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Relevant bibliographies by topics / BSCW

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Journal articles

Academic literature on the topic 'BSCW'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "BSCW"

1

Fischer, Thomas, Stella Gypser, Maria Subbotina, and Maik Veste. "Synergic hydraulic and nutritional feedback mechanisms control surface patchiness of biological soil crusts on tertiary sands at a post-mining site." Journal of Hydrology and Hydromechanics 62, no. 4 (2014): 293–302. http://dx.doi.org/10.2478/johh-2014-0038.

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Abstract In a recultivation area located in Brandenburg, Germany, five types of biocrusts (initial BSC1, developed BSC2 and BSC3, mosses, lichens) and non-crusted mineral substrate were sampled on tertiary sand deposited in 1985- 1986 to investigate hydrologic interactions between crust patches. Crust biomass was lowest in the non-crusted substrate, increased to the initial BSC1 and peaked in the developed BSC2, BSC3, the lichens and the mosses. Water infiltration was highest on the substrate, and decreased to BSC2, BSC1 and BSC3. Non-metric multidimensional scaling revealed that the lichens and BSC3 were associated with water soluble nutrients and with pyrite weathering products, thus representing a high nutrient low hydraulic feedback mode. The mosses and BSC2 represented a low nutrient high hydraulic feedback mode. These feedback mechanisms were considered as synergic, consisting of run-off generating (low hydraulic) and run-on receiving (high hydraulic) BSC patches. Three scenarios for BSC succession were proposed. (1) Initial BSCs sealed the surface until they reached a successional stage (represented by BSC1) from which the development into either of the feedback modes was triggered, (2) initial heterogeneities of the mineral substrate controlled the development of the feedback mode, and (3) complex interactions between lichens and mosses occurred at later stages of system development.

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2

Stahl, Gerry. "Groupware goes to school: adapting BSCW to the classroom." International Journal of Computer Applications in Technology 19, no. 3/4 (2004): 162. http://dx.doi.org/10.1504/ijcat.2004.004045.

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3

Korichi, Ahmed, and Brahim Belattar. "Towards a Web Based Simulation Groupware: Experiment with BSCW." Information Technology Journal 7, no. 2 (2008): 332–37. http://dx.doi.org/10.3923/itj.2008.332.337.

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4

Horstmann, Thilo, and Richard Bentley. "Distributed authoring on the Web with the BSCW shared workspace system." StandardView 5, no. 1 (1997): 9–16. http://dx.doi.org/10.1145/253452.253464.

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5

Thorsteinsson, Gisli, and Thomas Page. "BSCW As A Managed Learning Environment For International In-Service Teacher Education." i-manager's Journal on School Educational Technology 3, no. 2 (2007): 43–48. http://dx.doi.org/10.26634/jsch.3.2.782.

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6

Chen, Xiang, Tao Wang, Shulin Liu, et al. "Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions." Remote Sensing 12, no. 7 (2020): 1158. http://dx.doi.org/10.3390/rs12071158.

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Biological soil crusts (BSCs) are a major functional vegetation unit, covering extensive parts of drylands worldwide. Therefore, several multispectral indices have been proposed to map the spatial distribution and coverage of BSCs. BSCs are composed of poikilohydric organisms, the activity of which is sensitive to water availability. However, studies on dry and wet BSCs have seldom considered the mixed coverage gradient that is representative of actual field conditions. In this study, in situ spectral data and photographs of 136 pairs of dry and wet plots were collected to determine the influence of moisture conditions on BSC coverage detection. Then, BSC spectral reflectance and continuum removal (CR) reflectance responses to wetting were analyzed. Finally, the responses of four commonly used indices (i.e., normalized difference vegetation index (NDVI); crust index (CI); biological soil crust index (BSCI); and band depth of absorption feature after CR in the red band, (BD_red)), calculated from in situ hyperspectral data resampled to two multispectral data channels (Landsat-8 and Sentinel-2), were compared in dry and wet conditions. The results indicate that: (i) on average, the estimated BSC coverage using red-green-blue (RGB) images is 14.98% higher in wet than in dry conditions (P < 0.001); (ii) CR reflectance features of wet BSCs are more obvious than those of dry BSCs in both red and red-edge bands; and (iii) NDVI, CI, and BSCI for BSC coverage of 0%–60% under dry and wet conditions are close to those of dry and wet bare sand, respectively. NDVI and BD_red cannot separate dead wood and BSC with low coverage. This study demonstrates that low-coverage moss-dominated BSC is not easily detected by the four indices. In the future, remote-sensing data obtained during the rainy season with red and red-edge bands should be considered to detect BSCs.

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7

Bentley, Richard, Thilo Horstmann, and Jonathan Trevor. "The World Wide Web as Enabling Technology for CSCW: The Case of BSCW." Computer Supported Cooperative Work (CSCW) 6, no. 2-3 (1997): 111–34. http://dx.doi.org/10.1023/a:1008631823217.

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8

Pieper, Michael, and Dirk Hermsdorf. "BSCW for disabled teleworkers: usability evaluation and interface adaptation of an internet-based cooperation environment." Computer Networks and ISDN Systems 29, no. 8-13 (1997): 1479–87. http://dx.doi.org/10.1016/s0169-7552(97)00014-7.

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9

Huxor, A. "An active worlds interface to Basic Support for Cooperative Working (BSCW) to enhance chance encounters." Virtual Reality 4, no. 1 (1999): 4–14. http://dx.doi.org/10.1007/bf01434990.

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10

Chen, Xiang, Tao Wang, Shulin Liu, et al. "A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China." Remote Sensing 11, no. 11 (2019): 1286. http://dx.doi.org/10.3390/rs11111286.

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Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) < 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R2 = 0.947; ground validation, R2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems.

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