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184 results on '"Todorov, V."'

5. Tenecteplase versus standard of care for minor ischaemic stroke with proven occlusion (TEMPO-2): a randomised, open label, phase 3 superiority trial

Author

Salluzzi, Marina, Blenkin, Nicole, Dueck, Ashley, Doram, Craig, Zhang, Qiao, Kenney, Carol, Ryckborst, Karla, Bohn, Shelly, Collier, Quentin, Taylor, Frances, Lethebe, B. Cord, Jambula, Anitha, Sage, Kayla, Toussaint, Lana, Save, Supryia, Lee, Jaclyn, Laham, N, Sultan, A.A., Deepak, A., Sitaram, A., Demchuk, Andrew M., Lockey, A., Micielli, A., Wadhwa, A., Arabambi, B., Graham, B., Bogiatzi, Chrysi, Doshi, Darshan, Chakraborty, D., Kim, Diana, Vasquez, D, Singh, D, Tse, Dominic, Harrison, E., Smith, E.E., Teleg, E., Klourfeld, E., Klein, G., Sebastian, I.A., Evans, J, Hegedus, J, Kromm, J, Lin, K, Ignacio, K, Ghavami, Kimia, Ismail, M., Moores, M., Panzini, M.A., Boyko, M., Almekhlafi, M.A., Newcommon, Nancy, Maraj, N., Imoukhuede, O., Volny, O., Stys, Peter, Couillard, Phillipe, Ojha, P., Eswaradass, P., Joundi, Raed, Singh, R., Asuncion, R.M., Muir, R.T., Dey, S., Mansoor, S., Wasyliw, S., Nagendra, S., Hu, Sherry, Althubait, S., Chen, S., Bal, S., Van Gaal, Stephen, Peters, Steven, Ray, Sucharita, Chaturvedi, S., Subramaniam, Suresh, Fu, Vivian, Villaluna, K., Maclean, G., King-Azote, P., Ma, C., Plecash, A., Murphy, C., Gorman, J., Wilson, L., Zhou, L., Benevente, O., Teal, P., Yip, S., Mann, S., Dewar, B., Demetroff, M., Shamloul, R., Beardshaw, R., Roberts, S., Blaquiere, D., Stotts, G., Shamy, M., Bereznyakova, O., Fahed, R., Alesefir, W., Lavoie, Suzy, Hache, A., Collard, K, Mackey, A., Gosselin-Lefebvre, S., Verreault, S., Beauchamp, B., Lambourn, L., Khaw, A., Mai, L., Sposato, L., Bres Bullrich, M., Azarpazhooh, R., Fridman, S., Kapoor, A., Southwell, A., Bardi, E., Fatakdawala, I., Kamra, M, Lopes, K., Popel, N., Norouzi, V., Liu, A., Liddy, A.M., Ghoari, B., Hawkes, C., Enriquez, C.A., Gladstone, D.J., Manosalva Alzate, H.A., Khosravani, H., Hopyan, J.J., Sivakumar, K., Son, M., Boulos, M.I., Hamind, M.A., Swartz, R.H., Murphy, R., Reiter, S., Fitzpatrick, T., Bhandari, V., Good, J., Penn, M., Naylor, M., Frost, S., Cayley, A., Akthar, F., Williams, J., Kalman, L., Crellin, L., Wiegner, R., Singh, R.S., Stewart, T., To, W., Singh, S., Pikula, A., Jaigobin, C., Carpani, F., Silver, F., Janssen, H., Schaafsma, J., del Campo, M., Alskaini, M., Rajendram, P., Fairall, P., Granfield, B., Crawford, D., Jabs, J., White, L., Sivakumar, L., Piquette, L., Nguyen, T., Nomani, A., Wagner, A., Alrohimi, A., Butt, A., D'Souza, A., Gajurel, B., Vekhande, C., Kamble, H., Kalashyan, H., Lloret, M., Benguzzi, M., Arsalan, N., Ishaque, N., Ashayeriahmadabad, R., Samiento, R., Hosseini, S., Kazi, S., Das, S., Sugumar, T., Selchen, D., Kostyrko, P., Muccilli, A., Saposnik, A.G., Vandervelde, C., Ratnayake, K., McMillan, S., Katsanos, A., Shoamanesh, A., Sahlas, D.J., Naidoo, V., Todorov, V., Toma, H., Brar, J., Lee, J., Horton, M., Shand, E., Weatherby, S., Jin, A., Durafourt, B., Jalini, S., Gardner, A., Tyson, C., Junk, E., Foster, K., Bolt, K., Sylvain, N., Maley, S., Urroz, L., Peeling, L., Kelly, M., Whelan, R., Cooley, R., Teitelbaum, J., Boutayeb, A., Moore, A., Cole, E., Waxman, L., Ben-Amor, N., Sanchez, R., Khalil, S., Nehme, A., Legault, C., Tampieri, D., Ehrensperger, E., Vieira, L., Cortes, M., Angle, M., Hannouche, M., Badawy, M., Werner, K., Wieszmuellner, S., Langer, A., Gisold, A., Zach, H., Rommer, P., Macher, S., Blechinger, S., Marik, W., Series, W., Baumgartinger, M., Krebs, S., Koski, J., Eirola, S., Ivanoff, T., Erakanto, A., Kupari, L., Sibolt, G., Panula, J., Tomppo, L., Tiainen, M., Ahlstrom, M., Martinez Majander, N., Suomalainen, O., Raty, S., Levi, C., Kerr, E., Allen, J., Kaauwai, L.P., Belevski, L., Russell, M., Ormond, S., Chew, A., Loiselle, A., Royan, A., Hughes, B., Garcia Esperon, C., Pepper, E., Miteff, F., He, J., Lycett, M., Min, M., Murray, N., Pavey, N., Starling de Barros, R., Gangadharan, S., Dunkerton, S., Waller, S., Canento Sanchez, T., Wellings, T., Edmonds, G., Whittaker, K.A., Ewing, M., Lee, P., Singkang, R., McDonald, A., Dos Santos, A., Shin, C., Jackson, D., Tsoleridis, J., Fisicchia, L., Parsons, N., Shenoy, N., Smith, S., Sharobeam, A., Balabanski, A., Park, A., Williams, C., Pavlin-Premri, D., Rodrigues, E., Alemseged, F., Ng, F., Zhao, H., Beharry, J., Ng, J.L., Williamson, J., Wong, J.Z.W., Li, K., Kwan, M.K., Valente, M., Yassi, N., Yogendrakumar, V., McNamara, B., Buchanan, C., McCarthy, C., Thomas, G., Stephens, K., Chung, M., Chung, M.F., Tang, M., Busch, T., Frost, T., Lee, R., Stuart, N., Pachani, N., Menon, A., Borojevic, B., Linton, C.M., Garcia, G., Callaly, E.P., Dewey, H., Liu, J., Chen, J., Wong, J., Nowak, K., To, K., Lizak, N.S., Bhalala, O., Park, P., Tan, P., Martins, R., Cody, R., Forbes, R., Chen, S.K., Ooi, S., Tu, S., Dang, Y.L., Ling, Z., Cranefield, J., Drew, R., Tan, A., Kurunawai, C., Harvey, J., Mahadevan, J.J., Cagi, L., Palanikumar, L., Chia, L.N., Goh, R., El-Masri, S., Urbi, B., Rapier, C., Berrill, H., McEvoy, H., Dunning, R., Kuriakose, S., Chad, T., Sapaen, V., Sabet, A., Shah, D., Yeow, D., Lilley, K., Ward, K., Mozhy Mahizhnan, M., Tan, M., Lynch, C., Coveney, S., Tobin, K., McCabe, J., Marnane, M., Murphy, S., Large, M., Moynihan, B., Boyle, K., Sanjuan, E., Sanchis, M., Boned, S., Pancorbo, O., Sala, V., Garcia, L., Garcia-Tornel, A., Juega, J., Pagola, J., Santana, K., Requena, M., Muchada, M., Olive, M., Lozano, P.J., Rubiera, M., Deck, M., Rodriguez, N., Gomez, B., Reyes Munoz, F.J., Gomez, A.S., Sanz, A.C., Garcia, E.C., Penacoba, G., Ramos, M.E., de Lera Alfonso, M., Feliu, A, Pardo, L., Ramirez, P., Murillo, A., Lopez Dominguez, D., Rodriguez, J., Terceno Izaga, M., Reina, M., Viturro, S.B., Bojaryn, U., Vera Monge, V.A., Silva Blas, Y., R Siew, R., Agustin, S J, Seet, C., Tianming, T., d'Emden, A., Murray, A., Welch, A., Hatherley, K., Day, N., Smith, W., MacRae, E., Mitchell, E.S., Mahmood, A., Elliot, J., Neilson, S., Biswas, V., Brown, C., Lewis, A., Ashton, A., Werring, D., Perry, R., Muhammad, R., Lee, Y.C., Black, A., Robinson, A., Williams, A., Banaras, A., Cahoy, C., Raingold, G., Marinescu, M., Atang, N., Bason, N., Francia, N., Obarey, S., Feerick, S., Joseph, J., Schulz, U., Irons, R., Benjamin, J., Quinn, L., Jhoots, M., Teal, R., Ford, G., Harston, G., Bains, H., Gbinigie, I., Mathieson, P., Sim, C.H., Hayter, E., Kennedy, K., Binnie, L., Priestley, N., Williams, R., Ghatala, R., Stratton, S., Blight, A., Zhang, L., Davies, A., Duffy, H., Roberts, J., Homer, J., Roberts, K., Dodd, K., Cawley, K., Martin, M., Leason, S., Cotgreave, S., Taylor, T., Nallasivan, A., Haider, S., Chakraborty, T., Webster, T., Gil, A., Martin, B., Joseph, B., Cabrera, C., Jose, D., Man, J., Aquino, J., Sebastian, S., Osterdahl, M., Kwan, M., Matthew, M., Ike, N., Bello, P., Wilding, P., Fuentes, R., Shah, R., Mashate, S., Patel, T., Nwanguma, U., Dave, V., Haber, A., Lee, A., O'Sullivan, A., Drumm, B., Dawson, A.C., Matar, T., Roberts, D., Taylor, E., Rounis, E., El-Masry, A., O'Hare, C., Kalladka, D., Jamil, S., Auger, S., Raha, O., Evans, M., Vonberg, F., Kalam, S., Ali Sheikh, A., Jenkins, I.H., George, J., Kwan, J., Blagojevic, J., Saeed, M., Haji-Coll, M., Tsuda, M., Sayed, M., Winterkron, N., Thanbirajah, N., Vittay, O., Karim, R., Smail, R.C., Gauhar, S., Elmamoun, S., Malani, S., Pralhad Kelavkar, S., Hiden, J., Ferdinand, P., Sanyal, R., Varquez, R., Smith, B., Okechukwu, C., Fox, E., Collins, E., Courtney, K., Tauro, S., Patterson, C., McShane, D., Roberts, G., McIImoyle, J., McGuire, K., Fearon, P., Gordon, P., Isaacs, K., Lucas, K., Smith, L., Dews, L., Bates, M., Lawrence, S., Heeley, S., Patel, V., Chin, Y.M., Sims, D., Littleton, E., Khaira, J., Nadar, K., Kieliszkowska, A., Sari, B., Domingos Belo, C., Smith, E., Manolo, E.Y., Aeron-Thomas, J., Doheny, M., Garcia Pardo, M., Recaman, M., Tibajia, M.C., Aissa, M., Mah, Y., Yu, T., Meenakshisundaram, S., Heller, S., Alsukhni, R., Williams, O., Farag, M., Benger, M., Engineer, A., Bayhonan, S., Conway, S., Bhalla, A., Nouvakis, D., Theochari, E., Boyle, F., Teo, J., King-Robson, J., Law, K.Y., Sztriha, L., McGovern, A., Day, D., Mitchell-Douglas, J., Francis, J., Iqbal, A., Punjabivaryani, P., Anonuevo Reyes, J., Anonuevo Reyes, M., Pauls, M., Buch, A., Hedstrom, A., Hutchinson, C., Kirkland, C., Newham, J., Wilkes, G., Fleming, L., Fleck, N., Franca, A., Chwal, B., Oldoni, C., Mantovani, G., Noll, G., Zanella, L., Soma, M., Secchi, T., Borelli, W., Rimoli, B.P., da Cunha Silva, G.H., Machado Galvao Mondin, L.A., Barbosa Cerantola, R., Imthon, A.K., Esaki, A.S., Camilo, M., Vincenzi, O.C., ds Cruz, R.R., Morillos, M.B., Riccioppa Rodrigues, G.G., Santos Ferreira, K., Pazini, A.M., Pena Pereira, M.A., de Albuquerque, A.L.A., Massote Fontanini, C.E., Matinez Rubio, C.F., dos Santos, D.T., Dias, F.A., Alves, F.F.A., Milani, C., Pegorer Santos, B., Winckler, F., De Souza, J.T., Bonome, L.A.M., Cury Silva, V.A., Teodoro, R.S., Modolo, G.P., Ferreira, N.C., Barbosa dos Santos, D.F., dos Santos Moreira, J.C., Cruz Guedes de Morais, A.B., Vieira, J., Mendes, G., de Queiroz, J.P., Coutts, Shelagh B, Ankolekar, Sandeep, Appireddy, Ramana, Arenillas, Juan F, Assis, Zarina, Bailey, Peter, Barber, Philip A, Bazan, Rodrigo, Buck, Brian H, Butcher, Ken S, Camden, Marie-Christine, Campbell, Bruce C V, Casaubon, Leanne K, Catanese, Luciana, Chatterjee, Kausik, Choi, Philip M C, Clarke, Brian, Dowlatshahi, Dar, Ferrari, Julia, Field, Thalia S, Ganesh, Aravind, Ghia, Darshan, Goyal, Mayank, Greisenegger, Stefan, Halse, Omid, Horn, Mackenzie, Hunter, Gary, Imoukhuede, Oje, Kelly, Peter J, Kennedy, James, Kleinig, Timothy J, Krishnan, Kailash, Lima, Fabricio, Mandzia, Jennifer L, Marko, Martha, Martins, Sheila O, Medvedev, George, Menon, Bijoy K, Mishra, Sachin M, Molina, Carlos, Moussaddy, Aimen, Muir, Keith W, Parsons, Mark W, Penn, Andrew M W, Pille, Arthur, Pontes-Neto, Octávio M, Roffe, Christine, Serena, Joaquin, Simister, Robert, Singh, Nishita, Spratt, Neil, Strbian, Daniel, Tham, Carol H, Wiggam, M Ivan, Williams, David J, Willmot, Mark R, Wu, Teddy, Yu, Amy Y X, Zachariah, George, Zafar, Atif, Zerna, Charlotte, and Hill, Michael D

Published
2024
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7. Tackling adversity through innovation: A pilot study exploring VR as a tool to identify and diagnose depression

Author

S. Sutori and Karolinska Institutet: E. Eliasson, G. Hadlaczky, I. Todorov, V. Carli; University of Padua: F. Mura, V. Cardi; Polytechnic University of Valencia: V. Ortiz, M.A. Raya; University of Pisa: A.L. Alfeo, V. Catrambone, G. Valenza

Subjects
Psychiatry, RC435-571
Abstract

Abstract Introduction The final aim of the EXPERIENCE project is to enable individuals to record and share extended-personal realities in Virtual Reality (VR) - which entails the consideration of a person’s neurophysiological, psychological, and cognitive states. One prospective application is using this technology to aid in assessing symptoms of affective disorders. Objectives The objective is to test the ability of a pre-designed VR environment to differentiate between individuals with depressive symptoms and healthy controls (HCs) via machine learning algorithms. Methods Conducted as a pilot study in Italy, we recruited 100 volunteers, comprising 50 HCs and 50 individuals with moderate depressive symptoms assessed via the PHQ-9. Through a 40–60-minute VR engagement, comprehensive data on cognitive (inc. cognitive flexibility, sustained attention, working memory, processing speed), behavioral (exploration, attentional bias), and physiological (heart-rate variability, skin conductance) variables was collected. Subsequently, an explainable artificial intelligence model (xAI) was trained on data from 80% of the sample and tested on the remaining 20% in terms of accuracy for between-group classification. Results Following an iterative process that considered both the importance assigned to each variable in the different models and the theoretical relevance of these variables to depression the final model achieved an average accuracy of 71% (with individual trials ranging from 64.5% to 77.1%). Key predictors included exploratory behaviors and heart-rate variability during both exploration and cognitive tasks. Conclusions These results are comparable, however remain below the levels of accuracy achieved based on fMRI and DTI data alone (around 80%). Nonetheless, the EXPERIENCE system, slated for refinement beyond this pilot phase, shows potential in integrating multimodal data for evaluating affective disorder symptoms, aiming for a more objective screening and diagnostic approach at a lower cost. Acknowledgement The EXPERIENCE project is funded by the European Commission H2020 Framework Program, Grant No. 101017727. Disclosure of Interest None Declared

Published
2024
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11. Sensitivity analysis of the Unified Danish Eulerian Model with respect to emission levels and chemical reaction rates.

Author

Todorov, V., Dimov, I., Georgieva, R., and Ostromsky, Tz.

Subjects
*CHEMICAL kinetics, *SENSITIVITY analysis, *AMMONIUM sulfate, *AIR pollutants, *AIR pollution
Abstract

A number of numerical experiments with a large-scale air pollution model (Unified Danish Eulerian Model, UNI-DEM) have been carried out to compute Sobol sensitivity measures. Sensitivity analysis of ozone concentrations according to variations of rates of chemical reactions is developed. The sensitivity study has been done for the areas of four European cities (Genova, Milan, Manchester, and Edinburgh) with different geographical locations. Sensitivity analysis of the output of UNI-DEM according to emission levels is also provided. The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) is considered. Variance-based methods are most often used for providing sensitivity analysis. Sobol variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. Advanced stochastic methods based on modified Sobol sequences are used for computing the sensitivity measures. The numerical experiments show that the stochastic algorithms for the multidimensional integrals under consideration are one of the best stochastic techniques for computing the small in value sensitivity indices. [ABSTRACT FROM AUTHOR]

Published
2023
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12. Classical symmetric Diophantine systems.

Author

Stoenchev, M. and Todorov, V.

Subjects
*PRIME numbers, *EUCLIDEAN geometry
Abstract

This study delves into the examination of Diophantine systems, which are sets of polynomial equations with rational coefficients involving multiple variables. Specifically, the focus is on understanding the concept of local and global triviality within these systems. A Diophantine system is considered "locally trivial" if it possesses a non-zero solution in p-adic rational numbers for all prime numbers, including infinity. On the other hand, a system is deemed "globally trivial" if it has a non-zero rational solution in Q. While local triviality is a prerequisite for global triviality, it is important to note that the converse is not universally valid, as exemplified by Selmer's counterexample involving the equation 3x3+4y3+5z3=0. The main inquiry of this research revolves around investigating the conditions under which Diophantine systems exhibit equivalent notions of local and global triviality, thereby satisfying the "local-to-global principle". By analyzing various classes of Diophantine systems, the study sheds light on the existence of symmetric systems that arise in problems of Euclidean geometry, wherein the lengths of geometric elements are represented by integers. This paper is a continuous work of the [7]. [ABSTRACT FROM AUTHOR]

Published
2023
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13. A Hammersley and van der Corput sequences comparison for multidimensional sensitivity analysis.

Author

Todorov, V. and Georgiev, S.

