Int. J. Med. Sci. 2011, 8 http://www.medsci.org 106 IInntteerrnnaattiioonnaall JJoouurrnnaall ooff MMeeddiiccaall SScciieenncceess 2011; 8(2):106-113 © Ivyspring International Publisher. All rights reserved. Research Paper Segment-orientated analysis of two-dimensional strain and strain rate as assessed by velocity vector imaging in patients with acute myocardial in-farction Thomas Butz* , Corinna N. Lang*, Marc van Bracht, Magnus W. Prull, Hakan Yeni, Petra Maagh, Gunnar Plehn, Axel Meissner, Hans-Joachim Trappe Department of Cardiology and Angiology, Marienhospital Herne, Ruhr University Bochum, Hoelkeskampring 40, D-44625 Herne, Germany * Both authors contributed equally to this work  Corresponding author: Thomas Butz, MD, Department of Cardiology and Angiology, Marienhospital Herne, Ruhr-University Bochum, Hoelkeskampring 40, D-44625 Herne, Germany. Phone: +49 (0)2323 499-0; Fax: +49 (0)2323 499-360; Mail: Thomas.Butz@Marienhospital-Herne.de Received: 2010.11.14; Accepted: 2011.01.31; Published: 2011.02.01 Abstract Aims: Strain rate imaging techniques have been proposed for the detection of ischemic or viable myocardium in coronary artery disease, which is still a challenge in clinical cardiology. This retrospective comparative study analyzed regional left ventricular function and scaring with two-dimensional strain (2DS) in the first 4 to 10 days after acute anterior myocardial infarction (AMI). Methods and results: The study population consisted of 32 AMI patients with an LAD occlusion and successful reperfusion. The assessment of peak systolic 2DS and peak systolic strain rate (SR) was performed segment-oriented with the angle-independent speckle tracking algorithm Velocity Vector Imaging (VVI). The infarcted, adjacent and non-infarcted segments were revealed by late enhancement MRI (LE-MRI), which was used as reference for the comparison with 2DS. The infarcted segments showed a significant decrease of tissue ve-locities, 2DS and SR in comparison to the non-affected segments. Conclusion: 2DS and SR as assessed by VVI seem to be a suitable approach for echocar-diographic quantification of global and regional myocardial function as well as a promising tool for multimodal risk stratification after anterior AMI. Key words: Myocardial infarction, Two-dimensional strain, Strain rate imaging, Late Enhancement MRI Introduction After acute myocardial infarction (AMI) the dis-crimination of avital scar tissue and vital reversible harmed myocardium is crucial for the optimal indi-vidual therapy, and for risk stratification 1. Further intervention, such as a percutaneous coronary inter-vention (PCI) or a coronary artery bypass graft (CABG), is only indicated if the myocardium is hypokinetic due to insufficient blood supply (“hiber-nating” or “stunned” myocardium), but still viable. Until now only late enhancement magnetic resonance imaging (LE-MRI) has provided a certain distinction. However, its application is still limited due to high expense and restricted availability. Therefore, current studies are mostly concerned with the question if newly emerged parametric echocardiographic meth-ods, measuring left ventricular (LV) function and vi- Int. J. Med. Sci. 2011, 8 http://www.medsci.org 107 ability by the deformation indices two-dimensional Graphical Analysis of One-Dimensional Motion Graphical Analysis of OneDimensional Motion Bởi: OpenStaxCollege A graph, like a picture, is worth a thousand words Graphs not only contain numerical information; they also reveal relationships between physical quantities This section uses graphs of displacement, velocity, and acceleration versus time to illustrate onedimensional kinematics Slopes and General Relationships First note that graphs in this text have perpendicular axes, one horizontal and the other vertical When two physical quantities are plotted against one another in such a graph, the horizontal axis is usually considered to be an independent variable and the vertical axis a dependent variable If we call the horizontal axis the x-axis and the vertical axis the y-axis, as in [link], a straight-line graph has the general form y = mx+b Here m is the slope, defined to be the rise divided by the run (as seen in the figure) of the straight line The letter b is used for the y-intercept, which is the point at which the line crosses the vertical axis A straight-line graph The equation for a straight line is y = mx+b 1/17 Graphical Analysis of One-Dimensional Motion Graph of Displacement vs Time (a = 0, so v is constant) Time is