Modeling health care facility location for moving population groups

Locating public services for nomadic population groups is a difficult challenge as the locations of the targeted populations seasonally change. In this paper, the population groups are assumed to occupy different locations according to the time of the year, i.e., winter and summer. A binary integer programming model is formulated to determine the optimal number and locations of primary health units for satisfying a seasonally varying demand. This model is successfully applied to the actual locations of 17 seasonally varying nomadic groups in the Middle East. Computational tests are performed on different versions of the model in order analyze the tradeoffs among different performance measures.     

TRUST-TECH-based expectation maximization for learning finite mixture models

In spite of the initialization problem, the Expectation-Maximization (EM) algorithm is widely used for estimating the parameters of finite mixture models. Most popular model-based clustering techniques might yield poor clusters if the parameters are not initialized properly. To reduce the sensitivity of initial points, a novel algorithm for learning mixture models from multivariate data is introduced in this paper. The proposed algorithm takes advantage of TRUST-TECH (TRansformation Under STability-reTaining Equilibra CHaracterization) to compute neighborhood local maxima on likelihood surface using stability regions. Basically, our method coalesces the advantages of the traditional EM with that of the dynamic and geometric characteristics of the stability regions of the corresponding nonlinear dynamical system of the log-likelihood function. Two phases namely, the EM phase and the stability region phase, are repeated alternatively in the parameter space to achieve improvements in the maximum likelihood. The EM phase obtains the local maximum of the likelihood function and the stability region phase helps to escape out of the local maximum by moving towards the neighboring stability regions. The algorithm has been tested on both synthetic and real datasets and the improvements in the performance compared to other approaches are demonstrated. The robustness with respect to initialization is also illustrated experimentally.     

Zernike Moments-Based Gurumukhi Character Recognition

In this article, use of Zernike moments is presented for invariant recognition of Gurumukhi characters. Zernike moments belong to a class of continuous orthogonal moments defined over a unit circle. So, for a square image, computations of Zernike moments involve a certain square-to-circle mapping to map the pixel coordinates within the range of the unit circle. There exist two forms of mapping, which are used by various authors in their research works. Here, a computational framework has been proposed for calculation of Zernike moments using both mapping techniques, and a comparison between the performances of both of these mapping techniques is shown through a series of extensive experiments.     

Studies and characterization of RESOL/VAc–EHA/HMMM IPN systems in aqueous medium

Resol was solution‐blended with vinyl acetate‐2‐ethylhexyl acrylate (VAc–EHA) resin in an aqueous medium, in varying weight fractions, with hexamethoxymethylmelamine (HMMM) as a crosslinker and the data were compared with a control. The present work was aimed to obtain an optimum combination of high‐temperature resistance by synthesis of an interpenetrating network (IPN) of the resins. The control gave a semi‐IPN system, in which the resol crosslinked, while the acrylic did not, whereas the blend, where HMMM was the crosslinker, gave a full‐IPN system. FTIR spectra of the blends of resol/VAc–EHA/HMMM indicated the formation of new stretching, which was generated due to crosslinking reactions among VAc–EHA and the crosslinker HMMM. TGA showed that, with an increase in the VAc–EHA percent in semi‐IPNs, the decomposition temperature decreased gradually, whereas in case of full‐IPNs, the decomposition temperature increased with increase in the VAc–EHA percent. However, the full‐IPNs had a higher decomposition temperature than that of the semi‐IPNs, at the same resol/(VAc–EHA) ratio. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3581–3588, 2002     

A Methodology for Measuring Size-Dependent Chemical Composition of Ultrafine Particles

Ultrafine particulate matter (PM) consists of particles mostly emitted by combustion sources but also formed during gas-to-particle formation processes in the atmosphere. Various studies have shown these particles to be toxic. The very small mass of these particles has posed a great challenge in determining their size-dependent chemical composition using conventional aerosol sampling technologies. Implementing 2 technologies in series has made it possible to overcome these 2 problems. The first technology is the USC Ultrafine Concentrator, which concentrates ultrafine particles (i.e., 10-180 nm) by a factor of 20-22. Ultrafine particles are subsequently size fractionated and collected on suitable substrates using the NanoMOUDI, a recently developed cascade impactor that classifies particles in 5 size ranges from 10 to 180 nm. The entire system (concentrator + NanoMOUDI) was employed in the field at 2 different locations in the Los Angeles Basin in order to collect ultrafine particles in 3 consecutive 3 h time intervals (i.e., morning, midday, and afternoon). The results indicate a distinct mode in the 32-56 nm size range that is most pronounced in the morning and decreases throughout the day at Downey, CA (a "source" site), affected primarily by vehicular PM emissions. While the mass concentrations at the source site decrease with time, the levels measured at Riverside, CA (a "receptor" site), are highest in the afternoon with a minimum at midday. In Riverside, ultrafine EC (elemental carbon) and OC (organic carbon) concentrations were highly correlated only during the morning period, whereas these correlations collapsed later in the day. These results indicate that in this area, ultrafine PM is generated by primary emissions during the morning hours, whereas secondary aerosol formation processes become more important as the day progresses.     

Effective Diffusivity and Evaporative Cooling in Convective Drying of Food Material

This article presents mathematical models to simulate coupled heat and mass transfer during convective drying of food materials using three different effective diffusivities: shrinkage dependent, temperature dependent, and the average of those two. Engineering simulation software COMSOL Multiphysics was utilized to simulate the model in 2D and 3D. The simulation results were compared with experimental data. It is found that the temperature-dependent effective diffusivity model predicts the moisture content more accurately at the initial stage of the drying, whereas the shrinkage-dependent effective diffusivity model is better for the final stage of the drying. The model with shrinkage-dependent effective diffusivity shows evaporative cooling phenomena at the initial stage of drying. This phenomenon was investigated and explained. Three-dimensional temperature and moisture profiles show that even when the surface is dry, the inside of the sample may still contain a large amount of moisture. Therefore, the drying process should be dealt with carefully; otherwise, microbial spoilage may start from the center of the dried food. A parametric investigation was conducted after validation of the model.