Simulation Module Research Articles

Mixed Reality as a Teaching Tool for Medical Students in Neurosurgery.

Background and Objectives: Simulation-based learning within neurosurgery provides valuable and realistic educational experiences in a safe environment, enhancing the current teaching model. Mixed reality (MR) simulation can deliver a highly immersive experience through head-mounted displays and has become one of the most promising teaching tools in medical education. We aimed to identify whether an MR neurosurgical simulation module within the setting of an undergraduate neurosurgical hands-on course could improve the satisfaction of medical students. Materials and Methods: The quasi-experimental study with 223 medical students [120 in the conventional group (CG) and 103 in the MR-group (MRG)] was conducted at the University Hospital Münster, Münster, Germany. An MR simulation module was presented to the intervention group during an undergraduate neurosurgical hands-on course. Images of a skull fracture were reconstructed into 3D formats compatible with the MR-Viewer (Brainlab, Munich, Germany). Participants could interact virtually with the model and plan a surgical strategy using Magic Leap goggles. The experience was assessed by rating the course on a visual analog scale ranging from 1 (very poor) to 100 (very good) and an additional Likert-scale questionnaire. Results: The satisfaction score for CG and MRG were 89.3 ± 13.3 and 94.2 ± 7.5, respectively. The Wilcoxon rank-sum test showed that MR users (Mdn = 97.0, IQR = 4, n = 103) were significantly more satisfied than CG users (Mdn = 93.0, IQR = 10, n = 120; ln(W) = 8.99, p < 0.001) with moderate effect size (r^biserial = 0.30, CI95 [0.15, 0.43]), thus indicating that the utilization of MR-simulation is associated with greater satisfaction. Conclusions: This study reports a positive response from medical students towards MR as an educational tool. Feedback from the medical students encourages the adoption of disruptive technologies into medical school curricula.

Real-Time Selection System of Dispatching Rules for the Job Shop Scheduling Problem

Personalized market demands make the job shop scheduling problem (JSSP) increasingly complex, and the need for scheduling methods that can solve scheduling strategies quickly and easily has become very urgent. In this study, we utilized the variety and simplicity of dispatching rules (DRs) and constructed a DR real-time selection system with self-feedback characteristics by combining simulation techniques with decision tree algorithms using makespan and machine utilization as scheduling objectives, which are well adapted to the JSSP of different scales. The DR real-time selection system includes a simulation module, a learning module, and an application module. The function of the simulation module is to collect scheduling data in which is embedded a novel mathematical model describing the JSSP; the function of the learning module is to construct a DR assignment model to assign DR combinations to the job shop system, and the function of the application module is to apply the assigned DR combinations. Finally, a series of job shop systems are simulated to compare the DR assignment model with the NSGA-II and PSO algorithms. The aim is to verify the superiority of the DR assignment model and the rationality of the DR real-time selection system.

Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery

Although the maritime industry has the potential to lead smart maintenance methodologies, current maintenance routines within the sector focus on either reactive or preventive maintenance approaches. These approaches are increasingly conservative and often prompted by an increase of large costs or unnecessary maintenance actions. Attempts are being made, however, to optimise these costs and actions through the application of novel practices, such as the employment of prognostic-based maintenance, which is an approach that aims to minimise risk while maximising the lifespan of a system. In this respect, Mar-RUL is introduced to support Operations & Maintenance (O&M) decision making and address some of the main challenges that the sector is currently experiencing, including the lack of fault data analysis and the formalisation of deep learning technologies for the implementation of the Remaining Useful Life (RUL) prediction. In response, a degradation data simulation module is developed in tandem with an ensemble model comprised of three distinct deep learning architectures: Markov-Convolutional Neural Network (CNN), 1D-CNN, and Long Short-Term Memory (LSTM) neural network. To evaluate the performance of Mar-RUL, a case study on the turbocharger of a diesel generator of a tanker ship is presented. The case study results demonstrated that the application of time series imaging and ensemble methods can provide promising outcomes for the enhancement of RUL prediction.

