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Institution

Technion – Israel Institute of Technology

EducationHaifa, Israel
About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Upper and lower bounds. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.


Papers
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Journal ArticleDOI
01 Feb 1993
TL;DR: In this paper, the authors show that the total world consumption of fertilizer N, P2O5, and K2O in 1990/1991 was 78, 37, and 26 million tons per annum, respectively, with a projected yearly increase of demand of about 2 to 3%.
Abstract: Total world consumption of fertilizer N, P2O5, and K2O in 1990/1991 was 78, 37, and 26 million tons per annum, respectively, with a projected yearly increase of demand of about 2 to 3%. Trends in crop production (maize and wheat) in the last four decades show that N application rates increased about 15 times whereas its accumulation in grain increased only 3 to 4 times. At the same time nutrient recovery by crops remained relatively low (e.g. about 50% for N). This represents a potentially alarming situation from environmental, economic and resource conservation points of view and indicates an urgent need for improving efficiency of fertilizer use.

377 citations

Journal ArticleDOI
TL;DR: This work presents threshold DSS (digital signature standard) signatures where the power to sign is shared by n players such that for a given parameter t there is a consensus that n players should have the right to sign.
Abstract: We present threshold DSS (digital signature standard) signatures where the power to sign is shared by n players such that for a given parameter t

376 citations

Journal ArticleDOI
TL;DR: In this article, a new route to suppress grain growth and tune the sensitivity and selectivity of nanocrystalline SnO2 fibers was presented, where the Pd-loaded sensors have 4 orders of magnitude higher resistivity and exhibit significantly enhanced sensitivity to H2 and lower sensitivity to NO2 compared to their unloaded counterparts.
Abstract: This work presents a new route to suppress grain growth and tune the sensitivity and selectivity of nanocrystalline SnO2 fibers. Unloaded and Pd-loaded SnO2 nanofiber mats are synthesized by electrospinning followed by hot-pressing at 80 °C and calcination at 450 or 600 °C. The chemical composition and microstructure evolution as a function of Pd-loading and calcination temperature are examined using EDS, XPS, XRD, SEM, and HRTEM. Highly porous fibrillar morphology with nanocrystalline fibers comprising SnO2 crystallites decorated with tiny PdO crystallites is observed. The grain size of the SnO2 crystallites in the layers that are calcined at 600 °C decreases with increasing Pd concentration from about 15 nm in the unloaded specimen to about 7 nm in the 40 mol% Pd-loaded specimen, indicating that Pd-loading could effectively suppress the SnO2 grain growth during the calcination step. The Pd-loaded SnO2 sensors have 4 orders of magnitude higher resistivity and exhibit significantly enhanced sensitivity to H2 and lower sensitivity to NO2 compared to their unloaded counterparts. These observations are attributed to enhanced electron depletion at the surface of the PdO-decorated SnO2 crystallites and catalytic effect of PdO in promoting the oxidation of H2 into H2O. These phenomena appear to have a much larger effect on the sensitivity of the Pd-loaded sensors than the reduction in grain size.

376 citations

Journal ArticleDOI
TL;DR: Recent findings indicate that, for several proteins, the first ubiquitin moiety is fused linearly to the alpha-NH2 group of the N-terminal residue, which relates to the evolutionary requirement for an alternative mode of ubiquitination.

376 citations

Journal ArticleDOI
TL;DR: A new algorithm for rapid reconstruction of tissue‐specific genome‐scale models of human metabolism is presented, which is verified using standard cross‐validation procedures, and constructed the first genome-scale stoichiometric model of hepatic metabolism.
Abstract: The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

376 citations


Authors

Showing all 31937 results

NameH-indexPapersCitations
Robert Langer2812324326306
Nicholas G. Martin1921770161952
Tobin J. Marks1591621111604
Grant W. Montgomery157926108118
David Eisenberg156697112460
David J. Mooney15669594172
Dirk Inzé14964774468
Jerrold M. Olefsky14359577356
Joseph J.Y. Sung142124092035
Deborah Estrin135562106177
Bruce Yabsley133119184889
Jerry W. Shay13363974774
Richard N. Bergman13047791718
Shlomit Tarem129130686919
Allen Mincer129104080059
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023147
2022390
20213,397
20203,526
20193,273
20183,131