The Perception of Ecosystem Services of Mountain Farming and of a Local Cheese: An Analysis for the Touristic Valorization of an Inner Alpine Area

Mountain husbandry systems and their related products may directly or indirectly provide either ecosystem services (ESs) or disservices to humanity. The present study aims to evaluate the perception that a local mountain community has towards animal husbandry in the Lanzo Valleys (Piedmont, Italy) and towards the typical local dairy product, Toma di Lanzo, as well as to investigate the consumers\u2019 habits and preferences, to detect possible positive impacts on mountain tourism. A questionnaire was delivered to 233 respondents. The perception of the impact was scored using a five-point Likert scale. The results show a very positive perception of the product Toma di Lanzo because of its origin and type of processing, with different perceptions of the local society depending on age (p < 0.01), residence (p < 0.01), and education level (p < 0.05). The respondents had a very positive awareness of the impact of mountain livestock farming in the Lanzo Valleys. The most important perceived ESs are cultural identity and maintenance of local breeds. Women, non-residents, and respondents with an intermediate education level generally had a more positive perception of ESs. There was a very low perception of disservices derived from mountain animal farming. The main perceived obstacles to the spread of benefits derived from these farming systems were the scarce presence of specific supporting politics and the low income generated by mountain farming activities. The coexistence of touristic activities and extensive livestock farming systems has to be associated with a better promotion of mountain products like Toma di Lanzo to improve the sustainability of mountain regions

Renal function impairment predicts mortality in patients with chronic heart failure treated with resynchronization therapy

Background: The use of cardiac resynchronization therapy (CRT) and implantable cardioverter- defibrillator (ICD) for advanced heart failure (HF) is increasing. Renal dysfunction is a common condition in HF which is associated with a worse survival. The study aims at identifying in patients with advanced HF treated with CRT the effect of baseline glomerular filtration rate (GFR), GFR improvement and left ventricular ejection fraction (LVEF) change, after 6-months of CRT implant, on survival. Methods: The study population consisted of 375 advanced HF patients who received a CRT between 1999 and 2009, of these 277 received also an ICD implant. Clinical characteristics (New York Heart Association [NYHA] functional class, ischemic vs. non-ischemic etiology, atrial fibrillation, diabetes, hypertension, LVEF, QRS duration and GFR were recorded. The use of common used drugs was evaluated. Cox proportional hazards analysis was calculated in order to evaluate variables associated to mortality. Results: During a median follow-up of 43.0 months, 93 (24.8%) patients died. Patients deceased during the study had at baseline higher NYHA class and lower LVEF and GFR. In Cox regression analysis, GFR predicts long-term mortality (hazard ratio [HR] 0.983; 95% confidence interval [CI] 0.969–0.998; p = 0.023) independently from the effect of others covariates. In addition, a positive GFR improvement 6 months after CRT implant is significantly associated with a lower hazard of mortality (for each 10 mL/min of GFR improvement HR 0.86; 95% CI 0.75–0.99; p = 0.038). Conclusions: GFR is a significant predictor of mortality in advanced HF patients who received CRT. A GFR improvement 6 months after CRT implant is significantly associated with a lower hazard of mortality.

Neural Symbolic Architecture for Normative Agents

In this paper we propose a neural-symbolic architecture to represent and reason with norms in multi-agent systems. On the one hand, the architecture contains a symbolic knowledge base to represent norms and on the other hand it contains a neural network to reason with norms. The interaction between the symbolic knowledge and the neural network is used to learn norms. We describe how to handle normative reasoning issues like contrary to duties, dilemmas and exceptions by using a priority-based ordering between the norms in a neural-symbolic architecture