Blue Mountain and Blue Roses: An Exploration of the Feminine Psyche in Tennessee Williams’ The Glass Menagerie

Tennessee Williams, the remarkably outstanding American dramatist of the 1920s, through his plays, presents a marked concern for the identity crisis a woman faces. He projects the crisis arising out of the conflict between a woman’s own aspirations and the traditional role expectations. The Glass Menagerie (1945) depicts the life of two women- Amanda Wingfield and her daughter Laura Wingfield. Amanda is the typical Southern belle that suffered a reversal of economic and social fortune, who withdraws from reality into fantasy. Her daughter Laura, the physically and emotionally crippled heroine of the play is a self-less character who does not speak as much of others. She is extra-ordinarily sensitive and delicate; and her cripple isolates herself into her own illusory world with her own glass menagerie. This paper is an attempt to close study the women protagonists in this play and to reveal that they are a combination of a particular personality type. Williams seems to be interested in the personal and psychological aspects of his women. This paper tries to analyse the psyche of these women and prove that they seem to be more complex and complicated than portrayed in the work.

Variedades de Coffea arabica L. con manejo orgánico en Oaxaca

Mexico is an important producer of organic coffee, mainly harvested by producers with small shade plots who use a diversity of varieties. Considering organic cultivation standards, we evaluated fruit production, cherry-parchment industrial yield, and the sensory cup quality of 20 coffee varieties (10 tall stature and 10 compact stature) during six production cycles in Oaxaca, Mexico. The varieties with the highest average fruit production in six years were three compact stature: Colombia Brote Café (30.2 kg / plant), Oro Azteca (29.0 kg / plant), and Yellow Catuai (27 kg / plant). These varieties produce large beans suitable for a European-style preparation. The Aztec Gold varieties presented good aroma, acidity and body attributes. Tall stature varieties with greater intermediate yield were Batie, Dessie, Pluma Hidalgo 177, and Typica 947, with around 24 kg / plant. Pacamara and Blue Mountain had low fruit production. Some varieties achieved a good taste irrespectively of their stature. Abnormalities in beans and agroindustrial yields were similar for all varieties

‘The dense, murky water of the past’: Swamps, Nostalgia and Settlement Myth in Meir Shalev’s The Blue Mountain

This chapter examines the changing meanings of swamp drainage in Israel’s national mythology. Swamp drainage was undertaken in the early twentieth century by the Jewish National Fund and again after the establishment of the State of Israel. Once seen as a triumph of Zionist ingenuity, draining swamps was redefined in the late twentieth century as an emblem of Zionism’s environmental hubris. This chapter assesses this history through Meir Shalev’s magical realist novel The Blue Mountain (1988), situating Shalev’s text in its contemporary contexts of environmentalism and post-Zionism.

A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data

Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively.