Data relaying with constraints in hierarchical sensor networks

In this paper we present and discuss several optimisation problems that arise in the management of data flow in wireless sensor networks (WSNets). We consider a hierarchical architecture for WSNets composed of sensors, relays, and relay gateways. Sensors send data they generate at a known average bit rate to relays in one hop. The relay nodes use a multi-hop mechanism to reach a set of assigned gateways which then forward the data directly to the base station. We are concerned with finding an assignment of relay gateways to relays so that certain constraints are satisfied. We define a unified model in which constraints such as lifetime, data delay, and data flow splitting are formulated in terms of four optimisation problem in graphs.     

Intensity of Interaction in Supply of Business Advice and Client Impact: A Comparison of Consultancy,Business Associations and Government Support Initiatives for SMEs

This paper assesses the supply of business advice using new empirical evidence from a large-scale survey of SMEs. The chief focus of the paper is on a comparison of suppliers that operate in different environments of regulation, contract and reputation. The paper argues that interaction intensity varies with the level of information asymmetry of these different environments, between different types of service supplier and their clients. Interaction intensity between suppliers also varies as a result of the level of trust they enjoy: for example, the low trust enjoyed by consultants appears to encourage higher intensity of interaction which improves the tailoring of the service to the client's needs and enhances impact. The paper assesses interaction intensity using the existence of site visits and/or a written brief/contract as indicators. Although these measures have limitations, the paper demonstrates clear and significant differences between suppliers in terms of interaction intensity, use of contracts and impact in three broad categories: private-sector consultancy (low trust, high intensity, high impact), business associations (high trust, low intensity, moderate impact) and government support agencies (moderate trust, moderate to high intensity, moderate or low impact). Multivariate estimation methods demonstrate that significant differences in interaction intensity, use of contracts and impact by client type are much less important than differences in supplier type. This indicates that suppliers generally develop more into niche service fields or groups of services rather than niches related to types of firm.     

Distribution of Exposure Concentrations and Doses for Constituents of Environmental Tobacco Smoke

The ultimate goal of the research reported in this series of three articles is to derive distributions of doses of selected environmental tobacco smoke (ETS)-related chemicals for nonsmoking workers. This analysis uses data from the 16-City Study collected with personal monitors over the course of one workday in workplaces where smoking occurred. In this article, we describe distributions of ETS chemical concentrations and the characteristics of those distributions (e.g., whether the distribution was log normal for a given constituent) for the workplace exposure. Next, we present population parameters relevant for estimating dose distributions and the methods used for estimating those dose distributions. Finally, we derive distributions of doses of selected ETS-related constituents obtained in the workplace for people in smoking work environments. Estimating dose distributions provided information beyond the usual point estimate of dose and showed that the preponderance of individuals exposed to ETS in the workplace were exposed at the low end of the dose distribution curve. The results of this analysis include estimations of hourly maxima and time-weighted average (TWA) doses of nicotine from workplace exposures to ETS (extrapolated from 1 day to 1 week) and doses derived from modeled lung burdens of ultraviolet-absorbing particulate matter (UVPM) and solanesol resulting from workplace exposures to ETS (extrapolated from 1 day to 1 year).     

What are Research Expectations? A Comparative Study of Different Academic Disciplines

This paper is intended to assist professors, administrators, librarians and other members of university level committees that must consider research expectations and research quality in academic fields that they lack. While this is not a problem for field experts, it is a difficulty when people are asked to make decisions in areas of study other than their own. This is commonly the case for senior university professors, librarians and administrators in regards to university wide decisions. The paper investigates this gap, through a study of 27 academic fields in 348 highly regarded universities. We find that there are almost always statistically significant differences in activity between academic fields, regardless of the metric one considers. However, it is possible to understand these differences by comparing the distribution of a known academic field to that of a field that one is not familiar with. Tables and information are provided to assist in the comparison of different fields of study on metrics such as: departmental publications and researcher level metrics of publications, citations, H-index, and total number of co-authors. The information can also be used to support decisions associated with promotion to senior posts such as endowed chairs and professorships. Information regarding specific universities and researchers are included in the data supplement.     

