Benzyl alcohol oxidation with Pd-Zn/TiO2: computational and experimental studies

Pd–Zn/TiO2 catalysts containing 1 wt% total metal loading, but with different Pd to Zn ratios, were prepared using a modified impregnation method and tested in the solvent-free aerobic oxidation of benzyl alcohol. The catalyst with the higher Pd content exhibited an enhanced activity for benzyl alcohol oxidation. However, the selectivity to benzaldehyde was significantly improved with increasing presence of Zn. The effect of reduction temperature on catalyst activity was investigated for the catalyst having a Pd to Zn metal molar ratio of 9:1. It was found that lower reduction temperature leads to the formation of PdZn nanoparticles with a wide particle size distribution. In contrast, smaller PdZn particles were formed upon catalyst reduction at higher temperatures. Computational studies were performed to compare the adsorption energies of benzyl alcohol and the reaction products (benzaldehyde and toluene) on PdZn surfaces to understand the oxidation mechanism and further explain the correlation between the catalyst composition and its activity

Insight into nature of iron sulfide surfaces during the electrochemical hydrogen evolution and CO2 reduction reactions

Greigite and other iron sulfides are potential cheap, earth-abundant electrocatalysts for the hydrogen evolution reaction (HER), yet little is known about the underlying surface chemistry. Structural and chemical changes to a greigite (Fe3S4) modified electrode were determined at −0.6 V vs. SHE at pH 7, under conditions of the HER. In situ X-ray Absorption Spectroscopy (XAS) was employed at the Fe K-edge to show that iron-sulfur linkages were replaced by iron-oxygen units under these conditions. The resulting material was determined as 60% greigite and 40% iron hydroxide (goethite) with a proposed core-shell structure. A large increase in pH at the electrode surface (to pH 12) is caused by the generation of OH− as a product of the HER. Under these conditions iron sulfide materials are thermodynamically unstable with respect to the hydroxide. In situ IR spectroscopy of the solution near the electrode interface confirmed changes in the phosphate ion speciation consistent with a change in pH from 7 to 12 when −0.6 V vs. SHE is applied. Saturation of the solution with CO2 resulted in inhibition of the hydroxide formation, potentially due to surface adsorption of HCO3−. This study shows that the true nature of the greigite electrode under conditions of the HER is a core-shell greigite-hydroxide material and emphasises the importance of in situ investigation of the catalyst under operation in order to develop true and accurate mechanistic models

