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1. Resilienz der Gesundheitsforschung als Voraussetzung für Resilienz der Gesundheitsversorgung - das Infrastrukturkonzept 'PREparedness and PAndemic REsponse in Deutschland' (PREPARED) des Netzwerks Universitätsmedizin

Author: Hoffmann, J, Lang, C, Scheithauer, S, Fenz, D, Berens, M, Panchyrz, I, Harst, L, Adorjan, K, Apfelbacher, C, Ciesek, S, Denkinger, C, Drosten, C, Geffers, C, Geraedts, M, Greve, M, Haier, J, Hecker, R, Hoffmann, W, Karch, A, Koch, T, Krefting, D, Lieb, K, Meerpohl, J, Menk, M, Rehfuess, E, Skoetz, N, Sopka, S, Schildmann, J, Taddicken, M, van den Berg, N, von Lengerke, T, Winkler, E, Schmitt, J, PREPARED Study Group, Hoffmann, J, Lang, C, Scheithauer, S, Fenz, D, Berens, M, Panchyrz, I, Harst, L, Adorjan, K, Apfelbacher, C, Ciesek, S, Denkinger, C, Drosten, C, Geffers, C, Geraedts, M, Greve, M, Haier, J, Hecker, R, Hoffmann, W, Karch, A, Koch, T, Krefting, D, Lieb, K, Meerpohl, J, Menk, M, Rehfuess, E, Skoetz, N, Sopka, S, Schildmann, J, Taddicken, M, van den Berg, N, von Lengerke, T, Winkler, E, Schmitt, J, and PREPARED Study Group
Published: 2024

2. Quantification of surface changes and volume losses of selected rock types due to different cleaning processes

Author: Koch, T., (0000-0003-2416-6438) Fischer, C., Schad, F., Siegesmund, S., Koch, T., (0000-0003-2416-6438) Fischer, C., Schad, F., and Siegesmund, S.
Abstract: The restorative cleaning of natural stones has a special significance for the preservation of important cultural assets or the slowing of their deterioration. Organisms such as fungi, lichens or mosses, but also emission dirt such as soot soften and otherwise damage both the surface and the internal structure of the building stone. In order to quantify the effects and in particular the abrasiveness of selected cleaning methods, cleaning experiments were carried out on six different naturally and artificially weathered rocks using cold water under high pressure, hot water under high pressure as well as hot-water steam. The types of rocks studied include marble, limestone, granite, sandstone and tuff. Surface changes in roughness and topography were quantified using two surface-sensitive methods: confocal microscopy as well as 3D shadow triangulation. The two high-pressure cleaning methods were found to have a significantly stronger abrasive effect than steam cleaning when the distances were too small. The cleaning performance, which was compared using biologically weathered samples, was lowest for steam cleaning. However, the high temperatures of the steam also permanently eliminate much of the biological matter on and under the surface, as observed in the field test. The results presented should make it possible for the conservator to assess, which cleaning procedures to use for the different rock varieties depending on the degree of weathering.
Published: 2023

3. Short-term time series forecasting for multi-site municipal solid waste management

Author: Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Smart containers equipped with ultrasonic sensors at waste and recycle facilities allow waste and recycling companies to build a more efficient and data-driven approach for the collection of municipal solid waste (MSW). In this paper, we propose three time series algorithms that predict the MSW generation of six waste types, using data obtained from smart sensors placed inside 3,640 containers at facilities in six municipalities in the Netherlands. Per neighborhood and per waste type, three models are developed: a Seasonal NaIve Benchmark model, ensemble models of Error, trend, seasonality models with external variables (ETSX), and Quantile Regression models with external variables. According to the RMSE, the ETSX model is the outperforming model for 74% of the time. It is also found that poor weather conditions such as precipitation, wind gusts and thunderstorms result in less waste disposal. The proposed prediction models can be used for more efficient waste collection, in order to collect waste before the fll rate percentage exceeds 100%. In future studies the inclusion of spatial variables and clustering of the containers can be considered.
Published: 2023
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4. Hourly forecasting of traffic flow rates using spatial temporal graph neural networks

Author: Belt, E.A. (Eline), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Belt, E.A. (Eline), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.
Published: 2023
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5. Intraoperative ventilator settings and their association with postoperative pulmonary complications in neurosurgical patients: Post-hoc analysis of LAS VEGAS study

Author: Robba, C, Hemmes, S, Serpa Neto, A, Bluth, T, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Vidal Melo, M, Putensen, C, Jaber, S, Schmid, W, Severgnini, P, Wrigge, H, Battaglini, D, Ball, L, Gama De Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Van Mossevelde, V, Van Malderen, M, Dylst, D, Van Melkebeek, J, Beran, M, De Hert, S, De Baerdemaeker, L, Heyse, B, Van Limmen, J, Wyffels, P, Jacobs, T, Roels, N, De Bruyne, A, Van De Velde, S, Leva, B, Damster, S, Plichon, B, Juros-Zovko, M, Djonovic-Omanovic, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Azenic-Venzera, D, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Karanovic, N, Carev, M, Dropulic, N, Saric, J, Erceg, G, Dvorscak, M, Mazul-Sunko, B, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Mladic-Batinica, I, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, El Kafrawy, S, Abdelwahab, H, Metwally, T, Abdel-Razek, A, El-Shaarawy, A, Hasan, W, Ahmed, A, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Prades, A, Krassler, J, Merzky, S, De Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodoraki, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, De Michele, M, Bona, F, Giacoletto, G, Sardo, E, Giancarlo, L, Sottosanti, V, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, La Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Di Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Cortegiani, A, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Gecaj-Gashi, A, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, In 'T Veld, B, Werger, A, Dennesen, P, Severijns, C, De Jong, J, Hering, J, Van Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Mellin-Olsen, J, Andersson, E, Shafi-Kabiri, A, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, De Lurdes Goncalves Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Novak-Supe, B, Pekle-Golez, A, Jovanov, M, Strazisar, B, Markovic-Bozic, J, Novak-Jankovic, V, Voje, M, Grynyuk, A, Kostadinov, I, Spindler-Vesel, A, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, De Abajo Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, De Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Alvares-Santullano, C, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Creagh-Brown, B, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Fernandez-Bustamante, A, Luzier, E, Almagro, A, Melo, M, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Robba C., Hemmes S. N. T., Serpa Neto A., Bluth T., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Vidal Melo M. F., Putensen C., Jaber S., Schmid W., Severgnini P., Wrigge H., Battaglini D., Ball L., Gama De Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Van Mossevelde V., Van Malderen M. -C., Dylst D., Van Melkebeek J., Beran M., De Hert S., De Baerdemaeker L., Heyse B., Van Limmen J., Wyffels P., Jacobs T., Roels N., De Bruyne A., Van De Velde S., Leva B., Damster S., Plichon B., Juros-Zovko M., Djonovic-Omanovic D., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Azenic-Venzera D., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Karanovic N., Carev M., Dropulic N., Saric J. P., Erceg G., Dvorscak M. B., Mazul-Sunko B., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Mladic-Batinica I., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., El Kafrawy S., Abdelwahab H. H., Metwally T., Abdel-Razek A., El-Shaarawy A. M., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Prades A., Krassler J., Merzky S., De Abreu M. G., Uhlig C., Kiss T., Bundy A., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodoraki K., Gkiokas G., Tasoulis M. -K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., De Michele M., Bona F., Giacoletto G., Sardo E., Giancarlo L., Sottosanti V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., La Bella D., Mirabella L., D'Antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Di Parma A. L., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Cortegiani A., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Gecaj-Gashi A., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., In 'T Veld B., Werger A., Dennesen P., Severijns C., De Jong J., Hering J., Van Beek R., Ivars S., Jammer I., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Mellin-Olsen J., Andersson E., Shafi-Kabiri A., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., De Lurdes Goncalves Castro M., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Garotescu I. P., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. -T., Gherghina I., Costea D., Cindea I., Copotoiu S. -M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Novak-Supe B., Pekle-Golez A., Jovanov M., Strazisar B., Markovic-Bozic J., Novak-Jankovic V., Voje M., Grynyuk A., Kostadinov I., Spindler-Vesel A., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., De Abajo Larriba A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., De Andres Ibanez J. A., Pastor E., Soro M., Ferrando C., Defez M., Alvares-Santullano C. A., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Creagh-Brown B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Fernandez-Bustamante A., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., Tze Y., Robba, C, Hemmes, S, Serpa Neto, A, Bluth, T, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Vidal Melo, M, Putensen, C, Jaber, S, Schmid, W, Severgnini, P, Wrigge, H, Battaglini, D, Ball, L, Gama De Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Van Mossevelde, V, Van Malderen, M, Dylst, D, Van Melkebeek, J, Beran, M, De Hert, S, De Baerdemaeker, L, Heyse, B, Van Limmen, J, Wyffels, P, Jacobs, T, Roels, N, De Bruyne, A, Van De Velde, S, Leva, B, Damster, S, Plichon, B, Juros-Zovko, M, Djonovic-Omanovic, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Azenic-Venzera, D, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Karanovic, N, Carev, M, Dropulic, N, Saric, J, Erceg, G, Dvorscak, M, Mazul-Sunko, B, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Mladic-Batinica, I, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, El Kafrawy, S, Abdelwahab, H, Metwally, T, Abdel-Razek, A, El-Shaarawy, A, Hasan, W, Ahmed, A, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Prades, A, Krassler, J, Merzky, S, De Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodoraki, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, De Michele, M, Bona, F, Giacoletto, G, Sardo, E, Giancarlo, L, Sottosanti, V, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, La Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Di Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Cortegiani, A, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Gecaj-Gashi, A, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, In 'T Veld, B, Werger, A, Dennesen, P, Severijns, C, De Jong, J, Hering, J, Van Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Mellin-Olsen, J, Andersson, E, Shafi-Kabiri, A, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, De Lurdes Goncalves Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Novak-Supe, B, Pekle-Golez, A, Jovanov, M, Strazisar, B, Markovic-Bozic, J, Novak-Jankovic, V, Voje, M, Grynyuk, A, Kostadinov, I, Spindler-Vesel, A, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, De Abajo Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, De Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Alvares-Santullano, C, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Creagh-Brown, B, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Fernandez-Bustamante, A, Luzier, E, Almagro, A, Melo, M, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Robba C., Hemmes S. N. T., Serpa Neto A., Bluth T., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Vidal Melo M. F., Putensen C., Jaber S., Schmid W., Severgnini P., Wrigge H., Battaglini D., Ball L., Gama De Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Van Mossevelde V., Van Malderen M. -C., Dylst D., Van Melkebeek J., Beran M., De Hert S., De Baerdemaeker L., Heyse B., Van Limmen J., Wyffels P., Jacobs T., Roels N., De Bruyne A., Van De Velde S., Leva B., Damster S., Plichon B., Juros-Zovko M., Djonovic-Omanovic D., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Azenic-Venzera D., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Karanovic N., Carev M., Dropulic N., Saric J. P., Erceg G., Dvorscak M. B., Mazul-Sunko B., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Mladic-Batinica I., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., El Kafrawy S., Abdelwahab H. H., Metwally T., Abdel-Razek A., El-Shaarawy A. M., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Prades A., Krassler J., Merzky S., De Abreu M. G., Uhlig C., Kiss T., Bundy A., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodoraki K., Gkiokas G., Tasoulis M. -K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., De Michele M., Bona F., Giacoletto G., Sardo E., Giancarlo L., Sottosanti V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., La Bella D., Mirabella L., D'Antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Di Parma A. L., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Cortegiani A., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Gecaj-Gashi A., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., In 'T Veld B., Werger A., Dennesen P., Severijns C., De Jong J., Hering J., Van Beek R., Ivars S., Jammer I., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Mellin-Olsen J., Andersson E., Shafi-Kabiri A., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., De Lurdes Goncalves Castro M., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Garotescu I. P., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. -T., Gherghina I., Costea D., Cindea I., Copotoiu S. -M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Novak-Supe B., Pekle-Golez A., Jovanov M., Strazisar B., Markovic-Bozic J., Novak-Jankovic V., Voje M., Grynyuk A., Kostadinov I., Spindler-Vesel A., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., De Abajo Larriba A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., De Andres Ibanez J. A., Pastor E., Soro M., Ferrando C., Defez M., Alvares-Santullano C. A., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Creagh-Brown B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Fernandez-Bustamante A., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., and Tze Y.
Abstract: Background: Limited information is available regarding intraoperative ventilator settings and the incidence of postoperative pulmonary complications (PPCs) in patients undergoing neurosurgical procedures. The aim of this post-hoc analysis of the 'Multicentre Local ASsessment of VEntilatory management during General Anaesthesia for Surgery' (LAS VEGAS) study was to examine the ventilator settings of patients undergoing neurosurgical procedures, and to explore the association between perioperative variables and the development of PPCs in neurosurgical patients. Methods: Post-hoc analysis of LAS VEGAS study, restricted to patients undergoing neurosurgery. Patients were stratified into groups based on the type of surgery (brain and spine), the occurrence of PPCs and the assess respiratory risk in surgical patients in Catalonia (ARISCAT) score risk for PPCs. Results: Seven hundred eighty-four patients were included in the analysis; 408 patients (52%) underwent spine surgery and 376 patients (48%) brain surgery. Median tidal volume (VT) was 8 ml [Interquartile Range, IQR = 7.3-9] per predicted body weight; median positive end-expiratory pressure (PEEP) was 5 [3 to 5] cmH20. Planned recruitment manoeuvres were used in the 6.9% of patients. No differences in ventilator settings were found among the sub-groups. PPCs occurred in 81 patients (10.3%). Duration of anaesthesia (odds ratio, 1.295 [95% confidence interval 1.067 to 1.572]; p = 0.009) and higher age for the brain group (odds ratio, 0.000 [0.000 to 0.189]; p = 0.031), but not intraoperative ventilator settings were independently associated with development of PPCs. Conclusions: Neurosurgical patients are ventilated with low VT and low PEEP, while recruitment manoeuvres are seldom applied. Intraoperative ventilator settings are not associated with PPCs.
Published: 2020

