High-fidelity Simulation Model Research Articles

681 Application of Low-Cost Automated Progress Tracking System (APS) on Laparoscopic Appendicectomy Simulation Models (LaApSi) for the Laparoscopic Simulation Training in the Lower-Middle-Income-Countries (LMICs)

Abstract Aim To assess the effectiveness of the Low-Cost Automated Progress Tracking System on Laparoscopic Appendicectomy (LA) Simulation Models (LaApSi) for Laparoscopic Training in Lower-Middle-Income-Countries (LMICs). To assess and record the progression of trainees’ performance during simulation training. Method After the successful application and validation of the LaApSi model during laparoscopic courses at LMICs, the APS system has been prototyped to assess the trainee’s progression of LA. This system helps to assess surgical steps during simulated LA with the help of the LaApSi model integrated with wires at various anatomical positions giving real-time computer feedback and allowing users to assess the timing of operation, blood vessel excision and injury to the base of the appendix/caecum. Results The prototype version tested in LMIC showed excellent results in the detection of the above-mentioned parameters (shown in photos). This cost-effective model (LaApSi< £5 and docking system <£15) showed huge potential as a laparoscopic training model in LMICs. There will be additional validation of 16 APS models during the Laparoscopic Surgery Course in Nepal in Jan 2024 in collaboration with Nepalese Doctors Association-UK which we believe will further solidify its application in the LMICs Conclusions There is a need for the transition to high-fidelity low-cost laparoscopic simulation models from the traditional training methods in the LMICs which in the long-term address global surgery values. These systems have the potential for remote proctorship in the rural parts adapting the Hub & Spoke model and integrating AI and Machine Learning which eliminates the use of wires and sensors in the models.

Implementation and Validation of a High-fidelity Simulation Model for Surgical Resident Training: Management of Acute Intraoperative Hemorrhage during Robot-assisted Surgery

ObjectivesTo improve our previous simulation-based training module by using sustainable material to mold an anatomically accurate terrain and reproducing major vascular injuries encountered during robot-assisted nephrectomy. MethodsThe simulator was built with a pump, gauge, and valve linked via silicone tubing. Artificial blood was made from cornstarch, water, and red dye, and pumped through 3D-Med artificial vessels with the dimensions of an average renal artery. Silicone was used to emulate the pliability of organic tissue and mold an anatomically accurate terrain. Eight urologic residents participated in the pilot simulation. We employed validated assessment tools including Non-Technical Skills for Surgeons and Objective Structured Assessment of Technical Skills forms to guide debrief sessions moderated by an expert physician after individual performance evaluations. ResultsThe apparatus demonstrated high reproducibility across all simulation scenarios, enhancing resident problem-solving skills. Residents’ pre-simulation surveys revealed significant concern regarding their acute hemorrhage management. Residents’ post-simulation survey demonstrated average realism scores increased from 4.375 to 4.75. Residents also felt the simulator enhanced learning, offering valuable practice and knowledge applicable to their surgical specialty. ConclusionsThe management of acute hemorrhage during robot-assisted surgery remains a space for additional surgical education and training. Our simulation successfully provided a reliable, reproducible training for residents to practice their technical and non-technical skills in managing acute hemorrhage.

Automated optimal experimental design strategy for reduced order modeling of aerodynamic flow fields

Aerodynamic flow fields reveal essential physical insights (such as shocks) that substantially affect aerodynamic performance. However, conventional flow field computations require time-consuming simulations. Alternatively, reduced-order models (ROMs) allow fast flow field predictions, enabling real-time decision-making. Efficient sampling is required to train ROMs without incuring prohibitive costs. In this paper, we propose a fully automated optimal experimental design (Auto-OED) strategy on proper orthogonal decomposition (POD) for ROM-based rapid flow field predictions. Auto-OED uses two individual optimal experimental design (OED) strategies, automatically selects the number of POD bases for the first sampling strategy, and intelligently switches to the second strategy on the fly. We showcased the Auto-OED strategy on airfoil flow field predictions in the transonic regime. OED-based ROMs were constructed over 20 trials for robustness tests with a total of 40 training samples in each trial, including 16 random initial samples by Latin hypercube sampling (LHS) and 24 OED samples. The results demonstrated that the ROM predictions completely based on LHS had an error of 1.6×10−3 while the existing OED strategies-based ROMs over 20 trials achieved a mean error (μerr) of 7.5×10−4 with a standard deviation (σerr) of 9.0×10−5. In contrast, the best Auto-OED ROM achieved the lowest μerr of 7.5×10−4 and lowest σerr of 6.2×10−5. These error reductions confirm the viability of the proposed Auto-OED-based ROM in fluid field predictions and potentially other engineering applications of a similar type.