Catalysis for fuels: general discussion

SCI(E)EDITORIAL MATERIAL165-20519

Comparative study of entropy sensitivity to missing biosignal data

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.This work has been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Cirugeda Roldan, EM.; Cuesta Frau, D.; Miró Martínez, P.; Oltra Crespo, S. (2014). Comparative study of entropy sensitivity to missing biosignal data. Entropy. 16(11):5901-5918. doi:10.3390/e16115901S590159181611Garrett, D., Peterson, D. A., Anderson, C. W., & Thaut, M. H. (2003). Comparison of linear, nonlinear, and feature selection methods for eeg signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 141-144. doi:10.1109/tnsre.2003.814441Alcaraz, R. (2013). Journal of Medical and Biological Engineering, 33(3), 239. doi:10.5405/jmbe.1401Muller, K., Anderson, C. W., & Birch, G. E. (2003). Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 165-169. doi:10.1109/tnsre.2003.814484Gao, J., Hu, J., & Tung, W. (2011). Entropy measures for biological signal analyses. Nonlinear Dynamics, 68(3), 431-444. doi:10.1007/s11071-011-0281-2Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141Alcaraz, R., & Rieta, J. J. (2010). A novel application of sample entropy to the electrocardiogram of atrial fibrillation. Nonlinear Analysis: Real World Applications, 11(2), 1026-1035. doi:10.1016/j.nonrwa.2009.01.047Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Pincus, 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/bf01619355Abasolo, D., Hornero, R., Escudero, J., & Espino, P. (2008). A Study on the Possible Usefulness of Detrended Fluctuation Analysis of the Electroencephalogram Background Activity in Alzheimer’s Disease. IEEE Transactions on Biomedical Engineering, 55(9), 2171-2179. doi:10.1109/tbme.2008.923145Hwa, R. C., & Ferree, T. C. (2002). Scaling properties of fluctuations in the human electroencephalogram. Physical Review E, 66(2). doi:10.1103/physreve.66.021901Lee, J.-M., Kim, D.-J., Kim, I.-Y., Park, K.-S., & Kim, S. I. (2002). Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Computers in Biology and Medicine, 32(1), 37-47. doi:10.1016/s0010-4825(01)00031-2Jospin, M., Caminal, P., Jensen, E. W., Litvan, H., Vallverdu, M., Struys, M. M. R. F., … Kaplan, D. T. (2007). Detrended Fluctuation Analysis of EEG as a Measure of Depth of Anesthesia. IEEE Transactions on Biomedical Engineering, 54(5), 840-846. doi:10.1109/tbme.2007.893453Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6). doi:10.1103/physreve.64.061907Radhakrishnan, N., & Gangadhar, B. N. (1998). Estimating regularity in epileptic seizure time-series data. IEEE Engineering in Medicine and Biology Magazine, 17(3), 89-94. doi:10.1109/51.677174Burnsed, J., Quigg, M., Zanelli, S., & Goodkin, H. P. (2011). Clinical Severity, Rather Than Body Temperature, During the Rewarming Phase of Therapeutic Hypothermia Affect Quantitative EEG in Neonates With Hypoxic Ischemic Encephalopathy. Journal of Clinical Neurophysiology, 28(1), 10-14. doi:10.1097/wnp.0b013e318205134bDeffeyes, J. E., Harbourne, R. T., DeJong, S. L., Kyvelidou, A., Stuberg, W. A., & Stergiou, N. (2009). Use of information entropy measures of sitting postural sway to quantify developmental delay in infants. Journal of NeuroEngineering and Rehabilitation, 6(1), 34. doi:10.1186/1743-0003-6-34Moorman, J. R., Delos, J. B., Flower, A. A., Cao, H., Kovatchev, B. P., Richman, J. S., & Lake, D. E. (2011). Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring. Physiological Measurement, 32(11), 1821-1832. doi:10.1088/0967-3334/32/11/s08Zhang, D., Ding, H., Liu, Y., Zhou, C., Ding, H., & Ye, D. (2009). Neurodevelopment in newborns: a sample entropy analysis of electroencephalogram. Physiological Measurement, 30(5), 491-504. doi:10.1088/0967-3334/30/5/006Veiga, J., Lopes, A. J., Jansen, J. M., & Melo, P. L. (2011). Airflow pattern complexity and airway obstruction in asthma. Journal of Applied Physiology, 111(2), 412-419. doi:10.1152/japplphysiol.00267.2011Albuerne-Sanchez, L., Gonzalez-Camarena, R., Mejia-Avila, M., Carrillo-Rodriguez, G., Aljama-Corrales, T., & Charleston-Villalobos, S. (2013). Linear and Nonlinear Analysis of Base Lung Sound in Extrinsic Allergic Alveolitis Patients in Comparison to Healthy Subjects. Methods of Information in Medicine, 52(03), 266-276. doi:10.3414/me12-01-0037Jing Hu, Jianbo Gao, & Principe, J. C. (2006). Analysis of Biomedical Signals by the Lempel-Ziv Complexity: the Effect of Finite Data Size. IEEE Transactions on Biomedical Engineering, 53(12), 2606-2609. doi:10.1109/tbme.2006.883825Maestri, 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:10.1088/0967-3334/28/9/008Hornero, R., Aboy, M., Abasolo, D., McNames, J., & Goldstein, B. (2005). Interpretation of Approximate Entropy: Analysis of Intracranial Pressure Approximate Entropy During Acute Intracranial Hypertension. IEEE Transactions on Biomedical Engineering, 52(10), 1671-1680. doi:10.1109/tbme.2005.855722Escudero, J., Hornero, R., & Abásolo, D. (2009). Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings. Physiological Measurement, 30(2), 187-199. doi:10.1088/0967-3334/30/2/006Aboy, M., Hornero, R., Abasolo, D., & Alvarez, D. (2006). Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis. IEEE Transactions on Biomedical Engineering, 53(11), 2282-2288. doi:10.1109/tbme.2006.883696Garcia-Gonzalez, M. A., Fernandez-Chimeno, M., & Ramos-Castro, J. (2009). Errors in the Estimation of Approximate Entropy and Other Recurrence-Plot-Derived Indices Due to the Finite Resolution of RR Time Series. IEEE Transactions on Biomedical Engineering, 56(2), 345-351. doi:10.1109/tbme.2008.2005951Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. Artificial Intelligence in Medicine, 53(2), 97-106. doi:10.1016/j.artmed.2011.06.007Bao, Y., Li, H., Sun, X., Yu, Y., & Ou, J. (2012). Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring. Structural Health Monitoring: An International Journal, 12(1), 78-95. doi:10.1177/1475921712462936Cuesta-Frau, D., Pérez-Cortés, J. C., & Andreu-Garcı́a, G. (2003). Clustering of electrocardiograph signals in computer-aided Holter analysis. Computer Methods and Programs in Biomedicine, 72(3), 179-196. doi:10.1016/s0169-2607(02)00145-1Chon, K. H., Scully, C. G., & Lu, S. (2009). Approximate entropy for all signals. IEEE Engineering in Medicine and Biology Magazine, 28(6), 18-23. doi:10.1109/memb.2009.934629Hu, X., Miller, C., Vespa, P., & Bergsneider, M. (2008). Adaptive computation of approximate entropy and its application in integrative analysis of irregularity of heart rate variability and intracranial pressure signals. Medical Engineering & Physics, 30(5), 631-639. doi:10.1016/j.medengphy.2007.07.002Richman, 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:10.1080/03610920601036481Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xXie, H.-B., Chen, W.-T., He, W.-X., & Liu, H. (2011). Complexity analysis of the biomedical signal using fuzzy entropy measurement. Applied Soft Computing, 11(2), 2871-2879. doi:10.1016/j.asoc.2010.11.020Govindan, R. B., Wilson, J. D., Preißl, H., Eswaran, H., Campbell, J. Q., & Lowery, C. L. (2007). Detrended fluctuation analysis of short datasets: An application to fetal cardiac data. Physica D: Nonlinear Phenomena, 226(1), 23-31. doi:10.1016/j.physd.2006.10.019Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Effect of the Antiarrhythmic Agent Moricizine on Survival after Myocardial Infarction. (1992). New England Journal of Medicine, 327(4), 227-233. doi:10.1056/nejm199207233270403(1989). Preliminary Report: Effect of Encainide and Flecainide on Mortality in a Randomized Trial of Arrhythmia Suppression after Myocardial Infarction. New England Journal of Medicine, 321(6), 406-412. doi:10.1056/nejm198908103210629The Caps Investigators. (1986). The cardiac arrhythmia pilot study. The American Journal of Cardiology, 57(1), 91-95. doi:10.1016/0002-9149(86)90958-6Molina–Picó, A., Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., & Aboy, M. (2013). Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination. Computer Methods and Programs in Biomedicine, 110(1), 2-11. doi:10.1016/j.cmpb.2012.10.014Hausdorff, J. M., Mitchell, S. L., Firtion, R., Peng, C. K., Cudkowicz, M. E., Wei, J. Y., & Goldberger, A. L. (1997). Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. Journal of Applied Physiology, 82(1), 262-269. doi:10.1152/jappl.1997.82.1.262Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., & Goldberger, A. L. (2000). Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. Journal of Applied Physiology, 88(6), 2045-2053. doi:10.1152/jappl.2000.88.6.2045Cirugeda-Roldán, E. M., Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Vigil-Medina, L., & Varela-Entrecanales, M. (2014). A new algorithm for quadratic sample entropy optimization for very short biomedical signals: Application to blood pressure records. Computer Methods and Programs in Biomedicine, 114(3), 231-239. doi:10.1016/j.cmpb.2014.02.00

