Single machine scheduling with simple linear deterioration to minimize total absolute deviation of completion times

We consider a single machine scheduling problem with simple linear deterioration. Job processing times are assumed to be a simple linear function of a job-dependent growth rate and the job's starting time. We seek an optimal schedule, so as to minimize the total absolute deviation of completion times (TADC). We prove several interesting properties of an optimal schedule, and introduce two efficient heuristics which are tested against a lower bound.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Comparison of the ageing behaviour of PM 2124 Al alloy and Al-SiCp metal-matrix composite

The ageing response of 2124 Al-SiC particulate metal-matrix composite (MMC) and unreinforced alloy has been examined using hardness measurements and Arrhenius analysis. The formation of phases during precipitation has been studied using differential scanning calorimetry (DSC). The MMC exhibits accelerated ageing compared to unreinforced alloy, due to enhanced S formation. The activation energy for diffusion is lower in the MMC than in the unreinforced alloy. DSC scans show Guinier-Preston B (GPB) zone nucleation to occur at a lower temperature in the MMC, whilst the total volume of GPB zones formed is smaller than in the unreinforced alloy. A model has been proposed to explain the GPB zone formation behaviour, in which ease of GPB zone nucleation varies within the MMC, as a function of ageing time and of position within the matrix. S formation is enhanced in the MMC because of improved diffusion and a large increase in density of heterogeneous nucleation sites compared to the unreinforced alloy.     

Microwatt shot-noise measurement

We report a simple scheme for sensitive measurements of optical-noise spectra. Optical noise is separated from electronic noise when the output of an analog spectrum analyzer is real-time squared and then lock-in detected. This method directly yields the desired mean-square noise voltage, i.e., the power spectrum of the optical noise on a linear scale. To demonstrate this technique, the mean-square shot noise of a laser beam is measured and found to vary linearly with the laser power from several milliwatts down to one microwatt, in excellent quantitative agreement with predictions.     

Shrinkage estimation in general linear models

We propose a James-Stein-type shrinkage estimator for the parameter vector in a general linear model when it is suspected that some of the parameters may be restricted to a subspace. The James-Stein estimator is shown to demonstrate asymptotically superior risk performance relative to the conventional least squares estimator under quadratic loss. An extensive simulation study based on a multiple linear regression model and a logistic regression model further demonstrates the improved performance of this James-Stein estimator in finite samples. The application of this new estimator is illustrated using Ontario newborn infants data spanning four fiscal years.     

Coding of watershed and river hierarchy to support GIS-based hydrological analyses at different scales

A procedure is presented to automatically assign a modified Pfafstetter code to a stream network and its associated watersheds. The first step is to build a topological network according to the object-oriented Arc Hydro framework definition in ArcGIS. These network relations are then mapped into a code that fully represents the stream hierarchy of a basin in a single attribute. Its use does not depend on ArcGIS object orientation and facilitates simple hierarchical aggregation and querying using only the attribute tables of shapefile versions of the stream network and watershed datasets. The code was implemented for the whole of Austria to support the flexible presentation and user-defined aggregation of water balance results in the digital version of the Hydrological Atlas of Austria.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.     

Tactical combat movements: inter-individual variation in performance due to the effects of load carriage

An examination into the effects of carried military equipment on the performance of two tactical combat movement simulations was conducted. Nineteen Airfield Defence Guards performed a break contact (five 30-m sprints) and a fire and movement simulation (16 6-m bounds) in five load conditions (10–30 kg). Heavier loads significantly increased movement duration on the break contact (0.8%/kg load) and fire and movement (1.1%/kg). Performance deterioration was observed from the beginning to the end of the series of movements (bounds or sprints) with deterioration becoming significantly greater in heavier load conditions. Inter-individual variation between slower and faster participants showed a range in load effects; 0.6, 0.8%/kg for fast and 1.0, 1.4%/kg for slow (break contact, fire and movement, respectively). Velocity profiles revealed that the initial acceleration and peak velocity were the primary determinants of performance. As the duration of these tactical combat movements reflects periods of heightened vulnerability, these findings highlight important implications for commanders.

Practitioner Summary: Increasing amounts of carried military equipment impairs the performance of tactical combat movements. Examination of inter-individual variation in velocity profiles identified that the initial acceleration and the peak velocity achieved during sprints and bounds are key determinants of overall performance.