Willingness to Pay for Rural Landscape Improvements: Combining Mixed Logit and Random-Effects Models

This paper reports the findings from a discrete-choice experiment designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness-to-pay values for every individual in the sample. This departs from customary approaches in which the willingness-to-pay estimates are normally expressed as measures of central tendency of an a priori distribution. Random-effects models for panel data are subsequently used to identify the determinants of the individual-specific willingness-to-pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete-choice experiments, the analytical approach outlined in this paper is shown to add considerable explanatory power to the welfare estimates.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

An invasive-native mammalian species replacement process captured by camera trap survey random encounter models

Camera traps are used to estimate densities or abundances using capture-recapture and, more recently, random encounter models (REMs). We deploy REMs to describe an invasive-native species replacement process, and to demonstrate their wider application beyond abundance estimation. The Irish hare Lepus timidus hibernicus is a high priority endemic of conservation concern. It is threatened by an expanding population of non-native, European hares L. europaeus, an invasive species of global importance. Camera traps were deployed in thirteen 1 km squares, wherein the ratio of invader to native densities were corroborated by night-driven line transect distance sampling throughout the study area of 1652 km2. Spatial patterns of invasive and native densities between the invader’s core and peripheral ranges, and native allopatry, were comparable between methods. Native densities in the peripheral range were comparable to those in native allopatry using REM, or marginally depressed using Distance Sampling. Numbers of the invader were substantially higher than the native in the core range, irrespective of method, with a 5:1 invader-to-native ratio indicating species replacement. We also describe a post hoc optimization protocol for REM which will inform subsequent (re-)surveys, allowing survey effort (camera hours) to be reduced by up to 57% without compromising the width of confidence intervals associated with density estimates. This approach will form the basis of a more cost-effective means of surveillance and monitoring for both the endemic and invasive species. The European hare undoubtedly represents a significant threat to the endemic Irish hare.

Constructing random matrices to represent real ecosystems

Models of complex systems with n components typically have order n² parameters because each component can potentially interact with every other. When it is impractical to measure these parameters, one may choose random parameter values and study the emergent statistical properties at the system level. Many influential results in theoretical ecology have been derived from two key assumptions: that species interact with random partners at random intensities and that intraspecific competition is comparable between species. Under these assumptions, community dynamics can be described by a community matrix that is often amenable to mathematical analysis. We combine empirical data with mathematical theory to show that both of these assumptions lead to results that must be interpreted with caution. We examine 21 empirically derived community matrices constructed using three established, independent methods. The empirically derived systems are more stable by orders of magnitude than results from random matrices. This consistent disparity is not explained by existing results on predator-prey interactions. We investigate the key properties of empirical community matrices that distinguish them from random matrices. We show that network topology is less important than the relationship between a species’ trophic position within the food web and its interaction strengths. We identify key features of empirical networks that must be preserved if random matrix models are to capture the features of real ecosystems.

Interbirth interval is associated with childhood type 1 diabetes risk

Short interbirth interval has been associated with maternal complications and childhood autism and leukemia, possibly due to deficiencies in maternal micronutrients at conception or increased exposure to sibling infections. A possible association between interbirth interval and subsequent risk of childhood type 1 diabetes has not been investigated. A secondary analysis of 14 published observational studies of perinatal risk factors for type 1 diabetes was conducted. Risk estimates of diabetes by category of interbirth interval were calculated for each study. Random effects models were used to calculate pooled odds ratios (ORs) and investigate heterogeneity between studies. Overall, 2,787 children with type 1 diabetes were included. There was a reduction in the risk of childhood type 1 diabetes in children born to mothers after interbirth intervals <3 years compared with longer interbirth intervals (OR 0.82 [95% CI 0.72-0.93]). Adjustments for various potential confounders little altered this estimate. In conclusion, there was evidence of a 20% reduction in the risk of childhood diabetes in children born to mothers after interbirth intervals <3 years.

Hypermobility and musculoskeletal pain in children:a systematic review

Objective: To examine the evidence of an association between hypermobility and musculoskeletal pain in children. Methods: A systematic review of the literature was performed using the databases PubMed, EMBASE, NHS Evidence, and Medline. Inclusion criteria were observational studies investigating hypermobility and musculoskeletal pain in children. Exclusion criteria were studies conducted on specialist groups (i.e. dancers) or hospital referrals. Pooled odds ratios (ORs) were calculated using random effects models and heterogeneity was tested using ?(2)-tests. Study quality was assessed using the Newcastle-Ottawa Scale for case-control studies. Results: Of the 80 studies identified, 15 met the inclusion criteria and were included in the review. Of these, 13 were included in the statistical analyses. Analysing the data showed that the heterogeneity was too high to allow for interpretation of the meta-analysis (I(2) = 72%). Heterogeneity was much lower when the studies were divided into European (I(2) = 8%) and Afro-Asian subgroups (I(2) = 65%). Sensitivity analysis based on data from studies reporting from European and Afro-Asian regions showed no association in the European studies [OR 1.00, 95% confidence interval (CI) 0.79-1.26] but a marked relationship between hypermobility and joint pain in the Afro-Asian group (OR 2.01, 95% CI 1.45-2.77). Meta-regression showed a highly significant difference between subgroups in both meta-analyses (p <0.001). Conclusion: There seems to be no association between hypermobility and joint pain in Europeans. There does seem to be an association in Afro-Asians; however, there was a high heterogeneity. It is unclear whether this is due to differences in ethnicity, nourishment, climate or study design.

