Simulation Scenarios Research Articles

Simulation-Based Training in Clinical Event Debriefing Improves Leadership Performance.

Clinical event debriefing (CED) improves healthcare team performance and patient outcomes. Most pediatric emergency medicine (PEM) physicians do not receive formal training in leading CED. Our objectives were to develop a CED curriculum and evaluate its effect on performance, knowledge, comfort, and clinical practice. This was a single group pre-post-retention study. We developed a hybrid curriculum with simulation, an interactive module, and individual feedback. We invited faculty and fellows from the PEM division of our hospital to participate. During an in-person training day, participants led standardized clinical simulation scenarios followed by simulated CED with immediate feedback on their leadership performance. They watched an interactive module between scenarios. Participants returned for a retention assessment 2-6 months later with a third simulation and debrief. Participants completed surveys measuring attitudes, experiences, and knowledge. Participants also evaluated the curriculum.The primary outcome was CED leadership performance using a novel 21-item tool that we developed, the Debrief Leadership Tool for Assessment (DELTA). A blinded, trained rater measured performance with DELTA. Secondary outcomes included changes in knowledge and comfort and changes in clinical practice. Twenty-seven participants enrolled and completed all parts of the curriculum and assessments. Debrief leadership performance improved by a mean of 3.7 points on DELTA pre-training to post-training (95% confidence interval = 2.7, 4.6, P < 0.01) and by 1.4 points from pre-training to retention (95% confidence interval = 0.1, 2.8, P = 0.03). Knowledge and comfort also significantly improved from pre-training to post-training and were sustained at retention. Most (67%) participants changed their clinical practice of CED after completing the curriculum. All participants would recommend the training to other PEM physicians. A hybrid simulation-based curriculum in leading CED for PEM physicians was associated with improvement in CED leadership performance, knowledge, and comfort. PEM physicians incorporated training into their clinical practice.

Distribution characteristics and photochemical effects of isoprene in a coastal city of southeast China

As forest coverage and urban greening increase in China, the impact of biogenic volatile organic compounds (BVOCs) on urban atmospheric environment cannot be neglected. As one of the most abundant and reactive BVOCs globally, isoprene plays a crucial role in atmospheric radical chemistry. However, the reports on quantifying the impact of isoprene on atmospheric photochemistry remain scarce. This study selected Xiamen, a typical coastal city in Fujian Province, as the research area. By employing field observation and Photochemical Box Model (PBM), the study investigated the distribution characteristics, pollution mechanisms, and impact on O3 of the atmospheric photochemical active species isoprene in coastal urban area. The study found that the average concentration of isoprene in summer was 4.7 times higher than that in autumn. Isoprene degradation primarily occurred through oxidation by OH· radicals, reaching 88 % and 86 % in summer and autumn, respectively. Based on scenario simulations using the PBM model, it was observed that isoprene photochemistry led to increased daytime concentrations of ROx radicals (summer: 24 %, autumn: 23 %), peroxyacetyl nitrate (PAN, 49 %, 44 %), and formaldehyde (HCHO, 37 %, 38 %). Additionally, isoprene photochemistry enhanced reaction rates of HO₂· + NO (summer: 21 %, autumn: 16 %) and RO₂· + NO (50 %, 52 %), ultimately resulting in a 19 % and 15 % increase in net O3 production rates in summer and autumn, respectively. This study provides scientific evidence for exploring the photochemical mechanisms and pollution control strategies of O3 in coastal atmospheric environments.

