Reluctantly virtual : modelling copyright industry dynamics

During the evolution of the music industry, developments in the media environment have required music firms to adapt in order to survive. Changes in broadcast radio programming during the 1950s; the Compact Cassette during the 1970s; and the deregulation of media ownership during the 1990s are all examples of changes which have heavily affected the music industry. This study explores similar contemporary dynamics, examines how decision makers in the music industry perceive and make sense of the developments, and reveals how they revise their business strategies, based on their mental models of the media environment. A qualitative system dynamics model is developed in order to support the reasoning brought forward by the study. The model is empirically grounded, but is also based on previous music industry research and a theoretical platform constituted by concepts from evolutionary economics and sociology of culture. The empirical data primarily consist of 36 personal interviews with decision makers in the American, British and Swedish music industrial ecosystems. The study argues that the model which is proposed, more effectively explains contemporary music industry dynamics than music industry models presented by previous research initiatives. Supported by the model, the study is able to show how “new” media outlets make old music business models obsolete and challenge the industry’s traditional power structures. It is no longer possible to expose music at one outlet (usually broadcast radio) in the hope that it will lead to sales of the same music at another (e.g. a compact disc). The study shows that many music industry decision makers still have not embraced the new logic, and have not yet challenged their traditional mental models of the media environment. Rather, they remain focused on preserving the pivotal role held by the CD and other physical distribution technologies. Further, the study shows that while many music firms remain attached to the old models, other firms, primarily music publishers, have accepted the transformation, and have reluctantly recognised the realities of a virtualised environment.

Numerical modelling of load bearing light gauge steel frame wall systems exposed to realistic design fires

Fire safety has become an important part in structural design due to the ever increasing loss of properties and lives during fires. Conventionally the fire rating of load bearing wall systems made of Light gauge Steel Frames (LSF) is determined using fire tests based on the standard time-temperature curve in ISO834 [1]. However, modern commercial and residential buildings make use of thermoplastic materials, which mean considerably high fuel loads. Hence a detailed fire research study into the fire performance of LSF walls was undertaken using realistic design fire curves developed based on Eurocode parametric [2] and Barnett’s BFD [3] curves using both full scale fire tests and numerical studies. It included LSF walls without cavity insulation, and the recently developed externally insulated composite panel system. This paper presents the details of finite element models developed to simulate the full scale fire tests of LSF wall panels under realistic design fires. Finite element models of LSF walls exposed to realistic design fires were developed, and analysed under both transient and steady state fire conditions using the measured stud time-temperature curves. Transient state analyses were performed to simulate fire test conditions while steady state analyses were performed to obtain the load ratio versus time and failure temperature curves of LSF walls. Details of the developed finite element models and the results including the axial deformation and lateral deflection versus time curves, and the stud failure modes and times are presented in this paper. Comparison with fire test results demonstrate the ability of developed finite element models to predict the performance and fire resistance ratings of LSF walls under realistic design fires.

Numerical study of lid-driven square cavity with heat generation using LBM

In this study, the mixed convection heat transfer and fluid flow behaviors in a lid-driven square cavity filled with high Prandtl number fluid (Pr = 5400, ν = 1.2×10-4 m2/s) at low Reynolds number is studied using thermal Lattice Boltzmann method (TLBM) where ν is the viscosity of the fluid. The LBM has built up on the D2Q9 model and the single relaxation time method called the Lattice-BGK (Bhatnagar-Gross-Krook) model. The effects of the variations of non dimensional mixed convection parameter called Richardson number(Ri) with and without heat generating source on the thermal and flow behavior of the fluid inside the cavity are investigated. The results are presented as velocity and temperature profiles as well as stream function and temperature contours for Ri ranging from 0.1 to 5.0 with other controlling parameters that present in this study. It is found that LBM has good potential to simulate mixed convection heat transfer and fluid flow problem. Finally the simulation results have been compared with the previous numerical and experimental results and it is found to be in good agreement.

A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling

Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.

Representations for evolutionary design modelling

This research looked at using the metaphor of biological evolution as a way of solving architectural design problems. Drawing from fields such as language grammars, algorithms and cellular biology, this thesis looked at ways of encoding design information for processing. The aim of this work is to help in the building of software that support the architectural design process and allow designers to examine more variations.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

Modelling after-the-fact leakage for key exchange

Security models for two-party authenticated key exchange (AKE) protocols have developed over time to prove the security of AKE protocols even when the adversary learns certain secret values. In this work, we address more granular leakage: partial leakage of long-term secrets of protocol principals, even after the session key is established. We introduce a generic key exchange security model, which can be instantiated allowing bounded or continuous leakage, even when the adversary learns certain ephemeral secrets or session keys. Our model is the strongest known partial-leakage-based security model for key exchange protocols. We propose a generic construction of a two-pass leakage-resilient key exchange protocol that is secure in the proposed model, by introducing a new concept: the leakage-resilient NAXOS trick. We identify a special property for public-key cryptosystems: pair generation indistinguishability, and show how to obtain the leakage-resilient NAXOS trick from a pair generation indistinguishable leakage-resilient public-key cryptosystem.

Comparison of functionals for metal hexaboride band structure calculations

Density functional calculations of the electronic band structure for superconducting and semi-conducting metal hexaborides are compared using a consistent suite of assumptions and with emphasis on the physical implications of computed models. Spin polarization enhances mathematical accuracy of the functional approximations and adds significant physical meaning to model interpretation. For YB6 and LaB6, differences in alpha and beta projections occur near the Fermi energy. These differences are pronounced for superconducting hexaborides but do not occur for other metal hexaborides.

Reaction of Aromatic Peroxyl Radicals with Alkynes: A Mass Spectrometric and Computational Study Using the Distonic Radical Ion Approach

The reaction of the aromatic distonic peroxyl radical cations N-methyl pyridinium-4-peroxyl (PyrOO center dot+) and 4-(N,N,N-trimethyl ammonium)-phenyl peroxyl (AnOO center dot+), with symmetrical dialkyl alkynes 10?ac was studied in the gas phase by mass spectrometry. PyrOO center dot+ and AnOO center dot+ were produced through reaction of the respective distonic aryl radical cations Pyr center dot+ and An center dot+ with oxygen, O2. For the reaction of Pyr center dot+ with O2 an absolute rate coefficient of k1=7.1X10-12 cm3 molecule-1 s-1 and a collision efficiency of 1.2?% was determined at 298 K. The strongly electrophilic PyrOO center dot+ reacts with 3-hexyne and 4-octyne with absolute rate coefficients of khexyne=1.5X10-10 cm3 molecule-1 s-1 and koctyne=2.8X10-10 cm3 molecule-1 s-1, respectively, at 298 K. The reaction of both PyrOO center dot+ and AnOO center dot+ proceeds by radical addition to the alkyne, whereas propargylic hydrogen abstraction was observed as a very minor pathway only in the reactions involving PyrOO center dot+. A major reaction pathway of the vinyl radicals 11 formed upon PyrOO center dot+ addition to the alkynes involves gamma-fragmentation of the peroxy O?O bond and formation of PyrO center dot+. The PyrO center dot+ is rapidly trapped by intermolecular hydrogen abstraction, presumably from a propargylic methylene group in the alkyne. The reaction of the less electrophilic AnOO center dot+ with alkynes is considerably slower and resulted in formation of AnO center dot+ as the only charged product. These findings suggest that electrophilic aromatic peroxyl radicals act as oxygen atom donors, which can be used to generate alpha-oxo carbenes 13 (or isomeric species) from alkynes in a single step. Besides gamma-fragmentation, a number of competing unimolecular dissociative reactions also occur in vinyl radicals 11. The potential energy diagrams of these reactions were explored with density functional theory and ab initio methods, which enabled identification of the chemical structures of the most important products.

A comparison of the gas phase acidities of phospholipid headgroups: Experimental and computational studies

Proton-bound dimers consisting of two glycerophospholipids with different headgroups were prepared using negative ion electrospray ionization and dissociated in a triple quadrupole mass spectrometer. Analysis of the tandem mass spectra of the dimers using the kinetic method provides, for the first time, an order of acidity for the phospholipid classes in the gas phase of PE < PA << PG < PS < PI. Hybrid density functional calculations on model phospholipids were used to predict the absolute deprotonation enthalpies of the phospholipid classes from isodesmic proton transfer reactions with phosphoric acid. The computational data largely support the experimental acidity trend, with the exception of the relative acidity ranking of the two most acidic phospholipid species. Possible causes of the discrepancy between experiment and theory are discussed and the experimental trend is recommended. The sequence of gas phase acidities for the phospholipid headgroups is found to (1) have little correlation with the relative ionization efficiencies of the phospholipid classes observed in the negative ion electrospray process, and (2) correlate well with fragmentation trends observed upon collisional activation of phospholipid \[M - H](-) anions. (c) 2005 American Society for Mass Spectrometry.

Impact of passenger group dynamics on an airport evacuation process using an agent-based model

The safety of passengers is a major concern to airports. In the event of crises, having an effective and efficient evacuation process in place can significantly aid in enhancing passenger safety. Hence, it is necessary for airport operators to have an in-depth understanding of the evacuation process of their airport terminal. Although evacuation models have been used in studying pedestrian behaviour for decades, little research has been done in considering the evacuees’ group dynamics and the complexity of the environment. In this paper, an agent-based model is presented to simulate passenger evacuation process. Different exits were allocated to passengers based on their location and security level. The simulation results show that the evacuation time can be influenced by passenger group dynamics. This model also provides a convenient way to design airport evacuation strategy and examine its efficiency. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Sub-modelling for the ratchetting failure of insulated rail joints

Insulated rail joints are critical for train safety as they control electrical signalling systems; unfortunately they exhibit excessive ratchetting of the railhead near the endpost insulators. This paper reports a three-dimensional global model of these joints under wheel–rail contact pressure loading and a sub-model examining the ratchetting failures of the railhead. The sub-model employs a non-linear isotropic–kinematic elastic–plastic material model and predicts stress/strain levels in the localised railhead zone adjacent to the endpost which is placed in the air gap between the two rail ends at the insulated rail joint. The equivalent plastic strain plot is utilised to capture the progressive railhead damage adequately. Associated field and laboratory testing results of damage to the railhead material suggest that the simulation results are reasonable.

Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Promoting Statistical Literacy Through Data Modelling in the Early School Years

This chapter addresses data modelling as a means of promoting statistical literacy in the early grades. Consideration is first given to the importance of increasing young children’s exposure to statistical reasoning experiences and how data modelling can be a rich means of doing so. Selected components of data modelling are then reviewed, followed by a report on some findings from the third-year of a three-year longitudinal study across grades one through three.