Academics and data scientists from Durham University, a public research university in Durham, England, and UN Global Pulse (UNGP) have developed an agent-based model to simulate the spread of COVID-19 in the Cox’s Bazar refugee settlement in Bangladesh.

The researchers analyzed several operational interventions by modeling the interactions of over 900,000 Rohingya refugees and found that mask-wearing is highly effective to slow the spread of COVID-19. 

Researchers also established that handling of positive cases in isolation and treatment centers have little impact on the spread of COVID-19 in comparison to home isolation for individuals with mild symptoms, mainly due to the exceptionally high population density in the settlement and many facilities being communal that poses an increased risk of coronavirus transmission. 

Furthermore, at the time of the study, the simulation results indicated that the reopening of learning centers could lead to a higher infection rate in the refugee settlement, where social distancing is nearly impossible. This led the researchers to explore various mitigation strategies.

The study adapted the JUNE epidemic model to the settlement setting. The team took a scenario-based approach that focused on simulating the relative effectiveness of the above-mentioned interventions in the settlement.

The modeling followed a three-step process of (1) building a ‘digital twin’ of the Cox’s Bazar refugee settlement that (2) simulated the possible movement and interaction patterns among the residents and (3) implementation of operational interventions to simulate its effects on the spread of COVID-19 in the settlement. 

Virtual individuals were included in the model with different demographic attributes that mirrored real-world statistics. A simulation engine was designed by the researchers that captured the movement and interaction patterns of the people in the model.

Full results of the study have been published in the journal PLOS Computational Biology. 

The study findings have allowed decision-makers in the refugee settlement to set up new contingency plans for high case numbers and develop policies on the safe opening of various indoor spaces.

A mask-wearing strategy was rolled out, which included mask-making, and communication and engagement campaigns to increase correct mask usage, as the model showed how this could significantly reduce the spread of COVID-19 over time.

The model has been informed by data from UNHCR, the UN’s Refugee Agency, on geography, demographics, comorbidities, physical infrastructure, and other parameters obtained from real-world observations. 

The study results were presented in a series of reports that provided crucial insights and limitations relevant to this modeling approach to the World Health Organisation and UNHCR public health professionals operating in the settlement on the potential effectiveness of interventions to curb the spread of COVID-19.  

Chris Earney, Deputy Director of UNGP said: “The project has fulfilled its operational objectives successfully and the team is aiming to scale the model implementation further with future applications and partnerships.”

The JUNE open-source modeling framework has been developed by the researchers during the pandemic and was originally applied to simulating the spread of COVID-19 in England.

Professor Frank Krauss of Durham University said: “The work with the UN and the WHO is super-exciting and a very good example for the caliber of our Ph.D. students. It is great to see their enthusiasm, skills, and drive: this project started from zero, and within months we had a highly competitive COVID simulation for the UK, all while they also collaborated with international agencies to apply this to a completely new setting. This is nothing short of a truly excellent achievement!”               

The research conducted by UNGP has been supported by the Government of Sweden, and the William and Flora Hewlett Foundation and the Ph.D. students were supported by the UKRI-STFC grant.

The laws of quantum physics rule the microcosm. They determine, for example, how easily electrons move through a crystal and thus whether the material is a metal, a semiconductor, or an insulator. Quantum physics may lead to exotic properties in certain materials: In so-called topological insulators, only the electrons that can occupy some specific quantum states are free to move like massless particles on the surface, while this mobility is completely absent for electrons in the bulk. What's more, the conduction electrons in the "skin" of the material are necessarily spin-polarized and form robust, metallic surface states that could be utilized as channels in which to drive pure spin currents on femtosecond time scales (1 fs= 10-15 s). 

These properties open up exciting opportunities to develop new information technologies based on topological materials, such as ultrafast spintronics, by exploiting the spin of the electrons on their surfaces rather than the charge. In particular, optical excitation by femtosecond laser pulses in these materials represents a promising alternative to realize highly efficient, lossless transfer of spin information. Spintronic devices utilizing these properties have the potential of superior performance, as they would allow increasing the speed of information transport up to frequencies a thousand times faster than in modern electronics.

However, many questions still need to be answered before spintronic devices can be developed. For example, the details of exactly how the bulk and surface electrons from a topological material respond to the external stimulus i.e., the laser pulse, and the degree of overlap in their collective behaviors on ultrashort time scales.

A team led by HZB physicist Dr. Jaime Sánchez-Barriga has now brought new insights into such mechanisms. The team, which has also established a Helmholtz-RSF Joint Research Group in collaboration with colleagues from Lomonosov State University, Moscow, examined single crystals of elemental antimony (Sb), previously suggested to be a topological material. "It is a good strategy to study interesting physics in a simple system because that's where we can hope to understand the fundamental principles," Sánchez-Barriga explains. "The experimental verification of the topological property of this material required us to directly observe its electronic structure in a highly excited state with time, spin, energy, and momentum resolutions, and in this way, we accessed an unusual electron dynamics," adds Sánchez-Barriga.