Child Death in a Resource-Limited Setting: A Simulation Case for Pediatric Residents to Prepare for Global Health Electives.

Pediatric residents are increasingly pursuing global health electives. Differences in cultural norms and management around pediatric deaths in resource-limited settings can be emotionally overwhelming for residents. Educational resources are needed to better equip them for handling these stressful situations. We developed a predeparture simulation child death case to prepare pediatric residents for their global health elective. The simulation module included a clinical case followed by a multidisciplinary structured debriefing. The case featured a 5-year-old, malnourished child in hypovolemic shock who clinically deteriorates and dies. After obtaining a history and performing a physical examination, residents were expected to diagnose severe malnutrition, treat hypovolemic shock, and decide how far to extend resuscitation with the limited resources. Upon returning from abroad, residents were invited to complete a survey on the utility of the simulation case module in preparing for their elective. Twenty-nine residents participated in the simulation case module, and 18 completed the survey. Seventeen agreed or strongly agreed that the simulation module was a useful tool for preparation (Mdn = 4.5 on a 5-point Likert scale). Residents reflected that the simulation module helped manage expectations and provided them with an understanding of the cross-cultural differences in managing pediatric deaths in a resource-limited setting. Pediatric residents trained in resource-rich countries do not encounter death often. Postgraduate training programs could consider simulations like this one to prepare such residents for cross-cultural differences in managing pediatric deaths and build resiliency to operate in resource-limited settings.

Investigation of Numerical Simulation for Adaptive Optics System

In this study, the performance of the adaptive optics (AO) system was analyzed through a numerical computer simulation implemented in MATLAB. Making a phase screen involved turning computer-generated random numbers into two-dimensional arrays of phase values on a sample point grid with matching statistics. Von Karman turbulence was created depending on the power spectral density. Several simulated point spread functions (PSFs) and modulation transfer functions (MTFs) for different values of the Fried coherent diameter (ro) were used to show how rough the atmosphere was. To evaluate the effectiveness of the optical system (telescope), the Strehl ratio (S) was computed. The compensation procedure for an AO system was implemented. Analytical analysis was used to define the wave front and aberrations of the circular aperture telescope. Zernike polynomials were used to describe the residual error and figure out how much the compensation changed the measured turbulence values. The results of the computer simulation involving atmospheric turbulence reveal that elevating the ro values (4, 8, 12, 16, 20, 24, 28, 32) cm resulted in a 3.4% rise in S. However, when the adaptive optics system operated with a constant ro (20 cm), augmenting the Zernike aberration modes led to a remarkable 44% increase in S, signifying a substantial enhancement in the compensation procedure.

Optimal Leg Height of Landing Legs to Reduce Risk of ‎Damage from Regolith Ejecta by Retrorocket Exhausts

Over the past decade, there has been a rapid increase in rocket launches. 2022 was a record-breaking year for the ‎aerospace ‎industry, with 180 successful rocket launches into orbit, 44 more than the previous year. Reducing as ‎many risks as possible is ‎essential as interplanetary rocket launches and reusable booster landings become more ‎frequent. One such risk occurs when a ‎rocket/booster lands. During the landing process, the retrorockets spray debris ‎from the loose ground, which may damage the ‎rocket/landing module. Retrorockets are rocket engines that provide ‎a thrust opposing the spacecraft’s motion, causing it to ‎decelerate. This paper studies the effect of landing leg height ‎on ejecta velocity, the volume of debris ejected, and ground ‎surface temperature change. Four landing leg heights ‎were tested with an Estes® E-16 consumer model rocket motor: 0 mm, ‎‎50 mm, 75 mm, and 100 mm. The ‎experiment suggests that the optimal height above the ground’s surface for a simulated ‎landing module based on ‎the volume and velocity of the ejecta is 50 mm. Landing legs that elevate a model rocket this height ‎create an ‎average crater volume of 610.5 mL and a max crater diameter of 10.34 cm. After determining the optimal height, a ‎‎landing leg system was developed. This system was attached to an Aerodactyl TS® model rocket and utilized ‎landing legs that ‎elevated the rocket to a height of 50 mm above the ground at landing.‎‎

Modelling the Influence of the Tube Support Plate on the Eddy Current Testing of the Steam Generator: Numerical Tools and Experimental Validation

Eddy current testing (ECT) of Steam Generator (SG) tubes is part of the maintenance program of Nuclear Power Plants. Tube Support Plates (TSP) are usually only considered as extraneous signals for tube inspection. However, for long-term operation of SGs, there is a safety concern related to the build-up of deposits in the TSPs flow holes. The clogging-up of the TSPs flow holes affect the safe thermal-hydraulic operation of SGs. The deposits come from the feedwater train and are mostly iron oxides (magnetite) with a weak electrical conductivity and magnetic permeability. The ability of ECT probes to detect and characterize TSPs deposits is therefore of great interest. The most common and industrial ECT technique, the bobbin coil, averages the surrounding electromagnetic field over 360°, whereas the geometry of tri-foiled and quadri-foiled TSPs, and thus of clogging, is rather complex and non-axisymmetric, hence motivating the evaluation of the performance of conventional ECT rotating probes used for SGs tubes inspection. Although simulation is a powerful tool to support such a study, it requires dedicated models to be made available to NDT experts, and CEA develops ECT physical models in CIVA for this purpose. In addition to standard fast CIVA modules based on 3D semi-analytical or 2D numerical calculations restricted to canonical or axisymmetric parts, respectively, CEA develops a module dedicated to the simulation of SG tube inspection based on a 3D numerical simulation. This module allows tube deformation such as ovalization, bending or tube expansion. It also allows the addition of external objects such as anti-vibration bars, different geometries of tube support plates and now their clogging by deposits. This model is used to study various influent parameters and to perform benchmarks in the framework of a scientific collaboration between the CEA and the IRSN, related for example to the inspection of the U-bend tubes or of a friction wear under the anti-vibration bars. Here, the specificity of the TSPs and their clogging justifies further investigations between CEA and IRSN to qualify the simulation model before evaluating the influence of material and geometry properties of the deposit as well as the performance of the inspection techniques. We present the main characteristics of the simulation module in CIVA.

Simulation-based training in central venous catheterization for first-year postgraduate students: A prospective study

ABSTRACT Background: The current model of procedure training involves lectures, seminars, observations and personalised education in small groups or by watching videos on digital platforms. Central venous catheterisation (CVC) is an important skill; however, its training largely remains unstructured. There is a need of protocol-based uniform training policy. Aims: The aims and objectives of the study are to prepare and implement simulation-based CVC training module for the 1st year post-graduate students. Materials and Methods: After approval from the institutional ethical committee, 1st year post-graduate students of anaesthesia, medicine and surgery were enroled. Training modules and questionnaires were prepared with discussions amongst faculty. Students’ baseline knowledge regarding the CVC was noted on mannequins along with self-reported confidence. After that, they were subjected to training module. Students repeat knowledge and confidence regarding CVC were noted. In the next few months, procedures performed by post-graduate students were directly observed and rated. Students as well as faculty feedback regarding this teaching module was also collected. Results: Training module was associated with a statistically significant increase in knowledge and self-reported confidence regarding the knowledge and skill of CVC (P &lt; 0.05). Most of the students and faculty agreed regarding the training module utility. Conclusions: A structured simulation module for CVC helped to improve students’ knowledge and confidence about the CVC skill. The module was well accepted by both faculty and students.

