Environmental solutions of recovery of Amazonian forest mining operations

In order for the projects of recovery of degraded areas to be successful, it is necessary to have a perfect recovery of the soil where the revegetation will be implanted as an initial action in the recovery of the whole process. The use of native forest species fully adapted to these types of terrain is another aspect of great importance, once the non-selection of these species, even if abundant in the surrounding areas, as it is in our case, implies great mortality of individuals during the planting and their low fixation during the process. The establishment of a monitoring program that contemplates the advancements obtained in the soil, the vegetation and the return of wild animals also collaborate in the evaluation of the success of the process. And, finally, the effective participation of the mining company, accepting and applying the techniques tested and indicated by research, even if, initially, the return time is longer than expected, also guarantees the success of the process. The mining company not only implemented a partnership with important universities in Brazil to obtain solutions for the environmental problems but also applied the developed techniques and the monitoring program. In the present work, we have attempted to summarize important aspects to evaluate the advancements in the rehabilitation plan for those areas, being here presented some results of the monitoring of areas under different levels of recovery, in accordance with the techniques adopted. Biological parameters of the soil were used to verify the efficiency of these different techniques in the recovery process. This work is part of the monitoring program of areas in rehabilitation by the mining company, implemented as of 1999 and in partnership with universities. The microbial activity was determined through the quantification of the carbon and nitrogen microbial biomass (BMC and BMN) and the activity of the dehydrogenase evaluated in the mining floor and tailing areas in different levels of soil preparation and planting of native species. The analysis of the parameters studied revealed that the preparation of the soil, following the three years proposed by the methodology, was important for the success in establishing the rehabilitation process. Some of the areas analyzed already show some parameters with values close or superior to those found in the capoeira (secondary forest), the latter being the non-treated area. © 2010 WIT Press.

Mechanical tillage of degraded mining areas at Jamari national forest (Amazonian forest, Rondônia - BR)

According to the environmental legislation enforced in Brazil and the process of marketing globalization, the commitment of the nations to the preservation of the environment is intensified. By reason of nature's negative responses to its intensive use, awareness then appears from enterprises and agencies about how the anthropic action over the environment needs to be minimized, becoming a challenge: development and sustainability. In this context, the present work made use of the Mechanical tillage of the soil, as a technique to apply, in a large scale, the strategies and methods to recover mined areas that were researched and developed experimentally by researchers on a theme project about the recovering of degraded areas. This work was conducted in the Amazon ecosystem, inside the Jamari National Forest - Rondônia (FLONA do Jamari), in deactivated cassiterite mines. The objectives of this work were to: Develop a computational program capable of managing a database and assist in the selection of machines and preparation methods to execute the operations of topographical reconstitution and tillage of surfaces in areas degraded by the mineral exploitation of cassiterite. Use the program that was developed in the planning of costs and operational development, for the operations required in the strategies for recovering the areas. Analyze the vegetable productivity in the mobilized areas and the quality of the superficial mobilization, making use of indicators and tillage methods. Evaluate, through biological indicators, the efficiency of the recovery strategies and techniques that were mechanized and applied on the location. The results showed that the developed computational program (SGMAD) served the methodological purposes (the analysis of costs and operational capacity) established for the planning and the selection of the tillage machines and methods in the areas of mineral exploitation of cassiterite. The applied methods and quality of the superficial mobilization were significant to the development of leguminous plants in the areas. The use of biological indicators (microbial biomass and enzymatic activity) in the evaluation of the adopted techniques and strategies revealed that the planting of leguminous plants and their posterior incorporation have been promoting gradually positive alterations in some of the analyzed soil/substract parameters. © 2010 WIT Press.

Small-scale mining: a new entrepreneurial approach

Includes bibliography

Multi-relational algorithm for mining association rules in large databases

Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.

MR-Radix: a multi-relational data mining algorithm

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Preimplantation development regulatory pathway construction through a text-mining approach

Background: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. Results: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. Conclusions: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

TA-RE: An Exchange Language for Mining Software Repositories

Software repositories have been getting a lot of attention from researchers in recent years. In order to analyze software repositories, it is necessary to first extract raw data from the version control and problem tracking systems. This poses two challenges: (1) extraction requires a non-trivial effort, and (2) the results depend on the heuristics used during extraction. These challenges burden researchers that are new to the community and make it difficult to benchmark software repository mining since it is almost impossible to reproduce experiments done by another team. In this paper we present the TA-RE corpus. TA-RE collects extracted data from software repositories in order to build a collection of projects that will simplify extraction process. Additionally the collection can be used for benchmarking. As the first step we propose an exchange language capable of making sharing and reusing data as simple as possible.

Public participation in Michigan mining policy : the Kennecott Eagle Project case

Public participation is an important component of Michigan’s Part 632 Nonferrous Mining law and is identified by researchers as important to decision-making processes. The Kennecott Eagle Project, which is located near Marquette, Michigan, is the first mine permitted under Michigan’s new mining regulation, and this research examines how public participation is structured in regulations, how the permitting process occurred during the permitting of the Eagle Project, and how participants in the permitting process perceived their participation. To understand these issues, this research implemented a review of existing mining policy and public participation policy literature, examination of documents related to the Kennecott Eagle Project and completion of semi-structured, ethnographic interviews with participants in the decision-making process. Interviewees identified issues with the structure of participation, the technical nature of the permitting process, concerns about the Michigan Department of Environmental Quality’s (DEQ) handling of mine permitting, and trust among participants. This research found that the permitting of the Kennecott Eagle Mine progressed as structured by regulation and collected technical input on the mine permit application, but did not meet the expectations of some participants who opposed the project. Findings from this research indicated that current mining regulation in Michigan is resilient to public opposition, there is need for more transparency from the Michigan DEQ during the permitting process, and current participatory structures limit the opportunities for some stakeholder groups to influence decision-making.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

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

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