Multifidelity topology design of a maritime survey operation with UUVs

Multifidelity topology design of a maritime survey operation with UUVs

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

High-fidelity flexible multibody model considering torsional deformation for nonequatorial space elevator

Space elevators are a promising technology for future space transportation. The primary component of a space elevator is a tether deployed from a geostationary orbit towards the Earth and into deep space. Payloads can be transported using a climber moving along the tether. However, obtaining a deeper comprehension of the precise dynamics of a space elevator is challenging. The tether could have various deformations such as elongation, bending, and torsion owing to its flexible nature and the low level of vibration damping in space. Therefore, understanding those effects on the tether is essential for the design concept of a space elevator. Reproducing the behavior of space tether systems on the ground is challenging, which is primarily due to the scale of the system and the difficulty of accurately replicating a space environment. Thus, high-fidelity numerical analysis methods are required to analyze the dynamic response of space elevators precisely. Previously proposed analysis models of space tether systems with climbers have focused on elongation, bending, and rigid body motion of the tether, and the effect of torsional deformation has been neglected. However, a tether can be twisted easily owing to its low torsional stiffness. This study established high-fidelity numerical simulation models for space elevators considering large deformations including the torsion of a tether. This study developed a flexible multibody model with a moving climber using the absolute nodal coordinate formulation with 14 degrees of freedom. Further, the gravitational perturbation caused by the non-sphericity of the Earth was considered. Using the constructed model, the motion and deformation of nonequatorial space elevators caused by the climber motion and the disturbance in the space environment were investigated. The obtained results indicate that gravitational perturbation can induce torsional deformation, particularly at higher anchor latitudes.

Randomized Controlled Trial of Telementoring During Resource-Limited Patient Care Simulation Improves Caregiver Performance and Patient Survival.

To determine the impact of telementoring on caregiver performance during a high-fidelity medical simulation model (HFMSM) of a critically ill patient in a resource-limited setting. A two-center, randomized, controlled study using a HFMSM of a patient with community-acquired pneumonia complicated by acute respiratory distress syndrome. A notional clinic in a remote location staffed by a single clinician and nonmedical assistant. Clinicians with limited experience managing critically ill patients. Telemedicine (TM) support. The primary outcome was clinical performance as measured by accuracy, reliability, and efficiency of care. Secondary outcomes were patient survival, procedural quality, subjective assessment of the HFMSM, and perceived workload. TM participants (N = 11) performed better than non-TM (NTM, N = 12) in providing expected care (accuracy), delivering care more consistently (reliability), and without consistent differences in efficiency (timeliness of care). Accuracy: TM completed 91% and NTM 42% of expected tasks and procedures. Efficiency: groups did not differ in the mean (± sd) minutes it took to obtain an advanced airway successfully (TM 15.2 ± 10.5 vs. NTM 22.8 ± 8.4, p = 0.10) or decompress a tension pneumothorax with a needle (TM 0.7 ± 0.5 vs. NTM 0.6 ± 0.9, p = 0.65). TM was slower than NTM in completing thoracostomy (22.3 ± 10.2 vs. 12.3 ± 4.8, p = 0.03). Reliability: TM performed 13 of 17 (76%) tasks with more consistent timing than NTM. TM completed 68% and NTM 29% of procedural quality metrics. Eighty-two percent of the TM participants versus 17% of the NTM participants simulated patients survived (p = 0.003). The groups similarly perceived the HFMSM as realistic, managed their patients with personal ownership, and experienced comparable workload and stress. Remote expertise provided with TM to caregivers in resource-limited settings improves caregiver performance, quality of care, and potentially real patient survival. HFMSM can be used to study interventions in ways not possible with real patients.

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Bench test data are easily obtained, but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system. Despite the actual load information contained in the operating data of the control valve, its acquisition remains challenging. This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. Firstly, a system model is established, and the parameters in the model are analysed. Secondly, the bench and operating data of the system are collected. Then, the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and particle swarm optimisation (PSO) algorithm on system model parameters are compared. The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. Increasing load complexity leads to a decrease in model accuracy, highlighting the crucial role of the data fusion method in parameter estimation studies.

