Traditional Simulation Research Articles

Theory and Monte Carlo simulation of the ideal gas with shell particles in the canonical, isothermal-isobaric, grand canonical, and Gibbs ensembles.

Theories of small systems play an important role in the fundamental understanding of finite size effects in statistical mechanics, as well as the validation of molecular simulation results as no computer can simulate fluids in the thermodynamic limit. Previously, a shell particle was included in the isothermal-isobaric ensemble in order to resolve an ambiguity in the resulting partition function. The shell particle removed either redundant volume states or redundant translational degrees of freedom of the system and yielded quantitative differences from traditional simulations in this ensemble. In this work, we investigate the effect of including a shell particle in the canonical, grand canonical, and Gibbs ensembles. For systems comprised of a pure component ideal gas, analytical expressions for various thermodynamic properties are obtained. We also derive the Metropolis Monte Carlo simulation acceptance criteria for these ensembles with shell particles, and the results of the simulations of an ideal gas are in excellent agreement with the theoretical predictions. The system size dependence of various important ensemble averages is also analyzed.

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

Simulation Study on Rock Crack Expansion in CO2 Directional Fracturing

In underground construction projects, traversing hard rock layers demands concentrated CO2 fracturing energy and precise directional crack expansion. Due to the discontinuity of the rock mass at the tip of prefabricated directional fractures in CO2 fracturing, traditional simulations assuming continuous media are limited. It is challenging to set boundary conditions for high strain rate and large deformation processes. The dynamic expansion mechanism of the 3D fracture network in CO2 directional fracturing is not yet fully understood. By treating CO2 fracturing stress waves as hemispherical resonance waves and using a particle expansion loading method along with dynamic boundary condition processing, a 3D numerical model of CO2 fracturing is constructed. This model analyzes the dynamic propagation mechanism of 3D spatial fractures network in CO2 directional fracturing rock materials. The results show that in undirected fracturing, the fracture network relies on the weak structures near the rock borehole, whereas in directional fracturing, the crack propagation is guided, extending the fracture’s range. Additionally, the tip of the directional crack is vital for the re-expansion of the rock mass by high-pressure CO2 gas, leading to the formation of a symmetrical, umbrella-shaped structure with evenly developed fractures. The findings also demonstrate that the discrete element method (DEM) effectively reproduces the dynamic fracture network expansion at each stage of fracturing, providing a basis for studying the CO2 directional rock cracking mechanism.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

A novel 3D-printed educational model for the training of laparoscopic bile duct Exploration:a pilot study for beginning trainees

BackgroundLaparoscopic common bile duct exploration (LCBDE) is a minimally invasive procedure for the removal of bile duct stones that is often performed by experienced hepatobiliary surgeons; beginners do not easily master this approach. AimTo investigate the effectiveness and practicality of a three-dimensional printed (3DP) anatomical model based on radiographic images in the training of LCBDE techniques and formulate standardized educational workflows. MethodsColored LCBDE training models were produced using 3DP technology. Twenty standardized training trainees were randomly divided into two groups: a 3DP model training group and a traditional laparoscopic simulation training group. Both groups received the same number of teaching hours. After a 4-weeks training course, the trainees’ subjective and objective progress in basic knowledge and manipulations were evaluated and compared. ResultsCompared with traditional laparoscopic simulation, 3DP model simulation training is of great significance in improving trainers' understanding of surgical procedures and cooperation during the operation. Trainees with 3DP models training demonstrated a significant improvement in their understanding of the key points of surgery (χ2 = 6.139, p = 0.013) and skills scores, especially in core procedural steps operation. More importantly, the trainees showed higher levels of satisfaction and self-confidence while assisting in the surgery. ConclusionWith the development of 3DP models, improvements in the learning effect for theoretical understanding and practical skills were significant. The present study is the initial educational experience with 3DP models to facilitate the operational capabilities of the trainees for LCBDE.

Clock mesh synthesis through dynamic programming with physical parameters consideration

In response to the evolving technological landscape, the traditional clock network architecture faces challenges in meeting the complexities of modern System-on-Chip (SoC) designs. While the clock mesh topology offers resilience against On-Chip Variation (OCV) fluctuations, its manual implementation leaves room for advancements in methodology and swift analytical techniques. This paper introduces an innovative clock mesh synthesis approach, leveraging dynamic programming algorithms and emphasizing compliance with critical physical implementation parameters. Our experimental results demonstrate a significant 26.6% reduction in power consumption compared to baseline methodologies. Moreover, it achieves an impressive average runtime reduction of 78.0% when contrasted with traditional simulation methods. These findings underscore the potential of our methodology to enhance the efficiency and power management of clock mesh designs.

Simulated medication administration for vulnerable populations using scanning technology: a quasi-experimental pilot study

BackgroundMedication errors may occur due to shortcuts and pressures on time and resources on nurses. Nursing students are enculturated into these environments where their perceptions of norms around reporting and responding to medication errors are formative, yet simulated medication administration experiences are rarely reflective of the real-world environment. such as the standard use of medication scanning technology. The purpose of the present study is to test a pilot intervention, Medication Quick Response (QR) code scanning, and evaluate its effect on medication errors during simulation when compared to traditional simulation medication administration practices and to assess the students’ perceptions of the intervention.MethodsWe conducted a quasi-experimental, observational study involving Junior and Senior (3rd and 4th year) undergraduate, pre-licensure nursing students from Spring 2022 until Fall 2023. Seven simulations were conducted in pediatric and obstetric courses. The intervention group used non-patented, low cost QR scanning during medication administration. The control group used standard manual administration. Medication errors were measured based on the quantity, type of error, and degree of patient risk. A Qualtrics survey was used to assess the students’ perceptions of the intervention following simulation participation.ResultsA total of 166 students participated in the study. In each course, 7 groups were assigned to the intervention and 8 were assigned to the control. More than half of the groups made at least one medication error (n = 17), one-third of groups (n = 10) made a high-risk medication error. There was no statistically meaningful difference in the rate, type, or potential patient risk of medication errors between the intervention and control groups. The majority of participants (n = 53) felt that QR scanning more closely mimicked medication administration in clinical settings. Half of the participants responded that it improved their safety practices (n = 37).ConclusionsThe results of this pilot study indicate that while there is a high risk for error among pre-licensure nursing students, the use of QR scanning did not increase the risk of medication errors. The next study iteration will build upon these pilot findings to integrate the use of embedded medication errors, time management tasks, and a multi-site implementation.

An analytical scaling law model framework for short-time dust emission from belt conveyor

Aiming at explaining the short-time dust emission from belt conveyors, an analytical scaling law model framework is introduced, employing the same assumptions and wall models as traditional numerical simulations. Further, assuming a piecewise mean velocity profile, the Lagrangian mean velocity of dust particles can be written as a general expression. This expression is validated in various cases, including the 2D Belt, 3D Belt, 2D Tire, and 3D Tire cases. Numerical results show that the theoretical prediction, with key parameters fixed at us=5,κ=0.4187, and E=9.793, fits well with the simulated Lagrangian mean velocity profiles. For example, in the 2D Belt case, short-time agreement is achieved within t⩽1 s, indicating the model’s effectiveness in predicting dust dispersion within this time frame.

Efficacy of Virtual Preparation Simulators Compared to Traditional Preparations on Phantom Heads.

Virtual simulators are increasingly being introduced in dental education. This study investigates whether virtual simulators offer comparable or superior educational efficacy when compared to traditional phantom simulators. Participants were randomly allocated into groups: Virtual Preparation (SIM; n = 30) and Traditional Preparation (FRA; n = 30). Students were tasked with preparing tooth 36 for a full-cast crown during free practice for four days. Faculty staff provided feedback to both groups. Examinations were administered and graded by three examiners (preclinical and clinical consultants and a dental surgery consultant). Additionally, a survey was conducted to assess each training concept. The FRA group achieved significantly better grades in the preparation exam evaluations by all three examiners, compared to the SIM group. Interrater reliability showed only moderate agreement, with the clinical examiner giving better grades than the other two. The questionnaire results indicate that while participants managed with the virtual system, they preferred the analog system for exams and patient preparation. Virtual simulators do not seem to be as good when it comes to practicing for a preparation exam or clinical preparation, especially for unexperienced students. However, they still appear to be useful as an additional tool for introducing students to the topic of preparation.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

CFRP-RC fracture strength prediction: A novel approach using deep learning

Externally bonded carbon fiber-reinforced polymer (CFRP) composites have been widely adopted for strengthening and repairing aging concrete structures. However, premature fracture at the CFRP-concrete interface remains a significant concern, necessitating a better understanding of the interfacial bond behavior and fracture mechanisms. Traditional experimental studies and numerical simulations have limitations in accurately capturing the complex nonlinear factors influencing this fracture behavior. This paper explores the novel application of machine learning (ML), specifically artificial neural networks (ANNs), for predicting the fracture strength of CFRP-reinforced concrete (CFRP-RC) members. ANNs enable mapping the intricate relationships between input parameters (e.g., CFRP/concrete properties, geometric configurations) and fracture responses through iterative training on experimental data. Key aspects include comprehensive data collection, preprocessing techniques, feature selection methods like mean impact value (MIV) analysis, and neural network architecture design. The paper highlights successful ANN applications in predicting CFRP-RC bond strength, shear capacity, and overall fracture behavior. A systematic approach is proposed for developing robust ANN models tailored to CFRP-RC fracture prediction, encompassing data curation, input variable screening, network training/validation, and model interpretability analyses. By leveraging ML’s ability to handle multifaceted nonlinearities, this data-driven framework offers a powerful alternative to traditional methods, potentially enhancing the design, analysis, and performance evaluation of CFRP-strengthened concrete structures.

