Analysis of the Functioning of Clusters in Poland

The cluster concept has gained special significance after the publication of the works of M. Porter “The Competitive Advantage of Nations”(1990). But the most popular definition of industrial cluster was formed eight years later, when he wrote that clusters are:“geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g. universities, standards agencies, trade associations) in a particular field that compete but also cooperate”(Porter, 1998, p. 197). A cluster as a regionally focused form of economic activity generates positive effects for business and the region. Global researchers suggest that clusters help to increase the innovation and competitiveness of the country in which they are located. Since the 90s clusters have become an increasingly important element of economic development and innovation strategy of the European Union and its Member States. In years 2007–2013, clusters are expected to one of the objectives of support for EU regional policy. EU funds destined for cluster initiatives will help to take concrete actions by entrepreneurs interested in the cluster activity. Poland has recently joined the countries interested in popularizing the idea of clusters. Some specialized cluster studies have been carried out in Poland identifying clusters. This article gives an overview on policy support, formation and the functioning of clusters in Poland.

ECONOMIC EFFICIENCY OF PARTICIPATION IN A CLUSTER OF POTENTIAL PARTICIPANTS

A cluster is a geographic concentration of related companies, organizations, and institutions in a particular field that can be present in a region, state, or nation. Clusters arise because they raise a company's productivity, which is influenced by local assets and the presence of like firms, institutions, and infrastructure that surround it. That is why the necessity of an estimation of efficiency of cluster formations and their structural elements is investigated. Methods of estimation of economic efficiency of cluster activity are offered. A system of indicators of the efficiency of the functioning of cluster entities and their structural elements, based on the levels of cluster efficiency evaluation, is generalized. It is determined that the system of principles for the formation and functioning of clusters can be a motive for identifying existing and identifying promising cluster type entities in Ukraine.

Dynamika regionálních inovačních systémů ve střední a východní Evropě

This article examines regional innovation systems in Central and Eastern Europe, with particular attention to the regions with the highest innovation success. The articles also include a discussion of the presence of elements and dynamic of regional innovation systems in these countries. The Innovation performance of regions in Central and Eastern Europe is analyzed and selected progressive regions are further examined in terms of partial innovation and socio-economic indicators. Data on regions were obtained from the Regional Innovation Scoreboard 201at the NUTS II level. Detailed analysis is possible through a case study of the Malopolskie region. The analysis focuses primarily on the causes of the region's innovative progress, including the setting of favorable conditions for SMEs and the associated high cluster activity, the commercialization of research and the dissemination of external knowledge. On the basis of the overall analysis, RIS in Central and Eastern Europe are far below RIS in Western and Northern Europe and their results are very low compared to these regions.

Financial performance under stress: the case of the Norwegian maritime cluster

The present study investigates the Norwegian maritime industry in terms of its economic activity during the period 2001–2018. The purpose of the study is to determine the financial state and to conduct the cluster analysis of the companies which belong to the Blue Maritime Cluster of Møre and Romsdal County.The paper presents a structural analysis of key financial indicators of the maritime industry within four major segments: shipping companies, shipyards, ship equipment manufactures, and maritime design and service providers. The analysis sheds light on the impact of the 2015–2017 offshore crisis on the Norwegian maritime cluster activity, which makes up the essential components of the maritime industry.The author suggests using Harrington’s desirability function to measure the firms’ financial state of two main segments (shipping companies and shipyards) that belong to the Blue Maritime Cluster of the Norwegian North-Western coast, which remains the most important area in Norway for shipbuilding activities. The obtained results reveal that during the analyzed period (2001–2018), companies had a satisfactory level of financial sustainability (with the peak in 2002 for shipping firms and in 2011 for shipyards). Nevertheless, there were several fluctuations and the most significant troughs were fixed after 2014. Moreover, it was defined that government policy plays an important role in an increase in the productivity, competitiveness of the maritime industry and supports more environmentally friendly shipbuilding.

The Integrity of piRNA Clusters is Abolished by Insulators in the Drosophila Germline

Piwi-interacting RNAs (piRNAs) control transposable element (TE) activity in the germline. piRNAs are produced from single-stranded precursors transcribed from distinct genomic loci, enriched by TE fragments and termed piRNA clusters. The specific chromatin organization and transcriptional regulation of Drosophila germline-specific piRNA clusters ensure transcription and processing of piRNA precursors. TEs harbour various regulatory elements that could affect piRNA cluster integrity. One of such elements is the suppressor-of-hairy-wing (Su(Hw))-mediated insulator, which is harboured in the retrotransposon gypsy. To understand how insulators contribute to piRNA cluster activity, we studied the effects of transgenes containing gypsy insulators on local organization of endogenous piRNA clusters. We show that transgene insertions interfere with piRNA precursor transcription, small RNA production and the formation of piRNA cluster-specific chromatin, a hallmark of which is Rhino, the germline homolog of the heterochromatin protein 1 (HP1). The mutations of Su(Hw) restored the integrity of piRNA clusters in transgenic strains. Surprisingly, Su(Hw) depletion enhanced the production of piRNAs by the domesticated telomeric retrotransposon TART, indicating that Su(Hw)-dependent elements protect TART transcripts from piRNA processing machinery in telomeres. A genome-wide analysis revealed that Su(Hw)-binding sites are depleted in endogenous germline piRNA clusters, suggesting that their functional integrity is under strict evolutionary constraints.

A Double Error Dynamic Asymptote Model of Associative Learning

ABSTRACTIn this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed.