"I know what a Muslim really is": how political context predisposes the perceived need for an objective Muslim identity

This article explores the process by which Western Muslim young adults develop the need to experience an ‘objective’ religious identity. We interviewed 20 Western Muslim young adults born in Montreal, Berlin, and Copenhagen within the age range of 18–25, exploring their religious identity development. The interviews were semi-structured and open-ended. Thematic content analysis was used to explore patterns in their narratives. The participants disliked the perceived ethnocentric Muslim identity of their parents, which they sought to ‘purify’ for themselves from ‘cultural contamination’. There were two important elements underlying the process of religious identity objectification: experience of anti-Muslim political discourse and exposure to religious diversity in the aftermath of deterritorialisation

GPU-Based Volume Rendering of Noisy Multi-Spectral Astronomical Data

Traditional analysis techniques may not be sufficient for astronomers to make the best use of the data sets that current and future instruments, such as the Square Kilometre Array and its Pathfinders, will produce. By utilizing the incredible pattern-recognition ability of the human mind, scientific visualization provides an excellent opportunity for astronomers to gain valuable new insight and understanding of their data, particularly when used interactively in 3D. The goal of our work is to establish the feasibility of a real-time 3D monitoring system for data going into the Australian SKA Pathfinder archive. Based on CUDA, an increasingly popular development tool, our work utilizes the massively parallel architecture of modern graphics processing units (GPUs) to provide astronomers with an interactive 3D volume rendering for multi-spectral data sets. Unlike other approaches, we are targeting real time interactive visualization of datasets larger than GPU memory while giving special attention to data with low signal to noise ratio - two critical aspects for astronomy that are missing from most existing scientific visualization software packages. Our framework enables the astronomer to interact with the geometrical representation of the data, and to control the volume rendering process to generate a better representation of their datasets.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

Unleashing the Power of Distributed CPU/GPU Architectures: Massive Astronomical Data Analysis and Visualization case study

Upcoming and future astronomy research facilities will systematically generate terabyte-sized data sets moving astronomy into the Petascale data era. While such facilities will provide astronomers with unprecedented levels of accuracy and coverage, the increases in dataset size and dimensionality will pose serious computational challenges for many current astronomy data analysis and visualization tools. With such data sizes, even simple data analysis tasks (e.g. calculating a histogram or computing data minimum/maximum) may not be achievable without access to a supercomputing facility. To effectively handle such dataset sizes, which exceed today's single machine memory and processing limits, we present a framework that exploits the distributed power of GPUs and many-core CPUs, with a goal of providing data analysis and visualizing tasks as a service for astronomers. By mixing shared and distributed memory architectures, our framework effectively utilizes the underlying hardware infrastructure handling both batched and real-time data analysis and visualization tasks. Offering such functionality as a service in a "software as a service" manner will reduce the total cost of ownership, provide an easy to use tool to the wider astronomical community, and enable a more optimized utilization of the underlying hardware infrastructure.Comment: 4 Pages, 1 figures, To appear in the proceedings of ADASS XXI, ed. P.Ballester and D.Egret, ASP Conf. Serie

Perceptions of clinical dental students toward online education during the COVID-19 crisis: An Egyptian multicenter cross-sectional survey

Objectives: To evaluate the perceptions of clinical dental students on the role of online education in providing dental education during the COVID-19 crisis. Materials and Methods: A cross-sectional online survey was sent to four Egyptian dental schools from the 20th of January 2021 to the 3rd of February 2021. Survey questions included the demographics, uses, experiences, perceived benefits, and barriers of distance learning in dentistry during the COVID-19 pandemic. Responses were collected from the clinical dental school students. Categorical data were presented as frequencies (n) and percentages (%) and were analyzed using Fisher’s exact test. Results: Three hundred thirty-seven clinical dental students across four Egyptian dental schools responded. Most students used either Google Classroom or Microsoft Teams to access the online content. The data showed that the COVID-19 pandemic affected the academic performance of most participants (97.4%) with varying degrees. On average, students were neutral when asked to rate the online lectures, but did not find online practical education as effective (81.3%) as online theoretical teaching. The commonly described barriers to online teaching included loss of interaction with educators, inappropriateness in gaining clinical skills, and the instability of the internet connection. Conclusion: Despite the reported benefits, clinical dental students in Egypt preferred the hybrid approach in dental education as distance learning represented a prime challenge to gain adequate clinical dental skills

Multiobjective robustness for portfolio optimization in volatile environments

Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk, and an investor can choose her preferred point on the risk-return frontier. However, there are no guarantees that a low-risk solution will remain low-risk . if the environment changes, the relative positions of previously identified solutions may alter. A low-risk solution may become high-risk and vice versa. The robustness of a Multiobjective Genetic Programming (MOGP) algorithm such as SPEA2 is vitally important in the context of the real-world problem of portfolio optimisation. We explore robustness in this context, providing new definitions and a statistical measure to quantify the robustness of solutions. A new robustness measure is incorporated into a MOGP fitness function to bias evolution towards more robust solutions. This new system ("R-SPEA2") is compared against the original SPEA2 and we present our results

SHORT TERM URBAN TRAFFIC FORECASTING USING DEEP LEARNING

Abstract. In today's world, the number of vehicles is increasing rapidly in developing countries and China and remains stable in all other countries, while road infrastructure mostly remains unchanged, causing congestion problems in many cities. Urban Traffic Control systems can be helpful in counteracting congestion if they receive accurate information on traffic flow. So far, these data are collected by sensors on roads, such as Inductive Loops, which are rather expensive to install and maintain. A less expensive approach could be to use a limited number of sensors combined with Artificial Intelligence to forecast the intensity of traffic at any point in a city. In this paper, we propose a simple yet accurate short-term urban traffic forecasting solution applying supervised window-based regression analysis using Deep Learning algorithm. Experimental results show that is it possible to forecast the intensity of traffic with good accuracy just monitoring its intensity in the last few minutes. The most significant result, in our opinion, is that the machine can generate accurate predictions even with no knowledge of the current time, the day of the week or the type of the day (holiday, weekday, etc).</p

Primary breast sarcoma: case report

Primary breast sarcoma is a rare entity occurring in 0.5% of women with breast malignancy. Like in breast carcinoma, delay in its diagnosis has important clinical and treatment implications. The subject of this report presented at our breast unit with advanced breast lesion months after she noticed a small lump in her right breast. She had no clear diagnosis despite several consultations, in-patient treatments at two facilities in the city, breast ultrasonography, breast mammography and three fine needle aspiration cytology (FNAC) examinations. The patient needed multiple blood transfusions. A final FNAC showed ductal carcinoma. Histology following wide excision confirmed high-grade primary stromal breast sarcoma. She required adjuvant combination chemotherapy. A combination of diagnostic failures and patient fault caused delay in subject's treatment. Lesion progression during delay which influenced the pattern of physical morbidity, tumour prognosis and need for adjuvant treatment. Embracing the concept of breast care in dedicated breast units may minimise such treatment delays. East African Medical Journal Vol.81(7) 2004: 375-37