The Simulation of the Non-standard Five-axis Automatic Drilling Machine Tool Based on DELMIA

The structure and motion characteristics of a non-standard five-axis automatic drilling machine tool were analyzed, and the virtual simulation models were established based on DELMIA. In order to verify the correctness of the NC code and to simulate the machining process, the VBA method was used to develop a simulation module of an automatic drilling machine tool based on DELMIA.

Design and implementation of a “crisis resource management” training program for the initial assessment of polytrauma patients

IntroductionTo describe the design and implementation of a Crisis Resource Management (CRM) training program for the initial assessment of polytrauma patients. MethodsProspectively implemented CRM training program in acute-care tertiary hospital by hospital personnel involved in the care of polytraumatisms. The program has a blended format and 23-h duration, including 11 h of online theoretical training followed by 12-h simulation modules and practical cases devoted to the roles of members of the trauma team, functioning of the polytrauma room, and key aspects of teamwork. The Human Factors Attitude Survey (HFAS) was used to assess attitudes related to non-technical skills, and the End-of-Course Critique (ECC) survey to evaluate satisfaction with training. We evaluated changes in the pre- and post-training assessments. ResultsEighty staff personnel (26% specialists, 16% residents, 29% nurses, 14% nursing assistant, 15% stretcher bearer) participated in three editions of the program. Theoretical knowledge improved from a mean (SD) of 5.95 (1.7) to 8.27 (2.1) (P < .0001). In the HFAS, statistically significant differences in 18 of 23 attitudinal markers were observed, with improvements in all items of “leadership” and “roles”, in 4 of 5 items of “situational awareness”, and in 4 of 8 items of “communication”. Mean values obtained in the ECC questionnaire were also very high. ConclusionsA CRM training model developed for the initial care of polytrauma patients improved theoretical knowledge and participants perceptions and attitudes regarding leadership, communication, roles, and situational awareness of members of the trauma team.

(Invited) Application of Open-Source, Python-Based Tools for the Simulation of Electrochemical Systems

There is a rich history of mathematical modeling of electrochemical systems. These simulations are useful 1) to refine our understanding of systems that contain complex, coupled phenomena, 2) to design and control electrochemical devices, and 3) to help novices in developing confidence and intuition for the behavior of electrochemical systems.Regardless of the application, cyclic voltammetry, storage batteries, secondary current distributions, or corrosion to name a few, elucidating the relationship between current and potential is central to understanding how electrochemical systems behave. Here, we report on historical and future perspectives of simulating electrochemical systems with open-source, python-based tools. The presentation includes a tutorial of the formulation of problems based on underlying engineering and electrochemistry principles.Within R1 universities in the United States, excellent resources are available at little to no cost for the simulation of electrochemical systems. However, the price for these tools can be prohibitive for most engineers and scientists working in industry. Access to these tools is even worse in low- and lower-middle-income countries. Actively supporting open-source software promotes a more inclusive scientific and research community that is essential to confronting the challenges facing society. Python was chosen because it is open-source.FEniCSx, a popular open-source computing platform for solving partial differential equations,1-2 is applied to the solution of primary and secondary current distributions for two- and three-dimensional geometries. FEniCSx is used on both desktop computers as well as within high performance computing environments, such as Georgia Tech’s PACE.Simulations have long been known to increase interactions between instructors and teachers as well as to help students visualize content.3-4 Recently, tools developed in python have been applied to simple electrochemical systems. 5-6. Because of the low barrier to entry and access to numerous computational packages, such as numpy, matplotlib, and scipy, the Anaconda distribution of python is promoted. A series of dynamic simulations are designed to help students improve their understanding of electrochemical systems. These simulations feature animation and extensive use of widgets that allow students to adjust parameters and immediately observe the results. A. Logg, K. A. Mardal, G. N. Wells. Automated solution of differential equations by the finite element method, Lecture Notes in Computational Science and Engineering, 84 LNCSE (2012).A, Logg and G. N. Wells. DOLFIN: Automated finite element computing, ACM Transactions on Mathematical Software, 37.2 (2010).T. de Jong, W. R. van Joolingen, Scientific Discovery Learning with Computer Simulations of Conceptual Domains, Review of Educational Research, 68, 179-201 (1998).R. E. West, C. R. Graham, Five Powerful Ways Technology Can Enhance Teaching and Learning in Higher Education, Educational Technology, 45, 20-27 (2005).X. Wang, Z. Wang, Animated Electrochemistry Simulation Modules, J. Chem. Educ., 99, 752-758 (2022).T.F. Fuller, J.N. Harb, Using Python Simulations for Inquiry-Based Learning of Electrochemical Systems, ECS Meeting s, (2021). DOI 10.1149/MA2021-02511503mtgabs

