Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data

Nowadays, the world is fighting a dangerous form of Coronavirus that represents an emerging pandemic. Since its early appearance in China Wuhan city, many countries undertook several strict regulations including lockdowns and social distancing measures. Unfortunately, these procedures have badly impacted the world economy. Detecting and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading disease. For helping this hard effort, this research presents an innovative case study based on big data processing techniques to build a complete tracking system able to identify the central areas of infected/suspected people, and the new suspected cases using health records integration with mobile stations spatio-temporal data logs. The main idea is to identify the positive cases historical movements by tracking their phone location for the last 14 days (i.e., the virus incubation period). Then, by acquiring the citizen’s mobile phone locations for the same period, the system will be able to measure the Euclidean distances between positive case locations and other nearby people to identify the incontact suspected-cases using parallel clustering and classification techniques. Moreover, the daily change of the clusters size and its centroids will be used to predict new regions of infection, as well as, new cases. Moreover, this approach will support infection avoidance by alerting people approaching areas of high probability of infection using their mobile GPS location. This case study has been developed as a simulation system consisting of three components; positive cases/citizens movement’s data generation subsystem, big data processing platform including CPU/GPU tasks, and data visualization/map geotagging subsystem. The processing of such a big data system requires intensive computing tasks. Therefore, GPU tasks carried out to achieve high performance and accelerate the data processing. According to the simulated system results, data partitioning and processing speed up measures have been examined.     

电火花加工中电位差法放电位置的在线检测

在电火花加工（ＥＤＭ）过程中，在线检测放电位置具有十分重要的意义，本文对电位误差法ＥＤＭ放电位置的在线检测作了进一步的研究，并证明该检测方法不受脉冲宽度。供电分支导线长度和电极材料电阻率的影响。     

Alumina/tungsten nanocomposites obtained by Spark Plasma Sintering

In the present work, the parameters controlling the alumina/W nanopowders Spark Plasma Sintering (SPS) process are studied. The important and crucial role of C diffusion from the SPS die in the microstructure, fraction of porosity and new phases formation (W₂C) is discussed. It is also pointed out the importance of the processing parameters of the starting powder on the density and mechanical properties of the final compacts. In this regard, dense alumina/nW compacts containing 4 vol.% W with a hardness value as high as 24.6 ± 0.9 GPa were obtained.     

以淀粉为填充剂的碳坯渗硅制备反应烧结碳化硅陶瓷

探索了一条高性能RBSC低成本制造的新途径,本研究以石油焦粉为碳质原料制坯,玉米淀粉为填充剂调整碳坯的密度,纯碳素坯经高温渗硅得到密度为3.12g/cm³,强度为580MPa的反应烧结碳化硅陶瓷.研究结果表明掺加淀粉后素坯中含有更多的微孔,烧结体晶粒平均尺寸为2-4μm,晶粒细化是材料性能比传统RBSC材料高的原因.