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The analysis and management of Hydrology time series is used for the development of models that allow predictions on future evolutions. After identifying the trends and the seasonal components, a residual analysis can be done to correlate them and make a prediction based on a statistical model. Programming language R contains multiple packages for time series analysis: ‘hydroTSM’ package is adapted to the time series used in Hydrology, package ‘TSA’ is used for general interpolation and statistical analysis, while the ‘forecast’ package includes exponential smoothing, all having outstanding capabilities in the graphical representation of time series. The purpose of this paper is to present some applications in which we use time series of precipitation and temperature from Fagaras in the time period 1966-1982. The data was analyzed and modeled by using the R language.


We are studying the economic phenomenon of the unemployment in Maramures County of Romania. To obtain plausible conclusions regarding this study we apply different types of regression: the linear regression, polynomial regression, spline and B-spline regression. In this paper we focus on the numerical side of the research and we compare the predicted values, the graphic representation of the evolution, the future predictions and the errors generated by the regressions mentioned above. The calculations are performed in R, a programming language for statistical computing. An implementation in R is given.


This article deals with a possibility to identify parameters of a selected growth model of two populations coupled by a predator-prey interaction from a set of observed data. It starts with a brief description of the Gause-type model and of a property (interior equilibrium stability) important from a point of view of an application. Subsequently, data for four forms of the trophic function are simulated and then, a noise was added to the simulated data such that the coefficients of variation equal to 0.2, 0.3 and 0.4. For each data set, the parameters are estimated using a procedure implemented in the R-language package and the coordinates of equilibrium are computed. Then we can evaluate the effect of changing variation to the values of parameters and (un)stability of the equilibrium.


The problem of small area prediction is considered under a Linear Mixed Model. The article presents a proposal of an empirical best linear unbiased predictor under a model with two correlated random effects. The main aim of the simulation analyses is a study of an influence of the occurrence of a correlation between random effects on properties of the predictor. In the article, an increase of the accuracy due to the correlation between random effects and an influence of model misspecification in cases of the lack of correlation between random effects are analyzed. The problem of the estimation of the Mean Squared Error of the proposed predictor is also considered. The Monte Carlo simulation analyses and the application were prepared in R language.


Clinicopathological investigations are essential for the evaluation of the health status of ruminants. Apart from species-specific reference intervals, the effect of common biological factors should be considered for an accurate interpretation of laboratory data. The aim of this study was to evaluate the effect of season on hematologic and biochemical analytes, and serum total thyroxine and cortisol in adult rams of two breeds. Four blood samples (one every season) were collected from each ram. Complete blood count was performed on the Advia 120 (Siemens Healthcare Diagnostics, USA), while the differential leukocyte count was manually conducted. Biochemical and hormonal analyses were performed on Flexor E (Vital Scientific, The Netherlands), AVL 9180 (Roche Diagnostics, Belgium), and Immulite 1000 (Siemens Healthcare Diagnostics, USA), respectively. Linear mixed effects models (R language) were employed for statistical analyses. Forty-three (26 Chios, 17 Florina), adult, clinically healthy rams were included. Statistically significant (p<0.05), mostly breed-independent seasonal differences were observed in almost all of the analytes. However, when assessing these differences in view of the respective reference intervals, only a few of them were considered biologically important. Specifically, mild hyperglycemia and mild decrease in the concentration of total calcium and inorganic phosphorus were detected in winter, while a mild increase in thyroxine concentration (autumn) and creatine kinase activity (spring and summer) was also noted. In conclusion, seasonal effects should be considered when evaluating laboratory results in rams; however, season does not appear to have an essential effect on the clinicopathological profile of rams reared in the Mediterranean region.


Increased urine albumin concentration (UALB) or urine albumin-to-creatinine ratio (UACR) at admission has been associated with systemic disease and increased morbidity and mortality in critically ill canine patients. The objective of this study was to assess the prognostic value of UALB and UACR for the survival, as well as for the development and duration of systemic inflammatory response syndrome (SIRS) in puppies with canine parvoviral enteritis (CPVE). Unvaccinated puppies, aged 1-12 months with confirmed CPVE, hospitalized for ≥5 days were included. Urine was collected at admission via cystocentesis; albumin was measured immunoturbidimetrically and creatinine spectrophotometrically. The presence of SIRS was daily evaluated. Statistical analysis was conducted using R language. Twenty-six dogs were enrolled; 12/26 (46%) developed SIRS during hospitalization, while 5/26 (19%) died. A significant correlation was found between UALB and UACR (ϱ=0.868, p<0.001). The dogs with SIRS had higher median UALB [0.5 (0-12.7) mg/dL] and UACR [4.2 (0-2,093) mg/g] compared to dogs without SIRS [UALB= 0.1 (0-0.8) mg/dL, UACR= 1.6 (0-5.6) mg/g], but the differences were non-significant (p>0.05). SIRS duration was significantly correlated with UACR (ϱ=0.427, p=0.030), but not with UALB (ϱ=0.386, p=0.052). The non-survivors had higher median UALB [0.6 (0.1-12.7) mg/dL] and UACR [19.6 (0.7-2,093) mg/g] compared to survivors [UALB= 0.2 (0-1.5) mg/dL, UACR= 2.3 (0-16.9) mg/g], but the differences were non-significant (p>0.05). UACR appears to be a prognostic indicator of SIRS duration in puppies with CPVE. However, a large-scale study is warranted to confirm the usefulness of UALB and UACR for clinical risk assessment in puppies with CPVE.

:// McCracken E. Decoding women's magazines. London: Macmillan, 1993. Bell A. Language of news media. Oxford: Blackwell, 1991. Wernick A. Promotional culture. London: Sage, 1991. Fowler R, Kress G. Critical linguistics. In: Fowler R, Hodge B, Kress G, Trew T, editors. Language and control. London: Routledge and Keagen Paul, 1979: 185-213. Fowler R. Language in the news. London, New York: Routledge, 1991.

: Multidisciplinary team. Second edition. Siriphan Offset Publisher, 2002: 271-314. 13. Schmelzeisen R. Language development in children with cleft palate. Folia Phoniatr Logop.1996; 48:92-7. 14. Landis PA. Training a paraprofessional in speech pathology: a pilot project in South Vietnam. ASHA. 1973; 15:342-4. 15. Jones H. The development of an access approach in a community based disability program. Asia Pac Disabil Rehabil J. 1997; 8:39-41. 16. Willcox DS. Cleft palate rehabilitation: interim strategies in Indonesia. Cleft Palate Craniofac J. 1994; 31:316-20. 17. D’Antonio LL

časopis, 37:49–56. Petráš, R., Mecko, J., Kulla, L., 2017: Economic value production of trees as a criterion of their maturity in an uneven-aged forest. Central European Forestry Journal, 63:188–194. Pretzsch, H., Biber, P, Schütze, G., Uhl, E., Rötzer, T., 2014: Forest stand growth dynamics in Central Europe have accelerated since 1870. Nature Communications, 5:4967. R Core Team, 2015: R: Language and Environment for Statistical Computing. R foundation for Statistical Computing, Vienna Austria. . Roessiger, J, Griess V. C., Härtl, F., Clasen

). The tourism-economy causality in the United States: A subindustry level examination. Tourism Management, 30, 553-558. Tang, C.F., & Abosedra, S. (2016). Tourism and growth in Lebanon: new evidence from bootstrap simulation and rolling causality approaches. Empirical Economics, 50, 679-696. Traykov, M., Trencheva, M., Stavrova, E., Mavrevski, R., & Trenchev, I. (2018). Risk analysis in the economics through R Language, WSEAS transactions on business and economics, 15, 180-186. 365907-603.pdf