6. Intraoperative ventilator settings and their association with postoperative pulmonary complications in neurosurgical patients: Post-hoc analysis of LAS VEGAS study

Author: Robba, C, Hemmes, S, Serpa Neto, A, Bluth, T, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Vidal Melo, M, Putensen, C, Jaber, S, Schmid, W, Severgnini, P, Wrigge, H, Battaglini, D, Ball, L, Gama De Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Van Mossevelde, V, Van Malderen, M, Dylst, D, Van Melkebeek, J, Beran, M, De Hert, S, De Baerdemaeker, L, Heyse, B, Van Limmen, J, Wyffels, P, Jacobs, T, Roels, N, De Bruyne, A, Van De Velde, S, Leva, B, Damster, S, Plichon, B, Juros-Zovko, M, Djonovic-Omanovic, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Azenic-Venzera, D, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Karanovic, N, Carev, M, Dropulic, N, Saric, J, Erceg, G, Dvorscak, M, Mazul-Sunko, B, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Mladic-Batinica, I, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, El Kafrawy, S, Abdelwahab, H, Metwally, T, Abdel-Razek, A, El-Shaarawy, A, Hasan, W, Ahmed, A, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Prades, A, Krassler, J, Merzky, S, De Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodoraki, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, De Michele, M, Bona, F, Giacoletto, G, Sardo, E, Giancarlo, L, Sottosanti, V, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, La Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Di Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Cortegiani, A, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Gecaj-Gashi, A, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, In 'T Veld, B, Werger, A, Dennesen, P, Severijns, C, De Jong, J, Hering, J, Van Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Mellin-Olsen, J, Andersson, E, Shafi-Kabiri, A, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, De Lurdes Goncalves Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Novak-Supe, B, Pekle-Golez, A, Jovanov, M, Strazisar, B, Markovic-Bozic, J, Novak-Jankovic, V, Voje, M, Grynyuk, A, Kostadinov, I, Spindler-Vesel, A, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, De Abajo Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, De Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Alvares-Santullano, C, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Creagh-Brown, B, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Fernandez-Bustamante, A, Luzier, E, Almagro, A, Melo, M, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Robba C., Hemmes S. N. T., Serpa Neto A., Bluth T., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Vidal Melo M. F., Putensen C., Jaber S., Schmid W., Severgnini P., Wrigge H., Battaglini D., Ball L., Gama De Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Van Mossevelde V., Van Malderen M. -C., Dylst D., Van Melkebeek J., Beran M., De Hert S., De Baerdemaeker L., Heyse B., Van Limmen J., Wyffels P., Jacobs T., Roels N., De Bruyne A., Van De Velde S., Leva B., Damster S., Plichon B., Juros-Zovko M., Djonovic-Omanovic D., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Azenic-Venzera D., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Karanovic N., Carev M., Dropulic N., Saric J. P., Erceg G., Dvorscak M. B., Mazul-Sunko B., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Mladic-Batinica I., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., El Kafrawy S., Abdelwahab H. H., Metwally T., Abdel-Razek A., El-Shaarawy A. M., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Prades A., Krassler J., Merzky S., De Abreu M. G., Uhlig C., Kiss T., Bundy A., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodoraki K., Gkiokas G., Tasoulis M. -K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., De Michele M., Bona F., Giacoletto G., Sardo E., Giancarlo L., Sottosanti V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., La Bella D., Mirabella L., D'Antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Di Parma A. L., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Cortegiani A., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Gecaj-Gashi A., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., In 'T Veld B., Werger A., Dennesen P., Severijns C., De Jong J., Hering J., Van Beek R., Ivars S., Jammer I., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Mellin-Olsen J., Andersson E., Shafi-Kabiri A., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., De Lurdes Goncalves Castro M., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Garotescu I. P., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. -T., Gherghina I., Costea D., Cindea I., Copotoiu S. -M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Novak-Supe B., Pekle-Golez A., Jovanov M., Strazisar B., Markovic-Bozic J., Novak-Jankovic V., Voje M., Grynyuk A., Kostadinov I., Spindler-Vesel A., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., De Abajo Larriba A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., De Andres Ibanez J. A., Pastor E., Soro M., Ferrando C., Defez M., Alvares-Santullano C. A., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Creagh-Brown B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Fernandez-Bustamante A., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., Tze Y., Robba, C, Hemmes, S, Serpa Neto, A, Bluth, T, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Vidal Melo, M, Putensen, C, Jaber, S, Schmid, W, Severgnini, P, Wrigge, H, Battaglini, D, Ball, L, Gama De Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Van Mossevelde, V, Van Malderen, M, Dylst, D, Van Melkebeek, J, Beran, M, De Hert, S, De Baerdemaeker, L, Heyse, B, Van Limmen, J, Wyffels, P, Jacobs, T, Roels, N, De Bruyne, A, Van De Velde, S, Leva, B, Damster, S, Plichon, B, Juros-Zovko, M, Djonovic-Omanovic, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Azenic-Venzera, D, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Karanovic, N, Carev, M, Dropulic, N, Saric, J, Erceg, G, Dvorscak, M, Mazul-Sunko, B, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Mladic-Batinica, I, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, El