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

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

MULTISPECTRAL AND HIGH-RESOLUTION IMAGES AS SOURCES FOR ARCHAEOLOGICAL SURVEYS. NEW DATA FROM IRAQI KURDISTAN

The paper presents the results of a two-year archaeological survey carried out in the Iraqi Kurdistan, namely within the Navkur plain that has been extensively explored by the University of Udine since 2012. The surveys were planned in advance using Remote Sensing products available online and processed with Google Earth Engine, a large-scale cloud computing service specifically designed to process geospatial big data and especially satellite imagery. Images from Landsat 5, Landsat 7 and Sentinel-2 platforms were selected, processed and assessed. After two years, an overall number of 46 new and previously unknown sites have been localized and surveyed, contributing to the knowledge of the past history of this portion of the Kurdistan region and testing the use of Remote Sensing cloud-computing applications in the context of Near Eastern archaeological research

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

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

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

Multi-temporal analysis to support the management of torrent control structures

In the last decade with increasing frequency of extreme weather events, an accurate, sustainable, and effective planning of torrent control structures has become essential to reduce hydro-geomorphic risk. Quite often in planning interventions, there is a lack of information on the effectiveness of existing structures, the evolution of the ongoing hydro-geomorphic process, and a priori in-depth study to analyze the sediment morphology dynamics and the interaction with possible existing torrent control structures. Nowadays, High-Resolution Topography data (HRT) greatly simplifies the monitoring of sediment morphology dynamics and the understanding of the interaction with torrent control structures over time. Therefore, thanks to repeated HRT surveys, it is possible to derive multi-temporal Digital Terrain Models (DTMs), and DTMs of Difference (DoDs) to quantify the morphological changes and study continuously the catchment morphodynamics. This information can be very valuable to support watershed management plans if combined with up-to-date field surveys that identify the existing torrent control structures, and asses their current status and functionality. The present work aims at introducing a methodological approach based on the integration of the sediment morphology dynamics data over large time spans (e.g., from 2003 to 2022), obtained by multi-temporal DoDs (realized from three DTMs at 1 m resolution), with an updating cadastre of torrent control structures enriched by a very simple, quick, and user-friendly Maintenance Priority index (MPi). The proposed workflow proved to be very useful in the test basins analysed, providing more complete data on torrent control structures and sediment dynamics evidence to stakeholders compared to the past. Moreover, it served as a proxy to assess the long-term effectiveness of the management interventions. The approach also helped to constantly identify the areas most prone to hazards, improve the intervention planning, and find more appropriate solutions or direct the maintenance works. Finally, the suggested workflow could be the starting point to outline up-to-date guidelines to be used in other catchments equipped with torrent control structures, emphasizing possible intervention priorities on where decision-makers could better invest resources. By providing current information and accurate tools to realize a more complete decision-making chain, which is often neglected, it is certainly possible to support more sustainable and effective risk management decisions

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

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

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

[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