Air and oxy-fuel combustion kinetics of low rank lignites

The air and oxy-fuel combustion processes of two low-grade lignite coals were investigated by thermogravimetric analysis (TGA) method. Coals were provided from two different coal mines in the Aegean region of Turkey. Oxy-fuel combustion experiments were carried out with three different gas mixtures of 21% O₂–79% CO₂; 40% O₂–60% CO₂ and 50% O₂–50% CO₂ at 950 °C and heating rates of 10 °C/min, 20 °C/min and 40 °C/min. The kinetics of the oxy-fuel combustion of coals were studied by using four different methods namely, Coats-Redfern (model-fitting method), Friedman (FR), Flynn–Wall–Ozawa's (FWO) and Kissinger–Akahira–Sunose's (KAS) methods. The apparent activation energies of combustion process calculated by FWO method are slightly but systematically higher than that calculated by the KAS and FR methods for the oxy-fuel atmospheres. Combustion behavior of both coals in the oxy-fuel combustion environment could vary significantly, likely due to their characteristics such ash and volatile matter contents.     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

A two-dimensional material for high capacity supercapacitors: S-doped graphene

In this work, sulfur-doped graphene-coated electrodes are prepared by cyclic voltammetry in different potential ranges and different cycles (from 10 to 50) for selective modification of electrodes by different functional groups. The prepared electrodes are characterized by spectroscopic, microscopic and electrochemical methods. In scanning electron microscopic analysis, formation of graphene layers and their porous structure have been determined. Electrochemical impedance spectroscopic and cyclic voltammetric analyses are also used in electrochemical characterization of the electrodes. Then, the prepared sulfur-doped graphene-coated electrodes by using cyclic voltammetry in one-step and low cost are used as electrode materials of supercapacitor for the first time in the literature. Since the mesoporous structure of the electrodes prepared in lower potential ranges increases, specific capacitance of the electrodes increases from 74 to 1833 mF cm⁻² with 10 mA cm⁻² current density. This result shows that specific capacitances of prepared electrodes are higher than those of the electrodes prepared with metal-doped in the literature.     

Buckling lengths of irregular frame columns

In several design codes and specifications, simplified formulae and diagrams are given for determining the buckling lengths of frame columns. It is shown that these formulae may yield rather erroneous results, especially for irregular frames. This is due to the fact that the code formulae utilise only local stiffness distributions. In this paper, a simplified procedure for determining approximate values for the buckling loads of both regular and irregular frames is developed. The procedure utilises lateral load analysis of frames and yields errors on the order of 5%, which may be considered suitable for design purposes. The proposed procedure is applied to several numerical examples and it is shown that all the errors are in the acceptable range and on the safe side.     

Cu, Cr and As distribution in soils adjacent to CCA-treated utility poles in Eastern Blacksea Region of Turkey

In this study, the main objective was to asses the distribution of Cu, Cr, and As in soils adjacent to CCA-treated utility poles in Eastern Blacksea Region of Turkey (Trabzon, Rize and Artvin ) and determine the influence of soil composition.

Surface (0–5 cm) and subsurface soil samples (30–40 cm) were collected near CCA-treated utility poles and control soil samples away from CCA-treated utility poles were also collected. Water holding capacity, pH and mechanical properties of soil samples were determined for both depth levels.

Results showed that Cu, Cr and As concentration in soil samples taken from all three cities in 0–5 cm depth was higher than soil samples taken from 30–40 cm depth. Cu, Cr and As concentrations were much higher in soil samples taken from city of Rize     

Thin film based semi-active resonant marker design for low profile interventional cardiovascular MRI devices

Objectives

A new microfabrication method to produce low profile radio frequency (RF) resonant markers on catheter shafts was developed. A semi-active RF resonant marker incorporating a solenoid and a plate capacitor was constructed on the distal shaft of a 5 Fr guiding catheter. The resulting device can be used for interventional cardiovascular MRI procedures.

