Mining in Peru: Indigenous and Peasant Communities vs. The State and Mining Capital

The Peruvian economy depends for its growth on the export of natural resources and investment in the mining and hydrocarbon sectors. Peruvian governments and mining corporations have confronted anti-mining protests in different ways. While the current government has introduced policies of social inclusion to soften the negative effects of the operations of mining capital and policies of dialogue to engage social actors with the essence of governmental policies, mining companies use corporate social responsibility programs as a cover for the devastating effects of their operations on the environment and the livelihoods and habitats of the indigenous and peasant communities. Curiously, in the current context of the declining commodity prices and export volumes the Peruvian government strengthens its extractivist model of development. This article argues that whatever government that follows the rules of capital cannot but favor the corporations. It points out the main adversaries of the indigenous and peasant communities and the problems to transform the locally and/or regionally struggle into a nationwide battle for another development model.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

(Table 3, page 8) Chemical composition and physical cahracteristics of manganese nodules from the Pacific Ocean

This paper reviews the state of the art in processing and extraction of ocean floor manganese nodules. It briefly reviews the mining sites where the abundant rich nodules occur and also discusses the metal distribution in nodules in view of economical processing and extraction of these metal values. The paper discloses in a detailed manner the physical and chemical characteristics of nodules, including porosity, surface area, water content and the effect of temperature on crystal structure of major constituents of nodules. In the extraction aspect of nodules, the paper reviews two different extraction schemes revealed in the literature, namely hydrometallurgical treatment and pyrometallurgical treatment. The hydrometallurgical treatments include acid leaching, ammonia leaching, leaching with reducing agents and leaching after high temperature pre-treatments such as in sulfating rousting, while the pyrometallurgical processes include smelting, chlorination-vaporization and segregation. The paper also covers metal recovery processes from leach liquor. An economic survey of processing nodules has been made in terms of problems associated with metal-marketing, and impact of metal production from nodules on mineral industries.

Extraction and purification of immunoglobulin G with aqueous biphasic systems

The immune system is able to produce antibodies, which have the capacity to recognize and to bind to foreign molecules or pathogenic organisms. Currently, there are a diversity of diseases that can be treated with antibodies, like immunoglobulins G (IgG). Thereby, the development of cost-efficient processes for their extraction and purification is an area of main interest in biotechnology. Aqueous biphasic systems (ABS) have been investigated for this purpose, once they allow the reduction of costs and the number of steps involved in the process, when compared with conventional methods. Nevertheless, typical ABS have not showed to be selective, resulting in low purification factors and yields. In this context, the addition of ionic liquids (ILs) as adjuvants can be a viable and potential alternative to tailor the selectivity of these systems. In this work, ABS composed of polyethylene glycol (PEG) of different molecular weight, and a biodegradable salt (potassium citrate) using ILs as adjuvants (5 wt%), were studied for the extraction and purification of IgG from a rabbit source. Initially, it was tested the extraction time, the effect on the molecular weight of PEG in a buffer solution of K3C6H5O7/C6H8O7 at pH≈7, and the effect of pH (59) on the yield (YIgG) and extraction efficiency (EEIgG%) of IgG. The best results regarding EEIgG% were achieved with a centrifugation step at 1000 rpm, during 10 min, in order to promote the separation of phases followed by 120 min of equilibrium. This procedure was then applied to the remaining experiments. The results obtained in the study of PEGs with different molecular weights, revealed a high affinity of IgG for the PEG-rich phase, and particularly for PEGs of lower molecular weight (EEIgG% of 96 % with PEG 400). On the other hand, the variation of pH in the buffer solution did not show a significant effect on the EEIgG%. Finally, it was evaluated the influence of the addition of different ILs (5% wt) on the IgG extraction in ABS composed of PEG 400 at pH≈7. In these studies, it was possible to obtain EEIgG% of 100% with the ILs composed of the anions [TOS]-, [CH3CO2]-and Cl-, although the obtained YIgG% were lower than 40%. On the other hand, the ILs composed of the anions Br-, as well as of the cation [C10mim]+, although not leading to EEIgG% of 100%, provide an increase in the YIgG%. ABS composed of PEG, a biodegradable organic salt and ILs as adjuvants, revealed to be an alternative and promising method to purify IgG. However, additional studies are still required in order to reduce the loss of IgG.

Data mining for lifetime prediction of metallic components

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model

Mining in Northern British Columbia

The history of the settlement of the province is tied to patterns of exploration and min development. In Northern British Columbia the Cariboo goldfields provided the impetus for settlement of the region and the beginning for mining to extend into the watern and northern regions in a series of minor gold rushes. The northern half of the province has a geological diverse mineral base that supports a wide variety of mining, and a gradual improvement of exploration and mining methods due to scientific knowledge and technology provided opportunities for lode gold and base metal mines to be developed. The success of mining is based on world ore prices and competitive markets that impact the economic viability of developing a mine. Mining faces increasing pressures in the northern half of the province due to other resource values, such as tourism or protected areas, that claim and compete for a similar land base.

The use of narrative text for injury surveillance research : a systematic review

Objective: To summarise the extent to which narrative text fields in administrative health data are used to gather information about the event resulting in presentation to a health care provider for treatment of an injury, and to highlight best practise approaches to conducting narrative text interrogation for injury surveillance purposes.----- Design: Systematic review----- Data sources: Electronic databases searched included CINAHL, Google Scholar, Medline, Proquest, PubMed and PubMed Central.. Snowballing strategies were employed by searching the bibliographies of retrieved references to identify relevant associated articles.----- Selection criteria: Papers were selected if the study used a health-related database and if the study objectives were to a) use text field to identify injury cases or use text fields to extract additional information on injury circumstances not available from coded data or b) use text fields to assess accuracy of coded data fields for injury-related cases or c) describe methods/approaches for extracting injury information from text fields.----- Methods: The papers identified through the search were independently screened by two authors for inclusion, resulting in 41 papers selected for review. Due to heterogeneity between studies metaanalysis was not performed.----- Results: The majority of papers reviewed focused on describing injury epidemiology trends using coded data and text fields to supplement coded data (28 papers), with these studies demonstrating the value of text data for providing more specific information beyond what had been coded to enable case selection or provide circumstantial information. Caveats were expressed in terms of the consistency and completeness of recording of text information resulting in underestimates when using these data. Four coding validation papers were reviewed with these studies showing the utility of text data for validating and checking the accuracy of coded data. Seven studies (9 papers) described methods for interrogating injury text fields for systematic extraction of information, with a combination of manual and semi-automated methods used to refine and develop algorithms for extraction and classification of coded data from text. Quality assurance approaches to assessing the robustness of the methods for extracting text data was only discussed in 8 of the epidemiology papers, and 1 of the coding validation papers. All of the text interrogation methodology papers described systematic approaches to ensuring the quality of the approach.----- Conclusions: Manual review and coding approaches, text search methods, and statistical tools have been utilised to extract data from narrative text and translate it into useable, detailed injury event information. These techniques can and have been applied to administrative datasets to identify specific injury types and add value to previously coded injury datasets. Only a few studies thoroughly described the methods which were used for text mining and less than half of the studies which were reviewed used/described quality assurance methods for ensuring the robustness of the approach. New techniques utilising semi-automated computerised approaches and Bayesian/clustering statistical methods offer the potential to further develop and standardise the analysis of narrative text for injury surveillance.

Field and Service Robotics Results of the 5th International Conference

The 5th International Conference on Field and Service Robotics (FSR05) was held in Port Douglas, Australia, on 29th - 31st July 2005, and brought together the worlds' leading experts in field and service automation. The goal of the conference was to report and encourage the latest research and practical results towards the use of field and service robotics in the community with particular focus on proven technology. The conference provided a forum for researchers, professionals and robot manufacturers to exchange up-to-date technical knowledge and experience. Field robots are robots which operate in outdoor, complex, and dynamic environments. Service robots are those that work closely with humans, with particular applications involving indoor and structured environments. There are a wide range of topics presented in this issue on field and service robots including: Agricultural and Forestry Robotics, Mining and Exploration Robots, Robots for Construction, Security & Defence Robots, Cleaning Robots, Autonomous Underwater Vehicles and Autonomous Flying Robots. This meeting was the fifth in the series and brings FSR back to Australia where it was first held. FSR has been held every 2 years, starting with Canberra 1997, followed by Pittsburgh 1999, Helsinki 2001 and Lake Yamanaka 2003.

"Criatividade na Educação e Formação de Talentos" (Creativity in Education and Development of Talent)

Historically, 'Creativity' has had a complex set of meanings. Not long ago, 'Creativity' had a kind of marginal or peripheral status, being seen as the province of a gifted few; in many cases it was associated almost exclusively with the arts and with artists. But these traditional attitudes to creativity are changing. Mainstream businesses are employing people with creative skills as diverse as writing, directing, graphic design and event management. So what we’re beginning to see is an innovation framework and creative content adding value not just to SMEs, but to traditional industries such as manufacturing and mining, and to wider service industries. And this is why Education is such an important element, particularly with a focus on innovation, and on creative people and the contributions they make across different parts of the economy.

Social Impact of Mining Survey : Aggregate Results Queensland Communities

This is the final report from a study into the social impact of mining in Queensland.

Improving web database search incorporating users query information

The growing importance and need of data processing for information extraction is vital for Web databases. Due to the sheer size and volume of databases, retrieval of relevant information as needed by users has become a cumbersome process. Information seekers are faced by information overloading - too many result sets are returned for their queries. Moreover, too few or no results are returned if a specific query is asked. This paper proposes a ranking algorithm that gives higher preference to a user’s current search and also utilizes profile information in order to obtain the relevant results for a user’s query.

Process mining manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.