WELFARE, PRODUCTIVITY AND QUALITATIVE TRAITS OF EGG IN LAYING HENS REARED UNDER DIFFERENT REARING SYSTEMS

The welfare, production performance and some qualitative characteristics of eggs obtained under three different rearing systems (conventional, organic and organic-plus) were compared. Three homogeneous groups, each of 120 White Leghorn hens, fed the same diets, were assigned to different rearing systems and data were recorded for 1 year. The welfare indicators were the following: first impact, behavioural patterns, tonic immobility and plumage status. Productive performance was recorded (% deposition; egg weight) and some qualitative traits (Haugh index, yolk colour, yolk, albumen and egg shell weight) were evaluated. Well-being was greatly affected by rearing system. The best welfare status was observed in hens of the organic-plus group, whereas the worst was in the conventional group (caged hens). Caged hens showed little interest or fear of observers, at times they had high tonic immobility and some aggressive pecking; the status of their plumage was very poor. On the contrary, caged hens produced more eggs, even if their qualitative traits (Haugh index and yolk colour) were worse than the organicplus eggs. The intense motor activity of organic hens and the concurrent intake of grass reduced their productive level; further egg deposition seemed more affected by seasonal variation

Adversarial Data Augmentation for HMM-based Anomaly Detection

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for gener- ating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant perfor- mance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification), and the use of HMMs (in contrast to neural networks)

Comparing the Hydraulic Properties of Forested and Grassed Soils on an Experimental Hillslope in a Mediterranean Environment

AbstractThis experimental research compares the physical and hydraulic properties of two adjacent soils, one covered with a native forest of Mediterranean maquis, and the other with spontaneous grass. The latter replaced the previous natural forest. The aim is to quantify the significant differences in the soil properties caused by the removal of the natural vegetation. Although the soil texture was similar in the different land uses, the soil under the forest had a higher organic matter content, a lower apparent density and a higher water content at saturation than the grassed soil. The analysis of the water retention characteristics indicated that the retained water content of the forest soil exceeded that of the grassed soil in the range from saturation to -50cm of water tension. This suggests that changing the land use altered the soil pore structure within this range. The hydraulic conductivity of the forest soil exceeded that of the grassed soil at water tensions of -10, -5 and -3cm. Conversely the hydraulic conductivity of the grassed soil was similar to that of the forest soil at -1cm of water tension and at saturation. This result was probably due to the hydraulic activation of the desiccation cracks in the grassed soil. This increased the amount of infiltrated water in saturated and near-saturated soil conditions.This work shows that changes in land use have an unfavorable impact on the physical and hydraulic properties of the soil. Soil covered with grass is more vulnerability to water erosion than that under forest, and there is likely to be general worsening of flow regimes

eXplainable Modeling (XM): Data Analysis for Intelligent Agents

Intelligent agents perform key tasks in several application domains by processing sensor data and taking actions that maximize reward functions based on internal models of the environment and the agent itself. In this paper we present eXplainable Modeling (XM), a Python software which supports data analysis for intelligent agents. XM enables to analyze state-models, namely models of the agent states, discovered from sensor traces by data-driven methods, and to interpret them for improved situation awareness. The main features of the tool are described through the analysis of a real case study concerning aquatic drones for water monitoring

The Time of Flight System of the AMS-02 Space Experiment

The Time-of-Flight (TOF) system of the AMS detector gives the fast trigger to the read out electronics and measures velocity, direction and charge of the crossing particles. The new version of the detector (called AMS-02) will be installed on the International Space Station on March 2004. The fringing field of the AMS-02 superconducting magnet is $1.0\div2.5$ kG where the photomultiplers (PM) are installed. In order to be able to operate with this residual field, a new type of PM was chosen and the mechanical design was constrained by requiring to minimize the angle between the magnetic field vector and the PM axis. Due to strong field and to the curved light guides, the time resolution will be $150\div180$ ps, while the new electronics will allow for a better charge measurement.Comment: 5 pages, 4 figures. Proc. of 7th Int. Conf. on Adv. Tech. and Part. Phys., 15-19 October 2001,Como (Italy

Monte Carlo Tree Search Planning for continuous action and state space

Sequential decision-making in real-world environments is an important problem of artificial intelligence and robotics. In the last decade reinforcement learning has provided effective solutions in small and simulated environments but it has also shown some limits on large and real-world domains characterized by continuous state and action spaces. In this work, we aim to evaluate some state-of-the-art algorithms based on Monte Carlo Tree Search planning in continuous state/action spaces and propose a first version of a new algorithm based on action widening. Algorithms are evaluated on a synthetic domain in which the agent aims to control a car through a narrow curve for reaching the goal in the shortest possible time and avoiding the car going off the road. We show that the proposed method outperforms the state-of-the-art techniques