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This book explores the complex relationship between nationalism and liberal thought in the Arab East during the first half of the twentieth century. Examining this formative period through reformist Islam, Arab secularism and Arab literature, the book situates major shifts in the political ideologies and practices of Arab liberals within a historical context. Contributions from renowned scholars in the field show how rather than fundamentally contradicting each other, these two schools of thought are closely linked. Many key demands of liberalism - most notably constitutionalism, the rule of law, individual rights, and popular participation - have been central to the nationalist agenda, while other issues have proven more controversial: inter-confessional tolerance, secularism, and the goals of state-sponsored education. Although a strong nation-state was pivotal to the nationalist imagination during most of the twentieth century, a powerful critique of unchecked state power took shape as Arab countries experienced a half-century of authoritarian government. In analyzing these issues, the chapters demonstrate how the rise and fall of liberalism across the region was not determined solely by religion or culture, but by the ideas of influential intellectuals and politicians. Advancing our understanding of political ideology and practice in the Arab East, this volume will be of great interest to students and scholars of political science, history and the Middle East. Introduction Part I: Nationalism and Liberal Thought 1. The role of traditional religious scholars in Iraqi politics from the Young Turk period until 1920: the example of Yusuf al-Suwaydi Thomas Eich 2. Who is \"liberal\" in 1930s Iraq? Education as a contested terrain in a nascent public sphere Peter Wien 3. Liberal champions of pan-Arabism: Syria’s second Íizb al-Sha'b Fred H. Lawson 4. Nation, state, and democracy in the writings of Zaki al-Arsuzi Dalal Arsuzi-Elamir 5. Nationalism as a cause: Arab nationalism in the writings of Ghassan Kanafani Orit Bashkin Part II: Arab intellectuals and liberal thought 6. Modernity, romanticism, and religion: contradictions in the writings of Farah Antun Alexander Flores 7. Progress and liberal thought in al-Hil'al, al-Man'ar, and al-Muqta'aaf before World War I Thomas Philipp 8. Liberal democracy versus fascist totalitarianism in Egyptian intellectual discourse: the case of Salama Musa and 'al-Majalla al-JadÐda' Israel Gershoni 9. The \"failure\" of radical nationalism and the \"silence\" of liberal thought in the Arab world Christoph Schumann Christoph Schumann is Professor of Politics and Contemporary History of the Middle East at the University of Erlangen-Nuremberg, Germany. His research focuses on political ideologies in the Middle East and Muslims in the West, and he has previously written on the topics of liberalism in the Mediterranean and radical nationalism in Syria and Lebanon.

This volume analyzes a century of intellectual debates, political ideologies, and literary media in order to track the emergence, spread and decline of liberal thought as a response to both authoritarian rule and Westernization in the Eastern Mediterranean.

In order to sketch the emergence of an American Muslim discourse of political participation, I will analyze mainstream publications of Muslim organizations, such as the Islamic Society of North America (ISNA), the Muslim Students Association (MSA) and the American Muslim Council (AMC). First, he presents his readers with a number of ready-made apologetic answers to counter common anti-Islamic clichés that might come up in a dialogue like polygamy, the meaning of jihad, and Islam's assumed opposition to democracy.74 Then he goes on to explain why the Muslim community should give priority to building Islamic schools rather than mosques, in order to save Muslims from the corrupting influences of American society.75 Finally, with respect to American domestic politics, he depicts Islam in the United States as embattled by various enemies such as Zionism, the Christian right and the military-industrial complex (al-murakkab al-sina'i al-askari).76 Yet, despite this, he quotes the optimistic outlook of a friend who said with reference to the assumed Muslim voting block that \"Islam in America was a giant who hopefully will wake up.\"

Supervised super-resolution deep convolutional neural networks (CNNs) have gained significant attention for their potential in reconstructing velocity and scalar fields in turbulent flows. Despite their popularity, CNNs currently lack the ability to accurately produce high-frequency and small-scale features, and tests of their generalizability to out-of-sample flows are not widespread. Generative adversarial networks (GANs), which consist of two distinct neural networks (NNs), a generator and discriminator, are a promising alternative, allowing for both semi-supervised and unsupervised training. The difference in the flow fields produced by these two NN architectures has not been thoroughly investigated, and a comprehensive understanding of the discriminator's role has yet to be developed. This study assesses the effectiveness of the unsupervised adversarial training in GANs for turbulence reconstruction in forced homogeneous isotropic turbulence. GAN-based architectures are found to outperform supervised CNNs for turbulent flow reconstruction for in-sample cases. The reconstruction accuracy of both architectures diminishes for out-of-sample cases, though the GAN's discriminator network significantly improves the generator's out-of-sample robustness using either an additional unsupervised training step with large eddy simulation input fields and a dynamic selection of the most suitable upsampling factor. These enhance the generator's ability to reconstruct small-scale gradients, turbulence intermittency, and velocity-gradient probability density functions. The extrapolation capability of the GAN-based model is demonstrated for out-of-sample flows at higher Reynolds numbers. Based on these findings, incorporating discriminator-based training is recommended to enhance the reconstruction capability of super-resolution CNNs.

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative adversarial network (SR-GAN) is employed to upsample the grid-filtered velocity fields by a factor of two, enabling the evaluation of both scale-similarity and the dynamic Smagorinsky contributions. A priori analyses of forced homogeneous isotropic turbulence show that the SR-GAN accurately reconstructs fine-scale flow features and generalizes well across different filter sizes and higher Reynolds number flow configurations, even for unseen input fields. The DMSRM reproduces SFS stresses and energy dissipation more accurately than the traditional DMM. A posteriori LES calculations further confirm that DMSRM predicts the energy spectrum and intermittency more accurately than DMM, even for different LES grid-scale resolutions and for higher Reynolds numbers than those used for training. Unlike DMM, DMSRM yields realistic backscatter and physically consistent SFS energy dissipation. These improvements arise from the physically accurate super-resolved fields generated by the SR-GAN, from which SFS stresses are directly computed. The result is a closure that accurately reproduces stress magnitudes and dissipation while reducing reliance on additional dissipation from the dynamic term. The DMSRM formulation achieves a balance of physical fidelity, robustness, and computational efficiency, offering a promising alternative to traditional DMMs for turbulence LES modeling.

Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps.