Strasheela: Design and usage of a music composition environment based on the Oz programming model

Strasheela provides a means for the composer to create a symbolic score by formally describing it in a rule-based way. The environment defines a rich music representation for complex polyphonic scores. Strasheela enables the user to define expressive compositional rules and then to apply them to the score. Compositional rules can restrict many aspects of the music - including the rhythmic structure, the melodic structure and the harmonic structure - by constraining the parameters (e.g. duration or pitch) of musical events according to some numerical or logical relation. Strasheela combines this expressivity with efficient search strategies.

Enantiomeric programming in tripodal transition metal scaffolds

A new route to the isolation of the enantiopure tris- chelate complex (Delta/Lambda)- fac-[Ru( L-1)(3)] 21 (where L-1 is 2,2'-bipyridine-5-carboxylic acid) is demonstrated, where the transition metal centre retains the memory of the chirality present in a simple tripodal tether used to control the metal centred geometry.

Linear quadratic and tumour control probability modelling in external beam radiotherapy

The standard linear-quadratic (LQ) survival model for external beam radiotherapy is reviewed with particular emphasis on studying how different schedules of radiation treatment planning may be affected by different tumour repopulation kinetics. The LQ model is further examined in the context of tumour control probability (TCP) models. The application of the Zaider and Minerbo non-Poissonian TCP model incorporating the effect of cellular repopulation is reviewed. In particular the recent development of a cell cycle model within the original Zaider and Minerbo TCP formalism is highlighted. Application of this TCP cell-cycle model in clinical treatment plans is explored and analysed.

A role of cellular prion protein in programming T-cell cytokine responses in disease.

The cellular prion protein (PrPC) is widely expressed in neural and non-neural tissues, but its function is unknown. Elucidation of the part played by PrPC in adaptive immunity has been a particular conundrum: increased expression of cell surface PrPC has been documented during T-cell activation, yet the functional significance of this activation remains unclear, with conflicting data on the effects of Prnp gene knockout on various parameters of T-cell immunity. We show here that Prnp mRNA is highly inducible within 8–24 h of T-cell activation, with surface protein levels rising from 24 h. When measured in parallel with CD69 and CD25, PrPC is a late activation antigen. Consistent with its up-regulation being a late activation event, PrP deletion did not alter T-cell-antigen presenting cell conjugate formation. Most important, activated PrP0/0 T cells demonstrated much reduced induction of several T helper (Th) 1, Th2, and Th17 cytokines, whereas others, such as TNF- and IL-9, were unaffected. These changes were investigated in the context of an autoimmune model and a bacterial challenge model. In experimental autoimmune encephalomyelitis, PrP-knockout mice showed enhanced disease in the face of reduced IL-17 responses. In a streptococcal sepsis model, this constrained cytokine program was associated with poorer local control of infection, although with reduced bacteremia. The findings indicate that PrPC is a potentially important molecule influencing T-cell activation and effector function.

Semi-formal Models to Support Program Development: Autonomic Management within Component Based Parallel and Distributed Programming

Functional and non-functional concerns require different programming effort, different techniques and different methodologies when attempting to program efficient parallel/distributed applications. In this work we present a "programmer oriented" methodology based on formal tools that permits reasoning about parallel/distributed program development and refinement. The proposed methodology is semi-formal in that it does not require the exploitation of highly formal tools and techniques, while providing a palatable and effective support to programmers developing parallel/distributed applications, in particular when handling non-functional concerns.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Modelling the performance of self-compacting SIFCON of cement slurries containing limestone powder using genetic programming technique

The paper explores the potential of applicability of Genetic programming approach (GP), adopted in this investigation, to model the combined effects of five independent variables to predict the mini-slump, the plate cohesion meter, the induced bleeding test, the J-fiber penetration value, and the compressive strength at 7 and 28 days of self-compacting slurry infiltrated fiber concrete (SIFCON). The variables investigated were the proportions of limestone powder (LSP) and sand, the dosage rates of superplasticiser (SP) and viscosity modifying agent (VMA), and water-to-binder ratio (W/B). Twenty eight mixtures were made with 10-50% LSP as replacement of cement, 0.02-0.06% VMA by mass of cement, 0.6-1.2% SP and 50-150% sand (% mass of binder) and 0.42-0.48 W/B. The proposed genetic models of the self-compacting SIFCON offer useful modelling approach regarding the mix optimisation in predicting the fluidity, the cohesion, the bleeding, the penetration, and the compressive strength.

A programming model for BSP with partitioned synchronisation

A BSP superstep is a distributed computation comprising a number of simultaneously executing processes which may generate asynchronous messages. A superstep terminates with a barrier which enforces a global synchronisation and delivers all ongoing communications. Multilevel supersteps can utilise barriers in which subsets of processes, interacting through shared memories, are locally synchronised (partitioned synchronisation). In this paper a state-based semantics, closely related to the classical sequential programming model, is derived for distributed BSP with partitioned synchronisation.