Successive nonparametric estimation of conditional distributions

Spatial characterization of non-Gaussian attributes in earth sciences and engineering commonly requires the estimation of their conditional distribution. The indicator and probability kriging approaches of current nonparametric geostatistics provide approximations for estimating conditional distributions. They do not, however, provide results similar to those in the cumbersome implementation of simultaneous cokriging of indicators. This paper presents a new formulation termed successive cokriging of indicators that avoids the classic simultaneous solution and related computational problems, while obtaining equivalent results to the impractical simultaneous solution of cokriging of indicators. A successive minimization of the estimation variance of probability estimates is performed, as additional data are successively included into the estimation process. In addition, the approach leads to an efficient nonparametric simulation algorithm for non-Gaussian random functions based on residual probabilities.

Computational properties of min/max autocorrelation factors

Minimum/maximum autocorrelation factor (MAF) is a suitable algorithm for orthogonalization of a vector random field. Orthogonalization avoids the use of multivariate geostatistics during joint stochastic modeling of geological attributes. This manuscript demonstrates in a practical way that computation of MAF is the same as discriminant analysis of the nested structures. Mathematica software is used to illustrate MAF calculations from a linear model of coregionalization (LMC) model. The limitation of two nested structures in the LMC for MAF is also discussed and linked to the effects of anisotropy and support. The analysis elucidates the matrix properties behind the approach and clarifies relationships that may be useful for model-based approaches. (C) 2003 Elsevier Science Ltd. All rights reserved.

Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Use of a probabilistic neural network to reduce costs of selecting construction rock

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Introduction to Special issue on the Symposium on the Application of Neural Networks to the Earth Sciences

Papers in this issue of Natural Resources Research are from the “Symposium on the Application of Neural Networks to the Earth Sciences,” held 20–21 August 2002 at NASA Moffet Field, Mountain View, California. The Symposium represents the Seventh International Symposium on Mineral Exploration (ISME-02). It was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the US Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations always have been wide-ranging—talks presented at this symposium are no exception.

Engineering across cultures : new learning resources for intercultural competency in engineering

BACKGROUND The work described in this paper has emerged from an ALTC/OLT funded project, Exploring Intercultural Competency in Engineering. The project indentified many facets of culture and intercultural competence that go beyond a culture-as-nationality paradigm. It was clear from this work that resources were needed to help engineering educators introduce students to the complex issues of culture as they relate to engineering practice. A set of learning modules focussing on intercultural competence in engineering practice were developed early on in the project. Through the OLT project, these modules have been expanded into a range of resources covering various aspects of culture in engineering. Supporting the resources, an eBook detailing the ins and outs of intercultural competency has also been developed to assist engineering educators to embed opportunities for students to develop skills in unpacking and managing cross-cultural challenges in engineering practice. PURPOSE This paper describes the key principles behind the development of the learning modules, the areas they cover and the eBook developed to support the modules. The paper is intended as an introduction to the approaches and resources and extends an invitation to the community to draw from, and contribute to this initial work. DESIGN/METHOD A key aim of this project was to go beyond the culture-as-nationality approach adopted in much of the work around intercultural competency (Deardorff, 2011). The eBook explores different dimensions of culture such as workplace culture, culture’s influence on engineering design, and culture in the classroom. The authors describe how these connect to industry practice and explore what they mean for engineering education. The packaged learning modules described here have been developed as a matrix of approaches moving from familiar known methods through complicated activities relying to some extent on expert knowledge. Some modules draw on the concept of ‘complex un-order’ as described in the ‘Cynefin domains’ proposed by Kurtz and Snowden (2003). RESULTS Several of the modules included in the eBook have already been trialled at a variety of institutions. Feedback from staff has been reassuringly positive so far. Further trials are planned for second semester 2012, and version 1 of the eBook and learning modules, Engineering Across Cultures, is due to be released in late October 2012. CONCLUSIONS The Engineering Across Cultures eBook and learning modules provide a useful and ready to employ resource to help educators tackle the complex issue of intercultural competency in engineering education. The book is by no means exhaustive, and nor are the modules, they instead provide an accessible, engineering specific guide to bringing cultural issues into the engineering classroom.

Strategies to engage engineering students in group project work

Project focused group work is significant in developing social and personal skills as well as extending the ability to identify, formulate and solve engineering problems. As a result of increasing undergraduate class sizes, along with the requirement for many students to work part-time, group projects, peer and collaborative learning are seen as a fundamental part of engineering education. Group formation, connection to learning objectives and fairness of assessment has been widely reported as major issues that leave students dissatisfied with group project based units. Several strategies were trialled including a study of formation of groups by different methods across two engineering disciplines over the past 2 years. Other strategies involved a more structured approach to assessment practices of civil and electrical engineering disciplines design units. A confidential online teamwork management tool was used to collect and collate student self and peer assessment ratings and used for both formative feedback as well as assessment purposes. Student satisfaction and overall academic results in these subjects have improved since the introduction of these interventions. Both student and staff feedback highlight this approach as enhancing student engagement and satisfaction, improved student understanding of group roles, reducing number of dysfunctional groups whilst requiring less commitment of academic resources.

