Showing papers by "University of Cyprus published in 1996"

The firm as a distributed knowledge system : A constructionist approach

Abstract: The organizational problem firms face is the utilization of knowledge which is not, and cannot be, known by a single agent. Even more importantly, no single agent can fully specify in advance what kind of practical knowledge is going to be relevant, when and where. Firms, therefore, are distributed knowledge systems in a strong sense: they are decentered systems, iacking an overseeing 'mind'. The knowledge they need to draw upon is inherently indeterminate and continually emerging; it is not self-contained. Individuals' stock of knowledge consists of (a) role-related normative expectations; (b) dispositions, which have been formed in the course of past socializations; and (c) local knowledge of particular circumstances of time and place. A firm has greater-or-lesser control over normative expectations, but very limited control over the other two. At any point in time, a firm's knowledge is the indeterminate outcome of individuals attempting to manage the inevitable tensions between normative expectations, dispositions, and local contexts.

The Export Development Process: An Integrative Review of Empirical Models

Abstract: Despite widespread research on the export behavior of firms, no integrative review of the empirical work in relation to export development models exists in the extant literature. This article reviews the main models on the subject, identifies their structural characteristics, evaluates the methodologies used for their validation, and analyzes the key conceptual issues emerging from their assessment. While valuable inroads into the mechanics and constituent parts of the export development process have been made, this avenue of empirical inquiry has attracted criticism on structural, methodological and conceptual grounds. Future research in the field should harness the eclectic contribution afforded by existing theory and, at the same time, enhance its status with contributions from marketing, business and other disciplines.

Parental involvement: Definition and outcomes

Abstract: The purpose of this study was to seek a definition of the concept of parental involvement and to examine the relationship that may exist between school achievement and specific parental involvement activities. Data were collected from 852 parents of sixth-grade students of both genders. It was found that the involvement of parents in their children's educational process is a complex behavior that may take different forms, not all of which are related to the child's school achievement. The following six types of parental involvement were identified through factor analysis: learning at home, volunteering and decision making at school, and four different parenting tendencies (emphasizing achievement, pressure, control, and personality development). Parenting through emphasis on achievement and through personality development had a positive, statistically significant correlation with actual school achievement, while parenting through pressure had a negative such correlation. A positive correlation was also found between parental volunteering at school and the child's actual school achievement. Learning at home and parenting through control had no significant correlation with achievement. The findings of this study suggest that the six distinct types of behavior that make up parental involvement have differential effects in relation to school achievement.

Export market expansion strategy: Differences between market concentration and market spreading

Abstract: In light of certain important gaps observed in the export marketing literature, a study was conducted to investigate the presence of significant differences in the profile and behaviour between the two generic export market expansion strategic alternatives of concentration and spreading. Following a review of the extant literature, certain firm characteristics, export marketing effort and policy elements, and export‐related perception variables were viewed as potentially important discriminating factors between these two approaches. Based upon data generated from a focus on firms in a small EU country, the findings suggest the existence of several significant differences between market concentration and market spreading. These differences facilitate the development of extensive export profiles of concentrators and spreaders. The implications of the study for managers and public policy markers are discussed, and future research directions identified.

Formal Reasoning and Science Teaching

Abstract: Performance of 195 seventh-, eighth-, and ninth-grade students on the Test of Logical Thinking (TOLT) was used to identify differences related to five reasoning modes among the three classes and between male and female students. TOLT scores revealed substantial deficiencies in the development of student reasoning abilities, and only ninth-grade students had significantly better (p < .05) performance than seventh-grade students which was related to proportional reasoning problems. There were no significant differences between male and female students. Data were also analyzed using multiple regression and factor analysis. The results do not corroborate basic premises of Piagetian theory and indicate the need for neo-Piagetian views to explain cognitive development.

A singular function boundary integral method for the laplace equation

Abstract: The authors present a new singular function boundary integral method for the numerical solution of problems with singularities which is based on approximation of the solution by the leading terms of the local asymptotic expansion. The essential boundary conditions are weakly enforced by means of appropriate Lagrange multipliers. The method is applied to a benchmark Laplace-equation problem, the Motz problem, giving extremely accurate estimates for the leading singular coefficients. The method converges exponentially with the number of singular functions and requires a low computational cost. Comparisons are made to the analytical solution and other numerical methods.

Acceptance and resolution simulation studies for the dielectron spectrometer HADES at GSI

Abstract: Design studies for a second generation dilepton spectrometer to be built at the SIS accelerator of GSI Darmstadt are presented. The basic design parameters of this system are specified and the different detector components for charged particle tracking and for lepton identification are described. The geometrical acceptance for lepton pairs is given. Results on single track momentum resolution and on lepton pair mass resolution are reported.

A stochastic programming model for funding single premium deferred annuities

Abstract: Single Premium Deferred Annuities (SPDAs) are investment vehicles, offered to investors by insurance companies as a means of providing income past their retirement age. They are mirror images of insurance policies. However, the propensity of individuals to shift part, or all, of their investment into different annuities creates substantial uncertainties for the insurance company. In this paper we develop amultiperiod, dynamic stochastic program that deals with the problem of funding SPDA liabilities. The model recognizes explicitly the uncertainties inherent in this problem due to both interest rate volatility and the behavior of individual investors. Empirical results are presented with the use of the model for the funding of an SPDA liability stream using government bonds, mortgage-backed securities and derivative products.

Computer-aided classification of breast cancer nuclei.

Abstract: Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists regarding the hormonal status of the individual tumors. These factors influence the choice of treatment and help in predicting patient survival and probability of recurrence. The objective of this paper is to introduce a new computer-aided system for the classification of breast cancer nuclei based on neural networks. Currently, medical experts assess steroid receptors in breast cancer biopsy slides mostly manually using four- or five-level grading schemes. These schemes are based on the assessment of two parameters: number of nuclei positive and their staining intensity. Available computerized systems define their own grading schemes based on automated measurements of low-level features, such as optical density, texture, area, and others. However, the findings produced by these systems may not be readily comprehensible by the majority of medical experts who have been accustomed to manual assessment schemes. Moreover, findings from one system cannot be directly compared to findings obtained from other computerized systems. To date, no standardized assessment scheme exists for computerized systems, while interobserver and intraobserver variabilities limit the utility of the routinely used manual assessment schemes. In this paper a new system for computer-aided biopsy analysis is introduced. Here, we focus on the system's nuclear classification module. The input to this module consists of a set of six local and global features: optical density, two chromaticity indices, a variance based texture measure, global nuclei density mean, and variance. The output of the nuclei classification module consists of a membership label in a zero to four grading scheme for each detected nucleus. The classification module is based on a feedforward neural network trained in a supervised fashion to classify the nuclear feature vectors. The sample data comprises 3015 nuclei from 28 images that were classified by a human expert. A Sammon plot visualization of the six dimensional input feature space shows that the classification problem is quite difficult. The neural network used in the classification module achieved 72% accuracy. Our result indicate that by using a nuclear classification module such as the one introduced in this paper it is possible to translate low-level system measurements into a vocabulary that is familiar to medical experts. Thus, a contribution is made to the standardization of grading schemes in addition to improving the accuracy in grading breast cancer nuclei.

Product standardization or adaptation: the Japanese approach

Abstract: Focuses on the issue of product standardization versus adaptation, with special reference to the practices of Japanese multinational companies (MNCs) operating in the Middle East. Reveals that the degree of adaptation of Japanese goods is generally moderate, with labelling, packaging and internal features attracting most alterations. Product adaptations were more profound among firms producing consumer goods, as well as those having a long presence in the Arab market. Also suggests that the impact of factors affecting the standardization/ adaptation decision differed according to the specific product aspect, with demographic and political‐legal forces being the most influential overall. With respect to future product strategy, participant firms stated that they would proceed more or less as at present, the only exception being some additional adaptations as regards external characteristics of the product.

Genetics-based machine learning for the assessment of certain neuromuscular disorders

Abstract: Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks (ANN's) in classifying EMG data trained with backpropagation or Rohonen's self-organizing feature maps algorithm has recently been demonstrated. The objective of this study is to investigate how genetics-based machine learning (GBML) can be applied for diagnosing certain neuromuscular disorders based on EMG data. The effect of GBML control parameters on diagnostic performance is also examined. A hybrid diagnostic system is introduced that combines both neural network and GBML models. Such a hybrid system provides the end-user with a robust and reliable system, as its diagnostic performance relies on more than one learning principle. GBML models demonstrated similar performance to neural-network models, but with less computation. The diagnostic performance of neural network and GBML models is enhanced by the hybrid system.

Environmental scanning in educational organizations: uses, approaches, sources and methodologies

Abstract: Suggests that, in today’s world of uncertainty, it is of vital importance for an individual or an educational institution to stay abreast of environmental changes which may affect their future Presents up‐to‐date information on environmental scanning and how it can be used to benefit educational organizations For modern educational institutions, it is necessary to establish a systematic method of collecting information regarding external influences on educational institutions However, it is quite difficult for any individual to be knowledgeable about all occurrences that may affect an institution, and it often requires special expertise to realize how an event or trend might affect an institution in the future An institution must tailor specific sources of external information, devise a method to collect the information, and use the information effectively in the planning process and in building the future Argues that environmental scanning has proved to be successful for school systems and should be accorded the importance it deserves Furthermore, scanning provides all individuals with the opportunity to contribute to the strategic planning process which is of vital importance for the survival of an organization

Solving multistage stochastic network programs on massively parallel computers

Abstract: Multi-stage stochastic programs are typically extremely large, and can be prohibitively expensive to solve on the computer. In this paper we develop an algorithm for multistage programs that integrates the primal-dual row-action framework with proximal minimization. The algorithm exploits the structure of stochastic programs with network recourse, using a suitable problem formulation based on split variables, to decompose the solution into a large number of simple operations. It is therefore possible to use massively parallel computers to solve large instances of these problems. The algorithm is implemented on a Connection Machine CM-2 with up to 32K processors. We solve stochastic programs from an application from the insurance industry, as well as random problems, with up to 9 stages, and with up to 16392 scenarios, where the deterministic equivalent programs have a half million constraints and 1.3 million variables.

Textures for neutrino mass matrices.

Abstract: We give a classification of heavy Majorana neutrino mass matrices with up to three texture zeros, assuming the Dirac masses of the neutrinos to be of the same form as the ones of the up quarks in the five texture zero solutions for the quark matrices. This is the case for many unified and partially unified models. We find that it is possible to have solutions which account for the solar and atmospheric neutrino problems as well as the COBE observations simultaneously, and we motivate the existence of such solutions from symmetries. {copyright} {ital 1996 The American Physical Society.}

A combinatorial treatment of balancing networks

Abstract: Balancing networks, originally introduced by Aspnes et al. (Proceedings of the 23rd Annual ACM Symposium on Theory of Computing, pp. 348-358, May 1991), represent a new class of distributed, low-contention data structures suitable for solving many fundamental multi-processor coordination problems that can be expressed as balancing problems. In this work, we present a mathematical study of the combinatorial structure of balancing networks, and a variety of its applications.Our study identifies important combinatorial transfer parameters of balancing networks. In turn, necessary and sufficient combinatorial conditions are established, expressed in terms of transfer parameters, which precisely characterize many important and well studied classes of balancing networks such as counting networks and smoothing networks. We propose these combinatorial conditions to be “balancing analogs” of the well known Zero-One principle holding for sorting networksWithin the combinatorial framework we develop, our first application is in deriving combinatorial conditions, involving the transfer parameters, which precisely delimit the boundary between counting networks and sorting networks.

Tentative study of flow patterns in the North Aegean Sea using NOAA-AVHRR images and 2D model simulation

Abstract: A statistical technique for image processing, the maximum cross correlation (MCC) method, was utilized on sequences of NOAA-AVHRR thermal data in order to explore the surface advective current dynamics at the discharge region of the Hellespont in the North Aegean Sea. A 2D numerical flow model was also used in order to simulate the barotropic flow pattern of the surface water layer. The model was forced with diurnal wind fields obtained for the same period as the satellite infrared images. The currents (magnitude and direction) derived from the two methods compare satisfactorily despite the fact that some model simplifications were made.

Understanding Social Reforms : A Conceptual Analysis

Abstract: We attempt here to explain why reforming social systems is not an easy job and what can be done about it. Vickers' concept of ‘appreciative systems’ is re-examined and further developed. It is argued that appreciative systems are socially established ways of perceiving, consisting of a set of cognitive categories, values and interests which are grounded on social practices. The latter are constituted by certain historically developed self-understandings shared by individuals. Social practices are self-referential and, therefore, particularly resistant to reform. It is argued that the role of policy makers should be seen as consisting of two components. First, inventing and supplying social systems with new appreciative systems, and secondly, regularly providing social systems with information about their own functioning as well as the functioning of other systems. That information, spread throughout a system, has potentially reforming effects. These claims are illustrated with examples from UK and American public life.

Improved lattice operators: Case of the topological charge density.

Abstract: We analyze the properties of a class of improved lattice topological charge density operators, constructed by a smearinglike procedure. By optimizing the choice of the parameters introduced in their definition we find operators having (i) a better statistical behavior as estimators of the topological charge density on the lattice, i.e., less noisy, (ii) a multiplicative renormalization much closer to one, and (iii) a large suppression of the perturbative tail (and other unphysical mixings) in the corresponding lattice topological susceptibility. {copyright} {ital 1996 The American Physical Society.}