BibSonomyburst/tag/variabilityBibSonomy RSS feed for /tag/variability2012-02-15T17:11:19+01:00http://www.bibsonomy.org/bibtex/21d1b87f986edd56df67aaf536341ef44/bunkebunke2012-01-13T16:00:23+01:00Transfemoral amputation Prosthetics Osseointegration Transducer Gait Variability <span class="authorEditorList"><a href="/author/Lee">Winson C.C. Lee</a>, <a href="/author/Frossard">Laurent A. Frossard</a>, <a href="/author/Hagberg">Kerstin Hagberg</a>, <a href="/author/Haggstrom">Eva Haggstrom</a>, <a href="/author/Gow">David Lee Gow</a>, <a href="/author/Gray">Steven Gray</a>, and <a href="/author/BrÃ¥nemark">Rickard BrÃ¥nemark</a> </span><em>Med Eng Phys</em> <em>30(7):825--833</em> (<em>September 2008</em>)Fri Jan 13 16:00:23 CET 2012Med Eng Phys#sep#7825--833Magnitude and variability of loading on the osseointegrated implant of transfemoral amputees during walking302008Transfemoral amputation Prosthetics Osseointegration Transducer Gait Variability This study directly measured the load acting on the abutment of the osseointegrated implant system of transfemoral amputees during level walking, and studied the variability of the load within and among amputees. Twelve active transfemoral amputees (age: 54±12 years, mass: 84.3±16.3kg, height: 17.8±0.10m) fitted with an osseointegrated implant for over 1 year participated in the study. The load applied on the abutment was measured during unimpeded, level walking in a straight line using a commercial six-channel transducer mounted between the abutment and the prosthetic knee. The pattern and the magnitude of the three-dimensional forces and moments were revealed. Results showed a low step-to-step variability of each subject, but a high subject-to-subject variability in local extrema of body-weight normalized forces and moments and impulse data. The high subject-to-subject variability suggests that the mechanical design of the implant system should be customized for each individual, or that a fit-all design should take into consideration the highest values of load within a broad range of amputees. It also suggests specific loading regime in rehabilitation training are necessary for a given subject. Thus the loading magnitude and variability demonstrated should be useful in designing an osseointegrated implant system better able to resist mechanical failure and in refining the rehabilitation protocol.http://www.bibsonomy.org/bibtex/261ba7b2a789ebbed1225d8af62cabf32/mikimiki2011-10-03T06:40:29+02:00quasar variability <span class="authorEditorList"><a href="/author/Schmidt">Kasper B. Schmidt</a>, <a href="/author/Rix">Hans-Walter Rix</a>, <a href="/author/Shields">Joseph C. Shields</a>, <a href="/author/Knecht">Matthias Knecht</a>, <a href="/author/Hogg">David W. Hogg</a>, <a href="/author/Maoz">Dan Maoz</a>, and <a href="/author/Bovy">Jo Bovy</a> </span>(<em>2011</em>)<em>cite arxiv:1109.6653Comment: Accepted for publication in ApJ, 17 pages, 14 figures . </em>Mon Oct 03 06:40:29 CEST 2011cite arxiv:1109.6653Comment: Accepted for publication in ApJ, 17 pages, 14 figuresThe Color Variability of Quasars2011quasar variability We quantify quasar color-variability using an unprecedented variability database - ugriz photometry of 9093 quasars from SDSS Stripe 82, observed over 8 years at ~60 epochs each. We confirm previous reports that quasars become bluer when brightening. We find a redshift dependence of this blueing in a given set of bands (e.g. g and r), but show that it is the result of the flux contribution from less-variable or decayed emission lines in the different SDSS bands at different redshifts. After correcting for this effect, quasar color-variability is remarkably uniform, and independent not only of redshift, but also of quasar luminosity and black hole mass. The color variations of individual quasars, as they vary in brightness on year timescales, are much more pronounced than the ranges in color seen in samples of quasars across many orders of magnitude in luminosity. This indicates distinct physical mechanisms behind quasar variability and the observed range of quasar luminosities at a given black hole mass - quasar variations cannot be explained by changes in the mean accretion rate. We do find some dependence of the color variability on the characteristics of the flux variations themselves, with fast, low-amplitude, brightness variations producing more color variability. The observed behavior could arise if quasar variability results from flares or ephemeral hot spots in an accretion disc. [1109.6653] The Color Variability of Quasarshttp://www.bibsonomy.org/bibtex/23baa51c0466712ddd0bbff1f83687677/mikimiki2011-06-11T14:02:14+02:00blazar variability <span class="authorEditorList"><a href="/author/Shaohua">Zhang Shaohua</a>, <a href="/author/Huiyuan">Wang Huiyuan</a>, <a href="/author/Hongyan">Zhou Hongyan</a>, <a href="/author/Tinggui">Wang Tinggui</a>, and <a href="/author/Peng">Jiang Peng</a> </span>(<em>2011</em>)<em>cite arxiv:1106.1587Comment: 7 page, 2 figures, 1 table . </em>Sat Jun 11 14:02:14 CEST 2011cite arxiv:1106.1587Comment: 7 page, 2 figures, 1 tableDiscovery of A Variable Broad Absorption Line in the BL Lac object PKS 0138-0972011blazar variability We report the discovery of a Broad Absorption Line (BAL) of \sim 10^4 km s-1 in width