Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Non-destructive structural integrity assessment of a decommissioned rail wagon system

This paper presents a multi-criteria based approach for nondestructive diagnostic structural integrity assessment of a decommissioned flatbed rail wagon (FBRW) used for road bridge superstructure rehabilitation and replacement applications. First, full-scale vibration and static test data sets are employed in a FE model of the FBRW to obtain the best ‘initial’ estimate of the model parameters. Second, the ‘final’ model parameters are predicted using sensitivity-based perturbation analysis without significant difficulties encountered. Consequently, the updated FBRW model is validated using the independent sets of full-scale laboratory static test data. Finally, the updated and validated FE model of the FBRW is used for structural integrity assessment of a single lane FBRW bridge subjected to the Australian bridge design traffic load.

Improving PLDA speaker verification using WMFD and linear-weighted approaches in limited microphone data conditions

This paper proposes the addition of a weighted median Fisher discriminator (WMFD) projection prior to length-normalised Gaussian probabilistic linear discriminant analysis (GPLDA) modelling in order to compensate the additional session variation. In limited microphone data conditions, a linear-weighted approach is introduced to increase the influence of microphone speech dataset. The linear-weighted WMFD-projected GPLDA system shows improvements in EER and DCF values over the pooled LDA- and WMFD-projected GPLDA systems in inter-view-interview condition as WMFD projection extracts more speaker discriminant information with limited number of sessions/ speaker data, and linear-weighted GPLDA approach estimates reliable model parameters with limited microphone data.

Educational interventions for preventing vascular catheter bloodstream infections in critical care: Evidence map, systematic review and economic evaluation

Background Bloodstream infections resulting from intravascular catheters (catheter-BSI) in critical care increase patients' length of stay, morbidity and mortality, and the management of these infections and their complications has been estimated to cost the NHS annually £19.1–36.2M. Catheter-BSI are thought to be largely preventable using educational interventions, but guidance as to which types of intervention might be most clinically effective is lacking. Objective To assess the effectiveness and cost-effectiveness of educational interventions for preventing catheter-BSI in critical care units in England. Data sources Sixteen electronic bibliographic databases – including MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, Cumulative Index to Nursing and Allied Health Literature (CINAHL), NHS Economic Evaluation Database (NHS EED), EMBASE and The Cochrane Library databases – were searched from database inception to February 2011, with searches updated in March 2012. Bibliographies of systematic reviews and related papers were screened and experts contacted to identify any additional references. Review methods References were screened independently by two reviewers using a priori selection criteria. A descriptive map was created to summarise the characteristics of relevant studies. Further selection criteria developed in consultation with the project Advisory Group were used to prioritise a subset of studies relevant to NHS practice and policy for systematic review. A decision-analytic economic model was developed to investigate the cost-effectiveness of educational interventions for preventing catheter-BSI. Results Seventy-four studies were included in the descriptive map, of which 24 were prioritised for systematic review. Studies have predominantly been conducted in the USA, using single-cohort before-and-after study designs. Diverse types of educational intervention appear effective at reducing the incidence density of catheter-BSI (risk ratios statistically significantly < 1.0), but single lectures were not effective. The economic model showed that implementing an educational intervention in critical care units in England would be cost-effective and potentially cost-saving, with incremental cost-effectiveness ratios under worst-case sensitivity analyses of < £5000/quality-adjusted life-year. Limitations Low-quality primary studies cannot definitively prove that the planned interventions were responsible for observed changes in catheter-BSI incidence. Poor reporting gave unclear estimates of risk of bias. Some model parameters were sourced from other locations owing to a lack of UK data. Conclusions Our results suggest that it would be cost-effective and may be cost-saving for the NHS to implement educational interventions in critical care units. However, more robust primary studies are needed to exclude the possible influence of secular trends on observed reductions in catheter-BSI.

Electrochemical phase formation (ECPF): Nucleation growth vis-a-vis adsorption models

The nucleataon growth model of electrochemical phase formation is analysed for the hnear potential sweep input Apart from deducing diagnostic criteria and method~ of estimating model parameters, the predictions of the nucleation growth model are compared and contrasted with those of a sample adsorption model A dastlnCtlOn is made possible between adsorption and phase transition, which seems useful for understanding the nature of ECPF phenomena, especially underpotentlal deposition (UPD).

