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Institution

Lancaster University

EducationLancaster, Lancashire, United Kingdom
About: Lancaster University is a education organization based out in Lancaster, Lancashire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 13080 authors who have published 44563 publications receiving 1692277 citations. The organization is also known as: The University of Lancaster & Lancaster University.


Papers
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Journal ArticleDOI
11 Jun 2004-Science
TL;DR: This work shows how aboveground and belowground components are closely interlinked at the community level, reinforced by a greater degree of specificity between plants and soil organisms than has been previously supposed.
Abstract: All terrestrial ecosystems consist of aboveground and belowground components that interact to influence community- and ecosystem-level processes and properties. Here we show how these components are closely interlinked at the community level, reinforced by a greater degree of specificity between plants and soil organisms than has been previously supposed. As such, aboveground and belowground communities can be powerful mutual drivers, with both positive and negative feedbacks. A combined aboveground-belowground approach to community and ecosystem ecology is enhancing our understanding of the regulation and functional significance of biodiversity and of the environmental impacts of human-induced global change phenomena.

3,683 citations

Journal ArticleDOI
TL;DR: Overall, this review shows that soil microbes must be considered as important drivers of plant diversity and productivity in terrestrial ecosystems.
Abstract: Microbes are the unseen majority in soil and comprise a large portion of lifes genetic diversity. Despite their abundance, the impact of soil microbes on ecosystem processes is still poorly understood. Here we explore the various roles that soil microbes play in terrestrial ecosystems with special emphasis on their contribution to plant productivity and diversity. Soil microbes are important regulators of plant productivity, especially in nutrient poor ecosystems where plant symbionts are responsible for the acquisition of limiting nutrients. Mycorrhizal fungi and nitrogenfixing bacteria are responsible for c. 5‐20% (grassland and savannah) to 80% (temperate and boreal forests) of all nitrogen, and up to 75% of phosphorus, that is acquired by plants annually. Free-living microbes also strongly regulate plant productivity, through the mineralization of, and competition for, nutrients that sustain plant productivity. Soil microbes, including microbial pathogens, are also important regulators of plant community dynamics and plant diversity, determining plant abundance and, in some cases, facilitating invasion by exotic plants. Conservative estimates suggest that c. 20 000 plant species are completely dependent on microbial symbionts for growth and survival pointing to the importance of soil microbes as regulators of plant species richness on Earth. Overall, this review shows that soil microbes must be considered as important drivers of plant diversity and productivity in terrestrial ecosystems.

3,673 citations

Journal ArticleDOI
TL;DR: A functional model is proposed in which structural encoding processes provide descriptions suitable for the analysis of facial speech, for analysis of expression and for face recognition units, and it is proposed that the cognitive system plays an active role in deciding whether or not the initial match is sufficiently close to indicate true recognition.
Abstract: The aim of this paper is to develop a theoretical model and a set of terms for understanding and discussing how we recognize familiar faces, and the relationship between recognition and other aspects of face processing. It is suggested that there are seven distinct types of information that we derive from seen faces; these are labelled pictorial, structural, visually derived semantic, identity-specific semantic, name, expression and facial speech codes. A functional model is proposed in which structural encoding processes provide descriptions suitable for the analysis of facial speech, for analysis of expression and for face recognition units. Recognition of familiar faces involves a match between the products of structural encoding and previously stored structural codes describing the appearance of familiar faces, held in face recognition units. Identity-specific semantic codes are then accessed from person identity nodes, and subsequently name codes are retrieved. It is also proposed that the cognitive system plays an active role in deciding whether or not the initial match is sufficiently close to indicate true recognition or merely a ‘resemblance’; several factors are seen as influencing such decisions. This functional model is used to draw together data from diverse sources including laboratory experiments, studies of everyday errors, and studies of patients with different types of cerebral injury. It is also used to clarify similarities and differences between processes responsible for object, word and face recognition.

3,604 citations

Journal ArticleDOI
TL;DR: The results from a proof-of-concept prototype suggest that VM technology can indeed help meet the need for rapid customization of infrastructure for diverse applications, and this article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them.
Abstract: Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement.

3,599 citations

Book
01 Jan 1990
TL;DR: The Emergence of Soft Systems Thinking as discussed by the authors is a seminal work in the field of soft systems thinking, and it can be found in the Soft Systems Methodology--the Parts.
Abstract: The Emergence of Soft Systems Thinking. Soft Systems Methodology--the Whole. Soft Systems Methodology--the Parts. Soft Systems Methodology--the Whole Revisited. Soft Systems Methodology--the Context. Conclusion. Appendix. Bibliography. Indexes.

3,531 citations


Authors

Showing all 13361 results

NameH-indexPapersCitations
David Miller2032573204840
H. S. Chen1792401178529
John Hardy1771178171694
Yang Gao1682047146301
Gavin Davies1592036149835
David Tilman158340149473
David Cameron1541586126067
A. Artamonov1501858119791
Steven Williams144137586712
Carmen García139150396925
Milos Lokajicek139151198888
S. R. Hou1391845106563
Roger Jones138998114061
Alan D. Baddeley13746789497
Pavel Shatalov136109791536
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023150
2022467
20212,620
20202,881
20192,593
20182,505