Perfect samplers for mixtures of distributions

Summary. We consider the construction of perfect samplers for posterior distributions associated with mixtures of exponential families and conjugate priors, starting with a perfect slice sampler in the spirit of Mira and co-workers. The methods rely on a marginalization akin to Rao–Blackwellization and illustrate the duality principle of Diebolt and Robert. A first approximation embeds the finite support distribution on the latent variables within a continuous support distribution that is easier to simulate by slice sampling, but we later demonstrate that the approximation can be very poor. We conclude by showing that an alternative perfect sampler based on a single backward chain can be constructed. This alternative can handle much larger sample sizes than the slice sampler first proposed.     

The use of resighting data to estimate the rate of population growth of the snail kite in Florida

The rate of population growth ( u ) is an important demographic parameter used to assess the viability of a population and to develop management and conservation agendas. We examined the use of resighting data to estimate u for the snail kite population in Florida from 1997-2000. The analyses consisted of (1) a robust design approach that derives an estimate of u from estimates of population size and (2) the Pradel (1996) temporal symmetry (TSM) approach that directly estimates u using an open-population capture-recapture model. Besides resighting data, both approaches required information on the number of unmarked individuals that were sighted during the sampling periods. The point estimates of u differed between the robust design and TSM approaches, but the 95% confidence intervals overlapped substantially. We believe the differences may be the result of sparse data and do not indicate the inappropriateness of either modelling technique. We focused on the results of the robust design because this approach provided estimates for all study years. Variation among these estimates was smaller than levels of variation among ad hoc estimates based on previously reported index statistics. We recommend that u of snail kites be estimated using capture-resighting methods rather than ad hoc counts.     

Slope Estimation for Bivariate Longitudinal Outcomes Adjusting for Informative Right Censoring Using Discrete Survival Model: Application to the Renal Transplant Cohort

Patients undergoing renal transplantation are prone to graft failure which causes lost of follow-up measures on their blood urea nitrogen and serum creatinine levels. These two outcomes are measured repeatedly over time to assess renal function following transplantation. Loss of follow-up on these bivariate measures results in informative right censoring, a common problem in longitudinal data that should be adjusted for so that valid estimates are obtained. In this study, we propose a bivariate model that jointly models these two longitudinal correlated outcomes and generates population and individual slopes adjusting for informative right censoring using a discrete survival approach. The proposed approach is applied to the clinical dataset of patients who had undergone renal transplantation. A simulation study validates the effectiveness of the approach.     

Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

Strategic learning with scenarios

Since the oil shocks upset the business world in the 1970s, the use of multiple scenario analysis has been increasingly propagated as an approach to deal effectively with the many long-run uncertainties that surround business organisations. Since its introduction, the scenario approach has undergone some considerable changes and it is now claimed fulfils a diverse range of functions. Newly-added functions include the stretching of managers' mental models and the triggering and acceleration of processes of organisational learning. Although these functions currently get most of the attention in academic and management journals in recent years, a satisfying explanation of how scenarios fulfil these functions is still missing in the scenario literature. The scenario methodology seems to tell only part of the story suggesting that construing and using scenarios ‘simply’ consists of sequentially completing several distinct phases. If multiple scenario analysis really is able to fulfil the wide range of functions ascribed to it another, more dynamic process has to be hidden behind the rather static phase model. The scenario literature does not give any insight into this latter process. This article aims to increase the understanding of multiple scenario analysis by unravelling some of the mysteries surrounding it. For this purpose, the role of scenarios in strategic management is studied from a cognitive perspective. It appears that scenarios can deal effectively with several bottlenecks that potentially hinder organisational learning on a strategic level in organisations.