BioRxToolbox: A computational framework to streamline genetic circuit design in molecular data communications

ABSTRACT Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules and engineered cells is challenging. Diffusing molecules may fall behind their schedule to arrive at the receiver, interfering with symbols of subsequent time slots and distorting the signal. Existing theoretical molecular communication models often focus solely on the characteristics of a communication channel and fail to provide an end-to-end system response since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. In this paper, we present a model-based and computational framework called BioRxToolbox for designing diffusion-based and end-to-end molecular communication systems coupled with synthetic genetic circuits. We describe a novel framework to encode information as a sequence of bits, each transmitted from the sender as a burst of molecules, control cellular behavior at the receiver, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. Here, BioRxToolbox is demonstrated using a biological design and computational simulations for various communication scenarios. This toolbox facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

The effects of deception on memory: a comparative study of actors and eyewitnesses accounts

Deception is a common occurrence in daily life and has been shown to impair memory. This study investigated the memory-undermining effects of deception in a simulated daily life scenario, focusing on the potential moderating effect of the liars’ role (i.e., actor vs. eyewitness). In a 2 × 2 between-subjects design, 128 participants were randomly assigned to one of four conditions: actors who told the truth, actors who lied, eyewitnesses who told the truth, and eyewitnesses who lied. Participants engaged in a simulated shopping task, followed by an interview where they either provided truthful or deceptive responses about the items they bought (actors) or observed (eyewitnesses). Two days later, participants completed a series of memory tests assessing item memory, source memory, destination memory, and non-believed memories. Results showed that deception impaired source memory, with a greater impairment observed for actors than eyewitnesses. Deception also led to more non-believed memories and impaired item and destination memory, regardless of the liars’ role. These findings suggest that the role of liars moderates the effects of deception on memory, with self-related deception (actors) leading to greater memory impairments than other-related deception (eyewitnesses).

Quadrivalent Conjugate Vaccine and Invasive Meningococcal Disease in US Adolescents and Young Adults

Beginning in 2005, the US implemented routine immunization of adolescents with a quadrivalent conjugate vaccine (MenACWY) for the prevention of invasive meningococcal disease (IMD). To assess whether MenACWY immunization was associated with a reduced IMD burden among the US adolescent population and how the downward trajectory of IMD that began in the mid-1990s might have evolved in the absence of vaccination efforts. In this decision analytical study, a bayesian hierarchical Poisson regression model was developed to investigate the potential trajectory of IMD among US adolescents and young adults without vaccination and evaluate the direct association of vaccination with IMD burden. The model included the entire age-stratified US population and was fitted to national incidence data for serogroups C, W, and Y from January 1, 2001, to December 31, 2021, with stratification by vaccination status for IMD cases. Simulated counterfactual scenario of absent vaccination from 2005 to 2021, while retaining the incidence rate of IMD for unvaccinated individuals estimated during model fitting. The main outcomes were the estimated numbers of IMD cases and deaths averted by MenACWY vaccination among US adolescents and young adults aged 11 to 23 years. Among the entire US population from 2005 to 2021, MenACWY vaccination prevented an estimated 172 (95% credible interval [CrI], 85-345) cases of IMD among US adolescents 11 to 15 years of age and 328 (95% CrI, 164-646) cases of IMD among those aged 16 to 23 years. Absent vaccination, the cumulative incidence of IMD in these age groups would have been at least 59% higher than reported over the same period with vaccination. Using case fatality rates of unvaccinated individuals derived from national data, vaccination averted an estimated 16 (95% CrI, 8-31) deaths among adolescents aged 11 to 15 years and 38 (95% CrI, 19-75) deaths among those aged 16 to 23 years. This decision analytical model suggests that the MenACWY vaccination program in the US was associated with a reduced burden of meningococcal disease. Without vaccination, the incidence rates per 100 000 adolescents and young adults would have been substantially higher than those observed during the vaccine era.

