Monitoring the Petermann Ice Island with TanDEM-X

This paper presents the processing of TanDEM-X acquisitions for the monitoring of the topography of the Petermann ice island. In this particular case the area under study is continuously moving and the acquisition geometry is changing, so the processing of the iceberg’s DEMs is challenging and additional effects are to be considered. The SAR processing chain used is presented and the results obtained summarized, showing the effects and limitations observed during the process

Early neurodevelopmental outcome of low birth weight infants surviving neonatal intraventricular hemorrhage

Peer Reviewe

Rainfall partitioning after thinning in two low-biomass semiarid forests: Impact of meteorological variables and forest structure on the effectiveness of water-oriented treatments

[EN] Water-oriented forest management is an urgent need in semiarid catchments. In the case of low-biomass forests and shrublands, the magnitude, efficiency and temporal duration of thinning effects on rainfall partitioning needs further attention. This work studies the effects of juvenile thinning and shrub clearing on stemflow (Stf), throughfall (Thr) and interception (It) in two low-biomass forests (CAL: post-fire Aleppo pine saplings with 74% of basal area, BA, removed; and HU: evergreen oak coppice with 41% of BA removed), as well as the relative contribution of the event meteorology. The effects are compared with a control plot during the first 3¿4¿years. Stf rate (%) decreased with density and, on a tree scale, it was enhanced by the treatment only in the bigger oaks. Event Thr increased from 55 to 81% and from 68 to 86% of gross rainfall (Pg) for CAL and HU respectively after thinning, resulting in about 15% less intercepted Pg. High evaporative conditions and an open (ventilated) forest structure led to high It rates in the controls when comparing with other studies, thus making the treatments more efficient in net precipitation (Pn) gain (Pg intercepted decreased 17% or 2.3% per unit of LAI or BA removed respectively). In general, depths (mm) were mostly explained (>75%) by the rainfall characteristics of the event (e.g. amount, duration, intensity), with a limited contribution from forest structure (e.g. cover, LAI) and event meteorology (e.g. temperature, wind speed, vapor pressure deficit). On the contrary, when expressed as rates (% of Pg), forest structure and event-meteorology gained importance (explaining 25¿65%), especially in the drier site (CAL). In this site, the low gain in Pn (~25¿mm per year on average) was offset with no temporal dampening during the span of this study, as observed in the wetter site (HU), where plant growth tended to mitigate the effect of the treatment by the end of the study. The results presented here make a contribution to a better understanding of the effects of water-oriented forest management in low-biomass semiarid forests.This study is a component of research projects: HYDROSIL (CGL2011-28776-C02-02), SILWAMED (CGL2014-58127-C3-2) and CEHYRFO-MED (CGL2017-86839-C3-2-R) funded by the Spanish Ministry of Science and Innovation and the FEDER fund of the EU. The authors are grateful to the Valencia Regional Government (CMAAUV, Generalitat Valenciana), Serra municipality, VAERSA and ACCIONA for their support in allowing the use of the experimental forest and for their assistance in carrying out the fieldwork.Campo García, ADD.; González Sanchís, MDC.; Lidón, A.; Ceacero Ruiz, CJ.; Garcia-Prats, A. (2018). Rainfall partitioning after thinning in two low-biomass semiarid forests: Impact of meteorological variables and forest structure on the effectiveness of water-oriented treatments. Journal of Hydrology. 565:74-86. https://doi.org/10.1016/j.jhydrol.2018.08.013S748656

Boosting the activity of transition metal carbides towards methane activation by nanostructuring

The interaction of methane with pristine surfaces of bulk MoC and Mo2C is known to be weak. In contrast, a series of X-ray photoelectron spectroscopy (XPS) experiments, combined with thermal desorption mass spectroscopy (TDS), for MoCy (y = 0.5-1.3) nanoparticles supported on Au(111)¿which is completely inert towards CH4¿show that these systems adsorb and dissociate CH4 at room temperature and low CH4 partial pressure. This industrially-relevant finding has been further investigated with accurate density functional theory (DFT) based calculations on a variety of MoCy supported model systems. The DFT calculations reveal that the MoCy/Au(111) systems can feature low C-H bond scission energy barriers, smaller than the CH4 adsorption energy. Our theoretical results for bulk surfaces of Mo2C and MoC show that a simple Brønsted-Evans-Polanyi (BEP) relationship holds for C-H bond scission on these systems. However, this is not the case for methane activation on the MoCy nanoparticles as a consequence of their unique electronic and chemical properties. The discovery that supported molybdenum carbide nanoparticles are able to activate methane at room temperature paves the road towards the design of a new family of active carbide catalysts for methane activation and valorisation, with important implications in climate change mitigation and carbon cycle closure

