Showing 2301 - 2310 of 5714 Items

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Miniature of Bowdoin Orient, v. 58, no. 22
Bowdoin Orient, v. 58, no. 22

Date: 1929-01-23

Access: Open access

Miniature of Bowdoin Orient, v. 60, no. 14
Bowdoin Orient, v. 60, no. 14

Date: 1930-11-05

Access: Open access

Miniature of Bowdoin Orient, v. 59, no. 9
Bowdoin Orient, v. 59, no. 9

Date: 1929-10-02

Access: Open access

Miniature of Bowdoin Orient, v. 60, no. 4
Bowdoin Orient, v. 60, no. 4

Date: 1930-05-07

Access: Open access

Miniature of Bowdoin Orient, v. 61, no. 8
Bowdoin Orient, v. 61, no. 8

Date: 1931-06-18

Access: Open access

Miniature of Bowdoin Orient, v. 62, no. 22
Bowdoin Orient, v. 62, no. 22

Date: 1933-02-15

Access: Open access

Miniature of Bowdoin Orient, v. 62, no. 16
Bowdoin Orient, v. 62, no. 16

Date: 1932-11-16

Access: Open access

Miniature of Old Master Drawings at Bowdoin College
Old Master Drawings at Bowdoin College

Date: 1985-01-01

Creator: David P. Becker

Access: Open access

Catalogue of an exhibition held at Bowdoin College Museum of Art, Brunswick Me., May 17-July 7 1985; Sterling and Francine Clark Art Institute, Williamstown Mass., Sept. 14-Oct. 27 1985; Helen Foresman Spencer Museum of Art, Uniersity of Kansas, Lawrence Kansas, Jan. 19-Mar. 2, 1986; Art Gallery of Ontario, Toronto Canada, May 17-June 29, 1986.
Miniature of Non-intrusive load identification for smart outlets
Non-intrusive load identification for smart outlets

Date: 2015-01-12

Creator: Sean Barker, Mohamed Musthag, David Irwin, Prashant Shenoy

Access: Open access

An increasing interest in energy-efficiency combined with the decreasing cost of embedded networked sensors is lowering the cost of outlet-level metering. If these trends continue, new buildings in the near future will be able to install 'smart' outlets, which monitor and transmit an outlets power usage in real time, for nearly the same cost as conventional outlets. One problem with the pervasive deployment of smart outlets is that users must currently identify the specific device plugged into each meter, and then manually update the outlets meta-data in software whenever a new device is plugged into the outlet. Correct meta-data is important in both interpreting historical outlet energy data and using the data for building management. To address this problem, we propose Non-Intrusive Load Identification (NILI), which automatically identifies the device attached to a smart outlet without any human intervention. In particular, in our approach to NILI, we identify an intuitive and simple-to-compute set of features from time-series energy data and then employ well-known classifiers. Our results achieve accuracy of over 90% across 15 device types on outlet-level energy traces collected from multiple real homes.
Miniature of A strategy for the selective imaging of glycans using caged metabolic precursors
A strategy for the selective imaging of glycans using caged metabolic precursors

Date: 2010-07-21

Creator: Pamela V. Chang, Danielle H. Dube, Ellen M. Sletten, Carolyn R. Bertozzi

Access: Open access

Glycans can be imaged by metabolic labeling with azidosugars followed by chemical reaction with imaging probes; however, tissue-specific labeling is difficult to achieve. Here we describe a strategy for the use of a caged metabolic precursor that is activated for cellular metabolism by enzymatic cleavage. An N-azidoacetylmannosamine derivative caged with a peptide substrate for the prostate-specific antigen (PSA) protease was converted to cell-surface azido sialic acids in a PSA-dependent manner. The approach has applications in tissue-selective imaging of glycans for clinical and basic research purposes. © 2010 American Chemical Society.

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