Measurement of inclusive D*+- and associated dijet cross sections in photoproduction at HERA

Inclusive photoproduction of D*+- mesons has been measured for photon-proton centre-of-mass energies in the range 130 < W < 280 GeV and a photon virtuality Q^2 < 1 GeV^2. The data sample used corresponds to an integrated luminosity of 37 pb^-1. Total and differential cross sections as functions of the D* transverse momentum and pseudorapidity are presented in restricted kinematical regions and the data are compared with next-to-leading order (NLO) perturbative QCD calculations using the "massive charm" and "massless charm" schemes. The measured cross sections are generally above the NLO calculations, in particular in the forward (proton) direction. The large data sample also allows the study of dijet production associated with charm. A significant resolved as well as a direct photon component contribute to the cross section. Leading order QCD Monte Carlo calculations indicate that the resolved contribution arises from a significant charm component in the photon. A massive charm NLO parton level calculation yields lower cross sections compared to the measured results in a kinematic region where the resolved photon contribution is significant.Comment: 32 pages including 6 figure

Measurement of Jet Shapes in Photoproduction at HERA

The shape of jets produced in quasi-real photon-proton collisions at centre-of-mass energies in the range $134-277$ GeV has been measured using the hadronic energy flow. The measurement was done with the ZEUS detector at HERA. Jets are identified using a cone algorithm in the $\eta - \phi$ plane with a cone radius of one unit. Measured jet shapes both in inclusive jet and dijet production with transverse energies $E^{jet}_T>14$ GeV are presented. The jet shape broadens as the jet pseudorapidity ($\eta^{jet}$) increases and narrows as $E^{jet}_T$ increases. In dijet photoproduction, the jet shapes have been measured separately for samples dominated by resolved and by direct processes. Leading-logarithm parton-shower Monte Carlo calculations of resolved and direct processes describe well the measured jet shapes except for the inclusive production of jets with high $\eta^{jet}$ and low $E^{jet}_T$. The observed broadening of the jet shape as $\eta^{jet}$ increases is consistent with the predicted increase in the fraction of final state gluon jets.Comment: 29 pages including 9 figure

Measurement of the Diffractive Cross Section in Deep Inelastic Scattering using ZEUS 1994 Data

The DIS diffractive cross section, $d\sigma^{diff}_{\gamma^* p \to XN}/dM_X$, has been measured in the mass range $M_X < 15$ GeV for $\gamma^*p$ c.m. energies $60 < W < 200$ GeV and photon virtualities $Q^2 = 7$ to 140 GeV$^2$. For fixed $Q^2$ and $M_X$, the diffractive cross section rises rapidly with $W$, $d\sigma^{diff}_{\gamma^*p \to XN}(M_X,W,Q^2)/dM_X \propto W^{a^{diff}}$ with $a^{diff} = 0.507 \pm 0.034 (stat)^{+0.155}_{-0.046}(syst)$ corresponding to a $t$-averaged pomeron trajectory of \bar{\alphapom} = 1.127 \pm 0.009 (stat)^{+0.039}_{-0.012} (syst) which is larger than \bar{\alphapom} observed in hadron-hadron scattering. The $W$ dependence of the diffractive cross section is found to be the same as that of the total cross section for scattering of virtual photons on protons. The data are consistent with the assumption that the diffractive structure function $F^{D(3)}_2$ factorizes according to \xpom F^{D(3)}_2 (\xpom,\beta,Q^2) = (x_0/ \xpom)^n F^{D(2)}_2(\beta,Q^2). They are also consistent with QCD based models which incorporate factorization breaking. The rise of \xpom F^{D(3)}_2 with decreasing \xpom and the weak dependence of $F^{D(2)}_2$ on $Q^2$ suggest a substantial contribution from partonic interactions

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Measurement of the F2 structure function in deep inelastic e+^{+}p scattering using 1994 data from the ZEUS detector at HERA

We present measurements of the structure function \Ft\ in e^+p scattering at HERA in the range 3.5\;\Gevsq < \qsd < 5000\;\Gevsq. A new reconstruction method has allowed a significant improvement in the resolution of the kinematic variables and an extension of the kinematic region covered by the experiment. At \qsd < 35 \;\Gevsq the range in x now spans 6.3\cdot 10^{-5} < x < 0.08 providing overlap with measurements from fixed target experiments. At values of Q^2 above 1000 GeV^2 the x range extends to 0.5. Systematic errors below 5\perc\ have been achieved for most of the kinematic urray, W