Application Of Simulation Research Articles

Robust estimation method for panel interval-valued data model with fixed effects

Panel data model with fixed effects is widely used in economic and administrative applications. However, the presence of factors: measurement errors, data variability and outliers may potentially decrease the accuracy of the model prediction. In this paper, we use panel interval-valued data to represent measurement errors and data volatility of observations. Further, we propose a corresponding panel interval-valued data model with fixed effects, in which both the response and explanatory variables are interval-valued data. To reduce the impact of outliers on our model, we propose a robust estimation method based on the iterative weighted least squares technique. Later, Monte Carlo simulation and empirical application demonstrate that our model is a suitable tool for analyzing the behaviour of panel interval-valued data.

Predicting multiphase flow behavior of methane in shallow unconfined aquifers using conditional deep convolutional generative adversarial network

Numerical modeling is an essential tool for geoscience applications involving multiphase flow behavior. Performing numerical simulation is, however, computationally intensive due to multi-scale, non-linear, and multi-physics nature of mechanisms controlling multiphase flow dynamics. Additionally, the computational challenges associated with traditional numerical simulations limit their applicability for repetitive simulations required in inverse modeling, uncertainty analysis, and optimization tasks. To overcome these limitations, surrogate modeling has recently emerged as a viable solution. A surrogate model, which functions as a regression model, is trained using numerical simulation data. It approximates the input–output relationships within a dynamic fluid environment. We design surrogate models, based on conditional deep convolutional generative adversarial network (cDC-GAN), to map the cross-domain between input and output pairs in a multiphase multicomponent system, focusing on methane (CH4) migration in shallow unconfined aquifers. The cDC-GAN technique is selected due to its ability to generate high-fidelity surrogate models that effectively capture complex spatial distributions and heterogeneities. This technique excels in handling high-dimensional data, learning the intricate relationships between inputs and outputs, and producing realistic representations of subsurface phenomena. The trained cDC-GANs consider capillary entry pressure heterogeneity as input because this geologic attribute places first-order control on CH4 migration in subsurface. The outputs include CH4 saturation, water saturation, residual saturation of CH4, CH4 mole fraction, and CH4 molality. For each output, a separate cDC-GAN model is trained. Once trained, cDC-GANs can at any given time determine CH4 distribution in a shallow unconfined aquifer, both qualitatively and quantitatively. The cDC-GAN approach can be considered as a valuable complement to numerical simulations for predicting multiphase flow behavior in other geoscience-, energy-, and environmental applications such as contaminant transport, hydrocarbon production, and geological carbon dioxide and hydrogen storage.

New metabolites of 2-ethylhexyl salicylate in human urine after simulated real-life dermal sunscreen application

2-Ethylhexyl salicylate (EHS) is an organic UV filter which is used in sunscreen and other personal care products. The dermal uptake of EHS was studied in several dermal-exposure experiments. This paper aims to coherently assess urine samples after dermal exposure for the biomarkers EHS, 5OH-EHS, 5oxo-EHS, and 5cx-EPS as well as further biomarkers of interest, specifically 4OH-EHS, 4oxo-EHS, 2OH-EHS, and 6OH-EHS, for the first time. Samples from 18 participants of a pre-existing dermal exposure study under real-life conditions were reassessed using a comprehensive LC-MS/MS method. EHS accounts for 34 % of the cumulative excretion of all analytes within 24 h after exposure, followed by 5OH-EHS (19 %), 5cx-EPS (18 %), 4OH-EHS (15 %) and 5oxo-EHS (11 %). Further metabolites were only quantified in minor amounts. EHS as the most prominent excretion parameter in this study demonstrates the missing first-pass effect after dermal absorption. Furthermore, the applied novel comprehensive analytical procedure revealed oxidation at the ω (5cx-EPS, 6OH-EHS), ω-1 (5OH-EHS, 5oxo-EHS), and ω-2 positions (4OH-EHS, 4oxo-EHS) in the main chain of the ethylhexyl group as well as oxidation in the side chain (2OH-EHS). The presented data are of high relevance for a reliable toxicological risk assessment of dermal exposure to EHS.

Monte Carlo Simulation Application for Project Scheduling Improvements in The Shipping Industry

Monte Carlo Simulation Application for Project Scheduling Improvements in The Shipping Industry

Asymptotic analysis of the stress concentration between two adjacent stiff inclusions in all dimensions

In the region between two closely located stiff inclusions, the stress, which is the gradient of a solution to the Lamé system with partially infinite coefficients, may become arbitrarily large as the distance between interfacial boundaries of inclusions tends to zero. The primary aim of this paper is to give a sharp description in terms of the asymptotic behavior of the stress concentration, as the distance between interfacial boundaries of inclusions goes to zero. For that purpose, we capture all the blow‐up factor matrices, whose elements comprise of some certain integrals of the solutions in the case when two inclusions are touching. Then, we are able to establish the asymptotic formulas of the stress concentration in the presence of two close‐to‐touching ‐convex inclusions in all dimensions. Furthermore, an example of curvilinear squares with rounded‐off angles is also presented for future application in numerical computations and simulations.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

Assessments of multiple precipitation products and application in hydrodynamic simulations: A case of casualty‐inducing mountain torrents in Sichuan, Southwest China

AbstractMountain torrents are frequent and severe hazards, causing serious economic losses and personnel casualties, and in particular, they are the most dangerous threat in the complex terrain. Multi‐source precipitation products should be used to drive the hydrological models to improve understanding of the processes of mountain torrents. Fifteen sets of precipitation products are evaluated against the ground observations in situ within a serious mountain torrent caused by a severe rainstorm at June 26, 2020 in Sichuan. The multi‐source merged precipitation products performed better than the others. Satellite‐based precipitation performed better than model‐based products, while IMERG‐Early and FY2G performed better than other satellite‐based precipitation products. The reanalysis datasets are relatively worse than the observed products. The operational NWPs strongly underestimated precipitation in the mountainous areas. Furthermore, the related floods caused by the rainstorm is simulated based on various precipitation products by a raster‐based hydrodynamic two‐dimensional model of FloodArea. Most observation‐based precipitation products represented the flooding area well, while the model‐based precipitation products underestimated the severity of the mountain torrents. Finally, the risk of mountain torrents under variant return‐period rainfall is analyzed. The result shows the potential of real‐time torrent monitoring and forecasting using high‐resolution DEM and precipitation products.

Pragmatic Prediction Model of Droplet Trajectory in a Turbine Cascade

Abstract Droplet deposition on a turbine cascade is important for the turbine system performance but its experimental analysis is difficult. Thus, the simulation of a droplet liquid phase in a gas flow field was studied to optimize turbine cascade design. However, the computational fluid dynamics (CFD)-based approach for such droplet problems requires enormous costs. Thus, the application of CFD simulation in the turbine blade's early-stage design is challenging, requiring iterative optimization for adjusting design with performance prediction. Therefore, this study proposed an analytical prediction method, having a reasonable cost and moderate accuracy, as an alternative to the whole multi-phase numerical simulation approach. The proposed method predicts droplet motion using the outline of the turbine blade and gas–liquid physical properties. Furthermore, the approach was validated by performing a three-dimensional Eulerian–Lagrangian simulation with low-pressure turbine blade T106. It was found that the droplet trajectories in the turbine cascade are governed by Stokes number. Furthermore, the streamlines of the gas flow were characterized by the shape of the turbine blade. The proposed model reproduced droplet trajectories obtained using CFD within an error margin of 10%. Consequently, it was concluded that the proposed analytical model is a promising approach to predict the droplet trajectory in a turbine cascade, obtained by the three-dimensional numerical simulation at a low cost.

Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment.

The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to encode conditional shape information for the classification of neonatal resuscitation equipment from less than 100 natural images. The model is also strengthened by incorporating a reliability score, which enriches the prediction with an estimation of classification reliability. The model, whose performance is cross-validated, reached a median accuracy performance of over 99% (and a lower limit of 73.4% for the least accurate model/fold) using only 87 meta-training images. During the test phase on complex natural images, performance was slightly degraded due to a sub-optimal segmentation strategy (FastSAM) required to maintain the real-time inference phase (median accuracy 87.25%). This methodology proves to be excellent for applying complex classification models to contexts (such as neonatal resuscitation) that are not available in public databases. Improvements to the automatic segmentation strategy prior to the extraction of conditional information will allow a natural application in simulation and hospital settings.

Improving Students' Cognitive Understanding of Natural Selection Topics Through PhET Simulations

Natural selection is one of the topics of evolution material that is considered difficult for students, so it is necessary to have a practicum to help in understanding this material. Limited laboratory equipment in schools is an obstacle to learning evolution, so learning media are needed to support evolution learning. One of the learning media that can be applied is PhET simulation. PhET simulation supports evolution learning because it provides simulations in the form of processes of how living things experience natural selection mechanisms. This service program aims to describe students' cognitive understanding of the topic of natural selection with the help of the PhET simulation application. This activity is expected to be used as reference material for teachers who conduct practicums with the help of the PhET simulation application. The subjects who participated in this service activity were 30 students in class XII MIPA. The service activities were carried out on June 16–23, 2023, at SMAN 4 Tuban. The service activities carried out include training, discussion, and guidance. Based on the results of the program, it can be seen that the trainees were very enthusiastic about participating in the PhET simulation activities, the use of PhET simulation was easy to apply, and the trainees were easier to understand the material with the help of this application (87% of the time).

