Styrene-butadiene branched star-shaped asphalt modifiers: Synthesis and mechanical characterization

Abstract

The synthesis and the corresponding characterization of styrene-butadiene (SB), branched, star-shaped copolymers was investigated as part of a research project on asphalt modification using polymers with precise molecular structures. The method of anionic polymerization was followed to prepare samples of block copolymers of SB, a synthesis method that controls chain-architecture, molecular weight distribution, monomer distribution, and the average molecular weight. The research studies are the synthesis of block copolymers including linear, three- and four-arms constructs, depending on the coupling agent used. The techniques of nuclear magnetic resonance (1NMR), differential scanning calorimetry (DSC), gel permeation chromatography (GPC), and rheology were carried out to characterize the copolymers. From the results of the 1NMR, DSC, and GPC analyses, all star-shaped copolymers investigated showed a similar block copolymer composition. Furthermore, the rheological behavior of one of the synthesized star-shaped copolymers was nearly the same as a four-branched commercial copolymer. Rheologically, the four-arm block copolymer sample had the largest storage modulus (G′) among the branched copolymers synthetized, indicating that such architecture produces a highly structured material. In regard to polymer-modified asphalt formulations, the three-branched copolymer architecture yielded better elastic behavior than the four-branch version. In summary, the findings of this investigation provide new insight about a polymer system that may offer advantages in industrial asphalt paving applications.     

On reactive routing protocols in ZigBee wireless sensor networks

ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On‐Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision‐making process. The objective of the fuzzy routine is to determine, during the route‐discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route‐creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes.     

A Class of Incomplete Riemann Solvers Based on Uniform Rational Approximations to the Absolute Value Function

In this paper we propose a new class of incomplete Riemann solvers, based on approximations in the \(L^\infty \) -norm to the absolute value function in \([-1,1]\) by means of rational functions, for the numerical approximation of the solution of hyperbolic systems of conservation laws. The main idea relies on the construction of a numerical approximation to the viscosity matrix by using an appropriate rational real function \(R(x)\) , that approximates the function \(|x|\) uniformly in \([-1,1]\) , evaluated at the Jacobian of the fluxes of the hyperbolic system computed at some average value (for example, Roe averages). In addition to the Jacobians of the fluxes we shall use either the maximum in absolute value of the characteristic speeds in each cell or an upper bound of them. Thus, the resulting approximate Riemann solver is incomplete in the sense that we do not use the complete spectral decomposition of the Jacobian. Moreover, the new class of Riemann solvers consists of a hierarchy of schemes running from the more dissipative to the less dissipative ones, and having as limiting case a Roe-like scheme. According to the order of the approximation of the generating rational function used, the degree of dissipation can be dosed for particular applications. We study different rational approximations: Newman-type functions, iterative generated Halley functions, and also Chebyshev polynomial approximants. We test our basic algorithms for different initial value Riemann problems for ideal gas dynamics (HD) and magnetohydrodynamics (MHD) to observe their behavior with respect to challenging scenarios in numerical simulations, including some standard numerical pathologies (e. g., heat conduction, postshock oscillations and overheating) and the formation of compound waves in ideal MHD. We also examine our proposed schemes, by computing the numerical approximation of different initial value problems for nonconservative multilayer shallow water equations, where it has been observed that intermediate waves can be properly captured for an appropriate degree of approximation of the generating rational function used. Our numerical tests indicate that the proposed schemes are robust, running stable and accurate with a satisfactory time step restriction (CFL constant), and the computational cost is more advantageous with respect to schemes that use a complete spectral decomposition of the Jacobians.     

The effect of pre-warming on performance during simulated firefighting exercise

This study examined the effect of active pre-warming on speed and quality of performance during simulated firefighting exercise. Twelve male firefighters performed two trials in counterbalanced order. They were either pre-warmed by 20-min cycling at 1.5 Watt kg⁻¹ body mass (WARM) or remained thermoneutral (CON) prior to a simulated firefighting activity. After the pre-warming, gastrointestinal temperature (P < 0.001), skin temperature (P = 0.002), and heart rate (P < 0.001) were higher in WARM than in CON. During the firefighting activity, rating of perceived exertion, thermal sensation and discomfort were higher for WARM than for CON. Finish time of the firefighting activity was similar, but the last task of the activity was completed slower in WARM than in CON (P = 0.04). In WARM, self-reported performance quality was lower than in CON (P = 0.04). It is concluded that pre-warming reduces the speed during the last part of simulated firefighting activity and reduces self-reported quality of performance.     

Pervaporation of 50 wt % ethanol–water mixtures with poly(1‐trimethylsilyl‐1‐propyne) membranes at high temperatures

Poly(1‐trimethylsilyl‐1‐propyne) (PTMSP) membranes have been used to separate ethanol–water mixtures by pervaporation. This polyacetylene is known to present high affinity toward ethanol, showing high selectivity and ethanol permeation flux. The performance of this polymer in the separation of alcohol–water solutions has been evaluated over long periods (572 h) at a high temperature (75°C) to examine the deterioration of the transport properties in the separation of 50 wt % ethanol–water solutions. Although PTMSP membranes present good characteristics for the separation of gases and liquid mixtures, their organic selectivity decrease with the operating time because of the relaxation processes of the polymeric chains, which affect the free volume of the polymer, the deterioration being more evident for concentrated solutions. The effects of the operation temperature on the characteristic parameters of pervaporation have also been studied to establish how this variable affects the performance of PTMSP membranes. The selectivity increases slightly with the operation temperature, but the effect of the temperature on the separation factor decreases as membranes are degraded with the operation time. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2843–2848, 2007     

Event-driven,pattern-based methodology for cost-effective development of standardized personal health devices

Experiences applying standards in personal health devices (PHDs) show an inherent trade-off between interoperability and costs (in terms of processing load and development time). Therefore, reducing hardware and software costs as well as time-to-market is crucial for standards adoption. The ISO/IEEE11073 PHD family of standards (also referred to as X73PHD) provides interoperable communication between PHDs and aggregators. Nevertheless, the responsibility of achieving inexpensive implementations of X73PHD in limited resource microcontrollers falls directly on the developer. Hence, the authors previously presented a methodology based on patterns to implement X73-compliant PHDs into devices with low-voltage low-power constraints. That version was based on multitasking, which required additional features and resources. This paper therefore presents an event-driven evolution of the patterns-based methodology for cost-effective development of standardized PHDs. The results of comparing between the two versions showed that the mean values of decrease in memory consumption and cycles of latency are 11.59% and 45.95%, respectively. In addition, several enhancements in terms of cost-effectiveness and development time can be derived from the new version of the methodology. Therefore, the new approach could help in producing cost-effective X73-compliant PHDs, which in turn could foster the adoption of standards.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.     

Abnormal behavior detection using dominant sets

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\) -Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.     

Blocking the Routes to Triviality with Depth Relevance

In Rogerson and Restall’s (J Philos Log 36, 2006, p. 435), the “class of implication formulas known to trivialize (NC)” (NC abbreviates “naïve comprehension”) is recorded. The aim of this paper is to show how to invalidate any member in this class by using “weak relevant model structures”. Weak relevant model structures verify deep relevant logics only.