Creating efficiencies in teaching : where are the right resources when you need them?

Early career engineering academics are encouraged to join and contribute to established research groups at the leading edge of their discipline. This is often facilitated by various staff development and support programs. Given that academics are often appointed primarily on the basis of their research skills and outputs, such an approach is justified and is likely to result in advancing the individual academic’s career. It also enhances their capacity to attract competitive research funding, while contributing to the overall research performance of their institution, with further potential for an increased share of government funding. In contrast, there is much less clarity of direction or availability of support mechanisms for those academics in their role as teachers. Following a general induction to teaching and learning at their institution, they would commonly think about preparing some lecture materials, whether for delivery in a face-to-face or on-line modality. Typically they would look for new references and textbooks to act as a guide for preparing the content. They would probably find out how the course has been taught before, and what laboratory facilities and experiments have been used. In all of these and other related tasks, the majority of newly appointed academics are guided strongly by their own experiences as students, rather than any firm knowledge of pedagogical principles. At a time of increased demands on academics’ time, and high expectations of performance and productivity in both research and teaching, it is essential to examine possible actions to support academics in enhancing their teaching performance in effective and efficient ways. Many resources have been produced over the years in engineering schools around the world, with very high intellectual and monetary costs. In Australia, the last few years have seen a surge in the number of ALTC/OLT projects and fellowships addressing a range of engineering education issues and providing many resources. There are concerns however regarding the extent to which these resources are being effectively utilised. Why are academics still re-inventing the wheel and creating their own version of teaching resources and pedagogical practice? Why do they spend so much of their precious time in such an inefficient way? A symposium examining the above issues was conducted at the AAEE2012 conference, and some pointers to possible responses to the above questions were obtained. These are explored in this paper and supplemented by the responses to a survey of a group of engineering education leaders on some of the aspects of these research questions. The outcomes of the workshop and survey results have been analysed in view of the literature and the ALTC/OLT sponsored learning and teaching projects and resources. Other factors are discussed, including how such resources can be found, how their quality might be evaluated, and how assessment may be appropriately incorporated, again using readily available resources. This study found a strong resonance between resources reuse with work on technology acceptance (Davis, 1989), suggesting that technology adoption models could be used to encourage resource sharing. Efficient use of outstanding learning materials is an enabling approach. The paper provides some insights on the factors affecting the re-use of available resources, and makes some recommendations and suggestions on how the issue of resources re-use might be incorporated in the process of applying and completing engineering education projects.

Exploring sustainability themes in engineering accreditation and curricula

Purpose This paper aims to present key findings from an inquiry into engineering accreditation and curricula renewal. The research attempted to ascertain conceptions of requisite sustainability themes among engineering academics and professionals. The paper also reflects on the potential role of professional engineering institutions (PEIs) in embedding sustainability through their programme accreditation guidelines and wider implications in terms of rapid curricula renewal. Design/methodology/approach This research comprised an International Engineering Academic Workshop held during the 2010 International Symposium on Engineering Education in Ireland, on “accreditation and sustainable engineering”. This built on the findings of a literature review that was distributed prior to the workshop. Data collection included individual questionnaires administered during the workshop, and notes scribed by workshop participants. Findings The literature review highlighted a wide range of perspectives across and within engineering disciplines, regarding what sustainability/sustainable development (SD) themes should be incorporated into engineering curricula, and regarding language and terminology. This was also reflected in the workshop discussions. Notwithstanding this diversity, clusters of sustainability themes and priority considerations were distilled from the literature review and workshop. These related to resources, technology, values, ethics, inter- and intra-generational equity, transdisciplinarity, and systems and complex thinking. Themes related to environmental and economic knowledge and skills received less attention by workshop participants than represented in the literature. Originality/value This paper provides an appreciation of the diversity of opinion regarding priority sustainability themes for engineering curricula, among a group of self-selected engineering academics who have a common interest in education for SD. It also provides some insights and caveats on how these themes might be rapidly integrated into engineering curricula.

Dilemmas in Framing Research Studies in Engineering Education

There has been considerable debate about the need for more empirical, evidence based studies of the impact of various interventions and practices in engineering education. A number of resources including workshops to guide engineering faculty in the conduct of such studies have emerged over recent years. This paper presents a critique of the evolution of engineering education research and its underlying assumptions in the context of the systemic reform currently underway in engineering education. This critique leads to an analysis of the ways in which our current understanding of engineering, engineering education and research in engineering education is shaped by the traditions and cultural characteristics of the profession and grounded, albeit implicitly, in a particular suite of epistemological assumptions. It is argued that the whole enterprise of engineering education needs to be radically reconceptualized. A pluralistic approach to framing scholarship in engineering education is then proposed based on the principles of demonstrable practicality, critical interdisciplinarity and holistic reflexivity. This new framework has implications for engaging and developing faculty in the context of new teaching and learning paradigms, for the evaluation of the scholarship of teaching and for the research-teaching nexus.