The aim was to understand how fast excited electrons in the bulk and on the surface of Sb react to the external energy input and to explore the mechanisms governing their response. "By controlling the time delay between the initial laser excitation and the second pulse that allows us to probe the electronic structure, we were able to build up a full time-resolved picture of how excited states leave and return to equilibrium on ultrafast time scales. The unique combination of time and spin-resolved capabilities also allowed us to directly probe the spin-polarization of excited states far out-of-equilibrium," said Dr. Oliver J. Clark.

The data show a "kink" structure in transiently occupied energy-momentum dispersion of surface states, which can be interpreted as an increase in effective electron mass. The authors were able to show that this mass enhancement plays a decisive role in determining the complex interplay in the dynamical behaviors of electrons from the bulk and the surface, also depending on their spin, following the ultrafast optical excitation.

"Our research reveals which essential properties of this class of materials are the key to systematically control the relevant time scales in which lossless spin-polarized currents could be generated and manipulated," explained Sánchez-Barriga. These are important steps on the way to spintronic devices which based on topological materials possess advanced functionalities for ultrafast information processing.

The study has been selected as an outstanding publication by the academic journal Physics of Fluids 

In the field of industrial engineering, using simulations to model, predict, and even optimize the response of a system or device is widespread, as it is less expensive and less complex -and, sometimes, less dangerous- than fabricating and testing several prototypes.

This type of simulation study uses numerical methods that, depending on the problem to be addressed -for example, reducing the aerodynamic forces of an aircraft by changing its shape or using the minimum possible amount of material on elements under loading without breaking- require the simulation of a wide variety of possible combinational cases, which entails high computational costs.

The researchers from the School of Industrial Engineering of the University of Malaga in Spain Francisco Javier Granados Ortiz and Joaquín Ortega Casanova have taken a step further by developing a novel computational design optimization method that reduces these simulation costs by using artificial intelligence.

Faster and cost-efficient designs

They have developed a new methodology with Machine Learning algorithms to predict whether a combination of the design parameters of a problem will be useful or not, based on the objective pursued, and thus guide the design process.

"This method enables us to obtain faster-optimized designs by discarding simulations of little or no interest, thus saving not only physical prototype fabrication costs but also those related to simulation," explained the researchers of the Area of Fluid Mechanics. Particularly, this procedure has been applied to the design of a mechanical mixer that produces a significant increase in heat/mass transfer between two fluids thanks to vortex shedding, which results in an oscillating flow. "Based on the design parameters of the mixer, with our method we have verified that this flow can be controlled and achieve an efficient increase in mixing, but, at the same time, a decrease in pressure drop within it," said Ortega Casanova.

Melbourne researchers able to predict landslides

A mathematical model which can predict landslides that occur unexpectantly has been developed by two University of Melbourne scientists, with colleagues from GroundProbe-Orica and the University of Florence.

Professors Antoinette Tordesillas and Robin Batterham led the work over five years to develop and test the model SSSAFE (Spatiotemporal Slope Stability Analytics for Failure Estimation), which analyses slope stability over time to predict where and when a landslide or avalanche is likely to occur.

In a study, the research team was able to predict landslides, which often cause severe disruption, economic damage, and deaths, of various sizes and speeds and in different environments.

"The key to the success of this model is that it works across a vast range of spatial or temporal scales and is informed by the physics of failure in soil and rock bodies," said Professor Tordesillas.

"It can be used at a mine, where millimeter precision measurements of the surface motion of a rock face are made every few minutes. And it can also be used in a rural area, where the only available data is a satellite radar image taken every few days to weeks."

The SSSAFE model was initially developed for mine monitoring, where landslides are a constant threat, but using publicly available satellite data, the team was able to retrospectively predict the 2017 Xinmo landslide, which buried a township in China.

"For Xinmo, the model highlighted significant movement at what became the rock avalanche source, 10 months before the disaster occurred," said Professor Tordesillas. "If we can use this model, along with freely available satellite data to recognize potential future landslide sites well before they happen, actions can be taken to protect communities, saving many lives."

With SSSAFE exploiting big data analytics, network science, and physics, Professor Tordesillas hopes her research will be used by industry and governments worldwide to help early warning systems (EWS) in mitigating landslide hazards in the face of climate change.

"Very few studies have used remote sensing data to detect precursors of slope failure. Crucially, little is known about how to interpret this data from known physics of granular failure to better understand and predict events leading to catastrophic landslides. We achieved both in SSSAFE," she said.