PERFORMANCE AND BEHAVIOUR OF CHICKENS WITH DIFFERENT GROWING RATE REARED ACCORDING TO THE ORGANIC SYSTEM

The performance and the behaviour of three different chicken strains, reared according to the EEC-Regulation 1804/1999 organic system, were compared. The strains had very slow (Robusta maculata), slow (Kabir) and fast (Ross) growing rates, respectively. The trial was carried out on 200 chickens (male and female) per strain. Rearing lasted 81 days as required by the EEC Regulations. At slaughter age, 20 birds per group were killed. Robusta maculata and Kabir chickens showed more intense walking activity and better foraging aptitude; their antioxidant capacity was also superior. Ross chickens had a good growth rate and feed conversion index, reaching an excellent body weight, but the mortality and the culling rate were high indicating that fast-growing strains do not adapt well to organic production. Robusta maculata showed the worst productive performance although the mortality was low and Kabir birds gave intermediate results. The carcass traits were the best in Ross and the poorest in Robusta maculata. Male chickens were heavier and leaner than females

Learning state-variable relationships for improving POMCP performance

We address the problem of learning state-variable relationships across different episodes in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and we represent the acquired knowledge with Markov Random Fields (MRFs). We propose three different methods to compute MRF parameters while the agent acts in the environment. Our tech- niques acquire information from agent action outcomes, and from the belief of the agent, which summarizes the knowledge acquired from observations. We also propose a stopping criterion to deter- mine when the MRF is accurate enough and the learning process can be stopped. Results show that the proposed approach allows to effectively learn state-variable probabilistic constraints and to outperform standard POMCP with no computational overhead

Blood Rheology in Marine Mammals

The field of blood oxygen transport and delivery to tissues has been studied by comparative physiologists for many decades. Within this general area, the particular differences in oxygen delivery between marine and terrestrial mammals has focused mainly on oxygen supply differences and delivery to the tissues under low blood flow diving conditions. Yet, the study of the inherent flow properties of the blood itself (hemorheology) is rarely discussed when addressing diving. However, hemorheology is important to the study of marine mammals because of the critical nature of the oxygen stores that are carried in the blood during diving periods. This review focuses on the essential elements of hemorheology, how they are defined and on fundamental rheological applications to marine mammals. While the comparative rationale used throughout the review is much broader than the particular problems associated with diving, the basic concepts focus on how changes in the flow properties of whole blood would be critical to oxygen delivery during diving. This review introduces the reader to most of the major rheological concepts that are relevant to the unique and unusual aspects of the diving physiology of marine mammals

COMPARISON OF TWO CHICKEN GENOTYPES ORGANICALLY REARED: OXIDATIVE STABILITY AND OTHER QUALITATIVE TRAITS

The effect of genotype on the oxidative stability and other qualitative traits of chicken meat was studied. Two groups of 200 chicks (Ross 205 and Kabir) were reared according to the organic farming system. At 81 d of age 20 birds per group were slaughtered and after refrigeration (24 h at 4°C) of the carcasses, Pectoralis major muscles were excised for analyses.Samples were analysed after 0, 24, 48, 72 and 96 hours of storage at 4°C under continuous fluorescent illumination (2300 lux). The analyses concerned the chemical composition and the shear force (only at time 0) and the progress of several traits as pH, CIELAB values, Thiobarbituric Acid Reactive Substances (TBARS), panel test and fatty acid composition (at 0 and after 96 h). Genotype greatly affected the physico-chemical characteristics and the sensory evaluation. The meat from Ross chickens showed high TBARS values, perhaps due to selection for growth rate that reduced their adaptability to greater space allowance and to poorer environmental conditions; these higher TBARS values were also negatively correlated to lightness and yellowness. The initial level of TBARS affected the oxidative stability of breast meat during storage. The amount of TBARS showed significantly negative relationship with the sensory evaluation; breast meat of Kabir had higher scores for liking when the level of malondialdehyde was less than 2.5 mg kg-1

Convolutional Neural Network and Stochastic Variational Gaussian Process for Heating Load Forecasting

Heating load forecasting is a key task for operational planning in district heating networks. In this work we present two advanced models for this purpose, namely a Convolutional Neural Network (CNN) and a Stochastic Variational Gaussian Process (SVGP). Both models are extensions of an autoregressive linear model available in the literature. The CNN outperforms the linear model in terms of 48-h prediction accuracy and its parameters are interpretable. The SVGP has performance comparable to the linear model but it intrinsically deals with prediction uncertainty, hence it provides both load estimations and confidence intervals. Models and performance are analyzed and compared on a real dataset of heating load collected in an Italian network

Learning environment properties in Partially Observable Monte Carlo Planning

We tackle the problem of learning state-variable relationships in Partially Observable Markov Decision Processes to improve planning performance on mobile robots. The proposed approach extends Partially Observable Monte Carlo Planning (POMCP) and represents state-variable relationships with Markov Random Fields. A ROS-based implementation of the approach is proposed and evaluated in rocksample, a standard benchmark for probabilistic planning under uncertainty. Experiments have been performed in simulation with Gazebo. Results show that the proposed approach allows to effectively learn state- variable probabilistic constraints on ROS-based robotic platforms and to use them in subsequent episodes to outperform standard POMC

SUSTAINABILITY OF POULTRY PRODUCTION USING THE EMERGY APPROACH: COMPARISON OF CONVENTIONAL AND ORGANIC REARING SYSTEMS

The organic production system is an important strategy, compatible with sustainable agriculture, avoiding the use of chemical compounds,limiting the intensity of production and providing controls along the entire chain of production. The aim of this study is to compare conventional and organic poultry production in terms of emergy analysis. The main differences in the two systems were the emergy cost for poultry feed and for cleaning/sanitization of the buildings between successive productive cycles. In both production systems the poultry feed represented more than 50% of the emergy flow. Regarding the agronomic phase, it was shown that almost all the organic crops, avoiding chemical fertilizers and pesticides, saved around 60% emergy. The emergetic costs for housing of the birds were very similar in both systems. Relating the emergy results with productive performance it is possible to show that, although the annual productive performance was much lower in organic than in conventional (206%), transformity of organic poultry was around 10% lower. Comparison of the organic poultry system with a conventional one from the viewpoint of sustainability showed that all the emergy-based indicators are in favour of the organic farming system with a higher efficiency in transforming the available inputs in the final product, a higher level of renewable inputs, a higher level of local inputs and a lower density of energy and matter flows

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

Unsupervised activity recognition for autonomous water drones

We propose an automatic system aimed at discovering relevant activities for aquatic drones employed in water monitoring applications. The methodology exploits unsupervised time series segmentation to pursue two main goals: i) to support on-line decision making of drones and operators, ii) to support off-line analysis of large datasets collected by drones. The main novelty of our approach consists of its unsupervised nature, which enables to analyze unlabeled data. We investigate different variants of the proposed approach and validate them using an annotated dataset having labels for activity \u201cupstream/downstream navigation\u201d. Obtained results are encouraging in terms of clustering purity and silhouette which reach values greater than 0.94 and 0.20, respectively, in the best models