Mark-Houwink-Sakurada coefficients for conventional poly(methyl methacrylate) in tetrahydrofuran

Summary Intrinsic viscosities [] in tetrahydrofuran (THF) at 30°C are reported for ten narrow molecular weight distribution poly(methyl methacrylate) (PMMA) samples covering nearly two orders of magnitude in molecular weight. By combining the viscosity results with absolute weight-average molecular weights determined via low-angle laser light scattering (LALLS) experiments, we have accurately determined the Mark-Houwink-Sakurada (MHS) parameters for this polymer-solvent pair (K=7.56x10⁵ and a=0.731, when [] is in dL g^-1).     

Reference structure tomography

Reference structure tomography (RST) uses multidimensional modulations to encode mappings between radiating objects and measurements. RST may be used to image source-density distributions, estimate source parameters, or classify sources. The RST paradigm permits scan-free multidimensional imaging, data-efficient and computation-efficient source analysis, and direct abstraction of physical features. We introduce the basic concepts of RST and illustrate the use of RST for multidimensional imaging based on a geometric radiation model.     

Numerical investigation of the oxy-fuel combustion in large scale boilers adopting the ECO-Scrub technology

The present paper presents a three-dimensional numerical investigation of a pulverized-fuel, tangentially-fired utility boiler located at Florina/Greece under air, partial and full oxy-fuel conditions. Heat and mass transfer and major species concentration, such as CO₂, CO and O₂ are calculated; whilst the results for the reference air case scenario studied are in good agreement with the corresponding operational data measured in the plant, both for combustion calculations and NO_x emissions. Results for the partial and full oxy-fuel operation scenarios are in line with similar experimental and numerical investigations found in the recent literature. This numerical investigation of oxy-fuel conditions scenarios prior to their implementation under real scale conditions demonstrates the utmost of its importance, since significant results regarding the operation of a boiler in terms of lignite particle trajectories and burning rates are attained. Furthermore, NO_x calculations have been performed for all the examined case studies.     

Detection and Identification of Probiotic Lactobacillus plantarum Strains by Multiplex PCR Using RAPD-Derived Primers

Lactobacillus plantarum 2035 and Lactobacillus plantarum ACA-DC 2640 are two lactic acid bacteria (LAB) strains that have been isolated from Feta cheese. Both display significant potential for the production of novel probiotic food products. The aim of the present study was the development of an accurate and efficient method for the molecular detection and identification of the above strains in a single reaction. A multiplex PCR assay was designed for each strain, based on specific primers derived from Random Amplified Polymorphic DNA (RAPD) Sequenced Characterized Amplified Region (SCAR) analysis. The specificity of the assay was tested with a total of 23 different LAB strains, for L. plantarum 2035 and L. plantarum ACA-DC 2640. The multiplex PCR assay was also successfully applied for the detection of the above cultures in yogurt samples prepared in our lab. The proposed methodology may be applied for monitoring the presence of these strains in food products, thus evaluating their probiotic character. Moreover, our strategy may be adapted for other novel LAB strains with probiotic potential, thus providing a powerful tool for molecular discrimination that could be invaluable to the food industry.     

A rule-based semi-automatic method to map burned areas: exploring the USGS historical Landsat archives to reconstruct recent fire history

This study presents a new semi-automatic method to map burned areas by using multi-temporal Land Remote Sensing Satellite Program (Landsat) Thematic Mapper (TM) and Enhanced TM Plus (ETM+) images. The method consists of a set of rules that are valid especially when the post-fire satellite image has been captured shortly after the fire event. The overall accuracy of the method when applied to two case studies in Mt Parnitha and Samos Island in Greece were 95.69% and 93.98%, respectively. The commission and omission errors for Mt Parnitha were 6.92% and 10.24%, while those for Samos Island were 3.97% and 8.80%, respectively. Between the two types of error, it is preferred to minimize omission errors, since commission errors can be easily identified as part of product quality assessment and algorithm tuning procedures. The rule-based approach minimizes human interventions and makes it possible to run the mapping algorithm for a series of images that would otherwise need extensive time investment. In case of failure to capture burned areas correctly, it is possible either to make some adjustments by modifying the thresholding coefficients of the rules, or to discard some of the rules, since some editing is usually required to correct errors following the automated extraction procedures. When this method was applied to a series of US Geological Survey (USGS) Landsat TM and ETM+ archived satellite images covering the periods 1984–1991 and 1999–2009, a total of 1773 fires were identified and mapped from six different scenes that covered Attica and the Peloponnese in Greece. The majority of uncaptured burned areas corresponded to fires with size classes of 0–1 ha and 1–5 ha, where the loss in capturing fire scars is generally significant. This was expected since it is possible that small fires, identified and recorded by forest authorities, may not have been captured by satellite data due to limitations arising either from the spatial resolution of the sensor or imposed by the temporal series, which do not systematically cover the full period.     

Innovative seismic design optimization with reliability constraints

Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.     

Intelligent Network Provisioning for Dynamically Downloadable Applications in Beyond 3G Mobile Networks

Mobile communications beyond 3G will integrate different (but complementary) access technologies into a common platform to deliver value-added services and multimedia content in an optimum way. However, the numerous possible configurations of mobile networks complicated the dynamic deployment of mobile applications. Therefore, research is intensely seeking a service provisioning framework that is technology-independent, supports multiple wireless network technologies, and can interwork high-level service management tasks to network management operations. This paper presents an open value chain paradigm, a model for downloadable applications and a mediating platform for service provisioning in beyond 3G mobile settings. Furthermore, we introduce mechanisms that support a coupled interaction between service deployment and network configuration operations, focusing on the dynamic provisioning of QoS state to data path devices according to the requirements of dynamically downloadable mobile value-added services (VAS).

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive Run Length Smoothing Algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique.