Realistic Simulation Research Articles

The impact of simulation-based training in medical education: A review.

Simulation-based training (SBT) has emerged as a transformative approach in medical education, significantly enhancing healthcare professionals' learning experience and clinical competency. This article explores the impact of SBT, tracing its historical development and examining the various types of simulations utilized today, including high-fidelity mannequins, virtual reality environments, standardized patients, and hybrid simulations. These methods offer a safe and controlled environment for students to practice and hone technical and non-technical skills, ultimately improving patient safety and clinical outcomes. The benefits of SBT are manifold, including enhanced skill acquisition, error reduction, and the opportunity for repeated practice without risk to actual patients. Immediate feedback and structured debriefing further solidify learning, making Simulation an invaluable tool in medical education. However, the implementation of SBT is challenging. It requires substantial financial investment, specialized equipment, and trained faculty. Additionally, there are concerns about the realism of simulations and the transferability of skills to real-world clinical settings. Despite these challenges, numerous case studies and empirical research underscore the effectiveness of SBT compared to traditional methods. Looking ahead, advancements in technology, such as artificial intelligence and improved virtual reality applications, promise to enhance the efficacy and accessibility of simulation training. The integration of Simulation with other training modalities and its adoption in diverse global contexts highlight its potential to revolutionize medical education worldwide. This article affirms the crucial role of SBT in preparing the next generation of healthcare professionals and its ongoing evolution driven by technological innovations.

Swarm of Drones for Surveillance Monitoring of a Grounded Target: An event-triggered approach

In this paper, we present a novel formation control approach in the framework of Event-Triggered (ET) control to provide a solution to the surveillance problem. To do this, we identify two main challenges which are the switching topology of the drones and the limited bandwidth of the communication network, which are also valid in formation applications. To provide a solution to switching topologies, we propose a networked continuous controller that is robust in the presence of connection switching between drones and the target. Then, we propose a networked controller with ET communication in some aperiodic instants which reduces the required bandwidth and load within the communication network. We guarantee the stability of the developed ET controller and prove that the Zeno behavior cannot occur. To validate the method, we present realistic 3D simulation results conducted in the Simulink environment of Matlab® for different scenarios. The results of the study show the effectiveness of the proposed controller, especially for limited bandwidth channels as the ETC scheme has decreased the load within the communication network while resulting in a robust and efficient formation performance. We also considered moving target scenarios with missing possibilities to validate the robustness of the proposed method.

Assessing the impact of DIONISIO-SubChanFlow code coupling in nuclear fuel performance simulations

Realistic simulation of nuclear fuel performance requires not only validated models capable of describing the thermomechanical phenomena that take place within the fuel under irradiation conditions, but a detailed description of the thermal hydraulics of the channel surrounding the fuel rods, which provides the boundary conditions of the system. In this work, the main results and outlooks of coupling the thermal hydraulics code SubChanFlow with the fuel performance code DIONISIO are presented. To achieve this, an internal coupling was implemented, wherein DIONISIO is used as a master code controlling SubChanFlow as a thermal hydraulics subroutine replacing the simplified version already embedded in DIONISIO. Several tests were conducted to ensure the performance and quality of the coupling under normal operation conditions as a first approach. In addition, it was observed that the coupling demonstrated a significant improvement in the description of the cladding temperature and related variables, such as oxide thickness and hydrogen uptake, when compared with experimental data.

Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors.

Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was commonly used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. In this paper, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with various brain tumors to show that a Poisson distribution indeed models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence, and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, including PpIX (620 nm and 634 nm photostates), NADH, FAD, flavins, lipofuscin, melanin, elastin, and collagen, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, better sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to aid the surgeon better during brain tumor resection.