in the previously known BL Lac object PKS 0138-097, which we tentatively identified as a Mg II BAL. This is the first detection of a BAL, which is sometimes seen in powerful quasars with high accretion rates, in a BL Lac object. The BAL was clearly detected in its spectra of two epochs at a high luminosity state taken in the Sloan Digital Sky Survey (SDSS), while it disappeared in three SDSS spectra taken at a low luminosity state. The BAL and its variability pattern was also found in its historical multi-epoch spectra in the literature, but has been overlooked previously. In its high resolution radio maps, PKS 0138-097 shows a core plus an one-sided parsec-scale jet. The BAL variability can be interpreted as follows: The optical emission is dominated by the core in a high state and by the jet in a low state, and the BAL material is located between the core and jet so that the BAL appears only when the core is shining. Our discovery suggests that outflows may also be produced in active galactic nuclei at a low accreting state. [1106.1587] Discovery of A Variable Broad Absorption Line in the BL Lac object PKS 0138-097http://www.bibsonomy.org/bibtex/2a3001e5b34caebe10dd553a1e94a6d2b/meduzmeduz2011-05-09T11:37:55+02:00motion smooth\_pursuit\_eye\_movements variability <span class="authorEditorList"><a href="/author/Huang">Xin Huang</a>, and <a href="/author/Lisberger">Stephen G. Lisberger</a> </span><em>Journal of Neurophysiology</em> <em>101(6):3012--3030</em> (<em>Jun 1, 2009</em>)Mon May 09 11:37:55 CEST 2011Journal of Neurophysiology#jun#63012--3030Noise Correlations in Cortical Area {MT} and Their Potential Impact on {Trial-by-Trial} Variation in the Direction and Speed of {Smooth-Pursuit} Eye Movements1012009motion smooth\_pursuit\_eye\_movements variability 1Smooth-pursuit eye movements are variable, even when the same tracking target motion is repeated many times. We asked whether variation in pursuit could arise from noise in the response of visual motion neurons in the middle temporal visual area ({MT}). In physiological experiments, we evaluated the mean, variance, and trial-by-trial correlation in the spike counts of pairs of simultaneously recorded {MT} neurons. The correlations between responses of pairs of {MT} neurons are highly significant and are stronger when the two neurons in a pair have similar preferred speeds, directions, or receptive field locations. Spike count correlation persists when the same exact stimulus form is repeatedly presented. Spike count correlations increase as the analysis window increases because of correlations in the responses of individual neurons across time. Spike count correlations are highest at speeds below the preferred speeds of the neuron pair and increase as the contrast of a square-wave grating is decreased. In computational analyses, we evaluated whether the correlations and variation across the population response in {MT} could drive the observed behavioral variation in pursuit direction and speed. We created model population responses that mimicked the mean and variance of {MT} neural responses as well as the observed structure and amplitude of noise correlations between pairs of neurons. A vector-averaging decoding computation revealed that the observed variation in pursuit could arise from the {MT} population response, without postulating other sources of motor variation.http://www.bibsonomy.org/bibtex/2ae88ea6426e897d77e61258ab00cc88a/yevb0yevb02011-03-27T17:20:41+02:00behavioral economics,cross-cultural psychology,culture,evolutionary psychology,experiments,external research,cultural universals,population validity,generalizability,human variability <span class="authorEditorList"><a href="/author/Henrich">Joseph Henrich</a>, <a href="/author/Heine">Steven J. Heine</a>, and <a href="/author/Norenzayan">Ara Norenzayan</a> </span><em>Behavioral and Brain Sciences</em> <em>33(2-3):61--83</em> (<em>June 2010</em>)Sun Mar 27 17:20:41 CEST 2011Behavioral and Brain Sciencesjun2-361--83The weirdest people in the world?332010behavioral economics,cross-cultural psychology,culture,evolutionary psychology,experiments,external research,cultural universals,population validity,generalizability,human variability http://www.bibsonomy.org/bibtex/276bc5202859544da37fc50a236b6b8a3/jmaiorajmaiora2011-03-11T12:21:24+01:00ABDOMINAL ACCURACY ANEURYSM AORTIC-ANEURYSMS ENDOLUMINAL ENDOVASCULAR EXCLUSION FOLLOW-UP INTEROBSERVER MANAGEMENT REPAIR SAC STENT-GRAFT SURVEILLANCE SYSTEM VARIABILITY abdominal aneurysm aortic measurements rendering segmentation variability volume <span class="authorEditorList"><a href="/author/Yeung">K. K. Yeung</a>, <a href="/author/van der Laan">M. J. van der Laan</a>, <a href="/author/Wever">J. J. Wever</a>, <a href="/author/van Waes">P. F. G. M. van Waes</a>, and <a href="/author/Blankensteijn">J. D. Blankensteijn</a> </span><em>Journal of Endovascular Therapy</em> <em>10(5):887-893</em> (<em>2003</em>)Fri Mar 11 12:21:24 CET 2011Journal of Endovascular Therapy5887-893New post-imaging software provides fast and accurate volume data from CTA surveillance after endovascular aneurysm repair102003ABDOMINAL ACCURACY ANEURYSM AORTIC-ANEURYSMS ENDOLUMINAL ENDOVASCULAR EXCLUSION FOLLOW-UP INTEROBSERVER MANAGEMENT REPAIR SAC STENT-GRAFT SURVEILLANCE SYSTEM VARIABILITY abdominal aneurysm aortic measurements rendering segmentation variability volume Purpose: To quantify intra- and interobserver variabilities when measuring total aneurysm volume after endovascular aneurysm repair using the Vitrea 2 System and to compare it in terms of accuracy and processing time with the gold standard methods using the Easy Vision workstation. Methods: Total aneurysm volumes from 30 postendograft CTA datasets were randomly selected from a database consisting of similar to400 CTA datasets recorded in 89 patients. The intra- and interobserver variabilities were measured on the Vitrea workstation by 2 investigators. The intermodality variability was calculated for the same measurements using the Easy Vision workstation. The differences of each pair of measurements were plotted against their mean, and the repeatability coefficient (RC) was calculated. The mean differences were also expressed as a percentage of the first measurements. Results: The intraobserver mean difference was 1.6 mL (1.4%) with an RC of 10.8 mL (10.1%) and the interobserver mean difference was -1.4 mL (-1.4%) with an RC of 11.7 mL (10.2%). The intermodality mean difference was 1.8 mL (2.0%) with an RC of 15.8 mL (11.1%). The Vitrea workstation required a median of 8 minutes (interquartile range 7-10) for 1 observer and 6 minutes (interquartile range 5-8) for the other to perform a complete volume segmentation of each patient dataset compared to an estimated average of 30 minutes using the Easy Vision workstation. Conclusions: The Vitrea workstation provides fast and accurate volume data from spiral CTA follow-up of endovascular aneurysm repair. This software may enhance the acceptability of volume surveillance in daily practicehttp://www.bibsonomy.org/bibtex/23e37a8017851c5a53a0ae35c3bc00203/jmaiorajmaiora2011-03-11T12:21:24+01:00ACCURACY ANEURYSM ARTERIOGRAPHY COMPUTED-TOMOGRAPHY DIAMETER ENDOGRAFT INTEROBSERVER RECONSTRUCTION SYSTEM ULTRASOUND VARIABILITY <span class="authorEditorList"><a href="/author/Sprouse">L. R. Sprouse</a>, <a href="/author/Meier">G. H. Meier</a>, <a href="/author/Parent">F. N. Parent</a>, <a href="/author/Demasi">R. J. Demasi</a>, <a href="/author/Stokes">G. K. Stokes</a>, <a href="/author/Lesar">C. J. Lesar</a>, <a href="/author/Marcinczyk">M. J. Marcinczyk</a>, and <a href="/author/Mendoza">B. Mendoza</a> </span><em>Journal of Vascular Surgery</em> <em>40(3):443-447</em> (<em>2004</em>)Fri Mar 11 12:21:24 CET 2011Journal of Vascular Surgery3443-447Is three-dimensional computed tomography reconstruction justified before endovascular aortic aneurysm repair?402004ACCURACY ANEURYSM ARTERIOGRAPHY COMPUTED-TOMOGRAPHY DIAMETER ENDOGRAFT INTEROBSERVER RECONSTRUCTION SYSTEM ULTRASOUND VARIABILITY Objectives: The endovascular management of abdominal aortic aneurysm (AAA) relies on accurate preoperative imaging for proper patient selection and operative planning. Three-dimensional (3-D) computed tomography (CT) with reformatted images perpendicular to blood flow has gained popularity as a method of AAA assessment and image-based planning before endovascular aneurysm repair (EVAR). The current study was undertaken to determine the interobserver agreement of AAA measurements obtained with axial CT and reformatted 3-D CT and to compare the consistency of the 2 methods in selecting patients for EVAR. Methods: Eight observers assessed the axial CT and reformatted 3-D CT scans for 5 patients with AAAs to determine whether the patients were candidates for EVAR. 3-D CT with multiplanar reformatted images was performed by Medical Media Systems (AIMS). Each observer measured the length and diameter of the proximal neck, maximal AAA, aortic bifurcation, common iliac diameter, and aortic angulation. The proximal neck and common iliac arteries were also assessed for thrombus, calcification, and tortuosity. Agreement of the measurements on axial CT scans was compared with those on AIMS CT scans by calculating the K statistic. Complete agreement was defined as kappa = 1.0. The limits of agreement between observers were also calculated. Results: The cumulative interobserver agreement of MMS CT scans (kappa =.81) was greater than for axial CT scans (kappa=59). The kappa value for each of the diameter measurements was greater with the MMS CT scans. In 79% of cases the observers' measurements were less than 2 mm, from the mean with MMS CT, compared with 59% for axial CT. The kappa value for deciding whether a patient was an endograft candidate on the basis of aortic neck was greater with the MMS CT (0.92 vs 0.63). The limits of agreement between observers were also better with the MMS CT. Conclusions. The interobserver agreement in planning EVAR is significantly better with MMS CT compared with traditional axial CT. The routine use of MMS CT appears justified before EVAR to improve the accuracy and consistency of patient selectionhttp://www.bibsonomy.org/bibtex/241c7da4d7c8257ab901d4dfc721c53d5/sjpsjp2010-01-05T23:12:10+01:00Acute equations, human probit toxicity, variability <span class="authorEditorList"><a href="/author/Schubach">Simon Schubach</a> </span><em>Journal of Loss Prevention in the Process Industries</em> <em>10(5-6):309--315</em> (<em>1997</em>)Tue Jan 05 23:12:10 CET 2010Journal of Loss Prevention in the Process Industries5-6309--315A measure of human sensitivity in acute inhalation toxicity101997Acute equations, human probit toxicity, variability The prediction of the probability of death or injury following the inhalation of a toxic gas or vapour is used in risk analysis. The proportion of a population responding for a given endpoint (e.g. lethality) can be related to the received dose using a probit model. Some of the coefficients in the probit equations are based on data from animal testing. Generally, experimental test animals are bred to exhibit low variability. Animal variability in response to toxic exposures may not adequately represent human variability in response to toxic exposures to the tested chemical. It is suggested that some independently established measure of human variability be used in the formulation of the probit equation constants rather than those that arise solely from the fitting of the animal data.http://www.bibsonomy.org/bibtex/20f1b82670ce2ffc3379fcbf3ca57b2f7/hiddershidders2009-12-17T23:44:12+01:00conceptual-modeling variability <span class="authorEditorList"><a href="/author/Verelst">Jan Verelst</a> </span>(<em>2004</em>)Thu Dec 17 23:44:12 CET 2009A Framework for Classifying Variability in Conceptual Models2004conceptual-modeling variability bibrem uploadhttp://www.bibsonomy.org/bibtex/22a363bd4b2486b2d44182413e5f57b9d/hiddershidders2009-12-10T16:09:03+01:00conceptual-modeling myown variability <span class="authorEditorList"><a href="/author/Paredaens">Jan Paredaens</a>, <a href="/author/Hidders">Jan Hidders</a>, and <a href="/author/Verelst">Jan Verelst</a> </span><em>ORBEL 20, Quantitiative Methods for Decision Making, Proc. of the Annual conference of SOGESCI-B.B.W.B., The Belgian Operations Research Society, </em><em>page 75--77. </em>(<em>January 2006</em>)Thu Dec 10 16:09:03 CET 2009ORBEL 20, Quantitiative Methods for Decision Making, Proc. of the Annual conference of SOGESCI-B.B.W.B., The Belgian Operations Research SocietyJanuary 19-2075--77Towards a Formalization of variability in conceptual models of information systems2006conceptual-modeling myown variability bibrem uploadhttp://www.bibsonomy.org/bibtex/2a1684b2797505679b3aacbbb5dd59ab6/svancesvance2009-11-03T20:21:25+01:00DELTA-SCUTI NONRADIAL OSCILLATIONS; STARS; VARIABILITY <span class="authorEditorList"><a href="/author/KENNELLY">E. J. KENNELLY</a>, <a href="/author/WALKER">G. A. H. WALKER</a>, and <a href="/author/MERRYFIELD">W. J. MERRYFIELD</a> </span><em>ASTROPHYSICAL JOURNAL</em> <em>400(2):L71--L74</em> (<em>1992</em>)Tue Nov 03 20:21:25 CET 2009ASTROPHYSICAL JOURNAL2L71--L74TAU PEGASIA FOURIER REPRESENTATION OF LINE-PROFILE VARIATIONS4001992DELTA-SCUTI NONRADIAL OSCILLATIONS; STARS; VARIABILITY The complex line-profile variations, probably caused by nonradial pulsations, in the spectrum of the rapidly rotating delta Scuti star tau Peg are analyzed with a novel two-dimensional Fourier transform technique. It resolves the oscillations in temporal frequency nu and apparent azimuthal order m. The two-dimensional Fourier spectrum resembles the (1, nu) diagrams generated for the Sun. Advantages of the technique are (1) both frequency and mode of the oscillations are determined directly; (2) complex variations in the line profiles are resolved into individual modes; (3) modes of both high and low degree are resolved. For tau Peg, we identify four apparent modes having Absolute value of m congruent-to 3, 7, 11, and 15. After correcting for rotation, most of the modes have frequencies of approximately 17 cycles day-1. Assuming T(eff) congruent-to 8000 K and L congruent-to 40 L., all of the modes fall within the range of frequencies theoretically predicted to be unstable.http://www.bibsonomy.org/bibtex/259aa66a303173bdd8e8290806187c6ca/neilernstneilernst2009-09-22T17:54:36+02:00agents variability <span class="authorEditorList"><a href="/author/Penserini">Loris Penserini</a>, <a href="/author/Perini">Anna Perini</a>, <a href="/author/Susi">Angelo Susi</a>, and <a href="/author/Mylopoulos">John Mylopoulos</a> </span><em>Transactions on Autonomous and Adaptive Systems</em> (<em>2007</em>)Tue Sep 22 17:54:36 CEST 2009Transactions on Autonomous and Adaptive Systems4High variability design for software agents: Extending Tropos.22007agents variability Many classes of distributed applications, including e-business, e-government, and ambient intelligence, consist of networking infrastructures, where the nodes (peers)—be they software components, human actors or organizational units—cooperate with each other to achieve shared goals. The multi-agent system metaphor fits very well such settings because it is founded on intentional and social concepts and mechanisms. Not surprisingly, many agent-oriented software development methods have been proposed, including GAIA, PASSI, and Tropos. This paper extends the Tropos methodology, enhancing its ability to support high variability design through the explicit modelling of alternatives, it adopts an extended notion of agent capability and proposes a refined Tropos design process. The paper also presents an implemented software development environment for Tropos, founded on the Model-Driven Architecture (MDA) framework and standards. The extended Tropos development process is illustrated through a case study involving an e-commerce application. http://www.bibsonomy.org/bibtex/2d13494a23e4f43d9a93044aa7cefad80/j_sprengerj_sprenger2009-08-04T18:19:21+02:00UML variability <span class="authorEditorList"><a href="/author/Korherr">B. Korherr</a>, and <a href="/author/List">B. List</a> </span><em>Database and Expert Systems Applications, 2007. DEXA &#039;07. 18th International Conference on, </em><em>page 829-834. </em>(<em>September 2007</em>)Tue Aug 04 18:19:21 CEST 2009Database and Expert Systems Applications, 2007. DEXA '07. 18th International Conference onSept.829-834A UML 2 Profile for Variability Models and their Dependency to Business Processes2007UML variability Variability Models are designed for modelling variabilities of a software. Unfortunately they are not part of a well-known modelling framework for a higher usability, like the Unified Modelling Language. To address this limitation, we provide a UML 2 profile for variability models. Furthermore we show the dependency from the UML profile to activity diagrams to make the relationship between variability models and process models visible. This profile and its mapping are tested with example business processes.http://www.bibsonomy.org/bibtex/2848ce4225c972449ab090cda3d304b5f/hiddershidders2009-07-21T22:35:22+02:00software-engineering software-evolution variability <span class="authorEditorList"><a href="/author/Verelst">Jan Verelst</a> </span>(<em>1999</em>)Tue Jul 21 22:35:22 CEST 2009invloed van variabiliteit op de evolueerbaarheid van conceptuele modellen van informatiesystemen1999software-engineering software-evolution variability bibrem uploadhttp://www.bibsonomy.org/bibtex/2b61be2d457856203447b7c04a8d4a4c1/earthfareearthfare2009-05-19T18:00:18+02:00climate, variability <span class="authorEditorList"><a href="/author/Bertram">D. Bertram</a> </span><em>Progress In Oceanography</em> <em>49(1-4):283--307</em> (<em>2001</em>)Tue May 19 18:00:18 CEST 2009Progress In Oceanography1-4283--307The seasonal cycle revisited: interannual variation and ecosystem consequences492001climate, variability The annual seasonal cycle accounts for much of the total temporal variability of mid- and high-latitude marine ecosystems. Although the general pattern of the seasons repeats each year, climatic variability of the atmosphere and the ocean produce detectable changes in intensity and onset timing. We use a combination of time series data from oceanographic, zooplankton and seabird breeding data to ask if and how these variations in the timing of the spring growing season affect marine populations. For the physical environment, we develop an annual index of spring timing by fitting a non-linear 2-parameter periodic function to the average weekly SST data observed in British Columbia from 1 January to the end of August each year. For each year, the phase parameter describes the timing of seasonal warming (the timing index) and the amplitude parameter describes the magnitude of the temperature increase between the fitted winter minimum and summer maximum. For the zooplankton, which have annual and strongly synchronous cycles of biomass, productivity, and developmental sequence, we use copepodite stage composition to index the timing of the annual maximum. For seabirds, we examine (1975–1999) the timing of hatching, nestling growth performance, and diet for four species of alcids at Triangle Island, British Columbia's largest seabird colony and the world's largest population of the planktivorous Cassin's auklet. Temperature, zooplankton, and seabirds have all shown recent decadal trends toward ‘earlier spring’, but the magnitudes of the timing perturbations have differed from variable to variable and from year to year. Recent (1996–1999) extreme interannual variation in spring timing and April SST helped to facilitate a mechanistic investigation of oceanographic features that affect the reproductive performance of seabirds. Our results demonstrate a significant negative relationship between the annual spring timing index (and April mean SST) and nestling growth rates for both Cassin's auklet and rhinoceros auklet. Nestling growth rates were significantly lower in early, warm years. We demonstrate that low growth rates of Cassin's auklet occurred when copepod composition in nestling diet was low overall and copepods were scarce or absent in samples collected later in the season. We propose that when spring is early and warm, the duration of overlap of seabird breeding and copepod availability in surface waters becomes reduced, effectively creating a seasonal mismatch of prey and predator populations. Such a mismatch could explain the reduced reproductive performance of seabirds compared to years when spring was later and colder. The relationships we develop here can be used as simple predictive models to examine the effects of ocean climate change on seabird reproductive performance within our region.CiteULike: Everyone's libraryhttp://www.bibsonomy.org/bibtex/2f2be754c7074ba9964e7ed905fcb2963/earthfareearthfare2009-05-19T18:00:18+02:00climate, shift, variability <span class="authorEditorList"><a href="/author/Beaugrand">G. Beaugrand</a> </span><em>Progress In Oceanography</em> <em>60(2-4):245--262</em> (<em>March 2004</em>)Tue May 19 18:00:18 CEST 2009Progress In OceanographyMarch2-4245--262The North Sea regime shift: Evidence, causes, mechanisms and consequences602004climate, shift, variability This paper focuses on the ecosystem regime shift in the North Sea that occurred during the period 1982–1988. The evidence for the change is seen from individual species to key ecosystem parameters such as diversity and from phytoplankton to fish. Although many biological/ecosystem parameters and individual species exhibited a stepwise change during the period 1983–1988, some indicators show no evidence of change. The cause of the regime shift is likely to be related to pronounced changes in large-scale hydro-meteorological forcing. This involved activating of complex intermediate physical mechanisms which explains why the exact timing of the shift can vary from 1982 to 1988 (centred around two periods: 1982–1985 and 1987–1988) according to the species or taxonomic group. Increased sea surface temperature and possibly change in wind intensity and direction at the end of the 1970s in the west European basin triggered a change in the location of an oceanic biogeographical boundary along the European continental shelf. This affected both the stable and substrate biotope components of North Sea marine ecosystems (i.e. components related to the water masses and components which are geographically stable) circa 1984. Large-scale hydro-climatic forcing also modified local hydro-meteorological parameters around the North Sea after 1987 affecting the stable biotope components of North Sea ecosystems. Problems related to the detection and quantification of an ecosystem regime shift are discussed.CiteULike: Everyone's libraryhttp://www.bibsonomy.org/bibtex/29d24193696c67307beb18d158bedab40/bobsicabobsica2009-03-30T22:21:12+02:00ATMOSPHERE CHANGE CLOUDS EXPLORER GLOBAL LEO LOWER MEASUREMENTS MESOSPHERE MIDDLE NOCTILUCENT PARTICLE-SIZE SOLAR SOUTHERN-HEMISPHERE STRATOSPHERIC SUMMER TEMPERATURE THERMOSPHERE VARIABILITY WATER-VAPOR cloud mesospheric noctilucent polar remote scattering sensing <span class="authorEditorList"><a href="/author/DeLand">M. T. DeLand</a>, <a href="/author/Shettle">E. P. Shettle</a>, <a href="/author/Thomas">G. E. Thomas</a>, and <a href="/author/Olivero">J. J. Olivero</a> </span><em>Journal of Atmospheric And Solar-Terrestrial Physics</em> <em>68(1):9-29</em> (<em>2006</em>)<em>ISI Document Delivery No.: 002TQ Times Cited: 8 Cited Reference Count: 108 Cited References: AKMAEV RA, 2000, GEOPHYS RES LETT, V27, P2113 <span class="info">...<div>ISI Document Delivery No.: 002TQ Times Cited: 8 Cited Reference Count: 108 Cited References: AKMAEV RA, 2000, GEOPHYS RES LETT, V27, P2113 ALFRED JM, 2001, EOS T AGU, V82, S284 BAILEY SM, 2005, J GEOPHYS RES-ATMOS, V110 BARTH CA, 1983, GEOPHYS RES LETT, V10, P237 BARTH CA, 2003, J GEOPHYS RES-SPACE, V108 BEIG G, 2003, REV GEOPHYS, V41 BURTON SP, 2000, P QUADR OZ S SAPP JA, P325 CARBARY JF, 1999, J GEOPHYS RES-SPACE, V104, P10089 CARBARY JF, 2001, GEOPHYS RES LETT, V28, P725 CARBARY JF, 2004, GEOPHYS RES LETT, V31 CHANDRA S, 1997, GEOPHYS RES LETT, V24, P639 CHU X, 2001, GEOPHYS RES LETT, V26, P1937 DEBRESTIAN DJ, 1997, ADV SPACE RES, V19, P587 DEBRESTIAN DJ, 1997, J GEOPHYS RES-ATMOS, V102, P1971 DELAND MT, 2001, J ATMOS OCEAN TECH, V18, P914 DELAND MT, 2003, J GEOPHYS RES-ATMOS, V108 DELAND MT, 2005, UNPUB GEOPHYSICAL RE DONAHUE TM, 1972, J ATMOS SCI, V30, P515 EVANS WFJ, 1995, GEOPHYS RES LETT, V22, P2793 FIEDLER J, 2003, J GEOPHYS RES-ATMOS, V108 FLEMING EL, 1995, J ATMOS TERR PHYS, V57, P333 FOGLE B, 1973, CLIMATOLOGICAL RES, P263 FRENCH WJR, 2004, J ATMOS SOL-TERR PHY, V66, P493 GADSDEN M, 1989, NOCTILUCENT CLOUDS GADSDEN M, 1998, J ATMOS SOL-TERR PHY, V60, P1163 GADSDEN M, 2000, J ATMOS SOL-TERR PHY, V62, P31 GADSDEN M, 2002, MEMOIRS BRIT ASTRONO, V45 GARCIA RR, 1989, J GEOPHYS RES-ATMOSP, V94, P14605 GAVINE D, 2002, MEMOIRS BRIT ASTRONO, V45 GELINAS LJ, 2005, J GEOPHYS RES, V110, A1310 GERRARD AJ, 2004, J ATMOS SOL-TERR PHY, V66, P229 GLACCUM W, 1996, J GEOPHYS RES-ATMOS, V101, P14479 GOLDBERG RA, 2001, GEOPHYS RES LETT, V28, P1407 GOLITSYN GS, 1996, GEOPHYS RES LETT, V23, P1741 GRUZDEV AN, 2005, J GEOPHYS RES, V110, D3304 HERNANDEZ G, 2003, GEOPHYS RES LETT, V30 HERVIG M, 2001, GEOPHYS RES LETT, V28, P971 HERVIG M, 2003, GEOPHYS RES LETT, V30 HUANG TYW, 1993, J GEOPHYS RES-ATMOSP, V98, P20413 HUNTEN DM, 1980, J ATMOS SCI, V37, P1342 JENSEN E, 1989, J GEOPHYS RES-ATMOSP, V94, P14693 JOINER J, 1996, J GEOPHYS RES-SPACE, V101, P5239 KALASHNIKOVA O, 2000, GEOPHYS RES LETT, V27, P3293 KHOSRAVI R, 2002, J GEOPHYS RES-ATMOS, V107 KIRKWOOD S, 2002, GEOPHYS RES LETT, V29 KIRKWOOD S, 2003, J GEOPHYS RES-ATMOS, V108 KLOSTERMEYER J, 2001, J GEOPHYS RES-ATMOS, V106, P9749 KLOSTERMEYER J, 2002, J GEOPHYS RES-ATMOS, V107 LESLIE RJ, 1918, NATURE, V33, P245 LLEWELLYN E, 2004, CAN J PHYS, V82, P411 LUBKEN FJ, 2000, GEOPHYS RES LETT, V27, P3603 LUBKEN FJ, 2004, J GEOPHYS RES-ATMOS, V109 LUCKE RL, 1999, J GEOPHYS RES-ATMOS, V104, P18785 MARSH D, 2003, J GEOPHYS RES-ATMOS, V108 MAULDIN LE, 1985, OPT ENG, V24, P307 MCHUGH M, 2003, GEOPHYS RES LETT, V30 MEIER RR, 1991, SPACE SCI REV, V58, P1 MERKEL AW, 2003, GEOPHYS RES LETT, V30 MURRAY BJ, 2003, PHYS CHEM CHEM PHYS, V5, P4129 NEDOLUHA GE, 2003, J GEOPHYS RES-ATMOS, V108 OFFERMANN D, 2004, J ATMOS SOL-TERR PHY, V66, P437 OLIVERO JJ, 1986, J ATMOS SCI, V43, P1263 OLIVERO JJ, 2001, ADV SPACE RES, V28, P931 OLTMANS SJ, 2000, GEOPHYS RES LETT, V27, P3453 ONEIL RR, 2001, EOS T AGU SPR M S, V82 PORTMANN RW, 1995, GEOPHYS RES LETT, V22, P1733 RANDEL WJ, 2004, J ATMOS SCI, V61, P2133 RAPP M, 2003, J GEOPHYS RES-ATMOS, V108 REMSBERG EE, 2002, J GEOPHYS RES-ATMOS, V107 ROBLE RG, 1989, GEOPHYS RES LETT, V16, P1441 ROMEJKO VA, 2003, J GEOPHYS RES-ATMOS, V108 ROSENLOF KH, 2001, GEOPHYS RES LETT, V28, P1195 ROTTMAN G, 2004, GEOPH MONOG SERIES, V141, P111 RUSCH DW, 1991, J GEOPHYS RES-ATMOSP, V96, P12933 RUSSELL JM, 1993, J GEOPHYS RES-ATMOSP, V98, P10777 SEELE C, 1999, GEOPHYS RES LETT, V26, P1517 SHEPHERD GG, 1993, J GEOPHYS RES-ATMOSP, V98, P10725 SHEPHERD MG, 2004, J GEOPHYS RES-ATMOS, V109 SHETTLE EP, 2002, J GEOPHYSICAL RES, V107 SHETTLE EP, 2002, MEMOIRS BRIT ASTRONO, V45 SISKIND DE, 2003, J GEOPHYS RES-ATMOS, V108 SISKIND DE, 2005, J ATMOS SOL-TERR PHY, V67, P501 SONNEMANN GR, 2005, J ATMOS SOL-TERR PHY, V67, P177 STEVENS MH, 2001, GEOPHYS RES LETT, V28, P4449 STEVENS MH, 2003, GEOPHYS RES LETT, V30 STEVENS MH, 2005, J GEOPHYS RES-SPACE, V110 THAYER JP, 2003, J GEOPHYS RES-ATMOS, V108 THOMAS GE, 1984, J ATMOS TERR PHYS, V46, P819 THOMAS GE, 1985, PLANET SPACE SCI, V33, P1209 THOMAS GE, 1989, J GEOPHYS RES-ATMOSP, V94, P14673 THOMAS GE, 1989, NATURE, V338, P490 THOMAS GE, 1991, J GEOPHYS RES-ATMOSP, V96, P927 THOMAS GE, 1991, REV GEOPHYS, V29, P553 THOMAS GE, 1995, GEOPHYS MONOGR, V87, P185 THOMAS GE, 1996, J ATMOS TERR PHYS, V58, P1629 THOMAS GE, 2000, EOS T AGU, V81, S336 THOMAS GE, 2001, ADV SPACE RES, V28, P937 THOMAS GE, 2003, ADV SPACE RES, V32, P1737 THOMAS GE, 2003, EOS T AGU, V84, P352 TURCO RP, 1982, PLANET SPACE SCI, V30, P1147 VONCOSSART G, 1999, GEOPHYS RES LETT, V26, P1513 VONSAVIGNY C, 2004, ADV SPACE RES, V34, P851 VONZAHN U, 1998, GEOPHYS RES LETT, V25, P1289 VONZAHN U, 2003, EOS T AGU, V84, P261 VONZAHN U, 2004, ATMOS CHEM PHYS, V4, P2449 WARREN SG, 1997, J GEOPHYS RES-ATMOS, V102, P1991 WEATHERHEAD EC, 1998, J GEOPHYS RES-ATMOS, V103, P17149 WOODS TN, 2000, J GEOPHYS RES-SPACE, V105, P27195</div></span> . </em>Mon Mar 30 22:21:12 CEST 2009Journal of Atmospheric And Solar-Terrestrial PhysicsISI Document Delivery No.: 002TQ Times Cited: 8 Cited Reference Count: 108 Cited References: AKMAEV RA, 2000, GEOPHYS RES LETT, V27, P2113 ALFRED JM, 2001, EOS T AGU, V82, S284 BAILEY SM, 2005, J GEOPHYS RES-ATMOS, V110 BARTH CA, 1983, GEOPHYS RES LETT, V10, P237 BARTH CA, 2003, J GEOPHYS RES-SPACE, V108 BEIG G, 2003, REV GEOPHYS, V41 BURTON SP, 2000, P QUADR OZ S SAPP JA, P325 CARBARY JF, 1999, J GEOPHYS RES-SPACE, V104, P10089 CARBARY JF, 2001, GEOPHYS RES LETT, V28, P725 CARBARY JF, 2004, GEOPHYS RES LETT, V31 CHANDRA S, 1997, GEOPHYS RES LETT, V24, P639 CHU X, 2001, GEOPHYS RES LETT, V26, P1937 DEBRESTIAN DJ, 1997, ADV SPACE RES, V19, P587 DEBRESTIAN DJ, 1997, J GEOPHYS RES-ATMOS, V102, P1971 DELAND MT, 2001, J ATMOS OCEAN TECH, V18, P914 DELAND MT, 2003, J GEOPHYS RES-ATMOS, V108 DELAND MT, 2005, UNPUB GEOPHYSICAL RE DONAHUE TM, 1972, J ATMOS SCI, V30, P515 EVANS WFJ, 1995, GEOPHYS RES LETT, V22, P2793 FIEDLER J, 2003, J GEOPHYS RES-ATMOS, V108 FLEMING EL, 1995, J ATMOS TERR PHYS, V57, P333 FOGLE B, 1973, CLIMATOLOGICAL RES, P263 FRENCH WJR, 2004, J ATMOS SOL-TERR PHY, V66, P493 GADSDEN M, 1989, NOCTILUCENT CLOUDS GADSDEN M, 1998, J ATMOS SOL-TERR PHY, V60, P1163 GADSDEN M, 2000, J ATMOS SOL-TERR PHY, V62, P31 GADSDEN M, 2002, MEMOIRS BRIT ASTRONO, V45 GARCIA RR, 1989, J GEOPHYS RES-ATMOSP, V94, P14605 GAVINE D, 2002, MEMOIRS BRIT ASTRONO, V45 GELINAS LJ, 2005, J GEOPHYS RES, V110, A1310 GERRARD AJ, 2004, J ATMOS SOL-TERR PHY, V66, P229 GLACCUM W, 1996, J GEOPHYS RES-ATMOS, V101, P14479 GOLDBERG RA, 2001, GEOPHYS RES LETT, V28, P1407 GOLITSYN GS, 1996, GEOPHYS RES LETT, V23, P1741 GRUZDEV AN, 2005, J GEOPHYS RES, V110, D3304 HERNANDEZ G, 2003, GEOPHYS RES LETT, V30 HERVIG M, 2001, GEOPHYS RES LETT, V28, P971 HERVIG M, 2003, GEOPHYS RES LETT, V30 HUANG TYW, 1993, J GEOPHYS RES-ATMOSP, V98, P20413 HUNTEN DM, 1980, J ATMOS SCI, V37, P1342 JENSEN E, 1989, J GEOPHYS RES-ATMOSP, V94, P14693 JOINER J, 1996, J GEOPHYS RES-SPACE, V101, P5239 KALASHNIKOVA O, 2000, GEOPHYS RES LETT, V27, P3293 KHOSRAVI R, 2002, J GEOPHYS RES-ATMOS, V107 KIRKWOOD S, 2002, GEOPHYS RES LETT, V29 KIRKWOOD S, 2003, J GEOPHYS RES-ATMOS, V108 KLOSTERMEYER J, 2001, J GEOPHYS RES-ATMOS, V106, P9749 KLOSTERMEYER J, 2002, J GEOPHYS RES-ATMOS, V107 LESLIE RJ, 1918, NATURE, V33, P245 LLEWELLYN E, 2004, CAN J PHYS, V82, P411 LUBKEN FJ, 2000, GEOPHYS RES LETT, V27, P3603 LUBKEN FJ, 2004, J GEOPHYS RES-ATMOS, V109 LUCKE RL, 1999, J GEOPHYS RES-ATMOS, V104, P18785 MARSH D, 2003, J GEOPHYS RES-ATMOS, V108 MAULDIN LE, 1985, OPT ENG, V24, P307 MCHUGH M, 2003, GEOPHYS RES LETT, V30 MEIER RR, 1991, SPACE SCI REV, V58, P1 MERKEL AW, 2003, GEOPHYS RES LETT, V30 MURRAY BJ, 2003, PHYS CHEM CHEM PHYS, V5, P4129 NEDOLUHA GE, 2003, J GEOPHYS RES-ATMOS, V108 OFFERMANN D, 2004, J ATMOS SOL-TERR PHY, V66, P437 OLIVERO JJ, 1986, J ATMOS SCI, V43, P1263 OLIVERO JJ, 2001, ADV SPACE RES, V28, P931 OLTMANS SJ, 2000, GEOPHYS RES LETT, V27, P3453 ONEIL RR, 2001, EOS T AGU SPR M S, V82 PORTMANN RW, 1995, GEOPHYS RES LETT, V22, P1733 RANDEL WJ, 2004, J ATMOS SCI, V61, P2133 RAPP M, 2003, J GEOPHYS RES-ATMOS, V108 REMSBERG EE, 2002, J GEOPHYS RES-ATMOS, V107 ROBLE RG, 1989, GEOPHYS RES LETT, V16, P1441 ROMEJKO VA, 2003, J GEOPHYS RES-ATMOS, V108 ROSENLOF KH, 2001, GEOPHYS RES LETT, V28, P1195 ROTTMAN G, 2004, GEOPH MONOG SERIES, V141, P111 RUSCH DW, 1991, J GEOPHYS RES-ATMOSP, V96, P12933 RUSSELL JM, 1993, J GEOPHYS RES-ATMOSP, V98, P10777 SEELE C, 1999, GEOPHYS RES LETT, V26, P1517 SHEPHERD GG, 1993, J GEOPHYS RES-ATMOSP, V98, P10725 SHEPHERD MG, 2004, J GEOPHYS RES-ATMOS, V109 SHETTLE EP, 2002, J GEOPHYSICAL RES, V107 SHETTLE EP, 2002, MEMOIRS BRIT ASTRONO, V45 SISKIND DE, 2003, J GEOPHYS RES-ATMOS, V108 SISKIND DE, 2005, J ATMOS SOL-TERR PHY, V67, P501 SONNEMANN GR, 2005, J ATMOS SOL-TERR PHY, V67, P177 STEVENS MH, 2001, GEOPHYS RES LETT, V28, P4449 STEVENS MH, 2003, GEOPHYS RES LETT, V30 STEVENS MH, 2005, J GEOPHYS RES-SPACE, V110 THAYER JP, 2003, J GEOPHYS RES-ATMOS, V108 THOMAS GE, 1984, J ATMOS TERR PHYS, V46, P819 THOMAS GE, 1985, PLANET SPACE SCI, V33, P1209 THOMAS GE, 1989, J GEOPHYS RES-ATMOSP, V94, P14673 THOMAS GE, 1989, NATURE, V338, P490 THOMAS GE, 1991, J GEOPHYS RES-ATMOSP, V96, P927 THOMAS GE, 1991, REV GEOPHYS, V29, P553 THOMAS GE, 1995, GEOPHYS MONOGR, V87, P185 THOMAS GE, 1996, J ATMOS TERR PHYS, V58, P1629 THOMAS GE, 2000, EOS T AGU, V81, S336 THOMAS GE, 2001, ADV SPACE RES, V28, P937 THOMAS GE, 2003, ADV SPACE RES, V32, P1737 THOMAS GE, 2003, EOS T AGU, V84, P352 TURCO RP, 1982, PLANET SPACE SCI, V30, P1147 VONCOSSART G, 1999, GEOPHYS RES LETT, V26, P1513 VONSAVIGNY C, 2004, ADV SPACE RES, V34, P851 VONZAHN U, 1998, GEOPHYS RES LETT, V25, P1289 VONZAHN U, 2003, EOS T AGU, V84, P261 VONZAHN U, 2004, ATMOS CHEM PHYS, V4, P2449 WARREN SG, 1997, J GEOPHYS RES-ATMOS, V102, P1991 WEATHERHEAD EC, 1998, J GEOPHYS RES-ATMOS, V103, P17149 WOODS TN, 2000, J GEOPHYS RES-SPACE, V105, P2719519-29A quarter-century of satellite polar mesospheric cloud observations682006ATMOSPHERE CHANGE CLOUDS EXPLORER GLOBAL LEO LOWER MEASUREMENTS MESOSPHERE MIDDLE NOCTILUCENT PARTICLE-SIZE SOLAR SOUTHERN-HEMISPHERE STRATOSPHERIC SUMMER TEMPERATURE THERMOSPHERE VARIABILITY WATER-VAPOR cloud mesospheric noctilucent polar remote scattering sensing Satellite observations of polar mesospheric clouds (PMCs) are extremely valuable because they typically have daily coverage to characterize seasonal variations, sufficient detections for each season to give good statistics, quantitative information for physical analysis, and coverage of both hemispheres to evaluate global behavior. Continuous spectral measurements in the ultraviolet provide information about particle size distributions. A typical PMC season begins approximately 20 days before summer solstice at 80 degrees latitude, rises rapidly in occurrence frequency to 80-90%, and remains at that level until 50-60 days after solstice. Both occurrence frequency and brightness are latitude dependent, with higher values observed toward the poles. PMCs are normally observed at altitudes of 82-83 km, with higher altitudes at the start and end of each season. Hemispheric differences in behavior are also observed. Northern Hemisphere PMCs are consistently both more frequent and brighter than Southern Hemisphere clouds. Cloud height is generally anti-correlated with cloud brightness. The availability of extended PMC data sets from satellites provides the opportunity to evaluate long-term PMC variations over the past few decades. Analysis of these lengthy data sets shows a clear anti-correlation between seasonally averaged PMC parameters (occurrence frequency and brightness) and solar UV activity over the past two solar cycles, in agreement with model predictions. A time lag of similar to 1 year between the solar cycle and the PMC response is present in several data sets (solar variation leads PMC response). The cause is unknown. Multiple regression analysis also indicates long-term increases in both occurrence frequency and brightness, although there is not yet a consensus on the magnitude of the increase. These results are compared with information about concurrent variations in plausible source mechanisms such as mesospheric water vapor and temperature. (c) 2005 Elsevier Ltd. All rights reserved.Leo's paper references IIhttp://www.bibsonomy.org/bibtex/256e017c5ddbde1a58eeb9a60ec825a25/danidani2009-02-23T06:39:51+01:00variability <span class="authorEditorList"><a href="/author/NAKAZAKI">T. NAKAZAKI</a> </span><em>Japanese Journal of Animal Psychology</em> <em>50(1):33--40</em> (<em>2000</em>)Mon Feb 23 06:39:51 CET 2009Japanese Journal of Animal Psychology133--40{A review of operant variability studies. Issues and implications.}502000variability http://www.bibsonomy.org/bibtex/2f86f89effe486fcb75cc3dd5ebfdd682/leonardoleonardo2009-02-11T20:46:37+01:002008 statecharts variability <span class="authorEditorList"><a href="/author/Szasz">Nora Szasz</a>, and <a href="/author/Vilanova">Pedro Vilanova</a> </span><em>VaMoS, </em><em>page 131--140. </em>(<em>2008</em>)Wed Feb 11 20:46:37 CET 2009VaMoS131--140ICB Research ReportStatecharts and Variabilities20082008 statecharts variability 'slides':http://www.vamos-workshop.net/2008/slides/Presentation_15.pdf