117 research outputs found

    INTEGRATION OF PHOTOGRAMMETRY AND PORTABLE MOBILE MAPPING TECHNOLOGY FOR 3D MODELING OF CULTURAL HERITAGE SITES: THE CASE STUDY OF THE BZIZA TEMPLE

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    Abstract. In this paper, we present a multi-sensor approach employed to obtain the 3D model of the Roman temple of Bziza (Lebanon) and its surroundings, a work carried out as part of the archaeological Northern Lebanon Project (NoLeP). The integration of photogrammetry and portable mobile mapping technology was tested to overcome the weaknesses of each individual surveying method, with the aim of producing a complete and realistic 3D reconstruction of the whole site, as well as capturing at high-resolution the architectural features of the main structure. Moreover, this case study serves to further investigate the accuracy that can be reached with mobile laser scanners, highlighting benefits and limitations of this rapid and efficient mapping technique also in the field of Cultural Heritage documentation

    Role of Multipoles in Counterion-Mediated Interactions between Charged Surfaces: Strong and Weak Coupling

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    We present general arguments for the importance, or lack thereof, of the structure in the charge distribution of counterions for counterion-mediated interactions between bounding symmetrically charged surfaces. We show that on the mean field or weak coupling level, the charge quadrupole contributes the lowest order modification to the contact value theorem and thus to the intersurface electrostatic interactions. The image effects are non-existent on the mean-field level even with multipoles. On the strong coupling level the quadrupoles and higher order multipoles contribute additional terms to the interaction free energy only in the presence of dielectric inhomogeneities. Without them, the monopole is the only multipole that contributes to the strong coupling electrostatics. We explore the consequences of these statements in all their generality.Comment: 12 pages, 3 figure

    Towards higher current and voltage LCLs

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    LCLs are widely used devices for power control and distribution in satellites. Traditionally, P-type MOSFETs have been used due to their simplicity from the control perspective. Actual ESA standard defines LCLs up to class 10 (10A) and 50V. However, 100V bus voltage is common in high power platforms and the current trend is to increase even more this value, around 300V. In this new scenario, the classic concept of LCL design needs to be revised, and this work proposes a simple alternative for P-type MOSFETs that operates at high voltage and can be easily scaled up in curren

    CEREBROSPINAL FLUID DRAINAGE DEVICES: EXPERIMENTAL CARACTERIZATION

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    Hydrocephalus is a pathophysiology due to the excess of cerebrospinal fluid in the brain ventricles and it can be caused by congenital defects, brain abnormalities, tumors, inflammations, infections, intracranial hemorrhage and others. Hydrocephalus can be followed by significant rise of intraventricular pressure due to the excess of production of cerebrospinalfluid over the absorption, resulting in a weakening of intellectual functions, serious neurological damage (decreased movement, sensation and functions), critical physical disabilities and even death. A procedure for treatment involves the placement of a ventricular catheter into the cerebral ventricles to divert/drain the cerebrospinal fluid flow to a bag outside of the patient body – provisory treatment known as external ventricular drainage (EVD). Another option is the permanent treatment, internal ventricular drainage (IVD), promoting the cerebrospinal fluid drainage to other body cavity, being more commonly the abdominal cavity. In both cases, EVD and IVD, it is necessary to use of some type of neurological valve in order to control the flow of cerebrospinal fluid. In the present work is proposed an experimental procedure to test the hydrodynamic behavior of a complete drainage system, or parts of them, in order to verify its performance when subjected to pressure gradients found in the human body. Results show that the method is well adapted to quantify the pressure drop in neurological systems

    Acquired Haemophilia A. Which is the best therapeutic choice in older adults? Single center study of 4 cases

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    Acquired haemophilia A (AHA) is a rare bleeding disorder due to autoantibodies directed against coagulation factor VIII. The treatment is based on recombinant activated factor VII and activated prothrombin complex concentrate. However, mainly in older patients, severe thrombotic complications have been reported. Here we report the different therapeutic approaches in 4 cases of elderly patients with AHA and co-morbidities

    S.11.1 Influence of digital ulcer healing on disability and daily activity limitations in SSc

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    Objective. We previously showed that DU significantly increased global and hand disability with a significant impact on activities of daily living (ADLs) and work disability. This study aims to evaluate the impact of digital ulcer (DU) healing on disability and daily activity limitations in SSc. Methods. From January 2008 and June 2009, we prospectively evaluated 189 SSc patients for DU history, disability, employment and occupational status during meetings of the French SSc Patient Association (n = 86, 45.5%) or during hospitalization (n = 103, 54.5%)1. Among the 60 patients with at least one active DU at baseline (M0), 40 patients were followed longitudinally over 6 (3) months. These patients were evaluated for DU history, global and hand disability, health-related quality of life (HRQoL), daily activity limitation and employment status. Results. The median (IQR) age was 57.5 (43.5-68) years and the median (IQR) disease duration was 8.3 (3-16.5) years. Twenty-two (55%) patients had diffuse SSc and 34 (85%) were females. At baseline, a mean of 2.9 (2.8) DU per patient was reported. Thirty-three (82.5%) patients had ischaemic DU, 7 (17.5%) patients had >1 DU associated with calcinosis and 13 (32.5%) patients had mechanical DU. Thirteen (32.5%) patients had >4 DU at baseline. Among the 40 patients, 16 (40%) patients showed complete ulcer healing. In these patients with DU, the presence of calcinosis was associated with a lower probability of healing (P = 0.03). Comparison between healed and no-healed DU patients showed an improvement of hand disability provided by an improvement of the Cochin Hand Function score (P = 0.05)) and a trend towards HAQ domain dressing and grooming (P = 0.06) between M0 and M6 (3) visit in healed patients but not in no-healed patients. Concerning HRQoL, there were no difference for Mental and Physical component Scores of SF-36 but significant improvement of Bodily Pain score (P = 0.04) and Physical Role score (P = 0.05) between M0 and M6 (3) visit in patients with healed DU. The absence of healing was associated with significantly decreased work productivity (P = 0.05), whereas the performance in ADL was not significantly decreased (P = 0.15). Patients who were on sick-leave and who received some help for household tasks at the time of active DU were more likely to heal. Conclusion. For the first time, we provide prospective data with evidence that DU healing is associated with an improvement in hand function. Sick leave was associated with better healing of D

    Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

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    [EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.This study has been funded by Instituto de Salud Carlos III through the project PI17/00856 (Co-funded by the European Regional Development Fund, A way to make Europe). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Colás, A.; Vigil, L.; Vargas, B.; Cuesta Frau, D.; Varela, M. (2019). Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics. PLoS ONE. 14(12):1-15. https://doi.org/10.1371/journal.pone.0225817S1151412Goldstein, B., Fiser, D. H., Kelly, M. M., Mickelsen, D., Ruttimann, U., & Pollack, M. M. (1998). Decomplexification in critical illness and injury: Relationship between heart rate variability, severity of illness, and outcome. Critical Care Medicine, 26(2), 352-357. doi:10.1097/00003246-199802000-00040Varela, M. (2008). The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 1, 3-11. doi:10.2147/dmso.s3812Vikman, S., Mäkikallio, T. H., Yli-Mäyry, S., Pikkujämsä, S., Koivisto, A.-M., Reinikainen, P., … Huikuri, H. V. (1999). Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation. Circulation, 100(20), 2079-2084. doi:10.1161/01.cir.100.20.2079Wang, H., Naghavi, M., Allen, C., Barber, R. M., Bhutta, Z. A., Carter, A., … Coates, M. M. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459-1544. doi:10.1016/s0140-6736(16)31012-1Saudek, C. D., Derr, R. L., & Kalyani, R. R. (2006). Assessing Glycemia in Diabetes Using Self-monitoring Blood Glucose and Hemoglobin A1c. JAMA, 295(14), 1688. doi:10.1001/jama.295.14.1688Monnier, L., Colette, C., & Owens, D. R. (2008). Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it? Journal of Diabetes Science and Technology, 2(6), 1094-1100. doi:10.1177/193229680800200618Abdul-Ghani, M. A., Tripathy, D., & DeFronzo, R. A. (2006). Contributions of  -Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care, 29(5), 1130-1139. doi:10.2337/dc05-2179(2017). 2. Classification and Diagnosis of Diabetes:Standards of Medical Care in Diabetes—2018. Diabetes Care, 41(Supplement 1), S13-S27. doi:10.2337/dc18-s002Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: a high-risk state for diabetes development. The Lancet, 379(9833), 2279-2290. doi:10.1016/s0140-6736(12)60283-9DeFronzo, R. A., Banerji, M. A., Bray, G. A., Buchanan, T. A., Clement, S., … Tripathy, D. (2009). Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia, 53(3), 435-445. doi:10.1007/s00125-009-1614-2Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired Fasting Glucose and Impaired Glucose Tolerance: Implications for care. Diabetes Care, 30(3), 753-759. doi:10.2337/dc07-9920Ogata, H., Tokuyama, K., Nagasaka, S., Tsuchita, T., Kusaka, I., Ishibashi, S., … Yamamoto, Y. (2012). The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism, 61(7), 1041-1050. doi:10.1016/j.metabol.2011.12.007Kohnert, K.-D. (2015). Utility of different glycemic control metrics for optimizing management of diabetes. World Journal of Diabetes, 6(1), 17. doi:10.4239/wjd.v6.i1.17García Maset, L., González, L. B., Furquet, G. L., Suay, F. M., & Marco, R. H. (2016). Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes. Diabetes Technology & Therapeutics, 18(11), 719-724. doi:10.1089/dia.2016.0208Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987). Measurements of Glucose Control. Diabetes Care, 10(2), 225-237. doi:10.2337/diacare.10.2.225Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., & Stanley, H. E. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2466-2472. doi:10.1073/pnas.012579499Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Platiša, M. M., Bojić, T., Pavlović, S. U., Radovanović, N. N., & Kalauzi, A. (2016). Generalized Poincaré Plots-A New Method for Evaluation of Regimes in Cardiac Neural Control in Atrial Fibrillation and Healthy Subjects. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00038García-Puig, J., Ruilope, L. M., Luque, M., Fernández, J., Ortega, R., & Dal-Ré, R. (2006). Glucose Metabolism in Patients with Essential Hypertension. The American Journal of Medicine, 119(4), 318-326. doi:10.1016/j.amjmed.2005.09.010Lepot, M., Aubin, J.-B., & Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9(10), 796. doi:10.3390/w9100796Eke, A., Hermán, P., Bassingthwaighte, J., Raymond, G., Percival, D., Cannon, M., … Ikrényi, C. (2000). Physiological time series: distinguishing fractal noises from motions. Pflügers Archiv - European Journal of Physiology, 439(4), 403-415. doi:10.1007/s004249900135Eke, A., Herman, P., Kocsis, L., & Kozak, L. R. (2002). Fractal characterization of complexity in temporal physiological signals. Physiological Measurement, 23(1), R1-R38. doi:10.1088/0967-3334/23/1/201King, A. B., Philis-Tsimikas, A., Kilpatrick, E. S., Langbakke, I. H., Begtrup, K., & Vilsbøll, T. (2017). A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges. Diabetes Technology & Therapeutics, 19(4), 255-264. doi:10.1089/dia.2016.0405Colas, A., Vigil, L., Rodríguez de Castro, C., Vargas, B., & Varela, M. (2018). New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews, 34(5), e3002. doi:10.1002/dmrr.3002Henriques, T., Munshi, M. N., Segal, A. R., Costa, M. D., & Goldberger, A. L. (2014). «Glucose-at-a-Glance». Journal of Diabetes Science and Technology, 8(2), 299-306. doi:10.1177/1932296814524095Hinton, P. R. (2004). Statistics Explained. doi:10.4324/9780203496787Van Cauter, E., Blackman, J. D., Roland, D., Spire, J. P., Refetoff, S., & Polonsky, K. S. (1991). Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. Journal of Clinical Investigation, 88(3), 934-942. doi:10.1172/jci115396Qian, J., & Scheer, F. A. J. L. (2016). Circadian System and Glucose Metabolism: Implications for Physiology and Disease. Trends in Endocrinology & Metabolism, 27(5), 282-293. doi:10.1016/j.tem.2016.03.00

    Good behaviour with respect to the stiffness in the numerical integration of retarded functional differential equations

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    In this paper we obtain, for the global errors of a functional continuous Runge\u2013Kutta (FCRK) method as applied to a retarded functional differential equation (RFDE), a recursive relation similar to that obtained for the global errors of a one-step method as applied to an ordinary differential equation. After which, we introduce a notion of good behavior with respect to the stiffness of an FCRK method on a given family of RFDEs. Finally, we analyze this notion of \u201cgood behavior\u201d in the case of particular families of scalar semilinear RFDEs with nonvanishing delays
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