Use of recycled copper slag for blended cements

Copper slag is a by‐product generated during smelting to extract copper metal from the ore. The copper slag obtained may exhibit pozzolanic activity and may therefore be used in the manufacture of addition‐containing cements. In this paper the effect of the incorporation of the copper slag in cement is measured. Blends of copper slag with Portland cement generally possess properties equivalent to Portland cement containing fly ash, but very different to the silica fume incorporation. Copper slag and fly ash reduce the heat of hydration more effectively than silica fume in mortars. The replacement of 30% cement by copper slag reduces the flexural and compressive strength in a similar way to fly ash; however, after 28 days, the reduction is less than the percentage of substitution. Hydrated calcium aluminate phases were analysed using scanning electron microscopy (SEM) and X‐ray diffraction (XRD) techniques. The pozzolanic activity of copper slag is similar to that of fly ash and higher than silica fume. In the presence of low water/cement ratios, certain pozzolanic materials produce a very compact cement paste that limits the space available for hydration products, a determining factor in the formation of hydrated calcium aluminates. SEM was found to be a useful analytical technique when aluminates are formed and can be clearly detected by XRD. Copyright © 2008 Society of Chemical Industry     

Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.     

The effect of home storage conditions and packaging materials on the quality of frozen green beans

Home storage is the final step of the frozen foods distribution chain, and little is known on how it affects the products quality. The present research describes frozen green beans (Phaseolus vulgaris, L.) quality retention profile during the recommended ‘star marking’ system dates, at the storage temperatures of +5, −6, −12 and −18 °C (along 1, 4, 14 and 60 days, respectively).

The quality profile was assessed by a simulation system. Simulations were set by a response surface methodology to access the effect of different packaging materials (thermal conductivities and thickness), surface heat transfer coefficient, and refrigerator dynamics (effect of refrigeration cycles at the different storage temperatures) on the average retentions of Ascorbic Acid, total vitamin C, colour and flavour.

Green beans quality losses along frozen storage are significantly influenced by temperature, refrigerator dynamics and kinetic properties. Quality is also highly dependent on packaging materials thermal insulation (e.g. at temperatures above the melting point). Temperature cycles inside frozen chambers have a long term effect, and at the higher storage temperatures (e.g. T>−6 °C) are detrimental to frozen green beans quality after shorter periods.     

Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

It takes two to mimic: Behavioral consequences of self-construals.

The present studies demonstrated the moderation of self-construal orientation on mimicry. Recent research has indicated that an interdependent self-construal is associated with assimilation of the other to the self whereas an independent self-construal is associated with minimizing the influence of others on the self (H. R. Markus & S. Kitayama, 1991; D. Stapel & W. Koomen, 2001). Therefore, the authors hypothesized that an interdependent self-construal would be associated with more mimicry than an independent self-construal. When self-construal orientations were experimentally primed. as in Studies 1 and 2, independent self-construals produced less nonconscious mimicry than interdependent self-construals. When self-construals were examined as cultural differences with either a chronically dominant independent (Americans) or interdependent (Japanese) construal of the self, these results were replicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parametric modeling of somatosensory evoked potentials

In this paper, we examine methods of characterizing somatosensory evoked potentials (SEP's) in both the time and frequency domains. We have found that the truncated impulse response (TIR) method produced an accurate time domain model of the SEP signals at model orders greatly reduced from the original state space matrix. The TIR method was valuable for smoothing signals that were slightly corrupted by noise. In this case, the simulated data sequence was close to the original data sequence in the mean squared error sense. For signals that were greatly corrupted by noise, the TIR method was not able to perform as well. Therefore, the TIR method was not a feature extraction method but was valuable for data simulation. In the frequency domain, we have used the autoregressive moving average model (ARMA) to parameterize the SEP signal. An overdetermined set of Yule-Walker equations was created to determine the autoregressive (AR) parameters of the original data with the model order established by the singular value decomposition. From these AR parameters, a residual time series was generated which was used to find the moving average parameters. The resulting ARMA model was used to produce a simulated data sequence. The frequency domain characteristics of the simulated sequence and the corresponding power spectral density of the ARMA filter were very close to the periodogram of the original data sequence. Accurate parameterization was achieved for the SEP waveforms at low filter lengths.     

Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources

Awareness of the construction environment can be improved by automatic three-dimensional (3D) sensing and modeling of job sites in real time. Commercially available 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model dynamic objects in real time in an environment comprised of moving humans, equipment, and materials. Emerging prototype video range cameras offer an alternative by facilitating affordable, wide field of view, dynamic object tracking at frame rates better than 1?Hz (real time). This paper describes a methodology to model, detect, and track the position of static and moving objects in real time, based on data obtained from video range cameras. Experiments with this technology have produced results that indicate that video rate 3D data acquisition and analysis of construction environments can support effective modeling, detection, and tracking of project resources. This approach to job site awareness has inherent value and broad application. In combination with effective management practices and other sensing techniques, this technology has the potential to significantly improve safety on construction job sites.     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.