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News
October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.

 

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Combination of Top-down and Bottom-up DEA models using PCA: A Two-Stage Network DEA with Shared Input and Undesirable Output for Evaluation of the Road Transport Sector

Socio-Economic Planning Sciences. 2023. Vol. 89. Article 101706.
Omrani H., Yang Z., Karbasian A., Teplova T.

This paper presents a network data envelopment analysis (NDEA) model to assess road transport sector by considering energy and non-energy inputs, shared input, and desirable and undesirable outputs. The sector is considered as a two-stage framework where in stage 1, energy-environmental efficiency is calculated and in stage 2, technical efficiency is estimated. There are two general approaches for evaluation of the network structures using NDEA models: bottom-up and top-down. In the bottom-up approach of this study, a goal programming NDEA (GP-NDEA) model is proposed to calculate the efficiency of the two stages at the same time and then overall efficiency is obtained using the results of the two stages. In the top-down approach, both non-radial SBM-NDEA and RAM-NDEA models are applied to calculate the overall efficiency and then the efficiencies of stage 1 and stage 2 are estimated using the results of the overall efficiency. Three different models GP-NDEA, SBM-NDEA, and RAM-NDEA lead to different results and to obtain the final efficiencies and ranks, principal component analysis (PCA) method is applied. The efficiency generated by different bottom-up and top-down approaches are considered as indicators in PCA, and the final scores for both stages are calculated using PCA. To illustrate the capability of the proposed approach, an actual dataset of road transport sector in provinces of Iran is gathered and the results of the different models are analyzed.

Research target: Economics and Management
Language: English
Full text
DOI
Keywords: транспортэффективностьefficiencyметод главных компонентRoad transportSBM-NDEARAM-NDEAGoal programming NDEAPCASBM-NDEARAM-NDEAЦелевое программирование NDEA
Publication based on the results of:
Analysis of the behavior of financial assets under the influence of exogenous factors (2023)
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