Aral Sea; Irretrievable Loss or Irtysh Imports?

The Aral Sea has shrunk and become a large salt pan, because the water from the two rivers that used to feed the lake (Amu Darya and Syr Darya) is almost entirely used for irrigation. In this paper some possibilities to return to the original (1960) situation are studied. After discussing some of the alternatives, it is proposed to construct a canal along a more southerly route than the original Sibaral canal, starting from the Zaisan Lake along the Irtysh river. This solution requires the construction of a major tunnel through the Khrebet Tarbagataj mountain range. Thereafter, it will flow through the Balkash Lake, saving several hundred kilometers of canal construction, and discharge its water in the lower reaches of the Syr Darya. From here it will flow into Aral Sea, slowly restoring it towards its original (1960) level. Several flanking water saving measures are considered. Most of the drive to restore the Aral Sea is for ecological reasons. There may also be a serious climatic threat to avoid, although this is a matter of debate. It is found that the discharge of the major Siberian rivers into the Arctic Ocean is on the increase, and this may affect the great world ocean conveyor belt. This would have dire consequences for the climate in Western and Northern Europe. This could be avoided by diverting part of the water towards the Aral Sea. A restoration of the Aral Sea will have beneficial effects on climate, human health, fishery and ecology in general.     

The Lanchester–Betz–Joukowsky limit

The maximum efficiency of an ideal wind turbine rotor is well known as the ‘Betz limit’, named after the German scientist that formulated this maximum in 1920. In 1976, Bergey showed that the British scientist Lanchester derived the same maximum already in 1915. Betz and Lanchester were representatives of leading aerodynamic research schools in the first decades of the previous century. A study of early Russian publications on rotor aerodynamics now shows that the Russian aerodynamic school also produced the same result; its leader Joukowsky derived the maximum efficiency for an ideal wind turbine in 1920, the same year as Betz did. Consequently, in order to honour all, this ideal efficiency should be named the ‘Lanchester‐Betz‐Joukowsky limit’ in scientific writing. The well‐established and convenient name Betz limit is to be considered an easy abbreviation of this full name. Copyright © 2007 John Wiley & Sons, Ltd.     

Thermogravimetric analysis of vinylacetate-vinylbutyrate copolymers

The composition of copolymers of vinylacetate (VAc) and vinylbutyrate (VB) can be determined rapidly and satisfactorily by dynamic thermogravimetric analysis (TGA), in spite of the fact that both acetic and butyric acid are abstracted simultaneously and incompletely from their respective segments. The TGA results are compared with those obtained by gaschromatographic analysis of the reaction mixtures during copolymerization, and are within experimental error. Extension of this analysis method to other binary vinylester combinations seems to be possible.     

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide the entities into groups or clusters based on their attributes or characteristics. Clustering algorithms normally require both a method of measuring proximity between patterns and prototypes and a method for aggregating patterns. However sometimes feature vectors or patterns may not be available for objects and only the proximities between the objects are known. Even if feature vectors are available some of the features may not be numeric and it may not be possible to find a satisfactory method of aggregating patterns for the purpose of determining prototypes. Clustering of objects however can be performed on the basis of data describing the objects in terms of feature vectors or on the basis of relational data. The relational data is in terms of proximities between objects. Clustering of objects on the basis of relational data rather than individual object data is called relational clustering. The premise of this paper is that the proximities between the membership vectors, which are obtained as the objective of clustering, should be proportional to the proximities between the objects. The values of the components of the membership vector corresponding to an object are the membership degrees of the object in the various clusters. The membership vector is just a type of feature vector. Based on this premise, this paper describes another fuzzy relational clustering method for finding a fuzzy membership matrix. The method involves solving a rather challenging optimization problem, since the objective function has many local minima. This makes the use of a global optimization method such as particle swarm optimization (PSO) attractive for determining the membership matrix for the clustering. To minimize computational effort, a Bayesian stopping criterion is used in combination with a multi-start strategy for the PSO. Other relational clustering methods generally find local optimum of their objective function.     

A hybrid neural network for input that is both categorical and quantitative

The data on which a MLP (multilayer perceptron) is normally trained to approximate a continuous function may include inputs that are categorical in addition to the numeric or quantitative inputs. Examples of categorical variables are gender, race, and so on. An approach examined in this article is to train a hybrid network consisting of a MLP and an encoder with multiple output units; that is, a separate output unit for each of the various combinations of values of the categorical variables. Input to the feed forward subnetwork of the hybrid network is then restricted to truly numerical quantities. A MLP with connection matrices that multiply input values and sigmoid functions that further transform values represents a continuous mapping in all input variables. A MLP therefore requires that all inputs correspond to numeric, continuously valued variables and represents a continuous function in all input variables. A categorical variable, on the other hand, produces a discontinuous relationship between an input variable and the output. The way that this problem is often dealt with is to replace the categorical values by numeric ones and treat them as if they were continuously valued. However there is no meaningful correspondence between the continuous quantities generated this way and the original categorical values. The basic difficulty with using these variables is that they define a metric for the categories that may not be reasonable. This suggests that the categorical inputs should be segregated from the continuous inputs as explained above. Results show that the method utilizing a hybrid network and separating numerical from quantitative input, as discussed here, is quite effective. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 979–1001, 2004.     

Multi-Dimensional Scattered Ranking Methods for Geographic Information Retrieval*

Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introduce multi-dimensional scattered ranking, where textually and spatially similar documents are ranked spread in the list, instead of consecutively. The effect of this is that documents close together in the ranked list have less redundant information. We present various ranking methods of this type, efficient algorithms to implement them, and experiments to show the outcome of the methods.*This research is supported by the EU-IST Project No. IST-2001-35047 (SPIRIT).     

Preferred partitioning: the influence of coalescents on the build-up of mechanical properties in acrylic core–shell particles (I)

The partitioning of three coalescents of different polarity in different phases of multiphase acrylic particles was studied to provide a rationale for obtaining the desired performance of binders for wood coatings in terms of the ideal balance between hardness development, blocking resistance, and blushing resistance. Minimum film formation temperature- and aqueous differential scanning calorimetry-measurements on the hard phase polymer by itself showed the different extents to which both hydroplasticization and plasticization by the coalescent occur. Dynamic mechanical thermal analysis was subsequently used to visualize wet-T _g effects of three different coalescents in the hard and soft polymer phase of these multiphase acrylic particles. The results have important consequences for the formulation of such binders in applications for exterior wood coatings and coatings in general.     

Non-radiative recombination losses in polymer light-emitting diodes

We present a quantitative analysis of the loss of electroluminescence in light-emitting diodes (LEDs) based on poly[2-methoxy-5-(2′-ethylhexyloxy)-p-phenylenevinylene] (MEH-PPV) due to the combination of non-radiative trap-assisted recombination and exciton quenching at the metallic cathode. It is demonstrated that for an MEH-PPV LED the biggest efficiency loss, up to 45%, arises from extrinsic non-radiative recombination via electron traps. The loss caused by exciton quenching at the cathode proves only to be significant for devices thinner than 100 nm. Removal of electron traps by purification is expected to enhance the efficiency of polymer LEDs by more than a factor of two.