Estimation of Parameters of PTRC SRGM using Non-informative Priors

Reliability is an essentially important characteristic of software. The reliability of software has been assessed by considering Poisson Type occurrence of software failures and the failure intensity of one parameter say (η_1 ) Rayleigh class. Here, it is assumed that the software contains fixed number of inherent faults say (η_0 ). The scale parameter of Rayleigh density (η_1 ) and fixed number of inherent faults contained in software are the parameters of interest. The failure intensity and mean failure function of this Poisson Type Rayleigh Class (PTRC) Software Reliability Growth Model (SRGM) have been studied. The estimates of above parameters can be obtained by using maximum likelihood method. Bayesian technique has been used to about estimates of η_0 and η_1 if prior knowledge about these parameters is available. The prior knowledge about these parameters is considered in the form of non- informative priors for both the parameters. The proposed Bayes estimators are compared with their corresponding maximum likelihood estimators on the basis of risk efficiencies under squared error loss. The Monte Carlo simulation technique is used for calculating risk efficiencies. It is seen that both the proposed Bayes estimators can be preferred over corresponding MLEs for the proper choice of the values of execution time.

Third-order transport coefficients for electrons in N2 and CF4: effects of non-conservative collisions, concurrence with diffusion coefficients and contribution to the spatial profile of the swarm

Using a multi-term solution of the Boltzmann equation and Monte Carlo simulation technique we study behaviour of the third-order transport coefficients for electrons in model gases, including the ionisation model of Lucas and Saelee and modified Ness-Robson model of electron attachment, and in real gases, including N2 and CF4. We observe negative values in the E/n 0-profiles of the longitudinal and transverse third-order transport coefficients for electrons in CF4 (where E is the electric field and n 0 is the gas number density). While negative values of the longitudinal third-order transport coefficients are caused by the presence of rapidly increasing cross sections for vibrational excitations of CF4, the transverse third-order transport coefficient becomes negative over the E/n 0-values after the occurrence of negative differential conductivity. It is found that the accuracy of the two-term approximation for solving the Boltzmann equation is sufficient to investigate the behaviour of the third-order transport coefficients in N2, while for electrons in CF4 it produces large errors and is not even qualitatively correct . The influence of implicit and explicit effects of electron attachment and ionisation on the third-order transport tensor is investigated. In particular, we discuss the effects of attachment heating and attachment cooling on the third-order transport coefficients for electrons in the modified Ness-Robson model, while the effects of ionisation are studied for electrons in the ionisation model of Lucas and Saelee, N2 and CF4. The concurrence between the third-order transport coefficients and the components of the diffusion tensor, and the contribution of the longitudinal component of the third-order transport tensor to the spatial profile of the swarm are also investigated. For electrons in CF4 and CH4, we found that the contribution of the component of the third-order transport tensor to the spatial profile of the swarm between approximately 50 Td and 700 Td, is almost identical to the corresponding contribution for electrons in N2. This suggests that the recent measurements of third-order transport coefficients for electrons in N2 may be extended and generalized to other gases, such as CF4 and CH4.

Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

The goal of this research is to compare multiple linear regression coefficient estimations with multicollinearity. In order to quantify the effectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS), modified ridge regression method (MRR), and generalized Liu-Kejian method (LKM) are compared (AMSE). For this study, the simulation scenarios are 3 and 5 independent variables with zero mean normally distributed random error of variance 1, 5, and 10, three correlation coefficient levels; i.e., low (0.2), medium (0.5), and high (0.8) are determined for independent variables, and all combinations are performed with sample sizes 15, 55, and 95 by Monte Carlo simulation technique for 1,000 times in total. As the sample size rose, the AMSE decreased. The MRR and LKM both outperformed the LSM. At random error of variance 10, the MRR is the most suitable for all circumstances.

Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review

The use of renewable energy sources, especially wind energy, has been widely developed, mostly during the last decade. The main objective of the present study is to conduct a literature review focused on the evaluation under uncertainty of wind energy investment using the real options approach to find out whether public opposition (NIMBY projects) has been contemplated, and if so, what have been the flexible strategies applied for its intervention. Overall, 97 publications were analyzed, identifying 20 different models or approaches, which were grouped into eight categories: 1. Real options, 2. Optimization, 3. Stochastics, 4. Financial evaluation, 5. Probabilistic, 6. Estimation, 7. Numerical prediction, and 8. Others. The real options approach, present in 32% of the studies, was the most popular. Twenty-eight types of uncertainties were identified, which were grouped, for better analysis, into nine categories. In total, 62.5% of the studies included the price of electricity as a source of uncertainty; 18.8%, the velocity of wind; and 15.6%, the feed-in rates-subsidy. Both random and non-random techniques were applied to assess the real options and to model the uncertainties. When evaluating real options, the Monte Carlo simulation technique was the most preferred, with 16 (51.6%) applications, followed by non-randomized techniques, decision tree, and dynamic programming, with eight (25.8%) applications each. There is a marked tendency to use stochastic processes to model uncertainty, particularly geometric Brownian motion, which was used in 61.3% (19) of the studies in the sample. When searching for “real options AND (nimby OR public opposition)”, no study was found, which shows the possibility of developing research on this aspect to determine its impact on investments in wind energy projects.

Consumption-Based CO2 Emissions Accounting And Scenario Simulation In Asia And Pacific Region

This study uses a consumer-based accounting approach to evaluate CO2 emission factors of 17 major Asia and Pacific countries that distribute all emissions in the supply chain to the commodity up to the final consumption location due to the influence of a country's consumption patterns. In addition, the number of emissions connected with each country's consumption of products and services, mainly in Asia and the Pacific countries, has received little attention. This study contributes to understand the effects of the country's consumption of products and services on carbon emission peaks and formulate efficient carbon-mitigation plans for governments and decision-makers. The accelerating economic growth and industrialization have posed significant challenges to global carbon-mitigation efforts and climate change response; as a result, each country has been provided a higher emphasis on CO2 emission. The Monte Carlo simulation technique has been used to create a dynamic scenario simulation model to investigate possible future peaks of Asia and Pacific countries' carbon emissions, considering the uncertainties of factors. The result shows that total consumption-based CO2 emissions are remarkable in the three Asian countries, including China (387451.95 metric tons (Mt) CO2), Japan (185259.60 Mt CO2), and India (100720.46 Mt CO2). In South Korea, Brunei, and Taiwan, annual consumption emissions are 1.77, 1.62, and 1.49 tons of CO2 per person. In terms of final consumption, the household sector is the supreme noteworthy donor to consumption-based emissions, accounting for 27–56%. The household sector probably peak at 19.7 Gt CO2 as per the dynamic scenario simulation. As for three other types of final demand, the government expenditure will possibly reach at highest 44.0 Gt CO2 by the next 30 years while the capital formation will probably hit its highest emissions at 149.5 Gt CO2.

A risk-informed decision support tool for the strategic asset management of railway track infrastructure

The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure conditions by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget.

Health risk assessment of occupational exposure to heavy metals in a steel casting unit of a steelmaking plant using Monte–Carlo simulation technique

The present work aimed to evaluate the health risks of occupational exposure to heavy metals in a steel casting unit of a steel plant. To determine occupational exposure to heavy metals, personal air samples were taken from the workers’ breathing zones using the National Institute for Occupational Safety and Health method. Noncancer and cancer risks due to the measured metals were calculated according to the United States Environmental Protection Agency procedures. The results indicated that the noncancer risks owing to occupational exposure to lead (Pb) and manganese were higher than the recommended value in most of the workstations. The estimated cancer risk of Pb was also higher than the allowable value. Moreover, the results of sensitivity analysis indicated that the concentration, inhalation rate, and exposure duration were the most influencing variables contributing to the calculated risks. It was thus concluded that the present control measures were not adequate and further improvements were required for reducing the exposure levels.

Probabilistic Optimal Power Flow Using Linear Fuzzy Relation

Optimal Power Flow (OPF) is a very important tool for planning and analysis of power systems. In the recent times, uncertain renewable energy is being integrated into power systems in a large scale. Appropriate modeling of renewables in optimal power flow requires using stochastic models. Using stochastic models of renewables in optimal power flow is numerically and algorithmically challenging due to the complexity of stochastic models and nonlinear nature of bus power balance equations. Hitherto, Monte Carlo simulation technique and Cumulant technique have been proposed, but they are not computationally viable for large systems. In this thesis, we propose the use of linear fuzzy relation technique to relate stochastic models of dependent variables of optimal power flow formulation in terms of control variables that include power output of renewables. This fuzzy relation uses Hessian matrix of the LaGrangian of the optimal power flow formulation at optimal solution point. The technique is tested on a six bus system and results are reported. One can intuitively see that this technique can be easily extended to larger systems.