Synthesis and characterization of new thermally stable poly(ester‐imide)s derived from 2,2′‐bis(4‐trimellitimidophenoxy)biphenyl and 2,2′‐bis(4‐trimellitimidophenoxy)‐1,1′‐binaphthyl and various aromatic dihydroxy compounds

A series of new alternating aromatic poly(ester‐imide)s were prepared by the polycondensation of the preformed imide ring‐containing diacids, 2,2′‐bis(4‐trimellitimidophenoxy)biphenyl (2a) and 2,2′‐bis(4‐trimellitimidophenoxy)‐1,1′‐binaphthyl (2b) with various aromatic dihydroxy compounds in the presence of pyridine and lithium chloride. A model compound (3) was also prepared by the reaction of 2b with phenol, its synthesis permitting an optimization of polymerization conditions. Poly(ester‐imides) were fully characterized by FTIR, UV‐vis and NMR spectroscopy. Both biphenylene‐ and binaphthylene‐based poly(ester‐imide)s exhibited excellent solubility in common organic solvents such as tetrahydrofuran, m‐cresol, pyridine and dichloromethane. However, binaphthylene‐based poly(ester‐imide)s were more soluble than those of biphenylene‐based polymers in highly polar organic solvents, including N‐methyl‐2‐pyrrolidone, N,N‐dimethylacetamide, N,N‐dimethylformamide and dimethyl sulfoxide. From differential scanning calorimetry thermograms, the polymers showed glass‐transition temperatures between 261 and 315 °C. Thermal behaviour of the polymers obtained was characterized by thermogravimetric analysis, and the 10 % weight loss temperatures of the poly(ester‐imide)s was in the range 449–491 °C in nitrogen. Furthermore, crystallinity of the polymers was estimated by means of wide‐angle X‐ray diffraction. The resultant poly(ester‐imide)s exhibited nearly an amorphous nature, except poly(ester‐imide)s derived from hydroquinone and 4,4′‐dihydroxybiphenyl. In general, polymers containing binaphthyl units showed higher thermal stability but lower crystallinity than polymers containing biphenyl units. Copyright © 2005 Society of Chemical Industry     

Reconstruction of reflectance spectra using weighted principal component analysis

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 360–371, 2008     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

A talking profile to distinguish identical twins

Identical twins pose a great challenge to face recognition due to high similarities in their appearances. Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently, we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins. The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video. Given two talking profiles, we compute the similarities of the same type of face motion in both profiles and then perform the classification based on those similarities. To compute the similarity of each type of face motion, we give higher weights to more abnormal motions which are assumed to carry more identity signature information.     

An empirical study of control logic specifications for programmable logic controllers

This paper presents an empirical study of control logic specifications used to document industrial control logic code in manufacturing applications. More than one hundred input/output related property specifications from ten different reusable function blocks were investigated. The main purpose of the study was to provide understanding of how the specifications are expressed by industrial practitioners, in order to develop new tools and methods for specifying control logic software, as well as for evaluating existing ones. In this paper, the studied specifications are used to evaluate linear temporal logic in general and the specification language ST-LTL, tailored for functions blocks, in particular. The study shows that most specifications are expressed as implications, that should always be fulfilled, between input and output conditions. Many of these implications are complex since the input and output conditions may be mixed and involve sequences, timer issues and non-boolean variables. Using ST-LTL it was possible to represent all implications of this study. The few non-implication specifications could be specified in ST-LTL as well after being altered to suit the specification language. The paper demonstrates some advantages of ST-LTL compared to standard linear temporal logic and discusses possible improvements such as support for automatic rewrite of complex specifications.     

Color image DNA encryption using mRNA properties and non-adjacent coupled map lattices

This paper proposes a novel algorithm for encrypting color images. The innovation in this study is the use of messenger ribonucleic acid (mRNA) encoding to import into Deoxyribonucleic acid (DNA) encoding. For permutation of the plain image bits, we use Arnold’s Cat Map at the bit-level. Then, using Non-Adjacent Coupled Map Lattices (NCML), we apply diffusion operations to the permuted color channels. We also provide the upgrade of the diffusion phase with DNA encoding. In the proposed algorithm, the choices are random depending on the secret key, which is implemented using a simple logistic map. Hashing the string entered by the user, the secret key, parameters, and initial values are generated by the Double MD5 method. The results of tests and security analysis showed that the results of encryption with this scheme are effective, and the key space is large enough to withstand common attacks.

     

A study on melt recycling of bio-based polypropylene/thermoplastic starch compound

The compulsion to use bioplastics has increased significantly today. One of the important aspects of plastics is their recyclability. Therefore, the important question of this research is that although bio-based compounds containing starch are sensitive to thermal-mechanical recycling processes, are such products thermally recyclable? To answer the question, polypropylene (PP)/thermoplastic starch (TPS) compound granules were extruded up to five times, and in the other part, single-extruded granules were blended at different ratios with virgin granules by extrusion. In order to characterize these samples, Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, rotational disc rheometry, tensile properties, and appearance evaluation were used. The results showed that it is possible to recycle PP/TPS granules up to four times repetition of the extrusion operation and the fifth repetition also showed slight changes. There was also a blend of single-extruded granules with virgin material up to a 50:50% composition without significant variation.     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.