Simulation-based Model Research Articles

A simulation-based regional ground-motion model for Eastern Türkiye

A simulation-based regional ground-motion model for Eastern Türkiye

Silicone as a smart solution for simulating soft tissue-an iterative approach to developing a high-fidelity sustainable training model for laparoscopic appendectomy.

Laparoscopic appendectomy (LA) is an effective treatment for the surgical care of appendicitis, with this minimally invasive approach allowing patients to typically spend less time in hospital and promptly return to normal life activities. Residents can acquire the competence and confidence needed in a safe learning environment prior to real patient encounters through simulation-based learning of these techniques. We propose a low cost, sustainable, high fidelity simulation-based training model for LA to compliment regular resident practice of these skills. A team dedicated to developing this surgical simulation training model was established, equipped with the clinical knowledge and model engineering expertise. We used concepts of design-based research (DBR) to iteratively develop this model at key intervals. Our LA training model underwent four stages of model development prior to unified stakeholder consensus that this model was deemed effective and suitable for integration into formative surgical simulation curricula. This model simulates most of the key anatomical structures associated with performing an LA. In order to provide high fidelity haptic feedback, attempts were made to mimic the tensile properties of real tissue using different concentrations of silicone. The model can be utilized with laparoscopic box trainers of various sizes due to its scalability. It cost €9.67 to create, and single use appendix components cost €1.22 to build thereafter. Surgical residents can benefit from the platform that simulation-based education offers to develop the psychomotor skills necessary to perform LA in a safe learning environment. We describe a model for LA, which allows learners to develop their skill proficiency in this area under expert supervision.

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Monte Carlo simulations of the fracture resistance of an asphalt pavement layer

The purpose of the proposed numerical model is to analyze the cracking of the wearing course in a pavement overlay, assuming a pre-existing crack that passes through the binding layer and base. The computations employed the author's simulation-based Monte Carlo material model, which describes the failure process of a Semi-Circular Bend (SCB) specimen during standard laboratory testing of asphalt concrete. A key feature of this model is the incorporation of the random nature of material parameters, allowing for the simulation of result dispersion when analyzing a sufficiently large population of samples. The proposed FEM model and obtained material data were directly applied to the numerical analysis of the pavement structure. The comprehensive computational algorithm allows for a random description of the load that induces crack propagation in the pavement wearing course, leading to the creation of a histogram that defines the range of failure load dispersion. Such supporting calculations can assist in optimizing asphalt mix design and, in the future, may allow for the estimation of pavement structure reliability.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems - Part 2: Simulation aspects and application

Complex systems operations, such as Nuclear Power Plants (NPPs), generally require professional operating teams. Factors associated with teamwork, such as inappropriate communication and coordination, are important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has yet to be fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is a part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. Part 1 describes the theoretical fundaments of the model and details its elements. The current paper, Part 2, describes the simulation aspects and a full application of the method to an NPP four-steam generator feedwater system pipe break. It presents how to set up the simulation elements, such as hardware and humans within a team, using MATLAB Simulink. The method is demonstrated through a case study of an NPP four-steam generator feed water system pipe break. The results are discussed and assessed against theoretical and experimental findings.

Resource allocation for Hyphantria cunea invasive management:a novel simulation-based optimization model

In this study, we propose an integrated mixed-integer programming (MIP) model for allocating a limited budget to control a destructive invasive insect endangering the forest vegetation, Hyphantria cunea (H. cunea). Our model seeks to minimize the number of infected and dead trees within a preset planning period, factoring in various infestation scenarios, migration behaviors, resource limitations, intervention timing, and monitoring capabilities. Our modeling framework is novel in the sense that it depicts the transmission process of H. cunea as an infectious compartmental model. Our optimization model is also meaningful because it innovatively bridges the spread dynamics of H. cunea and the optimal resource allocation by using the time-varying number of infected trees that can be accepted in testing and surveillance. Our numerical test data and parameter settings have been collected from large-scale field surveys on H. cunea in Jiangsu Province over the past three years. The scenarios-based method offers significant computational advantages in searching for the best alternatives to real-world size problems in a rational time. Furthermore, our test results specify that the proposed model can not only aid in controlling H. cunea, but also can be adopted as a potential tool for managing other invasive species in the future.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

Accurate real-time modeling for multiple-blow forging

Numerical simulations are crucial for predicting outcomes in forging processes but often neglect dynamic interactions within forming tools and presses. This study proposes an approach for achieving accurate real-time prediction of forging outcomes. Initially, a simulation-based surrogate model is developed to replicate key process characteristics related to the billet, enabling prediction of geometry, deformation field, and forging load after an upsetting operation. Subsequently, this model is integrated with a mass-spring-damper model representing the behavior of forging machine and tools. This integration enables the prediction of blow efficiency and energy distribution after each blow, including plastic, elastic, damping, and frictional energy of the upsetting operation. The approach is validated by comparing predictions with experimental results. The coupled model outperformed Finite Element Method (FEM) predictions, exhibiting mean absolute errors (MAE) below 0.1 mm and mean absolute percentage errors (MAPE) below 1% in geometry predictions. Deformation field predictions showed errors below 0.05 mm/mm, and load-displacement curves closely matched experimental data. Blow efficiency predictions aligned well with experimental results, demonstrating a mean absolute error below 1.1%. The observed energy distribution correlated with literature findings, underscoring the model’s fidelity. The proposed methodology presents a promising approach for accurate real-time prediction of forging outcomes.

Perceptual representations in L1 and L2 spatial and abstract language processing: applying an innovative sentence-diagram verification paradigm.

Perceptual representations in language comprehension were examined using sentence-picture verification tasks. However, concerns have been raised regarding the suitability of concrete pictures for representing abstract concepts compared to image-schematic diagrams. To assess the perceptual representations of spatial and abstract domains in both first language (L1) and second language (L2) processing, the study tests bilingual speakers' mental imagery on the basis of the simulation-based L1 comprehension model and proposes a simulation-based L2 comprehension model, supported by empirical evidence from an innovative sentence-diagram verification paradigm. 41 adult L1 Mandarin Chinese speakers participated in the study. 21 participants completed the Chinese sentence-diagram verification task (Experiment 1), while 20 participants completed the translation-equivalent version in L2 English (Experiment 2). Participants read a sentence [e.g., A diligent worker walked into the office (spatial sense); A strong team headed into the final (abstract sense)] at their self-paced speed, followed by a congruent (e.g., into diagram) or incongruent diagram (e.g., out-of diagram), and made binary judgments to verify spatial configurations between the sentence and diagram. Semantic rating tasks in both Chinese and English were also conducted to validate congruency between diagrams and sentences in both languages. Results from Experiment 1 indicate overall compatibility effects on L1 Chinese processing, unaffected by directional verbs or abstractness of sense. Results from Experiment 2 reveal interference effects on L2 English processing, with interference observed only after reading sentences encoding spatial senses, not abstract senses. Aligning with previous findings using sentence-picture verification tasks, the current findings confirm the weaker mental simulation effects in L2 processing compared to L1 processing. These findings extend the existing simulation-based L1 comprehension model, provide empirical support for the proposed simulation-based L2 comprehension model, and validate the innovative sentence-diagram verification paradigm for examining image-schematic representations in spatial and abstract language processing among Chinese-English bilinguals. The paradigm holds significant potential for research on perceptual representations in processing a broader range of grammatical and semantic properties during both online and offline L1 and L2 comprehension.

A simulation-based training model to improve percutaneous endoscopic gastrostomy care skills of family caregivers: a randomized controlled study

A simulation-based training model to improve percutaneous endoscopic gastrostomy care skills of family caregivers: a randomized controlled study

Cyanobacterial blooms management: A simulation-based optimization method

Controlling cyanobacterial blooms is not only an engineering and technical issue but also an optimization problem in environment management. Under budget constraints, a novel simulation-based optimization model for cyanobacterial control is constructed in this study. The simulation model is used for simulating cyanobacteria growth and diffusion processes. The optimization model is utilized to determine the optimal search and treatment path. Through the interactive coupling of simulation modeling and resource allocation optimization, this research provides decision-makers with new operational guidelines for cyanobacterial control. Our test results demonstrate that the initial invasion frequency has a greater economic impact than invasion abundance. The nearby cells to the initial invasion are affected first, and then the influence radiates outward in a diffusion pattern. Using a slow search speed and a treatment frequency of every 10 days can achieve the lowest possible economic losses in most test scenarios. Moreover, we also find that the optimal search and treatment paths revolve around the initial invasion location. This study is a typical interdisciplinary research, which can assist water resource managers in making more accurate decisions regarding cyanobacteria removal paths, removal frequencies, and treatment speeds.

