Case-based analysis in user requirements modelling for knowledge construction

Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.

Modelling the lava dome extruded at Soufrière Hills Volcano, Montserrat, August 2005–May 2006. Part II: Rockfall activity and talus deformation

During many lava dome-forming eruptions, persistent rockfalls and the concurrent development of a substantial talus apron around the foot of the dome are important aspects of the observed activity. An improved understanding of internal dome structure, including the shape and internal boundaries of the talus apron, is critical for determining when a lava dome is poised for a major collapse and how this collapse might ensue. We consider a period of lava dome growth at the Soufrière Hills Volcano, Montserrat, from August 2005 to May 2006, during which a 100 × 106 m3 lava dome developed that culminated in a major dome-collapse event on 20 May 2006. We use an axi-symmetrical Finite Element Method model to simulate the growth and evolution of the lava dome, including the development of the talus apron. We first test the generic behaviour of this continuum model, which has core lava and carapace/talus components. Our model describes the generation rate of talus, including its spatial and temporal variation, as well as its post-generation deformation, which is important for an improved understanding of the internal configuration and structure of the dome. We then use our model to simulate the 2005 to 2006 Soufrière Hills dome growth using measured dome volumes and extrusion rates to drive the model and generate the evolving configuration of the dome core and carapace/talus domains. The evolution of the model is compared with the observed rockfall seismicity using event counts and seismic energy parameters, which are used here as a measure of rockfall intensity and hence a first-order proxy for volumes. The range of model-derived volume increments of talus aggraded to the talus slope per recorded rockfall event, approximately 3 × 103–13 × 103 m3 per rockfall, is high with respect to estimates based on observed events. From this, it is inferred that some of the volumetric growth of the talus apron (perhaps up to 60–70%) might have occurred in the form of aseismic deformation of the talus, forced by an internal, laterally spreading core. Talus apron growth by this mechanism has not previously been identified, and this suggests that the core, hosting hot gas-rich lava, could have a greater lateral extent than previously considered.

Monthly average temperature modelling

This research is associated with the goal of the horticultural sector of the Colombian southwest, which is to obtain climatic information, specifically, to predict the monthly average temperature in sites where it has not been measured. The data correspond to monthly average temperature, and were recorded in meteorological stations at Valle del Cauca, Colombia, South America. Two components are identified in the data of this research: (1) a component due to the temporal aspects, determined by characteristics of the time series, distribution of the monthly average temperature through the months and the temporal phenomena, which increased (El Nino) and decreased (La Nina) the temperature values, and (2) a component due to the sites, which is determined for the clear differentiation of two populations, the valley and the mountains, which are associated with the pattern of monthly average temperature and with the altitude. Finally, due to the closeness between meteorological stations it is possible to find spatial correlation between data from nearby sites. In the first instance a random coefficient model without spatial covariance structure in the errors is obtained by month and geographical location (mountains and valley, respectively). Models for wet periods in mountains show a normal distribution in the errors; models for the valley and dry periods in mountains do not exhibit a normal pattern in the errors. In models of mountains and wet periods, omni-directional weighted variograms for residuals show spatial continuity. The random coefficient model without spatial covariance structure in the errors and the random coefficient model with spatial covariance structure in the errors are capturing the influence of the El Nino and La Nina phenomena, which indicates that the inclusion of the random part in the model is appropriate. The altitude variable contributes significantly in the models for mountains. In general, the cross-validation process indicates that the random coefficient model with spatial spherical and the random coefficient model with spatial Gaussian are the best models for the wet periods in mountains, and the worst model is the model used by the Colombian Institute for Meteorology, Hydrology and Environmental Studies (IDEAM) to predict temperature.

Modelling the formation of necrotic regions in avascular tumours

The mechanisms underlying the formation of necrotic regions within avascular tumours are not well understood. In this paper, we examine the relative roles of nutrient deprivation and of cell death, from both the proliferating phase of the cell cycle via apoptosis and from the quiescent phase via necrosis, in changing the structure within multicellular tumour spheroids and particularly the accumulation of dead cell material in the centre. A mathematical model is presented and studied that accounts for nutrient diffusion, changes in cell cycling rates, the two different routes to cell death as well as active motion of cells and passive motion of the dead cell material. In studying the accumulation of dead cell matter we do not distinguish between the route by which each was formed. The resulting mathematical model is examined for a number of scenarios. Results show that in many cases the size of the necrotic core is closely correlated with low levels in nutrient concentration. However, in certain cases, particularly where the rate of necrosis is large, the resulting necrotic core can lead to regions of non-negligible nutrient concentration-dependent upon the mode of cell death.

