A welcome addition to the Routledge Critical Thinkers series, Judith Butler is the first guidebook on this renowned feminist and queer theory scholar, which will help not only students of literary criticism but also students of law, sociology, philosophy, film and cultural studies. Examining Butler's work through a variety of contexts, including the formation of gender performativity, identity and subjecthood, Sarah Salih address Butler's crucial ideas on the gender agenda, the body, pornography, race, gay self-expression and power and psychoanalysis. Concluding (...) with an annotated bibliography, this book will be the ideal starting point for all new to Butler. (shrink)

The Judith Butler Reader is a collection of writings that span her impressive career and trace her intellectual history. Judith Butler, author of influential books such as Gender Trouble, has built her international reputation as a theorist of power, gender, sexuality and identity Organized in active collaboration between Judith Butler and Sara Salih Collects together writings that span Butler’s impressive career as a critical philosopher, including selections from both well-known and lesser-known works Includes an introduction and editorial material to assist (...) students in their readings of theories that stand at the forefront of contemporary theoretical and political debates. (shrink)

Air temperature prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system, hybrid neural-fuzzy inference system, and adaptive neurofuzzy inference system for AT prediction. Modelling was done for three stations in North Dakota, USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly (...) variable data, such as AT, which shows a high positive correlation with average daily dew point, total solar radiation, and negative correlation with average wind speed. At the Robinson station, DENFIS performed the best with a coefficient of determination of 0.96 and a modified index of agreement of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure. (shrink)