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Mercury, Mining, and Empire
On the basis of an examination of the colonial mercury and
silver production processes and related labor systems, Mercury,
Mining, and Empire explores the effects of mercury pollution in
colonial Huancavelica, Peru, and Potosí, in present-day Bolivia.
The book presents a multifaceted and interwoven tale of what
colonial exploitation of indigenous peoples and resources left in
its wake. It is a socio-ecological history that explores the toxic
interrelationships between mercury and silver production, urban
environments, and the people who lived and worked in them. Nicholas
A. Robins tells the story of how native peoples in the region were
conscripted into the noxious ranks of foot soldiers of
proto-globalism, and how their fate, and that of their communities,
was-and still is-chained to it.
eBook
Combating Mountaintop Removal
Drawing on powerful personal testimonies of the hazards of mountaintop removal in southern West Virginia, Combating Mountaintop Removal critically examines the fierce conflicts over this violent and increasingly prevalent form of strip mining. Bryan T. McNeil documents the changing relationships among the coal industry, communities, environment, and economy from the perspective of local grassroots activist organizations and their broader networks. Focusing on Coal River Mountain Watch (CRMW), an organization composed of individuals who have personal ties to the coal industry in the region, the study reveals a turn away from once-strong traditional labor unions and the emergence of community-based activist organizations. By framing social and moral arguments in terms of the environment, these innovative hybrid movements take advantage of environmentalism's higher profile in contemporary politics. In investigating the local effects of globalization and global economics, McNeil tracks the profound reimagining of social and personal ideas such as identity, history, and landscape and considers their roles in organizing an agenda for progressive community activism.
eBook
Standing Our Ground
Standing Our Ground: Women, Environmental Justice, and the Fight to End Mountaintop Removal examines women's efforts to end mountaintop removal coal mining in West Virginia. Mountaintop removal coal mining, which involves demolishing the tops of hills and mountains to provide access to coal seams, is one of the most significant environmental threats in Appalachia, where it is most commonly practiced.
The Appalachian women featured in Barry's book have firsthand experience with the negative impacts of Big Coal in West Virginia. Through their work in organizations such as the Coal River Mountain Watch and the Ohio Valley Environmental Coalition, they fight to save their mountain communities by promoting the development of alternative energy resources. Barry's engaging and original work reveals how women's tireless organizing efforts have made mountaintop removal a global political and environmental issue and laid the groundwork for a robust environmental justice movement in central Appalachia.
eBook
Data mining models
Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to first describe the benefits of data mining in business, describe the process and typical business applications, describe the workings of basic data mining models, and demonstrate each with widely available free software. This second edition updates Chapter 1, and adds more details on Rattle data mining tools. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We will demonstrate use of R through Rattle. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use. We will demonstrate methods with a small but typical business dataset. We use a larger (but still small) realistic business dataset for Chapter 9.
Book
A New Algorithm for the Open-Pit Mine Production Scheduling Problem
For the purpose of production scheduling, open-pit mines are discretized into three-dimensional arrays known as block models. Production scheduling consists of deciding which blocks should be extracted, when they should be extracted, and what to do with the blocks once they are extracted. Blocks that are close to the surface should be extracted first, and capacity constraints limit the production in each time period. Since the 1960s, it has been known that this problem can be cast as an integer programming model. However, the large size of some real instances (3-10 million blocks, 15-20 time periods) has made these models impractical for use in real planning applications, thus leading to the use of numerous heuristic methods. In this article we study a well-known integer programming formulation of the problem that we refer to as C-PIT. We propose a new decomposition method for solving the linear programming relaxation (LP) of C-PIT when there is a single capacity constraint per time period. This algorithm is based on exploiting the structure of the precedence-constrained knapsack problem and runs in
O
(
mn log n
) in which
n
is the number of blocks and
m
a function of the precedence relationships in the mine. Our computations show that we can solve, in minutes, the LP relaxation of real-sized mine-planning applications with up to five million blocks and 20 time periods. Combining this with a quick rounding algorithm based on topological sorting, we obtain integer feasible solutions to the more general problem where multiple capacity constraints per time period are considered. Our implementation obtains solutions within 6% of optimality in seconds. A second heuristic step, based on local search, allows us to find solutions within 3% in one hour on all instances considered. For most instances, we obtain solutions within 1-2% of optimality if we let this heuristic run longer. Previous methods have been able to tackle only instances with up to 150,000 blocks and 15 time periods.
Journal Article