Institution
Lehigh University
Education•Bethlehem, Pennsylvania, United States•
About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.
Papers published on a yearly basis
Papers
More filters
••
Stockholm University1, Alfred Wegener Institute for Polar and Marine Research2, University of Hamburg3, United States Geological Survey4, Northern Arizona University5, University of Florida6, University of Alaska Fairbanks7, Lawrence Berkeley National Laboratory8, National Park Service9, University of Copenhagen10, Argonne National Laboratory11, Bowdoin College12, Lehigh University13
TL;DR: In this article, the authors presented revised estimates of permafrost organic carbon stocks, including quantitative uncertainty estimates, in the 0-3 m depth range in soils as well as for sediments deeper than 3 m in deltaic deposits of major rivers and in the Yedoma region of Siberia and Alaska.
Abstract: Soils and other unconsolidated deposits in the northern circumpolar permafrost region store large amounts of soil organic carbon (SOC). This SOC is potentially vulnerable to remobilization following soil warming and permafrost thaw, but SOC stock estimates were poorly constrained and quantitative error estimates were lacking. This study presents revised estimates of permafrost SOC stocks, including quantitative uncertainty estimates, in the 0–3 m depth range in soils as well as for sediments deeper than 3 m in deltaic deposits of major rivers and in the Yedoma region of Siberia and Alaska. Revised estimates are based on significantly larger databases compared to previous studies. Despite this there is evidence of significant remaining regional data gaps. Estimates remain particularly poorly constrained for soils in the High Arctic region and physiographic regions with thin sedimentary overburden (mountains, highlands and plateaus) as well as for deposits below 3 m depth in deltas and the Yedoma region. While some components of the revised SOC stocks are similar in magnitude to those previously reported for this region, there are substantial differences in other components, including the fraction of perennially frozen SOC. Upscaled based on regional soil maps, estimated permafrost region SOC stocks are 217 ± 12 and 472 ± 27 Pg for the 0–0.3 and 0–1 m soil depths, respectively (±95% confidence intervals). Storage of SOC in 0–3 m of soils is estimated to 1035 ± 150 Pg. Of this, 34 ± 16 Pg C is stored in poorly developed soils of the High Arctic. Based on generalized calculations, storage of SOC below 3 m of surface soils in deltaic alluvium of major Arctic rivers is estimated as 91 ± 52 Pg. In the Yedoma region, estimated SOC stocks below 3 m depth are 181 ± 54 Pg, of which 74 ± 20 Pg is stored in intact Yedoma (late Pleistocene ice- and organic-rich silty sediments) with the remainder in refrozen thermokarst deposits. Total estimated SOC storage for the permafrost region is ∼1300 Pg with an uncertainty range of ∼1100 to 1500 Pg. Of this, ∼500 Pg is in non-permafrost soils, seasonally thawed in the active layer or in deeper taliks, while ∼800 Pg is perennially frozen. This represents a substantial ∼300 Pg lowering of the estimated perennially frozen SOC stock compared to previous estimates.
1,168 citations
••
1,114 citations
••
TL;DR: In this article, an integrated theoretical model that explains how strategies for participating in the market for corporate control (acquisitions and divestitures) affect internal control is presented, and the model is extended to analyze the effect of different strategies on internal control.
Abstract: This research examines an integrated theoretical model that explains how strategies for participating in the market for corporate control (acquisitions and divestitures) affect internal control mec...
1,107 citations
••
TL;DR: In this paper, the general equations for crack-tip stress fields in anisotropic bodies are derived making use of a complex variable approach and stress intensity factors, which permit concise representation of the conditions for crack extension, are defined and evaluated directly from stress functions.
Abstract: The general equations for crack-tip stress fields in anisotropic bodies are derived making use of a complex variable approach. The stress-intensity-factors, which permit concise representation of the conditions for crack extension, are defined and are evaluated directly from stress functions. Some individual boundary value problem solutions are given in closed form and discussed with reference to their companion solutions for isotropic bodies.
1,098 citations
••
25 Jul 2010TL;DR: It is shown that by training a topic model on aggregated messages the authors can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems.
Abstract: Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential.We address the problem of using standard topic models in micro-blogging environments by studying how the models can be trained on the dataset. We propose several schemes to train a standard topic model and compare their quality and effectiveness through a set of carefully designed experiments from both qualitative and quantitative perspectives. We show that by training a topic model on aggregated messages we can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems. We also discuss how the state-of-the-art Author-Topic model fails to model hierarchical relationships between entities in Social Media.
1,096 citations
Authors
Showing all 12785 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yang Yang | 171 | 2644 | 153049 |
Gang Chen | 167 | 3372 | 149819 |
Yi Yang | 143 | 2456 | 92268 |
Mark D. Griffiths | 124 | 1238 | 61335 |
Michael Gill | 121 | 810 | 86338 |
Masaki Mori | 110 | 2200 | 66676 |
Kai Nan An | 109 | 953 | 51638 |
James R. Rice | 108 | 278 | 68943 |
Vinayak P. Dravid | 103 | 817 | 43612 |
Andrew M. Jones | 103 | 764 | 37253 |
Israel E. Wachs | 103 | 427 | 32029 |
Demetrios N. Christodoulides | 100 | 704 | 51093 |
Bert M. Weckhuysen | 100 | 767 | 40945 |
José Luis García Fierro | 100 | 1027 | 47228 |
Mordechai Segev | 99 | 729 | 40073 |