Room temperature methane capture and activation by Ni clusters supported on TiC(001): effects of metal-carbide interactions on the cleavage of the C-H bond

Methane is an extremely stable molecule, a major component of natural gas, and also one of the most potent greenhouse gases contributing to global warming. Consequently, the capture and activation of methane is a challenging and intensively studied topic. A major research goal is to find systems that can activate methane even at low temperature. Here, combining ultrahigh vacuum catalytic experiments followed by X-ray photoemission spectra and accurate density functional theory (DFT) based calculations, we show that small Ni clusters dispersed on the (001) surface of TiC are able to capture and dissociate methane at room temperature. Our DFT calculations reveal that two-dimensional Ni clusters are responsible of this chemical transformation, evidencing that the lability of the supported clusters appears to be a critical aspect in the strong adsorption of methane. A small energy barrier of 0.18 eV is predicted for CH4 dissociation into adsorbed methyl and hydrogen atom species. In addition, the calculated reaction free energy profile at 300 K and 1 atm of CH4 shows no effective energy barriers in the system. Comparing with other reported systems which activate methane at room temperature, including oxide and zeolite-based materials, indicates that a different chemistry takes place on our metal/carbide system. The discovery of a carbide-based surface able to activate methane at low temperatures paves the road for the design of new types of catalysts towards an efficient conversion of this hydrocarbon into other added-value chemicals, with implications in climate change mitigation

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

[EN] Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 +/- 4.3% and 76.2 +/- 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and by the Generalitat Valenciana (AICO/2019/220).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Monfort-Ortiz, R.; Martinez-Saez, C.; Perales, A.... (2020). Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. Sensors. 20(9):1-16. https://doi.org/10.3390/s20092681S116209Beck, S., Wojdyla, D., Say, L., Pilar Bertran, A., Meraldi, M., Harris Requejo, J., … Van Look, P. (2010). The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bulletin of the World Health Organization, 88(1), 31-38. doi:10.2471/blt.08.062554Zeitlin, J., Szamotulska, K., Drewniak, N., Mohangoo, A., Chalmers, J., … Sakkeus, L. (2013). Preterm birth time trends in Europe: a study of 19 countries. BJOG: An International Journal of Obstetrics & Gynaecology, 120(11), 1356-1365. doi:10.1111/1471-0528.12281Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75-84. doi:10.1016/s0140-6736(08)60074-4Petrou, S. (2005). The economic consequences of preterm birth duringthe first 10 years of life. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 10-15. doi:10.1111/j.1471-0528.2005.00577.xLucovnik, M., Chambliss, L. R., & Garfield, R. E. (2013). Costs of unnecessary admissions and treatments for «threatened preterm labor». American Journal of Obstetrics and Gynecology, 209(3), 217.e1-217.e3. doi:10.1016/j.ajog.2013.06.046Haas, D., Benjamin, T., Sawyer, R., & Quinney, S. (2014). Short-term tocolytics for preterm delivery &ndash; current perspectives. International Journal of Women’s Health, 343. doi:10.2147/ijwh.s44048Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Devedeux, D., Marque, C., Mansour, S., Germain, G., & Duchêne, J. (1993). Uterine electromyography: A critical review. American Journal of Obstetrics and Gynecology, 169(6), 1636-1653. doi:10.1016/0002-9378(93)90456-sChkeir, A., Fleury, M.-J., Karlsson, B., Hassan, M., & Marque, C. (2013). Patterns of electrical activity synchronization in the pregnant rat uterus. BioMedicine, 3(3), 140-144. doi:10.1016/j.biomed.2013.04.007Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yHoroba, K., Jezewski, J., Matonia, A., Wrobel, J., Czabanski, R., & Jezewski, M. (2016). Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybernetics and Biomedical Engineering, 36(4), 574-583. doi:10.1016/j.bbe.2016.06.004Vinken, M. P. G. C., Rabotti, C., Mischi, M., & Oei, S. G. (2009). Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstetrical & Gynecological Survey, 64(8), 529-541. doi:10.1097/ogx.0b013e3181a8c6b1Vrhovec, J., Macek-Lebar, A., & Rudel, D. (s. f.). Evaluating Uterine Electrohysterogram with Entropy. IFMBE Proceedings, 144-147. doi:10.1007/978-3-540-73044-6_36Diab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical Engineering & Physics, 36(6), 761-767. doi:10.1016/j.medengphy.2014.01.009Lemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Fergus, P., Idowu, I., Hussain, A., & Dobbins, C. (2016). Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing, 188, 42-49. doi:10.1016/j.neucom.2015.01.107Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE, 8(10), e77154. doi:10.1371/journal.pone.0077154Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., & Kendrick, K. M. (2015). Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLOS ONE, 10(7), e0132116. doi:10.1371/journal.pone.0132116Degbedzui, D. K., & Yüksel, M. E. (2020). Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals. Computers in Biology and Medicine, 119, 103677. doi:10.1016/j.compbiomed.2020.103677Borowska, M., Brzozowska, E., Kuć, P., Oczeretko, E., Mosdorf, R., & Laudański, P. (2018). Identification of preterm birth based on RQA analysis of electrohysterograms. Computer Methods and Programs in Biomedicine, 153, 227-236. doi:10.1016/j.cmpb.2017.10.018Vinken, M. P. G. C., Rabotti, C., Mischi, M., van Laar, J. O. E. H., & Oei, S. G. (2010). Nifedipine-Induced Changes in the Electrohysterogram of Preterm Contractions: Feasibility in Clinical Practice. Obstetrics and Gynecology International, 2010, 1-8. doi:10.1155/2010/325635Mas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yBradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi:10.1016/s0031-3203(96)00142-2Alexandersson, A., Steingrimsdottir, T., Terrien, J., Marque, C., & Karlsson, B. (2015). The Icelandic 16-electrode electrohysterogram database. Scientific Data, 2(1). doi:10.1038/sdata.2015.17Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Perales, A., & Ye-Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/5373810Terrien, J., Marque, C., & Karlsson, B. (2007). Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4352680Rooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). Low-complexity intrauterine pressure estimation using the Teager energy operator on electrohysterographic recordings. Physiological Measurement, 35(7), 1215-1228. doi:10.1088/0967-3334/35/7/1215Ahmed, M., Chanwimalueang, T., Thayyil, S., & Mandic, D. (2016). A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis. Entropy, 19(1), 2. doi:10.3390/e19010002Karmakar, C. K., Khandoker, A. H., Gubbi, J., & Palaniswami, M. (2009). Complex Correlation Measure: a novel descriptor for Poincaré plot. BioMedical Engineering OnLine, 8(1), 17. doi:10.1186/1475-925x-8-17Roy, B., & Ghatak, S. (2013). Nonlinear Methods to Assess Changes in Heart Rate Variability in Type 2 Diabetic Patients. Arquivos Brasileiros de Cardiologia. doi:10.5935/abc.20130181Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries. (2013). 2013 30th National Radio Science Conference (NRSC). doi:10.1109/nrsc.2013.6587953Aditya, S., & Tibarewala, D. N. (2012). Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. International Journal of Artificial Intelligence and Soft Computing, 3(2), 143. doi:10.1504/ijaisc.2012.049010Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Li, Z., Zhang, Q., & Zhao, X. (2017). Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries. International Journal of Distributed Sensor Networks, 13(9), 155014771773339. doi:10.1177/1550147717733391Murthy, H. S. N., & Meenakshi, D. M. (2015). ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison. Bonfring International Journal of Research in Communication Engineering, 5(2), 07-11. doi:10.9756/bijrce.8030Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153. doi:10.1016/j.knosys.2011.07.016Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: International Journal of Forecasting, 14(1), 35-62. doi:10.1016/s0169-2070(97)00044-7Lawrence, S., & Giles, C. L. (2000). Overfitting and neural networks: conjugate gradient and backpropagation. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. doi:10.1109/ijcnn.2000.857823Diab, A., Hassan, M., Boudaoud, S., Marque, C., & Karlsson, B. (2013). Nonlinear estimation of coupling and directionality between signals: Application to uterine EMG propagation. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2013.6610513Most, O., Langer, O., Kerner, R., Ben David, G., & Calderon, I. (2008). Can myometrial electrical activity identify patients in preterm labor? American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Mas-Cabo, J., Prats-Boluda, G., Ye-Lin, Y., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2019). Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomedical Signal Processing and Control, 52, 198-205. doi:10.1016/j.bspc.2019.04.001You, J., Kim, Y., Seok, W., Lee, S., Sim, D., Park, K. S., & Park, C. (2019). Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor. Journal of Electrical Engineering & Technology, 14(2), 897-916. doi:10.1007/s42835-019-00118-9Schuit, E., Scheepers, H., Bloemenkamp, K., Bolte, A., Duvekot, H., … van Eyck, J. (2015). Predictive Factors for Delivery within 7 Days after Successful 48-Hour Treatment of Threatened Preterm Labor. American Journal of Perinatology Reports, 05(02), e141-e149. doi:10.1055/s-0035-1552930Liao, J. B., Buhimschi, C. S., & Norwitz, E. R. (2005). Normal Labor: Mechanism and Duration. Obstetrics and Gynecology Clinics of North America, 32(2), 145-164. doi:10.1016/j.ogc.2005.01.001Ye-Lin, Y., Garcia-Casado, J., Prats-Boluda, G., Alberola-Rubio, J., & Perales, A. (2014). Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/47078