Застосування CAD/CAE-системи Solidworks у задачах аналізу міцності деталей верстатних пристосувань

The strength of parts is a critically important characteristic of the design of Clamping Fixtures. Calculating the strength of parts of Clamping Fixtures is necessary both when designing new structures and when checking existing ones. Traditional calculation methods, such as nominal stress calculation, although easy to use, have low accuracy. This creates a need for more accurate and efficient strength analysis methods. Modern CAD/CAE systems, in particular SolidWorks with the Simulation application, provide powerful tools for engineering calculations and finite element modeling. The algorithm of static calculation presented in the article on the example of the "Grip" part demonstrates the sequence of actions during strength analysis in the SolidWorks environment. This algorithm includes creation of a 3D model, generation of a finite-element mesh, material selection, determination of anchoring and loading conditions, calculation and analysis of results. An important stage of the analysis is the choice of the material of the part. At the same time, it should be taken into account that the databases of foreign CAD/CAE systems may not contain some domestic materials, which requires the selection of analogues with similar characteristics. The system allows you to visualize the results of calculations in the form of colored diagrams of stress and strain distribution. This greatly facilitates the analysis and interpretation of the results, allowing the critical areas of the structure to be quickly identified. The use of CAD/CAE systems for strength analysis of machine tool parts has a number of advantages compared to traditional methods. It increases the accuracy of calculations, reduces the time for their implementation, allows you to easily make changes to the design and instantly assess their impact on the strength of the part. The proposed method allows you to carry out both design and verification calculations of details of Clamping Fixtures. This makes it a versatile tool for structural engineers. The implementation of such strength analysis methods is particularly relevant both for the educational process and for real production. In terms of training, it allows students to gain practical skills in working with modern engineering systems. In production, it helps to improve the quality of design, reduce the number of errors and optimize structures.

Application Research of Virtual Simulation System From Complexity Perspective

The “Intelligent Manufacturing Technology and its Application” in higher vocational colleges has some problems such as limited practical training resources, high maintenance costs and security risks in the running process. Even if there is a simulation, it is a simple simulation. The repetitive imitation of traditional practical training links, and the virtual simulation teaching does not fully reflect the characteristics of virtuality, interactivity and spatiotemporality. Because of the existing problems in the training simulation development and application of the current curriculum, the development and application of the virtual simulation system under the complexity perspective is proposed. The results show that this application can improve the effectiveness of virtual system applications.

Assessment order and faking behavior

AbstractPersonality testing is a critical component of organizational assessment and selection processes. Despite nearly a century of research recognizing faking as a concern in personality assessment, the impact of order effects on faking has not been thoroughly examined. This study investigates whether the sequence of administering personality and cognitive ability measures affects the extent of faking. Previous research suggests administering personality measures early in the assessment process to mitigate adverse impact; however, models of faking behavior and signaling theory imply that test order could influence faking. In two simulated applicant laboratory studies (Study 1 N = 172, Study 2 N = 174), participants were randomly assigned to complete personality measures either before or after cognitive ability tests. Results indicate that participants who completed personality assessments first exhibited significantly higher levels of faking compared to those who took cognitive ability tests first. These findings suggest that the order of test administration influences faking, potentially due to the expenditure of cognitive resources during cognitive ability assessments. To enhance the integrity of selection procedures, administrators should consider the sequence of test administration to mitigate faking and improve the accuracy of personality assessments. This study also underscores the need for continued exploration of contextual factors influencing faking behavior. Future research should investigate the mechanisms driving these order effects and develop strategies to reduce faking in personality assessments.

The Effect Of Simulated Patient Education On Critical Thinking Trends In Medical School Students

Introduction &Objective: Critical thinking consists of mental processes such as reasoning, analysis, and evaluation. It is an objective super-thinking way open to self-correction and change. The main component of the current education methods is the acquisition of critical thinking. Simulated patient applications in medical education is an effective method that enables the student to acquire cognitive and psychomotor behaviors with active participation in the learning process. The aim of this study is to determine the change in the critical thinking tendencies of the third-year students of Akdeniz University Faculty of Medicine before and after simulated patient applications. Method: Third-year students of Akdeniz University Faculty of Medicine in the 2018-2019 academic year participated in the study. The students were asked to apply the Critical Thinking Disposition scale forms before the simulated patient applications. In the second stage of the study, the scale was reapplied to the students. The scale consists of 49 items. In our study, the results of the change between the first and the second applications of Critical Thinking Disposition scale were compared with the Paired Sample Test. Results &conclusion: A total of 345 students participated in the study. The mean score obtained from the Critical Thinking Disposition scale of the whole group was 191.63 (SD = 21.07) in the fall term and 193.69 (SD = 23.63) in the spring term. Although there was an increase in mean values of CTHD scale, the difference was not statistically significant (t(344)=-1.09, p = 0.277). The multidimensional nature of critical thinking skills and the need for continuity in order to keep this skill sharp are considered effective on this result. Different applications can be added to the training program to develop this skill.

