A representative and comprehensive review of the electrical and thermal properties of polymer composites with carbon nanotube and other nanoparticle fillers

In this review we present the results of our literature investigation into the electrical and thermal properties of carbon nanotube polymer composites. A short selection of data relating to conductive polymer composites with various fillers is provided for comparison. The effects of filler properties such as type and size, the use of hybrid fillers, fabrication methods for polymer composites and the importance of the modeling of the electronic and thermal transport mechanisms are discussed, as are more general factors influencing the properties of these composites. This review represents a comprehensive survey and constructive study and should serve as a useful reference tool for industrial and academic researchers working in this field. © 2017 Society of Chemical Industry     

Recovery of manganese from iron containing sulfate solutions by precipitation

Worldwide consumption of manganese is increasing, nevertheless huge amounts of manganese from hydrometallurgical processes still end up as waste since the recovery of manganese from multi-metal solutions at low concentrations is not considered feasible. Poor iron control typically prevents the production of high purity manganese. This work studies a number of precipitants in manganese recovery and iron separation from sulfate solutions. The precipitation reagents were compared from the point of view of selectivity and economy. Carbonate precipitation is a fast and effective method for the recovery of manganese from bulk solutions. Subsequent leaching of metal carbonate is also easier and consumes less acid than, for example, hydroxide or sulfide precipitates. In order to avoid gypsum formation, soda ash should be used instead of limestone. It was found that efficient selective iron removal from MnSO₄ solutions is achieved with combined O₂ or air oxidation and CaCO₃ precipitation at pH >5.8 and at a redox potential of >200 mV. Effective mixing and sufficient retention time are essential to make the method technically efficient and economically feasible.     

Effects of Ospemifene on Drug Metabolism Mediated by Cytochrome P450 Enzymes in Humans in Vitro and in Vivo

The objective of these investigations was to determine the possible effects of the novel selective estrogen receptor modulator, ospemifene, on cytochrome P450 (CYP)-mediated drug metabolism. Ospemifene underwent testing for possible effects on CYP enzyme activity in human liver microsomes and in isolated human hepatocytes. Based on the results obtained in vitro, three Phase 1 crossover pharmacokinetic studies were conducted in healthy postmenopausal women to assess the in vivo effects of ospemifene on CYP-mediated drug metabolism. Ospemifene and its main metabolites 4-hydroxyospemifene and 4′-hydroxyospemifene weakly inhibited a number of CYPs (CYP2B6, CYP2C9, CYP2C19, CYP2C8, and CYP2D6) in vitro. However, only CYP2C9 activity was inhibited by 4-hydroxyospemifene at clinically relevant concentrations. Induction of CYPs by ospemifene in cultured human hepatocytes was 2.4-fold or less. The in vivo studies showed that ospemifene did not have significant effects on the areas under the plasma concentration-time curves of the tested CYP substrates warfarin (CYP2C9), bupropion (CYP2B6) and omeprazole (CYP2C19), demonstrating that pretreatment with ospemifene did not alter their metabolism. Therefore, the risk that ospemifene will affect the pharmacokinetics of drugs that are substrates for CYP enzymes is low.     

Silicate Release from Sand-Manipulated Sediment Cores: Biogenic or Adsorbed Si?

The influence of an addition of either sand and/or spring-bloom algae on the efflux of nutrients from intact sediment cores from the Baltic Sea was studied in a flow-through experiment. The addition of sand significantly increased the efflux of silicon (Si) from sediment, but the algal addition did not. The effects on phosphorus (P) were not as clear, and fluxes of nitrogen (NH₄ and NO_2?+?3) remained relatively unaffected by the additions. The small effect of the algal addition was caused by the short time-period covered by the experiment and possibly by adsorption of released Si by the sediment. A follow-up bottle experiment showed that despite the apparently lower content of easily available Si and biogenic silica, BSi, in the sand, the sand-induced Si efflux was caused by release of Si from the sand itself, rather than by indirectly increasing the dissolution of BSi present in the sediment.     

A framework for generating tunable test functions for multimodal optimization

Multimodal function optimization, where the aim is to locate more than one solution, has attracted growing interest especially in the evolutionary computing research community. To evaluate experimentally the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and various levels of difficulty. The available selection of multimodal test problems is, however, rather limited and no general framework exists. This paper describes an attempt to construct a software framework which includes a variety of easily tunable test functions. The aim is to provide a general and easily expandable environment for testing different methods of multimodal optimization. Several function families with different characteristics are included. The framework implements new parameterizable function families for generating desired landscapes. Additionally the framework implements a selection of well known test functions from the literature, which can be rotated and stretched. The software module can easily be imported to any optimization algorithm implementation compatible with the C programming language. As an application example, 8 optimization approaches are compared by their ability to locate several global optima over a set of 16 functions with different properties generated by the proposed module. The effects of function regularity, dimensionality and number of local optima on the performance of different algorithms are studied.     

Water hydraulic manipulator for fail safe and fault tolerant remote handling operations at ITER

Department of Intelligent Hydraulics and Automation (IHA) of Tampere University of Technology has been involved in the European Fusion program since 1994 within the ITER reactor maintenance activities. In this paper we discuss the design and development of a six degrees of freedom water hydraulic manipulator with a force feedback for teleoperation tasks. The manipulator is planned to be delivered to Divertor Test Platform 2 (DTP2) during year 2008. The paper also discusses the possibility to improve the fail safe and redundant operation of the manipulator. During the design of the water hydraulic manipulator, special provisions have been made in order to meet the safety requirements such as servo valve block for redundant operation and safety vane brakes for fail safe operation.     

Risk assessment of a timber frame building by using CRISP simulation

The fire risk assessment model CRISP2 was applied to a 4‐storey apartment building. The case building was an actual 4‐storey timber‐framed building. Partly predetermined design alternatives were used in sequential simulations. CRISP2 cannot take into account the frame‐material of the building in a satisfactory way, because the wall thickness, structural fire resistance or lining materials in the fire room cannot be modelled. It was found that adding smoke alarms almost halved the risk level. The risk levels 1–2×10^?5 obtained are not far from comparable levels of fire death statistics from Finland, the United Kingdom, Sweden and Norway. Copyright © 2002 John Wiley & Sons, Ltd.     

多层帘式涂布和一种新型压光技术

帘式涂布是一种低冲击预计量涂布方式,可用于纸和纸板的涂布、帘式涂布技术所产生的涂层为仿形涂层,因而能提供良好的纤维覆盖。在多层帘式涂布中,可以同时进行多层涂布。与单层帘式涂布相比,多层帘式涂布具有许多优点：投资成本更低、设计不同涂层性质更具灵活性、具有更大的涂布操作空间及涂层更薄等,     

Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets

In this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the training phase of the classifier. The earlier version of differential evolution classifier that applied individually optimized distance measure for each new data set to be classified is generalized here so, that instead of optimizing a single distance measure for the given data set, we take a further step by proposing an approach where distance measures are optimized individually for each feature of the data set to be classified. In particular, distance measures for each feature are selected optimally from a predefined pool of alternative distance measures. The optimal distance measures are determined by differential evolution algorithm, which is also determining the optimal values for all free parameters of the selected distance measures in parallel. After determining the optimal distance measures for each feature together with their optimal parameters, we combine all featurewisely determined distance measures to form a single total distance measure, that is to be applied for the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; A sample belongs to the class represented by the nearest prototype vector when measured with the above referred optimized total distance measure. During the training process the differential evolution algorithm determines optimally the class vectors, selects optimal distance metrics for each data feature, and determines the optimal values for the free parameters of each selected distance measure. Based on experimental results with nine well known classification benchmark data sets, the proposed approach yield a statistically significant improvement to the classification accuracy of differential evolution classifier.