Institutions and firm ownership structure:evidence from Central and Eastern Europe

FDI plays a key role in development, particularly in resource-constrained transition economies of Central and Eastern Europe with relatively low savings rates. Gains from technology transfer play a critical role in motivating FDI, yet potential for it may be hampered by a large technology gap between the source and host country. While the extent of this gap has traditionally been attributed to education, skills and capital intensity, recent literature has also emphasized the possible role of institutional environment in this respect. Despite tremendous interest among policy-makers and academics to understand the factors attracting FDI (Bevan and Estrin, 2000; Globerman and Shapiro, 2003) our knowledge about the effects of institutions on the location choice and ownership structure of foreign firms remains limited. This paper attempts to fill this gap in the literature by examining the link between institutions and foreign ownership structures. To the best of our knowledge, Javorcik (2004) is the only papers, which use firm-level data to analyse the role of institutional quality on an outward investor’s entry mode in transition countries. Our paper extends Javorcik (2004) in a number of ways: (a) rather than a cross-section, we use panel data for the period 1997-2006; (b) rather than a binary variable, we use the percentage foreign ownership as continuous variable; (c) we consider multi-dimensional institutional variables, such as corruption, intellectual property rights protection and government stability. We also use factor analysis to generate a composite index of institutional quality and see how stronger institutional environment could affect foreign ownership; (d) we explore how the distance between institutional environment in source and host countries affect foreign ownership in a host country. The firm-level data used includes both domestic and foreign firms for the period 1997-2006 and is drawn from ORBIS, a commercially available dataset provided by Bureau van Dijk. In order to examine the link between institutions and foreign ownership structures, we estimate four log-linear ownership equations/specifications augmented by institutional and other control variables. We find evidence that the decision of a foreign firm to either locate its subsidiary or acquire an existing domestic firm depends not only on factor cost differences but also on differences in institutional environment between the host and source countries.

Applications of fractals to image data compression

Digital image processing is exploited in many diverse applications but the size of digital images places excessive demands on current storage and transmission technology. Image data compression is required to permit further use of digital image processing. Conventional image compression techniques based on statistical analysis have reached a saturation level so it is necessary to explore more radical methods. This thesis is concerned with novel methods, based on the use of fractals, for achieving significant compression of image data within reasonable processing time without introducing excessive distortion. Images are modelled as fractal data and this model is exploited directly by compression schemes. The validity of this is demonstrated by showing that the fractal complexity measure of fractal dimension is an excellent predictor of image compressibility. A method of fractal waveform coding is developed which has low computational demands and performs better than conventional waveform coding methods such as PCM and DPCM. Fractal techniques based on the use of space-filling curves are developed as a mechanism for hierarchical application of conventional techniques. Two particular applications are highlighted: the re-ordering of data during image scanning and the mapping of multi-dimensional data to one dimension. It is shown that there are many possible space-filling curves which may be used to scan images and that selection of an optimum curve leads to significantly improved data compression. The multi-dimensional mapping property of space-filling curves is used to speed up substantially the lookup process in vector quantisation. Iterated function systems are compared with vector quantisers and the computational complexity or iterated function system encoding is also reduced by using the efficient matching algcnithms identified for vector quantisers.

The design of a real-time nowcasting system for localised weather

National meteorological offices are largely concerned with synoptic-scale forecasting where weather predictions are produced for a whole country for 24 hours ahead. In practice, many local organisations (such as emergency services, construction industries, forestry, farming, and sports) require only local short-term, bespoke, weather predictions and warnings. This thesis shows that the less-demanding requirements do not require exceptional computing power and can be met by a modern, desk-top system which monitors site-specific ground conditions (such as temperature, pressure, wind speed and direction, etc) augmented with above ground information from satellite images to produce `nowcasts'. The emphasis in this thesis has been towards the design of such a real-time system for nowcasting. Local site-specific conditions are monitored using a custom-built, stand alone, Motorola 6809 based sub-system. Above ground information is received from the METEOSAT 4 geo-stationary satellite using a sub-system based on a commercially available equipment. The information is ephemeral and must be captured in real-time. The real-time nowcasting system for localised weather handles the data as a transparent task using the limited capabilities of the PC system. Ground data produces a time series of measurements at a specific location which represents the past-to-present atmospheric conditions of the particular site from which much information can be extracted. The novel approach adopted in this thesis is one of constructing stochastic models based on the AutoRegressive Integrated Moving Average (ARIMA) technique. The satellite images contain features (such as cloud formations) which evolve dynamically and may be subject to movement, growth, distortion, bifurcation, superposition, or elimination between images. The process of extracting a weather feature, following its motion and predicting its future evolution involves algorithms for normalisation, partitioning, filtering, image enhancement, and correlation of multi-dimensional signals in different domains. To limit the processing requirements, the analysis in this thesis concentrates on an `area of interest'. By this rationale, only a small fraction of the total image needs to be processed, leading to a major saving in time. The thesis also proposes an extention to an existing manual cloud classification technique for its implementation in automatically classifying a cloud feature over the `area of interest' for nowcasting using the multi-dimensional signals.

Representation and parameterisation issues in genetic algorithms

A multi-chromosome GA (Multi-GA) was developed, based upon concepts from the natural world, allowing improved flexibility in a number of areas including representation, genetic operators, their parameter rates and real world multi-dimensional applications. A series of experiments were conducted, comparing the performance of the Multi-GA to a traditional GA on a number of recognised and increasingly complex test optimisation surfaces, with promising results. Further experiments demonstrated the Multi-GA's flexibility through the use of non-binary chromosome representations and its applicability to dynamic parameterisation. A number of alternative and new methods of dynamic parameterisation were investigated, in addition to a new non-binary 'Quotient crossover' mechanism. Finally, the Multi-GA was applied to two real world problems, demonstrating its ability to handle mixed type chromosomes within an individual, the limited use of a chromosome level fitness function, the introduction of new genetic operators for structural self-adaptation and its viability as a serious real world analysis tool. The first problem involved optimum placement of computers within a building, allowing the Multi-GA to use multiple chromosomes with different type representations and different operators in a single individual. The second problem, commonly associated with Geographical Information Systems (GIS), required a spatial analysis location of the optimum number and distribution of retail sites over two different population grids. In applying the Multi-GA, two new genetic operators (addition and deletion) were developed and explored, resulting in the definition of a mechanism for self-modification of genetic material within the Multi-GA structure and a study of this behaviour.

A corpus of 714 full-color images of depth-rotated objects

A set of full-color images of objects is described for use in experiments investigating the effects of in-depth rotation on the identification of three-dimensional objects. The corpus contains up to 11 perspective views of 70 nameable objects. We also provide ratings of the "goodness" of each view, based on Thurstonian scaling of subjects' preferences in a paired-comparison experiment. An exploratory cluster analysis on the scaling solutions indicates that the amount of information available in a given view generally is the major determinant of the goodness of the view. For instance, objects with an elongated front-back axis tend to cluster together, and the front and back views of these objects, which do not reveal the object's major surfaces and features, are evaluated as the worst views.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Analysis of organisational climate to improve the adequacy of primary health care in the State of Kuwait

This study covers two areas of contribution to the knowledge, firstly it tried to investigate rigourously the relationships of a number of factors believed that they may affect the climate perception, classified into three types to arrive to prove a hypothesis of the important role that qualification and personal factors play in shaping the climate perception, this is in contrast with situational factors. Secondly, the study tries to recluster the items of a wide-range applied scale for the measurement of climate named HAY in order to overcome the cross-cultural differences between the Kuwaiti and the American society, and to achieve a modified dimensions of climate for a civil service organisation in Kuwait. Furthermore, the study attempts to carry out a diagnostic test for the climate of the Ministry of Public Health in Kuwait, aiming to diagnose the perceived characteristics of the MoPH organisation, and suggests a number of areas to be given attention if an improvement is to be introduced. The study used extensively the statistical and the computer facilities to make the analysis more representing the field data, on the other hand this study is characterised by the very highly responsive rate of the main survey which would affect the findings reliability. Three main field studies are included, the first one was to conduct the main questionnaire where the second was to measure the "should be" climate by the experts of MoPH using the DELPHI technique, and the third was to conduct an extensive meeting with the very top management team in MoPH. Results of the first stage were subject to CLUSTER analysis for the reconstruction of the HAY tool, whereas comparative analysis was carried on between the results of the second and third stages on one side, the first from the other.

Mathematical modelling of patient risks in a hospital environment, using multiple regression analysis

The aim of this research work was primarily to examine the relevance of patient parameters, ward structures, procedures and practices, in respect of the potential hazards of wound cross-infection and nasal colonisation with multiple resistant strains of Staphylococcus aureus, which it is thought might provide a useful indication of a patient's general susceptibility to wound infection. Information from a large cross-sectional survey involving 12,000 patients from some 41 hospitals and 375 wards was collected over a five-year period from 1967-72, and its validity checked before any subsequent analysis was carried out. Many environmental factors and procedures which had previously been thought (but never conclusively proved) to have an influence on wound infection or nasal colonisation rates, were assessed, and subsequently dismissed as not being significant, provided that the standard of the current range of practices and procedures is maintained and not allowed to deteriorate. Retrospective analysis revealed that the probability of wound infection was influenced by the patient's age, duration of pre-operative hospitalisation, sex, type of wound, presence and type of drain, number of patients in ward, and other special risk factors, whilst nasal colonisation was found to be influenced by the patient's age, total duration of hospitalisation, sex, antibiotics, proportion of occupied beds in the ward, average distance between bed centres and special risk factors. A multi-variate regression analysis technique was used to develop statistical models, consisting of variable patient and environmental factors which were found to have a significant influence on the risks pertaining to wound infection and nasal colonisation. A relationship between wound infection and nasal colonisation was then established and this led to the development of a more advanced model for predicting wound infections, taking advantage of the additional knowledge of the patient's state of nasal colonisation prior to operation.

Identification of industrial sources of airborne heavy metal pollution in urban areas

The research examines the deposition of airborne particles which contain heavy metals and investigates the methods that can be used to identify their sources. The research focuses on lead and cadmium because these two metals are of growing public and scientific concern on environmental health grounds. The research consists of three distinct parts. The first is the development and evaluation of a new deposition measurement instrument - the deposit cannister - designed specifically for large-scale surveys in urban areas. The deposit cannister is specifically designed to be cheap, robust, and versatile and therefore to permit comprehensive high-density urban surveys. The siting policy reduces contamination from locally resuspended surface-dust. The second part of the research has involved detailed surveys of heavy metal deposition in Walsall, West Midlands, using the new high-density measurement method. The main survey, conducted over a six-week period in November - December 1982, provided 30-day samples of deposition at 250 different sites. The results have been used to examine the magnitude and spatial variability of deposition rates in the case-study area, and to evaluate the performance of the measurement method. The third part of the research has been to conduct a 'source-identification' exercise. The methods used have been Receptor Models - Factor Analysis and Cluster Analysis - and a predictive source-based deposition model. The results indicate that there are six main source processes contributing to deposition of metals in the Walsall area: coal combustion, vehicle emissions, ironfounding, copper refining and two general industrial/urban processes. |A source-based deposition model has been calibrated using facctorscores for one source factor as the dependent variable, rather than metal deposition rates, thus avoiding problems traditionally encountered in calibrating models in complex multi-source areas. Empirical evidence supports the hypothesised associatlon of this factor with emissions of metals from the ironfoundry industry.

Minimizing nasty surprises with better informed decision-making in self-adaptive systems

Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger 'surprises'. Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis.

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties

Benefit segmentation of international travelers to Macau, China

The purpose of this study is threefold: (1) to identify the underlying benefits sought by international visitors to Macau, China, which has emerged as a popular gambling destination in Asia; (2) to segment tourists visiting Macau by employing a cluster analysis based on the benefits sought; and (3) to examine any salient differences between the segment groups with regard to their behavioral characteristics, socio-economic characteristics, and demographic profiles. A convenience sample was used to collect data in the Macau International Airport, in the Macau Ferry Terminal, and at the border gate with Mainland China. A total 1,513 useful surveys were retained for data analysis. Cluster analysis discloses four distinct clusters: "convention and business seekers," "family and vacation seekers," "gambling and shopping seekers," and "multi-purpose seekers." Based on the results of our findings, several managerial implications are discussed. © Taylor & Francis Group, LLC.