Ricerc@Sapienza

Wildlife is frequently infected by intestinal protozoa, which may threaten their fitness and health. A diverse community of Eimeria species is known to occur in the digestive tract of mountain-dwelling ungulates, including chamois (genus Rupicapra). However, available data on Eimeria diversity in these taxa is at times inconsistent and mostly dated. In the present study, we aimed to revisit the occurrence of Eimeria spp.

We propose here an implementation of 2D hierarchical clustering tailored for power constrained and low-precision hardware. In many application fields such as smart sensor networks, having low computational capacity is mandatory for energy saving purposes. In this context, we aim to deploy a specific constrained hardware solution, using a parallel architecture with a low number of bits. The effectiveness of the proposed approach is corroborated by testing it on well-known 2D clustering datasets.

In this paper we propose a novel hardware implementation for a bidimensional unconstrained hierarchical clustering method, based on fuzzy logic and membership functions. Unlike classical clustering approaches, our work is based on an advanced algorithm that shows an intrinsic parallelism. Such parallelism can be exploited to design an efficient hardware implementation suitable for low-resources, low-power and highcomputational demanding applications like smart-sensors and IoT devices. We validated our design by an extensive simulation campaign on well known 2D clustering datasets.

A novel energy management system (EMS) synthesis procedure based on adaptive neurofuzzy inference systems (ANFISs) by hyperplane clustering is investigated in this paper. In particular, since it is known that clustering input–output samples in hyperplane space does not consider clusters’ separability in the input space, a Min–Max classifier is applied to properly cut and update those hyperplanes defined on scattered clusters in order to refine the ANFIS membership functions.

Plot-level redundancy or alpha redundancy is usually defined as the fraction of species diversity not expressed by functional or phylogenetic diversity. Redundancy is zero when all species in one plot are maximally dissimilar from each other. In contrast, redundancy tends to its maximum if the functional or phylogenetic differences between species tend to be minimal.To explore the ecological drivers of community assembly, ecologists also use dissimilarity measures between pairs of plots (a component of beta diversity).