Science and technology in the region: The output of regional science and technology, its strengths and its leading institutions

Summary We operationalize scientific output in a region by means of the number of articles (as in the SciSearch database) per year and technology output by means of the number of patent applications (as in the database of the European Patent Office) per priority year. All informetric analyses were done using the DIALOG online-system. The main research questions are the following: Which scientific and technological fields or topics are most influent within a region and which institutions or companies are mainly publishing articles or holding patents? Do the distributions of regional science and technology fields and of publishing institutions follow the well-known informetric function? Are there - as it is expected - only few fields and few institutions which dominate the region? Is there a connection between the economic power of a region and the regional publication and patent output? Examples studied in detail are seven German regions: Aachen, Düsseldorf, Hamburg, Köln (Cologne), Leipzig - Halle - Dessau, München (Munich), and Stuttgart. Three different indicators were used, science and technology attraction of a region (number of scientific articles and patents), science and technology intensity (articles and patents per 1,000 inhabitants), and science and technology density (articles and patents per 1 billion EURO gross value added). Top region concerning both attraction and intensity is Munich, concerning density it is Aachen.     

Various approaches for constructing an ensemble of metamodels using local measures

Metamodels are approximate mathematical models used as surrogates for computationally expensive simulations. Since metamodels are widely used in design space exploration and optimization, there is growing interest in developing techniques to enhance their accuracy. It has been shown that the accuracy of metamodel predictions can be increased by combining individual metamodels in the form of an ensemble. Several efforts were focused on determining the contribution (or weight factor) of a metamodel in the ensemble using global error measures. In addition, prediction variance is also used as a local error measure to determine the weight factors. This paper investigates the efficiency of using local error measures, and also presents the use of the pointwise cross validation error as a local error measure as an alternative to using prediction variance. The effectiveness of ensemble models are tested on several problems with varying dimensionality: five mathematical benchmark problems, two structural mechanics problems and an automobile crash problem. It is found that the spatial ensemble models show better performances than the global ensemble for the low-dimensional problems, while the global ensemble is a more accurate model than the spatial ensembles for the high-dimensional problems. Ensembles based on pointwise cross validation error and prediction variance provide similar accuracy. The ensemble models based on local measures reduce cross validation errors drastically, but their performances are not that impressive in reducing the error evaluated at random test points, because the pointwise cross validation error is not a good surrogate for the error at a point.     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

Unveiling the presence of mixed oxidation states of Europium in Li7+δEuxLa3−δZr2−δO12−δ garnet and its impact on the Li-ion conductivity

Recently, lanthanides have been employed by researchers to examine their impact on the structure and properties of Li₇La₃Zr₂O₁₂ (LLZO) garnets. In this regard, we developed Europium oxide (Eu₂O₃) doped LLZO (Li_7+δEu_xLa_3−δZr_2−δO_12−δ) solid electrolyte which demonstrates a cubic phase with the symmetry of Iad (No.230) at room temperature. In this investigation, different concentrations of Eu ranging from 0.1 to 0.6 atoms per formula unit (pfu) were doped into Li₇La₃Zr₂O₁₂ to evaluate the impact of Eu on the stability of the cubic phase and thereby the ionic conductivity. The results unveiled that upon doping Eu³⁺ ions, the Eu²⁺ state is also formed and is then self-doped into the structure in which Rietveld refinement coupled with XPS, EPR, and solid-state NMR suggests that Eu³⁺ ions most probably partially occupy Zr⁴⁺ (16a) site, the Eu²⁺ ions occupy La³⁺ (24d) site, and the Li⁺ ions occupy two different sites (24d and 96h). It was further found that such a site preference induces distortion at LaO₈ polyhedrons opening up the neck for Li-ions diffusion, thereby enhancing the ionic conductivity. Moreover, it was revealed that Li-ions probably hop from 96h to 24d and then to 96h site to generate the Li-ion movement. Overall, by introducing Eu ions into the LLZO structure, an enhanced bulk ionic conductivity of 0.30 × 10⁻³ S/cm at 298 K with a minimum electronic conductivity of 2.547 × 10⁻⁹ S/cm at 298 K was achieved.     

