Event Simulation Research Articles

Tall Fescue Mowing Height Effects under Simulated Athletic Field Traffic

Tall fescue (Schedonorus arundinaceus) offers an alternative to kentucky bluegrass (Poa pratensis) for use on athletic fields. Tall fescue has the ability to withstand athletic field traffic, but little is known about the best management practices such as optimal height of cut (HOC). A 2-year study was conducted on established ‘Snap Back’ tall fescue grown over a native soil root zone to determine optimal HOC under simulated athletic field traffic. Plots were maintained at various HOC treatments (1.5, 2, or 3 inches) for the duration of the growing season. Twenty-five simulated traffic events were applied each fall with a modified Baldree traffic simulator. The percentage of green cover (GC) loss per traffic event by HOC varied between years. In 2017, the 1.5-inch HOC improved traffic tolerance (–1.7% GC per event) compared with the other HOC treatments (–2.6% GC per event) in terms of percentage of GC. In 2018, the HOC did not have an impact on traffic tolerance. Differences in traffic tolerance between years could be a result of differences in precipitation (78 mm in 2017, 6 mm in 2018) during the period when traffic occurred, which suggest that the lower HOC performs better under wet conditions compared with the greater HOC. There were no differences among treatments for the safety variables measured (surface hardness, rotational resistance, and soil moisture).

Process‐Based Quantification of the Role of Wildfire in Shaping Flood Frequency

AbstractModerate to high severity wildfire can abruptly alter watershed properties and enhance extreme hydrologic responses such as debris flows and floods. The compounding effects of wildfire on flood hazard, represented here via flood frequency analysis (FFA; e.g., 100‐year flood) are of growing importance. Standard statistical FFA approaches are ill‐suited to examining this issue because wildfire‐affected flood peak observations are limited in number and violate the assumption of independent and identically distributed events. Here, we developed a process‐based FFA framework that integrates a stochastic rainfall generator, wildfire simulation, inverse modeling, and a physics‐based hydrological model to directly simulate the impacts of wildfire on FFA. We applied this framework in the upper Arroyo Seco watershed in Southern California, which experienced Moderate to high burn during the 2009 Station Fire. An FFA analysis, performed with simulated peak flows from the first year since fire demonstrates the 100‐year flood can be three times larger than simulations that only consider peak flows in non‐fire‐affected years. On the other hand, coupling process‐based FFA with stochastically simulated wildfire events and watershed's time‐varying hydrologic recovery yields “fire continuum FFA”, a concept introduced here for the first time. Fire continuum FFA accounts for multiple wildfires within very long synthetic time series. Variability in upper tail flood peaks is substantially higher in fire continuum results as compared with pre‐wildfire FFA. This result highlights the importance of wildfire inter‐arrival time and post‐wildfire recovery processes, both of which are expected to change as a result of climatic change and evolving fire management strategies.

Scalable and Interactive Simulation for IoT Applications With TinySim

Recent years, the rapid development of IoT (Internet of Things) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications, i.e., device heterogeneity, long-range communication and cloud/edge-device integration. Difficulties are raised by the above features towards IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4,000 devices within 11.2 physical-minutes for 10 simulation-minutes, which is about 3× faster than the state-of-art approach.

The Effect of Compost Type, Application Rate, and Maturity on Nutrient Leaching and Soil Nutrient Levels

ABSTRACT The application of compost to agricultural soils can increase nutrient leaching, causing several environmental problems. Conversely, proper management of compost application can reduce environmental risks. Agronomical trials were conducted in greenhouses in Morocco to compare the effects of varying compost application rates, types, and maturity, as well as the number of growing days, on nutrient availability and nitrate leaching in a loamy sandy soil. Additionally, the study examined the impacts of these factors on soil physical and chemical characteristics. The compost used in this study was derived from a combination of two cost-effective materials, municipal waste, and poultry manure. Tomato plants were cultivated in pots amended with compost over a period of 65 days. The soils were treated with eight composts (plus a control) obtained from municipal solid waste (MSW) mixed with poultry manure (PM) at two ratios, 3:2 and 2:3 weight weight−1 (ww−1) (C1 and C2, respectively), used at two different ages (105 days and 173 days) and at two doses (32.5 g pot−1 and 65 g pot−1, equivalent to 40 Mg ha−1 and 80 Mg ha−1). Two simulated leaching events (the first after planting and the second at harvest) were performed. The concentration of chemical oxygen demand (COD) and total Kjeldahl nitrogen (TKN) in leachate varied with the number of growing days and increased in the leachate of the non-amended soil after planting. At both sampling times, compost C1, aged 173 days (d), significantly decreased COD in the leachate compared to compost C1 aged 105 d. Compost increased nitrate leaching compared with the non-amended soil. Nitrogen leaching was primarily in the form of nitrate, ranging from 61% to 78% over the whole experimental period. Compost also decreased leachate concentrations of iron (Fe) and chromium (Cr), for other metals, such as cobalt (Co) and nickel (Ni), values were very low. Moreover, compost increased total organic carbon by up to twofold at both sampling times, with the most substantial increase observed with a dose of 65 g pot−1 of compost C2 (aged 105 days). Most compost treatments also increased available phosphorus (P), inorganic nitrogen (N), total nitrogen (TNK), cation exchange capacity (CEC), and electrical conductivity (EC) at both sampling times but had no effect on pH. The metal content in the soil exhibited a decrease compared to the control soil. Overall, our study suggests that nitrogen leaching can be reduced by optimizing compost rates, properties, and stability to synchronize with crop needs.

Processing biological samples from simulated radiological terrorist events using Rapid DNA instruments

Two commercially available portable Rapid DNA instruments were evaluated for their ability to process 1 µL and 10 µL saliva samples deposited on metal and plastic surfaces and contaminated with surrogates of cesium (Cs)-137, strontium (Sr)-90 and cobalt (Co)-60; radioactive materials potentially released during a nuclear weapon accident or a radiological dispersal device detonation. A comparable success rate was noted for both Rapid DNA instruments when considering the number of complete and balanced DNA profiles, the number of profiles with a minimum of 10 autosomal STR loci (out of 23 [FlexPlex™ 27] or 21 [GlobalFiler™ Express]), and the possibility to search a national DNA database in Canada and the United States. Cobalt had an adverse impact on the quality of the megaplex short tandem repeat (STR) DNA profiles derived on each instrument for two of the three contamination levels tested in this study, i.e., 0.05 M and 0.1 M as reflected by a reduced number of detected alleles and decreased profile peak heights. Strontium exhibited some adverse effect on the Rapid DNA results when used at the highest contamination level (0.1 M) whereas cesium had none. No new artifacts were observed in the Rapid DNA profiles of samples spiked with the non-radiogenic surrogates. Importantly, in the context of a radiological/nuclear (RN) event, the ANDE™ 6C offers the possibility to dispose of all radioactive materials associated with contaminated samples quickly using a chip on which all steps of the Rapid DNA process are performed whereas the RapidHIT™ ID accumulates radioactive materials for many days before disposal. An individual handling 25 samples in a week (5 per day) on the RapidHIT™ ID at a 30.5 cm distance with a 5 min exposure to the radioactive source estimated at every run would exceed the 0.042 µSv/5 min limit with gamma dose rates for Cs at 0.13 mSv and for Co at 3.8 mSv. Beta dose rates calculated for the surrogate isotopes at the three concentrations tested were also above the recommended radiation exposure limit of 1 mSv/yr (0.042 µSv/5 min). Various potential mechanisms of action behind the interference noted for Sr and Co at high concentrations are presented. These elements may play a role in the steps prior to PCR (at the DNA molecule by binding to bases or to phosphate groups), during PCR (at the DNA polymerase as cofactors for catalytic sites), or even during amplified DNA fragment detection (as fluorescence quenchers).

Simulation of In-Space Fragmentation Events

In the next years, the space debris population is expected to progressively grow due to in-space collisions and break-up events; in addition, anti-satellite tests can further affect the debris environment by generating large clouds of fragments. The simulation of these events allows identifying the main parameters affecting fragmentation and obtaining statistically accurate populations of generated debris, both above and below detection thresholds for ground-based observatories. Such information can be employed to improve current fragmentation models and to reproduce historical events to better understand their influence on the non-detectable space debris population. In addition, numerical simulation can also be used as input to identify the most critical objects to be removed to reduce the risk of irreversible orbit pollution. In this paper, the simulation of historical in-orbit fragmentation events is discussed and the generated debris populations are presented. The presented case-studies include the COSMOS-IRIDIUM collision, the COSMOS 1408 anti-satellite test, the 2022-151B CZ-6A in-orbit break-up, and a potential collision of ENVISAT with a spent rocket stage; for these events, results are presented in terms of cumulative fragments distributions and debris orbital distributions.

Deterministic seismic hazard analysis from physics-based earthquake simulations in the Eastern Betics (SE Iberia)

The low frequency of large destructive earthquakes in the Eastern Betics (SE Iberian Peninsula), not recorded in the instrumental seismic catalog, complicate the estimation of the maximum ground motion that could be reached. Knowing the characteristics and behavior of these large seismic ruptures is crucial for the study of seismic hazard. In this work, we have used a physics-based earthquake simulator to generate a 100 kyr catalog of seismic ruptures associated with seismogenic faults bordering the quaternary Guadalentin and Bajo Segura basins. From these ruptures, we have selected maximum earthquake scenarios, and those associated with certain probabilities of exceeding a severe magnitude from the synthetic catalog. The estimation of maximum peak accelerations (PGA) by means of ground motion prediction equations include a possible site amplification that we have calculated from a detailed geotechnical classification of soils. According to the simulations, the modeled faults have the capacity to generate maximum earthquakes with magnitudes ranging MW 6.7–7.4. Densely populated urban areas located along the Guadalentin valley, such as Lorca, could be subject to site-dependent PGA values close to 0.6 g on the Alhama de Murcia fault trace. Similar ground motion would affect the coastline of SE Alicante province when evaluating the maximum simulated event in the Bajo Segura offshore fault. But already from magnitudes around MW ∼ 6, PGA estimations exceed critical values of 0.5 g at the south side of the Bajo Segura basin and the city of Murcia, highly dependent of ground-motion amplification induced by thick deposits of very soft and saturated soils. Our scenarios reflect that other factors such as fault geometry, kinematics and the capability observed in the simulations to generate multi-fault ruptures also affect the spatial distribution of maximum ground motion in this region. We expect that this evaluation could contribute as an improvement guideline for future strong motion prediction studies prior to the settlement of new urban infrastructures in the Eastern Betics.

ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study.

The transition to clinical clerkships can be difficult for medical students, as it requires the synthesis and application of preclinical information into diagnostic and therapeutic decisions. ChatGPT-a generative language model with many medical applications due to its creativity, memory, and accuracy-can help students in this transition. This paper models ChatGPT 3.5's ability to perform interactive clinical simulations and shows this tool's benefit to medical education. Simulation starting prompts were refined using ChatGPT 3.5 in Google Chrome. Starting prompts were selected based on assessment format, stepwise progression of simulation events and questions, free-response question type, responsiveness to user inputs, postscenario feedback, and medical accuracy of the feedback. The chosen scenarios were advanced cardiac life support and medical intensive care (for sepsis and pneumonia). Two starting prompts were chosen. Prompt 1 was developed through 3 test simulations and used successfully in 2 simulations. Prompt 2 was developed through 10 additional test simulations and used successfully in 1 simulation. ChatGPT is capable of creating simulations for early clinical education. These simulations let students practice novel parts of the clinical curriculum, such as forming independent diagnostic and therapeutic impressions over an entire patient encounter. Furthermore, the simulations can adapt to user inputs in a way that replicates real life more accurately than premade question bank clinical vignettes. Finally, ChatGPT can create potentially unlimited free simulations with specific feedback, which increases access for medical students with lower socioeconomic status and underresourced medical schools. However, no tool is perfect, and ChatGPT is no exception; there are concerns about simulation accuracy and replicability that need to be addressed to further optimize ChatGPT's performance as an educational resource.

