INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support) : overview and key principles

Non-communicable diseases (NCDs) dominate disease burdens globally and poor nutrition increasingly contributes to this global burden. Comprehensive monitoring of food environments, and evaluation of the impact of public and private sector policies on food environments is needed to strengthen accountability systems to reduce NCDs. The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) is a global network of public-interest organizations and researchers that aims to monitor, benchmark and support public and private sector actions to create healthy food environments and reduce obesity, NCDs and their related inequalities. The INFORMAS framework includes two ‘process’ modules, that monitor the policies and actions of the public and private sectors, seven ‘impact’ modules that monitor the key characteristics of food environments and three ‘outcome’ modules that monitor dietary quality, risk factors and NCD morbidity and mortality. Monitoring frameworks and indicators have been developed for 10 modules to provide consistency, but allowing for stepwise approaches (‘minimal’, ‘expanded’, ‘optimal’) to data collection and analysis. INFORMAS data will enable benchmarking of food environments between countries, and monitoring of progress over time within countries. Through monitoring and benchmarking, INFORMAS will strengthen the accountability systems needed to help reduce the burden of obesity, NCDs and their related inequalities.

The distribution of absorptive capacity among construction supply chain participants

The absorptive capacity of organisations is one of the key drivers of innovation performance in any industry. This research seeks to refine our understanding of the relationship between absorptive capacity and innovation performance, with a focus on characterising the absorptive capacity of the different participant groups within the Australian road industry supply chain. One of the largest and most comprehensive surveys ever undertaken of innovation in road construction was completed in 2011 by the Queensland University of Technology (QUT), based on the Australian road industry. The survey of over 200 construction industry participants covered four sectors, comprising suppliers (manufacturers and distributors), consultants (engineering consultants), contractors (head and subcontractors) and clients (state government road agencies). The survey measured the absorptive capacity and innovation activity exhibited by organisations within each of these participant groups, using the perceived importance of addressing innovation obstacles as a proxy for innovation activity. One of the key findings of the survey is about the impact of participant competency on product innovation activity. The survey found that the absorptive capacity of industry participants had a significant and positive relationship with innovation activity. Regarding the distribution of absorptive capacity, the results indicate that suppliers are more likely to have high levels of absorptive capacity than the other participant groups, with 32% of suppliers showing high absorptive capacity, ahead of contractors (18%), consultants (11%), and clients (7%). These results support the findings of previous studies in the literature and suggest the importance of policies to enhance organisational learning, particularly in relation to openness to new product ideas.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

A typology of music distribution models

A typology of music distribution models is proposed consisting of the ownership model, the access model, and the context model. These models are not substitutes for each other and may co‐exist serving different market niches. The paper argues that increasingly the economic value created from recorded music is based on con‐text rather than on ownership. During this process, access‐based services temporarily generate economic value, but such services are destined to eventually become commoditised.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.

A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration

This paper deals with an efficient hybrid evolutionary optimization algorithm in accordance with combining the ant colony optimization (ACO) and the simulated annealing (SA), so called ACO-SA. The distribution feeder reconfiguration (DFR) is known as one of the most important control schemes in the distribution networks, which can be affected by distributed generations (DGs) for the multi-objective DFR. In such a case, DGs is used to minimize the real power loss, the deviation of nodes voltage and the number of switching operations. The approach is carried out on a real distribution feeder, where the simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving the DFR problem.

Optimal distributed generation placement in radial distribution systems

Distributed generation (DG) resources are commonly used in the electric systems to obtain minimum line losses, as one of the benefits of DG, in radial distribution systems. Studies have shown the importance of appropriate selection of location and size of DGs. This paper proposes an analytical method for solving optimal distributed generation placement (ODGP) problem to minimize line losses in radial distribution systems using loss sensitivity factor (LSF) based on bus-injection to branch-current (BIBC) matrix. The proposed method is formulated and tested on 12 and 34 bus radial distribution systems. The classical grid search algorithm based on successive load flows is employed to validate the results. The main advantages of the proposed method as compared with the other conventional methods are the robustness and no need to calculate and invert large admittance or Jacobian matrices. Therefore, the simulation time and the amount of computer memory, required for processing data especially for the large systems, decreases.

Effectiveness of reactive power capability of photo voltaic inverters to maintain acceptable voltage profile in a residential distribution system

Integration of rooftop PVs and increasing peak demand in the residential distribution networks has resulted in unacceptable voltage profile. Curtailing PV generation to alleviate overvoltage problem and making regular network investment to cater peak demand is not always feasible. Reactive capability of the PV inverter can be a solution to address voltage dip and over voltage problems to some extent. This paper proposes an algorithm to utilize reactive capability of PV inverters and investigate their effectiveness on feeder length and R/X ratio of the line. Feeder loading level for a particular R/X ratio to have acceptable voltage profile is also investigated. Furthermore, the need of appropriate feeder distances and R/X ratio for acceptable voltage profile, which can be useful for suburban design and distribution planning, is explored.

