7,421 research outputs found
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization
Background and Aims: Prediction of phenotypic traits from new genotypes under
untested environmental conditions is crucial to build simulations of breeding
strategies to improve target traits. Although the plant response to
environmental stresses is characterized by both architectural and functional
plasticity, recent attempts to integrate biological knowledge into genetics
models have mainly concerned specific physiological processes or crop models
without architecture, and thus may prove limited when studying genotype x
environment interactions. Consequently, this paper presents a simulation study
introducing genetics into a functional-structural growth model, which gives
access to more fundamental traits for quantitative trait loci (QTL) detection
and thus to promising tools for yield optimization. Methods: The GreenLab model
was selected as a reasonable choice to link growth model parameters to QTL.
Virtual genes and virtual chromosomes were defined to build a simple genetic
model that drove the settings of the species-specific parameters of the model.
The QTL Cartographer software was used to study QTL detection of simulated
plant traits. A genetic algorithm was implemented to define the ideotype for
yield maximization based on the model parameters and the associated allelic
combination. Key Results and Conclusions: By keeping the environmental factors
constant and using a virtual population with a large number of individuals
generated by a Mendelian genetic model, results for an ideal case could be
simulated. Virtual QTL detection was compared in the case of phenotypic traits
- such as cob weight - and when traits were model parameters, and was found to
be more accurate in the latter case. The practical interest of this approach is
illustrated by calculating the parameters (and the corresponding genotype)
associated with yield optimization of a GreenLab maize model. The paper
discusses the potentials of GreenLab to represent environment x genotype
interactions, in particular through its main state variable, the ratio of
biomass supply over demand
Using numerical plant models and phenotypic correlation space to design achievable ideotypes
Numerical plant models can predict the outcome of plant traits modifications
resulting from genetic variations, on plant performance, by simulating
physiological processes and their interaction with the environment.
Optimization methods complement those models to design ideotypes, i.e. ideal
values of a set of plant traits resulting in optimal adaptation for given
combinations of environment and management, mainly through the maximization of
a performance criteria (e.g. yield, light interception). As use of simulation
models gains momentum in plant breeding, numerical experiments must be
carefully engineered to provide accurate and attainable results, rooting them
in biological reality. Here, we propose a multi-objective optimization
formulation that includes a metric of performance, returned by the numerical
model, and a metric of feasibility, accounting for correlations between traits
based on field observations. We applied this approach to two contrasting
models: a process-based crop model of sunflower and a functional-structural
plant model of apple trees. In both cases, the method successfully
characterized key plant traits and identified a continuum of optimal solutions,
ranging from the most feasible to the most efficient. The present study thus
provides successful proof of concept for this enhanced modeling approach, which
identified paths for desirable trait modification, including direction and
intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen
Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System
Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here by permission of Taylor & Francis for personal use, not for redistribution. The definitive version was published in Journal of Operational Oceanography 10 (2017): 115-126, doi:10.1080/1755876X.2017.1322026.This paper outlines strategies that would advance coastal ocean modeling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the U.S. Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of U.S. based researchers with a cross-section of coastal oceanography and ocean modeling expertise and community representation drawn from Regional and U.S. Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modeling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3-8 year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, U.S. Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal-academic partnerships benefiting IOOS stakeholders and end users.2018-05-2
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