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.     

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.     

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.     

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.     

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.     

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。     

Investigation of Efficiency Deterioration Causes in Process Centrifugal Compressor Operation

The high operating efficiency of centrifugal compressor is a basic requirement to maintain the gas productivity and machine availability. However, there are several factors influencing the stage efficiency in the operating environment leading to a deterioration in the compressor performance in addition to its impact on the mechanical integrity of the internal components. These variables can be classified into three main groups which are suction parameters variation, flow profile distortion, and compressor component damage. The determination of the root cause will help for a proper maintenance plan and to reduce the machine downtime. Hence, this paper will introduce a new approach to identify the inefficient compressor operation causes based on the available operation data. The investigated case is a three-stage gas transport centrifugal compressor driven by 2.9 MW two-shaft gas turbine. The outcomes from the conducted optimisation are compared with the measured discharge parameters and the findings from the internal inspection observation to emphasize the outcomes from the derived approach.     

Sparse episode identification in environmental datasets: The case of air quality assessment

Sparse episode identification in environmental datasets is not only a multi-faceted and computationally challenging problem for machine learning algorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services, poses the need for robust, efficient and, more importantly, understandable forecasting models. Additionally, these models need to provide decision-makers with “summarized” and valuable knowledge, that has to be subjected to a thorough evaluation procedure, easily translated to services and/or actions in actual decision making situations, and integratable with existing Environmental Management Systems (EMSs).On this basis, our current study investigates the potential of various machine learning algorithms as tools for air quality (AQ) episode forecasting and assesses them – given the corresponding domain-specific requirements – using an evaluation procedure, tailored to the task at hand. Among the algorithms employed in the experimental phase, our main focus is on ZCS-DM, an evolutionary rule-induction algorithm specifically designed to tackle this class of problems – that is classification problems with skewed class distributions, where cost-sensitive model building is required.Overall, we consider this investigation successful, in terms of its aforementioned goals and constraints: obtained experimental results reveal the potential of rule-based algorithms for urban AQ forecasting, and point towards ZCS-DM as the most suitable algorithm for the target domain, providing the best trade-off between model performance and understandability.     

The phytate and phytase of soybean tempeh

Soybeans were fermented into tempeh by Rhizopus oligosporus NRRL 2710. The phytic acid content of soybeans was reduced by about one-third as a result of this fermentation, while an equivalent amount of phosphate was released in the tempeh. The reduction of phytic acid was due to phytase elaborated by the mould of the fermentation. The pH optimum of this enzyme was 5.6 and the K_m of the phytase-phytate reaction was 0.28 × 10^?3 M.