Institution
Ryerson University
Education•Toronto, Ontario, Canada•
About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors emphasize the potential for improving green-roof function by understanding the interactions between its ecosystem elements, especially the relationships among growing media, soil biota, and vegetation.
Abstract: Green roofs (roofs with a vegetated surface and substrate) provide ecosystem services in urban areas, including improved storm-water management, better regulation of building temperatures, reduced urban heat-island effects, and increased urban wildlife habitat. This article reviews the evidence for these benefits and examines the biotic and abiotic components that contribute to overall ecosystem services. We emphasize the potential for improving green-roof function by understanding the interactions between its ecosystem elements, especially the relationships among growing media, soil biota, and vegetation, and the interactions between community structure and ecosystem functioning. Further research into green-roof technology should assess the efficacy of green roofs compared to other technologies with similar ends, and ultimately focus on estimates of aggregate benefits at landscape scales and on more holistic cost-benefit analyses.
1,137 citations
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TL;DR: An end-to-end spectral–spatial residual network that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification and achieves the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets.
Abstract: In this paper, we designed an end-to-end spectral–spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.
1,105 citations
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TL;DR: An analytical model for predicting the yield strength of particulate-reinforced metal matrix nanocomposites has been developed in this article, where the strengthening effects involving Orowan strengthening effect, enhanced dislocation density due to the residual plastic strain caused by the difference in the coefficients of thermal expansion between the matrix and particles, and loadbearing effect have been taken into account in the model.
1,042 citations
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TL;DR: The RAVDESS is a validated multimodal database of emotional speech and song consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent, which shows high levels of emotional validity and test-retest intrarater reliability.
Abstract: The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.
1,036 citations
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TL;DR: In this article, the authors identify and analyze the published definitions of green supply chain management (GSCM) and sustainable supply chain Management (SSCM) and two different sets of key characteristics for business sustainability (economic, environmental, social, stakeholder, volunteer, resilience, and long-term focuses) and SCM (i.e., flow, coordination, stake holder, relationship, value, efficiency, and performance focuses) were proposed.
1,020 citations
Authors
Showing all 7846 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eleftherios P. Diamandis | 110 | 1064 | 52654 |
Michael D. Taylor | 97 | 505 | 42789 |
Peter Nijkamp | 97 | 2407 | 50826 |
Anthony B. Miller | 93 | 416 | 36777 |
Muhammad Shahbaz | 92 | 1001 | 34170 |
Rakesh Kumar | 91 | 1959 | 39017 |
Marc A. Rosen | 85 | 770 | 30666 |
Bjorn Ottersten | 81 | 1058 | 28359 |
Barry Wellman | 77 | 219 | 34234 |
Bin Wu | 73 | 464 | 24877 |
Xinbin Feng | 72 | 413 | 19193 |
Roy Freeman | 69 | 254 | 22707 |
Xiaokang Yang | 68 | 518 | 17663 |
Amir H. Gandomi | 67 | 375 | 22192 |
Konstantinos N. Plataniotis | 63 | 595 | 16695 |