Enhancement of the non-invasive electroenterogram to identify intestinal pacemaker activity

Surface recording of electroenterogram (EEnG) is a non-invasive method for monitoring intestinal myoelectrical activity. However, surface EEnG is seriously affected by a variety of interferences: cardiac activity, respiration, very low frequency components and movement artefacts. The aim of this study is to eliminate respiratory interference and very low frequency components from external EEnG recording by means of empirical mode decomposition (EMD), so as to obtain more robust indicators of intestinal pacemaker activity from external EEnG signal. For this purpose, 11 recording sessions were performed in an animal model under fasting conditions and in each individual session the myoelectrical signal was recorded simultaneously in the intestinal serosa and the external abdominal surface in physiological states. Various parameters have been proposed for evaluating the efficacy of the method in reducing interferences: the signal-to-interference ratio (S/I ratio), attenuation of the target and interference signals, the normal slow wave percentage and the stability of the dominant frequency (DF) of the signal. The results show that the S/I ratio of the processed signals is significantly greater than the original values (9.66±4.44 dB vs. 1.23±5.13 dB), while the target signal was barely attenuated (-0.63±1.02 dB). The application of the EMD method also increased the percentage of the normal slow wave to 100% in each individual session and enabled the stability of the DF of the external signal to be increased considerably. Furthermore, the variation coefficient of the DF derived from the external processed signals is comparable to the coefficient obtained using internal recordings. Therefore the EMD method could be a very useful tool to improve the quality of external EEnG recording in the low frequency range, and therefore to obtain more robust indicators of the intestinal pacemaker activity from non invasive EEnG recordingsThe authors would like to thank D Alvarez-Martinez, Dr C Vila and the Veterinary Unit of the Research Centre of 'La Fe' University Hospital (Valencia, Spain), where the surgical interventions and recording sessions were carried out, and the R+D+I Linguistic Assistance Office at the UPV for their help in revising this paper. This research study was sponsored by the Ministerio de Ciencia y Tecnologia de Espana (TEC2007-64278) and by the Universidad Politecnica de Valencia, as part of a UPV research and development Grant Programme.Ye Lin, Y.; Garcia Casado, FJ.; Prats Boluda, G.; Ponce, JL.; Martínez De Juan, JL. (2009). Enhancement of the non-invasive electroenterogram to identify intestinal pacemaker activity. PHYSIOLOGICAL MEASUREMENT. 30(9):885-902. https://doi.org/10.1088/0967-3334/30/9/002S885902309Amaris, M. A., Sanmiguel, C. P., Sadowski, D. C., Bowes, K. L., & Mintchev, M. P. (2002). Digestive Diseases and Sciences, 47(11), 2480-2485. doi:10.1023/a:1020503908304Bass, P., & Wiley, J. N. (1965). Electrical and extraluminal contractile-force activity of the duodenum of the dog. The American Journal of Digestive Diseases, 10(3), 183-200. doi:10.1007/bf02233747Bradshaw, L. A., Allos, S. H., Wikswo, J. P., & Richards, W. O. (1997). Correlation and comparison of magnetic and electric detection of small intestinal electrical activity. American Journal of Physiology-Gastrointestinal and Liver Physiology, 272(5), G1159-G1167. doi:10.1152/ajpgi.1997.272.5.g1159Camilleri, M., Hasler, W. L., Parkman, H. P., Quigley, E. M. M., & Soffer, E. (1998). Measurement of gastrointestinal motility in the GI laboratory. Gastroenterology, 115(3), 747-762. doi:10.1016/s0016-5085(98)70155-6Chen, J. D. Z., & Lin, Z. (1993). Adaptive cancellation of the respiratory artifact in surface recording of small intestinal electrical activity. Computers in Biology and Medicine, 23(6), 497-509. doi:10.1016/0010-4825(93)90097-kChen, J., & McCallum, R. W. (1991). Electrogastrography: measuremnt, analysis and prospective applications. Medical & Biological Engineering & Computing, 29(4), 339-350. doi:10.1007/bf02441653Chen, J. D. Z., Schirmer, B. D., & McCallum, R. W. (1993). Measurement