usually an independent variable that other quantities, such as displacement, depend upon A graph of displacement versus time would, thus, have x on the vertical axis and t on the horizontal axis [link] is just such a straight-line graph It shows a graph of displacement versus time for a jet-powered car on a very flat dry lake bed in Nevada Graph of displacement versus time for a jet-powered car on the Bonneville Salt Flats Using the relationship between dependent and independent variables, we see that the − slope in the graph above is average velocity v and the intercept is displacement at time zero—that is, x0 Substituting these symbols into y = mx+b gives − x = v t + x0 or − x = x0 + v t Thus a graph of displacement versus time gives a general relationship among displacement, velocity, and time, as well as giving detailed numerical information about a specific situation The Slope of x vs t The slope of the graph of displacement x vs time t is velocity v slope = Δx Δt =v 2/17 Graphical Analysis of One-Dimensional Motion Notice that this equation is the same as that derived algebraically from other motion equations in Motion Equations for Constant Acceleration in One Dimension From the figure we can see that the car has a displacement of 400 m at time 0.650 m at t = 1.0 s, and so on Its displacement at times other than those listed in the table can be read from the graph; furthermore, information about its velocity and acceleration can also be obtained from the graph Determining Average Velocity from a Graph of Displacement versus Time: Jet Car Find the average velocity of the car whose position is graphed in [link] Strategy The slope of a graph of x vs t is average velocity, since slope equals rise over run In this case, rise = change in displacement and run = change in time, so that slope = Δx Δt − = v Since the slope is constant here, any two points on the graph can be used to find the slope (Generally speaking, it is most accurate to use two widely separated points on the straight line This is because any error in reading data from the graph is proportionally smaller if the interval is larger.) Solution Choose two points on the line In this case, we choose the points labeled on the graph: (6.4 s, 2000 m) and (0.50 s, 525 m) (Note, however, that you could choose any two points.) Substitute the x and t values of the chosen points into the equation Remember in calculating change (Δ) we always use final value minus initial value − v = Δx Δt = 2000 m−525 m 6.4 s − 0.50 s , yielding − v = 250 m/s Discussion 3/17 Graphical Analysis of One-Dimensional Motion This is an impressively large land speed (900 km/h, or about 560 mi/h): much greater than the typical highway speed limit of 60 mi/h (27 m/s or 96 km/h), but considerably shy of the record of 343 m/s (1234 km/h or 766 mi/h) set in 1997 Graphs of Motion when a is constant but a ≠ The graphs in [link] below represent the motion of the jet-powered car as it accelerates toward its top speed, but only during the time when its acceleration is constant Time starts at zero for this motion (as if measured with a stopwatch), and the displacement and velocity are initially 200 m and 15 m/s, respectively 4/17 Graphical Analysis of One-Dimensional Motion Graphs of motion of a jet-powered car during the time span when its acceleration is constant (a) The slope of an x vs t graph is velocity This is shown at two points, and the instantaneous velocities obtained are plotted in the next graph Instantaneous velocity at any point is the slope of the tangent at that point (b) The slope of the v vs t graph is constant for this part of the motion, indicating constant acceleration (c) Acceleration has the constant value of 5.0 m/s2 over the time interval ...A structural model of one-dimensional thin silica nanowires D.J. Zhang, R.Q. Zhang * Department of Physics and Materials Science, City University of Hong Kong, 83 Tat Chee Avenue, Koloon, Hong Kong SAR, China Received 27 June 2004; in final form 28 June 2004 Available online 31 July 2004 Abstract We report a new structural model of silica molecular wire based on spiro union two-membered ring (SU-2MR) units. As revealed by density functional calculations, the SU-2MR wire is formed by parallel 2MRs bridged by oxygen atoms and is energetically more favorable, thermally more stable and chemically more reactive at the tip than the edge-sharing two-membered ring molecular chain proposed early. The SU-2MR molecular chain would be considered as an appropriate structural model of one-dimensional