Telling stories of culture through literature

Grounded on Raymond Williams‘s definition of knowable community as a cultural tool to analyse literary texts, the essay reads the texts D.H.Lawrence wrote while travelling in the Mediterranean (Twilight in Italy, Sea and Sardinia and Etruscan Places) as knowable communities, bringing to the discussion the wide importance of literature not only as an object for aesthetic or textual readings, but also as a signifying practice which tells stories of culture. Departing from some considerations regarding the historical development of the relationship between literature and culture, the essay analyses the ways D. H. Lawrence constructed maps of meaning, where the readers, in a dynamic relation with the texts, apprehend experiences, structures and feelings; putting into perspective Williams‘s theory of culture as a whole way of life, it also analyses the ways the author communicates and organizes these experiences, creating a space of communication and operating at different levels of reality: on the one hand, the reality of the whole way of Italian life, and, on the other hand, the reality of the reader who aspires to make sense and to create an interpretative context where all the information is put, and, also, the reality of the writer in the poetic act of writing. To read these travel writings as knowable communities is to understand them as a form that invents a community with no other existence but that of the literary text. The cultural construction we find in these texts is the result of the selection, and interpretation done by D.H.Lawrence, as well as the product of the author‘s enunciative positions, and of his epistemological and ontological filigrees of existence, structured by the conditions of possibility. In the rearticulation of the text, of the writer and of the reader, in a dynamic and shared process of discursive alliances, we understand that Lawrence tells stories of the Mediterranean through his literary art.

Analysis of temperature time-series: embedding dynamics into the MDS method

Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

Multidimensional scaling visualization of earthquake phenomena

Earthquakes are associated with negative events, such as large number of casualties, destruction of buildings and infrastructures, or emergence of tsunamis. In this paper, we apply the Multidimensional Scaling (MDS) analysis to earthquake data. MDS is a set of techniques that produce spatial or geometric representations of complex objects, such that, objects perceived to be similar/distinct in some sense are placed nearby/distant on the MDS maps. The interpretation of the charts is based on the resulting clusters since MDS produces a different locus for each similarity measure. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analyzed. The events, characterized by their magnitude and spatiotemporal distributions, are divided into groups, either according to the Flinn–Engdahl seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Space-time and Space-frequency correlation indices are proposed to quantify the similarities among events. MDS has the advantage of avoiding sensitivity to the non-uniform spatial distribution of seismic data, resulting from poorly instrumented areas, and is well suited for accessing dynamics of complex systems. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools, for understanding the global behavior of earthquakes.