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition

Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target’s prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions.

Hydrochemical Evolution Process and Mechanism of Groundwater in the Hutuo River Alluvial Fan, North China

Due to extensive groundwater exploitation, a groundwater funnel has persisted in the Hutuo River alluvial fan in Shijiazhuang since the 1980s, lasting nearly 40 years and significantly impacting the groundwater chemical characteristics. In this study, based on the groundwater level and chemistry data, the hydrochemical evolution processes and mechanisms of the groundwater during the 1980 groundwater funnel period and the post-2015 artificial governance period were investigated using traditional hydrogeochemical methods and inverse hydrogeochemical simulations. The results show the following: (1) The ion concentrations gradually increased along the groundwater flow path, where they displayed a pattern of lower levels in the northwest and higher levels in the southeast. From 1980 to 2021, the concentrations of major ions were increased. (2) In 1980s, the groundwater hydrochemical type predominantly exhibited HCO3—Ca. From 1980 to 2015, the hydrochemical types diversified into HCO3·Cl—Ca, HCO3—Ca·Mg, and HCO3·SO4—Ca types. Following the artificial governance, the groundwater level rise led to an increase in the concentrations of SO42− and Mg2+. Post-2015, the prevailing hydrochemical type changed to HCO3·SO4—Ca·Mg. (3) The changes in the groundwater level and ion concentrations were quantitatively strongly correlated and exhibited spatial similarity. (4) In the 1980s, the groundwater hydrochemical composition was primarily controlled by the dissolution of albite, dolomite, halite, and quartz; reverse cation exchange; and groundwater exploitation. Since 2015, the hydrochemical composition has mainly been influenced by the dissolution of albite, calcite, and quartz; positive cation exchange; river–groundwater mixing; and industrial activities, with increasing intensities of both water–rock interactions and human activities.

Discrete event simulation and Digital Twins in warehouse logistics: a bibliometric and content analysis-based systematic literature review

ABSTRACT In Warehouse Logistics (WL), the growing adoption of Industry 4.0 automations coupled with the demand for efficient and almost instantly responsive decision-making has already created a paradigm shift from the traditional Discrete Event Simulation (DES) models into innovative new ones utilizing the inherent functionalities of ‘Digital Twins’ (DTs), one of today’s most disruptive Industry 4.0 technologies. DTs are ‘real-world aware’ through the creation of exact software replicas of actual systems, allowing the convergence of physical and virtual entities and facilitating real-time responsive decision-making. In this paper, a bibliometric and content analysis-based systematic literature review is performed to explore the adoption, implementation and evaluation of DES and DTs in WL. The authors focus on how and to what extent these two methods are integrated into the various WL theoretical and real-life applications to enhance a plethora of performance factors as well as look into their exploitation, as standalone solutions or in tandem with one another and other modelling and simulation approaches, technologies, algorithms, methods, methodologies and strategies in different contexts and environments. Based on the review results, a conceptual framework is proposed combining the multiple perspectives examined, contributing to the systematization and standardization of the field and pinpointing future research directions.

Thermal-electrical analogy for simulations of superconducting power cables

Due the integration of superconducting technologies into electrical networks around the world, its precise simulations in power grids are increasingly becoming a desirable outcome. Over the years, sophisticated methods have been used to model superconducting power cables and, in this way, predict its behavior under different conditions. Most of the available models, however, are not advisable to be used and embedded in power system simulators. In this manuscript, we focus on the development of a method specifically for modeling superconducting cables in traditional power grid simulators. We start developing the model through a case example, within the basic concepts are introduced and later expanded to general cases. The model also incorporates the transient behavior of the cooling media, which is mandatory for such cables. It has been observed that the proposed model requires less computational effort and is able to deliver accurate results when compared to more advanced methods.

