Soluble and conductive polypyrrole copolymers containing silicone tegomers

Soluble and processable conductive copolymers of silicone tegomers and pyrrole were developed. This was easily accomplished by the oxidative polymerization of pyrrole monomer by Ce(IV) salt in the presence of silicone tegomers with hydroxyl chain ends. The resulting copolymers were soluble in dimethylformamide. The products were characterized by Fourier transform infrared, ¹H‐NMR, and four‐point probe conductivity, and their surface properties were investigated with contact‐angle measurements. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2896–2901, 2003     

Soft handoff algorithms for CDMA cellular networks

This paper discusses the design of soft handoff algorithms for cellular communication systems. The handoff process is modeled as a hybrid system and handoff design is cast as an optimization problem based on such a model. Performance is evaluated in terms of call quality, average number of active base stations, average number of active set updates, and average amount of interference. A soft handoff algorithm, which achieves a tradeoff between these performance criteria, is obtained using principles of dynamic programming. One key feature of the algorithm is that it incorporates the effects of mobility and shadow fading in the handoff decision. Different diversity combining schemes are considered including selective combining, equal gain combining (EGC), and various optimized combining (OC) methods in the soft handoff mode. For EGC and OC, Wilkinson's and Schwartz and Yeh's methods are used to compute the statistics for the power sum of the signals. Simulation results indicate that the performance of the handoff algorithm is a function of the different combining schemes and of the different methods used to compute the statistics of the power sum. Moreover, it is observed that interference cancellation is important in order for the algorithm to be viable for cellular systems which experience interference due to using nonorthogonal multiple access.     

Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

Behaviour of sand–clay mixtures for road pavement subgrade

Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey

This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R²=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate.     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Compression testing of a sintered Ti6Al4V powder compact for biomedical applications

In this study, the compression deformation behavior of a Ti6Al4V powder compact, prepared by the sintering of cold compacted atomized spherical particles (100–200 μm) and containing 36–38% porosity, was investigated at quasi-static (1.6×10⁻³–1.6×10⁻¹ s⁻¹) and high strain rates (300 and 900 s⁻¹) using, respectively, conventional mechanical testing and Split Hopkinson Pressure Bar techniques. Microscopic studies of as-received powder and sintered powder compact showed that sintering at high temperature (1200 °C) and subsequent slow rate of cooling in the furnace changed the microstructure of powder from the acicular alpha () to the Widmanstätten (+β) microstructure. In compression testing, at both quasi-static and high strain rates, the compact failed via shear bands formed along the diagonal axis, 45° to the loading direction. Increasing the strain rate was found to increase both the flow stress and compressive strength of the compact but it did not affect the critical strain for shear localization. Microscopic analyses of failed samples and deformed but not failed samples of the compact further showed that fracture occurred in a ductile (dimpled) mode consisting of void initiation and growth in phase and/or at the /β interface and macrocracking by void coalescence in the interparticle bond region.     

Transferrin-Decorated Niosomes with Integrated InP/ZnS Quantum Dots and Magnetic Iron Oxide Nanoparticles: Dual Targeting and Imaging of Glioma

The development of multifunctional nanoscale systems that can mediate efficient tumor targeting, together with high cellular internalization, is crucial for the diagnosis of glioma. The combination of imaging agents into one platform provides dual imaging and allows further surface modification with targeting ligands for specific glioma detection. Herein, transferrin (Tf)-decorated niosomes with integrated magnetic iron oxide nanoparticles (MIONs) and quantum dots (QDs) were formulated (PEGNIO/QDs/MIONs/Tf) for efficient imaging of glioma, supported by magnetic and active targeting. Transmission electron microscopy confirmed the complete co-encapsulation of MIONs and QDs in the niosomes. Flow cytometry analysis demonstrated enhanced cellular uptake of the niosomal formulation by glioma cells. In vitro imaging studies showed that PEGNIO/QDs/MIONs/Tf produces an obvious negative-contrast enhancement effect on glioma cells by magnetic resonance imaging (MRI) and also improved fluorescence intensity under fluorescence microscopy. This novel platform represents the first niosome-based system which combines magnetic nanoparticles and QDs, and has application potential in dual-targeted imaging of glioma.