Simulation Project Research Articles

Pyrrhic victory? Towards a decision support system for simulation projects in industrial practice based on Fuzzy cognitive mapping

Simulation has become a popular methodology to solve complex non-linear problems in research and practice. Yet, particularly in industrial practice, assessing the individual applicability of simulation projects remains an intricate, time-consuming and precarious challenge. This paper models simulation applicability by tapping into expert knowledge of simulation engineers with multiple years of simulation project experience. We decompose simulation applicability into a number of interconnected concept variables, which ultimately control the likelihood of project success and failure. The system is modeled in the form of a Fuzzy Cognitive Map, in which the nodes represent positive or negative project influencers in variables and link their interconnectedness. In doing so, this paper is the first to exceed the scope of simulation success factors or systemic risk assessment by addressing the need to provide holistic decision support on the context-dependent applicability of simulation projects. We contextualize organizational, environmental and methodology-related cause-effect relationships to develop a Fuzzy Decision Support System (FDSS) prototype that can guide scholars and practitioners when planning to adopt a simulation project for industrial purposes. The approach is illustrated using three applications, where the assessment has been conducted for (1) a large, (2) a medium-sized, as well as (3) a small organization, and evaluated through sensitivity analyses and a comparative survey. Ultimately, our study contributes to existing knowledge on success and risk factors for simulation projects, extends the realms of FCM to the case of simulation projects and sets a starting point for additional research on the context-specific applicability of the simulation methodology.

Eco-Sustainable Energy Production in Healthcare: Trends and Challenges in Renewable Energy Systems

The shift from fossil fuels to renewable energy systems represents a pivotal step toward the realization of a sustainable society. This study aims to analyze representative scientific literature on eco-sustainable energy production in the healthcare sector, particularly in hospitals. Given hospitals’ substantial electricity consumption, the adoption of renewable energy offers a reliable, low-CO2 emission solution. The COVID-19 pandemic has underscored the urgency for energy-efficient and environmentally-responsible approaches. This brief review analyzes the development of experimental, simulation, and optimization projects for sustainable energy production in healthcare facilities. The analysis reveals trends and challenges in renewable energy systems, offering valuable insights into the potential of eco-sustainable solutions in the healthcare sector. The findings indicate that hydrogen storage systems are consistently coupled with photovoltaic panels or solar collectors, but only 14% of the analyzed studies explore this potential within hospital settings. Hybrid renewable energy systems (HRES) could be used to meet the energy demands of healthcare centers and hospitals. However, the integration of HRES in hospitals and medical buildings is understudied.

MF+C: Linking suboptimal projections to detail on handheld devices

Mobile Focus + Context (mF+C) involves using a handheld device as a focus screen for content on an immersive display or mobile projector. In this work we examine how using a focus device can mitigate poor context image quality due to environmental factors. In an exploratory study we compare three techniques for linking focus and context: lens-focus (Lens), where the device works as a mobile lens held parallel to and in front of the context, center-focus (Centered), where the user holds the device in the center of the projection and pans both context and focus by swiping, and marker-focus (Marker), where the focus region is highlighted on the context, and the user pans the focus by swiping. Participants performed searching, tracing, and detail acquisition tasks with maps and electronics diagrams under a range of simulated projection conditions. All techniques were able to mitigate poor projection quality and performed comparably in time and precision, but the effectiveness of each technique was impacted by task type. There was lower variation in time between participants using Lens for tracing tasks versus the other techniques, but wider variation for searching tasks. Tasks completed using sub-optimal projections involved more time spent looking at the context image than tasks with clear projections, however this difference is less pronounced for the Lens technique. We propose a hybrid Lens-Marker approach for mobile Focus+Context applications in dynamic environments.

Predicting the impact of feedback on matter clustering with machine learning in CAMELS

ABSTRACT Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at z = 0 using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations project, containing thousands of $(25\, h^{-1}\, {\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and active galactic nucleus (AGN) feedback strength and subgrid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\, h\, \mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive haloes (M200c > 1013.5 M⊙) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of haloes across the full mass range 1010 ≤ Mhalo/M⊙<1015 can predict the effect of galaxy formation on the matter power spectrum on scales k = 1.0–20.0 $h\, \mathrm{Mpc}^{-1}$.

Cone-beam pair-wise data consistency conditions in helical CT.