of electrical activity of the human small intestine using surface electrodes. IEEE Transactions on Biomedical Engineering, 40(6), 598-602. doi:10.1109/10.237682Garcia-Casado, J., Martinez-de-Juan, J. L., & Ponce, J. L. (2005). Noninvasive Measurement and Analysis of Intestinal Myoelectrical Activity Using Surface Electrodes. IEEE Transactions on Biomedical Engineering, 52(6), 983-991. doi:10.1109/tbme.2005.846730Gordon, A. D. (1987). A Review of Hierarchical Classification. Journal of the Royal Statistical Society. Series A (General), 150(2), 119. doi:10.2307/2981629Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. doi:10.1098/rspa.1998.0193Irimia, A., & Bradshaw, L. A. (2005). Artifact reduction in magnetogastrography using fast independent component analysis. Physiological Measurement, 26(6), 1059-1073. doi:10.1088/0967-3334/26/6/015Lammers, W. J. E. P., & Stephen, B. (2008). Origin and propagation of individual slow waves along the intact feline small intestine. Experimental Physiology, 93(3), 334-346. doi:10.1113/expphysiol.2007.039180Liang, H. (2001). Adaptive independent component analysis of multichannel electrogastrograms. Medical Engineering & Physics, 23(2), 91-97. doi:10.1016/s1350-4533(01)00019-4Liang, J., Cheung, J. Y., & Chen, J. D. Z. (1997). Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks. Annals of Biomedical Engineering, 25(5), 850-857. doi:10.1007/bf02684169Liang, H., Lin, Z., & McCallum, R. W. (2000). Artifact reduction in electrogastrogram based on empirical mode decomposition method. Medical & Biological Engineering & Computing, 38(1), 35-41. doi:10.1007/bf02344686Zhi-Yue Lin, Chen, Z., & Jian De. (1994). Time-frequency representation of the electrogastrogram-application of the exponential distribution. IEEE Transactions on Biomedical Engineering, 41(3), 267-275. doi:10.1109/10.284945Lin, Z. Y., & Chen, J. D. Z. (1994). Recursive running DCT algorithm and its application in adaptive filtering of surface electrical recording of small intestine. Medical & Biological Engineering & Computing, 32(3), 317-322. doi:10.1007/bf02512529Lin, Z., & Chen, J. D. Z. (1995). Comparison of three running spectral analysis methods for electrogastrographic signals. Medical & Biological Engineering & Computing, 33(4), 596-604. doi:10.1007/bf02522520Maestri, R., Pinna, G. D., Porta, A., Balocchi, R., Sassi, R., Signorini, M. G., … Raczak, G. (2007). Assessing nonlinear properties of heart rate variability from short-term recordings: are these measurements reliable? Physiological Measurement, 28(9), 1067-1077. doi:10.1088/0967-3334/28/9/008Martinez-de-Juan, J. ., Saiz, J., Meseguer, M., & Ponce, J. . (2000). Small bowel motility: relationship between smooth muscle contraction and electroenterogram signal. Medical Engineering & Physics, 22(3), 189-199. doi:10.1016/s1350-4533(00)00032-1Mintchev, M. P., Kingma, Y. J., & Bowes, K. L. (1993). Accuracy of cutaneous recordings of gastric electrical activity. Gastroenterology, 104(5), 1273-1280. doi:10.1016/0016-5085(93)90334-9Prats-Boluda, G., Garcia-Casado, J., Martinez-de-Juan, J. L., & Ponce, J. L. (2007). Identification of the slow wave component of the electroenterogram from Laplacian abdominal surface recordings in humans. Physiological Measurement, 28(9), 1115-1133. doi:10.1088/0967-3334/28/9/012Quigley, E. M. M. (1996). GASTRIC AND SMALL INTESTINAL MOTILITY IN HEALTH AND DISEASE. Gastroenterology Clinics of North America, 25(1), 113-145. doi:10.1016/s0889-8553(05)70368-xSeidel, S. A., Bradshaw, L. A., Ladipo, J. K., Wikswo, J. P., & Richards, W. O. (1999). Noninvasive detection of ischemic bowel. Journal of Vascular Surgery, 30(2), 309-319. doi:10.1016/s0741-5214(99)70142-4Tomomasa, T., Morikawa, A., Sandler, R. H., Mansy, H. A., Koneko, H., Masahiko, T., … Itoh, Z. (1999). Gastrointestinal Sounds and Migrating Motor Complex in Fasted Humans. American Journal of Gastroenterology, 94(2), 374-381. doi:10.1111/j.1572-0241.1999.00862.xWang, Z. S., Cheung, J. Y., & Chen, J. D. Z. (1999). Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network. Medical & Biological Engineering & Computing, 37(1), 80-86. doi:10.1007/bf0251327