Alcohol and the Risk of Barrett's Esophagus: A Pooled Analysis from the International BEACON Consortium

OBJECTIVES: Results from studies examining the association between alcohol consumption and the risk of Barrett's esophagus have been inconsistent. We assessed the risk of Barrett's esophagus associated with total and beverage-specific alcohol consumption by pooling individual participant data from five case–control studies participating in the international Barrett's and Esophageal Adenocarcinoma Consortium.
METHODS: For analysis, there were 1,282 population-based controls, 1,418 controls with gastroesophageal reflux disease (GERD), and 1,169 patients with Barrett's esophagus (cases). We estimated study-specific odds ratios (ORs) and 95% confidence intervals (95% CI) using multivariable logistic regression models adjusted for age, sex, body mass index (BMI), education, smoking status, and GERD symptoms. Summary risk estimates were obtained by random-effects models. We also examined potential effect modification by sex, BMI, GERD symptoms, and cigarette smoking.
RESULTS: For comparisons with population-based controls, although there was a borderline statistically significant inverse association between any alcohol consumption and the risk of Barrett's esophagus (any vs. none, summary OR=0.77, 95% CI=0.60–1.00), risk did not decrease in a dose-response manner (Ptrend=0.72). Among alcohol types, wine was associated with a moderately reduced risk of Barrett's esophagus (any vs. none, OR=0.71, 95% CI=0.52–0.98); however, there was no consistent dose–response relationship (Ptrend=0.21). We found no association with alcohol consumption when cases were compared with GERD controls. Similar associations were observed across all strata of BMI, GERD symptoms, and cigarette smoking.
CONCLUSIONS: Consistent with findings for esophageal adenocarcinoma, we found no evidence that alcohol consumption increases the risk of Barrett's esophagus.

Risk factors (excluding hormone therapy) for endometrial hyperplasia: a systematic review.

Objective: To conduct a systematic review of risk factors associated with the development of Endometrial Hyperplasia (EH).

Data sources: Ovid MEDLINE, EMBASE and Web of Science databases were searched from inception to 30 June 2015.

Study eligibility: Fifteen observational studies that reported on EH risk in relation to lifestyle factors (n=14), medical history (n=11), reproductive and menstrual history (n=9) and measures of socio-economic status (n=2) were identified. Pooled relative risk estimates and corresponding 95% confidence intervals (CI) were able to be derived for EH and Body Mass Index (BMI), smoking, diabetes and hypertension, using random effects models comparing high versus low categories.

Results: The pooled relative risk for EH when comparing women with the highest versus lowest BMI was 1.82 (95% CI 1.22–2.71; n=7 studies, I2=90.4%). No significant associations were observed for EH risk for smokers compared with non-smokers (RR 0.88, 95% CI 0.66-1.17; n=3, I2=0.0%), hypertensive versus normotensive women (RR 1.51, 95% CI 0.72–3.15; n=5 studies, I2=79.1%), or diabetic versus non-diabetic women (RR 1.77, 95% CI 0.79–3.96; n=5 studies, I2=31.8%) respectively although the number of included studies was limited. There were mixed reports on the relationship between age and risk of EH. Too few studies reported on other factors to reach any conclusions in relation to EH risk.

Conclusions: A high BMI was associated with an increased risk of EH, providing additional rationale for women to maintain a normal body weight. No significant associations were detected for other factors and EH risk, however relatively few studies have been conducted and few of the available studies adequately adjusted for relevant confounders. Therefore, further aetiological studies of endometrial hyperplasia are warranted.

How do grazers achieve their distribution? A continuum of models from random diffusion to the ideal free distribution using biased random walks

A conceptual model is described for generating distributions of grazing animals, according to their searching behavior, to investigate the mechanisms animals may use to achieve their distributions. The model simulates behaviors ranging from random diffusion, through taxis and cognitively aided navigation (i.e., using memory), to the optimization extreme of the Ideal Free Distribution. These behaviors are generated from simulation of biased diffusion that operates at multiple scales simultaneously, formalizing ideas of multiple-scale foraging behavior. It uses probabilistic bias to represent decisions, allowing multiple search goals to be combined (e.g., foraging and social goals) and the representation of suboptimal behavior. By allowing bias to arise at multiple scales within the environment, each weighted relative to the others, the model can represent different scales of simultaneous decision-making and scale-dependent behavior. The model also allows different constraints to be applied to the animal's ability (e.g., applying food-patch accessibility and information limits). Simulations show that foraging-decision randomness and spatial scale of decision bias have potentially profound effects on both animal intake rate and the distribution of resources in the environment. Spatial variograms show that foraging strategies can differentially change the spatial pattern of resource abundance in the environment to one characteristic of the foraging strategy.</

Goal Recognition through Goal Graph Analysis

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

Microscopic models for dielectric relaxation in disordered systems

It is shown how the Debye rotational diffusion model of dielectric relaxation of polar molecules (which may be described in microscopic fashion as the diffusion limit of a discrete time random walk on the surface of the unit sphere) may be extended to yield the empirical Havriliak-Negami (HN) equation of anomalous dielectric relaxation from a microscopic model based on a kinetic equation just as in the Debye model. This kinetic equation is obtained by means of a generalization of the noninertial Fokker-Planck equation of conventional Brownian motion (generally known as the Smoluchowski equation) to fractional kinetics governed by the HN relaxation mechanism. For the simple case of noninteracting dipoles it may be solved by Fourier transform techniques to yield the Green function and the complex dielectric susceptibility corresponding to the HN anomalous relaxation mechanism.

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.