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

Factors Influencing the Movement of Livestock Guardian Dogs in the Edwards Plateau of Texas: Implications for Efficacy, Behavior, and Territoriality

Livestock guardian dog (Canis lupus familiaris; LGD) breeds of domestic dog worldwide provide a degree of control over predation losses. The application of LGDs as a wildlife damage management tool evolved as a cultural practice in the Old World. In the 1970s, this tool emerged in North America. Despite several decades of science and application, gaps still exist in our knowledge regarding applications for LGDs. From February 2016 to November 2017, we deployed global positioning system transmitters on 4 LGDs on a 20-km2 ranch in Menard County, Texas, USA operated by Texas A&M AgriLife Research to investigate their fine scale movement and activity patterns, site fidelity to livestock management units (i.e., pastures), and fidelity to anthropogenic features, such as feed and water locations. The LGDs remained within study site boundaries for 90% of the study period. Additionally, daily activity patterns differed for dogs associated primarily with sheep (Ovis aries) and goats (Capra aegagrus hircus). All of the LGDs we studied were active throughout the 24-hour day. We determined that feed and water locations concentrated LGD activity to an extent, likely reflecting a livestock affinity for water sources, and provide an additional method by which to distribute them over the landscape. Our results, based on a small sample size, suggest that LGDs may provide effective association with livestock management areas, maintain a high fidelity to area perimeter boundaries, and distribute themselves across the area of use

Does repetitive task training improve functional activity after stroke? A Cochrane systematic review and meta-analysis.

Repetitive task training resulted in modest improvement across a range of lower limb outcome measures, but not upper limb outcome measures. Training may be sufficient to have a small impact on activities of daily living. Interventions involving elements of repetition and task training are diverse and difficult to classify: the results presented are specific to trials where both elements are clearly present in the intervention, without major confounding by other potential mechanisms of action

Aphid Species Specializing on Milkweed Harbor Taxonomically Similar Bacterial Communities That Differ in Richness and Relative Abundance of Core Symbionts

Host plant range is arguably one of the most important factors shaping microbial communities associated with insect herbivores. However, it is unclear whether host plant specialization limits microbial community diversity or to what extent herbivores sharing a common host plant evolve similar microbiomes. To investigate whether variation in host plant range influences the assembly of core herbivore symbiont populations we compared bacterial diversity across three milkweed aphid species (Aphis nerii, Aphis asclepiadis, Myzocallis asclepiadis) feeding on a common host plant (Asclepias syriaca) using 16S rRNA metabarcoding. Overall, although there was significant overlap in taxa detected across all three aphid species (i.e. similar composition), some structural differences were identified within communities. Each aphid species harbored bacterial communities that varied in terms of richness and relative abundance of key symbionts. However, bacterial community diversity did not vary with degree of aphid host plant specialization. Interestingly, the narrow specialist A.asclepiadis harbored significantly higher relative abundances of the facultative symbiont Arsenophonus compared to the other two aphid species. Although many low abundance microbes were shared across all milkweed aphids, key differences in symbiotic partnerships were observed that could influence host physiology or additional ecological variation in traits that are microbially-mediated. Overall, this study suggests overlap in host plant range can select for taxonomically similar microbiomes across herbivore species, but variation in core aphid symbionts within these communities may still occur

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed

Molecule Microscopy

Contains research objectives and reports on seven research projects.Whitaker Health Sciences FundFrancis L. Friedman ChairNational Institutes of Health (Grant AM-31546)National Institutes of Health (Grant AM-25535)International Business Machines, Inc