Decision Support Model for Allocating Maintenance Budgets for Bridges

This paper introduces a newly developed decision support model for allocating a budget for bridge maintenance. The model is built based on capturing the experts’ knowledge for the prioritization of criteria for selecting a bridge maintenance plan. A literature review and interviews with a group of nine local experts were carried out to identify the criteria for bridge prioritization for maintenance. A questionnaire survey was then designed and administered, utilizing multi-criteria decision-making techniques through face-to-face meetings with local bridge maintenance managers to determine the weights of the identified criteria and develop the utility curves for each criterion. The developed model consists of four major modules: (1) bridge ranking module; (2) prioritization module of bridge structural elements; (3) optimization-based simulation module; and (4) the outputs module. The model was validated in a hypothetical network of four bridges. The validation reveals that the model can assist bridge maintenance managers in setting a bridge maintenance plan, with a high level of confidence using efficient frontier analysis technique. Utilizing frontier analysis enables comparisons of mean costs of different bridge maintenance plans against different levels of risk to enable decision-makers to make informed decisions. The result of this study indicated that bridge structural condition was the most important criterion amongst all the criteria influencing the decision of bridge prioritization for maintenance, followed by traffic and bridge location. Moreover, bridge deck and parapet, followed by bearing pads, were found to be the most important of the bridge’s structural elements.

A perspective on large-scale simulation as an enabler for novel biorobotics applications.

Our understanding of the complex mechanisms that power biological intelligence has been greatly enhanced through the explosive growth of large-scale neuroscience and robotics simulation tools that are used by the research community to perform previously infeasible experiments, such as the simulation of the neocortex's circuitry. Nevertheless, simulation falls far from being directly applicable to biorobots due to the large discrepancy between the simulated and the real world. A possible solution for this problem is the further enhancement of existing simulation tools for robotics, AI and neuroscience with multi-physics capabilities. Previously infeasible or difficult to simulate scenarios, such as robots swimming on the water surface, interacting with soft materials, walking on granular materials etc., would be rendered possible within a multi-physics simulation environment designed for robotics. In combination with multi-physics simulation, large-scale simulation tools that integrate multiple simulation modules in a closed-loop manner help address fundamental questions around the organization of neural circuits and the interplay between the brain, body and environment. We analyze existing designs for large-scale simulation running on cloud and HPC infrastructure as well as their shortcomings. Based on this analysis we propose a next-gen modular architecture design based on multi-physics engines, that we believe would greatly benefit biorobotics and AI.

Nickel phosphate improved silicon nanowires arrays hybrid nanostructures for solar-driven photoelectrochemical hydrogen production