Data consistency conditions (DCC) are mathematical equations characterizing the redundancy in X-ray projections. They have been used to correct inconsistent projections before computed tomography (CT) reconstruction. This article investigates DCC for a helical acquisition with a cylindrical detector, the geometry of most diagnostic CT scanners. The acquired projections are analyzed pair-by-pair. The intersection of each plane containing the two source positions with the corresponding cone-beams defines two fan-beams for which a DCC can be computed. Instead of rebinning the two fan-beam projections to a conventional detector, we directly derive the DCC in detector coordinates. If the line defined by two source positions intersects the field-of-view (FOV), the DCC presents a singularity which is accounted for in our numerical implementation to increase the number of DCC compared to previous approaches which excluded these pairs of source positions. Axial truncation of the projections is addressed by identifying for which set of planes containing the two source positions the fan-beams are not truncated. The ability of these DCC to detect breathing motion has been evaluated on simulated and real projections. Our results indicate that the DCC can detect motion if the baseline and the FOV do not intersect. If they do, the inconsistency due to motion is dominated by discretization errors and noise. We therefore propose to normalize the inconsistency by the noise to obtain a noise-aware metric which is mostly sensitive to inconsistencies due to motion. Combined with a moving average to reduce noise, the derived DCC can detect breathing motion.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BackgroundConducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.ResultsBased on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.ConclusionsChina’s provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

Computed tomography simulation projection acquisition method of artistic relics based on voxel model

Computed tomography simulation projection acquisition method of artistic relics based on voxel model

Risk assessment of debris flow disaster in mountainous area of northern Yunnan province based on FLO-2D under the influence of extreme rainfall

The stratum in Yunlong mountain area is unstable, and it is more likely to have extreme damage to the mountain in extreme rainfall weather, and even cause the occurrence of mountain disasters such as debris flow. This paper conducted field geological surveys and used the FLO-2D professional simulation system to scientifically evaluate the risk probability of three debris flow in Yunlong County. The risk prediction before and after engineering control was compared for the county’s urban areas.This paper uses construction drawings and remote sensing images to assist in positioning methods for debris flow simulation. After dividing the debris flow simulation grid, the control project is added to the simulation project in the form of a plug-in, and specific engineering parameters are imported into the simulation project file by modifying its properties. The setting of the control project in numerical simulation is completed, solving the problem of setting engineering measures. By this method, the probability analysis of debris flow under three different rainfall frequencies of 1%, 2% and 5%, namely once in 20 years, once in 50 years and once in 100 years, is simulated, and the flow and sedimentation characteristics of debris flow before and after engineering control are compared. The results show that under 5% rainfall frequency, the simulated results before engineering control were compared with the actual deposition range, and the evaluation accuracy is within a reasonable error range. After debris flow engineering control, the proportion of high risk areas of debris flow in urban areas decreased significantly, especially when the frequency of rainfall was once in 100 years, and the area of non-danger areas increased by 5.92% compared with that before control. In the end, this paper combines the depth of mud, the velocity of mud and the outbreak frequency to get the debris flow risk assessment map before and after engineering treatment, and puts forward suggestions for further prevention and control measures of debris flow disaster. The research results of this paper will provide good guidance for the occurrence and control of debris flow hazards under the influence of extreme rainfall in the mountainous area of northern Yunnan.

A new projection correction based voting strategy for breast calcification artifact reduction

Objective. Digital Breast Tomosynthesis (DBT) is an imaging technique that combines traditional tomography with image processing and reconstruction techniques. In screening for breast cancer, high attenuation lesion will cause calcification hardening artifacts, which reduces the reconstructed image quality and limits diagnostic accuracy. We focus on the reconstruction artifacts that are caused by high-attenuation features in DBT, and aim to propose an efficient and accurate method to remove calcification artifacts and retain calcification information. Approach. The proposed method first introduces a new segmentation method, which can segment breast calcification accurately and effectively. Then an interpolation method is used to eliminate both the calcified area and artifact area in the projection images which are then used to reconstruct the image without artifacts and calcifications. Finally, the interpolated reconstructed image and the unprocessed reconstructed image are fused under the proposed voting strategy to obtain the DBT image with calcification artifacts removal. Main results. 18 groups of simulated projection data and 10 groups of real projection data collected by us are used to evaluate the proposed method. Experimental results show that our algorithm can effectively reduce the calcification artifact and preserve the effective information in the image as well. Significance. The proposed method utilizes a novel projection correction based voting fusion strategy for image fusion, and is advanced in reducing breast calcification artifacts compared with other state-of-the-art methods. Our work paves the way for more efficient and precise DBT breast cancer screening.

A Virtual Trading Project for the Derivative Securities Course

In this paper, I present a simulated trading project for the undergraduate Derivative Securities course which allows students to realistically trade both Futures and Option contracts. The project makes use of the Virtual Trading tool provided on the Chicago Board Options Exchange website, which processes virtual trades in real time using actual market data. Student response to the project's first implementation in Fall 2008 has been overwhelmingly positive. I provide Likert-style survey results describing students' perceptions of various aspects of the assignment, and identify directions for potential future modifications.

FOREVER22: Gas and metal outflow from massive galaxies in protocluster regions

Abstract We study gas and metal outflow from massive galaxies in protocluster regions at z = 3 − 9 by using the results of FOREVER22 simulation projects. Our simulations contain massive haloes with $M_{\rm h} \gtrsim 10^{13}~\rm {\rm M}_{\odot }$, showing high star formation rates of $> 100~\rm {\rm M}_{\odot }~yr^{-1}$ and hosting supermassive black holes with $M_{\rm BH} \gtrsim 10^{8}~\rm {\rm M}_{\odot }$. We show that the mass loading factor (ηM) sensitively depends on the halo mass and it is ηM = 1.2 (9.2) for $M_{\rm h}= 10^{13}~(10^{11})~{\rm {\rm M}_{\odot }}$. Once the halo mass exceeds $\sim 10^{12.5}~\rm {\rm {\rm M}_{\odot }}$, the outflow velocity of the gas rapidly decreases near a virial radius, and the gas returns to a galactic centre finally as a fountain flow. Also, the metal inflow and outflow rates sensitively depend on the halo mass and redshift. At z = 3, the inflow rate becomes larger than the outflow one if $M_{\rm h}\gtrsim 10^{13.0}~{\rm {\rm M}_{\odot }}$. Thus, we suggest that massive haloes cannot be efficient metal enrichment sources beyond virial radii that will be probed in future observations, e.g., studies of metal absorption lines with the Prime Focus Spectrograph on the Subaru telescope.

FOREVER22: the first bright galaxies with Population III stars at redshifts z ≃ 10–20 and comparisons with JWST data

ABSTRACT We study the formation of the first galaxies in overdense regions modelled by the FORmation and EVolution of galaxies in Extremely overdense Regions motivated by SSA22 (FOREVER22) simulation project. Our simulations successfully reproduce the star formation rates and the MUV–Mstar relations of candidate galaxies at z ∼ 10–14 observed by the JWST. We suggest that the observed galaxies are hosted by dark matter haloes with Mh ≳ 1010 M⊙ and are in short-period starburst phases. On the other hand, even simulated massive galaxies in overdense regions cannot reproduce the intense star formation rates and the large stellar masses of observed candidates at z ∼ 16. Also, we show that the contribution of Population III stars to the ultraviolet (UV) flux decreases as the stellar mass increases and it is a few per cent for galaxies with Mstar ∼ 107 M⊙. Therefore, a part of the observed flux by JWST could be the light from Population III stars. Our simulations suggest that the UV flux can be dominated by Population III stars and the UV slope shows β ≲ −3 if future observations would reach galaxies with Mstars ∼ 105 M⊙ at z ∼ 20 of which the mass fraction of Population III stars can be greater than 10 per cent.

Improved compact representation method for Monte Carlo phase spaces

Phase space files used in particle Monte Carlo simulations can be large, and thereby awkward to distribute and slow to load into memory. To circumvent this, compact source models and phase space representations have been developed. In this study, the aim is to increase the versatility of a previously presented method, based on principal component analysis whitening, by evaluating several modifications to the method on simulated cone-beam computed tomography projection images. Scaling the phase space components before whitening was seen to give the largest improvement and allowed for accurate modelling of phase spaces with a shifted detector and/or a bowtie filter present, configurations where the original method struggled. The modified method reproduced results using the original phase space to within a few percent both in energy fluence on the detector and in simulated projection and scatter images of a patient pelvis, without obvious position-dependence of the errors. Hence, the modified method was deemed sufficiently accurate for most applications relying on cone-beam computed tomography simulations. The final aim is to use the more versatile method for a broader range of phase space-based applications such as linear accelerator beam simulations and room shielding calculations.

The Effect of the Climate Change Looming for Indian Summer Monsoon

In recent years, with global temperature rise, extreme weather and climate events, especially high temperature heat waves, floods and droughts, occur more frequently. Recent observational and simulation projections with paleoclimate research reported that monsoon rainfall would become uncertain in the forthcoming century. One of the major problem in predicting the ISM is the accumulation of uncertainties arising from the complex role of Anthropocene activities on the climate system. This, in turn, would result in indecisions in the projected Gross Domestic Product (GDP), sustainable growth, and economy. About 21% of the Indian GDP is contributed by Agriculture (arguably the backbone of India's Economy) which itself highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper reviews the projection of the Indian Summer Monsoon (ISM) in looming climate change. Evaluating the uncertainties in future monsoon behavior predictions would facilitate informed decision-making while formulating strategies for a sustainable Economy and energy policy. Reliable predictions of future ISM in the context of climate change are required while formulating adaptation strategies for sustainable growth and water availability.

Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100 h −1 cMpc)3 with different cosmological parameters (Ω m and σ 8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 < R <27 h −1 cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.

Numerical modeling of wave and current patterns of a slope breakwater in the development plan

ABSTRACT This paper deals with numerical modelling of wave and current patterns in the coastal city of Genaveh port (Bushehr province, Persian Gulf) with a sloping breakwater. Three breakwater arm patterns were defined based on the studied area's marine currents. Meteorological model wind statistics (ECMWF) and wave statistics (ISWM) and data from monitoring and simulation projects of the northern coasts of the Persian Gulf were used to model the hydrodynamics of the study area after the breakwater construction. In this numerical modelling, mike 21-SW and HD modules were employed to simulate the wave patterns near the three proposed breakwater arms. Based on observations of the hydrodynamic condition and with consideration of the design wave exhibiting a 100-year return period, it is indicated that an optimal pattern would yield significant wave heights and wave speeds of 0.2 m and 0.25 m/s, respectively, within both the existing and newly constructed harbour basins.

The BACCO simulation project: biased tracers in real space

ABSTRACT We present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than 400 cosmological models in a 8D cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of a typical Emission-Line survey at z ∼ 1 with an accuracy of $1-2~{{\ \rm per\ cent}}$ up to non-linear scales $k\sim 0.7 h\, {\rm Mpc}^{-1}$.

Avoiding the Chill Pill- Results from a Simulation-Based Verbal De-escalation Training for Psychiatry Residents in a Community Mental Health Center

Background: Potentially dangerous encounters with uncooperative, belligerent, or even violent patients in the Emergency Department and Psychiatric Inpatient Units require skilled responses and timely management of episodes of acute agitation. Lack of training and knowledge in these scenarios could compromise the safety of both staff and patients. There have been improvements in knowledge and comfort when dealing with acutely agitated patients documented in previous studies that have used simulation training for junior psychiatry residents. Objectives: Understand the existing knowledge and skill gap and attempt to address it through simulation training. Methods: This is an IRB exempt quality improvement project, supported with funds from the Committee of Interns and Residents. The study was conducted over 3 years (2019-2022). All psychiatry residents (N=36) participated in an online module (4hrs, JCAHO and OMH accredited) and simulation training with 3 standardized patients (actors). All residents were blind to the clinical scenarios and observed by faculty. Each incoming class (2019-2022) has been trained yearly since. Pre and post intervention surveys asked questions about knowledge, comfort, perceived competence, prior training, verbal de-escalation skills (APSP-agitated patient simulation project survey) and the validated Management of Aggression and Violence Attitude Scale (MAVAS). Results: Data from pre- and post- intervention APSP responses were analyzed using the student’s paired t-test. Residents felt significantly more comfortable handling verbal de-escalation post training (score of 6 vs. 8, P&lt;0.0001) and more competent evaluating and managing agitated patients (6 vs. 8, p&lt;0.0001). They showed significant (p&lt;0.05) changes in attitudes around seclusion and restrains and causes of agitation on MAVAS. The most used de-escalation interventions were: Explaining the situation and process, Summarizing the content, and Identifying patient’s feelings, wants and needs. Triggering behaviors included interrupting/ talking over the patient, not prioritizing patient’s needs, insisting on using medications. Conclusion: This project showed simulation-based training successfully addressed an important practice and knowledge gap and lent structure to the tailored protocol-based approach for managing agitated patients, especially in busy ER settings. Residents appreciated the simulation-based training with real life scenarios – they found it useful and engaging and they welcomed the interaction with standardized patients (SPs) during the training.

FACE VALIDATION IN LARGE-SCALE SIMULATION PROJECTS

Face validation is an informal type of validation, usually applied in the early stages of a simulation project or model development process. Face validation is used to determine the reasonableness of a model or simulation when advanced validation methods such as statistical analysis or sensitivity analysis are not applicable. In large-scale simulation projects such as warfare simulation applications, which consist of a large number and variety of models that cannot be evaluated independently and contain high levels of uncertainty, face validation should be utilized, but no standard method for face validation can be proposed. In this paper, we review the literature and practical applications of face validation. We propose a process to improve the effectiveness of face validation. The proposed process is expected to help to realize a measurable, concrete and practical face validation, especially in large-scale projects involving high-fidelity complex models and constructive simulations.