High-fidelity 3D simulation of dust-loading behavior and clogging process of coarse nonwoven filter media considering collision effect

The filter lifetime of fibrous media is important for the energy-saving application of air filters and efficient delivery of clean air. The clogging process significantly affects the dust-loading behavior of fibrous filter media. Therefore, an effective methodology for determining the deposition of dust particles inside filter structures is necessary. This study conducted numerical dust loading simulations on a three-dimensional (3D) X-ray tomographic structure of coarse nonwoven filter media. Spherical particles with a density of 1808 kg/m3 were used in the simulations instead of plate-like dust particles with a higher silica density. A high-fidelity simulation model was successfully developed by considering collision effects. Key factors were determined, including the Hamaker constant for the adhesion energy, restitution coefficient for the particle kinetic energy, and sliding motion. Remarkably, the deviation between the simulated and experimental dust holding capacity was less than 1 %. The developed simulation model enabled a quantitative evaluation of the clogging process and 3D in-situ evolution of particle deposition and pore structures. It was observed that 95.6 % of the maximum deposited dust mass within the fibrous structure was captured at the clogging point. Several computational methods yielded accurate pore size evaluations and clear 3D pore structure visualizations. The developed methodology for high-fidelity simulation and 3D analysis can be further applied to other two-phase filtration and separation fields, including aerosol filtration, coalescence filtration, membrane fouling, micro/nanoplastic capture, and oil/water separation.

A Hybrid Algorithm Combining Data-Driven and Simulation-Based Reinforcement Learning Approaches to Energy Management of Hybrid Electric Vehicles

Energy management strategy (EMS) is one of the key technologies that improves the fuel efficiency of hybrid electric vehicles (HEVs) by governing the energy flow between the fuel tank and the electric energy storage. With the rapid development of artificial intelligence especially after the great success of AlphaGo, reinforcement learning (RL) has opened up a new window for EMS. Although many RL-based solutions have been successfully applied to EMS tasks, most current RL-based approaches only consider RL as an offline optimization tool, i.e., RL is used to solve optimization problems given a simulation model. This method may be utilized to realize optimal control in simulation, but there is no guarantee that a well-behaved performance can also be achieved in the real-physical world due to the simulation-to-real gap. Meanwhile, for many industrial EMS tasks especially when optimizing an existing controller, a high-fidelity simulator may be unavailable, but some logging data generated by some suboptimal existing controller and simple models of the principal powertrain components neglecting detailed component dynamics could exist. In this context, a hybrid algorithm combining data-driven and simulation-based RL approach is proposed that tries to learn experience from both the real logging data and simulated simple model. Based on the hardware-in-the-loop (HIL) results, a near optimal policy can be obtained (fuel consumption is decreased by approximately 6.10%) by this hybrid algorithm from a small batch logging data and a simple simulated model when compared with the dynamic programming (DP) method using a high-fidelity simulation model mimicking the real-physical world.

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

Optimal Tilt-Wing eVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus A3 Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples.

Exploiting the potential of large eddy simulations (LES) for ducted fuel injection investigation in non-reacting conditions

The diesel combustion research is increasingly focused on ducted fuel injection (DFI), a promising concept to abate engine-out soot emissions in compression-ignition engines. A large set of experiments carried out in constant volume vessel and numerical simulations, at medium-low computational cost, showed that the duct adoption in front of the injector nozzle activates several soot mitigation mechanisms, leading to quasi-zero soot formation in several engine-like operating conditions. However, although the simplified CFD modelling so far played a crucial role for the preliminary understanding of DFI technology, a more accurate turbulence description approach, combined with a large set of numerical experiments for statistical purposes, is of paramount importance for a robust knowledge of the DFI physical behaviour.In this context, the present work exploits the potential of large eddy simulations (LES) to analyse the non-reacting spray of DFI configuration compared with the unconstrained spray. For this purpose, a previously developed spray model, calibrated and validated in the RANS framework against an extensive amount of experimental data related to both free spray and DFI, has been employed. The tests have been carried out considering a single-hole injector in an optical accessible constant volume vessel, properly replicated in the simulation environment. This high-fidelity simulation model has been adapted for LES, firstly selecting the best grid settings, and then carrying out several numerical experiments for both spray configurations until achieving a satisfying statistical convergence. With this aim, the number of independent samples for the averaging procedure has been increased exploiting the axial symmetry characteristics of the present case study.Thanks to this approach, a detailed description of the main DFI-enabled soot mitigation mechanisms has been achieved, shrinking the knowledge gap in the physical understanding of the impact of spray-duct interaction.

