Human resource allocation management in multiple projects using sociometric techniques

This article describes a new application of key psychological concepts in the area of Sociometry for the selection of workers within organizations in which projects are developed. The project manager can use a new procedure to determine which individuals should be chosen from a given pool of resources and how to combine them into one or several simultaneous groups/projects in order to assure the highest possible overall work efficiency from the standpoint of social interaction. The optimization process was carried out by means of matrix calculations performed using a computer or even manually, and based on a number of new ratios generated ad-hoc and composed on the basis of indices frequently used in Sociometry.

Optimization of electroless copper plating on polyethylene films modified by surface grafting of vinyl ether of monoethanolamine

Metallized plastics have recently received significant interest for their useful applications in electronic devices such as for integrated circuits, packaging, printed circuits and sensor applications. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. There are several techniques for metal deposition on surface of polymers such as evaporation, sputtering, electroless plating and electrolysis. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. Polyethylene films were subjected to gamma-radiation induced surface graft copolymerization with vinyl ether of monoethanolamine. Electroless copper plating was carried out effectively on the modified films. The catalytic processes for the electroless copper plating in the presence and the absence of SnCl2 sensitization were studied and the optimum activation conditions that give the highest plating rate were determined. The effect of grafting degree on the plating rate is studied. Electroless plating conditions (bath additives, pH and temperature) were optimized. Plating rate was determined gravimetrically and spectrophotometrically at different grafting degrees. The results reveal that plating rate is a function of degree of grafting and increases with increasing grafted vinyl ether of monoethanolamine onto polyethylene. It was found that pH 13 of electroless bath and plating temperature 40°C are the optimal conditions for the plating process. The increasing of grafting degree results in faster plating rate at the same pH and temperature. The surface morphology of the metallized films was investigated using scanning electron microscopy (SEM). The adhesion strength between the metallized layer and grafted polymer was studied using tensile machine. SEM photos and adhesion measurements clarified that uniform and adhered deposits were obtained under optimum conditions.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Use of the statistical properties of the wavelet-transform coefficients for optimization of integration time in Fourier transform spectrometry

We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.

Optimization of a sea ice model using basinwide observations of Arctic sea ice thickness, extent, and velocity

A stand-alone sea ice model is tuned and validated using satellite-derived, basinwide observations of sea ice thickness, extent, and velocity from the years 1993 to 2001. This is the first time that basin-scale measurements of sea ice thickness have been used for this purpose. The model is based on the CICE sea ice model code developed at the Los Alamos National Laboratory, with some minor modifications, and forcing consists of 40-yr ECMWF Re-Analysis (ERA-40) and Polar Exchange at the Sea Surface (POLES) data. Three parameters are varied in the tuning process: Ca, the air–ice drag coefficient; P*, the ice strength parameter; and α, the broadband albedo of cold bare ice, with the aim being to determine the subset of this three-dimensional parameter space that gives the best simultaneous agreement with observations with this forcing set. It is found that observations of sea ice extent and velocity alone are not sufficient to unambiguously tune the model, and that sea ice thickness measurements are necessary to locate a unique subset of parameter space in which simultaneous agreement is achieved with all three observational datasets.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.