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Requirements of a project are found to change in various ways during the course of the same. Studies have investigated the effect of requirement volatility on different project parameters like effort, schedule, quality, etc. However, these studies have not looked into how different “patterns” of requirement volatility influence project quality; and which intervention strategies could be effective under the circumstances. This paper aims to address this issue.

The “system dynamics” approach has been used for carrying out the research. Based on a recent finding, we implemented different resource management policies on a validated software process model on waterfall systems development life cycle. Subsequently, we examined the efficacies of these resource management policies on project quality under requirement volatility.

Results indicate variations in quality metrics like error generation, error detection, and quality assurance effort across experimental scenarios as different patterns of requirement volatility and resource management policies impact the software project dynamics in different ways.

In absence of any imposed schedule penalty, the extent of variations in project parameters across the policy choices was not very significant. The results are also expected to differ depending upon the project development environment.

Findings are expected to assist project managers in deciding on the workforce augmentation plan that would favorably satisfy both the organization's objectives as well as the users' quality requirements under requirement volatility.

In present day context of shorter time to market and stringent quality requirements; meeting quality targets become difficult especially in scenarios where requirement volatility is a norm. This paper provides a dynamic view of the phenomenon of how quality gets affected; and explores the efficacy of different resource management strategies in improving quality under the experimental scenarios.

A novel modelling approach is proposed to study ozone distribution and destruction in indoor spaces. The level of ozone gas concentration in the air, confined within an indoor space during an ozone-based disinfection process, is analysed. The purpose of this work is to investigate how ozone is distributed in time within an enclosed space.

A computational methodology for predicting the space- and time-dependent ozone concentration within the room across the consecutive steps of the disinfection process (generation, dwelling and destruction modes) is proposed. The emission and removal of ozone from the air volume are possible by means of a generator located in the middle of the room. This model also accounts for ozone reactions and decay kinetics, and gravity effect on the air.

This work is validated against experimental measurements at different locations in the room during the disinfection cycle. The numerical results are in good agreement with the experimental data. This comparison proves that the presented methodology is able to provide accurate predictions of the time evolution of ozone concentration at different locations of the enclosed space.

This study introduces a novel computational methodology describing solute transport by turbulent flow for predicting the level of ozone concentration within a closed room during a COVID-19 disinfection process. A parametric study is carried out to evaluate the impact of system settings on the time variation of ozone concentration within the space considered.

Ventilation is driven by weather conditions, occupant actions and mechanical ventilation, and so can be highly variable. This paper reports on the development of two analysis algorithms designed to facilitate investigation of ventilation in occupied homes over time.

These algorithms facilitate application of the CO₂ concentration decay tracer gas technique. The first algorithm identifies occupied periods. The second identifies periods of decaying CO₂ concentration which can be assumed to meet the assumptions required for analysis.

The algorithms were successfully applied in four occupied dwellings, giving over 100 ventilation measurements during a six-month period for three flats. The specific implementation of the decay identification algorithm had important ramifications for the ventilation rates measured, highlighting the importance of interrogating the way that appropriate periods for analysis are identified.

The analysis algorithms provide robust, reliable and repeatable identification of CO₂ decay periods appropriate for ventilation rate analysis. The algorithms were coded in Python, and these have been made available via GitHub. As well as supporting future CO₂ tracer gas experiments, the algorithms could be adapted to different purposes, including the use of other tracer gases or exploring occupant exposure to indoor air pollution.

Empirical investigations of ventilation in occupied dwellings rarely aim to investigate the variability of ventilation. This paper reports on analysis methods which can be used to address this gap in the empirical evidence.

The gas assisted Iaser heating of engineering surfaces finds wide application in industry. Numerical simulation of the heating process may considerably reduce the cost spent on experimentation. In the present study, 2‐dimensional axisymmetric flow and energy equations are solved numerically using a control volume approach for the case of a gas assisted laser heating of steel surfaces. Various turbulence models including standard k‐ε, k‐ε YAP, low Reynolds number k‐ε and RSTM models are tested. The low Reynolds number k‐ε model is selected to account for the turbulence. Variable properties of both solid and gas are taken into account during the simulation. Air is considered as an assisting gas impinging the workpiece surface coaxially with the laser beam. In order to validate the presently considered methodology, the study is extended to include comparison of present predictions with analytical solution for the case available in the literature. It is found that the assisting gas jet has some influence on the temperature profiles. This effect is minimum at the irradiated spot center and it amplifies considerably in the gas side. In addition, account for the variable properties results in lower surface temperatures as compared to the constant properties case.

Reviews a number of contributions to migration analysis. Discusses some of the rudiments of migration modelling before turning attention to specific applications. Pays particular attention to articles which have modelled the interaction between labour and housing markets.

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

This paper aims to utilize purpose advanced fluid‐structure interaction, non‐linear dynamics, finite‐element analyses in order to investigate various phenomena and processes accompanying blast wave generation, propagation and interaction and to assess the blast‐wave‐mitigation potential of a piston‐cylinder assembly placed in front of the target structure.

The employed computational methods and tools are verified and validated by first demonstrating that they can quite accurately reproduce analytical solutions for a couple of well‐defined blast wave propagation and interaction problems.

The methods/tools are used to investigate the piston‐cylinder blast‐mitigation concept and the results obtained clearly reveal that significant blast‐mitigation effects can be achieved through the use of this concept. Furthermore, the results showed that the extent of the blast‐mitigation effect is a sensitive function of the piston‐cylinder geometrical parameters. Specifically, the mass of the piston and the length of the cylinder are found to be the dominant factors controlling the extent of the blast‐wave‐mitigation.

The work demonstrates that, when assessing the blast‐wave‐mitigation potential of the piston‐cylinder concept, it is critical that loading experienced by the piston be defined by explicitly modeling (fluid/structure) interactions between the blast wave(s) and the piston.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

This paper aims to give a perspective about the variety of techniques which are available and are being further developed in the area of coupled field formulations, with selective bibliography and practical examples, to help postgraduate students, researchers and designers working in design or analysis of electrical machinery.

This paper reviews the recent trends in coupled field formulations. The use of these formulations for designing and non‐destructive testing of electrical machinery is described, followed by their classifications, solutions and applications. Their advantages and shortcomings are discussed.

The paper gives an overview of research, development and applications of coupled field formulations for electrical machinery based on more than 160 references. All landmark papers are classified. Practical engineering case studies are given which illustrate wide applicability of coupled field formulations.

Problems which continue to pose challenges to researchers are enumerated and the advantages of using the coupled‐field formulation are pointed out.

This paper gives a detailed description of the application of the coupled field formulation method to the analysis of problems that are present in different electrical machines. Examples of analysis of generators and transformers with this formulation are presented. The application examples give guidelines for its use in other analyses.

The coupled‐field formulation is used in the analysis of rotational machines and transformers where reference data are available and comparisons with other methods are performed and the advantages are justified. This paper serves as a guide for the ongoing research on coupled problems in electrical machinery.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.