FAST Low-Energy Beamline Studies: Toward High-Peak 5D Brightness Beams for FAST-GREENS

The FAST beamline is the injector for the planned Gamma-Ray Electron ENhanced Source (GREENS) program, which aims to achieve the demonstration and first application of a high-efficiency, high-average-power free-electron laser at 515 nm. FAST-GREENS requires high 5D peak brightness; transverse normalized projected emittances of 3 mm-mrad and a peak current of 600 A are the minimum beam requirements for the FEL to reach the 10% efficiency goal. In this work, studies of the low-energy section of the FAST beamline are presented toward these ends, including preliminary measurements of beam compression and beam emittance. An effort toward developing a high-fidelity simulation model that could be later optimized for FAST-GREENS is presented.

Self-Optimizing Vapor Compression Cycles Online With Bayesian Optimization Under Local Search Region Constraints

Abstract Self-optimizing efficiency of vapor compression cycles (VCCs) involves assigning multiple decision variables simultaneously in order to minimize power consumption while maintaining safe operating conditions. Due to the modeling complexity associated with cycle dynamics (and other smart building energy systems), online self-optimization requires algorithms that can safely and efficiently explore the search space in a derivative-free and model-agnostic manner. This makes Bayesian optimization (BO) a strong candidate for self-optimization. Unfortunately, classical BO algorithms ignore the relationship between consecutive optimizer candidates, resulting in jumps in the search space that can lead to fail-safe mechanisms being triggered, or undesired transient dynamics that violate operational constraints. To this end, we propose safe LSR-BO, a global optimization methodology that builds on the BO framework while enforcing two types of safety constraints including black-box constraints on the output and local search region (LSR) constraints on the input. We provide theoretical guarantees that under standard assumptions on the performance and constraint functions, LSR-BO guarantees constraints will be satisfied at all iterations with high probability. Furthermore, in the presence of only input LSR constraints, we show the method will converge to the true (unknown) globally optimal solution. We demonstrate the potential of our proposed LSR-BO method on a high-fidelity simulation model of a commercial vapor compression system with both LSR constraints on expansion valve positions and fan speeds, in addition to other safety constraints on discharge and evaporator temperatures.

Model predictive control of heating in a low energy single-family house

This paper presents a control scheme that uses a Model Predictive Control (MPC) approach to manage the heating system of a low-energy single-family house. The house is equipped with an air-to-water Heat Pump (HP) and individually controlled hydronic underfloor heating circuits for each of its 11 heating zones. The MPC scheme is designed to maintain individual room comfort levels in each zone, while incorporating weather forecasts and following a heating reference to allow for load shifting for periods with low energy prices and high Photovoltaic (PV) production calculated by an upper level in a hierarchical control scheme. The focus of the design has been the model structure that allows for fast solutions to the MPC optimisation problem, while still capturing the high complexity, non-linear dynamics of the building. The controller is tested on a high-fidelity simulation model of the house, achieving disturbance rejection and system stabilisation. The rapid solving time makes repeated experiments and longer simulations feasible.

Surrogate model-based calibration of a flying Earth observation satellite

At the European Space Agency (ESA), Modeling and Simulation (M&S) plays a fundamental role during the lifetime of a spacecraft, being used from the design phase to the testing and during operations in space. M&S tools embed general physics-based models and disciplines characterized by configurable parameters which have to be calibrated in order to mimic the behavior of the actual flying spacecraft. However, their calibration requires a large number of simulations which are unfeasible to be obtained through computationally expensive high-fidelity simulation models. Thus, the inability to calibrate the high-fidelity simulation models poses limitations for the use of M&S tools during spacecraft operations.In this light, the present work proposes the use of a surrogate model-based approach for the calibration of simulation models of spacecraft. The approach integrates a computationally inexpensive deep-learning-based surrogate model, which mimics the high-fidelity simulation model without requiring the same computational burden, and a metaheuristic optimization algorithm, to identify the optimal values of the simulation model configurable parameters. This enhances the capabilities of M&S tools and allows their use in operations. The approach’s effectiveness is shown by its application to real flying Earth observation satellite data and simulation models.

