Parallel, self-organizing, hierarchical neural networks. II

For pt.I see IEEE Trans. Neural Networks, vol.1, p.167-78 (1990). Parallel, self-organizing, hierarchical neural networks (PSHNNs) involve a number of stages with error detection at the end of each stage, i.e., rejection of error-causing vectors, which are then fed into the next stage after a nonlinear transformation. The stages operate in parallel during testing. Statistical properties and the mechanisms of vector rejection of the PSHNN are discussed in comparison to the maximum likelihood method and the backpropagation network. The PSHNN is highly fault tolerant and robust against errors in the weight values due to the adjustment of the error detection bounds to compensate errors in the weight values. These properties are exploited to develop architectures for programmable implementations in which the programmable parts are reduced to on-off or bipolar switching operations for bulk computations and attenuators for pointwise operations     

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.     

Diffractive-phase-element design that implements several optical functions

A scheme for the design of diffractive phase elements (DPE's) that integrates several optical functions is presented in a consistent sense based on the general theory of amplitude-phase retrieval and the Yang-Gu algorithm [Appl. Opt. 33, 209 (1994)]. We extend the original Yang-Gu algorithm to treat a system illuminated by a beam of incident light whose components are at different wavelengths, and a set of equations for determining the phase distribution of the DPE is derived. The profile of a surface-relief DPE can be designed with an iterative algorithm. Numerical simulations are carried out for the design of one-dimensional DPE's capable of both demultiplexing different wavelength components and focusing each partial wave at predetermined positions. The influence of the extension of sampling points in the DPE's from ideal geometric points to physical spots on design results is also investigated. The numerical simulation results show that the new algorithm can be used successfully to design the desired DPE's. It is therefore expected to be useful in the design of DPE's for micro-optical systems.     

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.     

Image‐Based Edge Bundles: Simplified Visualization of Large Graphs

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graph's structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.     

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.     

Sink placement on a 3D terrain for border surveillance in wireless sensor networks

With their distributed nature and redundant operation capability, wireless sensor networks are very suitable for border surveillance scenarios that track intruders trying to breach to a safe side. In such scenarios, keeping the operation going on for as long as possible is the most important aspect of the network. We propose that by placing sink at a carefully selected coordinate will results in a longer living network. We also place restrictions on the candidate locations so that the sensing quality of the network is above a useful predetermined value and the sink is placed in a relatively safe location to avoid destruction. In order to find the suitable coordinates we propose a modified lifetime metric which takes quality and safety measures into account. We also propose a genetic algorithm which uses a discrete event simulator-in-the-loop over a three dimensional terrain to find locations for the sink that fits the given quality and safety restrictions. Using a three dimensional underlying terrain makes the proposed approach more realistic. The results obtained for various sensor network scenarios indicate that the proposed algorithm can find locations that increase the lifetime by also considering the sensing quality and safety.     

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.