10,000 stand in line for 46 jobs, circa 1977

Newspaper article about job seekers interested in the Anaconda American Brass job openings, Buffalo, NY

Copper Commando Index – vol. 3

Index for volume 3 (Aug. 1942-Aug. 1945) lists personal names, places, subjects; page and issue numbers.https://digitalcommons.mtech.edu/copper_commando_v3_4/1000/thumbnail.jp

Copper Commando Index – vol. 1

Index for volume 1 (Aug. 1942-Aug. 1945) lists personal names, places, subjects; page and issue numbers.https://digitalcommons.mtech.edu/copper_commando_v1/1001/thumbnail.jp

Copper Commando Index – vol. 2

Index for volume 2 (Aug. 1942-Aug. 1945) lists personal names, places, subjects; page and issue numbers.https://digitalcommons.mtech.edu/copper_commando_v2/1000/thumbnail.jp

The Amplifier - v. 8,(a-2) no. 2

In this issue...Anaconda Scholarships, homecoming, David Rovig, Berkeley Pit, National Reactor Testing Station, Anaconda Company, Wesley Club, Star Lanes Bowlinghttps://digitalcommons.mtech.edu/amplifier/1108/thumbnail.jp

Development and evaluation of braze alloys for vacuum furnace brazing

Copper and copper-base alloys tested for use in vacuum furnace brazing of rocket engine tubular thrust chamber

DeepSaucer: Unified Environment for Verifying Deep Neural Networks

In recent years, a number of methods for verifying DNNs have been developed. Because the approaches of the methods differ and have their own limitations, we think that a number of verification methods should be applied to a developed DNN. To apply a number of methods to the DNN, it is necessary to translate either the implementation of the DNN or the verification method so that one runs in the same environment as the other. Since those translations are time-consuming, a utility tool, named DeepSaucer, which helps to retain and reuse implementations of DNNs, verification methods, and their environments, is proposed. In DeepSaucer, code snippets of loading DNNs, running verification methods, and creating their environments are retained and reused as software assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer is confirmed by implementing it on the basis of Anaconda, which provides virtual environment for loading a DNN and running a verification method. In addition, the effectiveness of DeepSaucer is demonstrated by usecase examples

WHAT IS THE LENGTH OF A SNAKE?

The way that herpetologists have traditionally measuredlive snakes is by stretching them on a ruler andrecording the total length (TL). However, due to the thinconstitution of the snake, the large number of intervertebraljoints, and slim muscular mass of most snakes,it is easier to stretch a snake than it is to stretch anyother vertebrate. The result of this is that the length ofa snake recorded is infl uenced by how much the animalis stretched. Stretching it as much as possible is perhapsa precise way to measure the length of the specimenbut it might not correspond to the actual length ofa live animal. Furthermore, it may seriously injure a livesnake. Another method involves placing the snake in aclear plexiglass box and pressing it with a soft materialsuch as rubber foam against a clear surface. Measuringthe length of the snake may be done by outlining itsbody with a string (Fitch 1987; Frye 1991). However, thismethod is restricted to small animals that can be placedin a box, and in addition, no indications of accuracy of thetechnique are given. Measuring the snakes with a fl exibletape has also been reported (Blouin-Demers 2003)but when dealing with a large animals the way the tapeis positioned can produce great variance on the fi nal outcome.In this contribution we revise alternative ways tomeasuring a snake and propose a method that offers repeatableresults. We further analyze the precision of thismethod by using a sample of measurements taken fromwild populations of green anacondas (Eunectes murinus)with a large range of sizes