Antistatic packaging based on PTT/PTT-g-MA/ABS/MWCNT nanocomposites: Effect of the chemical functionalization of MWCNTs

Polymer/carbon nanotube nanocomposites have attracted high interest for a wide spectrum of applications, including antistatic packaging used to protect electronic devices against electrostatic discharge. Polytrimethylene terephthalate (PTT)/maleic-anhydride-grafted PTT (PTT-g-MA)/acrylonitrile butadiene styrene (ABS) blend-based multiwall carbon nanotubes (MWCNTs) nanocomposites were prepared through extrusion. It was conducted chemical functionalization on the MWCNTs by oxidation using nitric acid to introduce functional groups. The effect of the amount (0.5 or 1.0 wt%) and functionalization of MWCNTs on the nanocomposites was investigated. Despite the poor barrier properties of PTT/PTT-g-MA/ABS/MWCNT nanocomposites due to the presence of voids confirmed by scanning electron microscopy (SEM), the nanocomposites with functionalized MWCNT (MWCNTf) showed excellent barrier properties, indicating that the functionalization process improved the interaction between the MWCNTs and the matrix. The addition of MWCNTs into PTT/PTT-g-MA/ABS blend decreased the electrical resistivity by eight orders of magnitude. The use of MWCNTf may still disrupt the electrical network pathway and slightly decreasing the electrical resistivity, but the nanocomposites present the desired properties required for antistatic packaging.     

Analysis of surface integrity for minimum quantity lubricant—MQL in grinding

The quality of machined components is currently of high interest, for the market demands mechanical components of increasingly high performance, not only from the standpoint of functionality but also from that of safety. Components produced through operations involving the removal of material display surface irregularities resulting not only from the action of the tool itself, but also from other factors that contribute to their superficial texture. This texture can exert a decisive influence on the application and performance of the machined component. This article analyzes the behavior of the minimum quantity lubricant (MQL) technique and compares it with the conventional cooling method. To this end, an optimized fluid application method was devised using a specially designed nozzle, by the authors, through which a minimum amount of oil is sprayed in a compressed air flow, thus meeting environmental requirements. This paper, therefore, explores and discusses the concept of the MQL in the grinding process. The performance of the MQL technique in the grinding process was evaluated based on an analysis of the surface integrity (roughness, residual stress, microstructure and microhardness). The results presented here are expected to lead to technological and ecological gains in the grinding process using MQL.     

A novel supercapacitor-fuel cell hybrid cell

A monolithic hybrid fuel cell (MHFC) with a novel configuration was proposed in an effort to improve the fuel cell performance during instantaneous power changes. A modified direct methanol fuel cell (DMFC) with a layer of hydrous ruthenium dioxide (RuO2·xH2O) sandwiched between the anode catalyst layer and membrane was used to demonstrate the principle of the MHFC. Experimental results indicate that the RuO2·xH2O layer is equivalent to a resistor-capacitor transmission line and functions similar to a capacitor in parallel with the anode electrode. The improvement in dynamic response of the MHFC was experimentally confirmed under step current change and square current pulse operating. The ionic conductivity of the RuO2·xH2O layer was also obtained.     

A pseudo-spectral scheme for the approximate solution of a time-fractional diffusion equation

We consider an initial-boundary-value problem for a time-fractional diffusion equation with initial condition u₀(x) and homogeneous Dirichlet boundary conditions in a bounded interval [0, L]. We study a semidiscrete approximation scheme based on the pseudo-spectral method on Chebyshev–Gauss–Lobatto nodes. In order to preserve the high accuracy of the spectral approximation we use an approach based on the evaluation of the Mittag-Leffler function on matrix arguments for the integration along the time variable. Some examples are presented and numerical experiments illustrate the effectiveness of the proposed approach.     

Global Atmospheric Profiles from Reanalysis Information (GAPRI): a new database for earth surface temperature retrieval

This paper presents the Global Atmospheric Profiles derived from Reanalysis Information (GAPRI) database, which was designed for earth surface temperature retrieval. GAPRI is a comprehensive compilation of selected atmospheric vertical profiles at global scale which can be used for radiative transfer simulation in order to obtain generalized algorithms to estimate land surface temperature (LST). GAPRI includes information on geopotential height, atmospheric pressure, air temperature, and relative humidity derived from the European Centre for Medium-Range Weather Forecasts Re-Analysis data from year 2011. The atmospheric profiles are structured for 29 vertical levels and extracted from a global spatial grid of about 0.75° × 0.75° latitude–longitude with a temporal resolution of 6 hours. The selection method is based in the extraction of clear sky profiles over different atmospheric weather conditions such as tropical, mid-latitude summer, subarctic, and arctic, while also considering sea and land areas and day- and night-time conditions. The GAPRI database was validated by comparing land and sea surface temperature values derived from it to those obtained using other existing atmospheric profile databases and in situ measurements. Moreover, GAPRI was also compared to previous radiosonde atmospheric profiles using simulated split-window algorithms. Results show good agreement between GAPRI and previous atmospheric databases, thus demonstrating the potential of GAPRI for studies related to forward simulations in the thermal infrared range. GAPRI is a freely available database that can be modified according to the user’s needs and local atmospheric conditions.     

Statics Aware Grid Shells

We introduce a framework for the generation of polygonal gridshell architectural structures, whose topology is designed in order to excel in static performances. We start from the analysis of stress on the input surface and we use the resulting tensor field to induce an anisotropic nonEuclidean metric over it. This metric is derived by studying the relation between the stress tensor over a continuous shell and the optimal shape of polygons in a corresponding gridshell. Polygonal meshes with uniform density and isotropic cells under this metric exhibit variable density and anisotropy in Euclidean space, thus achieving a better distribution of the strain energy over their elements. Meshes are further optimized taking into account symmetry and regularity of cells to improve aesthetics. We experiment with quad meshes and hexdominant meshes, demonstrating that our gridshells achieve better static performances than stateoftheart gridshells.     

Portfolio approaches for constraint optimization problems

Within the Constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of Constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. The results obtained by measuring several evaluation metrics confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research.     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

Cross level semantic similarity: an evaluation framework for universal measures of similarity

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.