thin (0.4 nm) silica nanowires. Ó 2004 Elsevier B.V. All rights reserved. One-dimensional (1D) nanomaterials are being inten- sively researched because of their great potentials in mesoscopic physics and in nanod evices. Silica (SiO 2 ), which is the important component in glass, catalyst, Si-based microelectronic derives and optical fibers, is an increasingly important candidate to form 1D nano- materials. Significant progresses have been made in synthesizing silica nanowires with a variety of methods [1–6]. Recently, very long aligned silica nan owires with thin diameters of 5–10 nm has been synthesized by Hu et al. [6] through thermal oxidation of silicon wafers. Theoretical investigation of atomic structures of 1D quantum wires is fundamentally important for under- standing their overall properties and growth mechanism. In contrast to the intensive study on silicon nanowires [7,8], little has been done about silica nanowires in terms of their geometric and electronic structures. When forming bulk crystal, silica is a three-dimen- sional (3D) network of corner-sharing SiO 4 tetrahedra, frequently six-membered rings (refer to an Si–O–Si– OÁÁÁ ring containing six Si atoms). However, the smaller four-, three-, and two-membered rings have also been found to exist in the surface or inter ior of amorphous and crystalline silica, as well as vitreous silica [9–22]. In particular, two-membered rings (2MRs) exists not only in silica-w at high temperature [15], but also in Si–O-plasma reactions [16] as well as in the condensa- tion of vicinal hydroxyls or the thermodynamic rear- rangement of the pure silica structure at the surfaces of amorphous and crystalline silica at high temperature [17–22]. The structural diversity creates opportunities for ma- terials with designed structures and properties. Recently, Bromley et al. [23] proposed a structural model of silica molecular chains based on the edge-sharing 2MR (ES- 2MR) units. They found that the chains are energetically less stable than the corresponding molecular rings for n >11(n is the number of SiO 2 units) [23]. Here, we pro- pose a new model of thin silica molecular chains based on spiro union 2MR (SU-2MR) units, aiming at provid- ing insight into the growth of 1D silica nanowires by searching for the preponderant structures via quantum mechanical calculations. The insets of Fig. 1 show representative configura- tions of the SU-2MR molecular chains, and the ES- 2MR molecular chains and rings, respectively. To retain the stoichiometry, the chains are terminated at either end by non-bridging oxygen (NBO) atoms. Three other termination modes have also been Synthesis of one-dimensional SnO 2 nanorods via a hydrothermal technique O. Lupan a,b, Ã , L. Chow a , G. Chai c , H. Heinrich a,d,e , S. Park a , A. Schulte a a Department of Physics, University of Central Florida, PO Box 162385, Orlando, FL 32816-2385, USA b Department of Microelectronics and Semiconductor Devices, Technical University of Moldova, 168 Stefan cel Mare Blvd., Chisinau MD-2004, Republic of Moldova c Apollo Technologies, Inc. 205 Waymont Court, S111, Lake Mary, FL 32746, USA d Advanced Materials Processing and Analysis Center, University of Central Florida, Orlando, FL 32816, USA e Department of Mechanical, Materials, Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA article info Article history: Received 19 August 2008 Received in revised form 7 October 2008 Accepted 8 October 2008 Available online 17 October 2008 PACS: 81.10.Dn 61.46.Àw 61.46.Km 68.37.Lp 78.30.Fs Keywords: SnO 2 nanorod Crystal structure Semiconductors Hydrothermal synthesis Raman spectra abstract We have developed a simple solution process to synthesize tin oxide nanorods. The influence of precursors and the reaction temperature on the morphology of SnO 2 is investigated. SnO 2 nanorods are characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Raman spectroscopy. The as-grown SnO 2 nanorods are un iform in size with a radius of 50–100 nm and length of 1–2 mm. The nanorods grow direction is parallel to the [1 01] direction. Possible growth mechanism of SnO 2 nanorods is discussed. & 2008 Elsevier B.V. All rights reserved. 1. Introduction Controlled synthesis of nanostructures is an important step for the manufacturing of nanodevices. Performance of semiconductor nanodevices may depend on their morphology. Recently, one- dimensional (1D) materials have attracted great interest due to their potential applications as interconnects and functional components [1–5]. 1D oxide nanostructures showed interesting properties, chemical and thermal stability, diverse functionalities, high durability, owing to their high degree of crystallinity [3], and emerge as nanoscale building blocks for electronic and optoelec- tronic devices [4,5]. At the same time, the interest in developing parts per billion (ppb)-level gas sensors requires new approaches and new nanomaterials. One of the most important sensor materials is tin oxide (SnO 2 ), which is a low-cost, large-bandgap (3.6 eV, at 300 K), and n-type semiconductor [6]. SnO 2 ’s properties are greatly affected by the size and morphology, which define their further applications. Thus, designing SnO 2 1D nanorods and nanoarchitectures with well-defined morphologies is of impor- tance for fundamental research and high-tech applications. F abrication of SnO 2 nanorods has b een accomplished using s everal vapor deposition techniques, such as rapid oxidation [7],chemical vapor d eposition ( CVD) [8], and thermal ev aporation [9].Pengetal. [1 0] hav e recently reported a hydrotherma l synthesis of SnO 2 nanorods. However, organic reagents such as hexanol and sodium dodecylsulfate used in the synthesis of SnO 2 nanorods can lead t o undesirable impact on human health and on the envir onment [6]. Zhang et al. [11] also report ed a low-t emperatur e fabrication (at 200 1C for 18 h) via a hydro thermal process of crystalline SnO 2 nanorods. Vayssieres et al. [1 2] reported SnO 2 nanorods arrays gro wn on F-SnO 2 glasssubstratesbyaqueousthermohydrolysisat951C. In this work we report a simple, one-step low-temperature aqueous synthesis of SnO 2 1D nanorods without the need of templates or surfactants. 2. Experimental details The 1 Connectivity analysis of one-dimensional vehicular ad hoc networks in fading channels Neelakantan Pattathil Chandrasekharamenon ∗ and Babu AnchareV Department of Electronics and Communication Engineering, National Institute of Technology, Calicut 673601, India ∗ Corresponding author: neelakantan pc@nitc.ac.in Email address: AVB: babu@nitc.ac.in Abstract Vehicular ad hoc network (VANET) is a type of promising application-oriented network deployed along a highway for safety and emergency information delivery, entertainment, data collection, and communication. In this paper, we present an analytical model to investigate the connectivity properties of one-dimensional VANETs in the presence of channel randomness, from a queuing theoretic perspective. Connectivity is one of the most important issues in VANETs to ensure reliable dissemination of time- critical information. The effect of channel randomness caused by fading is incorporated into the analysis by modeling the transmission range of each vehicle as a random variable. With exponentially distributed inter-vehicle distances, we use an equivalent M/G/∞ queue for the connectivity analysis. Assuming that the network consists of a large number of finite clusters, we obtain analytical expressions for the average connectivity distance and the expected number of vehicles in a connected cluster, taking into account the underlying wireless channel. Three different fading models are considered for the analysis: Rayleigh, Rician and Weibull. The effect of log normal shadow fading is also analyzed. A distance- dependent power law model is used to represent the path loss in the channel. Further, the speed of each vehicle on the highway is assumed to be a Gaussian distributed random variable. The analytical model is useful to assess VANET connectivity properties in a fading channel. Keywords: connectivity distance; fading channels; highway; vehicle speed; vehicular ad hoc networks. 2 1. Introduction Vehicular Ad Hoc Networks (VANETs), which allow vehicles to form a self-organized net- work without the requirement of permanent infrastructures, are highly mobile wireless ad hoc networks targeted to support (i) vehicular safety-related applications such as emergency warning systems, collision avoidance through driver assistance, road condition warning, lane-changing assistance and (ii) entertainment applications [1]. VANET is a hybrid wireless network that supports both infrastructure-based and ad hoc communications. Specifically, vehicles on the road can communicate with each other through a multi-hop ad hoc connection. They can also access the Internet and other broadband services through the roadside infrastructure, i.e., base stations (BSs) or access points (APs) along the road. These types of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications have recently received significant interest from both academia and industry. The emerging technology for VANETs is Dedicated Short Range Communications (DSRC), for which in 1999, FCC has allocated 75 MHz of spectrum between 5,850 and 5,925 MHz. DSRC is based on IEEE 802.11 technology and is proceeding toward standardization under the standard IEEE 802.11p, while the entire communication stack is being standardized by the IEEE 1609 working group under the name wireless access in vehicular environments (WAVE) [1]. The goal of 802.11p standard is to provide V2V and V2I communications over the dedicated 5.9 GHz licensed frequency band and supports data rates This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Energy efficiency analysis of one-way and two-way relay systems EURASIP Journal on Wireless Communications and Networking 2012, 2012:46 doi:10.1186/1687-1499-2012-46 Can Sun (saga@ee.buaa.edu.cn) Chenyang Yang (cyyang@buaa.edu.cn) ISSN 1687-1499 Article type Research Submission date 29 September 2011 Acceptance date 14 February 2012 Publication date 14 February 2012 Article URL http://jwcn.eurasipjournals.com/content/2012/1/46 This peer-reviewed article was published immediately upon acceptance. It can be downloaded, printed and distributed freely for any purposes (see copyright notice below). For information about publishing your research in EURASIP WCN go to http://jwcn.eurasipjournals.com/authors/instructions/ For information about other SpringerOpen publications go to http://www.springeropen.com EURASIP Journal on Wireless Communications and Networking © 2012 Sun and Yang ; licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Energy efficiency analysis of one-way and two-way relay systems Can Sun ∗ and Chenyang Yang School of Electronics and Information Engineering, Beihang University, Beijing 100191, China ∗ Corresponding author: saga@ee.buaa.edu.cn Email address: CY: cyyang@buaa.edu.cn Abstract Relaying is supposed to be a low energy consumption technique since the long distance transmission is divided into several short distance transmissions. When the power consumptions (PCs) other than that consumed by transmitting information bits is taken into account, however, relaying may not be energy efficient. In this article, we study the energy efficiencies (EEs) of one-way relay transmission (OWRT) and two-way relay transmission (TWRT) by comparing with direct transmission (DT). We consider a system where two source nodes transmit to each other with the assistance of a half-duplex amplify-and-forward relay node. We first find the maximum EEs of DT, OWRT, and TWRT by optimizing the transmission time and the transmit powers at each node. Then we compare the maximum EEs of the three 1 strategies, and analyze the impact of circuit PCs and data amount. Analytical and simulation results show that relaying is not always more energy efficient than DT. Moreover, TWRT is not always more energy efficient than OWRT, despite that it is more spectral efficient. The EE of TWRT is higher than those of DT and OWRT in symmetric systems where the circuit PCs at each node are identical and the numbers of bits to be transmitted in two directions are equal. In asymmetric systems, however, OWRT may provide higher EE than TWRT when the numbers of bits in two directions differ significantly. 1 Introduction Since the explosive growth of wireless services is sharply increasing their contri- butions to the carbon footprint and the operating costs, energy efficiency (EE) has drawn more and more attention recently as a new design goal for various wireless communication systems [1–3], compared with spectral efficiency (SE) that has been the design focus for decades. A widely used performance metric for EE is the numb er of transmitted bits per unit of energy. When only transmit power is taken into account, the EE monotonically decreases with the increase of the SE [4] at least ... afterward 8/17 Graphical Analysis of One- Dimensional Motion Graphs of motion of a jet-powered car as it reaches its top velocity This motion begins where the motion in [link] ends (a) The slope of this... this motion (as if measured with a stopwatch), and the displacement and velocity are initially 200 m and 15 m/s, respectively 4/17 Graphical Analysis of One- Dimensional Motion Graphs of motion of. .. the acceleration of the jet car at a time of 25 s by finding the slope of the v vs t graph in [link](b) Strategy 9/17 Graphical Analysis of One- Dimensional Motion The slope of the curve at t