Modifications induced in soil porosity and in stability of soil aggregates were studied for 2 years on an Italian sandy loam soil. Aerobic and anaerobic sludges and their composted mixtures with the organic fraction of urban refuse were used and compared with manure. Addition rates were equivalent to 50 and 150 metric tons/ha of manure on the organic carbon basis. A control plot was also present. Porosity and pore size distribution were measured on thin sections prepared from undisturbed soil samples by using electro‐optical image‐analysis equipment. The stability of soil aggregates was determined by a wet‐sieving method.All organic materials increased the total porosity significantly at all sampling times. Differences between the two application rates were generally not significant. The improvement of total porosity caused by sludges and composts was comparable to that of manure. Modifications of pore size distribution were also observed. Stability of soil aggregates increased slightly in treated samples. The best stabilizing effect was shown by the anaerobic sludge.

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

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