This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

The pervasiveness of the Internet of Things (IoT) has enabled the administration of a large number of intelligent devices. However, IoT is based on centralised models, which introduce a number of problems, such as a single point of failure and security risks. Blockchain may offer a viable option for addressing these concerns. Practically, both blockchain and IoT are complex technologies posing further challenges in assessing application performance. The availability of a reliable simulation environment for Blockchain based IoT applications would be a major aid in the development and evaluation of such applications. Our research has found that currently there are no simulators with a comprehensive set of features, for the development and evaluation of blockchain based IoT applications, which is the main motivation for our work. The purpose of this study is to gather the opinions of experts regarding the creation of a simulation environment for IoT based blockchain applications. To do this, we utilise two separate investigations. First, a questionnaire is developed to ensure that the development of such simulation software would be of significant use. Second, interviews with participants are performed to gain their perspectives on the primary issues they face with blockchain-based IoT applications. In addition, the interviews focused on collecting the perspectives of participants on how blockchain may improve IoT and how to identify blockchain's applicability in IoT. Our findings demonstrate that the participants had a great deal of confidence in blockchain to resolve IoT issues. However, they lack the tools necessary to assess this concept. This highlights their requirement for a simulator to analyse the integration of blockchain and IoT.

Simulation Interoperability Standards Organization (SISO) SIW Conference PaperThe Extensible Modeling and Simulation Framework (XMSF) is a composable set of standards, profiles and recommended practices for Web-based modeling & simulation (M&S) (http://www.movesinstitute.org/xmsf/xmsf.html). A number of exemplar prototypes are in progress to demonstrate the ability of XML-based markup languages, Internet technologies and Web Services to enable a new generation of distributed M&S applications to emerge, develop and interoperate, including interoperability with command and control systems. This paper describes work being performed to enhance existing analytical models and tools while serving as proof of concept for XMSF ideas. Key applications include integration of the Naval Simulation System (NSS) with the Simkit open source Discrete Event Simulation library and integration of a variety of models and tools in the Flexible Asymmetric Simulation Technologies (FAST) program. The paper describes work completed, work in progress, and future opportunities

Terahertz (THz) communication is envisioned as one of the possible technologies for the sixth-generation (6G) communication system due to its rich spectrum. To evaluate the performance of THz communication, it is essential to propose THz channel models within the common framework of the geometry-based stochastic model (GBSM) in the 3rd Generation Partnership Project (3GPP). This paper focuses on THz channel modeling and simulation by a 3GPP-like GBSM, based on channel measurements. We first present channel measurements at 100 GHz in an indoor office scenario and 132 GHz in an urban microcellular scenario. Subsequently, channel characteristics such as path loss, delay spread, angle spread, K-factor, cluster characteristic, cross-correlations, and correlation distances are obtained and analyzed based on channel measurement. Additionally, the channel characteristics are modeled by the statistical distribution of 3GPP channel models, which can be used to reconstruct the channel impulse response (CIR). Furthermore, these obtained distributions are studied referring to the default models in the 3GPP, revealing the channel sparsity in the THz channel. For instance, in the case of line-of-sight links in the indoor office, the mean of the measured cluster number is 4 while the default value is 15. Finally, we propose the THz channel model and its simulation framework to reconstruct CIRs based on the obtained models, which aim at characterizing the sparser THz channels. The obvious channel sparsity is characterized in both scenarios, as the Gini factors obtained by the proposed model only have the maximum deviation of 0.04 for those of the measurement. Overall, these findings are helpful in understanding and modeling the THz channel, facilitating the application of THz communication techniques for 6G.

Natural language modeling (NLM) is a conceptual modeling language that can be used for requirements determination for business application systems. In this chapter we will show how the NLM methodology for requirements determination can be extended to serve as a blueprint for business (or management) simulation by providing an initial model for creating a business simulation. We will do this by defining the content of the communication documents for runtime management and we will subsequently show how this meta-UoD can be incorporated into an application UoD. This allows us to capitalize on conceptual models in a business that have been created for requirements determination by extending them with the conceptual model of runtime management. Subsequently, we will incorporate the simulation requirements into the latter UoD and we will give some guidelines on how the conceptual models for the “real life” runtime application can serve as a starting point for the conceptual sub-models for the simulation UoD.

The Extensible Modeling and Simulation Framework (XMSF) is defined as a composable set of standards, profiles and recommended practices for web-based modeling and simulation. Markup languages based on XML, Internet technologies, and Web services are combining to enable a new generation of distributed M&S applications to emerge, develop and interoperate. The purpose of this task is to develop the technical framework, coordinate with existing public standardization efforts, and demonstrate distributed exemplars of the developed framework. This report describes work accomplished in 2004 by the XMSF project team consisting of Naval Postgraduate School Modeling, Virtual Environments, and Simulation (MOVES) Institute, George Mason University (GMU), Science Applications International Corporation (SAIC), and Old Dominion University/Virginia Modeling, Analysis, and Simulation Center (ODU/VMASC). Principal areas of work included ongoing community education and development activities through conference and workshop participation, liaison with relevant standards organizations, and development of exemplar applications demonstrating the technical approaches and applicable standards relevant to XMSF precepts. The report proposes follow-on efforts for 2005, including continuation of current efforts and further standardization work with multicasting, web-enabled IEEE 1516 Run-Time Infrastructure, data modeling in C2 and M&S systems, and executable architectures.Approved for public release; distribution is unlimited.N6133904WX0013

Origami structures have been widely explored in robotics due to their many potential advantages. Origami robots can be very compact, as well as cheap and efficient to produce. In particular, they can be constructed in a flat format using modern manufacturing techniques. Rotational motion is essential for robotics, and a variety of origami rotational joints have been proposed in the literature. However, few of these are even approximately flat-foldable. One potential enabler of flat origami rotational joints is the inclusion of lightweight pneumatic pouches which actuate the origami's folds; however, pouch actuators only enable a relatively small amount of rotational displacement. The previously proposed Four-Vertex Origami is a flat-foldable structure which provides an angular multiplier for a pouch actuator, but suffers from a degenerate state. This paper presents a novel rigid origami, the Self-Lock Origami, which eliminates this degeneracy by slightly relaxing the assumption of flat-foldability. This joint is analysed in terms of a trade-off between the angular multiplier and the mechanical advantage. Furthermore, the Self-Lock Origami is a modular joint which can be connected to similar or different joints to produce complex movements for various applications; three different manipulator designs are introduced as a proof of concept.

Modeling and simulation of military operations requires human behavior models capable of learning from experi-ence in complex environments where feedback on action quality is noisy and delayed. This research examines the potential of reinforcement learning, a class of AI learning algorithms, to address this need. A novel reinforcement learning algorithm that uses the exponentially weighted average reward as an action-value estimator is described. Empirical results indicate that this relatively straight-forward approach improves learning speed in both benchmark environments and in challenging applied settings. Applications of reinforcement learning in the verification of the re-ward structure of a training simulation, the improvement in the performance of a discrete event simulation scheduling tool, and in enabling adaptive decision-making in combat simulation are presented. To place reinforcement learning within the context of broader models of human information processing, a practical cognitive architecture is devel-oped and applied to the representation of a population within a conflict area. These varied applications and domains demonstrate that the potential for the use of reinforcement learning within modeling and simulation is great.Approved for public release; distribution is unlimited.Lieutenant Colonel, United States Armyhttp://archive.org/details/learningfromnois109451731

Although strain-based models have been widely adopted in robotics, no comparison beyond the uniform bending test is commonly recognized to assess their performance. In addition, the increasing effort in prototyping continuum robots highlights the need to assess the applicability of these models and the necessity of comprehensive performance evaluation. To address this gap, this work investigates the shape reconstruction abilities of a third-order strain interpolation method, examining its ability to capture both individual and combined deformation effects. These results are compared and discussed against the Geometric-Variable Strain approach. Subsequently, simulation results are experimentally verified by reshaping a slender rod while recording the resulting configurations using cameras. The rod configuration is imposed using a manipulator displacing one of its tips and extracted through reflective markers, without the aid of any other external sensor -- i.e. strain gauges or wrench sensors placed along the rod. The experiments demonstrate good agreement between the model predictions and observed shapes, with average error of 0.58% of the rod length and average computational time of 0.32s per configuration, outperforming existing models.

Discrete event simulation has been applied to a wide range of applications areas due to its ability to represent stochastic systems over time. Maintenance, particularly field maintenance, is complex due to the interaction of different sub-systems of use, maintenance, repair and inventory and the conflicting demands of minimizing cost and maximizing availability. The area of simulation of maintenance systems receives little treatment in the literature and tends to focus on reliability modeling of individual assets. The work presented here documents research to fill this gap by specifying, creating and testing simulation functionality to rapidly model field maintenance systems

Influenza and respiratory syncytial virus (RSV) are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base the diagnosis of ARI on patients' symptoms alone and do not always conduct virological tests necessary to identify individual viruses, which limits the ability to study the interaction between multiple pathogens and make public health recommendations. We consider a stochastic kinetic model (SKM) for two interacting ARI pathogens circulating in a large population and an empirically motivated background process for infections with other pathogens causing similar symptoms. An extended marginal sampling approach based on the Linear Noise Approximation to the SKM integrates multiple data sources and additional model components. We infer the parameters defining the pathogens' dynamics and interaction within a Bayesian hierarchical model and explore the posterior trajectories of infections for each illness based on aggregate infection reports from six epidemic seasons collected by the state health department, and a subset of virological tests from a sentinel program at a general hospital in San Luis Potosí, México. We interpret the results based on real and simulated data and make recommendations for future data collection strategies. Supplementary materials and software are provided online.

Modeling and Simulation Support for System of Systems Engineering Applications provides a comprehensive overview of the underlying theory, methods, and solutions in modeling and simulation support for system of systems engineering. Highlighting plentiful multidisciplinary applications of modeling and simulation, the book uniquely addresses the criteria and challenges found within the field. … [Amazon.com]https://digitalcommons.odu.edu/msve_books/1008/thumbnail.jp

Canonical Correlation Analysis, CCA, is a widely used multivariate method in omics research for integrating high dimensional datasets. CCA identifies hidden links by deriving linear projections of features maximally correlating datasets. For standard CCA, observations must be independent of each other. As a result, it cannot properly deal with repeated measurements. Current CCA extensions dealing with these challenges either perform CCA on summarized data or estimate correlations for each measurement. While these techniques factor in the correlation between measurements, they are sub-optimal for high dimensional analysis and exploiting this datas longitudinal qualities. We propose a novel extension of sparse CCA that incorporates time dynamics at the latent level through longitudinal models. This approach addresses the correlation of repeated measurements while drawing latent paths, focusing on dynamics in the correlation structures. To aid interpretability and computational efficiency, we implement a penalty to enforce fixed sparsity levels. We estimate these trajectories fitting longitudinal models to the low dimensional latent variables, leveraging the clustered structure of high dimensional datasets, thus exploring shared longitudinal latent mechanisms. Furthermore, modeling time in the latent space significantly reduces computational burden. We validate our models performance using simulated data and show its real world applicability with data from the Human Microbiome Project. Our CCA method for repeated measurements enables efficient estimation of canonical correlations across measurements for clustered data. Compared to existing methods, ours substantially reduces computational time in high dimensional analyses as well as provides longitudinal trajectories that yield interpretable and insightful results.

Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques. [From Amazon.com]https://digitalcommons.odu.edu/vmasc_books/1003/thumbnail.jp

The continually growing demands for traffic as a result of advanced technologies in 5G and 6G systems offering services with intensive demands such as IoT and virtual reality applications has resulted in significant performance expectations of data center networks (DCNs). More specifically, DCNs are expected to meet high bandwidth connectivity, high throughput, low latency, and high scalability requirements. However, the current wired DCN architectures introduce large cabling requirements and limit the ability to reconfigure data centres as they expand. To that end, wireless technologies such as Optical Wireless Communication (OWC) have been proposed as a viable and cost-effective solution to meet the aforementioned requirements. This paper proposes the use of Infrared (IR) OWC systems that employ Wavelength Division Multiplexing (WDM) to enhance the DCN communication in the downlink direction; i.e. from Access Points (APs) in the ceiling, connected to spine switches, to receivers attached to the top of the racks representing leaf switches. The proposed systems utilize Angle Diversity Transmitters (ADTs) mounted on the room ceiling to facilitate inter-rack communication. Two different optical receiver types are considered, namely Angle Diversity Receivers (ADRs) and Wide Field-of-View Receivers (WFOVR). The simulation (i.e. channel modeling) results show that our proposed data center links achieve good data rates in the data centre up to 15 Gbps.

