System dynamics model of the knowledge management process

To effectively manage the challenges being faced by construction organisations in a fast changing business environment, many organisations are attempting to integrate knowledge management (KM) into their business operations. KM activities interact with each other and form a process which receives input from its internal business environment and produces outputs that should be justified by its business performance. This paper aims to provide further understanding on the dynamic nature of the KM process. Through a combination of path analysis and system dynamic simulation, this study found that: 1) an improved business performance enables active KM activities and provide feedback and guidance for formulating learning-based policies; and 2) effective human resource recruitment policies can enlarge the pool of individual knowledge, which lead to a more conducive internal business environment, as well as a higher KM activity level. Consequently, the desired business performance level can be reached within a shorter time frame.

Memory management and parallelization of data intensive all-to-all comparison in shared-memory systems

This thesis presents a novel program parallelization technique incorporating with dynamic and static scheduling. It utilizes a problem specific pattern developed from the prior knowledge of the targeted problem abstraction. Suitable for solving complex parallelization problems such as data intensive all-to-all comparison constrained by memory, the technique delivers more robust and faster task scheduling compared to the state-of-the art techniques. Good performance is achieved from the technique in data intensive bioinformatics applications.

Dubaization of Istanbul : insights from the knowledge-based urban development journey of an emerging local economy

Knowledge-based urban development (KBUD) has become a popular pursuit for cities especially from the developing countries to fast track the catching up process with their developed nation counterparts. Nevertheless, the KBUD progress for these cities is highly daunting and full of confronts. This paper aims to shed light on the major KBUD challenges of emerging local economies by undertaking an in-depth empirical investigation in one of these cities. The paper scrutinizes the prospects and constraints of Istanbul in her KBUD journey through comparative performance and policy context analyses. The findings reveal invaluable insights not only for Istanbul to reshape the policy context and better align the development with contemporary KBUD perspectives, but also for other emerging local economies to learn from these experiences.

Sex-specific roost selection by bats in clearfell harvested plantation forest : improved knowledge advises management

To maximize energetic savings, female bats often roost communally whilst pregnant or with non-volant dependents, whereas male bats more often roost alone; however, differences in selection of roosts by sex have not often been investigated. Better understanding of female colony locations could focus management to protect the majority of bats. New Zealand's long-tailed bat (Chalinolobus tuberculatus) roost in exotic plantation forest, where sex-specific roost selection has not been investigated, and therefore such management is not possible. We investigated sex-specific roost selection by long-tailed bats for the first time. Roosts and paired nonroosts were characterized testing predictions that males and females select roosts that differ from non-roosts, and males and females select different roosts. Females and males chose Pinus radiata roosts that differed from non-roost trees. Results suggest each sex chose roosts that maximized energetic savings. Female bats used roosts closer to water sources, that warmed earlier in the day, which allowed maintenance of high temperatures. Males appeared to choose roosts that allowed torpor use for long periods of the day. Males may be less selective with their roost locations than females, as they roosted further from water sources. This could allow persistence of male bats in marginal habitat. As all female long-tailed bats chose roosts within 150 m of waterways, management to protect bats could be focused here. To protect bats least able to escape when roosts are harvested, harvest of forest stands selected by female bats as roost sites should be planned when bats are not heavily pregnant nor have non-volant dependents.

Multimedia super corridor, Malaysia: Knowledge-based urban development lessons from an emerging economy

Purpose Knowledge-based urban development (KBUD) has been an effective strategy and an opportunity for emerging economies for catching up with the developed economies. The paper aims to investigate and provide insights on KBUD in the context of emerging economies. Design/methodology/approach The paper scrutinizes the Multimedia Super Corridor of Malaysia (MSC) by focusing on the planning, development and orchestration of the knowledge corridor. Findings The paper reveals a number of lessons and insights drawn from the development of MSC as the largest manifestation of KBUD initiative in Malaysia. Originality/value The paper provides lessons and recommendations on the planning, development and management of KBUD for emerging economies that are seeking a prosperous development.

