Articulation of information requirements for personalised knowledge and construction

The quality of information provision influences considerably knowledge construction driven by individual users’ needs. In the design of information systems for e-learning, personal information requirements should be incorporated to determine a selection of suitable learning content, instructive sequencing for learning content, and effective presentation of learning content. This is considered as an important part of instructional design for a personalised information package. The current research reveals that there is a lack of means by which individual users’ information requirements can be effectively incorporated to support personal knowledge construction. This paper presents a method which enables an articulation of users’ requirements based on the rooted learning theories and requirements engineering paradigms. The user’s information requirements can be systematically encapsulated in a user profile (i.e. user requirements space), and further transformed onto instructional design specifications (i.e. information space). These two spaces allow the discovering of information requirements patterns for self-maintaining and self-adapting personalisation that enhance experience in the knowledge construction process.

A real-time measurement system for long-life flood monitoring and warning applications

A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events.

Electronic structure of point defects in controlled self-doping of the TiO2 (110) surface: Combined photoemission spectroscopy and density functional theory study

Point defects in metal oxides such as TiO2 are key to their applications in numerous technologies. The investigation of thermally induced nonstoichiometry in TiO2 is complicated by the difficulties in preparing and determining a desired degree of nonstoichiometry. We study controlled self-doping of TiO2 by adsorption of 1/8 and 1/16 monolayer Ti at the (110) surface using a combination of experimental and computational approaches to unravel the details of the adsorption process and the oxidation state of Ti. Upon adsorption of Ti, x-ray and ultraviolet photoemission spectroscopy (XPS and UPS) show formation of reduced Ti. Comparison of pure density functional theory (DFT) with experiment shows that pure DFT provides an inconsistent description of the electronic structure. To surmount this difficulty, we apply DFT corrected for on-site Coulomb interaction (DFT+U) to describe reduced Ti ions. The optimal value of U is 3 eV, determined from comparison of the computed Ti 3d electronic density of states with the UPS data. DFT+U and UPS show the appearance of a Ti 3d adsorbate-induced state at 1.3 eV above the valence band and 1.0 eV below the conduction band. The computations show that the adsorbed Ti atom is oxidized to Ti2+ and a fivefold coordinated surface Ti atom is reduced to Ti3+, while the remaining electron is distributed among other surface Ti atoms. The UPS data are best fitted with reduced Ti2+ and Ti3+ ions. These results demonstrate that the complexity of doped metal oxides is best understood with a combination of experiment and appropriate computations.

Hydrogen-bonded interpolymer complexes. Formation, structure and applications

Noncovalent interactions play key roles in many natural processes leading to the self-assembly of molecules with the formation of supramolecular structures. One of the most important forces responsible for self-assembly is hydrogen bonding, which also plays an important role in the self-assembly of synthetic polymers in aqueous solutions. Proton-accepting polymers can associate with proton-donating polymers via hydrogen bonding in aqueous solutions and form polymer-polymer or interpolymer complexes. There has been an increased interest among researchers in hydrogen-bonded interpolymer complexes since the first pioneering papers were published in the early 1960s. Several hundred research papers have been published on various aspects of complex formation reactions in solutions and interfaces, properties of interpolymer complexes and their potential applications. This book focuses on the latest developments in the area of interpolymer complexation via hydrogen bonding. It represents a collection of original and review articles written by recognized experts from Germany, Greece, Kazakhstan, Poland, Romania, Russia, UK, Ukraine, and the USA. It highlights many important applications of interpolymer complexes, including the stabilization of colloidal systems, pharmaceuticals, and nanomaterials.

Self assembly of a model amphiphilic phenylalanine peptide/polyethylene glycol block copolymer in aqueous solution

There has been great interest recently in peptide amphiphiles and block copolymers containing biomimetic peptide sequences due to applications in bionanotechnology. We investigate the self-assembly of the peptide-PEG amphiphile FFFF-PEG5000 containing the hydrophobic sequence of four phenylalanine residues conjugated to PEG of molar mass 5000. This serves as a simple model peptide amphiphile. At very low concentration, association of hydrophobic aromatic phenylalanine residues occurs, as revealed by circular dichroism and UV/vis fluorescence experiments. A critical aggregation concentration associated with the formation of hydrophobic domains is determined through pyrene fluorescence assays. At higher concentration, defined beta-sheets develop as revealed by FTIR spectroscopy and X-ray diffraction. Transmission electron microscopy reveals self-assembled straight fibril structures. These are much shorter than those observed for amyloid peptides, the finite length may be set by the end cap energy due to the hydrophobicity of phenylalanine. The combination of these techniques points to different aggregation processes depending on concentration. Hydrophobic association into irregular aggregates occurs at low concentration, well-developed beta-sheets only developing at higher concentration. Drying of FFFF-PEG5000 solutions leads to crystallization of PEG, as confirmed by polarized optical microscopy (POM), FTIR and X-ray diffraction (XRD). PEG crystallization does not disrupt local beta-sheet structure (as indicated by FTIR and XRD). However on longer lengthscales the beta-sheet fibrillar structure is perturbed because spheruilites from PEG crystallization are observed by POM. (C) 2009 Elsevier B.V. All rights reserved.

