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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
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Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Packet Level Performance Assessment of mmWave Backhauling Technology for 3GPP NR Systems

IEEE Access. 2019. No. 7. P. 9860–9871.
Ometov A., Moltchanov D., Komarov M. M., Volvenko S., Koucheryavy E.

Recently standardized millimeter-wave (mmWave) band 3GPP New Radio systems are expected to bring extraordinary rates to the air interface efficiently providing commercial-grade enhanced mobile broadband services in hotspot areas. One of the challenges of such systems is efficient offloading of the data from access points (AP) to the network infrastructure. This task is of special importance for APs installed in remote areas with no transport network available. In this paper, we assess the packet level performance of mmWave technology for cost-efficient backhauling of remote 3GPP NR APs connectivity “islands”. Using a queuing system with arrival processes of the same priority competing for transmission resources, we assess the aggregated and per-AP packet loss probability as a function environmental conditions, mmWave system specifics, and generated traffic volume. We show that the autocorrelation in aggregated traffic provides a significant impact on service characteristics of mmWave backhaul and needs to be compensated by increasing either emitted power or the number of antenna array elements. The effect of autocorrelation in the per-AP traffic and background traffic from other APs also negatively affects the per-AP packet loss probability. However, the effect is of different magnitude and heavily depends on the load fraction of per-AP traffic in the aggregated traffic stream. The developed model can be used to parameterize mmWave backhaul links as a function of the propagation environment, system design, and traffic conditions.

Priority areas: IT and mathematics
Language: English
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Keywords: bandwidth mathematical modelwireless communications 5G mobile communicationmmWave
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