Influence of the ethylene–(methacrylic acid)–Zn2+ ionomer on the thermal and mechanical properties of blends of poly(propylene) (PP)/ethylene–(vinyl alcohol) copolymer (EVOH)

The thermal and mechanical properties and the morphologies of blends of poly(propylene) (PP) and an ethylene–(vinyl alcohol) copolymer (EVOH) and of blends of PP/EVOH/ethylene–(methacrylic acid)–Zn²⁺ ionomer were studied to establish the influence of the ionomer addition on the compatibilization of PP/EVOH blends and on their properties. The oxygen transmission rate (O₂TR) values of the blends were measured as well. PP and EVOH are initially incompatible as was determined by tensile tests and scanning electronic microscopy. Addition of the ionomer Zn²⁺ led to good compatibility and mechanical behaviour was improved in all blends. The mechanical properties on extruded films were studied for 90/10 and 80/20 w/w PP/EVOH blends compatibilized with 10 % of ionomer Zn²⁺. These experiments have shown that the tensile properties are better than in the injection‐moulded samples. The stretching during the extrusion improved the compatibility of the blends, diminishing the size of EVOH domains and enhancing their distribution in the PP matrix. As was to be expected, the EVOH improved the oxygen permeation of the films, even in compatibilized blends. Copyright © 2004 Society of Chemical Industry     

Preparation and characterization of proton‐exchange hybrid membranes

Two types of composite were prepared, based on a thermoplastic polymer, polyvinylidene fluoride (PVDF), and an elastomer, ethylene‐propylene‐diene terpolymer (EPDM), respectively. We obtained both series by addition of an inorganic proton‐conducting antimonic acid derivative (HSb) and polystyrene crosslinked with a small percentage of divinylbenzene (PS‐co‐DVB). From these composites, membranes were obtained and subjected to a heterogeneous‐phase sulfonation reaction with chlorosulfonic acid. All experimental materials were characterized from a morphological and electrical point of view, by means of techniques such as differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), non‐isothermal crystallization and complex impedance analysis. Copyright © 2005 Society of Chemical Industry     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

Paste Residence Time in a Spouted Bed Dryer. III: Effect of Paste Properties and Quality Interactions

The aim of this study was to evaluate the effects of paste properties on residence time during drying in a spouted bed dryer with inert bodies. The effect of paste solids content, surface tension, and viscosity on the residence time distribution and the mean residence times were studied using factorial experimental designs. The inert bodies used were glass and polyethylene beads. The mean residence times varied from 13.6 to 16.3 and 12.2 to 17.7 min for drying on glass and polyethylene beads, respectively. The analysis of variance showed that mean residence times significantly depended on solids content and surface tension for glass beads and also on viscosity for polyethylene beads. The residence time distributions for all conditions studied fitted well to the perfect mixing cell when applying the continuous stirred vessels in series model analysis. The powder density, flowability, and particle size depended on paste properties and inert type.     

Cpk index estimation using data transformation

Process capability indices (PCIs) are used in industry to assess percentages of nonconforming parts. An underlying assumption is that the output process measurements are distributed as normal random variables. When normal distributions are assumed, but different distributions are present - such as skew, heavy-tailed, and short-tailed distributions - the percentages of nonconforming parts are significantly different than the computed PCIs indicate. Data arising from nonnormal distributions can sometimes be transformed to conform to the normality assumption and the PCI's computed for the transformed data. In this paper, the effect of the transformation on the estimate of nonconforming parts is examined for three examples of nonnormal distributions - gamma, lognormal, and Weibull. The results of this experimental analysis suggest that data transformation can be useful for estimating an interval for C_pk values and the number of nonconforming parts.     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Variability for oil and fatty acid composition in castorbean varieties

Thirty-six castorbean varieties were surveyed for oil and fatty acid composition, in order to determine variability of these seed compounds. A large variability of seed oil percentage was observed, ranging from 39.6 to 59.5%. Concerning the fatty acids, little variability was observed for ricinoleic acid, which was the most abundant in the oil, ranging from 83.65 to 90.00%. The other fatty acids appeared in small concentrations and showed a small range: 0.87 to 2.35, 0.68 to 1.84, 2.96 to 5.64, 3.19 to 5.98, and 0.34 to 0.91%, for palmitic, stearic, oleic, linoleic, and linolenic acid, respectively. Non-significant correlations were observed between fatty acids and seed oil percentage. However, significant correlations were observed among fatty acid concentrations: positive and negative ones. These significant correlations could be associated with the biosynthetic pathways of the fatty acids, which are not fully elucidated. They suggest, however, that selection for a particular fatty acid will tend to increase those positively correlated, and decrease those negative ones. Selection and plant breeding techniques could then be applied to modify the oil content of the castorbean seeds, considering the variability observed. For the fatty acid composition, however, the variability was not large enough to make substantial changes in their concentrations by selection procedures. More varieties should be surveyed to find out if such variability is available.     

3D Semantic Scene Completion: A Survey

International Journal of Computer Vision - Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years...     

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.