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Evaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.

Monte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.

The review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.

Performance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

The purpose of this paper is to propose a bearing replacement strategy which employs the Monte Carlo simulation method. In this contribution the method is used to estimate the economic impact on the selection of a particular bearing change strategy. The simulation demonstrates that it is possible to identify the most cost‐effective approach and thus suggests a suitable bearing replacement policy, which in turn allows engineers to develop the appropriate maintenance schedules for their company.

The paper develops the Monte Carlo method through a case study approach. Three case studies are presented. The first study is detailed and chronicles the design, development and implementation of the Monte Carlo method as a means of defining a bearing replacement strategy within a subject company. The second and third cases outline the application of the Monte Carlo method in two different environments. These applications made it possible to obtain proof of concept and also to further prove the effectiveness of the Monte Carlo simulation approach.

An effective development of the Monte Carlo approach is proposed and the effectiveness of the method is subsequently evaluated, highlighting the benefits to the host organization and how the approach led to significant improvement in machinery reliability through a bearing replacement strategy.

The design, development and implementation of a bearing replacement strategy provide a simple yet effective approach to achieving significant improvements in system reliability and performance through less downtime and greater cost savings. The paper offers practising maintenance managers and engineers a strategic framework for increasing productive efficiency and output.

The proposed bearing replacement strategy contributes to the existing knowledge base on maintenance systems and subsequently disseminates this information in order to provide impetus, guidance and support towards increasing the development companies in an attempt to move the UK manufacturing sector towards world‐class manufacturing performance.

Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.

The purpose of this paper is to recuperate Heinz von Foerster’s “Quantum Mechanical Theory of Memory” from Cybernetics: Circular, Causal, and Feedback Mechanisms in Biological and Social Systems and John von Neumann’s The Computer and the Brain for present-day, and future, applications in biophysics, theories of information and cognition, and quantum theories; the main objective is to ground cybernetic theory for a critical evaluation of the historical evolution of the Monte Carlo method, with potential for application to quantum computing.

Close-reading of selected texts, historiography, and case studies in current developments in the Monte Carlo method of high-energy particle physics (HEP) for developing a platform for bridging the apparently incommensurable differences between the physical-mathematical and the biological sciences.

First, usefulness of the cybernetic approach for historicizing the Monte Carlo method in relation to digital computing and quantum physics. Second, development of an inter/trans-disciplinary approach to the hard sciences through a critical re-evaluation of the historical texts of von Foerster and von Neumann for application to developments in quantum theory, biophysics, and computing.

This work is largely theoretical and uses dialectical thought experiments to engage between sciences operating across different ontological scales.

Consideration of developments of quantum computing and how that would change one’s perception of information, data, and the way in which analysis is currently performed with big data.

This is the first time that von Neumann and von Foerster have been contrasted and compared in relation to their epistemic compatibility, historical importance, and relevance for producing a creative approach to current scientific epistemology. This paper hopes to change how the authors view trans-disciplinary/inter-disciplinary practices in the sciences and produce new vistas of thought in the history and philosophy of science.

The purpose of this paper is to provide a Monte Carlo variance reduction method based on Control variates to solve Fredholm integral equations of the second kind.

A numerical algorithm consisted of the combined use of the successive substitution method and Monte Carlo simulation is established for the solution of Fredholm integral equations of the second kind.

Owing to the application of the present method, the variance of the solution is reduced. Therefore, this method achieves several orders of magnitude improvement in accuracy over the conventional Monte Carlo method.

Numerical tests are performed in order to show the efficiency and accuracy of the present paper. Numerical experiments show that an excellent estimation on the solution can be obtained within a couple of minutes CPU time at Pentium IV‐2.4 GHz PC.

This paper provides a new efficient method to solve Fredholm integral equations of the second kind and discusses basic advantages of the present method.

In smaller projects with limited resources, schedule updates are often not performed. In these situations, traditional delay analysis methods cannot be used as they all require updated schedules. The objective of this study is to develop a model that performs delay analysis by using only an as-planned schedule and the expense records kept on site.