Weak lensing combined with the kinetic Sunyaev–Zel’dovich effect: a study of baryonic feedback

ABSTRACT Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year 3 weak lensing (WL) and Atacama Cosmology Telescope DR5 kinematic Sunyaev–Zel’dovich (kSZ) to jointly constrain cosmological and astrophysical baryonic feedback parameters using a flexible analytical model, ‘baryonification’. First, using WL only, we compare the $S_8$ constraints using baryonification to a simulation-calibrated halo model, a simulation-based emulator model, and the approach of discarding WL measurements on small angular scales. We find that model flexibility can shift the value of $S_8$ and degrade the uncertainty. The kSZ provides additional constraints on the astrophysical parameters, with the joint WL + kSZ analysis constraining $S_8=0.823^{+0.019}_{-0.020}$. We measure the suppression of the non-linear matter power spectrum using WL + kSZ and constrain a mean feedback scenario that is more extreme than the predictions from most hydrodynamical simulations. We constrain the baryon fractions and the gas mass fractions and find them to be generally lower than inferred from X-ray observations and simulation predictions. We conclude that the WL + kSZ measurements provide a new and complementary benchmark for building a coherent picture of the impact of gas around galaxies across observations.

Transport Effects in Electrochemical Biochip Sensors with Redox Cycling Amplification: Computational and Experimental Analysis

In the last few decades, and especially after the COVID-19 pandemic, point-of-care diagnostics and field-deployable biosensors have attracted significant attention. Key components of these instruments are microchips that integrate microfluidics with a sensor (e.g., optical or electrochemical) to provide rapid, low-cost, real-time detection of analyte molecules in very small volumes. Next-generation electrochemical sensors have been miniaturized on solid-state platforms (e.g., arrays and vectors) where the area is limited. However, as the active surface area of the sensors decreases from a few square microns to sub-micron and nanometric scales, the measured currents drop, hence affecting the limit of detection. As the concentration of many analytes in the physiological environment is low, low signal-to-noise ratios have limited the miniaturization of standard electrochemical sensors. One way to increase signal at the sensor output is by redox amplification. To achieve amplification, a second working electrode must be incorporated close to the first working electrode and proper bias must be applied to both. Micrometer-sized interdigitated electrode arrays have been fabricated using robust and scalable processes based on optical lithography and reactive ion-etching techniques. In some cases, IDAs have been integrated into microfluidics devices. The physical and electrochemical phenomena that occur in microfluidic channels under flow have been previously evaluated and showed an increase of current under flow. The effect of redox cycling in microfluidic and nanofluidic systems has also been qualitatively demonstrated. Some studies measured the amplification in the cases with and without flow and showed that the flow itself amplifies the measured electrochemical current, though in most cases, it reduces the amplification factor.Towards a more quantitative investigation, analytical and computational modeling were used, providing meaningful insights into the physics of IDAs without requiring numerous device fabrications. In this work, we used COMSOL Multiphysics to create a full three-dimensional simulation-based model of a real device in order to study the effect of laminar flow on redox cycling, as well as a simplified one-dimensional model for the analysis of transition velocity between different amplification regimes. IDAs with 5-10 mm for both electrodes width and gap were studied numerically and experimentally. The results of the 3D and 1D simulations were consistent between themselves, and also with those of the physical experiments. The relationship between the two mass transfer methods — diffusion and convection — were analyzed at different flow velocities and for different electrode geometries.Here we report a study of transport effects on a microfluidic sensor-chip in which electrochemical sensing is amplified by redox cycling using an interdigitated electrode array (IDA). Our models indicate that there exist two dominant regimes: one which is limited by redox cycling diffusion—a unique feature of IDAs— which is dominant at relatively low flow rates, and another which is limited by convection and has dominant flow effects which are effective at high flow rates (see Figure). The transition velocity between the two sensing regimes depends on the width of the electrode and spacing. It was found to be Vflow [mm/sec]=0.335/L[μm] + 0.04 which is similar to the prediction of Vflow∝1/L obtained from the one-dimensional model.An understanding of the combined effects of redox cycling and convection flow is critical as microfluidic chips with electrochemical sensing amplified by redox cycling using IDAs are becoming widely used in diagnostic assays. The method for prediction of current under any flow rate described here will support technological development of more efficient and accurate devices. Figure 1

Micro-costing of genetic diagnostics in acute leukemia in Sweden: from standard-of-care to whole-genome sequencing

Aims and background Whole-genome sequencing (WGS) is increasingly applied in clinical practice and expected to replace standard-of-care (SoC) genetic diagnostics in hematological malignancies. This study aims to assess and compare the fully burdened cost (‘micro-costing’) per patient for Swedish laboratories using WGS and SoC, respectively, in pediatric and adult patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Methods The resource use and cost details associated with SoC, e.g. chromosome banding analysis, fluorescent in situ hybridization, and targeted sequencing analysis, were collected via activity-based costing methods from four diagnostic laboratories. For WGS, corresponding data was collected from two of the centers. A simulation-based scenario model was developed for analyzing the WGS cost based on different annual sample throughput to evaluate economy of scale. Results The average SoC total cost per patient was €2,465 for pediatric AML and €2,201 for pediatric ALL, while in adults, the corresponding cost was €2,458 for AML and €1,207 for ALL. The average WGS cost (90x tumor/30x normal; sequenced on the Illumina NovaSeq 6000 platform) was estimated to €3,472 based on an annual throughput of 2,500 analyses, however, with an annual volume of 7,500 analyses the average cost would decrease by 23% to €2,671. Conclusion In summary, WGS is currently more costly than SoC, however the cost can be reduced by utilizing laboratories with higher throughput and by the expected decline in cost of reagents. Our data provides guidance to decision-makers for the resource allocation needed when implementing WGS in diagnostics of hematological malignancies.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

Self-calibrated transit service monitoring using automated collected data

This paper proposes a self-calibrated transit service monitoring framework that aims to obtain the performance of a transit system using automated collected data. We first introduce an event-based transit simulation model, which allows the detailed simulation of passenger travel behavior in a transit system, including boarding, alighting, and transfer walking. To estimate passenger path choices, we assume the path choices can be modeled using a C-logit model, and propose a simulation-based optimization model to estimate the path choice parameters based on automated fare collection and automated vehicle location data. The path choices can be estimated on a daily basis, which enables the simulation model to adapt to dynamic passenger behavior changes, and output more accurate network performance indicators for regular service monitoring such as train load, passenger travel time, and crowding at platforms. The proposed system eliminates the need for conventional monitoring equipment such as cameras at platforms and scaling/weighing systems on trains. The Hong Kong Mass Transit Railway (MTR) system is used as the case study. Results show that the model can well estimate the path choice behavior of passengers in the system. The output passenger exit flows are closer to the actual one compared to the two benchmark models (shortest path and uniform path choice).

A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms

Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators of a business’s financial health, both of which are adversely impacted by economic uncertainty. However, these metrics may diverge as distinct objectives drive them. There exists a gap in the literature regarding effective strategies for managing the trade-off between these metrics under economic uncertainty. This study addresses this gap by introducing a simulation-based optimization model that integrates system dynamics simulation and genetic algorithms. The proposed model aims to balance economic profitability and working capital efficiency within inventory management under partial trade credit. A recent real case study demonstrates the model’s applicability and reveals its superiority over conventional system dynamics simulation modeling. With its capacity to inform strategic and tactical decision-making, this model emerges as a valuable tool for supply chain and financial managers seeking to ensure financial stability amidst economic volatility.

Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

Investigating the Impacts of A Simulation-Based Learning Model Using Simulation Virtual Laboratory on Engineering Students

Considering electrical engineering students at Universitas Negeri Medan as a case study, this research looks at how an SVL affected their grades. Integrating the TAM with the ABET Laboratory Learning Objectives, it provides a comprehensive framework for quality engineering and technology education. This research is the first of its kind to theoretically compare the two concepts in a VL context. This research examines the relationship between student performance in the classroom and the TAM's usability components as well as the ABET's learning objectives. The results from the surveys given to first-year Electrical Engineering students are analyzed using Structural Equation Modeling (SEM) and Partial Least Squares (PLS). Because it enhances student performance and satisfies their learning goals, the results demonstrate that utilizing simulation-based virtual laboratories (SVL) in engineering education offers substantial educational benefits.

Enhancing Biosignal Quality in Electrocardiogram Monitoring Garments: Validation of a Simulation-Based Contact Pressure Model

Enhancing Biosignal Quality in Electrocardiogram Monitoring Garments: Validation of a Simulation-Based Contact Pressure Model