The Voice as Transcursive Inscriber: The Relation of Body and Instrument Understood through the Workings of a Machine

This article examines the relationship of the body with a musical instrument; specifically it looks at the vital threshold conditions that occur during the interplay of voice and instrument. By examining the work ‘IKAS’ (1982) for solo saxophone by German composer Hans-Joachim Hespos, the unusual timbral relationships created between vocal and instrumental sounds are exposed. I argue that this particular work highlights the performer/instrument relation as one marked by Gilles Deleuze’s notion of the workings of a machine and a machine’s relation to a ‘flow’, in particular a machine’s function with view to the break in the flow. By turning towards Deleuze’s concept of the machine, this article offers a slightly different vocabulary for music analysis, one that more easily encompasses certain works of the twentieth century, specifically those that are more timbre- than pitch-based.

Reconfiguring Older Bodies in the Prison Time Machine

It is by mapping an area that the geographer comes to understand the contours and formations of a place. The “place” in this case is the prison world. This article serves to map moments in prison demonstrating how “old” female bodies are performed under the prison gaze. In this article I will illustrate how older women subvert, negotiate, or invoke discourse as a means of reinscribing the normalizing discourses that serve to confine and define older women's experiences in prison. Female elders in prison become defined and confined by regimes of femininity and ageism. They have to endure symbolic and actual intrusions of physical privacy, which serve to remind them of what they were, where they are, and what they have become. This article will critically explore the complexity and contradictions of time use in prison and how they impact on embodied identities. By incorporating the voices of elders, I hope to draw out the contradictions and dilemmas which they experience, thereby illustrating the relationship between time, their involvement in doing time, and the performance of time in a total institution (see Goffman, 1961), and the relationship between temporality and existence. The stories of the women show how their identities are caught within the movement and motion of time and space, both in terms of the time of “the real” on the outside and within prison time. This is the in-between space of carceral time within which women live and which they negotiate. It is by being caught in this network of carceral time that they are constantly being “remade” as their body/performance of identities alters within it. While only a small percentage of the female prison population in the United Kingdom are in later life, one has to question why criminological and gerontological literature fail to address the needs of a growing significant minority.

Analytical description of stochastic field-Line wandering in magnetic turbulence

A nonperturbative nonlinear statistical approach is presented to describe turbulent magnetic systems embedded in a uniform mean magnetic field. A general formula in the form of an ordinary differential equation for magnetic field-line wandering (random walk) is derived. By considering the solution of this equation for different limits several new results are obtained. As an example, it is demonstrated that the stochastic wandering of magnetic field-lines in a two-component turbulence model leads to superdiffusive transport, contrary to an existing diffusive picture. The validity of quasilinear theory for field-line wandering is discussed, with respect to different turbulence geometry models, and previous diffusive results are shown to be deduced in appropriate limits.

Stochastic protein folding simulation in the three-dimensional HP-model

We present results from three-dimensional protein folding simulations in the HP-model on ten benchmark problems. The simulations are executed by a simulated annealing-based algorithm with a time-dependent cooling schedule. The neighbourhood relation is determined by the pull-move set. The results provide experimental evidence that the maximum depth D of local minima of the underlying energy landscape can be upper bounded by D < n(2/3). The local search procedure employs the stopping criterion (In/delta)(D/gamma) where m is an estimation of the average number of neighbouring conformations, gamma relates to the mean of non-zero differences of the objective function for neighbouring conformations, and 1-delta is the confidence that a minimum conformation has been found. The bound complies with the results obtained for the ten benchmark problems. (c) 2008 Elsevier Ltd. All rights reserved.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Adding value to renewables: a one pot process combining microbial cells and hydrogen transfer catalysis to utilise waste glycerol from biodiesel production

Waste glycerol was converted to secondary amines in a one pot reaction, using Clostridium butyricum and catalytic hydrogen transfer-mediated amination.

Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.