Every semester, some of my students develop web pages for their projects. Here, I would like to showcase outcome from my students in Spring 2020 semester. It is interesting to note that most of this semester was conducted online.
The whole world seems to be waiting for a cure for Covid-19. Numerous companies and researchers around the world are racing to beat the clock in making a vaccine available for the masses. In their efforts to develop a vaccine, often times however, there are setbacks and even confusing results.
One such case is that of the Oxford’s vaccine which has very strange results. Nature reports that the developers have discovered 70% efficacy for a two-dose vaccine. This was analyzed a fortnight after the second dose. Read more here.
Cancer is a dreadful disease. In some ways, it is actually a disorder or malfunction of the body. While its details are still fuzzy, considering the large number and types of cancer, it appears as an emergence resulting from a lack of coordination of various systems inside the body.
Scientists have always been looking for the origins of Cancer. Recently they have found concrete proof of cancer existence in animals dating back to the dinosaurs.
David Evans, senior curator of Paleontology at the Royal Museum of Ontario in Toronto has identified bone cancer from a so-called “Centrosaurus” dinosaur from around 70–75 million years ago.
The study has been presented in the journal Lancet Oncology .
To the best of our current knowledge, moon is the only natural “official” satellite orbiting our planet.
This, however, has often been challenged by some researchers over time who call some near-earth objects with synchronized orbits as “second moons” [1, 2].
While a number of these observations have been discarded by the scientific community, there still are objects which include temporary satellites, quasi-satellites, trojans, horseshoe orbit objects and more.
This is actually an exception in the solar system with 70 known moons for Jupiter.
The Hubble telescope took this splendid shot of our moon in 1991.
Sepharial, A. The Science of Foreknowledge: Being a Compendium of Astrological Research, Philosophy, and Practice in the East and West.; Kessinger Publishing (reprint), 1997, pp. 39–50; ISBN 1-56459-717-2
Bakich, Michael E. The Cambridge Planetary Handbook. Cambridge University Press, 2000, ISBN0-521-63280-3, p. 148
Background Complex Adaptive Systems involve a large number of variables. These variables are essentially inputs from complex processes which can often be modeled as agents. The data generated from these systems can be considerably nonlinear and difficult to understand.
Problem Statement In systems which are very closely linked to the human world, often times, there is a need to make quick or at least, expensive decisions in a relatively shorter amount of time.
Research Outcome The presented research reaffirms the belief of the Complex Adaptive Systems (CAS) community that simpler models can be considerably better in terms of making decisions.
Casey Helgeson et al, Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions, Philosophy of Science (2020). DOI: 10.1086/711501
A’ndrea Elyse Messer Simpler models may be better for determining some climate risk by Blog
Google has been successfully able to demonstrate the effective use of real-time data in its apps. They have been adding new content and capabilities. In a recent blog post, Google has introduced a yet another capability in Google Maps which will be showing location-specific Covid-19 data.
In the past, google has been adding new and innovative data-centric mechanisms to its suite of apps. These have included features such as:
So, now Google Maps is adding an additional layer inside it which will use color-coding.
It is not hard to use. Simply speaking, you need to tap on the layers button (Typically showing up at the top right corner) and then click on the Covid-19 info.
What you will see next is the data of new cases per 100,000 people in the area – averaged over a seven day period for normalization purposes. This data is available for 220 countries and regions supported by Google Maps. It can even allow for drilling down to a particular region or city.
What is new?
Arguably, country and region-wide data on Covid-19 cases has already been available for a considerable time. However, what is new here is the integration of Google maps with the data. This opens up new avenues for conducting research, for health authorities to explore regions visually, or simply if you just want to be the smart-citizen by understanding how the spread is occurring. It adds on to existing google apps such as those helping to get around safely.
What is Missing?
While interesting, the data is just one aspect of the big picture. For one, the data presented seems to be instantaneous (i.e. non-temporal). In other words, we cannot explore trends and gradients by simply looking at the map. It also does not show recoveries, or information about local facilities such as hospitals. While there may be existing, localized apps for that, to my knowledge, there is no direct, public integration of any of these apps with Google maps.
JOURNAL OF SIMULATION (JoS) SPECIAL ISSUE ON “Modeling and Simulation in the Cloud Computing era”
Cloud computing has received considerable interest by the scientific and industrial community because, thanks to advances in Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), it allows the implementation of solutions to exploit computing and data storage capacity, network resources, and scalability rapidly. In this context, Cloud, Edge and Fog computing can offer suitable services to share and collaborate on M&S and perform complex simulation experiments faster and more efficiently using Modelling and Simulation as a Service (MSAAS). While MSAAS offers an ever-expanding number of possibilities, it also entails a considerable number of challenges. One of the key challenges is related to the fact that cloud infrastructures are massive at all scales, therefore developing MSAAS solutions is difficult without an adequate knowledge of the involved platforms and technologies.
The aim of this special issue is to provide a comprehensive guide on current ideas and results in M&S for Cloud computing and vice versa. Specifically, the issue aims at: (i) presenting the current state-of-the-art about M&S environments and frameworks based on open standards, recent extensions, and innovations related to Cloud computing technologies; and, (ii) identifying potential research directions and technologies that will drive innovations in M&S on Cloud Computing Infrastructures. Additionally, the special issue will also look for submissions employing Complex Systems related methodologies, toolkits, and frameworks such as involving Complex Social Networks, and Agent-based Modelling, among others.
Nowadays, there is research aiming at investigating the impact of Cloud computing on M&S techniques and methodologies. We believe that a journal special issue on “Modeling and Simulation in the Cloud Computing era” will be a timely contribution to a field that is gaining considerable research interest and is expected to be of increasing interest to commercial developers in a wide range of application domains. Moreover, we believe that the methodological and technological trends in the convergence of Cloud Computing and M&S disciplines need to be explored more in order to provide an exclusive research roadmap to both Cloud Computing and M&S communities. Furthermore, this special issue involves strongly scientific programming aspects related to mathematical models and quantitative analysis techniques that use heavily cloud computing solutions.
Manuscripts for the Special Issue must provide a novel contribution and must be carefully placed in relation to the relevant scientific literature. Authors clearly address research issues of M&S in the Cloud and show the use of existing methodologies, techniques, and tools for the development of future-generation of simulators based on Cloud computing services.
Topics of interest include, but are not limited to:
– High-performance simulation in the Cloud;
– Cloud-based parallel and distributed simulation;
– Simulation optimization approaches that leverage Cloud Computing;
– Dependability and performance analyses through Big data in the Cloud;
– Hybrid modelling research that combines the application of Cloud-based solution to one or more stages of a simulation study;
– Real-time simulations in the Cloud;
– Application-focused papers that also contribute to methodology;
– Literature review (invited; please contact the Guest Editors with a proposal);
– Invited viewpoint papers (invited; please contact the Guest Editors with a proposal).
Peer review of manuscripts submitted to the special issue is conducted according to agreed and ethical peer review standards (http://journalauthors.tandf.co.uk/preparation/ethics.asp) for the publication of articles, so as to ensure the integrity of peer review and assure the quality of published articles.
All authors, peer reviewers, and referees comply with the Publisher’s guidelines on the ethics of journal publishing (http://journalauthors.tandf.co.uk/preparation/ethics.asp) and respect the confidentiality of the review process, and that material under review shall be held to be the contributing author’s intellectual property unless and until otherwise assigned.
Please indicate that your article is for a special issue during the submission process; both in your cover letter and when asked by the ScholarOne system. You should be able to select the special issue title from a drop-down menu, which will help the Editorial Office and the Editors to correctly allocate your paper for peer review.
Please do not hesitate to contact the Guest Editors for any questions.
Manuscript submission deadline: January 15th, 2021
Notification of Acceptance/Rejection: March 06th, 2021
Submission of Revised Manuscripts: April 06th, 2021
Final notification of acceptance: June 21th, 2021
Journal Special Issue Publication Date: September 2021
SPECIAL ISSUE EDITORS:
Lead Guest Editor:
Alberto Falcone (firstname.lastname@example.org), Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Italy.
Alfredo Garro (email@example.com), Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Italy.
Navonil Mustafee (firstname.lastname@example.org), Centre for Simulation, Analytics and Modelling (CSAM), University of Exeter Business School, Exeter, United Kingdom.
Muaz A. Niazi (email@example.com), Computer Science Department, University, Islamabad, Pakistan.
Gabriel Wainer (firstname.lastname@example.org), Department of Systems and Computer Engineering, Carleton University, Canada.