In this paper we provide a numerical analysis of the M D N queueing system This system consists of an in nite bu er and N identical batch servers with batch capacity b It serves as a model for various types of workstations in the manufacturing context Using an approach presented by Crommelin we derive the distribution of the number of lots waiting in queue at arbitrary time instants and the dis...

The adsorption study was conducted on three Japanese soils with different soil properties to characterize the adsorption pattern of Arsenic (As). Double tube method was used to find out the effect of soil moisture levels on As adsorption. For this study, besides double tube method, conventional batch method also was used. The As adsorption showed two phase kinetics. An initial and rapid adsorpt...

In the modem hospital microbiology department, it is not feasible to investigate fully all routine urine specimens for the detection of bacterial infection of the urinary tract. Although increased numbers of leucocytes in the urine may enable selection of urines for comprehensive culture techniques, including direct antibiotic sensitivity testing, it is totally unacceptable to reject any specim...

A method for estimating thrombin clottable fibrinogen in a large number of plasma samples is described. This method is based on measuring the optical density difference of a diluted plasma and its corresponding serum after incubation at 59 degrees C in the presence of 10% NaCl. The method gave excellent correlation with previously described thrombin clottable estimates, but was easier to perfor...

A chemical batch scheduling problem is modeled in two different ways as a discrete optimization problem. Both models are used to solve the batch scheduling problem in a two-phase tabu search procedure. The method is tested on real world data.

We introduce an efficient online learning mechanism for nonnegative sparse coding in autoencoder neural networks. In this paper we compare the novel method to the batch algorithm non-negative matrix factorization with and without sparseness constraint. We show that the efficient autoencoder yields to better sparseness and lower reconstruction errors than the batch algorithms on the MNIST benchm...

This paper develops an efficient online algorithm based on K-SVD for learning multiple consecutive tasks. We first derive a batch multi-task learning method that builds upon the K-SVD algorithm, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while m...

Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This method incorporates two acceleration techniques: one is Nesterov’s acceleration method, and the othe...

Histograms are used extensively for selectivity estimation and approximate query processing. Workloadaware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users’ inter...

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