Materials and methods

Unlike current semi-active device visualization techniques that require rigid and bulky analog circuit components (capacitor and solenoid), we fabricated a low profile RF resonant marker directly on guiding the catheter surface by thin film metal deposition and electroplating processes using a modified physical vapor deposition system.

Results

The increase of the overall device profile thickness caused by the semi-active RF resonant marker (130 µm thick) was lowered by a factor of 4.6 compared with using the thinnest commercial non-magnetic and rigid circuit components (600 µm thick). Moreover, adequate visibility performance of the RF resonant marker in different orientations and overall RF safety were confirmed through in vitro experiments under MRI successfully.

Conclusion

The developed RF resonant marker on a clinical grade 5 Fr guiding catheter will enable several interventional congenital heart disease treatment procedures under MRI.

     

An adaptive normal estimation method for scanned point clouds with sharp features

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a robust normal estimation method is developed in order to effectively establish a proper neighborhood for each point in the scanned point cloud. In particular, for a point near sharp features, an anisotropic neighborhood is formed to only enclose neighboring points located on the same surface patch as the point. Neighboring points on the other surface patches are discarded. The developed method has been demonstrated to be robust towards noise and outliers in the scanned point cloud and capable of dealing with sparse point clouds. Some parameters are involved in the developed method. An automatic procedure is devised to adaptively evaluate the values of these parameters according to the varying local geometry. Numerous case studies using both synthetic and measured point cloud data have been carried out to compare the reliability and robustness of the proposed method against various existing methods.     

Examination of the ECG signal classification technique DEA-ELM using deep convolutional neural network features

Multimedia Tools and Applications - The accurate separation of ECG signals has become crucial to identify heart diseases. Machine learning methods are widely used to separate ECG signals. The aim...     

A Bi-Enzymatic Cascade Pathway towards Optically Pure FAHFAs**

Recently discovered endogenous mammalian lipids, fatty acid esters of hydroxy fatty acids (FAHFAs), have been proved to have anti-inflammatory and anti-diabetic effects. Due to their extremely low abundancies in vivo, forging a feasible scenario for FAHFA synthesis is critical for their use in uncovering biological mechanisms or in clinical trials. Here, we showcase a fully enzymatic approach, a novel in vitro bi-enzymatic cascade system, enabling an effective conversion of nature-abundant fatty acids into FAHFAs. Two hydratases from Lactobacillus acidophilus were used for converting unsaturated fatty acids to various enantiomeric hydroxy fatty acids, followed by esterification with another fatty acid catalyzed by Candida antarctica lipase A (CALA). Various FAHFAs were synthesized in a semi-preparative scale using this bi-enzymatic approach in a one-pot two-step operation mode. In all, we demonstrate that the hydratase-CALA system offers a promising route for the synthesis of optically pure structure-diverse FAHFAs.     

Identifying critical autonomous systems in the Internet

The Internet not only facilitates our daily activities, such as communication, entertainment and shopping but also serves as the enabling technology for many critical services, including finance, manufacturing, healthcare and transportation. On the other hand, a wide spectrum of attacks targets its communication infrastructure to disable or disrupt the network connectivity and traffic flow until recovery processes take place. Attacking all autonomous systems (ASes) in the Internet is typically beyond the capability of an adversary. Therefore, targeting a small number of ASes which results in the highest impact is the best strategy for attackers. Similarly, it is important for network practitioners to identify, fortify and secure those critical ASes to mitigate the impact of the attacks. In this study we introduce an intuitive and effective measure, IP address spatial path stress centrality, to assess and identify the critical ASes in the Internet. We compare IP address spatial path stress centrality to the three well-known and widely used centrality measures, namely customer-cone size, node degree and betweenness. We demonstrate that the proposed measure incorporates business relations and IP address spaces to achieve a better measure for identifying the critical ASes in the Internet.