Reliability of systems with dependent components based on lattice polynomial description

Reliability of a system is considered where the components' random lifetimes may be dependent. The structure of the system is described by an associated "lattice polynomial" function. Based on that descriptor, general framework formulas are developed and used to obtain direct results for the cases where a) the lifetimes are "Bayes-dependent", that is, their interdependence is due to external factors (in particular, where the factor is the "preliminary phase" duration) and b) where the lifetimes' dependence is implied by upper or lower bounds on lifetimes of components in some subsets of the system. (The bounds may be imposed externally based, say, on the connections environment.) Several special cases are investigated in detail

Assessment of performance indices of selected gas turbine power plants in Nigeria

In this study, performance assessment of selected gas turbine power plants in Nigeria was evaluated using performance indices. The results of the study showed that for the period under review (2006–2010), the percentage shortfalls from the target energy in the selected power plants range from 26.33% to 86.61% as against the acceptable value of 5–10%. The capacity factor of the selected power plants varies from 16.88% to 73.67% as against the international value of 50–80%. The plant use factor varies from 45.89% to 97.03% and the utilization factor varies from 6.31% to 93.074% as against the international best practice of over 95%. From this result, it can be concluded that the generating units were underutilized. This is due to inadequate routine maintenance and equipment fault development. The analyses of reliability indicators revealed that the mean time between failures varies from 5.42 to 378.44 h, the mean time to repair varies from 18.3 to 153.88 h and the plant availability varies from 12.86% to 91.31% as against the Institute of Electrical and Electronics Engineers recommended standard of 99.9%. Evaluation of operating figures of the selected power plants revealed that starting reliability (SR) and operating reliability vary from 71.95% to 93.9% and 5.33% to 55%, respectively. The SR of the selected power plants is low in value compared with standard value of 99.9%. The statistical analysis carried out on plant availability revealed that at 95% confidence level; there is a significant difference in availability of the selected power plants. This indicates differences in their systems installation, operation and maintenance. The performance indicator developed to evaluate the performance indices for the selected stations can also be applicable to other power stations in Nigeria and elsewhere. Measures to improve the performance indices of the plants have been suggested in this paper

Modelling the probability of microhabitat formation on trees using cross-sectional data

The rate of TreM formation per unit diameter growth was modelled as a function of tree diameter at breast height (DBH), and the model was calibrated considering cross-sectional observations TreMs on trees of different sizes. The model predicted realistic TreM formation rates at the tree and stand levels in forests dominated by Abies alba and Fagus sylvatica. This approach opens new perspectives to the analysis of forest biodiversity conservation strategies

Trends in the Statistical Assessment of Reliability

Changes in technology have had and will continue to have a strong effect on changes in the area of statistical assessment of reliability data. These changes include higher levels of integration in electronics, improvements in measurement technology and the deployment of sensors and smart chips into more products, dramatically improved computing power and storage technology, and the development of new, powerful statistical methods for graphics, inference, and experimental design and reliability test planning. This paper traces some of the history of the development of statistical methods for reliability assessment and makes some predictions about the future

Evaluating Gene Drive Approaches for Public Benefit

Gene drive approaches—those which bias inheritance of a genetic element in a population of sexually reproducing organisms—have the potential to provide important public benefits. The spread of selected genetic elements in wild populations of organisms may help address certain challenges, such as transmission of vector-borne human and animal diseases and biodiversity loss due to invasive animals. Adapting various naturally occurring gene drive mechanisms to these aims is a long-standing research area, and recent advances in genetics have made engineering gene drive systems significantly more technically feasible. Gene drive approaches would act through changes in natural environments, thus robust methods to evaluate potential research and use are important. Despite the fact that gene drive approaches build on existing paradigms, such as genetic modification of organisms and conventional biological control, there are material challenges to their evaluation. One challenge is the inherent complexity of ecosystems, which makes precise prediction of changes to the environment difficult. For gene drive approaches that are expected to spread spatially and/or persist temporally, responding to this difficulty with the typical stepwise increases in the scale of studies may not be straightforward after studies begin in the natural environment. A related challenge is that study or use of a gene drive approach may have implications for communities beyond the location of introduction, depending on the spatial spread and persistence of the approach and the population biology of the target organism. This poses a particular governance challenge when spread across national borders is plausible. Finally, community engagement is an important element of responsible research and governance, but effective community engagement for gene drive approaches requires addressing complexity and uncertainty and supporting representative participation in decision making. These challenges are not confronted in a void. Existing frameworks, processes, and institutions provide a basis for effective evaluation of gene drive approaches for public benefit. Although engineered gene drive approaches are relatively new, the necessities of making decisions despite uncertainty and governing actions with potential implications for shared environments are well established. There are methodologies to identify potential harms and assess risks when there is limited experience to draw upon, and these methodologies have been applied in similar contexts. There are also laws, policies, treaties, agreements, and institutions in place across many jurisdictions that support national and international decision making regarding genetically modified organisms and the potential applications of gene drive approaches, such as public health and biodiversity conservation. Community engagement is an established component of many decision-making processes, and related experience and conceptual frameworks can inform engagement by researchers. The existence of frameworks, processes, and institutions provides an important foundation for evaluating gene drive approaches, but it is not sufficient by itself. They must be rigorously applied, which requires resources for risk assessment, research, and community engagement and diligent implementation by governance institutions. The continued evolution of the frameworks, processes, and institutions is important to adapt to the growing understanding of gene drive approaches. With appropriate resources and diligence, it will be possible to responsibly evaluate and make decisions on gene drive approaches for public benefit

A framework to assess quality and uncertainty in disaster loss data

There is a growing interest in the systematic and consistent collection of disasterloss data for different applications. Therefore, the collected data must follow a set oftechnical requirements to guarantee its usefulness. One of those requirements is theavailability of a measure of the uncertainty in the collected data to express its quality for agiven purpose. Many of the existing disaster loss databases do not provide such uncertainty/qualitymeasures due to the lack of a simple and consistent approach to expressuncertainty. After reviewing existing literature on the subject, a framework to express theuncertainty in disaster loss data is proposed. This framework builds on an existinguncertainty classification that was updated and combined with an existing method for datacharacterization. The proposed approach is able to establish a global score that reflects theoverall uncertainty in a certain loss indicator and provides a measure of its quality

A general approach for automating FMECA

International audienc