E3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Fuzzy Bipolar Hypersoft Sets: A Novel Approach for Decision-Making Applications

This article presents a pioneering mathematical model, fuzzy bipolar hypersoft (FBHS) sets, which combines the bipolarity of parameters with the fuzziness of data. Motivated by the need for a comprehensive framework capable of addressing uncertainty and variability in complex phenomena, our approach introduces a novel method for representing both the presence and absence of parameters through FBHS sets. By employing two mappings to estimate positive and negative fuzziness levels, we bridge the gap between bipolarity, fuzziness, and parameterization, allowing for more realistic simulations of multifaceted scenarios. Compared to existing models like bipolar fuzzy hypersoft (BFHS) sets, FBHS sets offer a more intuitive and user-friendly approach to modeling phenomena involving bipolarity, fuzziness, and parameterization. This advantage is underscored by a detailed comparison and a practical example illustrating FBHS sets’ superiority in modeling such phenomena. Additionally, this paper provides an in-depth exploration of fundamental FBHS set operations, highlighting their robustness and applicability in various contexts. Finally, we demonstrate the practical utility of FBHS sets in problem-solving and introduce an algorithm for optimal object selection based on available information sets, further emphasizing the advantages of our proposed framework.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

An Approach for Identifying Dynamic Parameters in Robotic Systems with Inconsistent Joint Measurements

Abstract Achieving accurate robot control and realistic robot simulation relies on the precise modeling of robotic dynamics. Although the identification method for obtaining dynamic parameters has been developed for several years, joint inconsistency has largely been disregarded in prior studies. The inconsistency of joint actuators results in varying confidence levels of their measurement results, leading to the departure of the final identification parameters from valid values, thereby impacting the control performance. This paper presents a novel identification method that effectively addresses the issue of joint inconsistency by assigning distinct weights to each joint. The presented approach extends the least square (LS) method and incorporates the particle swarm optimization (PSO) algorithm to calculate the weights of each individual joint. This approach is referred to as PWLS (PSO-based weighted least square). Simulation experiments demonstrated the superior identification accuracy of the PWLS method compared to the LS method in a robot system characterized by joint inconsistency. Moreover, the experiments were performed on a 3-degree-of-freedom (DoFs) robotic limb, which exhibited improved identification performance in both the excitation and verification trajectories. These findings have promising implications for enhancing the control and simulation of robotic systems.

Phase-locked and phase-unlocked multicolor solitons in a fiber laser.

Multicolor solitons are nonlinear pulses composed of two or more solitons centered at different frequencies, propagating with the same group velocity. In the time domain, multicolor solitons consist of an envelope multiplying a more rapidly varying fringe pattern that results from the interference of these frequency components. Here, we report the observation in a fiber laser of a novel, to the best of our knowledge, type of dynamics in which different frequency components still have the same group velocity but have different propagation constants. This causes the relative phases between the constituent spectral components to change upon propagation, corresponding to the fringes moving under the envelope. This leads to small periodic energy variations that we directly measure. Our experimental results are in good agreement with realistic numerical simulations based on an iterative cavity map.

Systematic review of subjective validation methods for computerized colonoscopy simulators.

Introduction: In recent years, different approaches have been used to conduct a subjective assessment of colonoscopy simulators. The purpose of this paper is to review these different approaches, specifically the ones used for computerized simulators, as the first step for the design of a standard validation procedure for this type of simulators. Methods: A systematic review was conducted by searching papers after 2010 in PubMed, Google Scholar, ScienceDirect, and IEEE Xplore databases. Papers were screened and reviewed for procedures regarding the subjective validation of computerized simulators for traditional colonoscopy with an endoscope. Results: An initial search in the databases identified 2094 papers, of which 7 remained after exhaustive review and application of exclusion criteria. All studies used questionnaires for subjective validation, with "face" being the most common validity type tested, while "content" validity and "usability" were less prominent. Conclusions: A classification of subscales for testing face validity was derived from the studies. The Colonoscopy Simulator Realism Questionnaire (CSRQ) was selected as the guide to follow for the development of future questionnaires related to subjective validation. Mislabeling of the validity tested in the studies due to ambiguous interpretations of the validity types was a common occurrence observed in the reviewed studies.

Educational Transformation: Using Virtual Reality as a Learning Tool for The Future

Education continues to transform with the introduction of advanced technology such as Virtual Reality (VR). The use of VR as a future learning tool promises to revolutionize the way students learn and interact with course material. This research aims to explore the potential of VR to increase student engagement through a more interactive and immersive learning experience. The research method used is literature study. Through a literature study and analysis of recent developments, we see how VR can aid the understanding of complex concepts, such as science and mathematics, as well as improve understanding of historical content through realistic simulations. However, challenges such as device costs and curriculum integration need to be overcome to realize the full potential of this technology in education. It can be concluded that with the right strategy, VR can be the key to creating more personalized and effective learning experiences, leading to major transformations in the global education system. This research contributes to a more personalized and effective learning experience, which in turn can improve the quality of education and student learning outcomes

Application of Artificial Intelligence in Health Management Education: An Emerging Tool for Enhancing Clinical Reasoning Skills

This study explores the application of artificial intelligence in health management education, focusing on its potential to enhance students' clinical reasoning skills. With the increasing complexity of medical care, developing efficient clinical reasoning skills has become more crucial. The article analyzes the current state of AI in medical education and proposes several application models, including virtual case simulations, intelligent diagnostic systems, and adaptive learning platforms. Through case studies, we demonstrate how AI enhances clinical reasoning by increasing the realism of scenario simulations, providing instant feedback, and promoting multidimensional thinking. As an emerging tool, the results indicate that AI has significant potential to enhance medical students' clinical reasoning abilities, pointing the way for future innovations in medical education. Key words: Artificial Intelligence（AI）, Health Management, Clinical Reasoning Skills

Mengembangkan Jiwa Kepemimpinan dalam Berwirausaha di Era Globalisasi

In the ever-evolving era of globalization, leadership skills in entrepreneurship have become increasingly crucial for business practitioners. This article aims to develop and strengthen leadership qualities within the entrepreneurial context through a community service program. This program is designed to provide training and guidance to local entrepreneurs, specifically students at SMK Kartika XX-1 Makassar, with a focus on developing leadership skills, business management, and adaptation to global changes. The methods employed in this program include workshops, mentoring, and realistic business simulations. The results of the program indicate a significant improvement in the leadership abilities and business performance of the participants. Furthermore, the program helps entrepreneurs become better prepared to face challenges and seize opportunities in the global market. These findings demonstrate that with the right approach, leadership qualities can be effectively developed within the entrepreneurial community, thereby contributing to sustainable local economic growth.

Adaptive meta-reinforcement learning for AUVs 3D guidance and control under unknown ocean currents

Given the challenges posed by underwater communication constraints, Autonomous Underwater Vehicles (AUVs) must operate with enhanced autonomy while balancing operational safety and efficiency within complex marine settings. AUVs face a 3D guidance and control challenge, requiring complex adjustments to unknown ocean currents that affect their stability, maneuverability, and Guidance-Navigation-Control system efficiency. This study introduces and implements an off-policy meta-reinforcement learning framework to address AUVs’ 3D guidance and control challenges under unknown ocean currents. Our method, APE-SAC, utilizes an inference network to identify latent variables, streamlining the adaptation to unknown ocean currents by optimizing their strength and direction. Consequently, this framework redefines the issue of guidance and control under unknown current disturbances into a manageable learning task across multiple predefined scenarios. Utilizing the 3D dynamics of the REMUS AUV, we construct meta-Markov Decision Processes to detail the comprehensive state space, action space, and robust reward function tailored to the task’s specific demands. Furthermore, we develop a highly realistic simulation environment specifically designed for comprehensive experiments. Extensive results demonstrate that, compared to deep reinforcement learning baselines, APE-SAC exhibits outstanding performance in guiding and controlling AUVs under unknown ocean currents.

Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields.

