A Human View Model for Socio-Technical Interactions

The Human View was developed as an additional architectural viewpoint to focus on the human part of a system. The Human View can be used to collect and organize data in order to understand how human operators interact and impact the other elements of a system. This framework can also be used to develop a model to describe how humans interact with each other in network enabled systems. These socio-technical interactions form the foundation of the emerging area of Human Interoperability. Human Interoperability strives to understand the relationships required between human operators that impact collaboration across networked environments, including the effect of belonging to different organizations. By applying organizational relationship concepts from network theory to the Human View elements, and aligning these relationships with a model developed to identify layers of coalition interoperability, the conditions for different levels for Human Interoperability for network enabled systems can be identified. These requirements can then be captured in the Human View products to improve the overall network enabled system

'Noise trader risk' and Bayesian market making in FX derivatives: rolling loaded dice?

ABSTRACT This paper develops and simulates a model of a Bayesian market maker who transacts with noise and position traders in derivative markets. The impact of noise trading is examined relative to price determination in FX futures, noise transmission from futures to options, and risk-management behaviour linking the two markets. The model simulations show noise trading in futures results in wider bid–ask spreads, increased price volatility, and greater variation in hedging costs. Above all, the Bayesian market maker manages price-risk by trend chasing not for speculative purposes, but to avoid being caught on the wrong side of the market. The pecuniary effects from this risk-management strategy suggest that noise trading tends to constrain the market maker’s capacity to arbitrage; particularly when the underlying price is mean averting as opposed to a Martingale and trading sessions exhibit significant price volatility. Copyright r 2008 John Wiley & Sons, Ltd. Copyright r 2008 John Wiley & Sons, Ltd.Noise trading; market making; FX derivatives; Bayesian agent; noise transmission

Enhancing Simulation Composability and Interoperability Using Conceptual/Semantic/Ontological Models

(First paragraph) Two emerging trends in Modeling and Simulation (M&S) are beginning to dovetail in a potentially highly productive manner, namely conceptual modeling and semantic modeling. Conceptual modeling has existed for several decades, but its importance has risen to the forefront in the last decade (Taylor and Robinson, 2006; Robinson, 2007). Also, during the last decade, progress on the Semantic Web has begun to influence M&S, with the development of general modeling ontologies (Miller et al, 2004), as well as ontologies for modeling particular domains (Durak, 2006). An ontology, which is a formal specification of a conceptualization (Gruber et al, 1993), can be used to rigorously define a domain of discourse in terms of classes/concepts, properties/relationships and instances/individuals. For the Semantic Web, ontologies are typically specified using the Web Ontology Language (OWL). Although, conceptual modeling is broader than just semantics (it includes additional issues such as pragmatics (Tolk et al, 2008)), progress in the Semantic Web and ontologies is certainly beneficial to conceptual modeling. Benefits are accrued in many ways including the large knowledge bases being placed on the Web in numerous fields in which simulation studies are conducted and the powerful reasoning algorithms based on description logic being developed that allow the consistency of large specifications to be checked

The production of low-energy neutral oxygen beams by grazing-incidence neutralization

The Vanderbilt University neutral oxygen facility produces beams of low-energy neutral oxygen atoms by means of grazing-incidence collisions between ion beams and metal surfaces. Residual ions are reflected by applied electric fields. This method can utilize initial ion beams of either O(+) or O2(+) since a very large percentage of molecular oxygen ions are dissociated when they undergo grazing-incidence neutralization. The method of neutralization is applicable to low-energy beams and to all ions. Particular emphasis is on O and N2 beams for simulation of the low Earth orbit space environment. Since the beam is a pure O-neutral beam and since measurements of the interaction of the beam with solid surfaces are made spectroscopically, absolute reaction rates can be determined. The technique permits the beams to be used in conjunction with electron and photon irradiation for studies of synergistic effects. Comparisons of optical spectra of Kapton excited by 2.5-keV O, O(+), and O2(+) show significant differences. Optical spectra of Kapton excited by neutral oxygen beams of less than 1 keV have been recorded

A Human View Model for Socio-Technical Interactions

The Human View was developed as an additional architectural viewpoint to focus on the human part of a system. The Human View can be used to collect and organize data in order to understand how human operators interact and impact the other elements of a system. This framework can also be used to develop a model to describe how humans interact with each other in network enabled systems. These socio-technical interactions form the foundation of the emerging area of Human Interoperability. Human Interoperability strives to understand the relationships required between human operators that impact collaboration across networked environments, including the effect of belonging to different organizations. By applying organizational relationship concepts from network theory to the Human View elements, and aligning these relationships with a model developed to identify layers of coalition interoperability, the conditions for different levels for Human Interoperability for network enabled systems can be identified. These requirements can then be captured in the Human View products to improve the overall network enabled system

