‘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.

Amphoteric nano-, micro-, and macrogels, membranes, and thin films

This review describes the state-of the-art of nano-, micro- and macrogels, membranes, micro- and nanocapsules, as well as multilayered thin films exhibiting amphoteric character. The synthetic strategies and physicochemical properties of amphoteric materials are outlined in light of the stimuli-responsive behavior and their potential application in nanotechnology, biotechnology and medicine.

A thermoresponsive supramolecular polymer network comprising pyrene-functionalized gold nano-particles and a chain-folding polydiimide.

A thermoresponsive, supramolecular nanocomposite has been prepared by the addition of pyrenyl functionalized gold nanoparticles (AuNPs) to a polydiimide that contains receptor residues designed to form defined complexes with pyrene. The novel pyrenyl-functionalized AuNPs (P-AuNPs) were characterized by transmission electron microscopy, with surface functionalization confirmed by infrared and UV–visible spectroscopic analyses. Mixing solutions of the P-AuNPs and a π-electron-deficient polydiimide resulted in the formation of electronically complementary, chain-folded and π–π-stacked complexes, so affording a new supramolecular nanocomposite network which precipitated from solution. The P-AuNPs bind to the polydiimide via π–π stacking interactions to create supramolecular cross-links. UV–visible spectroscopic analysis confirmed the thermally reversible nature of the complexation process, and transmission electron microscopy (TEM), infrared spectroscopy (IR), and differential scanning calorimetry (DSC) were used to characterize the supramolecular-nanocomposite material. The supramolecular polymer network is insoluble at room temperature, yet may be dissolved at temperatures above 60 °C. The thermal reversibility of this system is maintained over five heat/cool cycles without diminishment of the network characteristics. In contrast to the individual components, the nanocomposite formed self-supporting films, demonstrating the benefit of the supramolecular network in terms of mechanical properties. Control experiments probing the interactions between a model diimide compound that can also form a π-stacked complex with the π-electron rich pyrene units on P-AuNPs showed that, while complexation was readily apparent, precipitation did not occur because a supramolecular cross-linked network system could not be formed with this system.

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.

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.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

P-found: grid-enabling distributed repositories of protein folding and unfolding simulations for data mining

The P-found protein folding and unfolding simulation repository is designed to allow scientists to perform data mining and other analyses across large, distributed simulation data sets. There are two storage components in P-found: a primary repository of simulation data that is used to populate the second component, and a data warehouse that contains important molecular properties. These properties may be used for data mining studies. Here we demonstrate how grid technologies can support multiple, distributed P-found installations. In particular, we look at two aspects: firstly, how grid data management technologies can be used to access the distributed data warehouses; and secondly, how the grid can be used to transfer analysis programs to the primary repositories — this is an important and challenging aspect of P-found, due to the large data volumes involved and the desire of scientists to maintain control of their own data. The grid technologies we are developing with the P-found system will allow new large data sets of protein folding simulations to be accessed and analysed in novel ways, with significant potential for enabling scientific discovery.

The data-mining of studies database 'HHdbIV'

This article clarifies what was done with the sub-7-man positions in data-mining Harold van der Heijden's 'HHdbIV' database of chess studies prior to its publication. It emphasises that only positions in the main lines of studies were examined and that the information about uniqueness of move was not incorporated in HHdbIV. There is some reflection on the separate technical and artistic dimensions of study evaluation.

Nano structures through self-assembly of protected hydrophobic amino acids: encapsulation of rhodamine B dye by proline-based nanovesicles

The development of novel molecules for the creation of nanometer structures with specific properties has been the current interest of this research. We have developed a set of molecules from hydrophobic omega- and alpha-amino acids by protecting the -NH(2) with Boc (t-butyloxycarbonyl) group and -CO(2)H with para-nitroanilide such as BocHN-Xx-CONH-(p-NO(2))center dot C(6)H(4), where Xx is gamma-aminobutyric acid (gamma-Abu), (L)-isoleucine, alpha-aminoisobutyric acid, proline, etc. These molecules generate various nanometer structures, such as nanofibrils, nanotubes and nanovesicles, in methanol/water through the self-assembly of bilayers in which the nitro benzene moieties are stacked in the middle and the Boc-protected amino acids parts are packed in the outer surface. The bilayers can be further stacked one over the other through hydrophobic interactions to form multilayer structure, which helps to generate different kinds of nanoscopic structures. The formation of the nanostructures has been facilitated through the participation of various noncovalent interactions, such as hydrophobic interactions, hydrogen bonding and aromatic p-stacking interactions. Fluorescence microscopy and UV studies reveal that the nanovesicles generated from pro-based molecule can encapsulate dye molecules which can be released by addition of acid (at pH 2). These single amino acid based molecules are both easy to synthesize and cost-effective and therefore offer novel scaffolds for the future design of nanoscale structures.

"The ladder that sends us to wealth" : artisanal mining and poverty reduction in Tanzania

This article explores the contribution that artisanal and small-scale mining (ASM) makes to poverty reduction in Tanzania, based on data on gold and diamond mining in Mwanza Region. The evidence suggests that people working in mining or related services are less likely to be in poverty than those with other occupations. However, the picture is complex; while mining income can help reduce poverty and provide a buffer from livelihood shocks, peoples inability to obtain a formal mineral claim, or to effectively exploit their claims, contributes to insecurity. This is reinforced by a context in which ASM is peripheral to large-scale mining interests, is only gradually being addressed within national poverty reduction policies, and is segregated from district-level planning.

Artisanal gold mining at the margins of mineral resource governance : a case from Tanzania

This article discusses the character of mineral resource governance at the margins of the state in Tanzania and the way artisanal gold miners are incorporated into mineral sector transformation. The landscape of mineral resource exploitation has changed dramatically over the past 20 years: processes of economic liberalisation have heralded massive foreign investment in large-scale gold mining, while also stimulating artisanal activities. Against this background, the article shows how artisanal gold miners are affected by contradictory processes: some have become integrated with state institutions and legal processes, while others, the large majority, are either further excluded or incorporated in ways that exacerbate insecurity and exploitation, underpinned by socio-economic inequalities. These processes are compounded by the actions of large-scale and medium-scale gold mining companies and by poor local governance. It is open to debate whether this will bring improved integration and welfare for artisanal mining communities or new forms of exclusion, although evidence suggests the latter.