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In the last ten years, automatic Text Categorization (TC) has been gaining an increasing interest from the research community, due to the need to organize a massive number of digital documents. Following a machine learning paradigm, this paper presents a model which regards TC as a classification task supported by a wrapper approach and combines the utilization of a Genetic Algorithm (GA) with a filter. First, a filter is used to weigh the relevance of terms in documents. Then, the top-ranked terms are grouped in several nested sets of relatively small size. These sets are explored by a GA which extracts the subset of terms that best categorize documents. Experimental results on the Reuters-21578 dataset state the effectiveness of the proposed model and its competitiveness with the learning approaches proposed in the TC literature.

The number of individuals aged sixty and older is expected to increase from 605 million in 2000 to over two billion in 2050. As a consequence the number of elderly medicare beneficiaries likely to be frail will triple. Our study explored the factors of successful aging in a sample of nonagenarians with no cognitive impairment, temporarily admitted to a rehabilitation hospital complex, with regard to quality of life, mood, clinical and cognitive conditions and personality traits. It was a descriptive study with forty nonagenarians of both genders, with no cognitive impairment, no psychiatric diseases and forty two younger patients with similar diseases and socio-demographic conditions, but aged <90. The sample was recruited randomly among patients temporarily admitted to Beata Vergine Consolata Rehabilitation Hospital Complex from 2009 to 2015. The nonagenarians examined showed common personality traits, better mood, Population Ageing