Set pair analysis and system dynamics coupling approach for structure simulation and variation trend evaluation of water resources spatial equilibrium system

AbstractThe implement of water resources spatial equilibrium (WRSE) schemes is fundamental task of integrated water resources management in China, in which, the evaluation and simulation analysis of WRSE system is of great concern for understanding the overall variation and feedback characteristics of WRSE system. Therefore, we utilized the ordered degree, entropy and connection number coupling model to evaluate the variation of WRSE system, and also employed system dynamics (SD) and scenario simulation integrated method to reveal the feedback characteristics between different equilibrium variables and subsystems, and thus the set pair analysis‐SD based approach for structure simulation and variation trend evaluation of WRSE system was constructed. The application results in Anhui province, China demonstrated that, the overall variation of provincial spatial equilibrium situation of WRSE system presented obvious improving trend, 2009–2019, the index of ordered degree and connection number entropy in Hefei, increased from the minimum of 0.6434 (Grade 3, unequilibrium) to maximum of 0.9985 (Grade 1, equilibrium). Moreover, the future variation of WRSE system will display stable improving trend during 2020 to 2029, in which, annual water resources availability and water resources utilization efficiency will have significant influence on WRSE system. And the research findings can be favorable to formulate water resources development and utilization strategies.

Three‐Way Data Reduction Based on Essential Information

ABSTRACTIn this article, the idea of essential information‐based compression is extended to trilinear datasets. This basically boils down to identifying and labelling the essential rows (ERs), columns (ECs) and tubes (ETs) of such three‐dimensional datasets that allow by themselves to reconstruct in a linear way the entire space of the original measurements. ERs, ECs and ETs can be determined by exploiting convex geometry computational approaches such as convex hull or convex polytope estimations and can be used to generate a reduced version of the data at hand. These compressed data and their uncompressed counterpart share the same multilinear properties and their factorisation (carried out by means of, for example, parallel factor analysis–alternating least squares [PARAFAC‐ALS]) yield, in principle, indistinguishable results. More in detail, an algorithm for the assessment and extraction of the essential information encoded in trilinear data structures is here proposed. Its performance was evaluated in both real‐world and simulated scenarios which permitted to highlight the benefits that this novel data reduction strategy can bring in domains like multiway fluorescence spectroscopy and imaging.

Optimizing Particle Density for Accurate Wave Simulation: A Comparative Study of Non-Breaking and Breaking Wave Conditions

This study investigates the optimal particle density for accurate wave simulation in non-breaking and breaking wave conditions using Smoothed Particle Hydrodynamics (SPH). Simulations were conducted using various particle densities, or particles per wavelength, to evaluate their impact on simulation accuracy and computational efficiency. In tests involving regular, non-breaking waves, particle densities in the range of np per L = 560 to 800 were found to provide satisfactory accuracy and computational efficiency. Increasing the particle density beyond np per L = 800 for more complex setups involving breaking waves did not significantly improve accuracy but resulted in much higher computational costs. Overall, the range of np per L = 560 to 800 offers the best balance between accuracy and efficiency, making it a practical choice for simulations with limited resources. This study underscores the trade-offs between accuracy and computational demand, providing guidance on the selection of particle density for various wave simulation scenarios.

Prediction and Scenario Simulation of Carbon Emissions Peak of Resource-Based Urban Agglomeration with Industrial Clusters—Case of Hubaoe Urban Agglomeration Inner Mongolia Autonomous Region, China

China has implemented a “dual-carbon” policy in response to the Paris Agreement’s global climate change objectives. Hohhot, Baotou, and Ordos (HBO-UA) is a resource-based urban agglomeration that is noteworthy for having significant heavy industry in China. Based on the extended STRIPAT model, which broadens the study indicators into six aspects—population, economics, technology, urbanization, industrial energy, and industrial structure—this paper develops a research framework of “Driving–Predicting–Simulating” for carbon emissions. According to the “one formula for one city” principle, driver models were constructed for Hohhot, Baotou, and Ordos, respectively. The following conclusions were drawn: (1) Population and urbanization are the dominant factors of carbon emissions in HBO-UA, following the economy and industrial energy. (2) Carbon emissions are multifactor-driven in Hohhot, double-factor-driven in Baotou, and single-factor-driven in Ordos. (3) Hohhot can achieve its carbon emissions peak under more efficient and lower policy costs, while Ordo is under great pressure to reduce carbon emissions. (4) We suggest multiple strategies to accomplish the “dual-carbon” goals for resource-based urban agglomeration with industrial clusters. These strategies include fostering diversified consumption by continuously enhancing urban functions, directing the transformation of the industrial structure, and fostering the growth of emerging industries.