Kafrawy, S, Abdelwahab, H, Metwally, T, Abdel-Razek, A, El-Shaarawy, A, Hasan, W, Ahmed, A, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Prades, A, Krassler, J, Merzky, S, De Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodoraki, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, De Michele, M, Bona, F, Giacoletto, G, Sardo, E, Giancarlo, L, Sottosanti, V, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, La Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Di Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Cortegiani, A, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Gecaj-Gashi, A, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, In 'T Veld, B, Werger, A, Dennesen, P, Severijns, C, De Jong, J, Hering, J, Van Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Mellin-Olsen, J, Andersson, E, Shafi-Kabiri, A, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, De Lurdes Goncalves Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Novak-Supe, B, Pekle-Golez, A, Jovanov, M, Strazisar, B, Markovic-Bozic, J, Novak-Jankovic, V, Voje, M, Grynyuk, A, Kostadinov, I, Spindler-Vesel, A, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, De Abajo Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, De Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Alvares-Santullano, C, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Creagh-Brown, B, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Fernandez-Bustamante, A, Luzier, E, Almagro, A, Melo, M, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Robba C., Hemmes S. N. T., Serpa Neto A., Bluth T., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Vidal Melo M. F., Putensen C., Jaber S., Schmid W., Severgnini P., Wrigge H., Battaglini D., Ball L., Gama De Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Van Mossevelde V., Van Malderen M. -C., Dylst D., Van Melkebeek J., Beran M., De Hert S., De Baerdemaeker L., Heyse B., Van Limmen J., Wyffels P., Jacobs T., Roels N., De Bruyne A., Van De Velde S., Leva B., Damster S., Plichon B., Juros-Zovko M., Djonovic-Omanovic D., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Azenic-Venzera D., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Karanovic N., Carev M., Dropulic N., Saric J. P., Erceg G., Dvorscak M. B., Mazul-Sunko B., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Mladic-Batinica I., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., El Kafrawy S., Abdelwahab H. H., Metwally T., Abdel-Razek A., El-Shaarawy A. M., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Prades A., Krassler J., Merzky S., De Abreu M. G., Uhlig C., Kiss T., Bundy A., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodoraki K., Gkiokas G., Tasoulis M. -K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., De Michele M., Bona F., Giacoletto G., Sardo E., Giancarlo L., Sottosanti V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., La Bella D., Mirabella L., D'Antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Di Parma A. L., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Cortegiani A., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Gecaj-Gashi A., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., In 'T Veld B., Werger A., Dennesen P., Severijns C., De Jong J., Hering J., Van Beek R., Ivars S., Jammer I., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Mellin-Olsen J., Andersson E., Shafi-Kabiri A., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., De Lurdes Goncalves Castro M., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Garotescu I. P., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. -T., Gherghina I., Costea D., Cindea I., Copotoiu S. -M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Novak-Supe B., Pekle-Golez A., Jovanov M., Strazisar B., Markovic-Bozic J., Novak-Jankovic V., Voje M., Grynyuk A., Kostadinov I., Spindler-Vesel A., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., De Abajo Larriba A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., De Andres Ibanez J. A., Pastor E., Soro M., Ferrando C., Defez M., Alvares-Santullano C. A., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Creagh-Brown B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Fernandez-Bustamante A., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., and Tze Y.
Abstract: Background: Limited information is available regarding intraoperative ventilator settings and the incidence of postoperative pulmonary complications (PPCs) in patients undergoing neurosurgical procedures. The aim of this post-hoc analysis of the 'Multicentre Local ASsessment of VEntilatory management during General Anaesthesia for Surgery' (LAS VEGAS) study was to examine the ventilator settings of patients undergoing neurosurgical procedures, and to explore the association between perioperative variables and the development of PPCs in neurosurgical patients. Methods: Post-hoc analysis of LAS VEGAS study, restricted to patients undergoing neurosurgery. Patients were stratified into groups based on the type of surgery (brain and spine), the occurrence of PPCs and the assess respiratory risk in surgical patients in Catalonia (ARISCAT) score risk for PPCs. Results: Seven hundred eighty-four patients were included in the analysis; 408 patients (52%) underwent spine surgery and 376 patients (48%) brain surgery. Median tidal volume (VT) was 8 ml [Interquartile Range, IQR = 7.3-9] per predicted body weight; median positive end-expiratory pressure (PEEP) was 5 [3 to 5] cmH20. Planned recruitment manoeuvres were used in the 6.9% of patients. No differences in ventilator settings were found among the sub-groups. PPCs occurred in 81 patients (10.3%). Duration of anaesthesia (odds ratio, 1.295 [95% confidence interval 1.067 to 1.572]; p = 0.009) and higher age for the brain group (odds ratio, 0.000 [0.000 to 0.189]; p = 0.031), but not intraoperative ventilator settings were independently associated with development of PPCs. Conclusions: Neurosurgical patients are ventilated with low VT and low PEEP, while recruitment manoeuvres are seldom applied. Intraoperative ventilator settings are not associated with PPCs.
Published: 2020

7. Beeinflusst NoL-Monitoring den Opioidbedarf bei Da-Vinci-Prostatektomien?

Author: Niebhagen, F., Golde, C., Koch, T., Hübler, M., Niebhagen, F., Golde, C., Koch, T., and Hübler, M.
Abstract: Hintergrund Die Gabe von Opioiden zur Schmerzunterdrückung spielt eine zentrale Rolle in der modernen Anästhesiologie. Messungen von Hypnosetiefe und Muskelrelaxierung sind im Gegensatz zur Schmerzmessung seit Jahren etabliert. Seit Kurzem ist das PMD200 („Pain Monitoring System“; Fa. Medasense Biometrics™ Ltd., Ramat-Gan, Israel) verfügbar. Dieser Schmerzmonitor misst nichtinvasiv und errechnet einen dimensionslosen Schmerzindex („nociceptor level“, NoL). Die Validität und Zuverlässigkeit des Verfahrens sind Gegenstand von klinischen Studien. Fragestellung Reduziert die Verwendung des PMD200 die Gabe von Analgetika während einer Da-Vinci-Prostatektomie? Material und Methoden In die Studie wurden 50 Patienten aufgenommen. Nach gewichtsadaptierter Sufentanilgabe zur Narkoseinduktion und einem 10 µg Bolus vor Hautschnitt erfolgte die intraoperative Analgesie durch subjektive Entscheidung (CONT) oder aufgrund eines erhöhten NoL-Index (INT). Die statistische Auswertung erfolgte durch Mann-Whitney-U-, Kolmogorow-Smirnow-Test und Levene-Statistik. Ergebnisse In der INT-Gruppe war die Anzahl der Sufentanilboli/h nicht signifikant geringer als in der CONT-Gruppe (p = 0,065). Die Varianz der Sufentanilgaben unterschied sich signifikant (p = 0,033). In der CONT-Gruppe war die Applikation normal verteilt (p = 0,2), in der INT-Gruppe hingegen nicht (p = 0,003). Diskussion Eine mögliche Interpretation der Daten ist, dass die Schmerzmittelgabe in der INT-Gruppe individualisierter erfolgte, d. h., es wurden nichterforderliche Schmerzmittelgaben vermieden, und gleichzeitig detektierte das NoL-Monitoring einzelne Patienten mit deutlich erhöhtem Schmerzmittelbedarf. Diese Schlussfolgerung ist nur unter der Voraussetzung zulässig, dass das PMD200 auch tatsächlich die Entität Schmerz misst., Background Administration of opioids to suppress pain plays a major role in modern anesthesia. Measuring depth of hypnosis and neuromuscular recovery are already well established, and devices for pain monitoring are available. Nonetheless pain monitoring is rare in clinical practice. Recently, the pain monitoring device PMD200 (Medasense Biometrics™ , Israel) was introduced. It non-invasively measures heart rate, heart rate variability, skin resistance, resistance variability, temperature and movement to calculate a nociception level (NoL) index. The NoL index range starts at zero, which is equivalent to being painless, and goes up to a value of 100. The validity and reliability of NoL monitoring is the content of current studies. Objective We tested the hypothesis if the use of the PMD200 significantly reduces opioid consumption during da Vinci prostatectomy. Material and methods A total of 50 male patients were included in this randomized, single blinded study. Exclusion criteria were arrhythmia because the pain monitoring device requires a sinus rhythm for reliable results. Patients received a weight-adjusted sufentanil bolus (0.3 µg/kg ideal body weight) during induction of anesthesia. Additionally, they received 10 µg of sufentanil before skin incision. Both groups received total intravenous anesthesia with propofol and continuous muscle relaxation through cis-atracurium. In the control group (CONT; n = 26), a standardized sufentanil bolus of 10 µg were administered by common criteria (heart rate/blood pressure increase, lacrimation, gut feeling) at the anesthesiologistʼs discretion. In the intervention group (INT; n = 24), patients received the standardized sufentanil bolus when the NoL index was above 25 for 2 min, which corresponds to the manufacturerʼs recommendation. The NoL index and bolus administrations were recorded for every patient. In the control group, the display of the pain monitor showing the NoL index was not visible for the anesthesiologist. P
Published: 2022