This study starts out by developing an approach that estimates activity duration ranges in a network schedule by using as-planned and as-built s-curves. Monte Carlo simulation is performed to generate candidate as-built schedules using these activity duration ranges. If necessary, the duration ranges are refined by a follow-up procedure that systematically relaxes the ranges and develops new as-built schedules. The candidate schedule that has the closest s-curve to the actual s-curve is considered to be the most realistic as-built schedule. Finally, the as-planned vs. as-built delay analysis method is performed to determine which activity(ies) caused project delay. This process is automated using Matlab. A test case is used to demonstrate that the proposed automated method can work well.

The automated process developed in this study has the capability to develop activity duration ranges, perform Monte Carlo simulation, generate a large number of candidate as-built schedules, build s-curves for each of the candidate schedules and identify the most realistic one that has an s-curve that is closest to the actual as-built s-curve. The test case confirmed that the proposed automated system works well as it resulted in an as-built schedule that has an s-curve that is identical to the actual as-built s-curve. To develop an as-built schedule using this method is a reasonable way to make a case in or out of a court of law.

Practitioners specifying activity ranges to perform Monte Carlo simulation can be characterized as subjective and perhaps arbitrary. To minimize the effects of this limitation, this study proposes a method that determines duration ranges by comparing as-built and as-planned cash-flows, and then by systematically modifying the search space. Another limitation is the assumption that the precedence logic in the as-planned network remains the same throughout construction. Since updated schedules are not available in the scenario considered in this study, and since in small projects the logic relationships are fairly stable over the short project duration, the assumption of a stable logic throughout construction may be reasonable, but this issue needs to be explored further in future research.

Delays are common in construction projects regardless of the size of the project. The critical path method (CPM) schedules of many smaller projects, especially in developing countries, are not updated during construction. In case updated schedules are not available, the method presented in this paper represents an automated, practical and easy-to-use tool that allows parties to a contract to perform delay analysis with only an as-planned schedule and the expense logs kept on site.

Since an as-built schedule cannot be built without updated schedules, and since the absence of an as-built schedule precludes the use of any delay analysis method that is acceptable in courts of law, using the method presented in this paper may very well be the only solution to the problem.

A numerical integration scheme using the Monte Carlo method is discussed to evaluate the singular integral in boundary elements. A numerical demonstration is given for some potential problems. Results evaluated by the Monte Carlo method are compared with the analytical ones for accuracy and computation time. Examination shows the validity and capability of the approach.

Although very significant and applicable, there have been no formal justifications for the use of Monte‐Carlo models and Markov chains in evaluating hospital admission decisions or concrete data supporting their use. For these reasons, this research was designed to provide a deeper understanding of these models. The purpose of this paper is to examine the usefulness of a computerized Monte‐Carlo simulation of admission decisions under the constraints of emergency departments.

The authors construct a simple decision tree using the expected utility method to represent the complex admission decision process terms of quality adjusted life years (QALY) then show the advantages of using a Monte‐Carlo simulation in evaluating admission decisions in a cohort simulation, using a decision tree and a Markov chain.

After showing that the Monte‐Carlo simulation outperforms an expected utility method without a simulation, the authors develop a decision tree with such a model. real cohort simulation data are used to demonstrate that the integration of a Monte‐Carlo simulation shows which patients should be admitted.

This paper may encourage researchers to use Monte‐Carlo simulation in evaluating admission decision implications. The authors also propose applying the model when using a computer simulation that deals with various CVD symptoms in clinical cohorts.

Aside from demonstrating the value of a Monte‐Carlo simulation as a powerful analysis tool, the paper's findings may prompt researchers to conduct a decision analysis with a Monte‐Carlo simulation in the healthcare environment.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

To find an easy and accurate method for evaluating the Pollaczek's integral in earth‐return path impedance calculation.

The Monte Carlo method of evaluating the Pollaczek's integral is introduced.

The Monte Carlo method is easy and accurate method for this computation.

Using proposed method in cases of earth stratification.

The proposed method can be used in power system transient software.

The proposed method introduces a computation method for calculation of Pollaczek's integral which is valuable for power engineers.