Subjects
*SENSITIVITY analysis, *AIR pollutants, *MATHEMATICAL models
Abstract

In this paper for the first time a Hammersley and van der Corput sequences comparison for multidimensional sensitivity analysis has been done. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. By definition, sensitivity analysis is a procedure for studying how sensitive are the output results of large-scale mathematical models to some uncertainties of the input data. The algorithms for multidimensional sensitivity analysis are applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters on the concentrations of important air pollutants. A deep experimental investigation is done to demonstrate the pros and cons of both approaches in the case study of the considered model. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Innovative Monte Carlo method for linear systems.

Author

Todorov, V., Apostolov, S., Georgiev, S., and Dimov, I.

Subjects
*MONTE Carlo method, *LINEAR systems
Abstract

A new Monte Carlo algorithm for solving systems of Linear Algebraic (LA) equations is presented and studied. The algorithm is based on the "Walk on Equations" Monte Carlo method recently developed by Ivan Dimov, Sylvain Maire and Jean Michel Sellier [4]. The advantageous of the new algorithm versus the older one is demonstrated especially for diagonally dominant matrix with dimension up to 1000. [ABSTRACT FROM AUTHOR]

Published
2023
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17. On generalized $V^n$-continua

Author

Karassev, A., Krupski, P., Todorov, V., and Valov, V.

Subjects
Mathematics - Geometric Topology, General Topology (math.GN), FOS: Mathematics, Geometric Topology (math.GT), Primary 54F45, Secondary 55M10, Mathematics - General Topology
Abstract

The notion of $V^n$-continua was introduced by Alexandroff \cite{ps} as a generalization of the concept of $n$-manifold. In this note we consider the cohomological analogue of $V^n$-continua and prove that any strongly locally homogeneous, generalized continuum $X$ with cohomological dimension $n$ is a generalized $V^n$-con\-ti\-nuum with respect to the cohomological dimension. In particular, every strongly locally homogeneous continuum of covering dimension $n$ is a $V^n$-continuum in the sense of Alexandroff. An analog of the Mazurkiewicz theorem that no subset of covering dimension $\le n-2$ cuts any region of the Euclidean $n$-space is also obtained for strongly locally homogeneous, generalized continua of cohomological dimension $n$., 7 pages

Published
2023

21. Advanced stochastic approaches for option pricing based on Sobol sequence.

Author

Todorov, V., Dimov, I., Georgieva, R., Dimitrov, Yu., Apostolov, S., and Stoenchev, M.

Subjects
*PRICES, *MULTIDIMENSIONAL databases, *IMAGE encryption, *DETERMINISTIC algorithms, *FINANCIAL engineering, *COMPUTATIONAL complexity, *FINANCIAL markets
Abstract

Recently Monte Carlo and quasi-Monte Carlo approaches have become a very attractive and necessary computational tools in finance. The field of computational finance is becoming more complicated with increasing number of applications. The pricing of options is a very important in financial markets today and especially difficult when the dimension of the problem goes higher. Monte Carlo and quasi Monte Carlo methods are appropriate for solving multidimensional problems, since their computational complexity increases polynomially, but not exponentially with the dimensionality. A comprehensive experimental study based on scrambling of the Sobol sequence is applied for the first time to evaluate European style options. The Sobol scrambling method is not only one of the best available algorithms for high dimensional integrals but also one of the few possible methods, because in this work we show that the deterministic algorithms need a huge amount of time for the evaluation of the multidimensional integral, as it was discussed in this paper. The numerical tests show that the method is efficient for multidimensional integration and especially for computing multidimensional integrals of a very high dimension. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Multidimensional sensitivity study of large-scale air pollution model based on optimal stochastic approaches.

Author

Todorov, V.

Subjects
*AIR pollution, *CHEMICAL kinetics, *CHEMICAL processes, *AIR pollutants
Abstract

In this paper a study the sensitivity of concentration variations of some of the most dangerous air pollutants with respect to the anthropogenic emissions levels and with respect to some chemical reactions rates has been performed. Quasi-Monte Carlo algorithms based on Sobol, Faure, and different lattice rules are used in our sensitivity study of the Unified Danish Eulerian model. Different parts of the large amount of output data, produced by Unified Danish Eulerian model, were used in various practical applications, where the reliability of this data should be properly estimated. Another reason to choose this model as a case study here is its sophisticated chemical scheme, where all relevant chemical processes in the atmosphere are accurately represented. The numerical tests show that the stochastic algorithms under consideration, and especially the optimal lattice rule, are efficient for the multidimensional integrals under consideration and especially for computing small by value sensitivity indices. Clearly, the progress in the area of air pollution modeling, is closely connected with the progress in reliable algorithms for multidimensional integration. [ABSTRACT FROM AUTHOR]

Published
2022
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23. A comparison of Monte Carlo methods for multidimensional integrals in air pollution modeling based on Latin Hypercube Sampling Edge Algorithm.

Author

Todorov, V., Dimov, I., Ostromsky, Tz., and Georgieva, R.

Subjects
*LATIN hypercube sampling, *MONTE Carlo method, *CHEMICAL kinetics, *AIR pollution, *CHEMICAL processes
Abstract

The present study is based on The Unified Danish Eulerian Model (UNI-DEM) as one of the most advanced large- scale mathematical models that describes adequately all physical and chemical processes. One of the most attractive features of UNI-DEM is its advanced chemical scheme the Condensed CBM IV, which consider a large number of chemical species and numerous reactions between them, of which the ozone is one of the most important pollutants for its central role in many practical applications of the results. The calculations are done in a large spatial domain, which covers completely the European region and the Mediterranean, for certain time period. A comprehensive experimental study of Monte Carlo algorithm based on modifications of the Latin Hypercube Sampling Edge and Random Algorithms for multidimensional numerical integration has been done. Samplings with different seeds has been analyzed. This comparison has been made for the first time for sensitivity analysis of UNI-DEM. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the six chemical reactions rates and four different groups of pollutants. [ABSTRACT FROM AUTHOR]

Published
2022
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24. Efficient Monte Carlo algorithms for integral equations.

Author

Todorov, V., Dimov, I., Georgieva, R., Dimitrov, Yu., Apostolov, S., and Stoenchev, M.

Subjects
*INTEGRAL equations, *ALGORITHMS, *COMPUTATIONAL complexity, *FREDHOLM equations
Abstract

In this paper we propose and analyse different stochastic methods for solving a class of integral equations, namely the second kind Fredholm integral equations. We study and compare different possible approaches to compute linear functionals of the integral under consideration. Error balancing of both stochastic and systematic errors has been discussed and applied during the numerical implementation of the algorithms. An almost optimal Monte Carlo algorithm for integral equations in a combination with the idea of balancing of both systematic and stochastic errors is analysed. Conclusions about the applicability and efficiency of the algorithms have been drawn. Meaningful numerical examples and experiments with experimental and theoretical relative errors are presented. It is shown that the balancing of errors reduce the computational complexity if the error is fixed. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Three-way compositional analysis of energy intensity in manufacturing

Author

Todorov V, Simonacci V, Pollice A, Salvati N, Schirripa Spagnolo F, Todorov, V, and Simonacci, V

Subjects
manufacturing value added, energy intensity, PARAFAC, compositional data
Abstract

Both the scientific and political communities agree that significant reductions in CO2 emissions are necessary to limit the magnitude and extent of climate change and of course the energy efficiency is one of the most interesting issues analyzed by economists and policy makers within this debate. Different measures of energy efficiency in manufacturing can be defined but broadly this is the ratio of the production output to the energy input, usually disaggregated by industry. We create a global data set of energy intensity in manufacturing and analyze its structure by country, time and industry applying parallel factor analysis (CP). Since we are interested in the structure of the energy intensity, the absolute values are no more relevant for the analysis and the nature of this data set is compositional which requires specific adaptation of the methodology and suitable software.

Published
2020

31. Pitfals in recognition and management of trigeminal neuralgia

Author

Antonaci, F. Arceri, S. Rakusa, M. Mitsikostas, D.D. Milanov, I. Todorov, V. Ramusino, M.C. Costa, A.

Abstract

Background: Trigeminal neuralgia (TN) is a severe, disabling form of painful cranial neuropathy. Even though TN has a typical clinical picture, diagnosis it is often missed or delayed in clinical practice. In order to investigate the occurrence of diagnostic and therapeutic errors in TN, we studied 102 patients suffering from TN recruited through a multicentric survey. Methods: We performed a Pubmed database search on errors and pittfalls in TN diagnosis and management. Then, patients with TN were consecutively enrolled in the period from February 2017 to October 2019, by several European Headache Centers participating in the study, following a call of the Headache and Pain Scientific Panels of the European Academy of Neurology (EAN). Diagnosis of Classical Trigeminal Neuralgia (CTN) was made according to the International Headache Society (IHS) criteria (Tölle et al., Pain Pract 6:153-160, 2006). All the patients were evaluated using telephone/frontal interviews conducted by headache/pain specialists using an ad hoc questionnaire. Results: A number of 102 patients were recruited, mostly females (F:M ratio 2.64:1). Eighty-six percent of the patients consulted a physician at the time they experienced the first pain attacks. Specialists consulted before TN diagnosis were: primary care physicians (PCP) (43.1%), dentists (in 30.4%), otorhinolaryngologists (3.9%), neurosurgeons (3.9%), neurologists or headache specialists (14.7%), others (8%). The final diagnosis was made mainly by a neurologist or headache specialist (85.3%), and the mean interval between the disease onset and the diagnosis made by a specialist was 10.8 ± 21.2 months. The "diagnostic delay"was 7.2 ± 12.5 months, and misdiagnoses at first consultation were found in 42.1% of cases. Instrumental and laboratory investigations were carried out in 93.1% of the patients before the final diagnosis of TN. Conclusion: While TN has typical features and it is well defined by the available international diagnostic criteria, it is still frequently misdiagnosed and mistreated. There is a need to improve the neurological knowledge in order to promptly recognize the clinical picture of TN and properly adhere to the specific guidelines. This may result in a favorable outcome for patients, whose quality of life is usually severely impaired. © 2020 The Author(s).

Published
2020

37. Monte Carlo methods for sensitivity studies of large-scale air pollution model.

Author

Ostromsky, Tz., Todorov, V., Dimov, I., and Todorov, Michail D

Subjects
*MONTE Carlo method, *AIR pollution, *LATIN hypercube sampling, *EULERIAN graphs, *NUMERICAL integration, *ENVIRONMENTAL sciences, *MATHEMATICAL models
Abstract

Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of application. Such a sophisticated model in the area of air pollution modeling is the Danish Eulerian Model, a powerful large scale air pollution model with a long development history. Over the years it was used successfully in different long-term environmental studies for the European region. In this talk we discuss some performance-critical achievements in the parallel optimization and a systematic approach for sensitivity analysis of the latest version of the Danish Eulerian Model, UNI-DEM. A comprehensive experimental study of Monte Carlo algorithms based on Latin Hypercube Sampling and Adaptive approach for multidimensional numerical integration has been done. The algorithms have been successfully applied to compute global Sobol sensitivity measures, corresponding to the influence of several input parameters on the concentrations of some of the air pollutants of highest importance. The numerical tests show that the Monte Carlo algorithms under consideration are efficient for the multidimensional integrals in this study. [ABSTRACT FROM AUTHOR]

Published
2020
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38. A comparison of advanced quasi Monte Carlo methods for multidimensional integrals in air pollution modeling.

Author

Todorov, V., Dimov, I., Ostromsky, Tz., and Todorov, Michail D

Subjects
*MONTE Carlo method, *CHEMICAL kinetics, *AIR pollution, *EMISSIONS (Air pollution), *AIR pollutants, *INTEGRALS
Abstract

Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. Several efficient quasi-Monte Carlo algorithms – the van der Corput sequence and lattice rules based on different generating vectors have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The numerical tests show that the van der Corput sequences under consideration are efficient for the multidimensional integrals under consideration and especially for computing small by value sensitivity indices. [ABSTRACT FROM AUTHOR]

Published
2020
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39. Fitting the CANDECOMP-PARAFAC model to compositional data: a combined SWATLD-ALS algorithm

Author

Simonacci V, Di Palma MA, Todorov V, Pratesi M, Perna C, Simonacci, V, Di Palma, Ma, and Todorov, V

Subjects
Compositional Data, SWATLD, log-ratio, Parafac-ALS, CP model
Abstract

Multidimensional compositional arrays require special analytical tools to be modeled. Specifically, the variation of the data can be captured by linear combinations of a defined number of parameters, capable of describing the complexity of the data. Usually these models are described as generalizations of Principal Component Analysis to higher order cases. Here the Candecomp/Parafac (CP) model is defined for compositional data contaminated with extreme observations by using a novel integrated SWATLD-ALS algorithm. Since the new procedure does not find a solution in the least square sense, it is expected to have a better performance in terms of sensitivity to outliers than ALS. However, due to the instability of its loss function, it should not be used alone.

Published
2016

40. Pitfals in recognition and management of trigeminal neuralgia.

Author

Antonaci, F., Arceri, S., Rakusa, M., Mitsikostas, D. D., Milanov, I., Todorov, V., Ramusino, M. Cotta, and Costa, A.

Subjects
DIAGNOSIS, DIAGNOSTIC errors, INTERVIEWING, MEDICAL cooperation, MEDICAL errors, MEDICATION errors, NEUROLOGISTS, QUESTIONNAIRES, RESEARCH, SURVEYS, TRIGEMINAL neuralgia
Abstract

Background: Trigeminal neuralgia (TN) is a severe, disabling form of painful cranial neuropathy. Even though TN has a typical clinical picture, diagnosis it is often missed or delayed in clinical practice. In order to investigate the occurrence of diagnostic and therapeutic errors in TN, we studied 102 patients suffering from TN recruited through a multicentric survey. Methods: We performed a Pubmed database search on errors and pittfalls in TN diagnosis and management. Then, patients with TN were consecutively enrolled in the period from February 2017 to October 2019, by several European Headache Centers participating in the study, following a call of the Headache and Pain Scientific Panels of the European Academy of Neurology (EAN). Diagnosis of Classical Trigeminal Neuralgia (CTN) was made according to the International Headache Society (IHS) criteria (Tölle et al., Pain Pract 6:153-160, 2006). All the patients were evaluated using telephone/frontal interviews conducted by headache/pain specialists using an ad hoc questionnaire. Results: A number of 102 patients were recruited, mostly females (F:M ratio 2.64:1). Eighty-six percent of the patients consulted a physician at the time they experienced the first pain attacks. Specialists consulted before TN diagnosis were: primary care physicians (PCP) (43.1%), dentists (in 30.4%), otorhinolaryngologists (3.9%), neurosurgeons (3.9%), neurologists or headache specialists (14.7%), others (8%). The final diagnosis was made mainly by a neurologist or headache specialist (85.3%), and the mean interval between the disease onset and the diagnosis made by a specialist was 10.8 ± 21.2 months. The "diagnostic delay" was 7.2 ± 12.5 months, and misdiagnoses at first consultation were found in 42.1% of cases. Instrumental and laboratory investigations were carried out in 93.1% of the patients before the final diagnosis of TN. Conclusion: While TN has typical features and it is well defined by the available international diagnostic criteria, it is still frequently misdiagnosed and mistreated. There is a need to improve the neurological knowledge in order to promptly recognize the clinical picture of TN and properly adhere to the specific guidelines. This may result in a favorable outcome for patients, whose quality of life is usually severely impaired. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

41. A Comparison of Quasi-Monte Carlo Methods Based on Faure and Sobol Sequences for Multidimensional Integrals in Air Pollution Modeling.

Author

Todorov, V., Dimov, I., Dimitrov, Yu., Ostromsky, Tz., and Georgieva, R.

Subjects
*AIR pollution, *MONTE Carlo method, *ATMOSPHERIC models, *INTEGRALS, *POLLUTANTS, *SENSITIVITY analysis
Abstract

Air pollution and meteorological models are examples of mathematical models with a lot of natural uncertainties in their input data sets and parameters. Sensitivity analysis is a powerful tool for studying and improving the reliability of such models. In this paper we present the results of a global sensitivity study of the Unified Danish Eulerian Model (UNI-DEM). One of the most important features of UNI-DEM is its advanced chemical scheme the Condensed CBM IV, which considers a large number of chemicals, and various reactions between them, of which the ozone is the most important pollutant because it is used in many practical applications. The stochastic methods based on Faure and Sobol sequences are used for computing the sensitivity measures. The numerical experiments show that the stochastic algorithms for the multidimensional integrals under consideration are efficient methods for computing the small value sensitivity indices. [ABSTRACT FROM AUTHOR]

Published
2019
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46. Efficient Quasi-Monte Carlo Methods for Multiple Integrals in Option Pricing.

Author

Todorov, V., Dimov, I., and Dimitrov, Yu.

Subjects
*INTEGRALS, *EXPONENTIAL functions, *INTEGRAL calculus, *VECTORS (Calculus), *PRICING, *MONTE Carlo method
Abstract

In the present paper we consider European style options with an exponential payoff function. The problem is transformed to evaluation of multidimensional integrals of the exponential function over the unit cube where the values of the parameters involved in the formula depend the values of the European options. We compare the performance of quasi-Monte Carlo methods based on lattice rules for multiple integrals up to 30 dimensions. The performance of a lattice rule depends on the choice of the generator vectors. When the integrand is sufficiently regular the lattice rules outperform not only the standard Monte Carlo methods, but also other types of methods using low discrepancy sequences. We consider "rank 1" rules whose lattices have a a single generator vector. The advantages and disadvantages of the different quasi-Monte Carlo methods for multidimensional integrals related to evaluation of European options are studied in the paper. [ABSTRACT FROM AUTHOR]

Published
2018
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48. Numerical Determination of the Right-Hand Side of Parabolic Systems with Point Measurements.

Author

Dimov, I., Kandilarov, J., Todorov, V., and Vulkov, L.

Subjects
NUMERICAL analysis, DEGENERATE parabolic equations, INVERSE problems, MATHEMATICAL decomposition, HEAT equation
Abstract

In this paper, we study the inverse problem for the reconstruction of the right-hand side of parabolic systems of equations with overdeterminations, given by point measurements. The numerical algorithm is based on a special decomposition of the numerical solution first proposed for the heat equation in [9, 11]. Numerical experiments show the capacity of the numerical algorithm for parabolic systems. [ABSTRACT FROM AUTHOR]

Published
2017
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49. Efficient Stochastic Approaches for Sensitivity Studies of an Eulerian Large-scale Air Pollution Model.

Author

Dimov, I., Georgieva, R., Todorov, V., and Ostromsky, Tz.

Subjects
STATISTICAL reliability, MATHEMATICAL models, MONTE Carlo method, LATIN hypercube sampling, LATTICE theory
Abstract

Reliability of large-scale mathematical models is an important issue when such models are used to support decision makers. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is crucial for improving the reliability of mathematical models. A comprehensive experimental study of Monte Carlo algorithms based on Sobol sequences for multidimensional numerical integration has been done. A comparison with Latin hypercube sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The concentration values have been generated by the Unified Danish Eulerian Model. The sensitivity study has been done for the areas of several European cities with different geographical locations. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing small by value sensitivity indices. It is a crucial element since even small indices may be important to be estimated in order to achieve a more accurate distribution of inputs influence and a more reliable interpretation of the mathematical model results. [ABSTRACT FROM AUTHOR]

Published
2017
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