Effect of multiple sources of disturbance on rocky benthic assemblages in some localities of Salento, Apulia

The first part of my work consisted in samplings conduced in nine different localities of the salento peninsula and Apulia (Italy): Costa Merlata (BR), Punta Penne (BR), Santa Cesarea terme (LE), Santa Caterina (LE), Torre Inserraglio (LE), Torre Guaceto (BR), Porto Cesareo (LE), Otranto (LE), Isole Tremiti (FG). I collected data of species percentage covering from the infralittoral rocky zone, using squares of 50x50 cm. We considered 3 sites for location and 10 replicates for each site, which has been taken randomly. Then I took other data about the same places, collected in some years, and I combined them together, to do a spatial analysis. So I started from a data set of 1896 samples but I decided not to consider time as a factor because I have reason to think that in this period of time anthropogenic stressors and their effects (if present), didn’t change considerably. The response variable I’ve analysed is the covering percentage of an amount of 243 species (subsequently merged into 32 functional groups), including seaweeds, invertebrates, sediment and rock. 2 After the sampling, I have been spent a period of two months at the Hopkins Marine Station of Stanford University, in Monterey (California,USA), at Fiorenza Micheli's laboratory. I've been carried out statistical analysis on my data set, using the software PRIMER 6. My explorative analysis starts with a nMDS in PRIMER 6, considering the original data matrix without, for the moment, the effect of stressors. What comes out is a good separation between localities and it confirms the result of ANOSIM analysis conduced on the original data matrix. What is possible to ensure is that there is not a separation led by a geographic pattern, but there should be something else that leads the differences. Is clear the presence of at least three groups: one composed by Porto cesareo, Torre Guaceto and Isole tremiti (the only marine protected areas considered in this work); another one by Otranto, and the last one by the rest of little, impacted localities. Inside the localities that include MPA(Marine Protected Areas), is also possible to observe a sort of grouping between protected and controlled areas. What comes out from SIMPER analysis is that the most of the species involved in leading differences between populations are not rare species, like: Cystoseira spp., Mytilus sp. and ECR. Moreover I assigned discrete values (0,1,2) of each stressor to all the sites I considered, in relation to the intensity with which the anthropogenic factor affect the localities. 3 Then I tried to estabilish if there were some significant interactions between stressors: by using Spearman rank correlation and Spearman tables of significance, and taking into account 17 grades of freedom, the outcome shows some significant stressors interactions. Then I built a nMDS considering the stressors as response variable. The result was positive: localities are well separeted by stressors. Consequently I related the matrix with 'localities and species' with the 'localities and stressors' one. Stressors combination explains with a good significance level the variability inside my populations. I tried with all the possible data transformations (none, square root, fourth root, log (X+1), P/A), but the fourth root seemed to be the best one, with the highest level of significativity, meaning that also rare species can influence the result. The challenge will be to characterize better which kind of stressors (including also natural ones), act on the ecosystem; and give them a quantitative and more accurate values, trying to understand how they interact (in an additive or non-additive way).

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.