Estimated impact of timely guidelines-based initiation of dual-combination anti-hypertensive therapy on long-term cardiovascular outcomes in a population of 1.4 million individuals

Abstract Background In most patients qualifying for anti-hypertensive treatment initiation, guidelines recommend dual-combination therapy. Purpose To estimate cardiovascular (CV) events rate with optimal and suboptimal adoption of guideline-based treatment in comparison with observed risk in a large population representing usual clinical practice. Methods A study population representing real-world clinical practice was developed from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics databases in England. Adults qualifying for dual BP-lowering therapy as per the 2018 ESC/ESH guidelines were identified during 15-year period (2005-2019). Primary endpoint was composite of nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure, and CV death. A Cox model relating patient characteristics to the risk of primary endpoint was estimated for overall population and subgroups (Atherosclerotic cardiovascular disease [ASCVD] and diabetes). Patients in the real-world cohort entered a Monte-Carlo simulation model which simulated CV events over time consistent with an individual-specific predicted risk from the Cox model based on their characteristics. Following scenarios were investigated: (1) untreated (reference); (2) optimal treatment (full adherence and persistence); (3) suboptimal adherence (50% adherence); (4) suboptimal persistence (50% discontinuation at 1-year). Scenario 5 represented observed clinical practice in databases. Ten-year event rates, ARRs and NNTs were estimated for each scenario with scenario 1. Results The observed 10-year event rate for the primary endpoint representing clinical practice (scenario 5) in the database was 17.8% (Tables 1 and 2). In scenario 2, the simulated 10-year event rates for the primary endpoint were 15.8% (absolute risk reduction [ARR] 6.6%, number needed to treat [NNT] 15) and 15.2% (ARR 7.1%, NNT 14) for Ramipril plus Amlodipine (R+A) and Irbesartan plus Amlodipine (I+A), respectively. In scenario 3, the simulated 10-year event rates for the primary endpoint were 18.8% (ARR 3.6%, NNT 28) and 18.5% (ARR 3.9%, NNT 26), respectively, for R+A and I+A. The most notable risk reductions were seen in ASCVD subgroup for scenario 2; ARR 11.2% and 12.3% for R+A and I+A, respectively. Similarly, in diabetes subgroup scenario 2, resulted in highest ARR (8.2% and 8.7%) and lowest NNT (12 for both), indicating good clinical benefit of treatment. Conclusions These findings indicate substantial opportunity for population-level risk reduction by optimal guidelines-based treatment initiation with dual-combination anti-hypertensive therapy as compared to observed clinical practice. Results suggest substantial population attributable risk for behaviors of suboptimal adherence and persistence. The opportunity for risk reduction by optimal treatment, and attributable risk due to suboptimal treatment, were especially high for individuals with ASCVD or diabetes.Table 1Table 2

A semi-parametric hourly space–time weather generator

Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.

Master equation model for solute transport in river basins: part II basin fluvial scale

At basin scale the physical phenomenon of diffusion involves the intricate spreading and dispersion of substances within complex systems as networks of interconnected channels, streams, and land surfaces. Understanding this process is crucial for many purposes as management and conservation of water resources. We extend the model application of our previous work (Part I, Rizzello et al. in Stoch Environ Res Risk Assess 37:3807–3817, 2023) from channel to basin scale. We use conservation of mass and momentum to formulate and apply the Master Equation system at basin scale. The results on simulated events highlight the transition of the model from channel scale to basin scale.

