Glacial stratigraphy and micromorphology of the Pine Point Pb-­‐Zn mining district, Northwest Territories

This investigation aims to gain a better understanding of the glacial history of the Pine Point Mining district, Northwest Territories, by examining the sedimentological properties of the glacial sediments including, geochemical analysis, heavy mineral concentrate analysis, clast macro-­‐fabrics, pebble lithologies, and micromorphological investigation. Four till units were identified, and three were associated with identified erosional bedrock features and streamlined landforms in the area, indicating a minimum of three ice flow directions. Sedimentological properties suggest that these units were all Type-­B tectomict/mélange till, emplaced as part of a soft subglacial deformable bed. The lack of ice-­‐marginal advance and retreat sequences within the studied till, suggests the Middle Wisconsinan Laurentide Ice margin was likely north and west of the Pine Point area, as opposed to along the margin of the Canadian Shield and Western Sedimentary Basin where it has been suggested to have existed.

Union Pacific Railway from Kansas City , Omaha, St. Joseph to Denver, San Francisco, Portland, Helena, Butte, Boise, Leadville, Durango, Deadwood and all cities and mining camps in the west schedule

Union Pacific Railway from Kansas City , Omaha, St. Joseph to Denver, San Francisco, Portland, Helena, Butte, Boise, Leadville, Durango, Deadwood and all cities and mining camps in the west schedule, Jan. 15, 1882.

Travels in México, during the years 1843 and 44 ; including a description of California, the principal cities and mining districts of that republic the Oregón territory, etc.

UANL

McNeill's code : Arranged to meet the requirements of mi ning, metallurgical and civil engineers ; directors of mining, smetling and other companies...

UANL

Constituting "Community" at the onset of the Pascua Lama Mining Project

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

The Gandarela Mountain Range and the “resistance space” against iron ore mining threat: a focus on the contrapositions

Full Text / Article complet

Environmental impact of sand mining

This thesis Entitled Environmental impact of Sand Mining :A case Study in the river catchments of vembanad lake southwest india.The entire study is addressed in nine chapters. Chapter l deals with the general introduction about rivers, problems of river sand mining, objectives, location of the study area and scope of the study. A detailed review on river classification, classic concepts in riverine studies, geological work of rivers and channel processes, importance of river ecosystems and its need for management are dealt in Chapter 2. Chapter 3 gives a comprehensive account of the study area - its location, administrative divisions, physiography, soil, geology, land use and living and non-living resources. The various methods adopted in the study are dealt in Chapter 4. Chapter 5 contains river characteristics like drainage, environmental and geologic setting, channel characteristics, river discharge and water quality of the study area. Chapter 6 gives an account of river sand mining (instream and floodplain mining) from the study area. The various environmental problems of river sand mining on the land adjoining the river banks, river channel, water, biotic and social / human environments of the area and data interpretation are presented in Chapter 7. Chapter 8 deals with the Environmental Impact Assessment (EIA) and Environmental Management Plan (EMP) of sand mining from the river catchments of Vembanad lake.

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

Fast computation of concept lattices using data mining techniques

We present a new algorithm called TITANIC for computing concept lattices. It is based on data mining techniques for computing frequent itemsets. The algorithm is experimentally evaluated and compared with B. Ganter's Next-Closure algorithm.

Mining minimal non-redundant association rules using frequent closed itemsets

The problem of the relevance and the usefulness of extracted association rules is of primary importance because, in the majority of cases, real-life databases lead to several thousands association rules with high confidence and among which are many redundancies. Using the closure of the Galois connection, we define two new bases for association rules which union is a generating set for all valid association rules with support and confidence. These bases are characterized using frequent closed itemsets and their generators; they consist of the non-redundant exact and approximate association rules having minimal antecedents and maximal consequences, i.e. the most relevant association rules. Algorithms for extracting these bases are presented and results of experiments carried out on real-life databases show that the proposed bases are useful, and that their generation is not time consuming.

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.