Veterinary (Livestock

In this paper, we present a soft computing modelof decision support systems for diagnosing diseases andprescribing herbal prescriptions by oriental medicine.Inputs to the model are severities of observed symptoms onpatients and outputs from the model are a diagnosis of disease states and corresponding herbal prescriptions.First, having used fuzzy inferences, the most seriousdisease state in which the patient appears to be infected isdetermined. Next, an herbal prescription written insuitable herbs with reasonable amounts for treating thatdisease state is given by neural networks. Finally, wedescribe an application of this model in rheumatismdiagnoses and show its evaluations

Fairness evaluating effects of prescriptions is indispensably importance in disease treatment. The
development of Intelligence Technologies nowadays makes it realizable for a moderation of subjective
prescription evaluations in medicine. In this paper, we present an application of Self-Organizing Map (SOM) to
visualization of herbal prescription effects in Oriental Medicine (OM). The application is used to estimate
influence of herbal ingredients prescribed for patients who have several concurrent diseases. Inputs to the SOM
are herbal treatment prescriptions, and an output is a map that shows disease states that can be influenced by
these prescriptions. Training data is 133 standard prescriptions with 221 popular herbs in 17 disease groups
collected from oriental medical text books by experienced doctors. First, the prescription problem in OM is
described. Next, the system structure and implementation of the proposed system are shown. Finally, the
visualization abilities of the proposed system verified by experienced doctors are presented

There has been mounting environmental criticism of the livestock industry. Ruminants such as cattle have been implicated as significant contributors to greenhouse gases and it is argued that it is less food efficient to feed plant matter to livestock rather than have humans consume it directly. Furthermore, in many cases, livestock destroy natural vegetation and cause accelerated soil erosion as a result of overstocking often for socio-economic reasons. In other cases, such as in the Amazon rainforests have been cleared to provide pastures for cattle. Writers such as David Pearce and Charles Perrings have criticised socio-economic arrangements in Africa, e.g., in Botswana where the livestock industry involves common-property and forms of government assistance for grazing which are environmentally damaging. Furthermore, intensive livestock farming particularly in developed countries is a major source of pollutants. While some of these criticisms are justified, it is pointed out that there are counter considerations and that the livestock industry appears to have been discriminated against in some developing countries. The final portion of the paper reviews the development of the livestock industry in Thailand, the environmental issues raised by this development and the sustainability of the industry including aspects of animal health and diseases for its sustainability.

Multi-trait selection – how to implement? • Need to combine – the relative economic weights – genetic parameters (heritabilities, correlations) to determine the weights we put on the observed phenotypes Index = b 1 P 1 + b 2 P 2 Issues with MT selection • We have to spread our selection efforts over several traits, each of them weighted economically • Selection for one trait gives also a correlated response for other traits We need weights for selection criteria • Index = b 1 P 1 + b 2 P 2 + …. + b n P n Selection index with more information sources (multiple regression) p = vector with phenotypes (criteria) A = breeding objective (single trait here) var(p) = P = matrix = cov(p,A) = G = vector = A p weights: b = P-1 G Selection index with more information sources and with more objective traits (multiple regression) p = vector with phenotypes (criteria) H = breeding objective (multiple traits here) = a 1 A 1 + a 2 A 2 var(p) = P = matrix = cov(p,A) = G = matrix =