Socio-Economic Vulnerability to COVID-19: The Spatial Case of Greater Kampala Metropolitan Area (GKMA)

COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socio-economic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability. The spatial approach presented in this paper contributes to providing a rapid assessment of the socio-economic vulnerability based on administrative decision units-parishes. This essentially equips the public health domain with the right diagnosis to subject the highly exposed and vulnerable communities to regulatory policy, increase resilience incentives in low adaptive areas and optimally deploy resources to avoid the emancipation of high susceptibility areas into an epicentre of Covid-19.

EFFECTS OF MODIFICATION OF THE CARBIDE CHARACTERISTICS THROUGH GRAIN BOUNDARY SERRATION ON CREEP-FATIGUE LIFE IN AUSTENITIC STAINLESS STEELS

Modification of the carbide characteristics through the grain boundary serration is investigated, using an AISI 316 and 304 stainless steels. In both steels, triangular carbides were observed at straight grain boundaries while planar carbides were observed at the serrated grain boundaries. The serrated grain boundary energy is observed to be much lower than that of the straight one. Therefore, the carbide morphology is found to be changed from triangular to planar along the serrated boundary to reduce the interfacial energy between the carbide and the matrix. The creep-fatigue properties of these steels at 873K have been investigated. The creep-fatigue life of the sample with planar carbide at the serrated grain boundary was found to be much longer than that with triangular carbide at the straight one. These results imply that the planar carbides with lower interfacial energy have higher cavitation resistance, resulting in the retardation of cavity nucleation and growth to increase creep-fatigue life.

An Efficient and Spam-Robust Proximity Measure Between Communication Entities

Electronic communication service providers are obliged to retain communication data for a certain amount of time by their local laws. The retained communication data or the communication logs are used in various applications such as crime detection, viral marketing, analytical study, and so on. Many of these applications rely on effective techniques for analyzing communication logs. In this paper, we focus on measuring the proximity between two communication entities, which is a fundamental and important step toward further analysis of communication logs, and propose a new proximity measure called ESP （Efficient and Spam-Robust Proximity measure）. Our proposed measure considers only the （graph- theoretically） shortest paths between two entities and gives small values to those between spam-like entities and others. Thus, it is not only computationally efficient but also spam-robust. By conducting several experiments on real and synthetic datasets, we show that our proposed proximity measure is more accurate, computationally efficient and spam-robust than the existing measures in most cases.