Pore size distribution and accessible pore size distribution in bituminous coals

The porosity and pore size distribution of coals determine many of their properties, from gas release to their behavior on carbonization, and yet most methods of determining pore size distribution can only examine a restricted size range. Even then, only accessible pores can be investigated with these methods. Small-angle neutron scattering (SANS) and ultra small-angle neutron scattering (USANS) are increasingly used to characterize the size distribution of all of the pores non-destructively. Here we have used USANS/SANS to examine 24 well-characterized bituminous and subbituminous coals: three from the eastern US, two from Poland, one from New Zealand and the rest from the Sydney and Bowen Basins in Eastern Australia, and determined the relationships of the scattering intensity corresponding to different pore sizes with other coal properties. The range of pore radii examinable with these techniques is 2.5nm to 7μm. We confirm that there is a wide range of pore sizes in coal. The pore size distribution was found to be strongly affected by both rank and type (expressed as either hydrogen or vitrinite content) in the size range 250nm to 7μm and 5 to 10nm, but weakly in intermediate regions. The results suggest that different mechanisms control coal porosity on different scales. Contrast-matching USANS and SANS were also used to determine the size distribution of the fraction of the pores in these coals that are inaccessible to deuterated methane, CD4, at ambient temperature. In some coals most of the small (~10nm) pores were found to be inaccessible to CD4 on the time scale of the measurement (~30min–16h). This inaccessibility suggests that in these coals a considerable fraction of inherent methane may be trapped for extended periods of time, thus reducing the effectiveness of methane release from (or sorption by) these coals. Although the number of small pores was less in higher rank coals, the fraction of total pores that was inaccessible was not rank dependent. In the Australian coals, at the 10nm to 50nm size scales the pores in inertinites appeared to be completely accessible to CD4, whereas the pores in the vitrinite were about 75% inaccessible. Unlike the results for total porosity that showed no regional effects on relationships between porosity and coal properties, clear regional differences in the relationships between fraction of closed porosity and coal properties were found. The 10 to 50nm-sized pores of inertinites of the US and Polish coals examined appeared less accessible to methane than those of the inertinites of Australian coals. This difference in pore accessibility in inertinites may explain why empirical relationships between fluidity and coking properties developed using Carboniferous coals do not apply to Australian coals.

Simulation of ion flux distribution in conductive and nonconductive nanotip patterns

The distribution of flux of carbon-bearing cations over nanopatterned surfaces with conductive nanotips and nonconductive nanoislands is simulated using the Monte-Carlo technique. It is shown that the ion current is focused to nanotip surfaces when the negative substrate bias is low and only slightly perturbed at higher substrate biases. In the low-bias case, the mean horizontal ion displacement caused by the nanotip electric field exceeds 10 nm. However, at higher substrate biases, this value reduces down to 2 nm. In the nonconductive nanopattern case, the ion current distribution is highly nonuniform, with distinctive zones of depleted current density around the nanoislands. The simulation results suggest the efficient means to control ion fluxes in plasma-aided nanofabrication of ordered nanopatterns, such as nanotip microemitter structures and quantum dot or nanoparticle arrays. © World Scientific Publishing Company.

Geographical distribution of human Schistosoma japonicum infection in the Philippines : tools to support disease control and further elimination

S. japonicum infection is believed to be endemic in 28 of the 80 provinces of the Philippines and the most recent data on schistosomiasis prevalence have shown considerable variability between provinces. In order to increase the efficient allocation of parasitic disease control resources in the country, we aimed to describe the small scale spatial variation in S. japonicum prevalence across the Philippines, quantify the role of the physical environment in driving the spatial variation of S. japonicum, and develop a predictive risk map of S. japonicum infection. Data on S. japonicum infection from 35,754 individuals across the country were geo-located at the barangay level and included in the analysis. The analysis was then stratified geographically for Luzon, the Visayas and Mindanao. Zero-inflated binomial Bayesian geostatistical models of S. japonicum prevalence were developed and diagnostic uncertainty was incorporated. Results of the analysis show that in the three regions, males and individuals aged ≥ 20 years had significantly higher prevalence of S. japonicum compared with females and children <5 years. The role of the environmental variables differed between regions of the Philippines. S. japonicum infection was widespread in the Visayas whereas it was much more focal in Luzon and Mindanao. This analysis revealed significant spatial variation in prevalence of S. japonicum infection in the Philippines. This suggests that a spatially targeted approach to schistosomiasis interventions, including mass drug administration, is warranted. When financially possible, additional schistosomiasis surveys should be prioritized to areas identified to be at high risk, but which were underrepresented in our dataset.

Particle size distribution effects on preferential deposition areas in metal foam wrapped tube bundle

This paper presents a numerical model for understanding particle transport and deposition in metal foam heat exchangers. Two-dimensional steady and unsteady numerical simulations of a standard single row metal foam-wrapped tube bundle are performed for different particle size distributions, i.e. uniform and normal distributions. Effects of different particle sizes and fluid inlet velocities on the overall particle transport inside and outside the foam layer are also investigated. It was noted that the simplification made in the previously-published numerical works in the literature, e.g. uniform particle deposition in the foam, is not necessarily accurate at least for the cases considered here. The results highlight the preferential particle deposition areas both along the tube walls and inside the foam using a developed particle deposition likelihood matrix. This likelihood matrix is developed based on three criteria being particle local velocity, time spent in the foam, and volume fraction. It was noted that the particles tend to deposit near both front and rear stagnation points. The former is explained by the higher momentum and direct exposure of the particles to the foam while the latter only accommodate small particles which can be entrained in the recirculation region formed behind the foam-wrapped tubes.

A normal-distribution based reputation model

Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.