Application of CFD Simulation to the Design of an Innovative Drying Chamber

Drying and sanitising equipment has been very common in industrial plants since the pandemic. These are devices that consume significant amounts of energy. The best solution is to use a drying chamber equipped with a heat pump, which allows partial recovery of the energy. In the design of the drying chamber, the drying time is important, which depends both on the parameters of the heat pump itself, and the geometry and airflow of the drying chamber. The geometry and airflow supply should be arranged to ensure a uniform distribution of velocity throughout the drying area. For this purpose, the use of CFD simulations has been proposed. A model was developed in ANSYS/FLUENT where all model parameters, including the optimal mesh density, turbulence models, etc., were determined. The model was verified on the experimental results of the basic design of the chamber. Then an innovative design was proposed that was modelled and optimised in terms of the distribution of the inlet’s perforation. The final design was made, and, at the same time, the simulation’s results were verified by measuring the velocity of airflow in the new design. Together with the optimisation of the heat pump, this made it possible to reduce the drying time by 50%, with a simultaneous reduction in the energy consumed.

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms.

Seracam: characterisation of a new small field of view hybrid gamma camera for nuclear medicine

BackgroundPortable gamma cameras are being developed for nuclear medicine procedures such as thyroid scintigraphy. This article introduces Seracam® – a new technology that combines small field of view gamma imaging with optical imaging – and reports its performance and suitability for small organ imaging.MethodsThe count rate capability, uniformity, spatial resolution, and sensitivity for 99mTc are reported for four integrated pinhole collimators of nominal sizes of 1 mm, 2 mm, 3 mm and 5 mm. Characterisation methodology is based on NEMA guidelines, with some adjustments necessitated by camera design. Two diagnostic scenarios – thyroid scintigraphy and gastric emptying – are simulated using clinically relevant activities and geometries to investigate application-specific performance. A qualitative assessment of the potential benefits and disadvantages of Seracam is also provided.ResultsSeracam’s performance across the measured characteristics is appropriate for small field of view applications in nuclear medicine. At an imaging distance of 50 mm, corresponding to a field of view of 77.6 mm × 77.6 mm, spatial resolution ranged from 4.6 mm to 26 mm and sensitivity from 3.6 cps/MBq to 52.2 cps/MBq, depending on the collimator chosen. Results from the clinical simulations were particularly promising despite the challenging scenarios investigated. The optimal collimator choice was strongly application dependent, with gastric emptying relying on the higher sensitivity of the 5 mm pinhole whereas thyroid imaging benefitted from the enhanced spatial resolution of the 1 mm pinhole. Signal to noise ratio in images was improved by pixel binning. Seracam has lower measured sensitivity when compared to a traditional large field of view gamma camera, for the simulated applications this is balanced by advantages such as high spatial resolution, portability, ease of use and real time gamma-optical image fusion and display.ConclusionThe results show that Seracam has appropriate performance for small organ 99mTc imaging. The results also show that the performance of small field of view systems must be considered holistically and in clinically appropriate scenarios.

Numerical simulation application in occupational health studies: a review

Most occupational hazardous agents in workplaces should be evaluated and controlled. Different methods exist for identifying, evaluating and controlling these agents, such as numerical simulation tools. Numerical simulations can help experts to improve occupational health. Due to the importance and abilities of numerical simulations, this study divided occupational hazardous agents into 10 subgroups. These subgroups included air pollution, ventilation, respiratory airways, noise and vibration, lighting, radiation, ergonomics, fire and explosion, risk assessment and personal protective equipment. Recent research studies in each subgroup were then reviewed, and the codes and software used in simulations were determined. The results show that Fluent software and k–ϵ turbulence models are the most used in occupational health studies simulations. Today, different codes and software have been developed for simulation, and we suggest their use in occupational health studies.

Design and application of a radar target simulator

Abstract To fulfill the need for debugging boundary scenes in radar data processing development, this paper proposes a simulator design method for the editing and simulation of diverse combat scenes as required. The simulator was developed based on the VPX platform, including external control equipment, resource scheduling, beam control, target simulation, and data processing. The motion model is based on linear and circular models, the clutter model algorithm is the zero memory nonlinear transformation method based on K distributions, and the noise model is designed as Gaussian white noise, characterized by a probability distribution in the Gaussian distribution and a constant power spectrum. The generated plots are condensed, and their quality is calculated, for which the rate of plot rejection can reach more than 50% by controlling the feature elements. Finally, the simulated target trajectory is visually verified through Matlab.