A comparative analysis of the Greek and Romanian public finances : 2000-2008

In this paper we analyze the situation of the Public Finances for Greece and Romania for the 2000-2008 period, attempting to identify the fundamental factors that lead to the poor situation of the two countries in the current period. Currently, Greece and Romania are facing difficult financial situations, confronted with record levels of public debt and deficits, being forced to close urgent financial agreements with the European Commission and IMF to avoid financial collapse. In the study we analyze the main parameters of the government finances of Greece and Romania and compare them with the average levels of the European Union.peer-reviewe

Nofish - A new stream cipher

The proposed algorithm is a synchronous stream cipher, more precisely a binary additive stream cipher because it using the XOR function to encrypt the plaintext. The design is based on HENKOS stream cipher (http://eprint.iacr.org/2004/080.pdf), the functions used in the internal state are kept, the initialization and mixing key part being modified with respect to its revealed weaknesses. This stream cipher uses a named key of 64 bytes (512 bits) as a secret key and no initialization vector. Nofish is free to use for any non-commercial purposes, and the reference source code can be found in the appendix

HENKOS Stream Cipher

The purpose of this paper is to revise the HENKOS Stream Cipher paper present in the archive at 2004/080 and to provide a clear description of the proposed HENKOS cryptographic algorithm

HENKOS Cryptanalysis-Related keys attack

This paper describes a series of vulnerabilities found on HENKOS algorithm (http://eprint.iacr.org/080) having a description below, regarding to the related key attacks, mounting this type of attack for a particular relation between keys and showing that is a practical attack, having results in real time

ANALYZING FISCAL BALANCE EVOLUTION FOR DEVELOPED AND EMERGENT COUNTRIES

The purpose of our paper is to identify the main factors which influence fiscal balance’s evolution and thereby propose solutions for configuring a sustainable fiscal policy. We have selected as independent variables some of the main macroeconomic measures, respectively public debt, unemployment rate, economy openness degree, population, consumer goods’ price index, current account balance, direct foreign investments and economic growth rate. Our research method uses two econometric models applied on a sample of 22 countries, respectively 14 developed and 8 emergent. The first model is a multiple regression and studies the connection between the fiscal balance and selected independent variables, whereas the second one uses first order differences and introduces economic freedom as a dummy variable to catch the dynamic influences of selected measures upon fiscal result. The time interval considered was 1999-2013. The results generated using the two models revealed that public debt, current account balance and economic growth significantly influence the fiscal balance. As a consequence, the governments need to plan and implement a fiscal policy which resonates with economy priorities and the phase of the economic cycle, as well as ensure a proper management of the public debt, stimulate sustainable economic growth and employment.JEL Codes - H6

A Deep Learning Approach for Classification of Physiotherapy Exercises Using Segmentation of Techniques

Physiotherapy exercises are necessary to patients to restore their functional abilities in many cases as disabilities, injury, or basic with complementary approach as balneotherapy. Different type of exercised and different template sessions are used depending on the medical diagnostics. The evaluation of effectiveness of these exercises are important for patient’s rehabilitation process as time and level of recovery of locomotor skills. A dataset publicly available (Physical Therapy Exercises) is used for classification of session of repeated exercises that includes movement executed correct (C), fast execution (F) and low-amplitude execution (L). A novel approach is proposed by using segmentation of signal using deep learning neural network followed by a convolutional neural network for classification of sequence of the labeled classes L,C, F, and N (a new class introduced to label the noise of sensor of exercised or incorrect movement of the patient. The signal is extensively analyzed in order to made and corresponding labeling for analyzing using sliding window with a drive user selected length. The accuracy of classification is greater than 96% and sensitivity is greater than 95% but the results can be better if the labelling of N class is more restrictive and the effect of imbalanced dataset is reduced

Study comparing the vibrations recorded by professional and non-professional male athletes in winter sports, skiing vs. snowboarding

Winter sports such as skiing and snowboarding are becoming increasingly popular among all age groups, as practicing these sports has seen an upward trend, which has led to an increase in the number of injuries and pathologies related to them. Practicing skiing/snowboarding entails a series of vibrations occurring in the equipment, their propagation along the kinetic chain impacting both in a positive and negative way the health of the person in question. The study was a comparison, skiing vs. snowboarding, between the vibrations experienced by professional and non-professional athletes, with the main objective of determining which of them produces greater vibrations and identifying the negative and positive effects they have. The study was performed under field conditions using sensors designed to record vibrations on the ski/snowboard (tip/nose and tail), as well as vibration sensors located in the ankle, knee, hip and lumbosacral areas, designed to record the propagation of vibrations along the kinetic chain. The results show a higher level of vibrations recorded on the ski than on the snowboard, while their transmission along the kinetic chain is inversely proportional. The conclusion relates to the choice of skiing/snowboarding. Therefore, due to the Whole-Body Vibration phenomenon, young people are more likely to choose snowboarding due to the possibility of increasing bone quality and quantity, while older people are rather fond of skiing, given its effect along the kinetic chain, which protects the skeletal system. Studies have provided evidence to suggest alpine skiing is an appropriate activity for elderly as a health-enhancing sport. Thus, perhaps alpine skiing could provide the physical activity needed to counteract age-related degradation processes and loss of function

Mathematical models applied to the prediction of doping in male athletes

The compartmental model is a mathematical model (usually described by a set of differential equations) that describes how individuals from different compartments (or groups) that represent a population, interacts. The model is suitable especially for epidemic model, modeling spread of disease but also in simulation of interaction among social groups. The compartmental model has few assumptions to be feasible: “the infection/contamination rate” can be a function of many parameters (seasonality, epidemic waves, dependence of social distancing, policy of awareness, policy, and so one). The main assumption is that the population is homogeneous but, in reality, the heterogeneity of population (spatial localization, seasonal, demography) plays an important role in accuracy of models. Our approach was based on another method that has been used in the last years, the inclusion of a temporal function including heterogeneity in the influence that conduct to doping similar to rate of infection from epidemic models. In this paper, a new model is proposed for quantitative analysis of doping in a particular selected sport. Almost all the models in doping use the biological markers and effect of doping in declared by athletes involved in use of banned substances in a quantitative analysis over a group of high-performance athletes. The proposed compartmental model SEDRS (Susceptible-Exposed-Doped-Recovered-Susceptible) includes the heterogeneity shaped by awareness, due to social interaction that transmit the anti-doping policy. These measures are patterned by social interaction, especially during competitions and training, and this approach is included in system of integrodifferential equations. A heterogeneous (SEDRS) model is numerically solved and the solutions show how the social factor can contribute to decay of doping phenomenon of male athletes and the quantifiable influence in a healthier atmosphere in sport. The scope of the paper is the prediction of doping cases based on SEDRS model