Compositional data analysis (CoDA) approaches to distance in information retrieval

Many techniques in information retrieval produce counts from a sample, and it is common to analyse these counts as proportions of the whole - term frequencies are a familiar example. Proportions carry only relative information and are not free to vary independently of one another: for the proportion of one term to increase, one or more others must decrease. These constraints are hallmarks of compositional data. While there has long been discussion in other fields of how such data should be analysed, to our knowledge, Compositional Data Analysis (CoDA) has not been considered in IR. In this work we explore compositional data in IR through the lens of distance measures, and demonstrate that common measures, naïve to compositions, have some undesirable properties which can be avoided with composition-aware measures. As a practical example, these measures are shown to improve clustering. Copyright 2014 ACM.

Importance of knowledge management processes in a project-based organization : a case study of research enterprise

Knowledge Management (KM) is vital factor to successfully undertake projects. The temporary nature of projects necessitates employing useful KM practices for tackling issues such as knowledge leakiness and rework. The Project Management Office (PMO) is a unit within organizations to facilitate and oversee organizational projects. Project Management Maturity Models (PMMM) shows the development of PMOs from immature to mature levels. The existing PMMMs have focused on discussing Project Management (PM) practices, however, the management of project knowledge is yet to be addressed, at various levels of maturity. This research project was undertaken to investigate the mentioned gap for addressing KM practices at the existing PMMMs. Due to the exploratory and inductive nature of this research, qualitative methods were chosen as the research methodology. In total, three cases selected from different industries: research; mining and government organizations, to provide broad categories for research and research questions were examined using the developed framework. This paper presents the partial findings of undertaken investigation of the research organisation with the lowest level of maturity. The result shows that knowledge creation and capturing are the most important processes, while knowledge transferring and reusing are not as important as the other two processes. In addition, it was revealed that provision of “knowledge about client” and “project management knowledge” are the most important types of knowledge that are required at this level of maturity. In conclusion, the outcomes of this paper shall provide powerful guidance to PMOs at lowest level of maturity from KM point of view.

Linking pedagogical knowledge practices and student outcomes in STEM education for primary schools

STEM education is a new frontier in Australia, particularly for primary schools. However, the E in STEM needs to have a stronger focus with science and mathematics concepts aligned to the presiding curricula. In addition, pedagogical knowledge practices such as planning, preparation, teaching strategies, assessment and so forth need to be connected to key concepts for developing a STEM education. One of the aims of this study was to understand how a pedagogical knowledge practice framework could be linked to student outcomes in STEM education. Specifically, this qualitative research investigated Year 4 students’ involvement in an integrated STEM education program that focused on science concepts (e.g., states of matter, testing properties of materials) and mathematics concepts (such as 3D shapes and metric measurements: millilitres, temperature, grams, centimetres) for designing, making and testing a strong and safe medical kit to insulate medicines at desirable temperatures. Eleven pedagogical knowledge practices (e.g., planning, preparation, teaching strategies, classroom management, and assessment) were used as a framework for understanding how teaching may be linked to student outcomes in STEM education. For instance, “planning” involved devising a student booklet as a resource for students to understand the tasks required of them, which also provided space for them to record ideas, results and information. Planning involved linking national and state curriculum documents to the STEM education activities. More studies are required around pedagogical knowledge frameworks to understand what students learn when involved in STEM education, particularly with the inclusion of engineering education.

Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions

Conservation planning and management programs typically assume relatively homogeneous ecological landscapes. Such “ecoregions” serve multiple purposes: they support assessments of competing environmental values, reveal priorities for allocating scarce resources, and guide effective on-ground actions such as the acquisition of a protected area and habitat restoration. Ecoregions have evolved from a history of organism–environment interactions, and are delineated at the scale or level of detail required to support planning. Depending on the delineation method, scale, or purpose, they have been described as provinces, zones, systems, land units, classes, facets, domains, subregions, and ecological, biological, biogeographical, or environmental regions. In each case, they are essential to the development of conservation strategies and are embedded in government policies at multiple scales.

Bioprocess engineering of microalgae to produce a variety of consumer products

Microalgae biotechnology has recently emerged into the lime light owing to numerous consumer products that can be harnessed from microalgae. Product portfolio stretches from straightforward biomass production for food and animal feed to valuable products extracted from microalgal biomass, including triglycerides which can be converted into biodiesel. For most of these applications, the production process is moderately economically viable and the market is developing. Considering the enormous biodiversity of microalgae and recent developments in genetic and metabolic engineering, this group of organisms represents one of the most promising sources for new products and applications. With the development of detailed culture and screening techniques, microalgal biotechnology can meet the high demands of food, energy and pharmaceutical industries. This review article discusses the technology and production platforms for development and creation of different valuable consumer products from microalgal biomass.

Further development of discrete computational techniques for calculation of restricted diffusion propagators in porous media

Magnetic resonance is a well-established tool for structural characterisation of porous media. Features of pore-space morphology can be inferred from NMR diffusion-diffraction plots or the time-dependence of the apparent diffusion coefficient. Diffusion NMR signal attenuation can be computed from the restricted diffusion propagator, which describes the distribution of diffusing particles for a given starting position and diffusion time. We present two techniques for efficient evaluation of restricted diffusion propagators for use in NMR porous-media characterisation. The first is the Lattice Path Count (LPC). Its physical essence is that the restricted diffusion propagator connecting points A and B in time t is proportional to the number of distinct length-t paths from A to B. By using a discrete lattice, the number of such paths can be counted exactly. The second technique is the Markov transition matrix (MTM). The matrix represents the probabilities of jumps between every pair of lattice nodes within a single timestep. The propagator for an arbitrary diffusion time can be calculated as the appropriate matrix power. For periodic geometries, the transition matrix needs to be defined only for a single unit cell. This makes MTM ideally suited for periodic systems. Both LPC and MTM are closely related to existing computational techniques: LPC, to combinatorial techniques; and MTM, to the Fokker-Planck master equation. The relationship between LPC, MTM and other computational techniques is briefly discussed in the paper. Both LPC and MTM perform favourably compared to Monte Carlo sampling, yielding highly accurate and almost noiseless restricted diffusion propagators. Initial tests indicate that their computational performance is comparable to that of finite element methods. Both LPC and MTM can be applied to complicated pore-space geometries with no analytic solution. We discuss the new methods in the context of diffusion propagator calculation in porous materials and model biological tissues.

Mapping geographical knowledge and skills needed for pre-service teachers in teacher education

In Australia, for more than two decades, a ‘social science’ integrated framework was the favoured approach for delivering subjects such as history and geography. However, such interdisciplinary approaches have continued to attract criticism from various parts of the academic and public spheres and since 2009, a return to teaching the disciplines has been heralded as the ‘new’ way forward. Using discourse analysis techniques associated with Foucauldian archaeology, the purpose of this paper is to examine the Australian Curriculum: Geography document to ascertain the discourses necessary for pre-service teachers to enact effective teaching of geography in a primary setting. Then, based on pre-service teachers’ online survey responses, the paper investigates if such future teachers have the knowledge and skills to interpret, deliver and enact the new geography curriculum in primary classrooms. Finally, as teacher educators, our interest lies in preparing pre-service teachers effectively for the classroom so the findings are used to inform the content of a teacher education course for pre-service primary teachers.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Sealing the leak on classical NTRU signatures

Initial attempts to obtain lattice based signatures were closely related to reducing a vector modulo the fundamental parallelepiped of a secret basis (like GGH [9], or NTRUSign [12]). This approach leaked some information on the secret, namely the shape of the parallelepiped, which has been exploited on practical attacks [24]. NTRUSign was an extremely efficient scheme, and thus there has been a noticeable interest on developing countermeasures to the attacks, but with little success [6]. In [8] Gentry, Peikert and Vaikuntanathan proposed a randomized version of Babai’s nearest plane algorithm such that the distribution of a reduced vector modulo a secret parallelepiped only depended on the size of the base used. Using this algorithm and generating large, close to uniform, public keys they managed to get provably secure GGH-like lattice-based signatures. Recently, Stehlé and Steinfeld obtained a provably secure scheme very close to NTRUSign [26] (from a theoretical point of view). In this paper we present an alternative approach to seal the leak of NTRUSign. Instead of modifying the lattices and algorithms used, we do a classic leaky NTRUSign signature and hide it with gaussian noise using techniques present in Lyubashevky’s signatures. Our main contributions are thus a set of strong NTRUSign parameters, obtained by taking into account latest known attacks against the scheme, a statistical way to hide the leaky NTRU signature so that this particular instantiation of CVP-based signature scheme becomes zero-knowledge and secure against forgeries, based on the worst-case hardness of the O~(N1.5)-Shortest Independent Vector Problem over NTRU lattices. Finally, we give a set of concrete parameters to gauge the efficiency of the obtained signature scheme.