Applications of Open Source Intelligence in Crisis Analysis—A COVID-19 Case Study

Author(s):  
A. K. Buvanasri ◽  
R. Meenakshi ◽  
S. Karthika
Author(s):  
Jeff Boleng ◽  
Marc Novakouski ◽  
Gene Cahill ◽  
Soumya Simanta ◽  
Edwin Morris

Author(s):  
Faried Effendy ◽  
Taufik ◽  
Bramantyo Adhilaksono

: Substantial research has been conducted to compare web servers or to compare databases, but very limited research combines the two. Node.js and Golang (Go) are popular platforms for both web and mobile application back-ends, whereas MySQL and Go are among the best open source databases with different characters. Using MySQL and MongoDB as databases, this study aims to compare the performance of Go and Node.js as web applications back-end regarding response time, CPU utilization, and memory usage. To simulate the actual web server workload, the flow of data traffic on the server follows the Poisson distribution. The result shows that the combination of Go and MySQL is superior in CPU utilization and memory usage, while the Node.js and MySQL combination is superior in response time.


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.


2021 ◽  
Vol 113 ◽  
pp. 101604
Author(s):  
Pablo Gutiérrez ◽  
Ary Rivillas ◽  
Daniel Tejada ◽  
Susana Giraldo ◽  
Andrea Restrepo ◽  
...  

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