Building macroscale models from microscale probabilistic models : a general probabilistic approach for nonlinear diffusion and multispecies phenomena

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

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.

Public preferences for biodiversity conservation and climate-change mitigation: A choice experiment using ecosystem services indicators

Potential conflicts exist between biodiversity conservation and climate-change mitigation as trade-offs in multiple-use land management. This study aims to evaluate public preferences for biodiversity conservation and climate-change mitigation policy considering respondents’ uncertainty on their choice. We conducted a choice experiment using land-use scenarios in the rural Kushiro watershed in northern Japan. The results showed that the public strongly wish to avoid the extinction of endangered species in preference to climate-change mitigation in the form of carbon sequestration by increasing the area of managed forest. Knowledge of the site and the respondents’ awareness of the personal benefits associated with supporting and regulating services had a positive effect on their preference for conservation plans. Thus, decision-makers should be careful about how they provide ecological information for informed choices concerning ecosystem services tradeoffs. Suggesting targets with explicit indicators will affect public preferences, as well as the willingness of the public to pay for such measures. Furthermore, the elicited-choice probabilities approach is useful for revealing the distribution of relative preferences for incomplete scenarios, thus verifying the effectiveness of indicators introduced in the experiment.

Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

STAR : Predicting recombination sites from amino acid sequence

Background Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone. Results We present STAR, Site Targeted Amino acid Recombination predictor, which produces a score indicating the structural disruption caused by recombination, for each position in an amino acid sequence. Example predictions contrasted with those of alternative tools, illustrate STAR'S utility to assist in determining useful recombination sites. Overall, the correlation coefficient between the output of the experimentally validated protein design algorithm SCHEMA and the prediction of STAR is very high (0.89). Conclusion STAR allows the user to explore useful recombination sites in amino acid sequences with unknown structure and unknown evolutionary origin. The predictor service is available from http://pprowler.itee.uq.edu.au/star.

Privacy concerns and purchase of travel product online

The travel and tourism industry has come to rely heavily on information and communication technologies to facilitate relations with consumers. Compiling consumer data profiles has become easier and it is widely thought that consumers place great importance on how that data is handled by firms. Lack of trust may cause consumers to have privacy concerns and may, in turn, have an adverse impact on consumers’ willingness to purchase online. Three specific aspects of privacy that have received attention from researchers are unauthorized use of secondary data, invasion of privacy, and errors. A survey study was undertaken to examine the effects of these factors on both prior purchase of travel products via the Internet and future purchase probability. Surprisingly, no significant relationships were found to indicate that such privacy concerns affect online purchase behavior within the travel industry. Implications for managers are discussed.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Stochastic ranking method for thermostatically controllable appliances to provide regulation services

Demand response can be used for providing regulation services in the electricity markets. The retailers can bid in a day-ahead market and respond to real-time regulation signal by load control. This paper proposes a new stochastic ranking method to provide regulation services via demand response. A pool of thermostatically controllable appliances (TCAs) such as air conditioners and water heaters are adjusted using direct load control method. The selection of appliances is based on a probabilistic ranking technique utilizing attributes such as temperature variation and statuses of TCAs. These attributes are stochastically forecasted for the next time step using day-ahead information. System performance is analyzed with a sample regulation signal. Network capability to provide regulation services under various seasons is analyzed. The effect of network size on the regulation services is also investigated.

Building the safety case for UAS operations in support of natural disaster response

A framework supporting the systematic development of safety cases for Unmanned Aircraft System (UAS) operations in a broad range of civil and commercial applications is presented. The case study application is the use of UAS for disaster response. In those States where regulations do not preclude UAS operations altogether, approvals for UAS operations can be granted on a case-by-case basis contingent on the provision of a safety case acceptable to the relevant National Airworthiness Authority (NAA). A safety case for UAS operations must show how the risks associated with the hazards have been managed to an acceptable level. The foundational components necessary for structuring and assessing these safety cases have not yet been proposed. Barrier-bow-tie models are used in this paper to structure the safety case for the two primary hazards of 1) a ground impact, and 2) a Mid-Air Collision (MAC). The models establish the set of Risk Control Variables (RCVs) available to reduce the risk. For the ground-impact risk model, seven RCVs are identified which in combination govern the probability of an accident. Similarly, ten RCVs are identified within the MAC model. The effectiveness of the RCVs and how they can implemented in terms of processes, policies, devices, practices, or other actions for each of the case-study applications are discussed. The framework presented can provide for the more systematic and consistent regulation of UAS through a "safety target" approach.

Use of physical activity self-management strategies by high school students

Teaching adolescents to use self-management strategies (SMS's) may be an effective approach to promoting lifelong physical activity (PA). However, the extent to which adolescents use SMS's and their impact on current PA have not been studied previously. The aims of this study were: 1) describe the prevalence of SMS use in adolescents; and 2) determine relationships between SMS use, PA self-efficacy, and PA participation. 197 students completed questionnaires measuring use of SMS's, self-efficacy, and PA behavior. The most prevalent SMS's (>30%) were thinking about the benefits of PA, making PA more enjoyable, choosing activities that are convenient, setting aside time to do PA, and setting goals to do PA. Less than 10% reported rewarding oneself for PA, writing planned activities in a book or calendar, and keeping charts of PA. SMS use was associated with increased self-efficacy (r = 0.47, P < .001) and higher levels of PA (r = 0.34 P < .001). A one unit difference in SMS scores was associated with a ~ 4-fold increase in the probability of being active (OR = 3.7, 95% CI = 1.8-7.4). Although strongly associated with PA, a relatively small percentage of adolescents routinely use SMS's.

Visit Perth, Fremantle and Rottnest Island in Western Australia

A travel article about Perth, Freemantle, and Rottnest Island. In all probability, the water at Cottesloe Beach is no bluer than at all the other beaches of Australia. But early afternoon on the day I arrive, that blue seems total, or perhaps even on the edge of some other colour spectrum still being invented. The blue of the west...

SEM, EDS and vibrational spectroscopic study of dawsonite NaAl(CO3)(OH)2

In this work we have studied the mineral dawsonite by using a combination of scanning electron microscopy with EDS and vibrational spectroscopy. Single crystals show an acicular habitus forming aggregates with a rosette shape. The chemical analysis shows a phase composed of C, Al, and Na. Two distinct Raman bands at 1091 and 1068 cm−1 are assigned to the CO32− ν1 symmetric stretching mode. Multiple bands are observed in both the Raman and infrared spectra in the antisymmetric stretching and bending regions showing that the symmetry of the carbonate anion is reduced and in all probability the carbonate anions are not equivalent in the dawsonite structure. Multiple OH deformation vibrations centred upon 950 cm−1 in both the Raman and infrared spectra show that the OH units in the dawsonite structure are non-equivalent. Raman bands observed at 3250, 3283 and 3295 cm−1 are assigned to OH stretching vibrations. The position of these bands indicates strong hydrogen bonding of the OH units in the dawsonite structure. The formation of the mineral dawsonite has the potential to offer a mechanism for the geosequestration of greenhouse gases.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Combining opinions for use in Bayesian networks: A measurement error approach

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.