Signatures of Oceanic Wind Events in Convection-Resolving WRF Model Simulations

Abstract Analyses of cloud-top temperature and lightning characteristics of 48 Weather Research and Forecasting (WRF) Model–simulated ocean-based wind events, with 1-min temporal and 0.5-km horizontal resolution, revealed signatures similar to the corresponding 13 observed events detected by buoys and Coastal-Marine Automated Network (C-MAN) stations as shown in prior research on ocean-based wind events by the first author. These events occurred in the eastern Gulf of Mexico and in the Atlantic Ocean from Florida northward through South Carolina. The coldest WRF cloud-top temperature (WCTT) and peak WRF-estimated lightning flash rate values of the model-simulated events, where each event was required to have a negative vertical velocity of at least 10 m s−1 in the lowest 2 km associated with a convective storm, occurred at an average of 4.2 and 1.1 min prior to the events, respectively. With 36 of the events, the peak estimated flash rate occurred within 5 min of the coldest WCTT. Cloud depth typically increased as the WCTT decreased, and the maximum depth occurred at an average of 2.9 min prior to the events. Thermal cooling and precipitation loading provided negative buoyancy needed to help drive the wind events. Environmental characteristics of the model-simulated ocean-based wind events also resembled those associated with land-based wet downbursts, including moist air near the surface, lapse rates near moist adiabatic, and low cloud bases.

Random forest-based multi-hazard loss estimation using hypothetical data at seismic and tsunami monitoring networks

This article presents a novel approach to estimate multi-hazard loss in a post-event situation, resulting from cascading earthquake and tsunami events with machine learning for the first time. The proposed methodology combines the power of random forest (RF) with data that are simulated at seismic and tsunami monitoring locations. The RF model is well-suited for predicting highly nonlinear multi-hazard loss because of its nonparametric regression and ensemble learning capabilities. The study targets the cities of Iwanuma and Onagawa in Tohoku, Japan, where seismic and tsunami monitoring networks have been deployed. To encompass a diverse range of future multi-hazard loss estimation, an RF model is constructed based on 4000 simulated earthquake events with peak ground velocity and tsunami wave amplitude captured at ground-motion monitoring sites and offshore wave monitoring sensors, respectively. The incorporation of 10 ground-motion monitoring sites and five offshore wave monitoring sensors significantly enhances the model’s forecasting power, leading to a notable 60% decrease in mean squared error and 20% increase in the R 2 value compared to scenarios where no monitoring sensors are utilized. By harnessing the capabilities of RF and leveraging detailed sensing data, RF achieves R 2 values over 90%, which can contribute to enhanced disaster risk management.

Dissecting Earth's Magnetosphere: 3D Energy Transport in a Simulation of a Real Storm Event

AbstractWe present new analysis methods of 3D MHD output data from the Space Weather Modeling Framework during a simulated storm event. Earth's magnetosphere is identified in the simulation domain and divided based on magnetic topology and the bounding magnetopause definition. Volume energy contents and surface energy fluxes are analyzed for each subregion to track the energy transport in the system as the driving solar wind conditions change. Two energy pathways are revealed, one external and one internal. The external pathway between the magnetosheath and magnetosphere has magnetic energy flux entering the lobes and escaping through the closed field region and is consistent with previous work and theory. The internal pathway, which has never been studied in this manner, reveals magnetically dominated energy recirculating between open and closed field lines. The energy enters the lobes across the dayside magnetospheric cusps and escapes the lobes through the nightside plasmasheet boundary layer. This internal circulation directly controls the energy content in the lobes and the partitioning of the total energy between lobes and closed field line regions. Qualitative analysis of four‐field junction neighborhoods indicate the internal circulation pathway is controlled via the reconnection X‐line(s), and by extension, the interplanetary magnetic field orientation. These results allow us to make clear and quantifiable arguments about the energy dynamics of Earth's magnetosphere, and the role of the lobes as an expandable reservoir that cannot retain energy for long periods of time but can grow and shrink in energy content due to mismatch between incoming and outgoing energy flux.

Medical cyber crises and biotechnological syndromes: a multisite clinical simulation study focused on digital health complaints

BackgroundBiotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Implanted technologies can malfunction (eg, runaway pacemakers, hacked insulin pumps), and consumer technologies can be exploited to impose adverse health effects (eg, technology-facilitated abuse, hacks on epilepsy websites inducing seizures). Through a series of clinical simulation events, our study aimed to (1) evaluate the ability of physicians to respond to biotechnological syndromes, (2) explore gaps in training impeding effective patient care in digital cases, and (3) identify clinical cases due to digital technology arising in the population. MethodsThis was a multisite clinical simulation study. Between Jan 1 and July 1, 2023, four half-day clinical simulation events focused on digital pathologies were delivered across three NHS sites in London and the East Midlands. Participants (n=14) ranged in seniority from clinical medical students through to hospital consultants. Ethics approval was attained from University College London. Participant performance was scored by one researcher, using mark schemes built from the Objective Structured Clinical Examinations (OSCEs) format of UK Medical Schools. Qualitative and quantitative feedback was collected from participants following each of the four scenarios. Participants were asked to identify clinical challenges present in each simulation, discuss cases within their own practice, and evaluate the usefulness of the educational material. FindingsParticipants reported a wide range of examples within their own practice (eg, insulin pumps malfunctioning due to Apple watches, cardiac arrests due to faults in ventilators). Participants described barriers to treatment in simulations, including low diagnostic suspicion of technological failures, little education on biotechnological mechanisms, a lack of available expertise, and uncertainty regarding effective therapeutics. In the subjective feedback, participants reported the lowest levels of confidence when managing cases relating to software issues in medical devices, both in terms of confidence in their own ability to deliver care (mean scores: 3·6/10 junior staff, 5·8/10 senior staff) and in their teams (3·8/10 juniors, 6·8/10 seniors). InterpretationIn our digital society, clinical cases related to technology are likely to increase in the population. At present, a lack of clinical awareness, education, training material, and appropriate guidelines are some of the barriers that health-care professionals face when treating these patients. FundingNone.

A11An evaluation of student views on the use of virtual simulation in undergraduate pharmacy education

In part due motivated by a lack of in-person placement opportunities for undergraduate pharmacy students during the COVID-19 pandemic, a UK university teaching team developed a programme of virtual simulated placement-style events to support undergraduate pharmacy students in developing skills and experience to support them in engaging with their foundation training. These experiences were developed at a time of significant change for undergraduate pharmacy training, as all new pharmacists being annotated as independent prescribers at the point of registration from 2026 onwards [1]. It has been reported that in medical students, the use of virtual patient simulation could improve clinical reasoning skills [2] but evidence of student views on the acceptability and implementation of virtual simulation in the target audience is limited and frequently not reflective of the style of self-directed simulation being utilized. This work aimed to evaluate final-year undergraduate pharmacy student views of the impact of the introduction of a programme of student-led virtual simulation on their education. In academic years 2020–2021 and 2021–2022, an electronic questionnaire was distributed to final-year students who had recently been introduced to and given access to a range of student-led virtual placement experiences in academic years. Prior to administering questionnaires to students, the study was approved by the relevant school research ethics committee. Questionnaires were formed of a mixture of qualitative and quantitative questions, and asked students about their experiences of engagement with virtual simulation and views on the potential applications of virtual simulation in the curriculum. Quantitative data were analysed by simple descriptive statistics, and a critical review of free-text responses was performed through grounded theory to identify emergent key themes. A total of 43 student questionnaires were collected, with 18 responses (41.9%) being received in the academic year 2020–2021 and 25 responses (58.1%) received in the academic year 2022. 88.4% of respondents agreed that the introduction of virtual simulation would enhance their educational experience. Four key themes emerged from qualitative data analysis: individuality and autonomy, convenience, immediacy, and control. Students most commonly believed that the second year of the 4-year Master of Pharmacy programme is the optimal time for the introduction of placement-style virtually simulated experiences. Final-year undergraduate pharmacy students believed that the introduction of a programme of student-led virtual simulation would enhance their educational experience. Students were found to value the convenience, control and autonomy of the introduction of student-led virtual simulation. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

On the use of SRIM for calculating arc-dpa exposure

We propose two methods for evaluating athermal recombination corrected (arc) displacement damage parameters in ion irradiations employing the computer code SRIM (Stopping and Range of Ions in Matter). The first method consists of post-processing the detailed SRIM output for all simulated damage events and re-calculating according to the arc damage model. In the second method, an approximate empirical formula is devised which gives the average displacements in the arc damage model as a function of the corresponding quantity according to the standard Norgett–Robinson–Torrens model, which is readily obtained from SRIM.

