Regimes of Polymer Behaviour in Drop-on-Demand Ink-Jetting

Three regimes of fast DoD jetting behaviour for solutions of mono-disperse linear polymers have been linked to the underlying polymer molecular chains and their fully extended length L in good solvents. This allows scaling laws in molecular weight to be predicted and applied to experimental jetting results from different DoD print heads. The higher extensional flows encountered in high speed jetting in viscous solvents can fully stretch linear molecules outside the nozzle, permitting jetting of higher polymer content than for purely elastic behaviour. These results are significant for DoD printing at raised jet speeds and will apply to any DoD print head jetting linear polymer solutions.

Managing water resources and their connection to land and energy

Effective management is a key to ensuring the current and future sustainability of land, water and energy resources. Identifying the complexities of such management is not an easy task, especially since past studies have focussed on studying these resources in isolation from one another. However, with rapid population growth and an increase in the awareness of a potential change in climatic conditions that may affect the demand for and supply of food, water and energy, there has been a growing need to integrate the planning decisions relating to these three resources. The paper shows the visualisation of linked resources by drawing a set of interconnected Sankey diagrams for energy, water and land. These track the changes from basic resource (e.g. coal, surface water, groundwater and cropland) through transformations (e.g. fuel refining and desalination) to final services (e.g. sustenance, hygiene and transportation). The focus here is on the water analysis aspects of the tool, which uses California as a detailed case study. The movement of water in California is traced from its source to its services by mapping the different transformations of water from when it becomes available, through its use, to further treatment, to final sinks (including recycling and reuse of that resource). The connections that water has with energy and land resources for the state of California are highlighted. This includes the amount of energy used to pump and treat water, and the amount of water used for energy production and the land resources which create a water demand to produce crops for food. By mapping water in this way, policy-makers and resource managers can more easily understand the competing uses of water (environment, agriculture and urban use) through the identification of the services it delivers (e.g. sanitation, agriculture, landscaping), the potential opportunities for improving the management of the resource (e.g. building new desalination plants, reducing the demand for services), and the connections with other resources which are often overlooked in a traditional sector-based management strategy.

Task-level control of the lateral leg spring model of cockroach locomotion

The Lateral Leg Spring model (LLS) was developed by Schmitt and Holmes to model the horizontal-plane dynamics of a running cockroach. The model captures several salient features of real insect locomotion, and demonstrates that horizontal plane locomotion can be passively stabilized by a well-tuned mechanical system, thus requiring minimal neural reflexes. We propose two enhancements to the LLS model. First, we derive the dynamical equations for a more flexible placement of the center of pressure (COP), which enables the model to capture the phase relationship between the body orientation and center-of-mass (COM) heading in a simpler manner than previously possible. Second, we propose a reduced LLS "plant model" and biologically inspired control law that enables the model to follow along a virtual wall, much like antenna-based wall following in cockroaches. © 2006 Springer.

The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand.

In this paper, we review the energy requirements to make materials on a global scale by focusing on the five construction materials that dominate energy used in material production: steel, cement, paper, plastics and aluminium. We then estimate the possibility of reducing absolute material production energy by half, while doubling production from the present to 2050. The goal therefore is a 75 per cent reduction in energy intensity. Four technology-based strategies are investigated, regardless of cost: (i) widespread application of best available technology (BAT), (ii) BAT to cutting-edge technologies, (iii) aggressive recycling and finally, and (iv) significant improvements in recycling technologies. Taken together, these aggressive strategies could produce impressive gains, of the order of a 50-56 per cent reduction in energy intensity, but this is still short of our goal of a 75 per cent reduction. Ultimately, we face fundamental thermodynamic as well as practical constraints on our ability to improve the energy intensity of material production. A strategy to reduce demand by providing material services with less material (called 'material efficiency') is outlined as an approach to solving this dilemma.

A simple criterion for filament break-up in drop-on-demand inkjet printing

Jets from drop-on-demand inkjet print-heads consist of a main drop with a trailing filament, which either condenses into the main drop, or breaks up into satellite drops. Filament behaviour is quantitatively similar to that of larger, free symmetrical filamentscan be predicted from the aspect ratio and Ohnesorge number. Symmetrical filaments generated from inkjet print-heads show the same behaviour. A simple model, based on competition between the processes of axial shortening and radial necking, predicts the critical aspect ratio below which the jet condenses into a single drop. The success of this simple criterion supports the underlying physical model. © 2013 American Institute of Physics.

Time-resolved particle image velocimetry within the nozzle of a drop-on-demand printhead

Time-resolved particle image velocimetry (PIV) has been performed inside the nozzle of a commercially available inkjet print-head to obtain the time-dependent velocity waveform. A printhead with a single transparent nozzle 80 μm in orifice diameter was used to eject single droplets at a speed of 5 m/s. An optical microscope was used with an ultra-high-speed camera to capture the motion of particles suspended in a transparent liquid at the center of the nozzle and above the fluid meniscus at a rate of half a million frames per second. Time-resolved velocity fields were obtained from a fluid layer approximately 200 μm thick within the nozzle for a complete jetting cycle. A Lagrangian finite-element numerical model with experimental measurements as inputs was used to predict the meniscus movement. The model predictions showed good agreement with the experimental results. This work provides the first experimental verification of physical models and numerical simulations of flows within a drop-on-demand nozzle. © 2012 Society for Imaging Science and Technology.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer

Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance. © 2013 Springer-Verlag.

Dopamine and performance in a reinforcement learning task: evidence from Parkinson's disease.

The role dopamine plays in decision-making has important theoretical, empirical and clinical implications. Here, we examined its precise contribution by exploiting the lesion deficit model afforded by Parkinson's disease. We studied patients in a two-stage reinforcement learning task, while they were ON and OFF dopamine replacement medication. Contrary to expectation, we found that dopaminergic drug state (ON or OFF) did not impact learning. Instead, the critical factor was drug state during the performance phase, with patients ON medication choosing correctly significantly more frequently than those OFF medication. This effect was independent of drug state during initial learning and appears to reflect a facilitation of generalization for learnt information. This inference is bolstered by our observation that neural activity in nucleus accumbens and ventromedial prefrontal cortex, measured during simultaneously acquired functional magnetic resonance imaging, represented learnt stimulus values during performance. This effect was expressed solely during the ON state with activity in these regions correlating with better performance. Our data indicate that dopamine modulation of nucleus accumbens and ventromedial prefrontal cortex exerts a specific effect on choice behaviour distinct from pure learning. The findings are in keeping with the substantial other evidence that certain aspects of learning are unaffected by dopamine lesions or depletion, and that dopamine plays a key role in performance that may be distinct from its role in learning.

Dopamine and performance in a reinforcement learning task: Evidence from Parkinson's disease

The role dopamine plays in decision-making has important theoretical, empirical and clinical implications. Here, we examined its precise contribution by exploiting the lesion deficit model afforded by Parkinson's disease. We studied patients in a two-stage reinforcement learning task, while they were ON and OFF dopamine replacement medication. Contrary to expectation, we found that dopaminergic drug state (ON or OFF) did not impact learning. Instead, the critical factor was drug state during the performance phase, with patients ON medication choosing correctly significantly more frequently than those OFF medication. This effect was independent of drug state during initial learning and appears to reflect a facilitation of generalization for learnt information. This inference is bolstered by our observation that neural activity in nucleus accumbens and ventromedial prefrontal cortex, measured during simultaneously acquired functional magnetic resonance imaging, represented learnt stimulus values during performance. This effect was expressed solely during the ON state with activity in these regions correlating with better performance. Our data indicate that dopamine modulation of nucleus accumbens and ventromedial prefrontal cortex exerts a specific effect on choice behaviour distinct from pure learning. The findings are in keeping with the substantial other evidence that certain aspects of learning are unaffected by dopamine lesions or depletion, and that dopamine plays a key role in performance that may be distinct from its role in learning. © 2012 The Author.

The use of Th in HTR: State of the art and implementation in Th/Pu fuel cycles

Nowadays nuclear is the only greenhouse-free source that can appreciably respond to the increasing worldwide energy demand. The use of Thorium in the nuclear energy production may offer some advantages to accomplish this task. Extensive R&D on the thorium fuel cycle has been conducted in many countries around the world. Starting from the current nuclear waste policy, the EU-PUMA project focuses on the potential benefits of using the HTR core as a Pu/MA transmuter. In this paper the following aspects have been analysed: (1) the state-of-the-art of the studies on the use of Th in different reactors, (2) the use of Th in HTRs, with a particular emphasis on Th-Pu fuel cycles, (3) an original assessment of Th-Pu fuel cycles in HTR. Some aspects related to Thorium exploitation were outlined, particularly its suitability for working in pebble-bed HTR in a Th-Pu fuel cycle. The influence of the Th/Pu weight fraction at BOC in a typical HTR pebble was analysed as far as the reactivity trend versus burn-up, the energy produced per Pu mass, and the Pu isotopic composition at EOC are concerned. Although deeper investigations need to be performed in order to draw final conclusions, it is possible to state that some optimized Th percentage in the initial Pu/Th fuel could be suggested on the basis of the aim we are trying to reach. Copyright © 2009 Guido Mazzini et al.

The temporal evolution of feedback gains rapidly update to task demands.

Recent theoretical frameworks such as optimal feedback control suggest that feedback gains should modulate throughout a movement and be tuned to task demands. Here we measured the visuomotor feedback gain throughout the course of movements made to "near" or "far" targets in human subjects. The visuomotor gain showed a systematic modulation over the time course of the reach, with the gain peaking at the middle of the movement and dropping rapidly as the target is approached. This modulation depends primarily on the proportion of the movement remaining, rather than hand position, suggesting that the modulation is sensitive to task demands. Model-predictive control suggests that the gains should be continuously recomputed throughout a movement. To test this, we investigated whether feedback gains update when the task goal is altered during a movement, that is when the target of the reach jumped. We measured the visuomotor gain either simultaneously with the jump or 100 ms after the jump. The visuomotor gain nonspecifically reduced for all target jumps when measured synchronously with the jump. However, the visuomotor gain 100 ms later showed an appropriate modulation for the revised task goal by increasing for jumps that increased the distance to the target and reducing for jumps that decreased the distance. We conclude that visuomotor feedback gain shows a temporal evolution related to task demands and that this evolution can be flexibly recomputed within 100 ms to accommodate online modifications to task goals.

Drop speeds from drop-on-demand ink-jet print heads

Measured drop speeds from a range of industrial drop-on-demand (DoD) ink-jet print head designs scale with the predictions of very simple physical models and results of numerical simulations. The main drop/jet speeds at a specified stand-off depend on fluid properties, nozzle exit diameter, and print head drive amplitude for fixed waveform timescales. Drop speeds from the Xaar, Spectra Dimatix, and MicroFab DoD print heads tested with (i) Newtonian, (ii) weakly elastic, and (iii) highly shear-thinning fluids all show a characteristic linear rise with drive voltage (setting) above an apparent threshold drive voltage. Jetting, simple modeling approaches, and numerical simulations of Newtonian fluids over the typical DoD printing range of surface tensions and viscosities were studied to determine how this threshold drive value and the slope of the characteristic linear rise depend on these fluid properties and nozzle exit area. The final speed is inversely proportional to the nozzle exit area, as expected from volume conservation. These results should assist specialist users in the development and optimization of DoD applications and print head design. For a given density, the drive threshold is determined primarily by viscosity, and the constant of proportionality k linking speed with drive above a drive threshold becomes independent of viscosity and surface tension for more viscous DoD fluid jetting. © 2013 Society for Imaging Science and Technology.

Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.