Methodology to improve water and energy use by proper irrigationscheduling in pressurised networks

With the aim of reducing energy consumption and improving water use in pressurised irrigation systems, the methodology to minimise energy consumption by grouping intakes of pressurised irrigation networks into sectors, as developed by Jimenez Bello et al. (2010a), was modified to enable irrigation intakes to operate during the scheduled period for each intake instead of operating during restricted irrigation periods of the same length. Moreover, a method was developed to detect the maximum number of intakes that can operate without extra energy if the source has sufficient head to feed at least some of the intakes. These methods were applied to a Mediterranean irrigation system, where the total cropped area was mainly citrus orchards. In this case study, water was allocated to two groups of intakes, one fed by gravity and the other by pumps. A saving of 36.3 % was achieved by increasing the total volume supplied by gravity, decreasing the injection pump head, and improving the pump performance. Therefore, all the intakes only operated during the irrigation periods at the minimum required pressure.This research was supported by funds from Climate-KIC AGADAPT and from EU 7th Framework Programme FIGARO projects. The authors wish to acknowledge the support provided by Picassent Sector XI staff. The revision of this paper was funded by "The Universitat Politecnica de Valencia, Spain".Jiménez Bello, MA.; Royuela Tomás, Á.; Manzano Juarez, J.; García Prats, A.; Martínez Alzamora, F. (2015). Methodology to improve water and energy use by proper irrigationscheduling in pressurised networks. Agricultural Water Management. 149:91-101. doi:10.1016/j.agwat.2014.10.026S9110114

Experimental study of the sensitivity of a porous silicon ring resonator sensor using continuous in-flow measurements

A highly sensitive photonic sensor based on a porous silicon ring resonator was developed and experimentally characterized. The photonic sensing structure was fabricated by exploiting a porous silicon double layer, where the top layer of a low porosity was used to form photonic elements by e-beam lithography and the bottom layer of a high porosity was used to confine light in the vertical direction. The sensing performance of the ring resonator sensor based on porous silicon was compared for the different resonances within the analyzed wavelength range both for transverse-electric and transverse-magnetic polarizations. We determined that a sensitivity up to 439 nm/RIU for low refractive index changes can be achieved depending on the optical field distribution given by each resonance/polarization

Commentaire ECJ-TF 1/2023 sur la décision de la CJUE du 16 février 2023 dans l'affaire Gallaher Limited (aff. C-707/20), sur la taxation des plus-values dans les transferts intra-groupes

peer reviewedIn this CFE Opinion Statement, submitted to the EU Institutions in June 2023, the CFE ECJ Task Force comments on the ECJ decision in Gallaher Limited (Case C-707/20), which provides further clarity on the scope of the fundamental freedoms, the correct comparator in establishing discrimination and the proportionality of discriminatory taxation of capital gains