Covid-19 and the Clash of Narratives: From Cold War to End of Time (1989-2023)

This paper discusses the impact of Covid-19 on Islamist narratives of end time scenarios that predict the annihilation of a corrupted world and its ultimate replacement by a world order based exclusively on Islam. It does this against the backdrop of Islam’s antagonistic relationship with the West, particularly from the ending of the Cold War in the early 1990s to the present day, a relationship conducted within the shadow of the US’s attempts to establish a new world order based exclusively on its own values and interests. In the light of the contrary predictions of Francis Fukuyama that the resulting Pax Americana will bring a century of peace and S. P. Huntington a century of conflict, the paper goes on to examine the vastly different world views of the United States on the one hand and Al Qaeda and Islamic State on the other and how they envisage the future unfolding. What the paper shows is that the advent of Covid-19 has served not only to convince traditional Islamic Scholars that Al Mahama, the great battle at the end of time, is well on the way and may even have started, but also to make Muslims in the streets more receptive to such a doomsday message

Usage pattern of Facebook among the students of Dhaka University: a study

The usage pattern of Facebook by the students of information science and library management (ISLM) department at Dhaka University was studied. Questionnaires were distributed to 160 B.A. (Honours) students of all four years of ISLM Department out of which 139 questionnaires were found usable. The study found that a large number of students create Facebook account after they enter the university. The present study also revealed that personality characteristics, gender, educational level, geographical area and age influence ISLM students’ patterns of Facebook use and their perceptions about Facebook. The findings of this study also indicate that use of Facebook would be a supplementary tool in university education

Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage

We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from 1000 to 100 million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. Our method employs a branch and bound strategy with novel and aggressive pruning techniques. For instance, we use the core number of a vertex in combination with a good heuristic clique finder to efficiently remove the vast majority of the search space. In addition, we parallelize the exploration of the search tree. During the search, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with large search spaces can be pruned by other processes. We apply the algorithm to two problems: to compute temporal strong components and to compress graphs.Comment: 11 page

A Comprehensive Sensitivity Analysis of the Weather Research and Forecasting Modeling System over Southern Ontario, Canada

Every year weather events cause billions of dollars property damage and take many lives globally. Preventing as much damage as possible is crucial, and one way to help is through having the most accurate advance warning of extreme weather events. Therefore, this thesis investigates the sensitivity of precipitation, temperatures, and surface energy fluxes (i.e., sensible heat flux (SHF), latent heat flux (LHF), and ground heat flux (GHF) in four cumulus cloud (CU), five cloud microphysics (MP), and four planetary boundary layer (PBL) parameterization schemes; over five years (2002, 2007, 2008, 2014, and 2015) with significantly different climatological atmospheric conditions; horizontal grid spacing; two seasons: winter and summer; and feedback between the nest and its parent domain, using the dynamical downscaling technique of the Weather Research and Forecasting (WRF) model. The main objectives are 1) to identify a combination of physics schemes that realistically reproduce observed atmospheric conditions, and 2) to improve current understanding of factors influencing the micro climate of southern Ontario, a region of complex land-water-atmosphere interactions. Ontario is also the most populous province and the largest manufacturing hub of Canada. WRF-simulated precipitation and temperature agree well with DAYMET model gridded observations, with correlation coefficients of nearly 0.3 to 0.8 and ˃0.9, respectively. Precipitation showed an average systematic bias for July of -50 to +30 mm and for January of -10 to +30 mm. The simulated precipitation was more sensitive to CU and PBL schemes. WRF-simulated temperatures showed good reproducing skill, with biases within the range of -1.0°C to +1.0° C in most parts of the domain. Model-predicted temperature was quite sensitive to PBL and MP schemes. Model-simulated precipitation variability increased when the horizontal grid resolution was refined from 8.0 to 2.67 km. However, simulated temperature variability decreased. Overall, the model performed better in the 2.67 km resolution simulation than in the highest resolution simulations (with grid spacing of 0.888 km), an unexpected finding that suggests the need for carefully designed high-resolution dynamical downscaling experiments. WRF's limitation to capture all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas may result from uncertainties in our understanding of the climate and our inability to parameterize sub-grid scale processes realistically. WRF reproduced the diurnal variability of the SHF very well but systematically overestimated LHF compared to eddy covariance (EC) tower measurements for June of 2007 and 2008. For the interior of all three domains in July 2002, spatial distribution was overestimated for SHF and underestimated for LHF, with biases ranging from -30 to +30 W/m² over most of the area when compared to the North America Land Data Assimilation System (NLDAS) model gridded analysis. WRF showed little sensitivity to the choice of PBL scheme, except for January 2002's LHF, the hottest January of the five studied. If forced with distinctively different annual climatological boundary conditions, such as extreme cold in January 2014 and below average temperatures in January 2015, the model's simulated spatial distribution of energy flux bias indicates behavior that clearly differs from NLDAS analysis. A large energy flux bias occurs over the smaller shallow northern lakes, perhaps due to incorrect representation of their water temperatures. Overall, the Kain-Fritsch (KF) CU, Yonsei University (YSU) PBL, and WRF Single-Moment 6-class (WSM6) microphysics parameterization schemes exhibit superior results over the domain studied. The WRF model shows a high skill score over southern Ontario while reproducing observed climate means and statistics. Nevertheless, the model's performance depends on the meteorological variables, season, and synoptic conditions. The Great Lakes strongly influence atmospheric conditions in southern Ontario, by affecting precipitation and surface temperatures, ranging from the diurnal to the seasonal timescales. These results affirm the need for extensive sensitivity analysis, for both research, and operational applications. However, the findings are limited by the shorter spin-up time and by having only one-month simulation, although WRF ran for a month in both the winter and summer over multiple years