Effect of leaf-cutting ant nests on plant growth in an oligotrophic Amazon rain forest

This study examined whether high nutrient concentrations associated with leaf-cutting ant nests influence plant growth and plant water relations in Amazon rain forests. Three nests of Atta cephalotes were selected along with 31 Amaioua guianensis and Protium sp. trees that were grouped into trees near and distant (>10 m) from nests. A 15N leaf-labelling experiment confirmed that trees located near nests accessed nutrients from nests. Trees near nests exhibited higher relative growth rates (based on stem diameter increases) on average compared with trees further away; however this was significant for A. guianensis (near nest 0.224 y−1 and far from nest 0.036 y−1) but not so for Protium sp. (0.146 y−1 and 0.114 y−1 respectively). Water relations were similarly species-specific; for A. guianensis, near-nest individuals showed significantly higher sap flow rates (16 vs. 5 cm h−1), higher predawn/midday water potentials (−0.66 vs. −0.98 MPa) and lower foliar δ13C than trees further away indicating greater water uptake in proximity to the nests while the Protium sp. showed no significant difference except for carbon isotopes. This study thus shows that plant response to high nutrient concentrations in an oligotrophic ecosystem varies with species. Lower seedling abundance and species richness on nests as compared with further away suggests that while adult plants access subterranean nutrient pools, the nest surfaces themselves do not encourage plant establishment and growth.

Tool cutting force modeling and wear estimation of micro -end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. ^ The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc. ^

Cutting through paradise: A political history of the Cross -Florida Barge Canal

The Cross-Florida Barge Canal, synonymous with boondoggle and waste, became the cause celebre of environmental activism in Florida of the late 1960s. Dramatic changes in Florida's and the nation's politics doomed the CFBC to failure. My purpose is to place in national context the important developments and personalities of Florida's most important environmental controversy. ^ The methodology involved a series of interviews with the most important actors in the canal drama and the environmental movement. Also utilized were regional collections in Florida Public Libraries, the Florida State Archives, personal papers housed at the University of Florida and Corps of Engineers documents. Results showed a clear connection between Florida activism and national environmental policy through the influence of key individuals. I concluded that the CFBC acted as a catalyst to Florida's environmental movement, serving as an indicator of a larger political change from the New Deal coalition to the Republican realignment of 1968. ^

Tool cutting force modeling and wear estimation of micro-end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc.