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This paper examines the development of Italian asylum policy, especially in the light of major changes in Italian politics & Italy's shift from being a country of emigration to a country of immigration. The last general elections in 1996 were won, for the first time, by a coalition of Center-Left-oriented parties (Ulivo). An analysis of the asylum policies of previous & current governments is developed. Refugee-related issues moved up the political agenda in the 1990s, especially after mass influxes of displaced people from eastern countries & from the Middle East. The programs of the two main party coalitions that ran for the 1996 elections are scrutinized as well as the actions of the coalitions that formed governments. Policy at both national & European Union levels is analyzed. Currently there is a new bill of law in place through which Ulivo is trying to enforce the constitutional rule on asylum & broaden awareness of the importance of European Union policy on asylum. This marks a turning point in Italian policy on asylum, but nevertheless, neither a complete & deep understanding of the issue, nor a clear policy, has yet been developed in Italy. 2 Tables, 23 References. Adapted from the source document.

SIMPLE SUMMARY: Scoring lesions in slaughtered pigs can provide useful feedback to the swine industry, although the systematic recording of lesions is very challenging and time consuming. Artificial intelligence offers interesting opportunities to solve highly repetitive tasks, such as those performed by veterinarians at postmortem inspection in high-throughput slaughterhouses and to consistently analyze large amounts of data. The present investigation indicates that enzootic pneumonia-like lesions can be effectively detected and quantified through artificial intelligence methods under routine slaughter conditions. ABSTRACT: The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.