Real-life Simulation Research Articles

On efficient median-based linear regression estimator for population mean

For the past decade, literatures in sampling theory show that estimation of mean is one of the challenging aspects which has received wide attention from research community with the aim of obtaining efficient estimators. In this research paper, we propose an efficient median-based linear regression estimator that utilizes both median of study variable and auxiliary variable at a time using Subramani and Lamichhane et al.’s methods of estimation. The bias and mean square error of the proposed estimators are derived up to first-order approximation alongside its efficiency condition. A real-life dataset and simulation study is used to compare the efficiency of the proposed estimator with some existing efficient estimators. Findings from the empirical and simulation analysis show a significant efficiency gain (more than 10%), hence the estimator is recommended for practical application.

EFFECTIVE METHODS FOR SUCCESSFUL INFORMATION SECURITY AWARENESS

Information security awareness is becoming increasingly important these days. It is not enough to have a well-developed physical and logical protection of the system and stored data; the users of these systems have to keep up with technological development and have to be sufficiently aware or cautious when using these systems. Information Security Awareness Programs provide the most effective solution for the improvement of users’ information security knowledge and digital competencies. The aim of this study is to help organisations in finding and providing an effective way of knowledge transfer. The study identifies the key elements of the implementation of the awareness programs and highlights the importance of communication channels and methods. The essay summarises and shows the most effective techniques that experts can use in order to draw the user’s attention toward information security, like real-life simulation scenarios, interactive games, themed awareness videos and other gamification techniques.

The Comparative Analysis of Phase Shifting Transformers

Phase shifting transformers (PSTs) are currently widely used in power systems to control power flow. In this manuscript, the results of the asymmetrical PST (APST) with the in-phase PST (called asymmetrical controllable PST-ACPST) were compared, allowing to control both longitudinal and quadrature voltage. The MATLAB simulation model of the ACPST was built to obtain influence of PST in selected models for selected parameters. Then the 30A laboratory PST device was built in the laboratory. The parameters of the MATLAB model were then adjusted to parameters of the real life PST model. This allowed verifying the results of the real life and computer simulations. Based on the ACPST simulation model, the APST model was built; for the given work conditions, the influence of both PSTs were compared. APST construction always resulted in higher output voltage than the input one. ACPST achieved the same power transfer for the lower output PST voltage, which is its main advantage. This dependency is a result of the greater ACPST angle compared to the classical APST. The ACPST also allowed adjusting longitudinal and quadrature voltages; therefore, this PST type can be installed in places where high flexibility is required, especially in systems with high renewable energy sources penetration.

Estimation of mean of a sensitive variable using efficient exponential-type estimators in stratified sampling

Sousa et al. [Improved mean estimation of a sensitive variable using auxiliary information in stratified sampling. J Stat Manage Syst. 2014;17(5–6):503–518] presented a ratio estimator for mean estimation in stratified sampling using an additive scrambled response model for a sensitive variable. In order to improve the estimation of mean, this study is motivated to introduce two difference-cum-exponential ratio estimators for mean estimation of a sensitive variable in stratified sampling. The theoretical discussion is presented to show that the two proposed estimators are more efficient than the available estimators including Sousa et al. [Improved mean estimation of a sensitive variable using auxiliary information in stratified sampling. J Stat Manage Syst. 2014;17(5–6):503–518] ratio and regression estimator in certain situations. A real-life application and simulation studies are presented to express the performance of the proposed estimator and existing estimators. The two studies provide evidence that the two proposed estimators perform more efficiently for mean estimation than the other available estimators.

Kinetic-Based Multiphase Flow Simulation.

Multiphase flows exhibit a large realm of complex behaviors such as bubbling, glugging, wetting, and splashing which emerge from air-water and water-solid interactions. Current fluid solvers in graphics have demonstrated remarkable success in reproducing each of these visual effects, but none have offered a model general enough to capture all of them concurrently. In contrast, computational fluid dynamics have developed very general approaches to multiphase flows, typically based on kinetic models. Yet, in both communities, there is dearth of methods that can simulate density ratios and Reynolds numbers required for the type of challenging real-life simulations that movie productions strive to digitally create, such as air-water flows. In this article, we propose a kinetic model of the coupling of the Navier-Stokes equations with a conservative phase-field equation, and provide a series of numerical improvements over existing kinetic-based approaches to offer a general multiphase flow solver. The resulting algorithm is embarrassingly parallel, conservative, far more stable than current solvers even for real-life conditions, and general enough to capture the typical multiphase flow behaviors. Various simulation results are presented, including comparisons to both previous work and real footage, to highlight the advantages of our new method.

Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty

Reliability assessment in presence of epistemic uncertainty leads to consider the failure probability as a quantity depending on the state of knowledge about uncertain input parameters. The input joint distribution is often learnt from a small-sized dataset provided by operating experience. The computed failure probability depends on the estimated marginal distributions and the estimated copula distribution. This paper develops a reliability-oriented sensitivity analysis procedure in order to measure the influence exerted by the data-driven modeling of both the margins and the copula. The proposed methodology is validated for both deterministic and stochastic reliability methods through an extensive simulation study including several analytical performance functions as well as a real-life simulation code dealing with the buckling of a laminated composite plate.

Isogeometric analysis of multi-patch solid-shells in large deformation

In the context of isogeometric analysis (IGA) of shell structures, the popularity of the solid-shell elements benefit from formulation simplicity and full 3D stress state. However some basic questions remain unresolved when using solid-shell element, especially for large deformation cases with patch coupling, which is a common scene in real-life simulations. In this research, after introduction of the solid-shell nonlinear formulation and a fundamental 3D model construction method, we present a non-symmetric variant of the standard Nitsche’s formulation for multi-patch coupling in association with an empirical formula for its stabilization parameter. An selective and reduced integration scheme is also presented to address the locking syndrome. In addition, the quasi-Newton iteration format is derived as solver, together with a step length control method. The second order derivatives are totally neglected by the adoption of the non-symmetric Nitsche’s formulation and the quasi-Newton solver. The solid-shell elements are numerically studied by a linear elastic plate example, then we demonstrate the performance of the proposed formulation in large deformation, in terms of result verification, iteration history and continuity of displacement across the coupling interface. In the context of isogeometric analysis (IGA), after introduction of the solid-shell nonlinear formulation and a fundamental 3D model construction method, we present a non-symmetric variant of the standard Nitsche’s formulation for multi-patch coupling in association with an empirical formula for its stabilization parameter. An selective and reduced integration scheme is also introduced to address the locking syndrome. In addition, the quasi-Newton iteration format is derived as solver, together with a step length control method. The second order derivatives are totally neglected by the adoption of the non-symmetric Nitsche’s formulation and the quasi-Newton solver. The performance of the proposed formulation in large deformation is demonstrated by several examples.

Robust Design and Analysis of Clinical Trials With Nonproportional Hazards: A Straw Man Guidance From a Cross-Pharma Working Group

Loss of power and clear description of treatment differences are key issues in designing and analyzing a clinical trial where nonproportional hazard (NPH) is a possibility. A log-rank test may be inefficient and interpretation of the hazard ratio estimated using Cox regression is potentially problematic. In this case, the current ICH E9 (R1) addendum would suggest designing a trial with a clinically relevant estimand, for example, expected life gain. This approach considers appropriate analysis methods for supporting the chosen estimand. However, such an approach is case specific and may suffer from lack of power for important choices of the underlying alternate hypothesis distribution. On the other hand, there may be a desire to have robust power under different deviations from proportional hazards. We would contend that no single number adequately describes treatment effect under NPH scenarios. The cross-pharma working group has proposed a combination test to provide robust power under a variety of alternative hypotheses. These can be specified for primary analysis at the design stage and methods appropriately accounting for combination test correlations are efficient for a variety of scenarios. We have provided design and analysis considerations based on a combination test under different NPH types and present a straw man proposal for practitioners. The proposals are illustrated with real life example and simulation.

Reinforcement learning for Pedestrian evacuation Simulation and Optimization during Pandemic and Panic situation

The pandemic of Covid-19 has drawn significant attention of the people around the world. The current situation has revealed that this virus infected more than 50 million people globally. On the other hand, the study on crowd simulation can demonstrate the behaviour of massive people that gather in the same location. The importance of this study can lead to safe evacuation in case an outbreak happens. This paper proposed a solution of innovative crowd simulation supported by reinforcement learning and pandemic factors. The study has successfully demonstrated the entering and leaving the various exit and entrance. The experiment is performed with a different mode such as two elevators for exit and entry, staircase and six lanes of gates. As a result of the experimental study, it reveals around 87% of agent behaviour has similarity compared to a real-life simulation performed by previous research. It means the simulation is reflecting the real-life human behaviour when the evacuation process has occurred. The future study can extend to a contagion model of crowd behaviour where an agent can influence each other in a particular situation.

