The Impact of the Global Financial Crisis on Education and Healthcare in the Economies of the Former Soviet Union – the Case of Moldova

This study reviews the impact of the global financial crisis on public service delivery in the Republic of Moldova. It provides a background of the country’s development in the period prior to the crisis (2000 to 2007/2008) and presents the factors which determined the country’s fiscal performance during the crisis (2008-2010). The main aim of the study is to describe the changes in education and health financing and the associated changes in service delivery during the crisis. The presentation of the reforms in the social sector is necessary to set the stage for discussion and is not a primary goal of this study. In particular, the study analyzes the size and dynamics of public financing of education and healthcare and their intra-sector structure, as well as crisis management. It measures the impact the financial crisis had on the quality and reliability of public services and analyzes policy measures undertaken by the government to mitigate crisis’ impact. Conclusions and recommendations derived from the study should enable national policy-makers and international institutions supporting public finance reforms to improve the targeting of limited public resources both between and within individual sectors.Economic Crisis, Economic Development, Fiscal Policy, Education, Healthcare

Using a laser measurement system for monitoring morphological changes on the Strug rock fall, Slovenia

A medium-ranged high performance handheld reflectorless laser measurement system, was used for a morphological survey on the Strug rock fall in W Slovenia in the period from August 2003 to August 2004. The purpose was to evaluate its potential for monitoring ground surface changes in rock fall source areas and to help evaluating morphological changes by measuring distance from fixed points. In the area, 21 fixed geodetic points have been established. Altogether, seven measurement sets with more than 5500 points have been gathered in the rock fall area. Choosing a point cloud with a density of less than 1 point per 10 m(2) on a very rough rock fall surface failed to be a good solution. The changes on larger areas were shown by displacements of selected significantly large-sized rock blocks with a volume of several m(3). Because only smaller changes were observed between the single field series, the rock fall surface generally remained unchanged. Local surface changes of the order of 1 m or more, were clearly shown by measurements in the selected referenced cross sections. The usage of these cross sections gave a possibility to evaluate volumetric changes on the surface. The laser measurement system provided a good replacement for the classical terrestrial geodetic survey equipment, especially when performing remote monitoring of morphological changes in rock fall hazard zones, however, the case is different when fixed points are to be measured precisely

Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis

Bayesian network modeling for evolutionary genetic structures

AbstractEvolutionary theory states that stronger genetic characteristics reflect the organism’s ability to adapt to its environment and to survive the harsh competition faced by every species. Evolution normally takes millions of generations to assess and measure changes in heredity. Determining the connections, which constrain genotypes and lead superior ones to survive is an interesting problem. In order to accelerate this process,we develop an artificial genetic dataset, based on an artificial life (AL) environment genetic expression (ALGAE). ALGAE can provide a useful and unique set of meaningful data, which can not only describe the characteristics of genetic data, but also simplify its complexity for later analysis.To explore the hidden dependencies among the variables, Bayesian Networks (BNs) are used to analyze genotype data derived from simulated evolutionary processes and provide a graphical model to describe various connections among genes. There are a number of models available for data analysis such as artificial neural networks, decision trees, factor analysis, BNs, and so on. Yet BNs have distinct advantages as analytical methods which can discern hidden relationships among variables. Two main approaches, constraint based and score based, have been used to learn the BN structure. However, both suit either sparse structures or dense structures. Firstly, we introduce a hybrid algorithm, called “the E-algorithm”, to complement the benefits and limitations in both approaches for BN structure learning. Testing E-algorithm against a standardized benchmark dataset ALARM, suggests valid and accurate results. BAyesian Network ANAlysis (BANANA) is then developed which incorporates the E-algorithm to analyze the genetic data from ALGAE. The resulting BN topological structure with conditional probabilistic distributions reveals the principles of how survivors adapt during evolution producing an optimal genetic profile for evolutionary fitness

Antibiotic residues in final effluents of European wastewater treatment plants and their impact on the aquatic environment

A comprehensive monitoring of a broad set of antibiotics in the final effluent of wastewater treatment plants (WWTPs) of 7 European countries (Portugal, Spain, Ireland, Cyprus, Germany, Finland, and Norway) was carried out in two consecutive years (2015 and 2016). This is the first study of this kind performed at an international level. Within the 53 antibiotics monitored 17 were detected at least once in the final effluent of the WWTPs, i.e.: ciprofloxacin, ofloxacin, enrofloxacin, orbifloxacin, azithromycin, clarithromycin, sulfapyridine, sulfamethoxazole, trimethoprim, nalidixic acid, pipemidic acid, oxolinic acid, cefalexin, clindamycin, metronidazole, ampicillin, and tetracycline. The countries exhibiting the highest effluent average concentrations of antibiotics were Ireland and the southern countries Portugal and Spain, whereas the northern countries (Norway, Finland and Germany) and Cyprus exhibited lower total concentration. The antibiotic occurrence data in the final effluents were used for the assessment of their impact on the aquatic environment. Both, environmental predicted no effect concentration (PNEC-ENVs) and the PNECs based on minimal inhibitory concentrations (PNEC-MICs) were considered for the evaluation of the impact on microbial communities in aquatic systems and on the evolution of antibiotic resistance, respectively. Based on this analysis, three compounds, ciprofloxacin, azithromycin and cefalexin are proposed as markers of antibiotic pollution, as they could occasionally pose a risk to the environment. Integrated studies like this are crucial to map the impact of antibiotic pollution and to provide the basis for designing water quality and environmental risk in regular water monitoring programs.Peer reviewe