Bayesian clustering of high-dimensional data via latent repulsive mixtures

SUMMARY Model-based clustering of moderate or large dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mixture model for a set of latent scores, which are then linked to the observations via a Gaussian latent factor model. This approach was recently investigated by Chandra et al. (2023). The authors use a factor-analytic representation and assume a mixture model for the latent factors. However, performance can deteriorate in the presence of model misspecification. Assuming a repulsive point process prior for the component-specific means of the mixture for the latent scores is shown to yield a more robust model that outperforms the standard mixture model for the latent factors in several simulated scenarios. The repulsive point process must be anisotropic to favour well-separated clusters of data, and its density should be tractable for efficient posterior inference. We address these issues by proposing a general construction for anisotropic determinantal point processes. We illustrate our model in simulations as well as a plant species co-occurrence dataset.

Research on Multi-Scenario Simulation of Urban Expansion for Beijing–Tianjin–Hebei Region Considering Multilevel Urban Flows

With the development of urban agglomerations in China, the study of the interactions between cities has become a popular and difficult issue. Exploring the interactions between cities can help decision-makers optimize regional resource allocation and improve regional spatial patterns. Combining the urban flow model and the patch-generating land use simulation (PLUS) model, this study simulates and analyzes the process of urban expansion in the Beijing–Tianjin–Hebei (BTH) region, and investigates the impact of urban hierarchical structure differences on urban expansion. In this study, the role and influence of inter-city economy flow, transportation flow, population flow, and information flow on the development of urban agglomerations are comprehensively considered, and a multilevel urban interaction model is constructed based on a hierarchical generalized linear model (HGLM). Based on the national BTH cooperation and development strategy, a multi-scenario simulation study of urban expansion is carried out using the HGLM-PLUS model. The results indicate the following: (1) compared to the traditional PLUS model, the coupled HGLM-PLUS model, which considers multilevel urban flows, improved the overall accuracy by 0.047, the Kappa coefficient by 0.207, and the figure of merit (FoM) index by 0.051; (2) under different simulation scenarios, the development trend under the cooperation and development policy in the BTH region is more stable, demonstrating a relatively smooth urbanization expansion trend; and (3) under the BTH cooperation and development background, the total area of construction land in the BTH region is expected to be maintained at around 1,164,500 km2 by 2040. The spatial expansion pattern will present a networked expansion with the core driving development, axes and belts connecting, and clusters breaking through.

Megavariate Methods Capture Complex Genotype-by-Environment Interactions.

Genomic prediction models that capture genotype-by-environment interaction are useful for predicting site-specific performance by leveraging information among related individuals and correlated environments, but implementing such models is computationally challenging. This study describes the algorithm of these scalable approaches, including two models with latent representations of genotype-by-environment interactions, namely MegaLMM and MegaSEM, and an efficient multivariate mixed model solver, namely PEGS, fitting different covariance structures (unstructured, XFA, HCS). Accuracy and runtime are benchmarked on simulated scenarios with varying numbers of genotypes and environments. MegaLMM and PEGS-based XFA and HCS models provided the highest accuracy under sparse testing with 100 testing environments. PEGS-based unstructured model was orders of magnitude faster than REML-based multivariate GBLUP while providing the same accuracy. MegaSEM provided the lowest runtime, fitting a model with 200 traits and 20,000 individuals in approximately 5 minutes, and a model with 2,000 traits and 2,000 individuals in less than 3 minutes. With the G2F data, the most accurate predictions were attained with the univariate model fitted across environments and by averaging environment-level GEBVs from models with HCS and XFA covariance structures.

A simulation-based bias analysis to assess the impact of unmeasured confounding when designing nonrandomized database studies.