Self-nucleation and crystallization kinetics of double crystalline poly(p-dioxanone)-b-poly(epsilon-caprolactone) diblock copolymers

The crystallization kinetics of each constituent of poly(p-dioxanone)-b-poly(epsilon-caprolactone) diblock copolymers (PPDX-b-PCL) has been determined in a wide composition range by differential scanning calorimetry and compared to that of the equivalent homopolymers. Spherulitic growth rates were also measured by polarized optical microscopy while atomic force microscopy was employed to reveal the morphology of one selected diblock copolymer. It was found that crystallization drives structure formation and both components form lamellae within mixed spherulitic superstructures. The overall isothermal crystallization kinetics of the PPDX block at high temperatures, where the PCL is molten, was determined by accelerating the kinetics through a previous self-nucleation procedure. The application of the Lauritzen and Ho. man theory to overall growth rate data yielded successful results for PPDX and the diblock copolymers. The theory was applied to isothermal overall crystallization of previously self-nucleated PPDX ( where growth should be the dominant factor if self-nucleation was effective) and the energetic parameters obtained were perfectly matched with those obtained from spherulitic growth rate data of neat PPDX. A quantitative estimate of the increase in the energy barrier for crystallization of the PPDX block, caused by the covalently bonded molten PCL as compared to homo-PPDX, was thus determined. This energy increase can dramatically reduce the crystallization rate of the PPDX block as compared to homo-PPDX. In the case of the PCL block, both the crystallization kinetics and the self-nucleation results indicate that the PPDX is able to nucleate the PCL within the copolymers and heterogeneous nucleation is always present regardless of composition. Finally, preliminary results on hydrolytic degradation showed that the presence of relatively small amounts of PCL within PPDX-bPCL copolymers substantially retards hydrolytic degradation of the material in comparison to homo-PPDX. This increased resistance to hydrolysis is a complex function of composition and its knowledge may allow future prediction of the lifetime of the material for biomedical applications.

A perspective on chaos theory: Potential applications to intelligent buildings

Mathematical models have been vitally important in the development of technologies in building engineering. A literature review identifies that linear models are the most widely used building simulation models. The advent of intelligent buildings has added new challenges in the application of the existing models as an intelligent building requires learning and self-adjusting capabilities based on environmental and occupants' factors. It is therefore argued that the linearity is an impropriate basis for any model of either complex building systems or occupant behaviours for control or whatever purpose. Chaos and complexity theory reflects nonlinear dynamic properties of the intelligent systems excised by occupants and environment and has been used widely in modelling various engineering, natural and social systems. It is proposed that chaos and complexity theory be applied to study intelligent buildings. This paper gives a brief description of chaos and complexity theory and presents its current positioning, recent developments in building engineering research and future potential applications to intelligent building studies, which provides a bridge between chaos and complexity theory and intelligent building research.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Towards self-ware via swarm-array computing

The work reported in this paper proposes Swarm-Array computing, a novel technique inspired by swarm robotics, and built on the foundations of autonomic and parallel computing. The approach aims to apply autonomic computing constructs to parallel computing systems and in effect achieve the self-ware objectives that describe self-managing systems. The constitution of swarm-array computing comprising four constituents, namely the computing system, the problem/task, the swarm and the landscape is considered. Approaches that bind these constituents together are proposed. Space applications employing FPGAs are identified as a potential area for applying swarm-array computing for building reliable systems. The feasibility of a proposed approach is validated on the SeSAm multi-agent simulator and landscapes are generated using the MATLAB toolkit.

Self-immolative linkers in polymeric delivery systems

There has been significant interest in the methodologies of controlled release for a diverse range of applications spanning drug delivery, biological and chemical sensors, and diagnostics. The advancement in novel substrate-polymer coupling moieties has led to the discovery of self-immolative linkers. This new class of linker has gained popularity in recent years in polymeric release technology as a result of stable bond formation between protecting and leaving groups, which becomes labile upon activation, leading to the rapid disassembly of the parent polymer. This ability has prompted numerous studies into the design and development of self-immolative linkers and the kinetics surrounding their disassembly. This review details the main concepts that underpin self-immolative linker technologies that feature in polymeric or dendritic conjugate systems and outlines the chemistries of amplified self-immolative elimination.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

An implicit pole-assignment self-tuning regulator

A new self-tuning implicit pole-assignment algorithm is presented which, through the use of a pole compression factor and different RLS model and control structures, overcomes stability and convergence problems encountered in previously available algorithms. Computational requirements of the technique are much reduced when compared to explicit pole-assignment schemes, whereas the inherent robustness of the strategy is retained.