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

The surface structure of BaO on Pt(111): (2x2)-reconstructed BaO(111)

The surface structure of BaO(111) has been determined using STM and computer modelling. The BaO(111) surface was prepared in thin film form on Pt(111) and presents a surface with twice the lattice parameter expected for that of the bulk termination, i.e. a (2 x 2) reconstruction. Computer modelling indicates that the bulk termination is unstable, but that the (2 x 2) reconstructed BaO(111) surface has a low surface energy and is hence a stable surface reconstruction. The (2 x 2) reconstruction consists of small, three-sided pyramids with (100) oriented sides and either oxygen or barium ions at the apices. Less regular surface reconstructions containing the same pyramids are almost equally stable, indicating that we may also expect less regular regions to appear with a fairly random distribution of these surface species. The simulations further suggest that a regular (4 x 4) reconstruction built up of bigger pyramids is even more energetically favourable, and some evidence is found for such a structure in the STM. (c) 2006 Elsevier B.V. All rights reserved.

Metal complexes of a dipyridine octaazamacrocycle: stability constants, structural and modelling studies

Two 28-membered octaazamacrocycles, [28]py(2)N(6) and Me-2[28]py(2)N(6), have been synthesized. The protonation constants of the N-methyl. derivative and the stability constants of its complexes with Ni2+, Cu2+, Zn2+, Cd2+, and Pb2+ were determined at 25degreesC in 0.10 mol dm(-3) KNO3. The high overall basicity of Me-2[28]py(2)N(6) is ascribed to the weaker repulsion between protonated contiguous charged ammonium sites separated by propyl chains. These studies together with NMR, UV-vis and EPR spectroscopies indicated the presence of mono- and di-nuclear species, The single crystal structure of the complex [Ni-2([28]py(2)N(6))(H2O)(4)]Cl-4.3H(2)O was determined, and showed each nickel centre in a distorted octahedral co-ordination environment. The nickel centres are held within the macrocycle at a large distance of 6.991(g) Angstrom from each other. The formation of mononuclear complexes was evaluated theoretically via molecular mechanics (MM) and molecular dynamics (MD) calculations and showed that these large macrocycles have sufficient flexibility to encapsulate metal ions with different stereo-electronic sizes. Structures for small and large metal ions are proposed.

Modelling for planning building design - a critique of available approaches

This paper reviews four approaches used to create rational tools to aid the planning and the management of the building design process and then proposes a fifth approach. The new approach that has been developed is based on the mechanical aspects of technology rather than subjective design issues. The knowledge base contains, for each construction technology, a generic model of the detailed design process. Each activity in the process is specified by its input and output information needs. By connecting the input demands of one technology with the output supply from another technology a map or network of design activity is formed. Thus, it is possible to structure a specific model from the generic knowledge base within a KBE system.

Crumpet structures: experimental and modelling studies

Crumpets are made by heating fermented batter on a hot plate at around 230°C. The characteristic structure dominated by vertical pores develops rapidly: structure has developed throughout around 75% of the product height within 30s, which is far faster than might be expected from transient heat conduction through the batter. Cooking is complete within around 3 min. Image analysis based on results from X-ray tomography shows that the voidage fraction is approximately constant and that there is continual coalescence between the larger pores throughout the product although there is also a steady level of small bubbles trapped within the solidified batter. We report here experimental studies which shed light on some of the mechanisms responsible for this structure, together with some models of key phenomena.Three aspects are discussed here: the role of gas (carbon dioxide and nitrogen) nuclei in initiating structure development; convective heat transfer inside the developing pores; and the kinetics of setting the batter into an elastic solid structure. It is shown conclusively that the small bubbles of carbon dioxide resulting from the fermentation stage play a crucial role as nuclei for pore development: without these nuclei, the result is not a porous structure, but rather a solid, elastic, inedible, gelatinized product. These nuclei are also responsible for the tiny bubbles which are set in the final product. The nuclei form the source of the dominant pore structure which is largely driven by the, initially explosive, release of water vapour from the batter together with the desorption of dissolved carbon dioxide. It is argued that the rapid evaporation, transport and condensation of steam within the growing pores provides an important mechanism, as in a heat pipe, for rapid heat transfer, and models for this process are developed and tested. The setting of the continuous batter phase is essential for final product quality: studies using differential scanning calorimetry and on the kinetics of change in the visco-elastic properties of the batter suggest that this process is driven by the kinetics of gelatinization. Unlike many thermally driven food processes the rates of heating are such that gelatinization kinetics cannot be neglected. The implications of these results for modelling and for the development of novel structures are discussed.