Growth curves with time-dependent explanatory variables

A class of growth models incorporating time-dependent factors and stochastic perturbations are introduced. The proposed model includes the existing growth models used in fisheries as special cases. Particular attention is given to growth of a population (in average weight or length) from which observations are taken randomly each time and the analysis of tag-recapture data. Two real data sets are used for illustration: (a) to estimate the seasonal effect and population density effect on growth of farmed prawn (Penaeus monodon) from weight data and (b) to assess the effect of tagging on growth of barramundi (Lates calcarifer)

Maximum likelihood estimation of mortality and growth with individual variability from multiple length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L-infinity. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

Adsorption isotherms for neutral organic compounds-A hierarchy in modelling: Part II

A two-state model allowing for size disparity between the solvent and the adsorbate is analysed to derive the adsorption isotherm for electrosorption of organic compounds. Explicity, the organic adsorbate is assumed to occupy "n" lattice sites at the interface as compared to "one" by the solvent. The model parameters are the respective permanent and induced dipole moments apart from the nearest neighbour distance. The coulombic interactions due to permanent and induced dipole moments, discreteness of charge effects, and short-range and specific substrate interactions have all been incorporated. The adsorption isotherm is then derived using mean field approximation (MFA) and is found to be more general than the earlier multi-site versions of Bockris and Swinkels, Mohilner et al., and Bennes, as far as the entropy contributions are concerned. The role of electrostatic forces is explicity reflected in the adsorption isotherm via the Gibbs energy of adsorption term which itself is a quadratic function of the electrode charge-density. The approximation implicit in the adsorption isotherm of Mohilner et al. or Bennes is indicated briefly.

The potential geographic distribution of the invasive weed Senna obtusifolia in Australia

Senna obtusifolia (sicklepod) is an invasive weed of northern Australia, where it significantly impacts agricultural productivity and alters natural ecosystem structure and function. Although currently restricted to northern regions, the potential for S. obtusifolia to spread south is not known. Using the eco-climatic model CLIMEX, this study simulated the potential geographic distribution of S. obtusifolia in Australia under two scenarios. Model parameters for both scenarios were derived from the distribution of S. obtusifolia throughout North and Central America. The first scenario used these base model parameters to predict the distribution of S. obtusifolia in Australia, whilst the second model predicted the distribution of a cold susceptible S. obtusifolia ecotype that is reported to occur in the USA. Both models predicted the potential for an extensive S. obtusifolia distribution, with the first model indicating suitable climatic conditions occurring predominantly in coastal regions from the Northern Territory, to far north Queensland and into northern Victoria. The cold susceptible ecotype displayed a comparatively reduced distribution in the southern parts of Australia, where inappropriate temperatures, a lack of thermal accumulation and cold stress restrict the invasion south to the coastal regions of central New South Wales. The extent of the predicted distribution of both ecotypes of S. obtusifolia reinforces the need for strategic management at a national scale.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

Soil carbon modelling as a tool for carbon balance studies in forestry

Soils represent a remarkable stock of carbon, and forest soils are estimated to hold half of the global stock of soil carbon. Topical concern about the effects of climate change and forest management on soil carbon as well as practical reporting requirements set by climate conventions have created a need to assess soil carbon stock changes reliably and transparently. The large spatial variability of soil carbon commensurate with relatively slow changes in stocks hinders the assessment of soil carbon stocks and their changes by direct measurements. Models therefore widely serve to estimate carbon stocks and stock changes in soils. This dissertation aimed to develop the soil carbon model YASSO for upland forest soils. The model was aimed to take into account the most important processes controlling the decomposition in soils, yet remain simple enough to ensure its practical applicability in different applications. The model structure and assumptions were presented and the model parameters were defined with empirical measurements. The model was evaluated by studying the sensitivities of the model results to parameter values, by estimating the precision of the results with an uncertainty analysis, and by assessing the accuracy of the model by comparing the predictions against measured data and to the results of an alternative model. The model was applied to study the effects of intensified biomass extraction on the forest carbon balance and to estimate the effects of soil carbon deficit on net greenhouse gas emissions of energy use of forest residues. The model was also applied in an inventory based method to assess the national scale forest carbon balance for Finland’s forests from 1922 to 2004. YASSO managed to describe sufficiently the effects of both the variable litter and climatic conditions on decomposition. When combined with the stand models or other systems providing litter information, the dynamic approach of the model proved to be powerful for estimating changes in soil carbon stocks on different scales. The climate dependency of the model, the effects of nitrogen on decomposition and forest growth as well as the effects of soil texture on soil carbon stock dynamics are areas for development when considering the applicability of the model to different research questions, different land use types and wider geographic regions. Intensified biomass extraction affects soil carbon stocks, and these changes in stocks should be taken into account when considering the net effects of forest residue utilisation as energy. On a national scale, soil carbon stocks play an important role in forest carbon balances.

Land leasing, land degradation and agricultural productivity in Finland

This study analysed whether the land tenure insecurity problem has led to a decline in long-term land improvements (liming and phosphorus fertilization) under the Common Agricultural Policy (CAP) and Nordic production conditions in European Union (EU) countries such as Finland. The results suggests that under traditional cash lease contracts, which are encouraged by the existing land leasing regulations and agricultural subsidy programs, the land tenure insecurity problem on leased land reduces land improvements that have a long pay-back period. In particular, soil pH was found to be significantly lower on land cultivated under a lease contract compared to land owned by the farmers themselves. The results also indicate that land improvements could not be reversed by land markets, because land owners would otherwise have carried out land improvements even if not farming by themselves. To reveal the causality between land tenure and land improvements, the dynamic optimisation problem was solved by a stochastic dynamic programming routine with known parameters for one-period returns and transition equations. The model parameters represented Finnish soil quality and production conditions. The decision rules were solved for alternative likelihood scenarios over the continuation of the fixed-term lease contract. The results suggest that as the probability of non-renewal of the lease contract increases, farmers quickly reduce investments in irreversible land improvements and, thereafter, yields gradually decline. The simulations highlighted the observed trends of a decline in land improvements on land parcels that are cultivated under lease contracts. Land tenure has resulted in the neglect of land improvement in Finland. This study aimed to analyze whether these challenges could be resolved by a tax policy that encourages land sales. Using Finnish data, real estate tax and a temporal relaxation on the taxation of capital gains showed some potential for the restructuring of land ownership. Potential sellers who could not be revealed by traditional logit models were identified with the latent class approach. Those landowners with an intention to sell even without a policy change were sensitive to temporal relaxation in the taxation of capital gains. In the long term, productivity and especially productivity growth are necessary conditions for the survival of farms and the food industry in Finland. Technical progress was found to drive the increase in productivity. The scale had only a moderate effect and for the whole study period (1976–2006) the effect was close to zero. Total factor productivity (TFP) increased, depending on the model, by 0.6–1.7% per year. The results demonstrated that the increase in productivity was hindered by the policy changes introduced in 1995. It is also evidenced that the increase in land leasing is connected to these policy changes. Land institutions and land tenure questions are essential in agricultural and rural policies on all levels, from local to international. Land ownership and land titles are commonly tied to fundamental political, economic and social questions. A fair resolution calls for innovative and new solutions both on national and international levels. However, this seems to be a problem when considering the application of EU regulations to member states inheriting divergent landownership structures and farming cultures. The contribution of this study is in describing the consequences of fitting EU agricultural policy to Finnish agricultural land tenure conditions and heritage.

Prediction of masonry compressive behaviour using a damage mechanics inspired modelling method

Masonry under compression is affected by the properties of its constituents and their interfaces. In spite of extensive investigations of the behaviour of masonry under compression, the information in the literature cannot be regarded as comprehensive due to ongoing inventions of new generation products – for example, polymer modified thin layer mortared masonry and drystack masonry. As comprehensive experimental studies are very expensive, an analytical model inspired by damage mechanics is developed and applied to the prediction of the compressive behaviour of masonry in this paper. The model incorporates a parabolic progressively softening stress-strain curve for the units and a progressively stiffening stress-strain curve until a threshold strain for the combined mortar and the unit-mortar interfaces is reached. The model simulates the mutual constraints imposed by each of these constituents through their respective tensile and compressive behaviour and volumetric changes. The advantage of the model is that it requires only the properties of the constituents and considers masonry as a continuum and computes the average properties of the composite masonry prisms/wallettes; it does not require discretisation of prism or wallette similar to the finite element methods. The capability of the model in capturing the phenomenological behaviour of masonry with appropriate elastic response, stiffness degradation and post peak softening is presented through numerical examples. The fitting of the experimental data to the model parameters is demonstrated through calibration of some selected test data on units and mortar from the literature; the calibrated model is shown to predict the responses of the experimentally determined masonry built using the corresponding units and mortar quite well. Through a series of sensitivity studies, the model is also shown to predict the masonry strength appropriately for changes to the properties of the units and mortar, the mortar joint thickness and the ratio of the height of unit to mortar joint thickness. The unit strength is shown to affect the masonry strength significantly. Although the mortar strength has only a marginal effect, reduction in mortar joint thickness is shown to have a profound effect on the masonry strength. The results obtained from the model are compared with the various provisions in the Australian Masonry Structures Standard AS3700 (2011) and Eurocode 6.