A mechanistic inter-species comparison of flicker sensitivity

AbstractThe general validity of both the Rovamo [Vision Res. 39 (1999) 533] and Barten (Contrast sensitivity of the human eye, SPIE Optical Engineering Press, 1999), modulation transfer function models for describing flicker sensitivity in vertebrates was examined using published data for goldfish, chickens, tree shrews, ground squirrels, cats, pigeons and humans. Both models adequately described the flicker response in each species at frequencies greater than approximately 1 Hz. At lower frequencies, response predictions differed between the two models and this was due, in part, to dissimilar definitions of the role played by lateral inhibition in the retina. Modelled flicker sensitivity for a matched retinal illuminance condition enabled a direct inter-species comparison of signal processing response times at the photoreceptor level. The modelled results also quantified differences between species in post-retinal signal processing capability. Finally, the relationship between flicker frequency response curves and the perception of temporal signals in real visual scenes was examined for each species. It is proposed that the area under the flicker sensitivity function may offer a single “figure of merit” for specifying overall sensitivity to time signals in a species’ environment

Estimation of the number and demographics of companion dogs in the UK

<p>Abstract</p> <p>Background</p> <p>Current estimates of the UK dog population vary, contain potential sources of bias and are based on expensive, large scale, public surveys. Here, we evaluate the potential of a variety of sources for estimation and monitoring of the companion dog population in the UK and associated demographic information. The sources considered were: a public survey; veterinary practices; pet insurance companies; micro-chip records; Kennel Club registrations; and the Pet Travel Scheme. The public survey and subpopulation estimates from veterinary practices, pet insurance companies and Kennel Club registrations, were combined to generate distinct estimates of the UK owned dog population using a Bayesian approach.</p> <p>Results</p> <p>We estimated there are 9.4 (95% CI: 8.1-11.5) million companion dogs in the UK according to the public survey alone, which is similar to other recent estimates. The population was judged to be over-estimated by combining the public and veterinary surveys (16.4, 95% CI: 12.5-21.5 million) and under-estimated by combining the public survey and insured dog numbers (4.8, 95% CI: 3.6-6.9 million). An estimate based on combining the public survey and Kennel Club registered dogs was 7.1 (95% CI: 4.5-12.9) million. Based on Bayesian estimations, 77 (95% CI: 62-92)% of the UK dog population were registered at a veterinary practice; 42 (95% CI: 29-55)% of dogs were insured; and 29 (95% CI: 17-43)% of dogs were Kennel Club registered. Breed demographics suggested the Labrador was consistently the most popular breed registered in micro-chip records, with the Kennel Club and with J. Sainsbury's PLC pet insurance. A comparison of the demographics between these sources suggested that popular working breeds were under-represented and certain toy, utility and miniature breeds were over- represented in the Kennel Club registrations. Density maps were produced from micro-chip records based on the geographical distribution of dogs.</p> <p>Conclusions</p> <p>A list containing the breed of each insured dog was provided by J. Sainsbury's PLC pet insurance without any accompanying information about the dog or owner.</p

Neural Predictive Control of Broiler Chicken Growth

Active control of the growth of broiler chickens has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent broiler chicken growth using a recently developed nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler weights accurately followed the desired growth curves ranging from $-12$ to +12% of the standard curve. The overall mean relative error between the desired and achieved broiler weight was 1.8% for the period from day 12 to day 51

Neural predictive control of broiler chicken and pig growth

Active control of the growth of broiler chickens and pigs has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health in broiler chickens. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent animal growth using a nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predictive control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler and pig growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler and pig weights accurately followed the desired growth curves ranging from to +12% and to +20% of the standard curve for broiler chickens and pigs, respectively. The overall mean relative error between the desired and achieved broiler or pig weight was 1.8% for the period from day 12 to day 51 and 10.5% for the period from week 5 to week 21, respectively

Livestock Production in the UK in the 21 st Century: A Perfect

animal