1,721,792 research outputs found
Aware: art fashion identity
Full text linkThis new book, published to coincide with an exhibition at the Royal Academy of Arts, London co-curated by Lucy Orta and Gabi Scardi (opened, 2/12/2010), looks at the relationship between the work of leading contemporary artists and fashion designers.
Aware – Art Fashion Identity reflects upon the relationship between our physical covering and constructed personal environments, our individual and social identities and the contexts in which we live. The book examines the role of clothing in cultural and personal stories, through the work of 30 artists. Issues of belonging and nationality, displacement and political and social confrontation are addressed in the work of Yinka Shonibare, Sharif Waked, Alicia Framis, Meschac Gaba, Dai Rees and Acconci Studio. The importance of performance in the presentation of fashion and clothing, and in highlighting the roles that we play in our daily life, is explored through the work of Hussein Chalayan, Gillian Wearing RA and Andreas Gursky, amongst others. As a mechanism of expression, the exploration of the role of clothing has been at the heart of the artistic practice of a number of contemporary artists, and has particular resonance for those attuned to the social situations of their times
Marine Strategy, una sfida ed un'opportunità per la Biologia Marina italiana
Full text linkThe Marine Strategy Framework Directive, which came into force in 2008, can be
regarded as the environmental pillar for the Integrated European Maritime Policy. In the first phase of
its implementation EU member Countries carried out an initial assessment of the ecological status, set
environmental targets and defined the concept of Good Ecological Status. While marine biologists from
Italian Universities and other research Institutions actively participated in this process, new challenges
will be brought by its next phases, requiring a deeper involvement of the scientific community and a truly
holistic approach
Immobilization of Saccharomyces cerevisiae cells by adhesion to polymeric matrices obtained by radiation-induced polymerization
Full text linkAn immobilization method based on the spontaneous adhesion of invertase-active cells of yeast (Saccharomyces cerevisiae) to tuff granules was described by Parascandola, Scardi, and Tartaglione. Compared with gel entrapment, immobilization by adhesion is much more simple and free from diffusional limitations. However, adhesion is a rather complicated process involving surface interactions between microbial cells and the so-called substratum, that is, the solid support to which they attach. Because there are still many unanswered questions about the mechanism of adhesion, the selection of suitable substrata for a given microbial species can be made only empirically. Thus, to find substrata better than tuff or insolubilized gelatin, polymeric hydrogels that were obtained by radiation-induced polymerization below 0 °C and that were employed successfully for immobilizing enzymes, cells and antibodies were considered. A dozen of such polymer matrices with different hydrophilicities were synthesized and assayed as possible substrata for S. cereviisae cells used in continuous ethanol production
Modeling Posidonia oceanica shoot density and rhizome primary production
Full text linkPosidonia oceanicameadows rank among the most important and most productive ecosystems in
the Mediterranean basin, due to their ecological role and to the goods and services they provide.
estimations of crucial ecological process such as meadows productivity could play a major role in an
environmental management perspective and in the assessment of P. oceanicaecosystem services.
In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica
shoot density and rhizome primary production using as predictive variables only environmental
factors retrieved from indirect measurements, such as maps. Our predictive models showed a good
level of accuracy in modeling both shoot density and rhizome productivity (R
2
= 0.761 and R
2
= 0.736,
respectively). Furthermore, as shoot density is an essential parameter in the estimation of P. oceanica
productivity, we proposed a cascaded approach aimed at estimating the latter using predicted values
of shoot density rather than observed measurements. In spite of the complexity of the problem, the
cascaded Random forest performed quite well (R2 = 0.637). While direct measurements will always
play a fundamental role, our estimates could support large scale assessment of the expected condition
of P. oceanica meadows, providing valuable information about the way this crucial ecosystem works
Fractal dimension of Posidonia oceanica meadows for the assessment of their ecological condition
No full textEcological analyses are aimed at characterizing the complexity of the structure of natural objects, yet their
heterogeneity is hardly described by the Euclidean concepts. For such purpose, the fractal geometry can be best
suited due to its ability in describing, with mathematical rigor, the inherent irregularity of nature. Fractal
dimension provides indeed a measurement of the complexity of the analyzed object in terms of space occupation.
In this study, we applied the fractal geometry to Posidonia oceanica in order to characterize the structural
complexity of its meadows, which are widely recognized as one of the most important coastal ecosystems in the
Mediterranean basin. For achieving our aim, we developed an ad hoc implementation of the Box-Counting algorithm based on the Moore neighborhood analysis. Our approach allowed to render the structural complexity of
P. oceanica meadows spatially explicit, thus expressing an intrinsic ecological property. The fractal analysis
suggested that the complexity of meadows structure is intimately connected with the ecological conditions of
P. oceanica. In fact, meadows in living and mixed conditions showed a higher fractal dimension, suggesting a
largely uniform and smooth structure. While the fractal dimension associated to the regressed ecological condition of P. oceanica meadows exhibited lower values, highlighting a more jagged and rough structure. Therefore,
the fractal theory may prove useful to both fundamental and applied ecological research focusing on P. oceanica
and its interactions with Mediterranean coastal ecosystems. In fact, the fractal analysis we performed could result
in an effective and straightforward approach for assessing the condition of P. oceanica at large spatial scale,
enhancing an integrated maritime spatial planning over the whole Mediterranean basin
A Machine Learning Approach to Chlorophyll a Time Series Analysis in the Mediterranean Sea
No full textUnderstanding the dynamics of natural system is a crucial task in ecology especially when climate change is taken into account. In this context, assessing the evolution of marine ecosystems is pivotal since they cover a large portion of the biosphere. For these reasons, we decided to develop an approach aimed at evaluating temporal and spatial dynamics of remotely-sensed chlorophyll a concentration. The concentrations of this pigment are linked with phytoplankton biomass and production, which in turn play a central role in marine environment. Machine learning techniques proved to be valuable tools in dealing with satellite data since they need neither assumptions on data distribution nor explicit mathematical formulations. Accordingly, we exploited the Self Organizing Map (SOM) algorithm firstly to reconstruct missing data from satellite time series of chlorophyll a and secondly to classify them. The missing data reconstruction task was performed using a large SOM and allowed to enhance the available information filling the gaps caused by cloud coverage. The second part of the procedure involved a much smaller SOM used as a classification tool. This dimensionality reduction enabled the analysis and visualization of over 37 000 chlorophyll a time series. The proposed approach provided insights into both temporal and spatial chlorophyll a dynamics in the Mediterranean Basin
Mining satellite data for extracting chlorophyll a spatio-temporal patterns in the Mediterranean Sea
No full textUnderstanding the evolution of natural systems spatio-temporal dynamics is paramount in modern ecology. We
focused on highlighting and analysing temporal and spatial dynamics of remotely-sensed chlorophyll a concentration. This pigment is linked with phytoplankton production, which in turn play a pivotal role in marine
environment. Satellite platforms offer a synoptic view of surface chlorophyll a concentration for the last two
decades. Coupling this source of information with statistical and Machine Learning techniques could help
highlighting eventual patterns.
We merged the Mediterranean chlorophyll a satellite data for the last two decades into a single dataset. We
tested several techniques for reconstructing missing data and performed a general analysis. Finally, we implemented a Dynamic Time Warping Self-Organizing Map algorithm to cluster our series showing that an elastic
distance measure outperforms a non-elastic one. The proposed satellite data management and analysis provided
insights on spatio-temporal chlorophyll a dynamics in the Mediterranean Basin
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