DIVERSITY OF ETHNICITY AND STATE INVOLVEMENT ON URBAN INFORMALITY IN BEIRUT

Urban informality has become the dominant feature of urban growth on Beirut City and its periphery. Beirut context, as the rest of Lebanese cities, sheds light on a new era of controversy on urban informality. The appearance of urban informality in Beirut is due to the ways that the state being involved on such areas and its affect on shaping the urban fabric, the ways that the influence of various sociopolitical circumstances the country being passed through by which informal areas being established, and the complexity of ethnicity structure within Lebanese society. Understanding the diversity of the state power and ethnicity structure of the society during various periods of the establishment of informal housing areas would enable the state and housing professionals to provide a clear policy strategy to tackle urban informality. Each marginal area needs special treatment according to its religion and ethnicity structure‚ to be remolded within the society.informality; urbanization; state; ethnicity; Lebanon.

Low Complexity V-BLAST MIMO-OFDM Detector by Successive Iterations Reduction

V-BLAST detection method suffers large computational complexity due to its successive detection of symbols. In this paper, we propose a modified V-BLAST algorithm to decrease the computational complexity by reducing the number of detection iterations required in MIMO communication systems. We begin by showing the existence of a maximum number of iterations, beyond which, no significant improvement is obtained. We establish a criterion for the number of maximum effective iterations. We propose a modified algorithm that uses the measured SNR to dynamically set the number of iterations to achieve an acceptable bit-error rate. Then, we replace the feedback algorithm with an approximate linear function to reduce the complexity. Simulations show that significant reduction in computational complexity is achieved compared to the ordinary V-BLAST, while maintaining a good BER performance.Comment: 6 pages, 7 figures, 2 tables. The final publication is available at www.aece.r

Semiclassical Hartree-Fock theory of a rotating Bose-Einstein condensation

In this paper, we investigate the thermodynamic behavior of a rotating Bose-Einstein condensation with non-zero interatomic interactions theoretically. The analysis relies on a semiclassical Hartree-Fock approximation where an integral is performed over the phase space and function of the grand canonical ensemble is derived. Subsequently, we use this result to derive several thermodynamic quantities including the condensate fraction, critical temperature, entropy and heat capacity. Thereby, we investigate the effect of the rotation rate and interactions parameter on the thermodynamic behavior. The role of finite size is discussed. Our approach can be extended to consider the rotating condensate in optical potential

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Characterization and therapeutic targeting of Parkinson’s-related LRRK2

More than a decade ago, and during the attempts to understand Parkinson’s disease (PD), scientists identified Leucine-Rich Repeat Kinase 2 (LRRK2) as a protein related to PD development. Since then, different research groups have focused on developing tools to investigate and target LRRK2 function and activity. LRRK2 is a uniquely large protein, that becomes active upon dimerization, i.e. when two units of the protein come together. Although a number of LRRK2 inhibitors has been developed over the past few years, these inhibitors result in side effects that were observed during animal and preclinical studies. Thus, different strategies to regulate the activity of LRRK2 are needed.In the work presented in this thesis, we provided an alternative approach by targeting the LRRK2 dimerization process. We designed, tested, and proved that small peptides can be used to occupy the site at which the two units of LRRK2 stick together. In this way, we prevented the formation of the active dimer form of LRRK2 and reduced its activity. Importantly, this approach didn’t result in the same side-effects of classical inhibitors upon testing in human cells. In order to better understand the function of LRRK2, we also identified nanobodies (small protein that can specifically bind to a target of interest) that can bind LRRK2 and regulate its activity. These nanobodies will help identifying the exact structure of LRRK2 and developing of new therapeutics

病原性グラム陰性細菌における薬剤耐性遺伝子の伝播に関する可動性遺伝因子の役割

内容の要約広島大学(Hiroshima University)博士(学術)Doctor of Philosophydoctora