Developing an On-Line Interactive Health Psychology Module.

On-line teaching material in health psychology was developed which ensured a range of students could access appropriate material for their course and level of study. This material has been developed around the concept of smaller 'content chunks' which can be combined into whole units of learning (topics), and ultimately, a module. On the basis of the underlying philosophy that the medium is part of the message, we considered interactivity to be a key element in engaging the student with the material. Consequently, the key aim of this development was to stimulate and engage students, promoting better involvement with the academic material, and hence better learning. It was hoped that this was achieved through the development of material including linked programmes and supporting material, small Java Scripts and basic email, forms and HTML additions. This material is outlined as are some of the interactive activities introduced, and the preliminary student and tutor experience described

Comparison of modelling techniques for milk-production forecasting

peer-reviewedThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions

Magnetic Flux Transport at the Solar Surface

After emerging to the solar surface, the Sun's magnetic field displays a complex and intricate evolution. The evolution of the surface field is important for several reasons. One is that the surface field, and its dynamics, sets the boundary condition for the coronal and heliospheric magnetic fields. Another is that the surface evolution gives us insight into the dynamo process. In particular, it plays an essential role in the Babcock-Leighton model of the solar dynamo. Describing this evolution is the aim of the surface flux transport model. The model starts from the emergence of magnetic bipoles. Thereafter, the model is based on the induction equation and the fact that after emergence the magnetic field is observed to evolve as if it were purely radial. The induction equation then describes how the surface flows -- differential rotation, meridional circulation, granular, supergranular flows, and active region inflows -- determine the evolution of the field (now taken to be purely radial). In this paper, we review the modeling of the various processes that determine the evolution of the surface field. We restrict our attention to their role in the surface flux transport model. We also discuss the success of the model and some of the results that have been obtained using this model.Comment: 39 pages, 15 figures, accepted for publication in Space Sci. Re

Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system

peer-reviewedThe aim of this experiment was to assess strategies to reduce milking time in a pasture-based automatic milking system (AMS). Milking time is an important factor in automatic milking because any reductions in box time can facilitate more milkings per day and hence higher production levels per AMS. This study evaluated 2 end-of-milking criteria treatments (teatcup removal at 30% and 50% of average milk flowrate at the quarter-level), 2 milking system vacuum treatments (static and dynamic, where the milking system vacuum could change during the peak milk flowrate period), and the interaction of these treatment effects on milking time in a Lely Astronaut A4 AMS (Maassluis, the Netherlands). The experiment was carried out at the research facility at Teagasc Moorepark, Cork, Ireland, and used 77 spring-calved cows, which were managed on a grass-based system. Cows were 179 DIM, with an average parity of 3. No significant differences in milk flowrate, milk yield, box time, milking time, or milking interval were found between treatments in this study on cows milked in an AMS on a pasture-based system. Average and peak milk flowrates of 2.15 kg/min and 3.48 kg/min, respectively, were observed during the experiment. Small increases in maximum milk flowrate were detected (+0.09 kg/min) due to the effect of increasing the system vacuum during the peak milk flow period. These small increases in maximum milk flowrate were not sufficient to deliver a significant reduction in milking time or box time. Furthermore, increasing the removal setting from 30% of the average milk flowrate to 50% of the average milk flowrate was not an effective means of reducing box time, because the resultant increase in removal flowrate of 0.12 kg/min was not enough to deliver practical or statistically significant decreases in milking time or box time. Hence, to make significant reductions in milking time, where cows have an average milk flow of 2 kg/min and yield per milking of 10 kg, end-of-milking criteria above 50% of average milk flowrate at the quarter level would be required

Doping Evolution of Magnetic Order and Magnetic Excitations in (Sr1−x_{1-x}Lax_x)3_3Ir2_2O7_7

