three decades of experimental work, competing and partially conflicting models persist regarding some of its most basic actions. Here we investigate the mechanical and functional consequences of dynamin scaffold shape changes and disassembly with the help of a geometrically and elastically realistic simulation model of helical dynamin-membrane complexes. Beyond changes of radius and pitch, we emphasize the crucial role of a third functional motion: an effective rotation of the filament around its longitudinal axis, which reflects alternate tilting of dynamin's PH binding domains and creates a membrane torque. We also show that helix elongation impedes fission, hemifission is reached via a small transient pore, and coat disassembly assists fission. Our results have several testable structural consequences and help to reconcile mutual conflicting aspects between the two main present models of dynamin fission-the two-stage and the constrictase model.

Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter

This study presents the results of the Fuzzy Logic based control application for a heaving wave energy converter operating in realistic sea-state conditions. It has been shown in literature that the power capture performance of a heaving wave energy converter depends on meeting the resonance condition with the incident wave frequency. In regular sea wave conditions, the task of setting the control parameters is a well-known easy task where the wave frequency is known. However, in irregular sea conditions, the wave frequency needed for control settings is not clearly defined. One of the approaches proposed in literature is to use of the dominant wave frequency that can be estimated by a model using the Discrete Fourier Transform approach. The presented model utilises the estimated wave frequency in Fuzzy Logic controller for determination of Power Take-Off control settings. This approach combines widely used fast tuning technique with a new slow tuning method. As a part of the study, the developed realistic simulation model and proposed Fuzzy Logic based controller are used to combine the fast and slow tuning techniques to form a novel hybrid control technique. The simulation results for this new control technique in realistic sea conditions are presented.

Quality of input data in emergency department simulations: Framework and assessment techniques

Operations research techniques are widely used to analyse and optimise emergency department operations. The complex and stochastic nature of an emergency department makes simulation a suitable and frequently used technique. Simulation can provide valuable insights to hospital managers on how to improve the efficiency of an emergency department. However, the output of the simulation study is only as reliable as the input data used as basis for simulation modelling. As a result, high quality input data are essential for the construction of a realistic simulation model. This paper provides a data quality framework that categorises possible data quality problems in electronic healthcare records of emergency departments. Electronic healthcare records are a common source of input data for emergency department simulations, but often suffer from data quality issues. For the data quality problems identified in the framework, data quality assessment techniques are described. These techniques enable researchers and practitioners to identify and quantify the potential data quality issues present in input data. In order to facilitate data quality assessment, an implementation to automate this process is developed and applied to a real-life case study. This case study demonstrates the need for thorough and structured data quality assessment. Possible ways to deal with identified data quality problems are also described.

A novel simulator model and standardized assessment tools for fine needle aspiration cytology training.

Fine needle aspiration (FNA) is an invaluable diagnostic procedure for evaluation of lesions; however, acquisition of diagnostic material is dependent on the skill of the practitioner. We report a novel patient simulator for teaching the FNA procedure and structured assessment tools for educators and learners. We created a novel simulator model for FNA training, employed a standardized teaching module, and assessed procedure utility in medical students. Groups of students completed training using a commercial version of the model, and underwent structured evaluation using an Objective Structured Assessment of Technical Skills (OSATS) form, and the Debriefing Assessment for Simulation in Healthcare (DASH) tool. In the initial phase, 178 students rated the training workshop between valuable and essential (4.2 on a 5-point Likert scale). In the second phase, for students evaluated with the OSATS form, the mean overall score was 33 out of 50 (range 26-43). The areas of weakness for the participants were: (a) compression after the FNA procedure, (b) completion of the informed consent, and (c) correct explanation of the procedure to the patient. For the group of students that completed the DASH questionnaire, the results were: 6.2 (assessment by students) and 6.7 (assessment by instructor) out of a maximum of 7. A realistic simulation model, in combination with a standardized training program with formal assessment methods is a valuable tool to teach FNA. We here describe a process for teaching the FNA procedure to interested educators and learners.