The investigation reported in this document focuses on identifying systems with symmetries using equivariant autoregressive reservoir computers. General results in structured matrix approximation theory are presented, exploring a two-fold approach. Firstly, a comprehensive examination of generic symmetry-preserving nonlinear time delay embedding is conducted. This involves analyzing time series data sampled from an equivariant system under study. Secondly, sparse least-squares methods are applied to discern approximate representations of the output coupling matrices. These matrices play a pivotal role in determining the nonlinear autoregressive representation of an equivariant system. The structural characteristics of these matrices are dictated by the set of symmetries inherent in the system. The document outlines prototypical algorithms derived from the described techniques, offering insight into their practical applications. Emphasis is placed on their effectiveness in the identification and predictive simulation of equivariant nonlinear systems, regardless of whether such systems exhibit chaotic behavior.

A tool has been developed to help healthcare professionals identify hospitalised patients most at risk of dying from COVID-19 using artificial intelligence (AI). The algorithm could help doctors to direct critical care resources to those in most immediate need, which the developers of the AI tool say could be especially valuable to resource-limited countries. And with no end in sight for the coronavirus pandemic, with new variants leading to fresh waves of sickness and hospitalisation, the scientists behind the tool say there is a need for generalised tools like this which can be easily rolled out. To develop the tool, scientists used biochemical data from routine blood samples taken from nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. Taking blood from so many patients meant the team were able to capture data from people with different immune statuses – vaccinated, unvaccinated and those with natural immunity – and from people infected with every variant of COVID-19.

IBM Corp. and Heifer International announced today that blockchain-based IBM Food Trust and artificial intelligence platform IBM Watson are being used to help small-scale coffee and cocoa farmers in Honduras get higher crop yields and access new markets. Food Trust uses blockchain technology to track products from farm to point of sale, providing transparency along the entire supply chain. It is currently being used by coffee farmers in the Cooperativa Regional Agroforestal Nuevas Ideas Limitada, a farmer cooperative, and cocoa farmers that are part of Heifer's Chocolate4All project. According to a study published by Heifer International small-scale farmers operate at an average of 46% to 59% loss because of excessive middlemen. Increased transparency enables end buyers more opportunity to understand whom they are buying from and thus can create a competitive advantage for small-scale coffee and cocoa farmers.

For centuries, Maya women in Guatemala, El Salvador, Honduras, Mexico, and Belize have been manually weaving clothing and other textile arts that encapsulate their beliefs, culture, rituals, and traditions. Worldwide, however, the knowledge of Maya culture and its rich colors and designs is in danger. High-fashion designers have been known to document this ancient weaving process to later on incorporate the knowledge into creating expensive products giving little to no credit to the artisans. Traditional indigenous garments worn by Maya people are more than just clothing, they unite and express the identity of ancestral communities. Initiatives such as Museo Ixchel del Traje Indigena aim to educate the public on the evolution of indigenous dress in diverse communities and the techniques used in its elaboration.

Roman is a data scientist at SAP Digital Interconnect and Agile Solutions, where he mainly works on building machine learning models and applying time series analysis to financial forecasting. He studied Systems Engineering and received a Bachelor of Mathematics with Computer Orientation at the National Autonomous University of Honduras (UNAH). This combination of degrees set him on the road of data science. He loves to learn, teach, help others, and spend time with his family.

In the Central American country of Honduras, a political story has been unfolding which deserves more coverage than it has been getting. Close to 40 people have been killed and more than 2,000 arrested, following the contested re-election of President Juan Orlando Hernandez to a second term in office. With 54 percent of the votes counted, the trend was a clear win for left-wing opposition candidate, Salvador Nasralla. But then the computer system mysteriously broke down. When it finally came back online a full day later, the vote count had been turned upside down: the right-wing incumbent, Juan Orlando Hernandez, was suddenly ahead.

Digital disruption is leaving no industry untouched, irrespective of geography. Digital disruptors and cloud natives are radically transforming business models. Digital transformation is no more a buzzword. We see technology evolving like never before and accelerating in scope and reach. While the year 2017 more or less stabilized new technologies, the coming year could see a significant incline. Emerging technologies that could have a long term impact include Blockchain, Artificial Intelligence, Big Data Analytics, and Cloud Computing (witnessing massive adoption of public and hybrid cloud models globally).