Delineation and modelling of a nucleolar retention signal in the coronavirus nucleocapsid protein

Unlike nuclear localization signals, there is no obvious consensus sequence for the targeting of proteins to the nucleolus. The nucleolus is a dynamic subnuclear structure which is crucial to the normal operation of the eukaryotic cell. Studying nucleolar trafficking signals is problematic as many nucleolar retention signals (NoRSs) are part of classical nuclear localization signals (NLSs). In addition, there is no known consensus signal with which to inform a study. The avian infectious bronchitis virus (IBV), coronavirus nucleocapsid (N) protein, localizes to the cytoplasm and the nucleolus. Mutagenesis was used to delineate a novel eight amino acid motif that was necessary and sufficient for nucleolar retention of N protein and colocalize with nucleolin and fibrillarin. Additionally, a classical nuclear export signal (NES) functioned to direct N protein to the cytoplasm. Comparison of the coronavirus NoRSs with known cellular and other viral NoRSs revealed that these motifs have conserved arginine residues. Molecular modelling, using the solution structure of severe acute respiratory (SARS) coronavirus N-protein, revealed that this motif is available for interaction with cellular factors which may mediate nucleolar localization. We hypothesise that the N-protein uses these signals to traffic to and from the nucleolus and the cytoplasm.

On the structure of Langmuir turbulence

The Stokes drift induced by surface waves distorts turbulence in the wind-driven mixed layer of the ocean, leading to the development of streamwise vortices, or Langmuir circulations, on a wide range of scales. We investigate the structure of the resulting Langmuir turbulence, and contrast it with the structure of shear turbulence, using rapid distortion theory (RDT) and kinematic simulation of turbulence. Firstly, these linear models show clearly why elongated streamwise vortices are produced in Langmuir turbulence, when Stokes drift tilts and stretches vertical vorticity into horizontal vorticity, whereas elongated streaky structures in streamwise velocity fluctuations (u) are produced in shear turbulence, because there is a cancellation in the streamwise vorticity equation and instead it is vertical vorticity that is amplified. Secondly, we develop scaling arguments, illustrated by analysing data from LES, that indicate that Langmuir turbulence is generated when the deformation of the turbulence by mean shear is much weaker than the deformation by the Stokes drift. These scalings motivate a quantitative RDT model of Langmuir turbulence that accounts for deformation of turbulence by Stokes drift and blocking by the air–sea interface that is shown to yield profiles of the velocity variances in good agreement with LES. The physical picture that emerges, at least in the LES, is as follows. Early in the life cycle of a Langmuir eddy initial turbulent disturbances of vertical vorticity are amplified algebraically by the Stokes drift into elongated streamwise vortices, the Langmuir eddies. The turbulence is thus in a near two-component state, with suppressed and . Near the surface, over a depth of order the integral length scale of the turbulence, the vertical velocity (w) is brought to zero by blocking of the air–sea interface. Since the turbulence is nearly two-component, this vertical energy is transferred into the spanwise fluctuations, considerably enhancing at the interface. After a time of order half the eddy decorrelation time the nonlinear processes, such as distortion by the strain field of the surrounding eddies, arrest the deformation and the Langmuir eddy decays. Presumably, Langmuir turbulence then consists of a statistically steady state of such Langmuir eddies. The analysis then provides a dynamical connection between the flow structures in LES of Langmuir turbulence and the dominant balance between Stokes production and dissipation in the turbulent kinetic energy budget, found by previous authors.

A hyperstable neural network for the modelling and control of nonlinear systems

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

Modelling small angle neutron scattering data from electrospun fibres

Electrospinning is a technique employed to produce nanoscale to microscale sized fibres by the application of a high voltage to a spinneret containing a polymer solution. Here we examine how small angle neutron scattering data can be modelled to analyse the polymer chain conformation. We prepared 1:1 blends of deuterated and hydrogenated atactic-polystyrene fibres from solutions in N, N-Dimethylformamide and Methyl Ethyl Ketone. The fibres themselves often contain pores or voiding within the internal structure on the length scales that can interfere with scattering experiments. A model to fit the scattering data in order to obtain values for the radius of gyration of the polymer molecules within the fibres has been developed, that includes in the scattering from the voids. Using this model we find that the radius of gyration is 20% larger than in the bulk state and the chains are slightly extended parallel to the fibre axis.

Wavelet filtered modelling applied to measurements of a waveguide's THz time domain response

A quasi-optical de-embedding technique for characterizing waveguides is demonstrated using wideband time-resolved terahertz spectroscopy. A transfer function representation is adopted for the description of the signal in the input and output port of the waveguides. The time domain responses were discretised and the waveguide transfer function was obtained through a parametric approach in the z-domain after describing the system with an ARX as well as with a state space model. Prior to the identification procedure, filtering was performed in the wavelet domain to minimize signal distortion and the noise propagating in the ARX and subspace models. The model identification procedure requires isolation of the phase delay in the structure and therefore the time-domain signatures must be firstly aligned with respect to each other before they are compared. An initial estimate of the number of propagating modes was provided by comparing the measured phase delay in the structure with theoretical calculations that take into account the physical dimensions of the waveguide. Models derived from measurements of THz transients in a precision WR-8 waveguide adjustable short will be presented.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.