US/UK Mental Models of Planning: The Relationship Between Plan Detail and Plan Quality

This paper presents the results of a research study applying a new cultural analysis method to capture commonalities and differences between US and UK mental models of operational planning. The results demonstrate the existence of fundamental differences between the way US and UK planners think about what it means to have a high quality plan. Specifically, the present study captures differences in how US and UK planners conceptualize plan quality. Explicit models of cultural differences in conceptions of plan quality are useful for establishing performance metrics for multinational planning teams. This paper discusses the prospects of enabling automatic evaluation of multinational team performance by combining recent advances in cultural modelling with enhanced ontology languages

What is a Good Plan? Cultural Variations in Expert Planners’ Concepts of Plan Quality

This article presents the results of a field research study examining commonalities and differences between American and British operational planners’ mental models of planning. We conducted Cultural Network Analysis (CNA) interviews with 14 experienced operational planners in the US and UK. Our results demonstrate the existence of fundamental differences between the way American and British expert planners conceive of a high quality plan. Our results revealed that the American planners’ model focused on specification of action to achieve synchronization, providing little autonomy at the level of execution, and included the belief that increasing contingencies reduces risk. The British planners’ model stressed the internal coherence of the plan, to support shared situational awareness and thereby flexibility at the level of execution. The British model also emphasized the belief that reducing the number of assumptions decreases risk. Overall, the American ideal plan serves a controlling function, whereas the British ideal plan supports an enabling function. Interestingly, both the US and UK would view the other’s ideal plan as riskier than their own. The implications of cultural models of plans and planning are described for establishing performance measures and designing systems to support multinational planning teams

Origins of elastic properties in ordered nanocomposites

We predict a diblock copolymer melt in the lamellar phase with added spherical nanoparticles that have an affinity for one block to have a lower tensile modulus than a pure diblock copolymer system. This weakening is due to the swelling of the lamellar domain by nanoparticles and the displacement of polymer by elastically inert fillers. Despite the overall decrease in the tensile modulus of a polydomain sample, the shear modulus for a single domain increases dramatically

What Americans Think of the New Insurance Marketplaces and Medicaid Expansion: Findings from the Commonwealth Fund Health Insurance Marketplace Survey, 2013

The Affordable Care Act's health insurance marketplaces are opening for enrollment on October 1, 2013. The Commonwealth Fund Health Insurance Marketplace Survey, 2013, finds that only two of five adults are aware of the marketplaces or of potential financial help that may be available to them to pay for plans purchased though the marketplaces. However, three of five adults who might be eligible for these new options said they were likely to take advantage of them. The survey also finds broad support for state expansion of the Medicaid program, even in states that have not yet decided to expand their programs. While outreach and education are critical to ensuring that those eligible for the new coverage options will enroll, the survey results suggest that eligible Americans will likely take advantage of the law's insurance reforms in the months and years to come

Bursts in discontinuous Aeolian saltation

Close to the onset of Aeolian particle transport through saltation we find in wind tunnel experiments a regime of discontinuous flux characterized by bursts of activity. Scaling laws are observed in the time delay between each burst and in the measurements of the wind fluctuations at the fluid threshold Shields number $\theta_c$. The time delay between each burst decreases on average with the increase of the Shields number until sand flux becomes continuous. A numerical model for saltation including the wind-entrainment from the turbulent fluctuations can reproduce these observations and gives insight about their origin. We present here also for the first time measurements showing that with feeding it becomes possible to sustain discontinuous flux even below the fluid threshold

Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark

Environmental impacts of grazed pastures

Large nitrogen (N) surplus and return of excreta-N in localised patches at high N rates in intensively grazed pasture systems markedly increases the risk of N losses to waterways and the atmosphere. Here are described the main routes of N input to grazed pastures, losses via N leaching, methane (CH4) and nitrous oxide (N2O) emissions. Furthermore farm N budgets and N use efficiency in relation to management strategies that can be applied to reduce N losses are discussed. Nitrate leaching increases exponentially with increased inputs and is closely related to urine patches, which also influence the leaching of dissolved organic N. High N2O emission rates in grazed pastures are related to fertiliser-N or N in excreta combined with compaction by animal treading. Grazing may considerably reduce CH3 emissions compared to indoor housing of cows. Pastures are occasionally cultivated due to sward deterioration followed by a rapid and extended period of N mineralization, contributing to an increased potential for losses. Good management of the pasture (e.g. reduced fertiliser input and reduced length of grazing) and of the mixed crop rotation during both the grassland and the arable phase (e.g. delayed ploughing time and a catch crop strategy) can considerably reduce the negative environmental impact of grazing. It is important to consider the whole farm system when evaluating environmental impact. In particular for green house gasses since the pasture may serve as a source of N2O and indirectly of CH3, but also as a sink of CO2 influenced by management practices on the farm

Clocking connector replaces adapter cables

Single cable using simplified, versatile clocking connector satisfies clocking variations that previously required many cables. Connector consists of specially fabricated grommet follower dial housing, dial assembly, and modified insert

Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y t = f(Yt-1 ,..., Yt-L ), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction

State-Space Inference and Learning with Gaussian Processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors