Mathesia

28/10/2019

A recent study shows that a Manhattan commuter willing to increase trip time by about 15% during the evening or morning rush hour might reduce the number of accidents they encounter on their route by up to 18%

The new algorithm will help city planners and others confront the issue of increased traffic congestion from rideshare services and autonomous vehicles.

28 Oct A new algorithm helps find tradeoffs between commute time and safety Posted at 15:36h in Data Science, Innovation Stories, Optimization by Francesca Gatti 0 Likes Developed a new computer algorithm that will help city planners and others confront the issue of increased traffic congestion from...

21/10/2019

Researchers have modeled the fluid dynamics of multi-rotor wind turbines, and how they interact in wind farms; the study demonstrates a clear advantage for a turbine model with 4 rotors.

- A turbine with 4 rotors costs approx. 15% less to construct than a turbine with 1 rotor, even though the blades cover the same area in total.
- A construction with 4 rotors is much lighter and therefore easier to transport.
- If one of the rotors stops working, the rest of the turbine will still produce energy, unlike ordinary wind turbines.

- In addition, the researchers have found that individual multi-rotor turbines actually produce slightly more energy than single-rotor turbines: approx. 2%

https://mathesia.com/computer-models-show-clear-advantages-in-new-types-of-wind-turbines/

Aarhus University

21 Oct Computer models show clear advantages in new types of wind turbines Posted at 14:08h in Innovation Stories, Modelling, Simulation by Francesca Gatti 0 Likes Researchers have modeled the fluid dynamics of multi-rotor wind turbines, and how they interact in wind farms; the study demonstrates a....

16/10/2019

Delays and disruptions in airline operations annually result in billions of dollars of additional costs to airlines, passengers and the economy. Airlines strive to mitigate these costs by creating schedules that are less likely to get disrupted or schedules that are easy to repair when there are disruptions. Researchers have found a solution using a mathematical optimization model.
- The approach can reduce extreme delays by as much as 20–30% on average.
- The buffers in scheduling cost airlines less than 5%.

Vikrant Vaze College University of Coimbra
https://mathesia.com/revamped-crew-scheduling-model-cuts-airline-delays-by-as-much-as-30/

14 Oct Revamped crew scheduling model cuts airline delays by as much as 30% Posted at 14:01h in Innovation Stories, Optimization by Francesca Gatti 0 Likes Delays and disruptions in airline operations annually result in billions of dollars of additional costs to airlines, passengers and the economy....

11/10/2019

Purchasing Power Parity Quantitative Analysis project launched!
Develop techniques that forecast different financial KPIs and you could win part of a 50.000EUR prize pool!

Winner will be decided by the highest rate achieved of index price, purchasing power parity, commission, average fulfilled request rate, P/L, and dividends

Learn more and join the challenge:
https://mathesia.com/project-detail/?ch_id=2156

08/10/2019

- "Everything as a service" is how we work and live today and it's a topic worth strategizing about. Thanks Gert Christen .org to mention the service of Mathesia in your latest Digigram Newsletter!

PoliHub

Gert Christen’s DIGIGRAM newsletter of September 2019. Topics are: XaaS – Everything as a service focusing on the Gig economy and ways of Crowdsourcing.

04/10/2019

Purchasing Power Parity Quantitative Analysis: applications are open!
The project aims to develop techniques that, given a set of parameters and sample , different financial KPIs.
Find out more on https://mathesia.com/project-detail/?ch_id=2156 and send your proposal!

01/10/2019

A team of researchers have developed machine learning algorithms which can successfully identify bullies and aggressors on Twitter with 90% accuracy.

Binghamton University

23 Sep New algorithm can distinguish cyberbullies with 90% accuracy Posted at 12:54h in Data Science, Innovation Stories, Numerical Science by Francesca Gatti 0 Likes A team of researchers, including faculty at Binghamton University, have developed machine learning algorithms which can successfully....

09/09/2019

Looking forward to Crowdsourcing Week event !
Join us in San Francisco!

PoliHub

09/09/2019

New Season - New Project!
Stay tuned for our new project involving purchasing power quantitive analysis for a first of it's kind company in the US! Check out www.mathesia.com for further updates and for more exciting projects.

26/07/2019

We are delighted to be mentioned in this article about the strategy and the network of Eni.

Mathesia is working with Eni in the Safety Pre-sense project where our network of experts have been tasked with the creation of a data-driven framework capable of identifying the most relevant and repeating causes and factors of incidence concerning safety.

Open innovation e intelligenza artificiale per far crescere le PMI: l'esempio di Eni a Campus Party 2019.

23/07/2019

Today we are in Santiago at the 1st Conference on Transfer between Mathematics & Industry – a satellite meeting of – talking about How to Crowdsource and

We will present the unique model of Mathesia and some of our projects:
- Safety Pre-Sense: A descriptive and correlation analysis of safety events, highlighting patterns and similarities useful for the next steps, and to identify other possible sources of information.
- CFD Optimization of Cooling towers: Fluid distribution analysis in cooling tower, improving the cooling tower performance by approach.
- Resource Allocation Optimization: of daily resource allocation: static optimization and dynamic reassignment of the jobs

19 Jul Mathesia @ Conference on Transfer between Mathematics & Industry Posted at 14:30h in Mathesia News by Francesca Gatti 0 Likes On July 23rd, Mathesia will participate to the 1st Conference on Transfer between Mathematics & Industry [CTMI 2019] – a satellite meeting of ICIAM 2019 – talking ...

20/07/2019

Lettuce is a big business, with 122,000 tonnes produced in the UK each year. Up to 30% of yield can be lost due to inefficiencies in the growing process as well as harvest strategies, which, if made up, could provide a significant economic boost.

The AirSurf technology uses deep learning combined with sophisticated, ultra-wide-scale imaging analysis to measure iceberg lettuce in a high-throughput mode. This is able to identify the precise quantity and location of lettuce plants, with the additional advantage of recognising crop quality, for example small, medium or large lettuce heads.

Combining this system with GPS allows farmers to precisely track size distribution of lettuce in fields, which can help increasing the precision and effectiveness of farming practice, including harvest time.

11 Jul AirSurf Lettuce, machine learning for crop optimization Posted at 12:51h in Data Science, Innovation Stories, Numerical Science, Optimization by Francesca Gatti 0 Likes Researchers at Earlham Institute (EI) in Norwich, in cooperation with G’s Growers, have developed a machine learning platf...