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Arnold Toynbee, Mankind’s Mood Divisive. Britain Leaves Wreck Behind (1972) https://www.academia.edu/150265053/Arnold_Toynbee_Mankind_s_Mood_Divisive_Britain_Leaves_Wreck_Behind_1972_
No full textADDITIONS, INTEGRATIONS, CORRECTIONS AND SUPPLEMENTS TO THE BIBLIOGRAPHY OF ARNOLD JOSEPH TOYNBEE, nos. 609-610, Additions to Part I, Works of Arnold J. Toynbe
A copula based BINAR(1) process with applications
No full textThe application of the simple first order integer-valued autoregressive model to multidimensional space allows the simultaneous modelling of multiple series. Despite this, existing models do not provide a great deal of flexibility for modelling dependence, allowing only positive correlations to be modelled. A copula is one of the most widely used tools in statistics to describe, analyze and model the relationship between random variables. As part of this study, we investigate a bivariate first order integer-valued autoregressive process in which cross-correlations are introduced through the use of copulas for the specification of the joint distribution of innovations. Throughout the paper, we emphasize the parametric case arising from the Poisson extended exponential marginals. An empirical illustration is provided using a bivariate financial time series and a bivariate criminal dataset
The creaky voice in disease: "{ { Maronna mia ’u teng’ ’mpizz’ ’e lengua } } [DGmaLc01N, F#72]"
No full textshort introduction to the creaky voice in diseas
eXplainable artificial intelligence for non-visual multiclass recognition of EHDA Modes
No full textElectrohydrodynamic atomization (EHDA) is a versatile technology applied
to different fields ranging from process industries to materials science
and medicine. Depending on the operating conditions, EHDA provides
different spray modes, which are mostly recognized either by highspeed
imaging or, less frequently, by current measurements. While high-speed
imaging is very successful for lab experiments, it may be difficult to
apply in field applications with limited optical access to the spray. To
this scope, this study specifically uses frequency-domain analysis of
emitted electric current signal data to propose an eXplainable
Artificial Intelligence (XAI)-based approach for multi-class recognition
of EHDA modes, improving the accuracy of electric current-based
classification and allowing an online control of the spray performances.
To this scope, a new dataset of experimental data for various liquid
types with different chemical-physical properties has been built. The
dataset is used to tune the XAI-based method through a supervised
learning approach. By combining advanced feature engineering and a
one-dimensional convolutional neural network (1D-CNN), the proposed
approach achieves accurate classification, making possible the
identification of dripping, intermittent, cone-jet, and the challenging
multi-jet modes, without the need for visual data. The use of post-hoc
XAI techniques ensures transparency, confirming that the model bases its
decisions on frequency patterns aligned with the physics of the process.
The proposed method demonstrates robustness and a certain adaptability,
being capable of classifying with appreciable accuracy EHDA modes for
liquids with physical properties different from those used for its
training, marking a significant advancement in EHDA process control.
This innovation lays the foundation for integrating AI-based
classification into closed-loop systems for real-time optimization,
addressing both academic and industrial challenges in process efficiency
and automation
Componente del Collegio del dottorato di ricerca di interesse nazionale in Studi europei presso l’Università di Genova.
No full text87Sr/86Sr isotope ratios in soils, vine leaves, grapes and wines of the Italian volcanic districts authenticate their respective terroirs
No full textInterest in the origin and traceability of agri-food products has led to an increasing number of publications using strontium isotope ratios as a geographic tracer. We used 87Sr/86Sr isotope systematics to authenticate the provenance of wine from different volcanic districts of Italy. Samples of soil, grapes, leaves and bottled wines from 6 different wineries were analysed. A detailed study of the different soil horizons from the Somma-Vesuvio area demonstrates the relationship between the 87Sr/86Sr of the soils and the different parts of the grapevine (root, steam, grape, grape pulp, grape seed, grape skin), and the soil characteristics (soil type, granulometry, root density) that control the 87Sr/86Sr of the end-products. Results showed that the geological characteristics of volcanic terranes of Italy, and in particular the northwest to southeast 87Sr/86Sr gradient, are inherited by the wines of each region, such that the wines can be discriminated and authenticated by their isotope ratio