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Abstract Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Research on drone systems is expanding rapidly and finds application in many fields. These systems provide powerful and precise means in several domains from surveillance to agriculture. If we look inside a drone system, we will find several parts: the aerial vehicle(s), the onboard sensors, a computing device, and the autopilot, a communication network, a ground control station, control software, and a security module, encrypting commands and messages. In a multi-drone application, the system becomes more complex and coordination along with synchronization of the Unmanned Aerial Vehicles (UAVs) forms the strategic parts of the whole system. This manuscript focuses on an Intelligence, Surveillance and Reconnaissance (ISR) application and investigates a complete HW/SW architecture for a number of deployments where the secure control architecture is based on the MAVLink protocol for automatic control of the UAVs fully enabled by audio and video acquisition and processing. Indeed, one of the key features and novelties introduced by this proposal is the combined use of audio and video inputs, in order to provide a more precise and robust detection of targets and the related reaction of the system. The proposed system setup, user control scheme, ciphering and synchronization methods, and the related experiments are explored for laying the basis for future ideas and developments. [ABSTRACT FROM AUTHOR]

Introduction: Porcine circovirus 2 (PCV-2) is a key pathogen for the swine industry at a global level. Nine genotypes, differing in epidemiology and potentially virulence, emerged over time, with PCV-2a, -2b, and -2d being the most widespread and clinically relevant. Conversely, the distribution of minor genotypes appears geographically and temporally restricted, suggesting lower virulence and different epidemiological drivers. In 2022, PCV-2e, the most genetically and phenotypically divergent genotype, was identified in multiple rural farms in North-eastern Italy. Since rural pigs often have access to outdoor environment, the introduction from wild boars was investigated. Methods: Through a molecular and spatial approach, this study investigated the epidemiology and genetic diversity of PCV-2 in 122 wild boars across different provinces of North-eastern Italy. Results: Molecular analysis revealed a high PCV-2 frequency (81.1%, 99/122), and classified the majority of strains as PCV-2d (96.3%, 78/81), with sporadic occurrences of PCV-2a (1.2%, 1/81) and PCV-2b (2.5%, 2/81) genotypes. A viral flow directed primarily from domestic pigs to wild boars was estimated by phylogenetic and phylodynamic analyses. Discussion: These findings attested that the genotype replacement so far described only in the Italian domestic swine sector occurred also in wild boars. and suggested that the current heterogeneity of PCV-2d strains in Italian wild boars likely depends more on different introduction events from the domestic population rather than the presence of independent evolutionary pressures. While this might suggest PCV-2 circulation in wild boars having a marginal impact in the industrial sector, the sharing of PCV-2d strains across distinct wild populations, in absence of a consistent geographical pattern, suggests a complex interplay between domestic and wild pig populations, emphasizing the importance of improved biosecurity measures to mitigate the risk of pathogen transmission. [ABSTRACT FROM AUTHOR]