A52Probing for Veins: A Cost-Effective, Reproducible Method for Teaching Ultrasound-Guided Peripheral Intravenous Access

Repeated attempts at peripheral intravenous (PIV) access cause increased discomfort and delays in treatment for patients in hospital. The use of ultrasound can improve success in PIV access, benefitting patient experience. Medical professionals are increasingly expected to use ultrasound when landmark techniques fail [1]. We sought to improve confidence using ultrasound for PIV cannulation with low-cost simulation models made from ADAMgel [2] or tofu in multiple teaching sessions. We organized teaching sessions with multiple specialties at St Richard’s Hospital. A total of 48 members of staff have been taught over four sessions. Prior to the teaching session learners were asked to complete a survey identifying previous ultrasound experience and rating confidence using ultrasound for PIV access. The teaching session consisted of an introductory multimedia presentation with a video demonstration, then practical experience. Models consisted of water-filled balloons placed in ADAMgel (Aqueous Dietary Fibre Antifreeze Mix gel) or tofu to simulate human tissue and veins, Models used to deliver teaching sessions. After ADAMgel became available this has been used exclusively due to it being more reusable compared to the tofu modelsA: tofu model with ultrasound image underneath.B: ADAMgel model with ultrasound image underneath The initial survey identified only 29% of staff had previously used ultrasound for PIV access, and half of these (15% overall) had only used it once or twice a year. Using a five-point Likert scale, 74% of respondents rated their confidence in using ultrasound as one or two out of five. All respondents thought they would benefit from further teaching in PIV access. Feedback after teaching sessions was favourable, with 95% of respondents finding the session very useful and 95% also believing it would increase their use of ultrasound in clinical practice. After the session, more than half rated their confidence in ultrasound cannulation as four or five out of five. We have demonstrated that there is a desire from medical staff to increase their competency in ultrasound-guided PIV cannulation. Using low-cost, high-fidelity simulation models with a blended learning method, we can deliver teaching sessions to a large number of medical staff. We hope to continue this teaching in collaboration with our sister sites throughout Sussex to increase confidence with ultrasound-guided PIV cannulation in this region. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

Feasibility of High-Fidelity Simulator Models for Minimally Invasive Spine Surgery in a Resource-Limited Setting: Experience From East Africa.

Spine surgery is a rapidly evolving specialty with a continuous need to learn new skills. In resource-limited settings such as Africa, the need for training is greater. The use of simulation-based training is important in different stages of skill acquisition, especially for high-stake procedures such as spine surgery. Among the available methods of simulation, the use of synthetic models has gained popularity among trainers. Twenty participants of a neurosurgery training course, most of whom (65%) were neurosurgery residents and fellows, were recruited. They had hands-on training sessions using a high-fidelity lumbar degenerative spine simulation model and hands-on theater experience. After this, they completed a survey to compare their experience and assess the effectiveness of the lumbar spine model in stimulating real patient and surgery experiences. The participants were from four African countries, and the majority were neurosurgery residents. There were varying levels of experience among the participants in minimally invasive spine surgery, with the majority either having no experience or having only observed the procedure. All the participants said that the high-fidelity lumbar spine model effectively simulated real minimally invasive spine setup and real bone haptics and was effective in learning new techniques. Most of the participants agreed that the model effectively simulated real dura and nerve roots (95%), real muscle (90%), real bleeding from bones and muscles (95%), and real cerbrospinal fluid in the subarachnoid space. Among them, 95% agreed that the model is effective in lumbar minimally invasive spine training in resource-limited settings. With the development of new and better surgical techniques, the use of high-fidelity models provides a good opportunity for learning and training, especially in resource-poor settings where there is a paucity of training facilities and personnel.

Indicators of surgical stress and influence of clinical experience, simulation models, and cadaveric laboratory on the stress response of third-year veterinary students performing first elective surgery.

To evaluate the role of simulation models and previous surgical experience on subjective and objective stress levels of students performing their 1st elective surgery within the veterinary curriculum. 141 third-year veterinary students. Using a pre-post experimental design, salivary alpha-amylase, and cortisol were evaluated as markers of physiologic stress response before students' first elective surgery. Student self-reported State-Trait Anxiety Inventory (STAI) scores and quantitative measures of experience were correlated to biomarker results. No association was found for change in salivary biomarkers of stress, alpha-amylase, and cortisol, between baseline and presurgical samples accounting for gender, age, type of elective surgery performed, previous surgical experience, or simulation model use. Salivary cortisol levels were markedly elevated falling between the 66th and 99th percentile compared to an age and gender-matched population. Salivary alpha-amylase levels were also 2 to 3 times higher than those recorded by other health professionals. Veterinary student STAI scores were high falling between the 65th and 73rd percentile compared to working adults in the general population. Veterinary students' salivary cortisol, alpha-amylase, and STAI scores fell into the upper 2/3rds of the general population, demonstrating a high level of stress. Simulation models and previous surgical experience were not associated with decreased stress. Further evaluation of the implementation of high-fidelity simulation models and the role of stress on performance is indicated.