An AI-assisted terahertz reconfigurable metamaterial in standard 180-nm CMOS

For large-scale terahertz (THz) reconfigurable metamaterials (RM), each unit element needs to be controlled by an independent voltage source. This leads to a huge solution space for control words to achieve the required RM performance specification. In this paper, we propose a fast AI-assisted control-word generation scheme to reduce the computation cost of electromagnetic (EM) simulations (Forward Model) and to speed up iterations for control word searches (Inverse Model). We demonstrate the effectiveness of our models by designing and fabricating a 12 × 9 RM array with a Bi-layer Bi-Split-Ring Resonator structure (B-BSRR) in standard 180 nm CMOS technology. The terahertz RM can be electrically reconfigured, providing active metamaterials with an amplitude modulation depth of around 0–1.7 dB at 0.49 THz. The results show that our Forward Model can predict the S-parameters between 100 GHz and 800 GHz with a mean absolute error of 0.61 dB. Our method is more than 200 times faster than traditional full-wave simulation methods. The Inverse Model can generate required control words within a 1.3 dB error margin in less than 200 iterations.

Artificial Intelligence Applied to Microwave Heating Systems: Prediction of Temperature Profile through Convolutional Neural Networks

Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly machine learning algorithms, have transformed data processing by increasing accuracy while decreasing computational time. This study tackles the difficulty of efficient and accurate modelling in microwave heating by combining convolutional neural networks (CNNs) with traditional simulation techniques. The major goal of this research is to use CNNs to forecast temperature profiles in a variety of industrial materials, including susceptors, semi-transparent, and microwave-transparent materials, under varying power settings and heating periods. This unique strategy greatly reduces prediction times, with up to 60-fold speed increases over standard methods. Our research is based on examining the electromagnetic and thermal responses of these materials under microwave heating. This study’s findings emphasise the need for extensive datasets and show the transformational potential of CNNs in optimising material processing. It uses artificial intelligence to pave the way for more effective and exact simulations, supporting breakthroughs in industrial microwave heating applications.

Using Mixed Reality to Teach Medical Students Lumbar Punctures

BackgroundGaining familiarity with bedside procedures during medical school is essential for preparing medical students for intern year. However, this is often not a core component of undergraduate medical education, and students often feel unprepared to perform bedside procedures upon graduation from medical school. Extended reality technologies have rapidly developed in the past few years, become more accessible, and show great promise in education, most recently in healthcare. We aim to show the feasibility and usability of a mixed-reality lumbar puncture simulated experience. MethodsWe created the mixed-reality simulation by filming a procedure conducted by an expert on a lumbar puncture simulator from the first-person view. We performed a study to assess the feasibility and usability of mixed-reality lumbar puncture training compared with traditional faculty-led high-fidelity mannikin-based simulation training. Fourth-year medical students were randomly selected to participate in the mixed-reality lumbar puncture training. Students watched the virtual first-person teaching via a virtual reality headset while mimicking hand movements on a lumbar puncture mannikin. Faculty taught students in the traditional high-fidelity group in small groups before practicing on the high-fidelity manikin. Students in both groups then performed the procedure entirely with performance assessed via a standardized checklist. Number of attempts to achieve a predetermined minimum passing score were recorded, and students were also surveyed regarding their experience with both simulations. ResultsFifty-nine percent of students felt comfortable using mixed reality as a training modality with less than 6% having any prior extended reality experience. More than 55% felt the mixed-reality experience was as effective as the traditional high-fidelity simulation training. Many students (72%) desired more mixed-reality opportunities. In terms of performance, 58.8% of students in the mixed-reality group vs 65.5% in the traditional group completed the checklist in 1 attempt (P = .79). ConclusionsWe demonstrate that mixed reality is an acceptable and effective modality to train health care professionals in lumbar punctures with opportunities to further enhance the immersive learning experience.

The evolution of simulation-based medical education research: From traditional to virtual simulations

BackgroundSimulation-based medical education (SBME) is a widely used method in medical education. This study aims to analyze publications on SBME in terms of countries, institutions, journals, authors, and keyword co-occurrence, as well as to identify trends in SBME research. MethodsWe retrieved the Publications on SBME from the Web of Science Core Collection (WoSCC) database from its inception to January 27, 2024. Microsoft Excel 2019, CiteSpace, and VOSviewer were used to identify the distribution of countries, journals, and authors, as well as to determine the research hotspots. ResultsWe retrieved a total of 11272 publications from WoSCC. The number of documents published in 2022 was the highest in the last few decades. The USA, the UK, and Canada were three key contributors to this field. The University of Toronto, Stanford University, and Harvard Medical School were the top major institutions with a larger number of publications. Konge, Lars was the most productive author, while McGaghie, William C was the highest cited author. BMC Medical Education has the highest number of publications among journals. The foundational themes of SBME are “Patient simulation,” “extending reality,” and “surgical skills.” ConclusionsSBME has attracted considerable attention in medical education. The research hotspot is gradually shifting from traditional simulations with real people or mannequins to virtual, digitally-based simulations and online education. Further studies will be conducted to elucidate the mechanisms of SBME. The utilization of SBME will be more rationalized.