Preparation and characterization of multilayer anti-reflective coatings via sol-gel process

Single layer and multilayer films consisting of SnO₂, Ta₂O₅, SiO₂, TiO₂, indium tin oxide (ITO) and antimony tin oxide (ATO) have been prepared by sol-gel dip coating technique. All of the multilayer films contained a SiO₂ top layer, which was composed of SiO₂ nanoparticles. The other films had polymeric character. Obtained films were characterized by ellipsometry, XRD, AFM and SEM. Light transmittance values of the films were compared. Films other than SiO₂ and Ta₂O₅ were found to have crystalline structure. Thickness values of the films were in the range of 30–115 nm and roughness values were in 1.2–23 nm range. Single layer porous silica provided 95% light transmittance, whereas ITO-TiO₂-SiO₂ multilayer film provided a light transmittance of 97.2%.     

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.     

Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: A case of hybrids

This paper aims to determine the factors that have an impact on the consumers’ willingness to pay a premium for hybrid automobiles in Turkey. A web-based random survey was conducted in different regions of Turkey. A questionnaire was administered to 1983 participants in January–March of 2009. The questionnaire was prepared by taking the issues raised in various sources into account. An ordered Probit model was used to meet the objective. Results show that variables such as income, gender, education, concerns about global warming, number of automobiles, importance of automobile performance, risk preference, attitude toward the alternative energy sources have an impact on the consumers’ willingness to pay a premium for hybrids. Findings suggest that consumers who have high income, higher educational level, and concerns about the global warming are more likely to pay a premium for hybrids. This study is expected to make important contributions to the current literature related to the consumers’ willingness to pay for hybrids by providing a research study from a developing country’s perspective. Results of this study also make important contributions to the policy and decision makers, environmental groups and automotive industry.     

Investigation of water gas-shift activity of Pt–MOx–CeO2/Al2O3 (M = K,Ni, Co) using modular artificial neural networks

The water gas shift activity of promoted Pt–CeO₂/Al₂O₃ catalysts were investigated in this work. The catalysts were prepared by incipient to wetness impregnation and tested using a microflow reaction system. It was found that K has beneficial effects under product-containing feed compositions while Co and Ni promoters worsen catalyst performance. The reaction temperature and feed H₂O/CO ratio positively affect the catalytic activity, whereas CO₂ and H₂ addition to the feed decreases CO conversion, as expected. The experimental results were also modeled using modular neural networks, at which the catalyst preparation and operational (reaction) variables were used together in the same network because they are interacting but processed differently because they are dissimilar in their form (i.e. categorical versus continuous) and their effects on catalytic activity. It was concluded that the effects of catalyst preparation and operational variables and their relative importance could be comprehended more accurately by using this approach, which may be also employed in other similar systems.     

Silicon Nanowires Driving Miniaturization of Microelectromechanical Systems Physical Sensors: A Review

The miniaturization of microelectromechanical systems (MEMS) physical sensors is driven by global connectivity needs and is closely linked to emerging digital technologies and the Internet of Things. Strong technical advantages of miniaturization such as improved sensitivity, functionality, and power consumption are accompanied by significant economic benefits due to semiconductor manufacturing. Hence, the trend to produce smaller sensors and their driving force resemble very much those of the miniaturization of integrated circuits (ICs) as described by Moore's law. In this respect, with its IC-, and MEMS-compatibility, and scalability, the silicon nanowire is frequently employed in frontier research as the sensor building block replacing conventional sensors. The integration of the silicon nanowire with MEMS has thus generated a multiscale hybrid architecture, where the silicon nanowire serves as the piezoresistive transducer and MEMS provide an interface with external forces, such as inertial or magnetic. This approach has been reported for almost all physical sensor types over the last decade. These sensors are reviewed here with detailed classification. In each case, associated technological challenges and comparisons with conventional counterparts are provided. Future directions and opportunities are highlighted.