Romanian glocalization : Case study on the Rosia Montana Gold Corporation Mining Project

At the end of the communist era, which was characterised as a closed social experiment, Romania found itself in the middle of a globalization process. Its industrial capacities have been considerably reduced through a poor and spendthrift management. There was a mass exodus of the labour force abroad and the educational background for the remaining part was no longer in agreement with the labour market. On these grounds, the vectors of globalization, in the form of foreign investments, entered Romania effortlessly. There even were local communities where the arrival of foreign investors was expected like a second coming of Christ. This is the context in which a Canadian company set forth the mining project Rosia Montana Gold Corporation. The implementation of the project should have started in 2005. Nevertheless, the project has not been effectively launched yet. This situation is based on what we call Romanian glocalization, namely a specific confrontation between global and local on Romanian land

Romanian glocalization : Case study on the Rosia Montana Gold Corporation Mining Project

At the end of the communist era, which was characterised as a closed social experiment, Romania found itself in the middle of a globalization process. Its industrial capacities have been considerably reduced through a poor and spendthrift management. There was a mass exodus of the labour force abroad and the educational background for the remaining part was no longer in agreement with the labour market. On these grounds, the vectors of globalization, in the form of foreign investments, entered Romania effortlessly. There even were local communities where the arrival of foreign investors was expected like a second coming of Christ. This is the context in which a Canadian company set forth the mining project Rosia Montana Gold Corporation. The implementation of the project should have started in 2005. Nevertheless, the project has not been effectively launched yet. This situation is based on what we call Romanian glocalization, namely a specific confrontation between global and local on Romanian land

Patrimonio Histórico Minero-Metalúrgico, Patrimonio Cultural/ Mining-Metallurgical Historical Heritage, Cultural Heritage

El panel se divide en tres secciones : Minería histórica , Patrimonio Minero y Museos.

A Methodological model to assist the optimization and risk management of mining investment decisions

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modelling of project “Net Present Value” (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project lifecycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.

On loader productivity, rock strength and explosive energy in metal mining

Twenty production blasts in two open pit mines were monitored, in rocks with medium to very high strength. Three different blasting agents (ANFO, watergel and emulsion blend) were used, with powder factors ranging between 0.88 and 1.45 kg/m3. Excavators were front loaders and rope shovels. Mechanical properties of the rock, blasting characteristics and mucking rates were carefully measured. A model for the calculation of the productivity of excavators is developed thereof, in which the production rate results as a product of an ideal, maximum, productivity rate times an operating efficiency. The maximum rate is a function of the dipper capacity and the efficiency is a function of rock density, strength, and explosive energy concentration in the rock. The model is statistically significant and explains up to 92 % of the variance of the production rate measurements.

Adapting autonomously classification data mining algorithms for ubiquitous devices

Abstract Due to recent scientific and technological advances in information sys¬tems, it is now possible to perform almost every application on a mobile device. The need to make sense of such devices more intelligent opens an opportunity to design data mining algorithm that are able to autonomous execute in local devices to provide the device with knowledge. The problem behind autonomous mining deals with the proper configuration of the algorithm to produce the most appropriate results. Contextual information together with resource information of the device have a strong impact on both the feasibility of a particu¬lar execution and on the production of the proper patterns. On the other hand, performance of the algorithm expressed in terms of efficacy and efficiency highly depends on the features of the dataset to be analyzed together with values of the parameters of a particular implementation of an algorithm. However, few existing approaches deal with autonomous configuration of data mining algorithms and in any case they do not deal with contextual or resources information. Both issues are of particular significance, in particular for social net¬works application. In fact, the widespread use of social networks and consequently the amount of information shared have made the need of modeling context in social application a priority. Also the resource consumption has a crucial role in such platforms as the users are using social networks mainly on their mobile devices. This PhD thesis addresses the aforementioned open issues, focusing on i) Analyzing the behavior of algorithms, ii) mapping contextual and resources information to find the most appropriate configuration iii) applying the model for the case of a social recommender. Four main contributions are presented: - The EE-Model: is able to predict the behavior of a data mining algorithm in terms of resource consumed and accuracy of the mining model it will obtain. - The SC-Mapper: maps a situation defined by the context and resource state to a data mining configuration. - SOMAR: is a social activity (event and informal ongoings) recommender for mobile devices. - D-SOMAR: is an evolution of SOMAR which incorporates the configurator in order to provide updated recommendations. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation.

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Equity pricing in the mining sector: evidence from NYSE and LSE

The study examines the Capital Asset Pricing Model (CAPM) for the mining sector using weekly stock returns from 27 companies traded on the New York Stock Exchange (NYSE) or on the London Stock Exchange (LSE) for the period of December 2008 to December 2010. The results support the use of the CAPM for the allocation of risk to companies. Most companies involved in precious metals (particularly gold), which have a beta value less than unity (Table 1), have been actuated as shelter values during the financial crisis. Values of R2 do not shown very explanatory power of fitted models (R2 < 70 %). Estimated coefficients beta are not sufficient to determine the expected returns on securities but the results of the tests conducted on sample data for the period analysed do not appear to clearly reject the CAPM

Self-configuring data mining for ubiquitous computing

Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm?s execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Educational innovation applied in the UNESCO chair of mining and industrial heritage

The “Innovatio Educativa Tertio Millennio” group has been 10 years developing educational innovation techniques, actually has reached the level of teaching on the technical teachers has developed, and share them with other groups, that can implement them in their teaching activities. UNESCO Chair of Mining and Industrial Heritage has been years working on heritage, and on the one hand teaching in conservation and maintenance of heritage, and on the other doing raise awareness of the meaning of heritage, the social value and as must be managed effectively. Recently these two groups work together, thus is spreading in a much more effective manner the concepts of heritage, its meaning, its value, and how to manage it and provide effective protection. On one hand being a work of dissemination based on internet and on radio broadcasting, and on the other one of teaching based on educational innovation, and courses, conferences, and face-to-face seminars or distance platforms.

"Innovatio educativa tertio millennio": 12 years evoluting from learning from learning to teaching in courses on educational innovation and collaboration with the UNESCO chair of mining and industrial heritage

Twelve years ago a group of teachers began to work in educational innovation. In 2002 we received an award for educational innovation, undergoing several stages. Recently, we have decided to focus on being teachers of educational innovation. We create a web scheduled in Joomla offering various services, among which we emphasize teaching courses of educational innovation. The “Instituto de Ciencias de la Educacion” in “Universidad Politécnica de Madrid” has recently incorporated two of these courses, which has been highly praised. These courses will be reissued in new calls, and we are going to offer them to more Universities. We are in contact with several institutions, radio programs, the UNESCO Chair of Mining and Industrial Heritage, and we are working with them in the creation of heritage courses using methods that we have developed.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.