Institution
University of Maryland, Baltimore County
Education•Baltimore, 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.
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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
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Pennsylvania State University1, University of Science and Technology of China2, University of Arkansas3, Durham University4, Millennium Institute5, Space Science Institute6, Pontifical Catholic University of Chile7, University of Maryland, Baltimore County8, Goddard Space Flight Center9, INAF10, Nanjing University11, Space Telescope Science Institute12, European Southern Observatory13, University of Naples Federico II14, Agenzia Spaziale Italiana15, Lund University16, University of Ferrara17, University of North Texas18
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
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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
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Fisk University1, Vanderbilt University2, Harvard University3, Lehigh University4, Northern Kentucky University5, Boston University6, Vassar College7, Andrés Bello National University8, Space Telescope Science Institute9, Ames Research Center10, University of California, Riverside11, Goddard Space Flight Center12, University of Maryland, Baltimore County13, University of Chicago14, University of Florida15, University of North Carolina at Chapel Hill16, George Mason University17, Massachusetts Institute of Technology18, University of Sydney19, INAF20, Aarhus University21, University of New South Wales22
411 citations
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Robert C. Gallo | 145 | 825 | 68212 |
Paul T. Costa | 133 | 406 | 88454 |
Igor V. Moskalenko | 132 | 542 | 58182 |
James Chiang | 129 | 308 | 60268 |
Alex K.-Y. Jen | 128 | 921 | 61811 |
Alan R. Shuldiner | 120 | 557 | 71737 |
Richard N. Zare | 120 | 1201 | 67880 |
Vince D. Calhoun | 117 | 1234 | 62205 |
Rita R. Colwell | 115 | 781 | 55229 |
Kendall N. Houk | 112 | 997 | 54877 |
Elliot K. Fishman | 112 | 1335 | 49298 |
Yoram J. Kaufman | 111 | 263 | 59238 |
Paulo Artaxo | 107 | 454 | 44346 |
Braxton D. Mitchell | 102 | 558 | 49599 |
Sushil Jajodia | 101 | 664 | 35556 |