Comparison of soil water sensing methods for irrigation management and research

Presented at the 2007 Central Plains irrigation conference on February 27-28 in Kearney, Nebraska.Includes bibliographical references.As irrigation water resources decrease and deficit irrigation becomes more common across the Great Plains, greater accuracy in irrigation scheduling will be required. With deficit irrigation a smaller amount of soil water is held in reserve and there is less margin for error. Researchers investigating deficit irrigation practices and developing management practices must also have accurate measures of soil water content - in fact, the two go hand in hand. New management practices for deficit irrigation will require more accurate assessments of soil water content if success is to be ensured. This study compared several commercial soil water sensing systems, four of them based on the electromagnetic (EM) properties of soil as influenced by soil water content, versus the venerable neutron moisture meter (NMM), which is based on the slowing of neutrons by soil water. While performance varied widely, the EM sensors were all less precise and less accurate in the field than was the NMM. Variation in water contents from one measurement location to the next was much greater for the EM sensors and was so large that these sensors are not useful for determining the amount of water to apply. The NMM is still the only sensor that is suitable for irrigation research. However, the NMM is not practical for on-farm irrigation management due to cost and regulatory issues. Unfortunately, our studies indicate that the EM sensors are not useful for irrigation management due to inaccuracy and variability. A new generation of EM sensors should be developed to overcome the problems of those currently available. In the meantime, tensiometers, electrical resistance sensors and soil probes may fill the gap for irrigation management based on soil water sensing. However, many farmers are successfully using irrigation scheduling based on crop water use estimates from weather station networks and reference ET calculations. When used in conjunction with direct field soil water observations to avoid over irrigation, the ET network approach has proved useful in maximizing yields

Improving C2 Effectiveness Based on Robust Connectivity

This chapter describes an approach to develop an improved metric for network effectiveness through the use of Cares\u27 (2005) Information Age Combat Model (IACM) as a context for combat (or competition) between networked forces. The IACM highlights the inadequacy of commonly used quantifiable metrics with regards to comparing networks that differ only by the placement of a few links. An agent-based simulation is used to investigate the potential value of the Perron-Frobenius Eigenvalue (λPFE) as an indicator of network effectiveness. The results validate this assumption. Another measurement is proven to be equally important, namely the robustness of a configuration. Potential applications from the domain of ballistic missile defense are included to show operational relevance

Executable Architecture Research at Old Dominion University

Executable Architectures allow the evaluation of system architectures not only regarding their static, but also their dynamic behavior. However, the systems engineering community do not agree on a common formal specification of executable architectures. To close this gap and identify necessary elements of an executable architecture, a modeling language, and a modeling formalism is topic of ongoing PhD research. In addition, systems are generally defined and applied in an operational context to provide capabilities and enable missions. To maximize the benefits of executable architectures, a second PhD effort introduces the idea of creating an executable context in addition to the executable architecture. The results move the validation of architectures from the current information domain into the knowledge domain and improve the reliability of such validation efforts. The paper presents research and results of both doctoral research efforts and puts them into a common context of state-of-the-art of systems engineering methods supporting more agility

Northern Texas High Plains

Presented at the 2002 USCID/EWRI conference, Energy, climate, environment and water - issues and opportunities for irrigation and drainage on July 9-12 in San Luis Obispo, California.Includes bibliographical references.Cotton (Gossypium hirsutum L.) is beginning to be produced on the Northern Texas High Plains as a lower water-requiring crop while producing an acceptable profit. Cotton is a warm season, perennial species produced like an annual yet it requires a delicate balance of water and water deficit controls to most effectively produce high yields in this thermally limited environment. This study measured the water use of cotton in near-fully irrigated, deficiently irrigated, and dryland regimes in a Northern Texas High Plains environment, which has a shortened cotton producing season, using precision weighing lysimeters in 2000 and 2001. The irrigated regimes were irrigated with a lateral-move sprinkler system. The water use data were used to develop crop coefficient data and compared with the FAO-56 method for estimating crop water use. Cotton yield, water use, and water use efficiency was found to be as good in this region as other more noted cotton regions. FAO-56 ET prediction procedures performed better for the more fully irrigated treatments in this environment

Modelling representation errors of atmospheric CO2 mixing ratios at a regional scale

Inverse modelling of carbon sources and sinks requires an accurate quality estimate of the modelling framework to obtain a realistic estimate of the inferred fluxes and their uncertainties. So-called "representation errors" result from our inability to correctly represent point observations with simulated average values of model grid cells. They may add substantial uncertainty to the interpretation of atmospheric CO2 mixing ratio data. We simulated detailed variations in the CO2 mixing ratios with a high resolution (2 km) mesoscale model (RAMS) to estimate the representation errors introduced at larger model grid sizes of 10 100 km. We found that meteorology is the main driver of representation errors in our study causing spatial and temporal variations in the error estimate. Within the nocturnal boundary layer, the representation errors are relatively large and mainly caused by unresolved topography at lower model resolutions. During the day, convective structures, mesoscale circulations, and surface CO2 flux variability were found to be the main sources of representation errors. Interpreting observations near a mesoscale circulation as representative for air with the correct footprint relative to the front can reduce the representation error substantially. The remaining representation error is 0.5 1.5 ppm at 20 100 km resolution