Dielectric heating is one of the most promising alternatives to conventional thermal treatment of fluid foods. Higher thermal efficiency and better heating uniformity of radio frequency and microwave processes have been proven successful in providing a similar or better bacterial and enzymatic inactivation in liquid and semi-solid foods while improving the sensory and nutritional qualities of the fresh product when compared to conventional pasteurization. However, further investigations are necessary to advance scaling up of applications at different frequencies and to better understand heat distribution and energy consumption of industrial dielectric heating operations.
This paper constitutes a step forward in resource-constrained scheduling theory. We introduce an O(n x alpha(n) x log n) checker for energetic reasoning (ER), where alpha(n) is Ackermann's inverse function. Until now, after more than 20 years of research, the best technique, recently obtained by Ouellet and Quimper, was in O(n log(2) n). We identify mathematical properties that allow the number of useful intervals to be reduced. We also discuss the interest of integrating this checker into industrial solvers developed for addressing optimisation problems.
Ground Penetrating Radar (GPR) is often regarded a prospecting tool of underground targets rather than rigorous measurement tool where measurement uncertainty must be accompanied. This paper attempts to make use of GPR as a measurement tool by developing a simple model and evaluating the uncertainty of underground target's depth estimation in two ways: (1) prior to GPR survey and (2) after GPR survey, for practical use by the partitioners. For (1), the expected uncertainty prior to GPR survey was modelled based on the estimation of the assumed host material's dielectric permittivity and the assumed two-way travel time subject to required target depth. For (2), the expanded uncertainty of the estimated cover depth by GPR was propagated from the errors in the measured pairs of (x(i), t(i)) of hyperbolas in the radargram. This effort attempts to simplify the complex computation in Xie et al. (2021a, 2021b) by assuming negligible uncertainties of buried target diameter and distance of GPR antenna separation. Experiments were carried out on various host materials to validate the model with a common offset antenna of nominal centre frequency at 400 MHz. The experimental results shows that (1) the expected uncertainty prior to survey was strongly dependent on the increase of the target depth. But in the evaluated uncertainty after the GPR survey, this strong dependency is replaced by the scattering noise in the reflection data (x(i), t(i)). (2) The maximum percentage SD (i.e. error) is 11.5% of detected depth at 95% confidence level. This percentage is smaller than the the most stringent accuracy requirement in PAS 128 (15% of the detected depth) (ICE, 2014). This work contributes to the accuracy evaluation of the estimated depths of underground targets. The expected uncertainty of depth prior to GPR survey can provide a reference for the practitioners and clients during engineering, e.g. underground trenchless tunneling project). And the evaluated uncertainty after GPR survey turns a uncertain survey to a serious measurement reflecting the quality and reliability of the estimation results by GPR.
The quantity of antimicrobials used in monogastric production (swine, poultry and rabbit) has dropped since the 2000s, and is now relatively stationary. The successive EcoAntibio plans have strengthened the momentum and contributed to drastically reducing the use of critically impor-tant antimicrobials. This results from the combined effect of regulatory changes, private voluntary actions implemented in the different sectors, as well as collective and individual professional approaches. Different preventive approaches have been implemented, based on a multifactorial approach of animal health, the refinement of diagnosis of health troubles, and analysis of the causes to define suitable preventive measures. The emphasis has been put on farm management, hygiene, biosecurity, vaccination, nutrition, and the use of alternative products. Antimicrobial prescription practices have also evolved, with establishment of consensual good treatment practice guidelines, generalization of bacteriological testing and antibiograms, correct compliance with dosage, and close health monitoring to tailor treatments. These changes rely on a good rela-tionship between the farmer, the veterinarian and the technician, which has been reinforced through support and training of farmers. Further rationalization of antibimicrobial use needs to target "at-risk farms >> and tailor-made actions.
The behaviour and movement of lame dairy cows at pasture have been studied little, yet they could be relevant to improve the automatic detection of lameness in cows in pasture-based systems. Our aim in this study is to identify behavioural and movement variables of dairy cows at pasture that could discriminate lameness scores. Individual cow behaviours were predicted from accelerometer data and movements measured using GPS data. Sixty-eight dairy cows from three pasture-based commercial farms were equipped with a 3-D accelerometer and a GPS sensor fixed on a neck collar for 1-5 weeks, depending on the farm, in spring and summer 2018. A lameness score was assigned to each cow by a trained observer twice a week. Behaviours were predicted every 10 s based on accelerometer data, and then combined with the GPS position. Segmentation on behavioural time series was used to delineate each behavioural bout within each outdoor period. Thirty-seven behavioural and movement variables were then calculated from the behavioural bouts for each cow. A partial least square discriminant analysis was performed to identify the variables that best discriminate lameness scores. Time spent grazing, grazing bout duration, duration before lying down in the pasture, time spent resting, number of resting bouts, distance travelled during grazing, and dispersion were the most discriminant variables in the PLS-DA (VIP > 1). Severely lame cows spent 4.5 times less time grazing and almost twice as much time resting as their sound congeners, especially in the lying position. Exploratory behaviour was also reduced for both moderately and severely lame cows, resulting in 1.2 and 1.7 times less distance travelled respectively, especially during grazing. These variables could be used as additional variables to improve the performance of existing lameness detection devices in pasture-based systems.
