21,661 research outputs found
MODELING OF AGRICULTURAL SYSTEMS
The authors present an overview of agricultural systems models. Beginning with why systems are modeled and for what purposes, the paper examines types of agricultural systems and associated model types. The broad categories range from pictorial (iconic) models to descriptive analogue models to symbolic (usually mathematical) models. The uses of optimization versus non-optimizing mechanistic models are reviewed, as are the scale and aggregation challenges associated with scaling up from the plant cell to the landscape or from a farm enterprise to a world market supply-demand equilibrium Recent modeling developments include the integration of formerly stand-alone biophysical simulation models, increasingly with a unifying spatial database and often for the purpose of supporting management decisions. Current modeling innovations are estimating and incorporating environmental values and other system interactions. At the community and regional scale, sociological and economic models of rural community structure are being developed to evaluate long-term community viability. The information revolution is bringing new challenges in delivering agricultural systems models over the internet, as well as integrating decision support systems with the new precision agriculture technologies.Farm Management,
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Unified radio and network control across heterogeneous hardware platforms
Experimentation is an important step in the investigation of techniques for handling spectrum scarcity or the development of new waveforms in future wireless networks. However, it is impractical and not cost effective to construct custom platforms for each future network scenario to be investigated. This problem is addressed by defining Unified Programming Interfaces that allow common access to several platforms for experimentation-based prototyping, research, and development purposes. The design of these interfaces is driven by a diverse set of scenarios that capture the functionality relevant to future network implementations while trying to keep them as generic as possible. Herein, the definition of this set of scenarios is presented as well as the architecture for supporting experimentation-based wireless research over multiple hardware platforms. The proposed architecture for experimentation incorporates both local and global unified interfaces to control any aspect of a wireless system while being completely agnostic to the actual technology incorporated. Control is feasible from the low-level features of individual radios to the entire network stack, including hierarchical control combinations. A testbed to enable the use of the above architecture is utilized that uses a backbone network in order to be able to extract measurements and observe the overall behaviour of the system under test without imposing further communication overhead to the actual experiment. Based on the aforementioned architecture, a system is proposed that is able to support the advancement of intelligent techniques for future networks through experimentation while decoupling promising algorithms and techniques from the capabilities of a specific hardware platform
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems
We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake
ecosystem model by augmenting the individual cognitive maps drawn by 8
scientists working in the area of shallow lake ecology. We calculated graph
theoretical indices of the individual cognitive maps and the collective
cognitive map produced by augmentation. The graph theoretical indices revealed
internal cycles showing non-linear dynamics in the shallow lake ecosystem. The
ecological processes were organized democratically without a top-down
hierarchical structure. The steady state condition of the generic model was a
characteristic turbid shallow lake ecosystem since there were no dynamic
environmental changes that could cause shifts between a turbid and a clearwater
state, and the generic model indicated that only a dynamic disturbance regime
could maintain the clearwater state. The model developed herein captured the
empirical behavior of shallow lakes, and contained the basic model of the
Alternative Stable States Theory. In addition, our model expanded the basic
model by quantifying the relative effects of connections and by extending it.
In our expanded model we ran 4 simulations: harvesting submerged plants,
nutrient reduction, fish removal without nutrient reduction, and
biomanipulation. Only biomanipulation, which included fish removal and nutrient
reduction, had the potential to shift the turbid state into clearwater state.
The structure and relationships in the generic model as well as the outcomes of
the management simulations were supported by actual field studies in shallow
lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to
understand the complex structure of shallow lake ecosystems as a whole and
obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
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