The fatigue and degradation mechanisms of hoisting ropes

Based upon specialised experience of rope mechanics spanning over 20 years, this paper reviews the processes of degradation and fatigue that are relevant to hoisting ropes in mines. The review is brought up to date with an account of the most recent work in this field, which identifies a torsional fatigue process and quantifies the impact of degradation upon the residual service life. A proper understanding of these processes is important in determining how different parameters of hoist design and operation interact to determine rope life. This knowledge is also important in informing decisions relating to rope discard based upon observed condition, as well is identifying the critical features that must be quantified reliably during inspection.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Intestinal injury and endotoxemia in children undergoing surgery for congenital heart disease

RATIONALE: Children with congenital heart disease are at risk of gut barrier dysfunction and translocation of gut bacterial antigens into the bloodstream. This may contribute to inflammatory activation and organ dysfunction postoperatively. OBJECTIVES: To investigate the role of intestinal injury and endotoxemia in the pathogenesis of organ dysfunction after surgery for congenital heart disease. METHODS: We analyzed blood levels of intestinal fatty acid binding protein and endotoxin (endotoxin activity assay) alongside global transcriptomic profiling and assays of monocyte endotoxin receptor expression in children undergoing surgery for congenital heart disease. MEASUREMENTS AND MAIN RESULTS: Levels of intestinal fatty acid binding protein and endotoxin were greater in children with duct-dependent cardiac lesions. Endotoxemia was associated with severity of vital organ dysfunction and intensive care stay. We identified activation of pathogen-sensing, antigen-processing, and immune-suppressing pathways at the genomic level postoperatively and down-regulation of pathogen-sensing receptors on circulating immune cells. CONCLUSIONS: Children undergoing surgery for congenital heart disease are at increased risk of intestinal mucosal injury and endotoxemia. Endotoxin activity correlates with a number of outcome variables in this population, and may be used to guide the use of gut-protective strategies.

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.

‘The Making of the Book’: Roy Fisher, the Circle Press and the poetics of book art

Between 1972 and 2001, the English late-modernist poet Roy Fisher provided the text for nine separate artist's books produced by Ron King at the Circle Press. Taken together, as Andrew Lambirth has written, the Fisher-King collaborations represent a sustained investigation of the various ways in which text and image can be integrated, breaking the mould of the codex or folio edition, and turning the book into a sculptural object. From the three-dimensional pop-up designs of Bluebeard's Castle (1973), each representing a part of the edifice (the portcullis, the armoury and so on), to ‘alphabet books’ such as The Half-Year Letters (1983), held in an ingenious french-folded concertina which can be stretched to over a metre long or compacted to a pocketbook, the project of these art books is to complicate their own bibliographic codes, and rethink what a book can be. Their folds and reduplications give a material form to the processes by which meanings are produced: from the discovery, in Top Down, Bottom Up (1990), of how to draw on both sides of the page at the same time, to the developments of The Left-Handed Punch (1987) and Anansi Company (1992), where the book becomes first a four-dimensional theatre space, in which a new version of Punch and Judy is played out by twelve articulated puppets, and then a location for characters that are self-contained and removable, in the form of thirteen hand-made wire and card rod-puppets. Finally, in Tabernacle (2001), a seven-drawer black wooden cabinet that stands foursquare like a sculpture (and sells to galleries and collectors for over three thousand pounds), the conception of the book and the material history of print are fully undone and reconstituted. This paper analyses how the King-Fisher art books work out their radically material poetics of the book; how their emphasis on collaboration, between artist and poet, image and text, and also book and reader – the construction of meaning becoming a co-implicated process – continuously challenges hierarchies and fixities in our conception of authorship; and how they re-think the status of poetic text and the construction of the book as material object.

A new algorithm for tracking people in a pervasive mining environment

This paper analyze and study a pervasive computing system in a mining environment to track people based on RFID (radio frequency identification) technology. In first instance, we explain the RFID fundamentals and the LANDMARC (location identification based on dynamic active RFID calibration) algorithm, then we present the proposed algorithm combining LANDMARC and trilateration technique to collect the coordinates of the people inside the mine, next we generalize a pervasive computing system that can be implemented in mining, and finally we show the results and conclusions.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

An ethical turn in African mining: voluntary regulation through fair trade

In recent decades there has been an ethical turn in expectations of how African mineral production and trade should be conducted. Good labour conditions, the absence of conflict and mining’s potential for securing economic, social and environmental benefits are being demanded in the jewellery trade. As a consequence the quality of precious and semi-precious metals and gemstones is now being judged on their ethical credentials in addition to their aesthetic and mineral qualities. Mineral production for industrial manufacture, particularly in the electronics industry, is also coming under scrutiny. Adding value through ethics is closely associated with the use of voluntary (non-state) regulation. This includes standards and associated certification and labels, which have been widely adopted by the minerals and metals sector in efforts to ensure improvements in the social and environmental conditions of production and to enable access to the profitable and expanding global ‘ethical market’. In this chapter, we focus on ethical trading schemes that incorporate voluntary regulation, by using artisanal gold mining in Tanzania and the sale of gold through international fair trade markets as an exemplar to consider the development dynamics that emerge from ethical schemes.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Public real estate and the term structure of interest rates: a cross-country study

Using a variation of the Nelson-Siegel term structure model we examine the sensitivity of real estate securities in six key global markets to unexpected changes in the level, slop and curvature of the yield curve. Our results confirm the time-sensitive nature of the exposure and sensitivity to interest rates and highlight the importance of considering the entire term structure of interest rates. One issue that is of particular of interest is that despite the 2007-9 financial crisis the importance of unanticipated interest rate risk weakens post 2003. Although the analysis does examine a range of markets the empirical analysis is unable to provide definitive evidence as to whether REIT and property-company markets display heightened or reduced exposure.

Mining in a changing climate: what scope for forestry-based legacies?

This paper provides an interdisciplinary perspective on mine reclamation in forested areas of Ghana, a country characterised by conflicts between mining and forest conservation. A comparison was made between above ground biomass (AGB) and soil organic carbon (SOC) content from two reclaimed mine sites and adjacent undisturbed forest. Findings suggest that on decadal timescales, reclaimed mine sites contain approximately 40% of the total carbon and 10% the AGB carbon of undisturbed forest. This raises questions regarding the potential for decommissioning mine sites to provide forestry-based legacies. Such a move could deliver a host of benefits, including improving the longevity and success of reclamation, mitigating climate change and delivering corollary enumeration for local communities under carbon trading schemes. A discussion of the antecedents and challenges associated with establishing forest-legacies highlights the risk of neglecting the participation and heterogeneity of legitimate local representatives, which threatens the equity of potential benefits and sustainability of projects. Despite these risks, implementing pilot projects could help to address the lack of transparency and data which currently characterises mine reclamation.