Discrete element model calibration for industrial raw material simulations

The use of computational fluid dynamics in continuous operation industries have become more prominent in recent times. Proposed system improvements through geometric changes or control strategies can be evaluated within a relatively shorter timeframe. Applications for discrete element methods (DEMs) in real life simulations, however, require validated material-calibration-methods. In this paper, the V-model methodology in combination with direct and bulk calibration approaches were followed to determine material model parameters, to simulate real life occurrences. For the bulk calibration approach a test rig with a containment hopper, deflection plate and settling zone was used. Screened material drains from the hopper, interacts with the deflection plate, and then settles at the material angle of repose. A high-speed camera captured material interaction with the rig, where footage was used during simulation validation. The direct measuring approach was used to determine particle size, shape and density, while confirming friction and restitution coefficients determined in the bulk calibration method. The test was repeated and validated for various geometrical changes. Three categories of validation were established, namely particle speed assessment, -trajectory assessment and -plate interaction assessment. In conclusion, the combination of direct and bulk calibration approaches was significant in calibrating the required material model parameters.

The Application of CDIO Model in the Contest-based Learning

According to the ‘721’ learning rule, of all the 100% of knowledge and skills acquired, 70%, 20% and 10% are acquired on the job, peers and university courses respectively. The CDIO model, originated from engineering education, offers a new vision and concept to codify the knowledge and skills required in the instruction of business course. As an effort to make the students excel in a real-life simulation contest, the contest-based training program aims to create an effective training syllabus embracing the four key elements, namely, the conceive-design-implement-operate procedure.

Preventing Outbreaks through Interactive, Experiential Real-Life Simulations

Operation Outbreak (OO) is a Bluetooth-based simulation platform that teaches students how pathogens spread and the impact of interventions, thereby facilitating the safe reopening of schools. OO also generates data to inform epidemiological models and prevent future outbreaks. Before SARS-CoV-2 was reported, we repeatedly simulated a virus with similar features, correctly predicting many human behaviors later observed during the pandemic.

Set-Membership Constrained Frequency-Domain Algorithm

In this brief, we propose a new frequency-domain adaptive filtering algorithm within the set-membership filtering (SMF) paradigm. Algorithms employing the previously proposed frequency-domain SMF (FD-SMF) concept are not capable of performing linear convolutions (i.e., linear filtering operations), but only circular convolutions. Here, we overcome such limitation by generalizing the FD-SMF concept to allow for linear convolutions, and we propose a new algorithm following this extended FD-SMF concept. Synthetic and real-life simulation results confirm that the proposed algorithm can perform quite better than its non-SMF counterpart, achieving faster convergence and requiring much fewer computations.

A New Transformed t-test for Skewed Data: A Goodness-of-fit Approach

A new transformed two-sample t-test has been proposed for testing equality of two population means for skewed distributions by means of a univariate normal goodness of fit to the combined sample. The small sample performance of the proposed test is compared with untransformed t-test and the non-parametric analogue of t-test via Wilcoxon rank sum test using real-life examples and simulation from skewed distributions with varying values of skewness, empirically. It reveals that the proposed new test is appropriate for estimating the level of significance and is more powerful than the untransformed t-test and the Wilcoxon rank sum test for skewed distributions.

Machine Learning and Virtual Reality on Body Movements' Behaviors to Classify Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.

Setting safer exposure limits for toxic substance combinations

Toxic stimuli (stressors) exposure limits are typically based on single toxic stimuli experiments, but are presently used for both toxic stimuli in isolation and in combination with other toxic stimuli (simultaneous co-exposure or exposures separated in time). In the combination case, typically less of each constituent of the combination is required to cause damage compared to the amount determined from single stressor experiments. Thus, exposure limits based on single toxic stimulus experiments are inadequate for setting limits for stressor combinations.This article presents a recommended simplified approach to improving regulatory exposure limits for toxic stimuli combinations, and a more expansive and expensive alternative to the recommended simplified approach. The recommended approach will partially compensate for the enhanced adverse effects of toxic stimuli combinations relative to adverse effects of toxic stimuli in isolation. The approach covers myriad categories of toxic stimuli reflective of real-life exposures due to lifestyle, iatrogenic, biotoxin, occupational/environmental, and psychosocial/socioeconomic conditions. The proposed approach 1) assumes that all potential toxic stimuli to which an individual might be exposed have the same mechanisms/modes of action on biological mechanisms, and are, thus, indistinguishable by the impacted organism; 2) normalizes the myriad stimuli by converting the doses of toxic stimuli exposures to the respective toxicity reference values (TRV) fractions; 3) sums all the TRVs fractions from these toxic stimuli exposures; and 4) divides all the single substance TRVs by the sum of fractions. While it is an additive approach conceptually, it differs from other additive approaches in the breadth of its inter-category coverage, in order to reflect true inter-category real-life simulation. The newly posited approach does not account for hormetic, antagonistic, or synergistic effects of toxic stimuli in combination. It does not adjust for 1) low-dose toxicants with adverse effects that have been under-reported, or 2) exposure limits like the Occupational Safety and Health Administration - Permissible Exposure Limits (OSHA PELs) that are orders of magnitude above levels shown by published single toxic stimuli studies to have caused adverse effects. Practical considerations for the application of this approach are presented.

Advanced three-dimensionally engineered simulation model for aortic valve and proximal aorta procedures.

A 3-dimensionally (3D) engineered model for simulation of aortic valve and proximal aortic procedures is a reliable tool both for training young surgeons and for simulating complex cases. To achieve a realistic simulation, the artificial model should reproduce the angles and orientations of the cardiac structures based on the patient's anatomical condition, reproduce tissue mechanical characteristics and be easy to obtain and easy to use. The goal of the study was the production and validation of realistic training models, based on the patient's actual anatomical characteristics, to provide training for aortic valve procedures. An anatomical model was manufactured using 3D printing and silicone casting. The digital anatomical model was obtained by segmenting computed tomography imaging. The segmented geometrical images were processed and a casting mould was designed. The mould was manufactured on a 3D printer. Silicone was cast into the mould; after curing, the finished model was ready. The realistic reproduction was evaluated by mechanical hardness tests and a survey by cardiac surgeons. Six 3D silicone models were produced that represented the patient's anatomy including aortic valve leaflets, aortic root with coronary ostia, ascending aorta and proximal arch. Aortic valve replacement was performed, and 100% of the participants evaluated the model in a survey as perfectly reproducing anatomy and surgical handling. We produced a realistic, cost-effective simulator for training purposes and for simulation of complex surgical cases. The model reproduced the real angulation and orientation of the aortic structures inside the mediastinum, permitting a real-life simulation of the desired procedure. This model offers opportunities to simulate various surgical procedures.

Simulation based learning to facilitate clinical readiness in diagnostic radiography. A meta-synthesis

IntroductionSimulation based learning (SBL) has been used in diagnostic radiography for teaching and learning purposes. However, the method of offering the SBL opportunities has not been placed in the context of whether the experiences provide the student with a real life simulation. Moreover, in the light of the current staff shortages and healthcare issues in South Africa, placing students into the clinical practice without sound grounding in the application of theory into practice would be unfair to the student and patients. Thus, SBL could provide the opportunity for the student to learn in a safe teaching and learning environment. MethodsA qualitative, meta- synthesis was undertaken using systematic literature searches. The existing literature in English was retrieved from databases (Medline, CINAHL and ScienceDirect). The keywords used were simulation based learning, simulation, radiography and diagnostic radiography. ResultsThemes and categories were developed from the literature. Theme 1: An authentic and realistic situation, which is relevant to the development of a professional in the context of the profession. Theme 2: Building confidence in a safe, reliable and nurturing environment. Theme 3: Active participation in a collaborative process. ConclusionThe meta synthesis revealed three major themes that can be used as a framework to motivate for the use of SBL in a diagnostic radiography programme. Implications for practiceThe implementation of SBL that could prepare students for the workplace. SBL focused on specific competencies for clinical readiness.

Model učenja engleskog jezika kod studenata sporta

The knowledge of an English language is one of the crucial skills for any person in the field of sport. The multicultural diversity at international competitions and a large number of sports professionals working and competing worldwide created a bigger demand for a more adequate level of linguistic knowledge. Therefore, as a result of it, it is crucial to develop a model of learning English at the institutions of higher education where the focus will be on practicing the communication competence in the field of physical culture that would primarily be context based. This means that the learning environment and the curriculum should be organized in such a way that the students are placed in different situations where they will have real life simulations of the different situations in sport or training. The important part of this model would also be the cultural aspect of language learning which would contribute to the development of intercultural communicative competence.

Emergency patient health service simulation as a supporter of smart health care

Providing health services for patient requests obtained from smart devices installed in each patient can be done if the service center has adequate resources and there are no criteria for emergency patients, but this will be different if in real life an emergency service request occurs. Providing emergency services to hospitals is growing, especially with the introduction and evolution of triage systems by giving priority services to patients based on their level of urgency. Providing services to emergency patients must be a priority in service delivery, because it can be ascertained that this emergency request is closely related to the safety of the patient’s life, such as an accident or sudden onset of disease. Service simulations in patients are carried out using Simulink, by limiting the availability of all the resources in the service center, and providing real life simulation events for 56 emergency patients, and the simulation concludes that priority queuing systems can serve all patient requests and perform as quickly as possible.