Re-engineering construction: going against the grain

An overtly critical perspective on 're-engineering construction' is presented. It is contended that re-engineering is impossible to define in terms of its substantive content and is best understood as a rhetorical label. In recent years, the language of re-engineering has heavily shaped the construction research agenda. The declared goals are to lower costs and improve value for the customer. The discourse is persuasive because it reflects the ideology of the 'enterprise culture' and the associated rhetoric of customer responsiveness. Re-engineering is especially attractive to the construction industry because it reflects and reinforces the existing dominant way of thinking. The overriding tendency is to reduce organizational complexities to a mechanistic quest for efficiency. Labour is treated as a commodity. Within this context, the objectives of re-engineering become 'common sense'. Knowledge becomes subordinate to the dominant ideology of neo-liberalism. The accepted research agenda for re-engineering construction exacerbates the industry's problems and directly contributes to the casualization of the workforce. The continued adherence to machine metaphors by the construction industry's top management has directly contributed to the 'bad attitudes' and 'adversarial culture' that they repeatedly decry. Supposedly neutral topics such as pre-assembly, partnering, supply chain management and lean thinking serve only to justify the shift towards bogus labour-only subcontracting and the associated reduction of employment rights. The continued casualization of the workforce raises real questions about the industry's future capacity to deliver high-quality construction. In order to appear 'relevant' to the needs of industry, it seems that the research community is doomed to perpetuate this regressive cycle.

Efficient operator pipelining in a bit serial genetic algorithm engine

The authors propose a bit serial pipeline used to perform the genetic operators in a hardware genetic algorithm. The bit-serial nature of the dataflow allows the operators to be pipelined, resulting in an architecture which is area efficient, easily scaled and is independent of the lengths of the chromosomes. An FPGA implementation of the device achieves a throughput of >25 million genes per second

Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management

A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

Implementing a generic systolic array for genetic algorithms

We have designed a highly parallel design for a simple genetic algorithm using a pipeline of systolic arrays. The systolic design provides high throughput and unidirectional pipelining by exploiting the implicit parallelism in the genetic operators. The design is significant because, unlike other hardware genetic algorithms, it is independent of both the fitness function and the particular chromosome length used in a problem. We have designed and simulated a version of the mutation array using Xilinix FPGA tools to investigate the feasibility of hardware implementation. A simple 5-chromosome mutation array occupies 195 CLBs and is capable of performing more than one million mutations per second. I. Introduction Genetic algorithms (GAs) are established search and optimization techniques which have been applied to a range of engineering and applied problems with considerable success [1]. They operate by maintaining a population of trial solutions encoded, using a suitable encoding scheme.

Systolic random number generation for genetic algorithms

A parallel hardware random number generator for use with a VLSI genetic algorithm processing device is proposed. The design uses an systolic array of mixed congruential random number generators. The generators are constantly reseeded with the outputs of the proceeding generators to avoid significant biasing of the randomness of the array which would result in longer times for the algorithm to converge to a solution. 1 Introduction In recent years there has been a growing interest in developing hardware genetic algorithm devices [1, 2, 3]. A genetic algorithm (GA) is a stochastic search and optimization technique which attempts to capture the power of natural selection by evolving a population of candidate solutions by a process of selection and reproduction [4]. In keeping with the evolutionary analogy, the solutions are called chromosomes with each chromosome containing a number of genes. Chromosomes are commonly simple binary strings, the bits being the genes.

Free amino acids and sugars in rye grain: implications for acrylamide formation

Acrylamide forms from free asparagine and sugars during cooking, and products derived from the grain of cereals, including rye, contribute a large proportion of total dietary intake. In this study, free amino acid and sugar concentrations were measured in the grain of a range of rye varieties grown at locations in Hungary, France, Poland, and the United Kingdom and harvested in 2005, 2006, and 2007. Genetic and environmental (location and harvest year) effects on the levels of acrylamide precursors were assessed. The data showed free asparagine concentration to be the main determinant of acrylamide formation in heated rye flour, as it is in wheat. However, in contrast to wheat, sugar, particularly sucrose, concentration also correlated both with asparagine concentration and with acrylamide formed. Free asparagine concentration was shown to be under genetic (G), environmental (E), and integrated (G × E) control. The same was true for glucose, whereas maltose and fructose were affected mainly by environmental factors and sucrose was largely under genetic control. The ratio of variation due to varieties (genotype) to the total variation (a measure of heritability) for free asparagine concentration in the grain was 23%. Free asparagine concentration was closely associated with bran yield, whereas sugar concentration was associated with low Hagberg falling number. Rye grain was found to contain much higher concentrations of free proline than wheat grain, and less acrylamide formed per unit of asparagine in rye than in wheat flour.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Effects of variety and nutrient availability on the acrylamide-forming potential of rye grain

Acrylamide is a probable human carcinogen that forms in plant-derived foods when free asparagine and reducing sugars react at high temperatures. The identification of rye varieties with low acrylamide-forming potential or agronomic conditions that produce raw material with low acrylamide precursor concentrations would reduce the acrylamide formed in baked rye foods without the need for additives or potentially costly changes to processes. This work compared five commercial rye varieties grown under a range of fertilisation regimes to investigate the effects of genotype and nutrient (nitrogen and sulphur) availability on the accumulation of acrylamide precursors. A strong correlation was established between the free asparagine concentration of grain and the acrylamide formed upon heating. The five rye varieties accumulated different concentrations of free asparagine in the grain, indicating that there is genetic control of this trait and that variety selection could be useful in reducing acrylamide levels in rye products. High levels of nitrogen fertilisation were found to increase the accumulation of free asparagine, showing that excessive nitrogen application should be avoided in order not to exacerbate the problem of acrylamide formation. This effect of nitrogen was mitigated in two of the varieties by the application of sulphur.

Spatial patterns of gluten protein and polymer distribution in wheat grain

The starchy endosperm is the major storage tissue in the mature wheat grain and exhibits quantitative and qualitative gradients in composition, with the outermost cell layers being rich in protein, mainly gliadins, and the inner cells being low in protein but enriched in high-molecular-weight (HMW) subunits of glutenin. We have used sequential pearling to produce flour fractions enriched in particular cell layers to determine the protein gradients in four different cultivars grown at two nitrogen levels. The results show that the steepness of the protein gradient is determined by both genetic and nutritional factors, with three high-protein breadmaking cultivars being more responsive to the N treatment than a low-protein cultivar suitable for livestock feed. Nitrogen also affected the relative abundances of the three main classes of wheat prolamins: the sulfur-poor ω-gliadins showed the greatest response to nitrogen and increased evenly across the grain; the HMW subunits also increased in response to nitrogen but proportionally more in the outer layers of the starchy endosperm than near the core, while the sulfur-rich prolamins showed the opposite trend.