16,054,665 research outputs found
Brevetto di invenzione industriale di un elettrodomestico, deposito n. VI2004A000276 del 3/12/2004
No full textDispositivo elettrodomestico per l'asciugatura di capi stes
Spread of Alsidium corallinum C. Ag. in a Tyrrhenian eutrophic lagoon dominated by opportunistic macroalgae
No full textIn 2007, the Rhodophyceae Alsidium corallinum C. Ag., a marine taxon, bloomed in the eutrophic lagoon of
Orbetello (Tuscany, Italy) for the first time, becoming the dominant species in spring and summer. In
November, its biomass collapsed. The hypothesis examined in this study is that the bloom expressed a relatively
low eutrophic level of the ecosystem after intense disposal of accumulated sedimentary organic
matter (OM) by dystrophic processes in the two years preceding the bloom. To verify the hypothesis, we
compared water physical–chemical variables, sediment redox (Eh) and OM, and standing crops of macroalgae
and seagrass from the database of routine monitoring between 2005 and 2008.Wealso used dissolved
nutrient data obtained in 2007 and 2008, as well as data on chlorophyll and total suspended matter in the
water column during the microalgal bloom of 2007, and C, N and P content in thalli of the Chlorophycea
Chaetomorpha linum and the Rhodophyceae Gracilariopsis longissima and A. corallinum obtained in 2007.
In 2007, unusually low values of dissolved inorganic nitrogen (DIN) were recorded. Combined with stable
values of soluble reactive phosphorus (SRPs), low DIN led to a reduction of about one order of magnitude in
the DIN:SRP atomic ratio with respect to the past and to 2008. G. longissima accumulated C, N and P more
than the other species and A. corallinum proved to be less demanding. Sediment OM was lower in the
autumn of years characterized by dystrophy, confirming that summer dystrophic events coincided with
maximum energy dissipation in this ecosystem. However, as soon as OM and DIN values increased
(2008), the vegetation shifted towards blooms of G. longissima and C. linum, while A. corallinum almost
disappeared. The results sustain the hypothesis that the bloom of A. corallinum was due to a decline in
DIN that limited G. longissima, and to intense turbidity of the water caused by microphytes that developed
after the dystrophic event of summer 2006. The latter probably limited the development of C. linum, which
could only develop at the edges of the lagoon
Modelling biogeochemical fluxes across a Mediterranean fish cage farm
No full textAn integrated approach is described for modelling interactions between off-shore fish cages and biogeochemical fluxes of carbon (C), nitrogen (N) and phosphorus (P). Two individual- based population dynamic models for European seabass Dicentrarchus labrax and gilthead seabream Sparus aurata were coupled with a Lagrangian deposition and a benthic degradation model. The individual models explicitly take into account the effects of water temperature and feed availability on fish growth. The integrated model was tested at a Mediterranean fish farm where a comprehensive set of in situ environmental and husbandry data was available. Tests were performed to compare the predicted and observed total organic carbon (TOC) concentrations in surface sediment under and near fish cages. At a local scale, the model output simulated the spatial
distribution of 4 biogeochemical indicators, namely: TOC concentrations, C fluxes towards the seabed and C:N and C:P ratios. These allowed the most impacted areas and more extended areas of intermediate organic enrichment to be identified. The model was also used for estimating the
mass balance of C, N and P, in order to determine the potential cumulative effects of multiple fish farms in the same area. The C, N and P fluxes among feed, fish and environment were calculated for each fish species over 24 mo of farm activity. The results showed that the amount of dissolved N directly released into the water column in inorganic form (ammonia/urea) was comparable to that deposited on the seafloor in particulate form as uneaten feed and faeces. A larger fraction of
P (about 65%) was released as faeces. Results from the integrated model yielded useful information for assessing the sustainability of an area for aquaculture activities that could be used to provide a scientific rationale for fish farm development in new areas
Sfruttare e Trasferire conoscenza a priori nelle Architetture di Deep Learning
No full textNell'ultimo decennio, il Deep Learning è diventato un argomento caldo oltre che uno strumento dirompente nel contesto del Machine Learning e della Computer Vision. Si basa su un paradigma di apprendimento in cui i dati (ad esempio, i video acquisiti da telecamere di video-sorveglianza poste su una strada pubblica) giocano un ruolo cruciale. Sfruttando un gran numero di esempi, è possibile imparare compiti complessi e simili a quelli svolti da esseri umani (ad esempio, riconoscere azioni anomale in un video-stream) con risultati impressionanti. Tuttavia, se la disponibilità di dati rappresenta la più grande forza delle tecniche di Deep Learning, essa nasconde anche la più grande debolezza: lo sviluppo di applicazioni e servizi è, infatti, spesso limitato da tale requisito, poiché l'acquisizione e il mantenimento di una enorme quantità di dati sono attività costose che richiedono personale esperto e attrezzature idonee.
