scispace - formally typeset
Search or ask a question
Institution

University of Maryland, Baltimore County

EducationBaltimore, Maryland, United States
About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors extended the TAM model to incorporate teachers' perceived usability and self-efficacy measures toward the technologies they are currently using, and found that the incorporation of perceived usability into the TAM explained more variance and was more influential to TAM elements than its absence, thereby supporting the importance, positive influence and necessity of evaluating usability when investigating educational technology acceptance and usage behavior.
Abstract: The Technology Acceptance Model (TAM) represents how users come to accept and use a given technology and can be applied to teachers’ use of educational technologies. Here the model is extended to incorporate teachers’ perceived usability and self-efficacy measures toward the technologies they are currently using. The authors administered a survey to K–12 teachers in two rural school districts in Virginia, and 99 teachers responded. We then analyzed the responses with both reliability statistics and general linear modeling techniques. The results indicated that the incorporation of perceived usability into the TAM explained more variance and was more influential to TAM elements than its absence, thereby supporting the importance, positive influence, and necessity of evaluating usability when investigating educational technology acceptance and usage behavior. Furthermore, the study found teachers’ technology self-efficacy (TSE) was more beneficial to the TAM than their computer self-efficacy (CSE); ...

415 citations

Journal ArticleDOI
TL;DR: In this article, the authors used wavdetect for initial source detection and ACIS Extract for photometric extraction and significance assessment, and created a main source catalog containing 1008 sources that are detected in up to three X-ray bands: 0.5-7.0 keV, 0.4 ×10-18, and 2.7 × 10-17 erg cm-2 s-1, respectively.
Abstract: We present X-ray source catalogs for the ≈7 Ms exposure of the Chandra Deep Field-South (CDF-S), which covers a total area of 484.2 arcmin2. Utilizing wavdetect for initial source detection and ACIS Extract for photometric extraction and significance assessment, we create a main source catalog containing 1008 sources that are detected in up to three X-ray bands: 0.5-7.0 keV, 0.5-2.0 keV, and 2-7 keV. A supplementary source catalog is also provided, including 47 lower-significance sources that have bright (Ks ≤ 23) near-infrared counterparts. We identify multiwavelength counterparts for 992 (98.4%) of the main-catalog sources, and we collect redshifts for 986 of these sources, including 653 spectroscopic redshifts and 333 photometric redshifts. Based on the X-ray and multiwavelength properties, we identify 711 active galactic nuclei (AGNs) from the main-catalog sources. Compared to the previous ≈4 Ms CDF-S catalogs, 291 of the main-catalog sources are new detections. We have achieved unprecedented X-ray sensitivity with average flux limits over the central ≈1 arcmin2 region of ≈1.9 ×10-17, 6.4 ×10-18, and 2.7 ×10-17 erg cm-2 s-1 in the three X-ray bands, respectively. We provide cumulative number-count measurements observing, for the first time, that normal galaxies start to dominate the X-ray source population at the faintest 0.5-2.0 keV flux levels. The highest X-ray source density reaches ≈50,500 deg-2, and 47% ± 4% of these sources are AGNs (≈23,900 deg-2). (Less)

415 citations

Journal ArticleDOI
TL;DR: Novel algorithms are described, with worst-case running times polynomial in n, to solve the data gathering problem with aggregation in sensor networks, and the results demonstrate that the proposed algorithms significantly outperform previous methods in terms of system lifetime.

411 citations

Journal ArticleDOI

411 citations

Journal ArticleDOI
TL;DR: The findings demonstrate that the balance of local and regional effects changes depending on location within riverine network with local (environmental) factors dictating community structure in headwaters, and regional (dispersal driven) forces dominating in mainstems.
Abstract: 1. Increasingly, ecologists conceptualize local communities as connected to a regional species pool rather than as isolated entities. By this paradigm, community structure is determined through the relative strengths of dispersal-driven regional effects and local environmental factors. However, despite explicit incorporation of dispersal, metacommunity models and frameworks often fail to capture the realities of natural systems by not accounting for the configuration of space within which organisms disperse. This shortcoming may be of particular consequence in riverine networks which consist of linearly -arranged, hierarchical, branching habitat elements. Our goal was to understand how constraints of network connectivity in riverine systems change the relative importance of local vs. regional factors in structuring communities. 2. We hypothesized that communities in more isolated headwaters of riverine networks would be structured by local forces, while mainstem sections would be structured by both local and regional processes. We examined these hypotheses using a spatially explicit regional analysis of riverine macroinvertebrate communities, focusing on change in community similarity with distance between local communities [i.e., distance-decay relationships; (DDRs)], and the change in environmental similarity with distance. Strong DDRs frequently indicate dispersal-driven dynamics. 3. There was no evidence of a DDR in headwater communities, supporting our hypothesis that dispersal is a weak structuring force. Furthermore, a positive relationship between community similarity and environmental similarity supported dynamics driven by local environmental factors (i.e., species sorting). In mainstem habitats, significant DDRs and community x environment similarity relationships suggested both dispersal-driven and environmental constraints on local community structure (i.e., mass effects). 4. We used species traits to compare communities characterized by low vs. high dispersal taxa. In headwaters, neither strength nor mode (in-network vs. out of network) of dispersal changed our results. However, outcomes in mainstems changed substantially with both dispersal mode and strength, further supporting the hypothesis that regional forces drive community dynamics in mainstems. 5. Our findings demonstrate that the balance of local and regional effects changes depending on location within riverine network with local (environmental) factors dictating community structure in headwaters, and regional (dispersal driven) forces dominating in mainstems.

410 citations


Authors

Showing all 8862 results

NameH-indexPapersCitations
Robert C. Gallo14582568212
Paul T. Costa13340688454
Igor V. Moskalenko13254258182
James Chiang12930860268
Alex K.-Y. Jen12892161811
Alan R. Shuldiner12055771737
Richard N. Zare120120167880
Vince D. Calhoun117123462205
Rita R. Colwell11578155229
Kendall N. Houk11299754877
Elliot K. Fishman112133549298
Yoram J. Kaufman11126359238
Paulo Artaxo10745444346
Braxton D. Mitchell10255849599
Sushil Jajodia10166435556
Network Information
Related Institutions (5)
Pennsylvania State University
196.8K papers, 8.3M citations

94% related

University of Illinois at Urbana–Champaign
225.1K papers, 10.1M citations

94% related

University of Washington
305.5K papers, 17.7M citations

93% related

University of California, San Diego
204.5K papers, 12.3M citations

93% related

Massachusetts Institute of Technology
268K papers, 18.2M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202371
2022165
20211,065
20201,091
2019989
2018929