Nucleation and Crystallization of New Glasses from Fly Ash Originating from Thermal Power Plants

The nucleation and crystallization kinetics of new glasses obtained by melting mixtures of a Spanish carbon fly ash with glass cullet and dolomite slag at 1500°C has been evaluated by a calculation method. These glasses, whose microstructure was examined by TEM carbon replica, were susceptible to controlled crystallization in the 800°–1100°C range. The resulting glass-ceramics developed acicular and branched wollastonite crystals or a network of dendritic pyroxene mixed with anorthite feldspar (SEM and EDX analysis). The time–temperature–transformation curves (processing of the XRD data) showed the crystallization kinetics and the critical cooling rate to be in the 12°–42°C/min range.     

Structure,microstructure and electrical properties of Cu2+ doped (K,Na,Li)(Nb,Ta,Sb)O3 piezoelectric ceramics

This work studies the effects of copper doping on the properties of the (K_0.44Na_0.52Li_0.04)(Nb_0.86Ta_0.10Sb_0.04)O₃ piezoelectric ceramic material. Cu²⁺ incorporation into the perovskite structure produces a transformation of the crystalline lattice from tetragonal to orthorhombic symmetry together with an increase of the secondary phase. The grain size of the ceramic samples is increased due to the formation of a liquid phase during sintering, which increases with the Cu²⁺ content. EDS analysis reveals that the secondary-phase regions present a Cu and Nb-rich composition, indicating that the Cu-excess accommodates through the formation of this secondary phase. Cu-doping induces a rapid increase of the orthorhombic–tetragonal phase transition temperature, while the tetragonal–cubic phase transition temperature is decreased, the latter becoming more diffuse with the increase of Cu content. The piezoelectric properties of the material are reduced with the copper concentration, whereas the mechanical quality factor increases by a factor of nearly four.     

Unconventional method used in synthesis of polyphosphoesters

Polyphosphonates, polyphosphates, polyarylazophosphonates and polyarylazophosphates were synthesized by a new alternative to the classical phase transfer catalysis, respectively, the modified inverse phase transfer catalysis (IPTC) polycondensation of various phenylphosphonic (phosphoric) dichlorides (phenylphosphonic dichloride, phenylphosphoric dichloride, p-chlorophenylphosphoric dichloride) with bisphenols: bisphenol A, tetrabromobisphenol A, bis-(4-hydroxyphenyl)methane (bisphenol F), 4,4′-dihydroxyazobenzene. The polymers were characterized by infrared spectroscopy and magnetic resonance (¹H-NMR, ³¹P-NMR, ¹³C-NMR) spectroscopy. Yields in the range of 63.5–85% and molecular weights (M _w) of ~2,000–8,200 g mol^?1 were obtained. Polyphosphonates were stable up 210–270 °C and polyphosphates up 190–220 °C in air atmosphere. For a correct estimation of the thermal behavior of similar compounds, a kinetic analysis using a modified version of Non Parametric Kinetic method for representative polyphosphonate and polyphosphate was realized. The studies made on the hydrolytic degradation of the synthesized polyphosphates show that the most stable polymer under alkali-catalyzed degradation is the polyphosphate obtained by IPTC of phenylphosphoric dichloride and bisphenol A.     

Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification

The constant pressure for making functional verification more agile has led to the conception of coverage driven verification (CDV) techniques. CDV has been implemented in verification testbenches using supervised learning techniques to model the relationship between coverage events and stimuli generation, providing a feedback between them. One commonly used technique is the classification- or decision-tree data mining, which has shown to be appropriate due to the easy modeling. Learning techniques are applied in two steps: training and application. Training is made on one or more sets of examples, which relate datasets to pre-determined classes. Precision of results by applying the predictive learning concept has shown to be sensitive to the size of the training set and the amount of imbalance of associated classes, this last meaning the number of datasets associated to each class is very different from each other. This work presents experiments on the manipulation of data mining training sets, by changing the size and reducing the imbalances, in order to check their influence on the CDV efficiency. To do that, a circuit example with a large input space and strong class imbalance was selected from the application domain of multimedia systems and another one, with a small input space that affects the coverage occurrences, was selected from the communication area.     

Entrepreneurial quality and regional development: Characterizing SME sectors in low income areas*

This paper analyses the characteristics of SMEs in less‐developed areas in comparison with more advanced economies. The conceptual framework includes different factors making up the so‐called ‘entrepreneurial quality’ of the SME sector: the personal characteristics of the entrepreneurs (motivations and previous experience), different dimensions of the SMEs’ entrepreneurial orientation, and the patterns of the SMEs’ insertion in national and global value chains. The empirical analysis uses data of SMEs in four different Spanish provinces representing examples of low income economies and comparatively high income ones, respectively. Results show the SMEs in the low income economies to be characterized by a lower entrepreneurial quality.     

A genetic algorithm for energy-efficiency in job-shop scheduling

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.     

Montmorillonite reinforced type A gelatin nanocomposites

This study investigates the structural, morphological, thermal, and mechanical properties of type A gelatin/montmorillonite (MMT) films as a function of MMT concentration. The variations of the X‐ray diffraction pattern suggest that the structure of the nanocomposites turns from intercalated to exfoliated on increasing clay loading up to 20 wt %. Simultaneously, gelatin interaction with clay negative sheets during gelling provokes a reduction of the triple helix content of the composite films, in agreement with the reduction of the relative intensity of the 1.1 nm diffraction reflection of gelatin and of the values of denaturation enthalpy. On the other hand, the increase of the denaturation and decomposition temperatures, the significant rise of the Young's modulus, as well as the swelling decrease observed as clay content increases, demonstrate a relevant stabilizing effect of MMT on gelatin. The reinforcement action of MMT allows to utilize a relatively low concentration of the crosslinking agent genipin to further stabilize the films. The synergic action of clay and genipin prevents dissolution of the nanocomposites in aqueous solution and enhances their mechanical properties. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40301.