New Bayesian parameter estimation methods have the capability to enable more physically realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates of uncertainties of force-field (FF) parameters and associated properties. However, the choice of which Bayesian parameter estimation algorithm to use has not been widely investigated, despite its impact on the effective exploration of parameter space. Here, using a case example of the Embedded Atom Method (EAM) FF parameters, we investigated the ramifications of several of the algorithm choices. We found that Ensemble Slice Sampling (ESS) and Affine-Invariant Ensemble Sampling (AIES) demonstrate a new level of superior performance, culminating in more accurate parameter and property estimations with tighter uncertainty bounds, compared to traditional methods such as Metropolis-Hastings (MH), Gradient Search (GS), and Uniform Random Sampler (URS). We demonstrate that Bayesian Uncertainty Quantification with ESS and AIES leads to significantly more accurate and reliable predictions of the FF parameters and properties. The results suggest that ESS and AIES should be used to obtain more accurate parameter and uncertainty estimations while providing deeper physical insights.

Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica.

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems' neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia's grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia's grasper, has not been characterized. Because of the importance of the musculature's properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior of Aplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle's peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex's kinematics in vivo.

Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion

PurposeThe purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.Design/methodology/approachThis study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.FindingsFEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.Originality/valueThe originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).

On the role of seamounts in upwelling deep-ocean waters through turbulent mixing

Turbulent mixing in the ocean exerts an important control on the rate and structure of the overturning circulation. However, the balance of processes underpinning this mixing is subject to significant uncertainties, limiting our understanding of the overturning's deep upwelling limb. Here, we investigate the hitherto primarily neglected role of tens of thousands of seamounts in sustaining deep-ocean upwelling. Dynamical theory indicates that seamounts may stir and mix deep waters by generating lee waves and topographic wake vortices. At low latitudes, stirring and mixing are predicted to be enhanced by a layered vortex regime in the wakes. Using three realistic regional simulations spanning equatorial to middle latitudes, we show that layered wake vortices and elevated mixing are widespread around seamounts. We identify scalings that relate mixing rate within seamount wakes to topographic and hydrographic parameters. We then apply such scalings to a global seamount dataset and an ocean climatology to show that seamount-generated mixing makes an important contribution to the upwelling of deep waters. Our work thus brings seamounts to the fore of the deep-ocean mixing problem and urges observational, theoretical, and modeling efforts toward incorporating the seamounts' mixing effects in conceptual and numerical ocean circulation models.

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots

The study aimed at investigating the impact of an innovative Wake Vortex Alert (WVA) avionics on pilots' operation and mental states, intending to improve aviation safety by mitigating the risks associated with wake vortex encounters (WVEs). Wake vortices, generated by jet aircraft, pose a significant hazard to trailing or crossing aircrafts. Despite existing separation rules, incidents involving WVEs continue to occur, especially affecting smaller aircrafts like business jets, resulting in aircraft upsets and occasional cabin injuries. To address these challenges, the study focused on developing and validating an alert system that can be presented to air traffic controllers, enabling them to warn flight crews. This empowers the flight crews to either avoid the wake vortex or secure the cabin to prevent injuries. The research employed a multidimensional approach including an analysis of human performance and human factors (HF) issues to determine the potential impact of the alert on pilots' roles, tasks, and mental states. It also utilizes Human Assurance Levels (HALs) to evaluate the necessary human factors support based on the safety criticality of the new system. Realistic flight simulations were conducted to collect data of pilots' behavioural, subjective and neurophysiological responses during WVEs. The data allowed for an objective evaluation of the WVA impact on pilots' operation, behaviour and mental states (mental workload, stress levels and arousal). In particular, the results highlighted the effectiveness of the alert system in facilitating pilots' preparation, awareness and crew resource management (CRM). The results also highlighted the importance of avionics able to enhance aviation safety and reducing risks associated with wake vortex encounters. In particular, we demonstrated how providing timely information and improving situational awareness, the WVA will minimize the occurrence of WVEs and contribute to safer aviation operations.