New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

A process mining-based investigation of adverse events in care processes

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

Interference and management of parthenium: The world's most important invasive weed

Parthenium (Parthenium hysterophorus L.) is one of the most aggressive herbaceous weeds of the Asteraceae family. It is widely distributed, almost across the world and has become the most important invasive weed. Comprehensive information on interference and control of this devastating species is required to facilitate better management decisions. A broad review on the interference and management of this weed is presented here. Inspite of its non-tropical origin, parthenium grows quite successfully under a wide range of environmental conditions. It is spreading rapidly in Australia, Western Africa, Asia, and Caribbean countries, and has become a serious weed of pastures, wastelands, roadsides, railwaysides, water courses, and agricultural crops. The infestations of parthenium have been reported to reduce grain and forage yields by 40–90%. The spread of parthenium has been attributed to its allelopathic activity, strong competitiveness for soil moisture and nutrients, and its capability to exploit natural biodiversity. Allelochemicals released from parthenium has been reported to decrease germination and growth of agronomic crops, vegetables, trees, and many other weed species. Growth promoting effects of parthenium extracts at low concentrations have also been reported in certain crops. Many pre- and post-emergence herbicides have been evaluated for the control of parthenium in cropped and non-cropped areas. The most effective herbicides are clomazone, metribuzin, atrazine, glyphosate, metsulfuron methyl, butachlor, bentazone, dicamba, and metsulfuron methyl. Extracts, residues, and essential oils of many allelopathic herbs (Cassia, Amaranthus, and Xanthium species), grasses (Imperata and Desmostachya species), and trees (Eucalyptus, Azadirachta, Mangifera species, etc.) have demonstrated inhibitory activities on seed germination and seedling growth of parthenium. Metabolites of several fungi, e.g., Fusarium oxysporun and Fusarium monilifonne, exhibit bioherbicidal activity against seeds and seedlings of this weed. Intercropping, displacement by competitive plant species like Cassia species, bisset bluegrass, florgen blugress, buffelgrass, along with the use of biological control agents like Mexican beetle, seed-feeding and stem-boring weevils, stem-galling and leaf-mining moth, and sap-feeding plant hopper, have been reported as possible strategies for the management of parthenium. An appropriate integration of these approaches could help minimize spread of parthenium and provide sustainable weed management with reduced environmental concerns.

Matrix Decomposition Methods for Data Mining : Computational Complexity and Algorithms

Matrix decompositions, where a given matrix is represented as a product of two other matrices, are regularly used in data mining. Most matrix decompositions have their roots in linear algebra, but the needs of data mining are not always those of linear algebra. In data mining one needs to have results that are interpretable -- and what is considered interpretable in data mining can be very different to what is considered interpretable in linear algebra. --- The purpose of this thesis is to study matrix decompositions that directly address the issue of interpretability. An example is a decomposition of binary matrices where the factor matrices are assumed to be binary and the matrix multiplication is Boolean. The restriction to binary factor matrices increases interpretability -- factor matrices are of the same type as the original matrix -- and allows the use of Boolean matrix multiplication, which is often more intuitive than normal matrix multiplication with binary matrices. Also several other decomposition methods are described, and the computational complexity of computing them is studied together with the hardness of approximating the related optimization problems. Based on these studies, algorithms for constructing the decompositions are proposed. Constructing the decompositions turns out to be computationally hard, and the proposed algorithms are mostly based on various heuristics. Nevertheless, the algorithms are shown to be capable of finding good results in empirical experiments conducted with both synthetic and real-world data.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

Discovering Math APIs by Mining Unit Tests

In today's API-rich world, programmer productivity depends heavily on the programmer's ability to discover the required APIs. In this paper, we present a technique and tool, called MATHFINDER, to discover APIs for mathematical computations by mining unit tests of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code to compute the expression by mapping its subexpressions to API method calls. For each subexpression, MATHFINDER searches for a method such that there is a mapping between method inputs and variables of the subexpression. The subexpression, when evaluated on the test inputs of the method under this mapping, should produce results that match the method output on a large number of tests. We implemented MATHFINDER as an Eclipse plugin for discovery of third-party Java APIs and performed a user study to evaluate its effectiveness. In the study, the use of MATHFINDER resulted in a 2x improvement in programmer productivity. In 96% of the subexpressions queried for in the study, MATHFINDER retrieved the desired API methods as the top-most result. The top-most pseudo-code snippet to implement the entire expression was correct in 93% of the cases. Since the number of methods and unit tests to mine could be large in practice, we also implement MATHFINDER in a MapReduce framework and evaluate its scalability and response time.