Unmeasured confounding is often raised as a source of potential bias during the design of nonrandomized studies, but quantifying such concerns is challenging. We developed a simulation-based approach to assess the potential impact of unmeasured confounding during the study design stage. The approach involved generation of hypothetical individual-level cohorts using realistic parameters, including a binary treatment (prevalence 25%), a time-to-event outcome (incidence 5%), 13 measured covariates, a binary unmeasured confounder (u1; 10%), and a binary measured "proxy" variable (p1) correlated with u1. Strengths of unmeasured confounding and correlations between u1 and p1 were varied in simulation scenarios. Treatment effects were estimated with (1) no adjustment, (2) adjustment for measured confounders (level 1), and (3) adjustment for measured confounders and their proxy (level 2). We computed absolute standardized mean differences in u1 and p1 and relative bias with each level of adjustment. Across all scenarios, level 2 adjustment led to improvement in the balance of u1, but this improvement was highly dependent on the correlation between u1 and p1. Level 2 adjustments also had lower relative bias than level 1 adjustments (in strong u1 scenarios: relative bias of 9.2%, 12.2%, and 13.5% at correlations of 0.7, 0.5, and 0.3, respectively, vs 16.4%, 15.8%, and 15.0% for level 1). An approach using simulated individual-level data is useful to explicitly convey the potential for bias due to unmeasured confounding while designing nonrandomized studies, and can be helpful in informing design choices. This article is part of a Special Collection on Pharmacoepidemiology.

Effects of atmospheric pressure change during flight on insulin pump delivery and glycaemic control of pilots with insulin-treated diabetes: an in vitro simulation and a retrospective observational real-world study.

Glycaemic control and clinical outcomes in diabetes are improved by continuous subcutaneous insulin infusion (CSII). Atmospheric pressure changes during flights may affect insulin delivery from pumps and cause unintended metabolic consequences, including hypoglycaemia, in people with type 1 diabetes. The present report evaluates both hypobaric flight simulation and real-world data in pilots using insulin pumps while flying. In the flight simulation part of this study, an in vitro study of insulin pumps was conducted in a hypobaric chamber, de-pressurised to 550 mmHg to mimic the atmospheric pressure changes in airliner cabins during commercial flights. Insulin delivery rates and bubble formation were recorded for standard flight protocol. Insulin infusion sets, without pumps, were tested in a simulated rapid decompression scenario. The real-world observational study was a 7.5-year retrospective cohort study in which pre- and in-flight self-monitored blood glucose (SMBG) values were monitored in pilots with insulin-treated diabetes. Commercial and private pilots granted a medical certificate to fly within the European Union Aviation Safety Agency approved protocol and receiving insulin either by pump or multiple daily injections (MDI) were included. In the flight simulation study, full cartridges over-delivered 0.60 U of insulin during a 20 min ascent and under-delivered by 0.51 U during descent compared with ground-level performance. During emergency rapid decompression, 5.6 U of excess insulin was delivered. In the real-world study, seven pilots using CSII recorded 4656 SMBG values during 2345 h of flying across 1081 flights. Only 33 (0.7%) values were outside an acceptable safe range (5.0-15.0 mmol/l [90-270 mg/dl]). No clinically significant fall in the median SMBG concentration was observed after aircraft ascent and no in-flight SMBG values were within the hypoglycaemic range (<4.0 mmol/l [<72 mg/dl]). Compared with pilots receiving MDI therapy, pilots using CSII recorded more SMBG values within the acceptable range (99.3% vs 97.5%), fewer values in the low red range (0.02% vs 0.1%), fewer in-flight out-of-range values (0.2% vs 1.3%) and maintained stricter glycaemic control during flight. Ambient pressure reduction during simulated flights results in bubble formation and expansion within insulin cartridges. This causes unintended delivery of small insulin doses independent of pre-determined delivery rates and represents the maximum amount of insulin that could be delivered and retracted. However, in vivo, pilots using CSII in-flight did not experience a fall in blood glucose or episodes of hypoglycaemia during these atmospheric pressure changes and the use of insulin pumps can be endorsed in view of their clinical benefits.

