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September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
September 17, 2026
'I Wish That People Would Place Greater Trust in Science'
When Tatiana Eremicheva chose Fundamental and Computational Linguistics as her field of study, she thought it would be about learning languages. Instead, she discovered it was about helping people. In this interview for the HSE Young Scientists project, she discusses science as a way of understanding the world, billiards as a team-building activity, and why learning to read is not always as easy as it seems.
September 15, 2026
Immunity to Chaos: How Personal Resources Help Us Cope with the Challenges of a Turbulent World
International conflicts, crises and digital overload—the modern world puts our minds to the test every day. Traditional psychology often focuses on the consequences: anxiety, depression, and psychosomatic disorders. But what if we looked at the problem differently—through the lens of the resources that prevent us from breaking down? Psychological immunity is precisely this set of resources. Alena Zolotareva and her group, Psychological Immunity as a Resource for Positive Functioning, are developing an integrative model of this phenomenon, adapting diagnostic tools and preparing for large-scale empirical research. Why do psychologists need to collaborate with medical professionals, and how could their research transform preventive care in clinics and corporations?

 

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Degree-based topological co-indices for QSPR modelling of benzenoid hydrocarbons: a comparative computational study

Chemical Papers. 2026.
Ahsan M., Imran Ali

Benzenoid hydrocarbons are structurally regular aromatic compounds, which makes them well suited for quantitative structure–property relationship (QSPR) studies. This study presents a QSPR analysis of nineteen benzenoid hydrocarbon species using degree-based topological co-indices. We developed a Python program to compute eleven degree-based topological co-indices from molecular graphs and evaluated their ability to explain variations in key physicochemical properties. Using SPSS, we evaluated linear, quadratic, and cubic regression models to correlate the computed co-indices with Henry’s law constant, molecular weight, -electron energy, and other physicochemical properties. The comparative analysis reveals that cubic regression models provide the strongest structure–property correlations, with the forgotten co-index  emerging as a particularly robust descriptor for Henry’s law constant. Overall, this study demonstrates that degree-based co-indices are effective and computationally efficient descriptors for capturing structure–property trends in benzenoid hydrocarbons. The predictive stability of the proposed models is confirmed through leave-one-out cross-validation, Y-randomization, and applicability-domain analysis.

Research target: Mathematics Chemistry Chemical Technologies
Language: English
DOI
Text on another site
Keywords: pythondata proessing using SPSSDegree-based co-indicesBenzenoid hydrocarbonsAdjacency matrixSPSSQSPR
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Added: December 10, 2024
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