Solar-driven photoelectrochemical (PEC) hydrogen production is a promising and environmentally friendly approach to relieve energy crisis and serve the carbon neutrality goal. Silicon nanowires (SiNWs), as one of p-type semiconductors, have more negative conduction band position than the water-resolved hydrogen potential, and wide range of light absorption due to their narrow band gap. Their PEC performance can be further improved by co-catalysts, and they can be used as photocathodes for hydrogen production. In this work, nickel phosphate (Ni3(PO4)2) was in situ decorated onto the SiNWs via a chemical vapor deposition (CVD). The Ni3(PO4)2 nanoparticles with diameters of about 20 to 50 nm were uniformly distributed on SiNWs arrays to construct heterostructures (Ni3(PO4)2-SiNWs). The Ni3(PO4)2-SiNWs composite showed a low onset potential to trigger water reduction. Compared with SiNWs, the Ni3(PO4)2-SiNWs composite had a much higher photocurrent and better PEC hydrogen production performance, which exhibited a stable photocurrent density of 16 mA cm−2 at 0 V (vs. reversible hydrogen electrode (RHE)) under simulated amplitude modulation (AM) 1.5G solar irradiation. This work provided a new solution to obtain nickel phosphate and fabricate the SiNWs-based photocathode.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia.

The strength of certain visual illusions, including contrast-contrast and apparent motion, is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived contrast-contrast and apparent motion illusions in individuals with schizophrenia, we propose a two-layer network, with top-down feedback from layer 2 to layer 1 that can model visual receptive fields (RFs) and their inhibitory and excitatory subfields. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia within certain clinical states and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups. We propose that the heterogeneity of visual and in general sensory processing in certain clinical states of schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts of contrast-contrast and apparent motion in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

Coupling effect between tunnel lining heat exchanger and subway thermal environment

If the waste heat generated during the long-term operation of a subway cannot be effectively eliminated, it will lead to the deterioration of the tunnel thermal environment and thermal pollution of the underground space. A subway source heat pump system (SSHPS) with a tunnel lining heat exchanger is an effective technique for addressing this problem. The present studies mainly focus on the design and performance optimisation of front-end heat exchangers and lack the analysis of the influence of heat exchanger operation on the thermal environment within the tunnels. Based on a demonstration project of an SSHPS in a cold region of China, this study established a coupled heat transfer model involving tunnel air, a lining heat exchanger, and surrounding rock and built a system simulation module in the TRNSYS platform. Based on the validated system simulation module, the coupling effect between the lining heat exchanger operation and the tunnel thermal environment was analysed under different tunnel environment conditions. The results show that when the heat produced in the tunnel increases at the rate of 100 W/m, the heat transfer between the heat exchanger and the tunnel air decreases by 4.35 W/m2 in the cooling season, and increases by 4.36 W/m2 in the heating season. In the cooling season, when the surrounding rock temperature rises by 2 °C, the heat released by the heat exchanger to the surrounding rock decreases by 3.6 W/m2, while that to the tunnel air increases by 0.68 W/m2. The results indicate that the heat generated by subway operations directly affects the heat transfer efficiency between the tunnel lining heat exchanger and tunnel air. The increase in the temperature of the surrounding rock causes the heat exchanger to release more heat to the tunnel air and vice versa. It is suggested that when the subway tunnel heat generation or heat exchanger operation has adverse effects on the tunnel environment, the operating condition of the tunnel heat exchanger should be adjusted accordingly to prevent the tunnel air temperature from exceeding the design limits. This study can provide a reference for the engineering design of subway tunnel lining heat exchangers.

Development and evaluation of a problem-based learning simulation module for home-visit nursing.

Although home-visit healthcare programs in Korea are expected to expand, providing hands-on experience to nursing students may be limited. This study aimed to develop and evaluate a problem-based learning (PBL) simulation module that reflects home-visit healthcare services provided by public health centers for pre-frail older adults. The simulation module, including PBL as prebriefing, was developed by the researchers and revised based on expert reviews. The module was evaluated using a mixed-method embedded one-group post-test-only design with focus group interviews (FGIs). Quantitative data (n=29) were collected between April and June, 2021. FGIs (n=10) were conducted twice in June 2021, and qualitative data were analyzed using an inductive content analysis approach. The average score of the Simulation Design Scale was 4.67±0.36. The overall mean score of the Educational Practices Questionnaire was 4.75±0.37. Three themes emerged from the FGIs: immersive learning experience, changes in perspective on nursing, and enhanced nursing competency. This PBL-based simulation module was evaluated as a systematic learning process in which nursing students could become self-directed learners, interacting and collaborating with colleagues, instructors, and environments. The module encourages them to practice home visit services.