We use resonant elastic and inelastic X-ray scattering at the Ir-$L_3$ edge to study the doping-dependent magnetic order, magnetic excitations and spin-orbit excitons in the electron-doped bilayer iridate (Sr$_{1-x}$La$_{x}$)$_3$Ir$_2$O$_7$ ($0 \leq x \leq 0.065$). With increasing doping $x$, the three-dimensional long range antiferromagnetic order is gradually suppressed and evolves into a three-dimensional short range order from $x = 0$ to $0.05$, followed by a transition to two-dimensional short range order between $x = 0.05$ and $0.065$. Following the evolution of the antiferromagnetic order, the magnetic excitations undergo damping, anisotropic softening and gap collapse, accompanied by weakly doping-dependent spin-orbit excitons. Therefore, we conclude that electron doping suppresses the magnetic anisotropy and interlayer couplings and drives (Sr$_{1-x}$La$_x$)$_3$Ir$_2$O$_7$ into a correlated metallic state hosting two-dimensional short range antiferromagnetic order and strong antiferromagnetic fluctuations of $J_{\text{eff}} = \frac{1}{2}$ moments, with the magnon gap strongly suppressed.Comment: 6 Pages, 3 Figures, with supplementary in Sourc

A method for assessing liner performance during the peak milk flow period

peer-reviewedThe objective of this study was to develop a method to quantify the milking conditions under which circulatory impairment of teat tissues occurs during the peak flow period of milking. A secondary objective was to quantify the effect of the same milking conditions on milk flow rate during the peak flow rate period of milking. Additionally, the observed milk flow rate was a necessary input to the calculation of canal area, our quantitative measure of circulatory impairment. A central composite experimental design was used with 5 levels of each of 2 explanatory variables (system vacuum and pulsator ratio), creating 9 treatments including the center point. Ten liners, representing a wide range of liner compression (as indicated by overpressure), were assessed, with treatments applied using a novel quarter-milking device. Eight cows (32 cow-quarters) were used across 10 separate evening milkings, with quarter being the experimental unit. The 9 treatments, with the exception of a repeated center point, were randomly applied to all quarters within each individual milking. Analysis was confined to the peak milk flow period. Milk flow rate (MFR) and teat canal cross sectional area (CA) were normalized by dividing individual MFR, or CA, values by their within-quarter average value across all treatments. A multiple explanatory variable regression model was developed for normalized MFR and normalized CA. The methods presented in this paper provided sufficient precision to estimate the effects of vacuum (both at teat-end and in the liner mouthpiece), pulsation, and liner compression on CA, as an indicator of teat-end congestion, during the peak flow period of milking. Liner compression (as indicated by overpressure), teat-end vacuum, vacuum in the liner mouthpiece, milk-phase time, and their interactions are all important predictors of MFR and teat-end congestion during the peak milk flow period of milking. Increasing teat-end vacuum and milk-phase time increases MFR and reduces CA (indicative of increased teat-end congestion). Increasing vacuum in the liner mouthpiece also acts to reduce CA and MFR. Increasing liner compression reduces the effects of teat-end congestion, resulting in increased MFR and increased CA at high levels of teat-end vacuum and milk-phase time. These results provide a better understanding of the balance between milking speed and milking gentleness

Structure and functional motifs of GCR1, the only plant protein with a GPCR fold?

Whether GPCRs exist in plants is a fundamental biological question. Interest in deorphanizing new G protein coupled receptors (GPCRs), arises because of their importance in signaling. Within plants, this is controversial as genome analysis has identified 56 putative GPCRs, including GCR1 which is reportedly a remote homologue to class A, B and E GPCRs. Of these, GCR2, is not a GPCR; more recently it has been proposed that none are, not even GCR1. We have addressed this disparity between genome analysis and biological evidence through a structural bioinformatics study, involving fold recognition methods, from which only GCR1 emerges as a strong candidate. To further probe GCR1, we have developed a novel helix alignment method, which has been benchmarked against the the class A – class B - class F GPCR alignments. In addition, we have presented a mutually consistent set of alignments of GCR1 homologues to class A, class B and class F GPCRs, and shown that GCR1 is closer to class A and /or class B GPCRs than class A, class B or class F GPCRs are to each other. To further probe GCR1, we have aligned transmembrane helix 3 of GCR1 to each of the 6 GPCR classes. Variability comparisons provide additional evidence that GCR1 homologues have the GPCR fold. From the alignments and a GCR1 comparative model we have identified motifs that are common to GCR1, class A, B and E GPCRs. We discuss the possibilities that emerge from this controversial evidence that GCR1 has a GPCR fol