Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.

On the determination of the number of factors using information criteria with data-driven penalty

As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liška (J Am Stat Assoc102:603–617, 2007) proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones, which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.

A national information infrastructure model for information warfare defence

Information infrastructures are an eclectic mix of open and closed networks, private and public systems, the Internet, and government, military, and civilian organisations. Significant efforts are required to provide infrastructure protection, increase cooperation between sectors, and identify points of responsibility. The threats to infrastructures are many and various, and are increasing daily: information warfare, hackers, terrorists, criminals, activists, and even competing organisations all pose significant threats that cannot be sufficiently dealt with using the current infrastructure model. We present a National Information Infrastructure model that is based on defence against threats such as information warfare.

Using information flow model to specify the Kanban-controlled shopfloor control system

The implementation of Kanban-based production control systems may be difficult in make-to-order environments such as job shops. The flexible manufacturing approach constitutes a promising solution to adapt the Kanban method to such environments. This paper presents an information flow modelling approach for specifying the operational planning and control functions of the Kanban-controlled shopfloor control system (KSCS) in a flexible manufacturing environment. By decomposing the KSCS control functionalities, we have created the system information flow model through the data flow diagrams of Structured Systems Analysis Methodology. The data flow diagrams serve effective system specifications for communicating the system operations to participants of different disciplines as well as the system model for the design and development of KSCS.

Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

Information needs of young adults with type 2 diabetes : a literature review

Background Type 2 diabetes (T2DM) is increasingly prevalent in young adults but there is very little information about what information they need to undertake diabetes self-management.

Aim To undertake a structured literature review to identify what information people with type 2 diabetes aged 25 to 45 years need to manage their diabetes and how they would like to receive it.

Methods A structured literature search was conducted. The MEDLINE, CINAHL, AMI, APAIS-Health databases were searched for articles published between 1980 and 2011. The reference list of journals and relevant websites were searched. Inclusion criteria were: literature about T2DM in young adults and literature about education and information needs of young adults with T2DM.

Results Only one article specifically focussed on the information needs of young people with T2DM and two reports included some information about T2DM and young adults. The limited data available suggested young adults with T2DM have specific information needs that are not sufficiently addressed in current resources, and have some needs different from young people with type 1 diabetes. Young people with T2DM want clear, consistent information from credible sources, delivered in a range of formats. They also want psychological support and counselling, age-specific education groups, after hours access to health professionals and education provided to family members and the community about T2DM in young people and about how to support people with T2DM.

Conclusion With the prevalence of T2DM in young adults continuing to increase, the need to address the specific information needs and learning styles of people with T2DM aged 25 to 45 is increasingly important.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

OBJECTIVES: To derive and validate a mortality prediction model from information available at ED triage. METHODS: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. RESULTS: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. CONCLUSIONS: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

Urban amphibian assemblages as metacommunities

1. Urban ecosystems are expanding throughout the world, and urban ecology is attracting increasing research interest. Some authors have questioned the value of existing ecological theories for understanding the processes and consequences of urbanization.
2. In order to assess the applicability of metacommunity theory to urban systems, I evaluated three assumptions that underlie the theory – the effect of patch area, the effect of patch isolation, and species–environment relations – using data on assemblages of pond-breeding amphibians in the Greater Melbourne area of Australia. I also assessed the relative impact of habitat fragmentation, habitat isolation, and changes to habitat quality on these assemblages.
3. Poisson regression modelling provided support for an important increase in species richness with patch area (pond size) and a decrease in species richness with increasing patch isolation, as measured by surrounding road cover. Holding all other variables constant, species richness was predicted to be 2·8–5·5 times higher at the largest pond than at the smallest, while the most isolated pond was predicted to have 12–19% of the species richness of the least isolated pond. Thus, the data were consistent with the first two assumptions of metacommunity theory evaluated.
4. The quality of habitat at a pond was also important, with a predicted 44–56% decrease in the number of species detected at ponds with a surrounding vertical wall compared with those with a gently sloping bank. This demonstrates that environmental differences between habitat patches were also influencing amphibian assemblages, providing support for the species-sorting and/or mass-effect perspectives of metacommunity theory.
5. Without management intervention, urbanization may lead to a reduction in the number of amphibian species persisting in urban ponds, particularly where increasing isolation of ponds by roads and associated infrastructure reduces the probability of re-colonization following local extinction.

Changes in physical activity following a genetic-based internet-delivered personalized intervention: randomized controlled trial (Food4Me)

Background: There is evidence that physical activity (PA) can attenuate the influence of the fat mass- and obesity-associated (FTO) genotype on the risk to develop obesity. However, whether providing personalized information on FTO genotype leads to changes in PA is unknown. Objective: The purpose of this study was to determine if disclosing FTO risk had an impact on change in PA following a 6-month intervention.

Methods: The single nucleotide polymorphism (SNP) rs9939609 in the FTO gene was genotyped in 1279 participants of the Food4Me study, a four-arm, Web-based randomized controlled trial (RCT) in 7 European countries on the effects of personalized advice on nutrition and PA. PA was measured objectively using a TracmorD accelerometer and was self-reported using the Baecke questionnaire at baseline and 6 months. Differences in baseline PA variables between risk (AA and AT genotypes) and nonrisk (TT genotype) carriers were tested using multiple linear regression. Impact of FTO risk disclosure on PA change at 6 months was assessed among participants with inadequate PA, by including an interaction term in the model: disclosure (yes/no) × FTO risk (yes/no).

Results: At baseline, data on PA were available for 874 and 405 participants with the risk and nonrisk FTO genotypes, respectively. There were no significant differences in objectively measured or self-reported baseline PA between risk and nonrisk carriers. A total of 807 (72.05%) of the participants out of 1120 in the personalized groups were encouraged to increase PA at baseline. Knowledge of FTO risk had no impact on PA in either risk or nonrisk carriers after the 6-month intervention. Attrition was higher in nonrisk participants for whom genotype was disclosed (P=.01) compared with their at-risk counterparts.

Conclusions: No association between baseline PA and FTO risk genotype was observed. There was no added benefit of disclosing FTO risk on changes in PA in this personalized intervention. Further RCT studies are warranted to confirm whether disclosure of nonrisk genetic test results has adverse effects on engagement in behavior change.

Tanis: a link between type 2 diabetes and inflammation?

Here we describe a novel protein, which we have named Tanis, that is implicated in type 2 diabetes and inflammation. In <i>Psammomys obesusi>, a unique polygenic animal model of type 2 diabetes and the metabolic syndrome, Tanis is expressed in the liver in inverse proportion to circulating glucose (<i>Pi> = 0.010) and insulin levels (<i>Pi> = 0.004) and in direct proportion with plasma triglyceride concentrations (<i>Pi> = 0.007). Hepatic Tanis gene expression was markedly increased (3.1-fold) after a 24-h fast in diabetic but not in nondiabetic P. obesus. In addition, glucose inhibited Tanis gene expression in cultured hepatocytes (<i>P i>= 0.006) as well as in several other cell types (<i>Pi> = 0.001–0.011). Thus, Tanis seems to be regulated by glucose and is dysregulated in the diabetic state. Yeast-2 hybrid screening identified serum amyloid A (SAA), an acute-phase inflammatory response protein, as an interacting protein of Tanis, and this was confirmed by Biacore experiments. SAA and other acute-phase proteins have been the focus of recent attention as risk factors for cardiovascular disease, and we contend that Tanis and its interaction with SAA may provide a mechanistic link among type 2 diabetes, inflammation, and cardiovascular disease.