Tuttavia, la progettazione delle moderne architetture di Deep Learning offre diversi gradi di libertà, i quali possono essere sfruttati per mitigare la mancanza di dati di allenamento, sia essa parziale che completa. L'idea di fondo è quella di compensare tale mancanza incorporando una conoscenza preliminare che gli umani (in particolare, colore che controllano e guidano il processo di apprendimento) detengono sul dominio in questione. Infatti, le regole e le proprietà intrinseche si estendono ben oltre i dati di formazione e spesso possono essere identificate e imposte al modello di learning. Se prendiamo in considerazione la classificazione delle immagini, il successo delle Reti Neurali Convoluzionali (CNN) rispetto alle soluzioni del passato (come le Reti Neurali Multistrato) può essere attribuito principalmente a tale pratica. Infatti, i principi di progettazione del suo elemento costitutivo fondamentale (cioè la convoluzione tra due segnali 2D) riflettono naturalmente ciò che sapevamo sulle immagini naturali: le correlazioni che sussistono tra le regioni vicine dell'immagine hanno fornito pertanto una potente intuizione per lo sviluppo di modelli efficienti ed efficaci come lo sono ancora le CNN.
Lo scopo di questa tesi riguarda l'indagine e la proposta di nuovi modi di modellare e iniettare la conoscenza a priori nelle architetture di Deep Learning. È importante sottolineare che tale discussione è trasversale: infatti, si concentra su diversi domini di dati (ad esempio, immagini, video, dati strutturati mediante un grafo, ecc.) e coinvolge diversi livelli della pipeline complessiva. Su quest'ultimo punto, il lettore viene guidato in questa ricerca attraverso la seguente triplice categorizzazione: i) approcci basati sui parametri, che limitano lo spazio delle soluzioni possibili a quelle regioni che riflettono le proprietà geometriche dei dati; ii) approcci goal-driven, che guidano il processo di apprendimento verso soluzioni che incarnano alcune proprietà vantaggiose; iii) approcci data-driven, che sfruttano i dati per estrarre la conoscenza da utilizzare successivamente per condizionare l'algoritmo di training. Insieme a una descrizione completa di entrambe le impostazioni e degli strumenti coinvolti, presentiamo ampi risultati sperimentali e studi di ablazione che dimostrano il valore delle tecniche proposte in questa ricerca.In the last decade, Deep Learning has arisen as a hot topic and a disruptive tool in the fields of Machine Learning and Computer Vision. It builds upon a learning paradigm in which data (e.g., videos acquired by surveillance cameras placed on a public road) play a crucial role. By leveraging a great number of data-points, it is possible to fit complex and human-like tasks (e.g., recognizing abnormal actions in a video-stream) with impressive results. However, if data availability represents the source of the greatest strength of Deep Learning techniques, it also reveals the greatest weakness: the development of applications and services is indeed often restrained by such a requirement, as the acquisition and maintenance of a huge amount of data are expensive activities that require expert staff and equipment.
However, the design of modern Deep Learning architectures offers several degrees of freedom that can be exploited to mitigate the lack of training data, either partial or complete. The underlying idea is to compensate for it by incorporating a prior knowledge that humans (specifically, those who control and guide the learning process) hold about the domain at hand. Indeed, intrinsic rules and properties extend far beyond training data and can often be identified and imposed on the learner. If we take image classification into account, the success of Convolutional Neural Networks (CNNs) over past solutions (such as Multi-Layered Neural Networks) can be mainly ascribed to such a practice. Indeed, the design principle of its fundamental building block (i.e., the convolution between two 2D-signals) naturally reflect what we knew about natural images: in this regard, the correlations that subsist between neighborhood regions of the image provided so a powerful insight for the development of efficient and effective models as CNNs still prove to be.
The ultimate aim of this thesis is the investigation and proposal of novel ways of modeling and injecting prior knowledge in Deep Learning architectures. Importantly, we conduct such a discussion across the board: in fact, it focuses on several data domains (e.g., images, videos, graph-structured data, etc.) and involves different levels of the overall training pipeline. On this latter point, we guide the reader towards this research by means of the following threefold categorization: i) parameter-based approaches, which limit the space of feasible solutions to those regions reflecting geometrical properties of the data; ii) goal-driven approaches, which guide the learning process towards solutions that embody some advantageous properties; iii) data-driven approaches, which exploit data to extract the knowledge to be used to condition the training algorithm. Along with a comprehensive description of both settings and tools involved, we present extensive experimental results and ablation studies that demonstrate the value of the techniques proposed in this research
Chemical composition of the essential oils of three taxa of Cytisus growing wild in Sicily, Italy
No full textThe genus Cytisus is native Canary Islands, Europe to Mediterranean, Morocco, Algeria and Tunisia and several species of the genus are used in folk medicine of different countries. In this work the chemical composition of the essential oils from the aerial parts of three taxa of this genus growing wild in Sicily, Cytisus scoparius (L.) Link, C. villosus Pourr. and C. aeolicus Guss., has been investigated. No one report has been published on the Sicilian accession of the former two species, and, at the best of our knowledge, C. aeolicus is devoid of any chemical investigation. Cytisus scoparius and C. aeolicus essential oils have similar composition characterised by the occurrence of almost the same amount of compounds belonging to "other" class (59.5-52.0%) and carbonyl compounds (22.2-19.6%). Cytisus villosus showed a different composition with hydrocarbons as the main class (52.0%), totally absent in the other two species
Differential involvement of protein kinase C alpha and epsilon in the regulated secretion of soluble amyloid precursor protein
No full textWe investigated the differential role of protein kinase C
(PKC) isoforms in the regulated proteolytic release of
soluble amyloid precursor protein (sAPPa) in SH-SY5Y
neuroblastoma cells. We used cells stably transfected with
cDNAs encoding either PKCa or PKCe in the antisense
orientation, producing a reduction of the expression of
PKCa and PKCe, respectively. Reduced expression of
PKCa and/or PKCe did not modify the response of the
kinase to phorbol ester stimulation, demonstrating translocation
of the respective isoforms from the cytosolic fraction
to specific intracellular compartments with an interesting
differential localization of PKCa to the plasma membrane and PKCe to Golgi-like structures. Reduced expression of
PKCa significantly impaired the secretion of sAPPa induced
by treatment with phorbol esters. Treatment of PKCadeficient
cells with carbachol induced a significant release of
sAPPa. These results suggest that the involvement of PKCa
in carbachol-induced sAPPa release is negligible. The
response to carbachol is instead completely blocked in
PKCe-deficient cells suggesting the importance of PKCe in
coupling cholinergic receptors with APP metabolism
Traitement chirurgical des cancer bronchopulmonaires envahissnt la paroi thoracique : resultats, facteurs prognostiques et implicaions therapeutiques
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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