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Qiang He

Bio: Qiang He is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 50, co-authored 389 publications receiving 8498 citations. Previous affiliations of Qiang He include Huazhong University of Science and Technology & Shanghai Jiao Tong University.


Papers
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TL;DR: A synthesis of 727 tests of the stress-gradient hypothesis in plant communities across the globe shows that plant interactions change with stress through an outright shift to facilitation (survival) or a reduction in competition (growth and reproduction).
Abstract: The study of positive species interactions is a rapidly evolving field in ecology. Despite decades of research, controversy has emerged as to whether positive and negative interactions predictably shift with increasing environmental stress as hypothesised by the stress-gradient hypothesis (SGH). Here, we provide a synthesis of 727 tests of the SGH in plant communities across the globe to examine its generality across a variety of ecological factors. Our results show that plant interactions change with stress through an outright shift to facilitation (survival) or a reduction in competition (growth and reproduction). In a limited number of cases, plant interactions do not respond to stress, but they never shift towards competition with stress. These findings are consistent across stress types, plant growth forms, life histories, origins (invasive vs. native), climates, ecosystems and methodologies, though the magnitude of the shifts towards facilitation with stress is dependent on these factors. We suggest that future studies should employ standardised definitions and protocols to test the SGH, take a multi-factorial approach that considers variables such as plant traits in addition to stress, and apply the SGH to better understand how species and communities will respond to environmental change.

685 citations

Journal ArticleDOI
TL;DR: Global sampling of microbial communities associated with wastewater treatment plants and application of ecological theory revealed a small, core bacterial community associated with performance and provides insights into the community dynamics in this environment.
Abstract: Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.

423 citations

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TL;DR: This work proposes EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game and designs a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to theEUA problem.
Abstract: Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

244 citations

Journal ArticleDOI
TL;DR: It is underscores that an enhanced understanding of interactions between climate change and local human impacts is of profound importance to improving predictions of climate change impacts, devising climate-smart conservation actions, and helping enhance adaption of coastal societies to climate change in the Anthropocene.

237 citations

Journal ArticleDOI
TL;DR: In this article, waste glass powder and class C fly ash (FC) were mixed at varying ratios (100:0, 75:25, 50:50, 25:75, 0:100) and activated by sodium hydroxide solutions of different concentrations.

213 citations


Cited by
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2002

9,314 citations