Adaptive Coordination Control Based on Alkaline Water Electrolyzers and Battery Driven by Wind Power

With the widespread use of clean energy sources, there has been a growing interest in obtaining green and pure hydrogen from wind power. However, wind energy is characterized by randomness and volatility, which results in a low grid connection rate for wind turbines and makes it difficult to integrate wind energy into the power grid. In this context, this article proposes an adaptive coordinated control strategy considering the wind turbine, alkaline electrolyzer, battery, and local loads. Using the DC bus voltage as the connecting signal, adaptive power control and voltage control methods are developed for the wind turbine, energy storage battery, and electrolyzer to achieve power matching in the system. Finally, the proposed research results are validated through Matlab/Simulink simulation module.

A Risk-Informed Decision-Support Framework for Optimal Operation of Hurricane-Impacted Transportation Networks

To reduce life threats and financial losses under hurricane weather and traffic conditions, stakeholders need to make a sequence of decisions (e.g., enforcing traffic control and/or broadcasting travel advisory) with the presence of various uncertainties, which could be formulated as a Markov decision process (MDP). To effectively solve the MDP for optimal operation of hurricane-impacted transportation networks, a risk-informed decision-support framework is proposed consisting of both a decision-making environment and tool. The decision-making environment involves essentially three coupled modules of hurricane hazard, transportation infrastructure, and traffic flow, where the uncertainties from natural environment, built environment, and human behavior are examined. Specifically, multiple correlated hazards for each specific storm are capsulated in the hurricane hazard module, representative hurricane-vulnerable infrastructure components and associated moving vehicles are evaluated in the transportation infrastructure module, and network-level traffic relations are addressed in the traffic flow module. In view of the complexity and intractability of a hurricane-transportation infrastructure-traffic system through full model representation, the efficient low-dimensional modeling approaches including both physics-based analytical and data-driven surrogate models are utilized in all these three simulation modules. Considering an explicit probabilistic model for the decision-making environment (and hence state transition function) is not available, the operation optimization (involving competing objectives of traffic safety and mobility) is accomplished through a deep reinforcement learning-based decision-making tool that learns to act optimally (involving both traffic control and travel advisory actions) by directly interacting with the environment. A hypothetical case study is conducted to verify the applicability and effectiveness of the proposed framework as a testbed for optimal operation of hurricane-impacted transportation networks.

AFM SMILER: A Scale Model Interactive Learning Extended Reality Toolkit for Atomic Force Microscopy Created With Digital Twin Technology

The Atomic Force Microscope (AFM) is a precision mechatronic system for nanoscale imaging of surfaces. Due to limited instrument access and lack of visualization techniques, understanding its principles can be challenging. Digital twin technology allows the creation of virtual representations of physical systems, which can be particularly useful to address challenges in AFM education. To realistically simulate nanoscale physics, we first developed new efficient algorithms for four virtual scale models, including cantilever mechanics, probe transducers, controller tuning, and contact mechanics. Second, three simulated experiment interactive learning modules are developed for instrument operation, including virtual imaging, system overview, and imaging modalities. In the end, three hardware systems are integrated for an extended reality experience, including a macroscopic AFM scale model, a haptic device for probe-sample interaction force feedback, and an upgraded low-cost educational AFM for nanoscale imaging and instrumentation. This completes the eight total modules for the AFM SMILER: A scale model interactive learning extended reality toolkit. Preliminary studies show the toolkit being helpful for AFM education. In addition to mechatronics and nanotechnology education, techniques developed in this work can be generally applied to computationally efficient realistic digital twin creation.