Failure modes of closed-cell polyurethane foams

The failure modes of closed-cell polyurethane foams were studied by applying the elliptic paraboloid failure surface criterion. A series of three polyurethane rigid foams (PUR-foams) were examined presenting different amounts of porosity from a highly porous material having a low density of 64 kg/m³ to a compact one with a density of 192 kg/m³. All these PUR-foams were of the same batch of material presenting a cell-wall density ₅=1200 kg/m³. Samples were tested in simple tension and compression along the three principal axes of anisotropy of the materials. It was shown that all three types of foams may be closely represented by transversely isotropic materials.The elliptic paraboloid failure surfaces (EPFS) for these three materials were defined from the six values of principal failure stresses in tension and compression. It was shown that the theoretically plotted paraboloid surfaces along all their principal-plane intersections were in good agreement with experiments.Since cellular materials collapse, either under elastic buckling in the compressive octant of the principal stress space, or under fast brittle fracture in the tensile octant, it was shown that the elliptic paraboloid failure surface is truncated by the intersection of the EPFS and an ellipsoid whose position and dimensions are interrelated with those of the EPFS. Again, experimental evidence with elastic buckling of foams corroborated the results of this theory.An important feature for the failure behavior of the foams was derived by this study according to which the foamed materials change mode of failure from a compression strong to a tension strong mode as their porosity is increased. In between they pass through a quasi-isotropic state.     

Thermodynamic study of polymer–solvent systems by reversed-flow gas chromatography

Reversed-flow gas chromatography, a new method for studying heterogeneous catalysis, diffusion, adsorption, evaporation, and other related phenomena, is now applied to the determination of activity of the solvent in a polymer–solvent system in the whole range of polymer concentration, as well as for the calculation of excess partial molar thermodynamic functions of mixing. The systems used were polystyrene in cyclohexane, polyvinylacetate in cyclohexanone and polyvinylpyrrolidone in methanol. In the first system, which was studied in detail, weight fraction activity coefficients (a₁/w₁) at several temperatures (333.8–348.1 K) and various polymer concentrations (0.020–0.131 g/g) were determined. From the latter parameters weight fraction activity coefficients at infinite dilution (a₁/w₁)^∞ were computed. Flory–Huggins interaction parameters x and solubility parameters δ₂ were also determined. Finally, the thermodynamic parameters found by the present method are compared with those determined by other techniques or calculated theoretically.     

Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques

Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.     

Blue-green algae as a source of protein

Anabaena flos-aquae, an N-fixing and photosynthesising blue-green alga, was grown in defined liquid media. Although this organism grew well in autotrophic solution, it rendered a higher yield in a medium containing 1% glucose. A good procedure for extracting the protein from the cells was heating them in 3 n HCl at 95°C for 10 min, adjusting the pH to 11 with 3n NaOH after cooling, letting them stand at room temperature for 1h, centrifuging out the debris and exposing the supernatant to 15,000 international lux of fluorescent lamp light for 10h. The treated solution contained 80% of the nitrogen of the cells and it had a pale yellow colour. The protein of the solution was further fractionated into a pH-4-insoluble fraction and an acetone-insoluble fraction. Both fractions were easily digested by pepsin in vitro and were deficient in sulphur-containing amino acids. The amino acid scores of the fractions were 74 and 60, respectively.     

Data mining for agent reasoning: A synergy for training intelligent agents

The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent technology can indeed be used in order to solve DM problems. Looking into the opposite direction though, knowledge extracted through DM has not yet been exploited on MASs. The inductive nature of DM imposes logic limitations and hinders the application of the extracted knowledge on such kind of deductive systems. This problem can be overcome, however, when certain conditions are satisfied a priori. In this paper, we present an approach that takes the relevant limitations and considerations into account and provides a gateway on the way DM techniques can be employed in order to augment agent intelligence. This work demonstrates how the extracted knowledge can be used for the formulation initially, and the improvement, in the long run, of agent reasoning.     

In vitro hydrolysis of plant protein concentrate with papain

A method was tested for measuring in vitro papain proteolysis of extracted plant protein to products soluble in trichloroacetic acid. The effects of incubation time, temperature and enzyme concentration were examined. Maximum proteolysis (82.9%) was obtained at 70°C for 24 h and a ratio of papain/protein substrate of approximately 1:10.     

倒装芯片结构中不流动底部填充工艺参数

介绍了倒装芯片贴装工艺中焊剂涂布技术的工艺控制、芯片贴装及回流过程的条件控制。讨论了贴装工艺中焊剂材料的热远配参数和改进型填补材料取代传统填补剂的可行性。     

An automated two-step NDVI-based method for the production of low-cost historical burned area map records over large areas

The recognition and understanding of long-term fire-related processes and patterns, such as the possible connection between the increased frequency of wildfires and global warming, requires the study of historical data records. In this study, a methodology was proposed for the automated production of long historical burned area map records over large-scale regions. The methodology was based on remotely sensed, high temporal resolution, normalized difference vegetation index (NDVI) data that could be easily acquired at medium or low spatial resolution. The proposed methodology was used to map the burned areas of the wildfires that occurred over the Peloponnese peninsula, Greece, during the summer of 2007. The method was built upon the NDVI data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT)-VEGETATION. The higher spatial resolution data of MODIS resulted in higher burned area user accuracy (91.10%) and kappa (0.85) values than the respective ones for VEGETATION (79.29% and 0.77). The majority of classification errors were located along the perimeter of the burned areas and were mainly attributed to spatial resolution limitations of the remotely sensed data. The commission errors located away from the fire perimeter were primarily attributed to topographically shaded areas and land-cover types spectrally similar to burned areas. The omission errors resulted primarily from the small size and elongated shape of remote burned areas. Owing to their geometry, they have a high proportion of mixed pixels, whose spectral properties failed to meet the strict set of criteria for core fire pixels. The benefits of the proposed methodology are maximized when applied to data of the highest available spatial resolution, such as those collected by MODIS and the Project for On-Board Autonomy – Vegetation (PROBA-V) and when land-cover types spectrally similar to burned areas are masked prior to its application.     

Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks

Learning classifier systems (LCS) are machine learning systems designed to work for both multi-step and single-step decision tasks. The latter case presents an interesting challenge for such algorithms, especially when they are applied to real-world data mining (DM) problems. The present investigation departs from the popular approach of applying accuracy-based LCS to single-step classification and aims to uncover the potential of strength-based LCS in such tasks. Although the latter family of algorithms have often been associated with poor generalization and performance, we aim at alleviating these problems by defining appropriate extensions to the traditional strength-based LCS framework. These extensions are detailed and their effect on system performance is studied through the application of the proposed algorithm on a set of artificial problems, designed to challenge its scalability and generalization abilities. The comparison of the proposed algorithm with UCS, its state-of-the-art accuracy-based counterpart, emphasizes the effects of our extended strength-based approach and validates its competitiveness in multi-class problems with various class distributions. Overall, our work presents an investigation of strength-based LCS in the domain of supervised classification. Our extensive analysis of the learning dynamics involved in these systems provides proof of their potential as real-world DM tools, inducing tractable rule-based classification models, even in the presence of severe class imbalances.