Low-cost optical tracking for collaborative applications in immersive virtual environments

We present a novel way of interacting with an immersive virtual environment which involves inexpensive motion-capture using the Wii Remote®. A software framework is also presented to visualize and share this information across two remote CAVETM-like environments. The resulting application can be used to assist rehabilitation by sending motion information across remote sites. The application’s software and hardware components are scalable enough to be used on a desktop computer when home-based rehabilitation is preferred.

Family hardship and cultural values: child labor in Malian artisanal gold mining communities

This paper contributes to the debate on child labor in small-scale mining communities, focusing specifically on the situation in sub-Saharan Africa. It argues that the child labor now widespread in many of the region’s small-scale mining communities is a product of a combination of cultural issues, household-level poverty and rural livelihood diversification. Experiences from Komana West, a subsistence gold panning area in Southern Mali, are drawn upon to make this case. The findings suggest that the sector’s child labor “problem” is far more nuanced than international organizations and policymakers have diagnosed.

Poverty traps in small-scale mining communities: the case of sub-Saharan Africa

This paper critically reflects on why, in many rural stretches of sub-Saharan Africa, scores of people engage in artisanal and small-scale mining (ASM) activity – low-tech, labour intensive mineral extraction – for lengthy periods of time. It argues that a large share of the region’s ASM operators have mounting debts which prevent them from pursuing alternative, less arduous, employment. The paper concludes with an analysis of findings from research carried out by the author in Talensi-Nabdam District, Northern Ghana, which captures the essence of the poverty trap now plaguing so many ASM communities in sub-Saharan Africa.

Determining the cost of in-field mitigation options to reduce sediment and phosphorus loss

The Mitigation Options for Phosphorus and Sediment (MOPS) project investigated the effectiveness of within-field control measures (tramline management, straw residue management, type of cultivation and direction, and vegetative buffers) in terms of mitigating sediment and phosphorus loss from winter-sown combinable cereal crops using three case study sites. To determine the cost of the approaches, simple financial spreadsheet models were constructed at both farm and regional levels. Taking into account crop areas, crop rotation margins per hectare were calculated to reflect the costs of crop establishment, fertiliser and agro-chemical applications, harvesting, and the associated labour and machinery costs. Variable and operating costs associated with each mitigation option were then incorporated to demonstrate the impact on the relevant crop enterprise and crop rotation margins. These costs were then compared to runoff, sediment and phosphorus loss data obtained from monitoring hillslope-length scale field plots. Each of the mitigation options explored in this study had potential for reducing sediment and phosphorus losses from arable land under cereal crops. Sediment losses were reduced from between 9 kg ha−1 to as much as 4780 kg ha−1 with a corresponding reduction in phosphorus loss from 0.03 kg ha−1 to 2.89 kg ha−1. In percentage terms reductions of phosphorus were between 9% and 99%. Impacts on crop rotation margins also varied. Minimum tillage resulted in cost savings (up to £50 ha−1) whilst other options showed increased costs (up to £19 ha−1 for straw residue incorporation). Overall, the results indicate that each of the options has potential for on-farm implementation. However, tramline management appeared to have the greatest potential for reducing runoff, sediment, and phosphorus losses from arable land (between 69% and 99%) and is likely to be considered cost-effective with only a small additional cost of £2–4 ha−1, although further work is needed to evaluate alternative tramline management methods. Tramline management is also the only option not incorporated within current policy mechanisms associated with reducing soil erosion and phosphorus loss and in light of its potential is an approach that should be encouraged once further evidence is available.

‘Everyone now is concentrating on the mining’: drivers and implications of rural economic transition in the Eastern Region of Ghana

Artisanal and small-scale mining (ASM) is replacing smallholder farming as the principal income source in parts of rural Ghana. Structural adjustment policies have removed support for the country’s smallholders, devalued their produce substantially and stiffened competition with large-scale counterparts. Over one million people nationwide are now engaged in ASM. Findings from qualitative research in Ghana’s Eastern Region are drawn upon to improve understanding of the factors driving this pattern of rural livelihood diversification. The ASM sector and farming are shown to be complementary, contrary to common depictions in policy and academic literature.

Multiple-project discount rates for cost-benefit analysis in construction projects: A formal risk model for microgeneration renewable energy technologies

The traditional economic approach for appraising the costs and benefits of construction project Net Present Values involves the calculation of net returns for each investment option under different discount rates. An alternative approach consists of multiple-project discount rates based on risk modelling. The example of a portfolio of microgeneration renewable energy technology (MRET) is presented to demonstrate that risks and future available budget for re-investment can be taken into account when setting discount rates for construction project specifications in presence of uncertainty. A formal demonstration is carried out through a reversed intertemporal approach of applied general equilibrium. It is demonstrated that risk and the estimated available budget for future re-investment can be included in the simultaneous assessment of the costs and benefits of multiple projects.

Evaluation of field wetlands for mitigation of diffuse pollution from agriculture: sediment retention, cost and effectiveness

Diffuse pollution, and the contribution from agriculture in particular, has become increasingly important as pollution from point sources has been addressed by wastewater treatment. Land management approaches, such as construction of field wetlands, provide one group of mitigation options available to farmers. Although field wetlands are widely used for diffuse pollution control in temperate environments worldwide, there is a shortage of evidence for the effectiveness and viability of these mitigation options in the UK. The Mitigation Options for Phosphorus and Sediment Project aims to make recommendations regarding the design and effectiveness of field wetlands for diffuse pollution control in UK landscapes. Ten wetlands have been built on four farms in Cumbria and Leicestershire. This paper focuses on sediment retention within the wetlands, estimated from annual sediment surveys in the first two years, and discusses establishment costs. It is clear that the wetlands are effective in trapping a substantial amount of sediment. Estimates of annual sediment retention suggest higher trapping rates at sandy sites (0.5–6 t ha�1 yr�1), compared to silty sites (0.02–0.4 t ha�1 yr�1) and clay sites (0.01–0.07 t ha�1 yr�1). Establishment costs for the wetlands ranged from £280 to £3100 and depended more on site specific factors, such as fencing and gateways on livestock farms, rather than on wetland size or design. Wetlands with lower trapping rates would also have lower maintenance costs, as dredging would be required less frequently. The results indicate that field wetlands show promise for inclusion in agri-environment schemes, particularly if capital payments can be provided for establishment, to encourage uptake of these multi-functional features.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.