Modelica is an object-oriented language for modeling of large and heterogeneous physical systems. Typical applications include mechatronic models in automo-tive and aerospace applications involving mechani-cal, electrical and hydraulic subsystems as well as control systems. Modeling of an ideal diode and Coulomb friction is discussed to illustrate the unique hybrid features of Modelica. The language has been designed to allow tools to generate efficient code automatically. Approaches supported by the Dynamic Modeling Laboratory Dymola from Dynasim are presented. Real-time simulation of an automatic gearbox is discussed to demonstrate the power of symbolic manipulation. A gearbox is inherently hybrid, since the structure varies during each gearshift. Friction is also an important phenomenon. It takes Dymola only a few seconds to translate a Modelica model of an automatic gearbox with 11 switching elements into efficient simulation code. A 500 MHz DEC alpha processor from dSPACE evaluates one Euler step including a possible mode switch in less than 0.18 m

Magnetic polymers are examples of composite soft materials in which the competition between the large configurational entropy of the soft substrate (polymer) and the magnetic interaction may give rise to rich equilibrium phase diagrams as well as non-standard critical phenomena. Here, we study a self-avoiding walk model decorated by Ising spins of value $0$ and $\pm 1$ that interact according to a Blume-Emery-Griffith-like Hamiltonian. By using mean-field approximations and Monte Carlo simulations, we report the existence of three distinct equilibrium phases: swollen disordered, compact ordered, and compact disordered. Notably, these phases are separated by phase boundaries that meet at multicritical points, whose nature and location are tunable and depend on the strength of the interactions. In our conclusion, we discuss the relevance of the phase diagrams we have obtained to the physics of magnetic polymers and their application to chromatin biophysics.

It is essential in the simulation of power electronics applications to model magnetic components accurately. In addition to modeling the nonlinear hysteresis behaviour, eddy currents and winding losses must be included to provide a realistic model. In practice the losses in magnetic components give rise to significant temperature increases which can lead to major changes in the component behaviour. In this paper a model of magnetic components is presented which integrates a nonlinear model of hysteresis, electro-magnetic windings and thermal behaviour in a single model for use in circuit simulation of power electronics systems. Measurements and simulations are presented which demonstrate the accuracy of the approach for the electrical, magnetic and thermal domains across a variety of operating conditions, including static thermal conditions and dynamic self heating

Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient, general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we map a broad pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Across a benchmark covering conditions from ambient to extreme, including high-pressure/high-temperature states and high-energy cascade damage, tabGAP provides a favorable balance of accuracy, robustness, transferability, and computational cost among the tested empirical and ML-IAPs.

Proceedings of the 6th IEEE International Workshop on Distributed Simulation and Real-Time Applications (DS-RT 02), abstract onlyThe MOVES Institute s mission is research, application and education in the grand challenges of modeling, virtual environments and simulation (MOVES). The institute s focus is on 3D visual simulation, networked virtual environments, computer-generated autonomy, human performance engineering, immersive technologies, defense/entertainment collaboration, and evolving operational modeling

The unsustainable take-make-dispose linear economy prevalent in healthcare contributes 4.4% to global Greenhouse Gas emissions. A popular but not yet widely-embraced solution is to remanufacture common single-use medical devices like electrophysiology catheters, significantly extending their lifetimes by enabling a circular life cycle. To support the adoption of catheter remanufacturing, we propose a comprehensive emission framework and carry out a holistic evaluation of virgin manufactured and remanufactured carbon emissions with Life Cycle Analysis (LCA). We followed ISO modelling standards and NHS reporting guidelines to ensure industry relevance. We conclude that remanufacturing may lead to a reduction of up to 60% per turn (-1.92 kg CO2eq, burden-free) and 57% per life (-1.87 kg CO2eq, burdened). Our extensive sensitivity analysis and industry-informed buy-back scheme simulation revealed long-term emission reductions of up to 48% per remanufactured catheter life (-1.73 kg CO2eq). Our comprehensive results encourage the adoption of electrophysiology catheter remanufacturing, and highlight the importance of estimating long-term emissions in addition to traditional emission metrics.