Variable Selection for Recurrent Events Using Heuristic Approaches: Identifying Informative Variables for Rehospitalization in Schizophrenia Patients

Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
 Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
 Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
 Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.

Implementing competing risks in discrete event simulation: the event-specific probabilities and distributions approach.

Background: Although several strategies for modelling competing events in discrete event simulation (DES) exist, a methodological gap for the event-specific probabilities and distributions (ESPD) approach when dealing with censored data remains. This study defines and illustrates the ESPD strategy for censored data. Methods: The ESPD approach assumes that events are generated through a two-step process. First, the type of event is selected according to some (unknown) mixture proportions. Next, the times of occurrence of the events are sampled from a corresponding survival distribution. Both of these steps can be modelled based on covariates. Performance was evaluated through a simulation study, considering sample size and levels of censoring. Additionally, an oncology-related case study was conducted to assess the ability to produce realistic results, and to demonstrate its implementation using both frequentist and Bayesian frameworks in R. Results: The simulation study showed good performance of the ESPD approach, with accuracy decreasing as sample sizes decreased and censoring levels increased. The average relative absolute error of the event probability (95%-confidence interval) ranged from 0.04 (0.00; 0.10) to 0.23 (0.01; 0.66) for 60% censoring and sample size 50, showing that increased censoring and decreased sample size resulted in lower accuracy. The approach yielded realistic results in the case study. Discussion: The ESPD approach can be used to model competing events in DES based on censored data. Further research is warranted to compare the approach to other modelling approaches for DES, and to evaluate its usefulness in estimating cumulative event incidences in a broader context.

Comparative study of morphological, anatomical, cell wall compositional, and leaf elemental features of traffic‐tolerant versus traffic‐sensitive tall fescue (Festuca arundinacea)

AbstractTurfgrass traffic tolerance is essential for athletic field aesthetics and performance. Tall fescue (Festuca arundinacea Scherb.; syn. Schedonorus arundinaceus [Schreb.] Dumort., nom. cons.) is utilized on athletic fields due to its high tolerance to traffic stress. However, the physiochemical characteristics and mechanisms associated with traffic tolerance are not understood. Therefore, identifying traffic‐tolerant characteristics would be beneficial for selecting future turfgrass varieties. The objective of this study was to identify morphological, mechanical, and anatomical characteristics of traffic‐tolerant and traffic‐sensitive tall fescue varieties. Simulated traffic was applied to the 2018 National Turfgrass Evaluation Program tall fescue trial at the Iowa State Horticulture Research Station. A modified Baldree traffic simulator applied 24 simulated traffic events yearly during August and September 2019–2021. The four most traffic‐tolerant/traffic‐sensitive varieties, each, were selected based on the average final percent green cover after simulated traffic. Evaluations included leaf width and thickness, leaf angle, plant density, intercellular void space, and maximum and final resistive force of three‐point bending testing. Additionally, cell wall constituents, hemicellulose, cellulose, lignin, and concentration of three elements (nitrogen [N], calcium [Ca], potassium [K]) were quantified in the leaf. Compared to traffic‐sensitive varieties, the traffic‐tolerant group (SE5302, SE5STAR, BGR‐TF3, and PST‐5DART) exhibited wider leaves (3.53 vs. 2.73 mm), lower plant density (23.4 vs. 38.6 plants 7 cm−2), larger intercellular void space (29,532 vs. 10,051 μm2), greater maximum resistive force (0.607 vs. 0.350 N), and greater final displacement force (0.531 vs. 0.288 N). The identification of these characteristics could be used as selection criteria for traffic‐tolerant varieties of tall fescue in future.