PUBLIC BUREAUCRACY COLLABORATION: BARRIERS AND CHALLENGES THE ACADEMIC PERSPECTIVE

The decentralization, privatization and globalization have created a world without boarders and highly competitive markets, forcing firms to constantly innovate in order to survive, improve performance and grow. One approach for firms to innovate is to collaborate with universities. University-Industry Collaboration (U-IC) has proved to be very effective in developed world, but in most cases quite ineffective in developing countries. While many research have identified success or failure factors in collaborative efforts in developed world, not much known about it in developing world, and even less in their public bureaucracies. The purpose of this paper is to briefly review the existing literature on U-IC, focus on the reality of politics in public bureaucracies, namely university and municipality in developing countries, and identify prerequisites needed to be addressed before contemplating on any joint effort

Barriers Affecting Contribution of Developing Countries Social Scientists in ISI Indexed Journals

The decades leading to the third millennium was marked with concerted efforts by governments to raise their knowledge generation and contribution profile in the community of nations. Thus, all kinds of financial and promotional incentives have been offered to academics and researchers to publish papers in international journals, particularly ISI journals. It is argued that quantity and quality of articles published in ISI journals is an indication of scientific capabilities of a country and a yardstick for assessing its development. This research study aims to identify barriers that academics face in publishing papers in ISI social/humanity science journals. A questionnaire based on extensive literature review and a series of unstructured interviews was developed and tested. A stratified sampling method was used to collect data from academics of four social/humanity science faculties of a provincial university in the northwest of Iran. The findings revealed that respondent’s perceived lack of proficiency in a foreign language, poor information technology infrastructure and inadequate access to international scientific databases and uncontrollable factors related to the nature of social science disciplines and political climate as the major barriers that prevent or de-motivate them to publish in ISI journals. The research findings were discussed and concluded, and recommendations were made to reduce or remove barriers to publishing in ISI journals

Exports and Economic Growth Nexus: The Case of Pakistan

This paper re-investigates the exports and economic growth nexus for Pakistan. The paper employs cointegration and multivariate Granger Causality Test [Toda and Yamamoto (1995)] to study the long-run and short-run dynamics among exports, imports, and real output growth over the 1960–2003 period. Results strongly support a long-run relationship among imports, exports, and output growth. Feedback effect between import and output growth and unidirectional causality from export to output growth was found. Nevertheless, the results do not confirm any significant causality between imports and exports.Exports, Economic Growth, Pakistan

Polymer Crosslinking: a new Strategy to Enhance Mechanical Properties and Structural Stability of Bioactive Glasses

The organic-inorganic hybrids fabricated by the sol-gel method are intrinsically bioactive materials with extensive applications in bone tissue engineering. The brittleness and limited water uptake capacity of these monoliths, however, restrict their applications for engineering the soft tissues and their interfaces with bone. To address these challenges, we developed a unique method in which polymer crosslinking was used to cease the over-condensation of a bioactive glass component and eradicate the formation of brittle structure. In this study, an organosilane-functionalized gelatin methacrylate was covalently bonded to a bioactive glass during the sol-gel process, and the condensation of silica networks was controlled by polymer-crosslinking. The physicochemical properties and mechanical strength of these hybrid hydrogels were then tuned by the incorporation of secondary crosslinking agents such as poly(ethylene glycol diacrylate). The resulting elastic hydrogels displayed tuneable compressive modulus in the range of 42 kPa to 530 kPa. The swelling behaviours of these hybrids and their structural integrities were also favourable for tissue engineering applications. Moreover, these hybrid hydrogels kept their structures for more than 28 days in simulated body fluid. The bioactivity of the constructs due to the presence of silica networks were confirmed by detecting nearly 2-fold increase in the alkaline phosphatase activity of the cultured bone progenitor cells on these hybrid hydrogels within 28 days of in vitro culture. Within the same period, in vivo studies on mice subcutaneous model showed that the hybrid hydrogels were highly biocompatible and well-tolerated. In summary, the bioactivity of the constructs, their tuneable physicochemical properties, the outstanding biocompatibility, and biodegradability of the hybrid hydrogels showed the high potential of the developed technique for fabrication of constructs for a variety of soft and hard tissue regeneration

On special subgroups of fundamental group

Suppose $\alpha$ is a nonzero cardinal number, $\mathcal I$ is an ideal on arc connected topological space $X$, and ${\mathfrak P}_{\mathcal I}^\alpha(X)$ is the subgroup of $\pi_1(X)$ (the first fundamental group of $X$) generated by homotopy classes of $\alpha\frac{\mathcal I}{}$loops. The main aim of this text is to study ${\mathfrak P}_{\mathcal I}^\alpha(X)$s and compare them. Most interest is in $\alpha\in\{\omega,c\}$ and $\mathcal I\in\{\mathcal P_{fin}(X),\{\varnothing\}\}$, where $\mathcal P_{fin}(X)$ denotes the collection of all finite subsets of $X$. We denote ${\mathfrak P}_{\{\varnothing\}}^\alpha(X)$ with ${\mathfrak P}^\alpha(X)$. We prove the following statements: $\bullet$ for arc connected topological spaces $X$ and $Y$ if ${\mathfrak P}^\alpha(X)$ is isomorphic to ${\mathfrak P}^\alpha(Y)$ for all infinite cardinal number $\alpha$, then $\pi_1(X)$ is isomorphic to $\pi_1(Y)$; $\bullet$ there are arc connected topological spaces $X$ and $Y$ such that $\pi_1(X)$ is isomorphic to $\pi_1(Y)$ but ${\mathfrak P}^\omega(X)$ is not isomorphic to ${\mathfrak P}^\omega(Y)$; $\bullet$ for arc connected topological space $X$ we have ${\mathfrak P}^\omega(X)\subseteq{\mathfrak P}^c(X) \subseteq\pi_1(X)$; $\bullet$ for Hawaiian earring $\mathcal X$, the sets ${\mathfrak P}^\omega({\mathcal X})$, ${\mathfrak P}^c({\mathcal X})$, and $\pi_1({\mathcal X})$ are pairwise distinct. So ${\mathfrak P}^\alpha(X)$s and ${\mathfrak P}_{\mathcal I}^\alpha(X)$s will help us to classify the class of all arc connected topological spaces with isomorphic fundamental groups.Comment: 29 page

Sparse signal representation for complex-valued imaging

We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. We propose a mathematical framework and an associated optimization algorithm for a sparse signal representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Multiple feature-enhanced synthetic aperture radar imaging

Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Sparse representation-based SAR imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data