The local and global dimensions of metalliferous pollution derived from a reconstruction of an eight thousand year record of copper smelting and mining at a desert-mountain frontier in southern Jordan

Grattan, J.P., Gilbertson, D.D., Hunt, C.O. (2007). The local and global dimensions of metaliferrous air pollution derived from a reconstruction of an 8 thousand year record of copper smelting and mining at a desert-mountain frontier in southern Jordan. Journal of Archaeological Science 34, 83-110

Stable isotope ratios indicate diet and habitat use in New World monkeys.

This paper demonstrates the use of stable isotope ratios of carbon and nitrogen in animal tissue for indicating aspects of species behavioral strategy. We analyzed hair from individuals representing four species of New World monkeys (Alouatta palliata, the mantled howler; Ateles geoffroyi, the spider monkey; Cebus capucinus, the capuchin; and Brachyteles arachnoides, the woolly-spider monkey or muriqui) for delta 13C and delta 15N using previously developed methods. There are no significant differences in either carbon or nitrogen ratios between sexes, sampling year, or year of analysis. Seasonal differences in delta 13C reached a low level of significance but do not affect general patterns. Variation within species was similar to that recorded previously within single individuals. The omega 13C data show a bimodal distribution with significant difference between the means. The two monkey populations living in an evergreen forest were similar to each other and different from the other two monkey populations that inhabited dry, deciduous forests. This bimodal distribution is independent of any particular species' diet and reflects the level of leaf cover in the two types of forest. The delta 15N data display three significantly different modes. The omnivorous capuchins were most positive reflecting a trophic level offset. The spider monkeys and the muriquis were similar to one another and significantly more positive than the howlers. This distribution among totally herbivorous species correlates with the ingestion of legumes by the howler monkey population. In combination, these data indicate that museum-curated primate material can be analyzed to yield information on forest cover and diet in populations and species lacking behavioral data.

Gold mining in the Peruvian Amazon: global prices, deforestation, and mercury imports.

Many factors such as poverty, ineffective institutions and environmental regulations may prevent developing countries from managing how natural resources are extracted to meet a strong market demand. Extraction for some resources has reached such proportions that evidence is measurable from space. We present recent evidence of the global demand for a single commodity and the ecosystem destruction resulting from commodity extraction, recorded by satellites for one of the most biodiverse areas of the world. We find that since 2003, recent mining deforestation in Madre de Dios, Peru is increasing nonlinearly alongside a constant annual rate of increase in international gold price (∼18%/yr). We detect that the new pattern of mining deforestation (1915 ha/year, 2006-2009) is outpacing that of nearby settlement deforestation. We show that gold price is linked with exponential increases in Peruvian national mercury imports over time (R(2) = 0.93, p = 0.04, 2003-2009). Given the past rates of increase we predict that mercury imports may more than double for 2011 (∼500 t/year). Virtually all of Peru's mercury imports are used in artisanal gold mining. Much of the mining increase is unregulated/artisanal in nature, lacking environmental impact analysis or miner education. As a result, large quantities of mercury are being released into the atmosphere, sediments and waterways. Other developing countries endowed with gold deposits are likely experiencing similar environmental destruction in response to recent record high gold prices. The increasing availability of satellite imagery ought to evoke further studies linking economic variables with land use and cover changes on the ground.

The Great War on the small screen: Representing the First World War in contemporary Britain

In Britain since the 1960s television has been the most influential medium of popular culture. Television is also the site where the Western Front of popular culture clashes with the Western Front of history. This book examines the ways in which those involved in the production of historical documentaries for this most influential media have struggled to communicate the stories of the First World War to British audiences. Documents in the BBC Written Archives Centre at Caversham, Berkshire, the Imperial War Museum, and the Liddell Hart Centre for Military Archives all inform the analysis. Interviews and correspondence with television producers, scriptwriters and production crew, as well as two First World War veterans who appeared in several recent documentaries provide new insights for the reader. Emma Hanna takes the reader behind the scenes of the making of the most influential documentaries from the landmark epic series The Great War (BBC, 1964) up to more recent controversial productions such as The Trench (BBC, 2002) and Not Forgotten: The Men Who Wouldn't Fight (BBC, 2008). By examining the production, broadcast and reception of a number of British television documentaries this book examines the difficult relationship between the war's history and its popular memory.