Fuzzy Logic Controller for Power Control of an Electric Arc Furnace

Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics have a significant impact on power quality. Because the active power of an electric arc depends on its length, a system for controlling the electrode positions is necessary. This paper presents a control system based on a fuzzy logic controller for the active power control of an electric arc furnace. Individual simulation scenarios were chosen with both reference values and the process taken into consideration. The reference, constant value, step variation, and the sequence of step variation were investigated, as well as step disturbances and the sequence of step disturbances from the viewpoint of the process. Furthermore, the procedure of changing the tap on a transformer was investigated. The proposed solution minimizes the time required for charge elaboration, but the main benefit is that there are no additional costs in the implementation process because the installation remains identical, with the only changes being improvements to soft control management.

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy.

The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored. We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches. We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains. Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort. This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.

Low-resource dynamic loading identification of nonlinear system using pretraining

To address the challenge of load identification in nonlinear systems, an audio neural network-based method called WaveNet is proposed that leverages its capability to capture long-term dependencies in mechanical systems, enabling accurate load identification. Unlike traditional dynamic load identification methods that often encounter difficulties with matrix solutions, this approach takes advantage of WaveNet’s capabilities, enhancing both accuracy and efficiency. We integrate pre-training and transfer learning techniques to address the data scarcity challenges often encountered in real-world engineering applications. By transferring features across distributed datasets, this method reduces the dependency on single-task data, thereby improving model robustness. The performance of the WaveNet method is rigorously evaluated against traditional benchmarks such as Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) benchmarks under random load conditions applied to a complex structural framework. The proposed method achieves a root mean squared error (RMSE) of 1.521 and a determination coefficient (R²) of 0.996 in the random load case, demonstrating superior accuracy compared to other approaches. Moreover, its applicability is verified through simulations of both impact load and harmonic load scenarios, showcasing the effectiveness of transfer learning in overcoming domain discrepancies. Finally, the method is tested in random experiments to validate its engineering applicability. The results highlight the significant accuracy improvement in low-resource tasks achieved through pre-training, showcasing the potential and value of the proposed method.

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies.

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.

A Hybrid Simulation‐Based Workshop Improves Knowledge and Confidence in the Management of Hemorrhagic Conversion of Stroke Among Interventional Neurology Trainees

Background Mechanical thrombectomy is the standard of care in acute ischemic stroke with anterior large vessel occlusion presenting within 6 hours of symptom onset, but complications do exist. We assessed the feasibility of a hybrid simulation‐based workshop at a national meeting for interventional neurology trainees focused on the management of acute ischemic stroke and tissue‐plasminogen activator‐related hemorrhage. Methods In this prospective, observational, hybrid simulation‐based study at a fellows’ workshop at a national conference, participants were asked to manage a patient with acute ischemic stroke in the neurointerventional suite followed by thrombolytic‐related hemorrhage leading to cerebral herniation during mechanical thrombectomy. We evaluated the participants’ ability to complete critical actions that were developed based on best practices and relevant American Heart Association guidelines and the Neurocritical Care Society's Emergency Neurological Life Support protocols. The primary outcome was the improvement in knowledge from a precourse to postcourse test. Secondary outcomes included participant reactions. Results Sixty trainees completed the simulation session in 8 groups. The mean sum of critical actions completed by trainees was 9.75/14 (70%). There was a moderate effect of the intervention on trainees’ knowledge from pretest (mean 3.8, SD = 0.3) to posttest (mean 4.3, SD = 0.3). The simulation scenario was described as moderately realistic, very engaging, and extremely satisfactory. Following the workshop, all fellows endorsed an increase in proficiency and confidence in neurological emergency management. Conclusion Simulation‐based workshops at national conferences are feasible and a potentially useful tool for safely educating a large audience of trainees who may not have access to high‐fidelity simulation platforms at their institutions.