Green chemistry: microwave-assisted facile synthesis of 6-imino-1,3,4-thiadiazenes from reaction of thiocarbohydrazones with malononitrile dimer

The reactions of thiocarbohydrazones with malononitrile dimer under microwave irradiation are reported. These reactions give 6-imino-1,3,4-thiadiazines in good yields. The structures of products were elucidated by MS, IR and NMR spectra together with elemental analyses. The reaction mechanism was discussed.     

Template‐Based Synthesis of Aluminum Nitride Hollow Nanofibers Via Plasma‐Enhanced Atomic Layer Deposition

Aluminum nitride (AlN) hollow nanofibers were synthesized via plasma‐enhanced atomic layer deposition using sacrificial electrospun polymeric nanofiber templates having different average fiber diameters (~70, ~330, and ~740 nm). Depositions were carried out at 200°C using trimethylaluminum and ammonia precursors. AlN‐coated nanofibers were calcined subsequently at 500°C for 2 h to remove the sacrificial polymeric nanofiber template. SEM studies have shown that there is a critical wall thickness value depending on the template's average fiber diameter for AlN hollow nanofibers to preserve their shapes after the template has been removed by calcination. Best morphologies were observed for AlN hollow nanofibers prepared by depositing 800 cycles (corresponding to ~69 nm) on nanofiber templates having ~330 nm average fiber diameter. TEM images indicated uniform wall thicknesses of ~65 nm along the fiber axes for samples prepared using templates having ~70 and ~330 nm average fiber diameters. Synthesized AlN hollow nanofibers were polycrystalline with a hexagonal crystal structure as determined by high‐resolution TEM and selected area electron diffraction. Chemical compositions of coated and calcined samples were studied using X‐ray photoelectron spectroscopy (XPS). High‐resolution XPS spectra confirmed the presence of AlN.     

Catalytic combustion of carbon tetrachloride

The catalytic combustion of carbon tetrachloride (CCl4) by metal oxide catalysts coated on the monolith support was investigated. The prepared catalysts were tested at temperatures between 300 and 800 degrees C and at varying gas hourly space velocity (GHSV) values with an excess air ratio of 3100%. The catalyst, whose composition was 18% Cr2O3, 2% Ce2O3 and 80% gamma-Al2O3, was found to almost completely oxidize CCl4. The operating conditions proposed are 5702 h(-1) for GHSV, 3100% excess air and a temperature slightly higher than 800 degrees C. The reaction rate expression was found to be independent of oxygen partial pressure but strongly dependent on CCl4 partial pressure.     

Effect of Fiber-Reinforced Polymer Wraps on Corrosion Activity and Concrete Cracking in Chloride-Contaminated Concrete Cylinders

This paper presents the results of an experimental study designed to investigate the effect of fiber-reinforced polymer (FRP) wraps on corrosion activity and concrete cracking in chloride-contaminated concrete cylinders. Thirty-five concrete cylinders, each having 102?mm diameter and 204?mm height, concentrically reinforced with one steel reinforcing bar, were subjected to accelerated corrosion exposure for 80?days. Test parameters included level of applied potential, presence of FRP wraps, and bar diameter. The corresponding current and concrete expansion were continuously monitored throughout the corrosion exposure. At the end of the test, the steel bars were extracted, cleaned of rust, and weighed to determine the actual steel mass loss. The results showed that, for the same applied fixed potential, FRP wraps effectively reduced the corresponding current, the concrete expansion, and the steel mass loss. For the same applied potential, the current density increased as the bar diameter decreased. For the same corrosion depth, the circumferential expansion of the cylinder caused by corrosion decreased as the concrete cover-to-bar diameter ratio (c/d) increased.     

Web-Services Environment for Collaborative Management of Product Life-Cycle Costs

There is an increasing need for effective tools for managing life cycle costs (LCC) of civil products. This paper presents a web-based semantic system for managing products’ life cycle costs. The basic architecture of the proposed system represents costs as a hierarchy of cost elements. Each cost element has a dollar value that could be deterministic, probabilistic, or fuzzy. Several factors (also represented in hierarchies) can have a set of impacts on the values of these costs. Through the analysis of different impact possibilities and probabilities, a decision maker can study various alternative scenarios and define the optimum set of costs and their values. A set of web services are used to capture cost elements, factors, and their impacts. The semantic nature of the system allows for it to be an integral part of a corporate memory system, where decision makers will be able to document and access lessons learned about LCC optimization. Finally, the proposed system includes a framework for a LCC-centered supply chain, where all stakeholders can collaborate in optimizing product LCC.     

Segmentation of ultrasound images by using a hybrid neural network

A hybrid neural network is presented for the segmentation of ultrasound images.

Feature vectors are formed by the discrete cosine transform of pixel intensities in region of interest (ROI). The elements and the dimension of the feature vectors are determined by considering only two parameters: The amount of ignored coefficients, and the dimension of the ROI.

First-layer-nodes of the proposed hybrid network represent hyperspheres (HSs) in the feature space. Feature space is partitioned by intersecting these HSs to represent the distribution of classes. The locations and radii of the HSs are found by the genetic algorithms.

Restricted Coulomb energy (RCE) network, modified RCE network, multi-layer perceptron and the proposed hybrid neural network are examined comparatively for the segmentation of ultrasound images.     

A multilayer incremental neural network architecture for classification

A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.     

Parasitic-aware analytical modeling of integrated CMOS inductively degenerated narrow-band low noise amplifiers

In this paper, we present an analytical modeling methodology for fully integrated inductively-degenerated CMOS narrow-band cascode Low Noise Amplifiers (LNA) that captures short channel transistor effects to enable rapid design space exploration in current and future process technologies. The modeling methodology captures the impact of parasitics on passive components, ESD-protection structures, and devices to accurately predict both impedance matching and noise figure. Our modeling is suitable for numerical optimization and fully automated synthesis for LNAs. The results indicate that the methodology models ESD-protected LNA circuits with 47.9% better accuracy in noise figure when compared with several current analytical modeling techniques with four orders of magnitude improvement in simulation time over circuit-level simulation. Given its speed and accuracy, the analytical modeling methodology is well-suited for design space exploration.