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Risk Prediction Models for Hypertensive Disorders of Pregnancy: Role of 12-Lead Electrocardiography

pressure education program working group on high blood pressure in pregnancy. Am J ObstetGynecol 2000; 183: S1-S22. http://dx.doi.org/10.1067/mob.2000.107928 7. Milne F, Redman C, Walker J et al. The pre-eclampsia community guideline (precog): How to screen for and detect onset of pre-eclampsia in the community. BMJ 2005; 330: 576-80. http://dx.doi.org/10.1136/bmj.330.7491.576 8. Angeli F, Angeli E, Reboldi G, Verdecchia P. Hypertensive disorders during pregnancy: Clinical applicability of risk prediction models. J Hypertens 2011; 29: 2320-3. http

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Bankruptcy Practice in Countries of Visegrad Four

Business & Accounting, 44(1-12), 3-34. Kasgari, A.A., Divsalar, M., Javid, M.R., & Ebrahimian, S.J. (2013). Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses. Neural Computing & Applications, 23(3-4), 927-936. Kirkos, E. (2015). Assessing Methodologies for Intelligent Bankruptcy Prediction. Artificial Intelligence Review, 43(1), 83-123. Kliestik, T., & Majerova, J. (2015). Selected issues of selection of significant variables in the prediction models. Proceedings of 10

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Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises

(1), 236-252. http://doi.org/10.1016/j.ejor.2016.03.008. Grice J.S., Dugan M.T. 2001. The limitations of bankruptcy prediction models: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17 (2), 151-166. Gruszczynski M. 2005. Strengths and Weaknesses of Bankruptcy Models. Materialy i Prace Instytutu Funkcjonowania Gospodarki Narodowej, 93, 185-187. Hołda A. 2001. Prognozowanie bankructwa jednostki w warunkach gospodarki polskiej z wykorzystaniem funkcji dyskryminacyjnej ZH. Rachunkowość, 5

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Prediction of Default of Small Companies in the Slovak Republic

References Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94 , 164-184. doi:10.1016/j.eswa.2017.10.040 Alaminos, D., Castillo, A. D., & Fernández, M. Á. (2016). A Global Model for Bankruptcy Prediction. Plos One, 11 (11). doi:10.1371/journal.pone.0166693 Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Distressed Firm and Bankruptcy

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Basic density and crown parameters of forest forming species within Steppe zone in Ukraine

Colendarum Ratio et Industria Lignaria , 12 (4): 43–53. V iana , H., A ranha , J., L opes , D., C ohen , W., 2012. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecological Modelling , 22 (6): 22–35. V iherä -A arnio A., V elling , P., 2017. Growth, wood density and bark thickness of silver birch originating from the Baltic countries and Finland in two Finnish provenance trials. Silva Fennica , 51 (4): article ID 7731, 18 p. V

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Forecasting and Predicting in Engineering Tasks

6. References 1. Anagnostopoulos T., Anagnostopoulos Ch., Hadjiefthymiades S.: An adaptive location prediction model based on fuzzy control. Computer Communications. Vol. 34. Iss. 7, May 2011. 2. Ben-lin Dai, Yu-long He, Fei-hu Mu, Ning Xu, Zhen Wu: Development of a traffic noise prediction model on inland waterway of China using the FHWA. Science of The Total Environment. Vol. 482–483, June 2014. 3. Chu-Hui Lee, Yu-lung Lo, Yu-Hsiang Fu: A novel prediction model based on hierarchical characteristic of web site. Expert Systems with Applications

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Bankruptcy Prediction Model for Private Limited Companies of Lithuania

. 69-74. Chudson, W. (1945). The Pattern of Corporate Financial Structure. New York: National Bureau of Economic Research. Dagilienė, L. et al. (2010). Bankroto prognozavimo svarba ir metodai.Verslas: teorija ir praktika, 11(2), p. 143-150. Du Jardin, P. (2009). Bankruptcy prediction models: How to choose the most relevant variables? Bankers, Markets & Investors, issue 98, January-February, p. 39-46. Grigaravičius, S. (2003). Corporate Failure Diagnosis: Reliability and Practice. Organizacijų vadyba

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

research works on microblog influence are abundant. However, research on the influence of microblog in specific fields, such as public health emergencies, is relatively insufficient. This study attempts to propose a microblog influence prediction model for public health emergencies, which is composed of user, time, and content features and which uses the random forest method ( Breiman, 2001 ) and the Best Match 25-based latent Dirichlet allocation model (LDA-BM25) ( Li, 2013 ). As this model is constructed specifically for public health emergencies, it highlights the

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Lake water level variations in Belarus

Abstract

Lake level is one of the most important lake characteristics which allows the results of different effects to be identified and detected. In this work time series of the water levels of Belorussian lakes were analysed in order to detect pattern variations, to evaluate quantitatively the transformation of the hydrological regime of lake ecosystems and to develop prediction models. The possibility of plotting predicting models of lake water levels one year in advance was shown. The complication in plotting predicting models is in its individuality, the huge volume of initial data and the impossibility of immediate assessment of the results. Additional complications are caused by the inhomogeneity of time series of water levels in lakes.

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Shelf-Life Prediction Model of Chitosan Coated Eggs at Different Storage Temperatures

, Y, Q., Lv, J, P. (2013).Establishment and Test of Prediction Model for UHT Milk Shelf Life. Scientia Agricultural Sinica . 46 (03): 586-594. 20. Yang, M, Z., Wang, X, L. (2015). Shelf-life prediction of instant flavor rice through kinetic models. Journal of Harbin University of Commerce, 31(06):710-714. 21. Liu, X, D., Jang, A., Kim, D, H., et al. (2009). Effect of combination of chitosan coating and irradiation on physicochemical and functional properties of chicken egg during room-temperature storage. Radiation Physics & Chemistry , 78(7): 589

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