Identification of Health and Welfare Parameters for Rabbit Production and Definition of an Evaluation Score

The knowledge on rabbit welfare may be improved by the use of correct tools for monitoring the different aspects of rabbit industrial farming. Therefore, the aim of this study was to define parameters related to health and welfare of animals in industrial farms with intensive husbandry. Health, management, environmental and productive parameters were firstly characterized and then a protocol to assess welfare of rabbits was define. The research was conducted on 8 industrial farms from 2004 to 2007 and around 30 inspections were done in each farm. At each visit, the health conditions were established by： （1） necropsy on animals of different productive category; （2） specific laboratory investigations based on the lesions observed; （3） checking the presence of parasites in environmental faecal samples; （4） bacteriological examination of vaginal, nasal and rectal swabs of rabbit of different age. The immune conditions and the efficacy of vaccinations were measured by determining anti-Myxomatosis and anti-Rabbit Haemorrhagic Disease antibodies using competitive ELISAs, and anti-Encephalitozoon cunicoli antibodies by immunocarbonassay. The environmental conditions were evaluated by measuring air temperature, relative humidity, ammonia concentration and bacterial/fungal count. Finally the productive parameters were also recorded and elaborated. All the entered values were then utilized for defining a score system to establish health and welfare conditions.

A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine

Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.