A memetic algorithm to solve an unrelated parallel machine scheduling problem with auxiliary resources in semiconductor manufacturing

In this paper, we propose a metaheuristic for solving an original scheduling problem with auxiliary resources in a photolithography workshop of a semiconductor plant. The photolithography workshop is often a bottleneck, and improving scheduling decisions in this workshop can help to improve indicators of the whole plant. Two optimization criteria are separately considered: the weighted flow time (to minimize) and the number of products that are processed (to maximize). After stating the problem and giving some properties on the solution space, we show how these properties help us to efficiently solve the problem with the proposed memetic algorithm, which has been implemented and tested on large generated instances. Numerical experiments show that good solutions are obtained within a reasonable computational time.     

PDQ: Parallel Distance Queries for deformable meshes

This paper presents a new algorithm to efficiently maintain Bounding-Volume Hierarchies (BVHs) for fast distance queries with deformable polygon meshes using multi-core architectures. The method involves inflating the bounding volumes in an efficient manner to guarantee the enclosure of the deformable model within the BVH at all times. This is done at low additional computation and memory cost without significantly degrading the quality of the BVH and also in a fashion that allows a simple parallel implementation. Additionally, to facilitate fast queries specifically for deforming meshes, we propose a novel algorithm for the bottom-up construction of BVHs that results in much faster distance queries.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.     

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.     

Blind digital watermarking in PDF documents using Spread Transform Dither Modulation

In this paper, a blind digital watermarking scheme for Portable Document Format (PDF) documents is proposed. The proposed method is based on a variant Quantization Index Modulation (QIM) method called Spread Transform Dither Modulation (STDM). Each bit of the secret message is embedded into a group of characters, more specifically in their x-coordinate values. The method exhibits experiments of two opposite objectives: transparency and robustness, and is motivated to present an acceptable distortion value that shows sufficient robustness under high density noises attacks while preserving sufficient transparency.     

Chlorophyllase biocatalysis in an aqueous/miscible organic solvent medium containing canola oil

Chlorophyllase catalyzes the bioconversion of chlorophyll into chlorophyllide by replacing the phytol group with a hydrogen atom. There is an increased interest in the biotechnological application of chlorophyllase for the removal of green pigments from edible oil and its potential as an alternative to the use of the conventional bleaching technique. Partially purified chlorophyllase, obtained from the alga Phaeodactylum tricornutum, was assayed for its hydrolytic activity in an aqueous/miscible organic solvent system containing refined-bleached-deodorized (RBD) canola oil, using chlorophyll and pheophytin as substrate models. The results indicated that chlorophyllase biocatalysis could be successfully carried out in an aqueous/miscible organic system containing RBD canola oil. The presence of 20% RBD canola oil decreased the hydrolytic activity of chlorophyllase by 2.2 and 6.7 times, using chlorophyll and pheophytin as substrates, respectively. In addition, acetone acted as an activator of chlorophyllase activity at low concentrations and an inhibitor at higher ones. The optimal reaction conditions for chlorophyllase biocatalysis in the aqueous/miscible organic system were determined to consist of 20% RBD oil and 10% acetone at a 200 rpm agitation speed and at a temperature and substrate concentration of 35°C and 12.6 μM for chlorophyll, and 30°C and 9.3 μM for pheophytin.     

Soluble phosphate glass fibres for repair of bone-ligament interface

Phosphate-based fibres of the generic composition (CaO)0.46-(Na2O)n-(Fe2O3)y-(P2O5)0.50 have been evaluated, in vitro, as three dimensional scaffolds for tissue engineering of the hard-soft tissue interface by assessing the fibre solubility and growth and functional gene expression of human cells. Primary human osteoblasts and fibroblasts were seeded onto scaffolds and maintained in culture for up to 21 days. Fluorescent immunolabeling revealed the spread cell morphology and significant proliferation pattern on these fibres, particularly on the 3 mol% Fe2O3-containing formulation. Real-time quantitative Polymerase Chain Reaction (rtQ-PCR) analysis of gene expression using TaqMan Probes was preformed and it has been established that committed cell differentiation was maintained by both cell types, and was strongly related to the 3 mol% Fe2O3 glass composition. These novel, readily manufactured, soluble glass fibres offer a biocompatible and biochemically favourable alternative in the search for suitable degradable materials used in Tissue Engineering.     

Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the α-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs.