Mechanical and dynamical mechanical properties of chloroprene rubber and cellulose II composites

Vulcanized composites of chloroprene rubber (CR) with cellulose II (Cel II) as a filler were investigated. Cel II, obtained by the coagulation of cellulose xanthate, was incorporated in the rubber by the traditional method. The filler content varied from 0 to 30 phr. For comparison purposes, carbon black (CB)–CR composites were also studied. The CB amount varied from 0 to 45 phr. The mechanical and dynamic mechanical properties were determined, and the CR composite containing 20 phr of Cel II showed the best set of properties. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 2425–2430, 2004     

Thermotropic polyester: the influence of the spacer on the physical properties

Thermotropic polyesters prepared from series of 4,4-dihydroxy-,w-diphenoxy alkanes and 4,4-dichloroformyl-,w-diphenoxy alkanes are well known. The substitution of a methylenic spacer by an oxyethylenic spacer in the main chain of these polyesters drastically modifies their physical properties, particularly the mesogenic transition temperatures, as can be observed by DSC and thermooptical analysis. The thermal stability and the polymer solubility in common organic solvents of these polymers did not show a significant change. The physical properties of the two thermotropic polyesters prepared in diphenyl ether were evaluated.     

Envelopes with microplastics generated from recycled plastic bags for crude oil sorption

Oil spill accidents in marine environments and the lack of disposal of post-consumer plastic are environmental problems worldwide. This study presents a sustainable alternative for both issues through envelopes filled with microplastics (MPs) from recycled bags for the sorption of spilled crude oil. Through particle size analysis by three different sieves (4, 9, and 20 mesh), different MP sizes were characterized by scanning electron microscope (SEM), Brunauer–Emmett–Teller (BET), density, contact angle (CA), and Fourier-transform infrared spectroscopy (FTIR). According to their sizes, the MPs were distributed in envelopes and submitted to crude oil sorption capacity and efficiency evaluation. Three MP particle sizes were obtained (from the largest to the smallest, according to the sieve mesh, MP4, MP9, and MP20). SEM images of samples exhibited irregular and porous surfaces, and MP4 had the smallest pore size (8.6 μm). BET showed that MP4 had the highest surface area (0.074 m²/g). The CA > 90° exposed that all samples were hydrophobic. FTIR spectrum demonstrated that the samples from the recycled bags were made of high density polyethylene (HDPE). The MP4 envelopes also had the best crude oil sorption results in capacity and efficiency (1.73 g/g and 68%, respectively), being a promising recycled sorbent in crude oil spillage applications.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.     

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas–Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the self-occlusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D + 3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation.     

Classifying unlabeled short texts using a fuzzy declarative approach

Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a pre-classified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named Bousi~Prolog , for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance.     

Investigation on the styrene-butadiene rubber cleavage with periodic acid under the influence of ultrasonic radiation

Summary The cleavage of styrene-butadiene rubber (SBR) with periodic acid (H₅IO₆) and simultaneous injection of ultrasonic radiation has been performed. Two frequencies, 25 and 40 kHz, respectively, have been used. The results demonstrate clearly that 40 kHz ultrasound radiation accelerates significantly the cleavage reaction. The Pearl String Model theory had been used to elucidate this process.