8. Short-term forecasting of off-street parking occupancy

Author: Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Leeuwen, M. (Marco) van, Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Leeuwen, M. (Marco) van, and Dugundji, E.R. (Elenna)
Abstract: Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.
Published: 2022
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9. A comparison of approaches for the time series forecasting of motorway traffic flow rate at hourly and daily aggregation levels

Author: Bijl, B. (Bas) van der, Gijsbertsen, B. (Bart), Loon, S. (Stan) van, Reurich, Y. (Yorran), Valk, T. (Tom) de, Koch, T. (Thomas), Dugundji, E.R. (Elenna), Bijl, B. (Bas) van der, Gijsbertsen, B. (Bart), Loon, S. (Stan) van, Reurich, Y. (Yorran), Valk, T. (Tom) de, Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. Congestion is generally associated with reduced average speed at a high traffic flow rate. This traffic flow rate is defined as the number of vehicles that pass a certain location at a given time. The modelling and prediction of this traffic flow rate may lead to valuable insights that may be used to reduce congestion and societal costs. This study aims to predict the traffic flow rate for 41 different locations in and around Amsterdam, The Netherlands. Using TBATS, SARIMAX and LSTM models, among others, the traffic flow rate of these locations has successfully been modelled. These models may provide accurate predictions for the future flow rate, which may be useful for the identification of infrastructure bottlenecks and the scheduling of maintenance. Considering the dramatic effects of the COVID-19 pandemic on the traffic flow rate, the inclusion of 2020 data with a number of external factors, could lead to an improvement of the results and the ability to model the future effects of the pandemic.
Published: 2022
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10. Year-ahead ambient temperature forecasting in pharmaceutical transport lanes thermal conditions

Author: Berg, A.P.B. (Annabel) van den, Bootsma, L.R. (Lotte), Bovenber, T.F.A. (Thomas), Moerbeek, A.R. (Rosa), Jong, E. (Eelco) de, Khalil, S. (Sergey), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Berg, A.P.B. (Annabel) van den, Bootsma, L.R. (Lotte), Bovenber, T.F.A. (Thomas), Moerbeek, A.R. (Rosa), Jong, E. (Eelco) de, Khalil, S. (Sergey), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: This research aims to predict year-ahead ambient temperature aggregated at monthly level per hour on airport locations using historical temperature data. In this research, an Extreme gradient boosting regression model, LSTM model and the benchmark model, a persistence model, are compared in accuracy. Models are fine-tuned on the cities with the highest variability in temperature and grid searches are implemented only for these cities (one per data source). Overall, we have seen that the LSTM model with output size 12 months x 24 hours predicts for the next year the best. The Persistence model is closely followed by the Extreme gradient boosting model, with a small deviation in the quantiles. The point predictions for each of the other models are a bit further of (with more than 3 degrees Celsius) and LSTM 365 days x 24 hours is the worst in this case. These models can be used to give an indication for the ambient temperature on lane level.
Published: 2022
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11. Bilateral oophorectomy and rate of colorectal cancer: A prospective cohort study

Author: Koch, T, Jorgensen, JT, Christensen, J, Duun-Henriksen, AK, Priskorn, L, Simonsen, MK, Dehlendorff, C, Andersen, ZJ, Juul, A, Brauner, E, Hickey, M, Koch, T, Jorgensen, JT, Christensen, J, Duun-Henriksen, AK, Priskorn, L, Simonsen, MK, Dehlendorff, C, Andersen, ZJ, Juul, A, Brauner, E, and Hickey, M
Abstract: Worldwide, colorectal cancer is the second most common cancer and third cause of cancer death in women. Estrogen exposure has been inversely associated with colorectal cancer. Oophorectomy reduces circulating estrogen, but the effect on colorectal cancer remains uncertain. The aim of this study was to examine the association between unilateral and bilateral oophorectomy and subsequent risk of colorectal cancer, and whether this association varied by menopausal status at time of oophorectomy, use of hormone replacement therapy (HRT) at baseline, hysterectomy and baseline body mass index (BMI). The study included 25 698 female nurses (aged ≥45 years) participating in the Danish Nurse Cohort. Nurses were followed from baseline until date of colorectal cancer, death, emigration or end of follow-up at December 31, 2018, whichever came first. We examined the association between oophorectomy and colorectal cancer (all ages and stratified by menopausal status). The potential modifying effects of hysterectomy, HRT use at baseline and BMI were investigated. During 542 140 person-years of follow-up, 863 (3.4%) nurses were diagnosed with colorectal cancer. Bilateral oophorectomy was associated with a 79% increased colorectal cancer rate, adjusted rate ratio (aRR) (95% confidence interval [CI]): 1.79 (1.33-2.42). Effect estimates following unilateral oophorectomy also showed higher rate of colorectal cancer, although less pronounced and nonstatistically significant (aRR) (95% CI): 1.25 (0.86-1.82). Similar results were seen when stratifying by menopausal status. The association was not modified by baseline HRT use, hysterectomy or BMI. Oophorectomy was associated with increased rate of colorectal cancer, with highest rates among women with bilateral oophorectomy.
Published: 2022

12. Cardiovascular mortality after bilateral oophorectomy: a prospective cohort study

Author: Olesen, CS, Koch, T, Uldbjerg, CS, Gregersen, LS, Christensen, J, Dehlendorff, C, Priskorn, L, Wilson, LF, Lim, Y-H, Jorgensen, JT, Andersen, ZJ, Juul, A, Abildgaard, J, Hickey, M, Braeuner, EV, Olesen, CS, Koch, T, Uldbjerg, CS, Gregersen, LS, Christensen, J, Dehlendorff, C, Priskorn, L, Wilson, LF, Lim, Y-H, Jorgensen, JT, Andersen, ZJ, Juul, A, Abildgaard, J, Hickey, M, and Braeuner, EV
Abstract: OBJECTIVES: Bilateral oophorectomy permanently reduces endogenous estrogen exposure and may increase cardiovascular mortality in women. This study aimed to investigate the association between bilateral oophorectomy and cardiovascular mortality and whether this association was conditional on hysterectomy or on the use of hormone therapy at the time of study entry. METHODS: A prospective cohort study of 25,338 female nurses aged ≥ 45 years within the Danish Nurse Cohort. Nurses were enrolled in 1993 or 1999 and followed until death, emigration, or end of follow-up on December 31, 2018, whichever came first. Exposure was bilateral oophorectomy. Outcome was cardiovascular mortality. Associations were estimated using Poisson regression models with log person-years as the offset. RESULTS: A total of 2,040 (8.1%) participants underwent bilateral oophorectomy. During a mean follow-up of 21.2 (SD: 5.6) years, 772 (3.0%) nurses died from cardiovascular disease. In adjusted analyses, a 31% higher rate of cardiovascular mortality was observed after bilateral oophorectomy (aMRR 1.31; 95% CI, 0.88-1.96) compared with women who retained their ovaries. No evidence of effect modification by use of hormone therapy at baseline or by hysterectomy on the association between bilateral oophorectomy and cardiovascular mortality was observed. CONCLUSION: Bilateral oophorectomy may be associated with cardiovascular mortality in women, but the estimate was not statistically significant. Additionally, we were unable to make firm conclusions regarding the possible modifying role of hormone therapy and hysterectomy on this potential association. Additional studies are needed to replicate this work.
Published: 2022

13. The long-term association between bilateral oophorectomy and depression: a prospective cohort study

Author: Brauner, EV, Wilson, LF, Koch, T, Christensen, J, Dehlendorff, C, Duun-Henriksen, AK, Priskorn, L, Abildgaard, J, Simonsen, MK, Jorgensen, JT, Lim, Y-H, Andersen, ZJ, Juul, A, Hickey, M, Brauner, EV, Wilson, LF, Koch, T, Christensen, J, Dehlendorff, C, Duun-Henriksen, AK, Priskorn, L, Abildgaard, J, Simonsen, MK, Jorgensen, JT, Lim, Y-H, Andersen, ZJ, Juul, A, and Hickey, M
Abstract: OBJECTIVE: Depression is a leading cause of disability globally and affects more women than men. Ovarian sex steroids are thought to modify depression risk in women and interventions such as bilateral oophorectomy that permanently change the sex steroid milieu may increase the risk of depression. This study aimed to investigate the associations between unilateral and bilateral oophorectomy and depression over a 25-year period (1993-2018) and whether this varied by age at oophorectomy or use of menopausal hormone therapy. METHODS: Twenty-five thousand one hundred eighty-eight nurses aged ≥45 years from the Danish Nurse Cohort were included. Nurses with depression prior to baseline were excluded. Poisson regression models, with log-transformed person-years as offset, were used to assess the associations between oophorectomy and incident depression. Nurses who retained their ovaries were the reference group. RESULTS: Compared with nurses with retained ovaries, bilateral oophorectomy was associated with a slightly higher rate of depression (rate ratio [RR], 1.08; 95% confidence interval [CI], 0.95-1.23), but without statistical significance. However, when stratified by age at oophorectomy, compared with nurses with retained ovaries, bilateral oophorectomy at age ≥51 years was associated with higher rates of depression (RR 1.16; 95% CI, 1.00-1.34), but not bilateral oophorectomy at age <51 years (RR 0.86; 95% CI, 0.69-1.07); P value for difference in estimates = 0.02. No association between unilateral oophorectomy and depression was observed. CONCLUSIONS: In this cohort of Danish female nurses, bilateral oophorectomy at age ≥51 years, but not at younger ages, was associated with a slightly higher rate of depression compared with those who retained their ovaries.
Published: 2022

14. Oophorectomy and rate of dementia: a prospective cohort study

Author: Uldbjerg, CS, Wilson, LF, Koch, T, Christensen, J, Dehlendorff, C, Priskorn, L, Abildgaard, J, Simonsen, MK, Lim, Y-H, Jorgensen, JT, Andersen, ZJ, Juul, A, Hickey, M, Brauner, E, Uldbjerg, CS, Wilson, LF, Koch, T, Christensen, J, Dehlendorff, C, Priskorn, L, Abildgaard, J, Simonsen, MK, Lim, Y-H, Jorgensen, JT, Andersen, ZJ, Juul, A, Hickey, M, and Brauner, E
Abstract: Objective: Globally, dementia disproportionally affects women, which is not fully explained by higher female longevity. Oophorectomy at any age leads to the permanent loss of ovarian sex steroids, potentially increasing the risk of dementia. We aimed to investigate the association between oophorectomy and dementia and whether this was conditional on age at oophorectomy, hysterectomy or use of hormone therapy (HT). Methods: A prospective study of 24,851 female nurses from the Danish Nurse Cohort. Nurses were followed from age 60 years or entry into the cohort, whichever came last, until date of dementia, death, emigration or end of follow-up (December 31, 2018), whichever came first. Poisson regression models with log-transformed person-years as offset were used to estimate the associations. Results: During 334,420 person-years of follow-up, 1,238 (5.0%) nurses developed dementia and 1,969 (7.9%)/ 1,016 (4.1%) contributed person-time after bilateral-/unilateral oophorectomy. In adjusted analyses, an 18% higher rate of dementia was observed following bilateral oophorectomy (aRR 1.18: 95% CI, 0.89-1.56) and 13% lower rate (aRR 0.87: 95% CI, 0.59-1.23) following unilateral oophorectomy compared to nurses who retained their ovaries. Similar effects were detected after stratification according to age at oophorectomy. No statistically significant modifying effects of hysterectomy or HT were detected (Pinteraction≥0.60). Conclusions: Bilateral, but not unilateral, oophorectomy was associated with an increased rate of incident dementia. We were unable to establish whether this association was conditional on hysterectomy or HT use. Although an increase in dementia after bilateral oophorectomy is biologically plausible, limited statistical power hampers the precision of the estimates.
Published: 2022

15. A comparison of approaches for the time series forecasting of motorway traffic flow rate at hourly and daily aggregation levels

Author: Bijl, B. (Bas) van der, Gijsbertsen, B. (Bart), Loon, S. (Stan) van, Reurich, Y. (Yorran), Valk, T. (Tom) de, Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Bijl, B. (Bas) van der, Gijsbertsen, B. (Bart), Loon, S. (Stan) van, Reurich, Y. (Yorran), Valk, T. (Tom) de, Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. Congestion is generally associated with reduced average speed at a high traffic flow rate. This traffic flow rate is defined as the number of vehicles that pass a certain location at a given time. The modelling and prediction of this traffic flow rate may lead to valuable insights that may be used to reduce congestion and societal costs. This study aims to predict the traffic flow rate for 41 different locations in and around Amsterdam, The Netherlands. Using TBATS, SARIMAX and LSTM models, among others, the traffic flow rate of these locations has successfully been modelled. These models may provide accurate predictions for the future flow rate, which may be useful for the identification of infrastructure bottlenecks and the scheduling of maintenance. Considering the dramatic effects of the COVID-19 pandemic on the traffic flow rate, the inclusion of 2020 data with a number of external factors, could lead to an improvement of the results and the ability to model the future effects of the pandemic.
Published: 2022
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16. Year-ahead ambient temperature forecasting in pharmaceutical transport lanes thermal conditions

Author: Berg, A.P.B. (Annabel) van den, Bootsma, L.R. (Lotte), Bovenber, T.F.A. (Thomas), Moerbeek, A.R. (Rosa), Jong, E. (Eelco) de, Khalil, S. (Sergey), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Berg, A.P.B. (Annabel) van den, Bootsma, L.R. (Lotte), Bovenber, T.F.A. (Thomas), Moerbeek, A.R. (Rosa), Jong, E. (Eelco) de, Khalil, S. (Sergey), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: This research aims to predict year-ahead ambient temperature aggregated at monthly level per hour on airport locations using historical temperature data. In this research, an Extreme gradient boosting regression model, LSTM model and the benchmark model, a persistence model, are compared in accuracy. Models are fine-tuned on the cities with the highest variability in temperature and grid searches are implemented only for these cities (one per data source). Overall, we have seen that the LSTM model with output size 12 months x 24 hours predicts for the next year the best. The Persistence model is closely followed by the Extreme gradient boosting model, with a small deviation in the quantiles. The point predictions for each of the other models are a bit further of (with more than 3 degrees Celsius) and LSTM 365 days x 24 hours is the worst in this case. These models can be used to give an indication for the ambient temperature on lane level.
Published: 2022
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17. Short-term forecasting of off-street parking occupancy

Author: Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Leeuwen, M. (Marco) van, Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Leeuwen, M. (Marco) van, and Dugundji, E.R. (Elenna)
Abstract: Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.
Published: 2022
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18. Mobility and accessibility in the metropolitan region of Amsterdam

Author: Koch, T. H. A. (Thomas) and Koch, T. H. A. (Thomas)
Published: 2022

19. Prenatal and postnatal exposures to endocrine disrupting chemicals and timing of pubertal onset in girls and boys:a systematic review and meta-analysis

Author: Uldbjerg, C S, Koch, T, Lim, Y-H, Gregersen, L. S., Olesen, C. S., Andersson, A-M, Frederiksen, H., Coull, B A, Hauser, R, Juul, A, Bräuner, E. V., Uldbjerg, C S, Koch, T, Lim, Y-H, Gregersen, L. S., Olesen, C. S., Andersson, A-M, Frederiksen, H., Coull, B A, Hauser, R, Juul, A, and Bräuner, E. V.
Abstract: BACKGROUND: Globally, the ages at pubertal onset for girls and boys have been decreasing during recent decades, partly attributed to excess body fat accumulation. However, a growing body of literature has recognized that endocrine disrupting chemicals (EDCs) may play an important role in this global trend, but the association has not yet been fully established.OBJECTIVE AND RATIONALE: EDCs can interfere with normal hormone function and metabolism and play a role in pubertal onset. We aimed to systematically identify and evaluate the current evidence on the timing of pubertal onset in girls and boys following prenatal or postnatal exposures to xenobiotic EDCs.SEARCH METHODS: Following PRISMA guidelines, we performed a systematic literature search of original peer-reviewed publications in the PubMed database through a block search approach using a combination of index MeSH and free text search terms. Publications were considered if they covered biomarkers of prenatal or postnatal exposures to xenobiotic EDCs (European Commission's list of category 1 EDCs) measured in maternal or child biospecimen and pubertal onset defined by the progression of the following milestones (and assessed in terms of the following measures): menarche (age), thelarche (Tanner staging) and pubarche (Tanner staging), in girls, and genital stage (Tanner staging), testicular volume (ml) and pubarche (Tanner staging), in boys.OUTCOMES: The literature search resulted in 703 references, of which we identified 52 publications fulfilling the eligibility criteria for the qualitative trend synthesis and 23 publications for the meta-analysis. The qualitative trend synthesis provided data on 103 combinations of associations between prenatal or postnatal exposure to EDC compounds groups and puberty outcomes and the meta-analysis enabled 18 summary risk estimates of meta-associations.WIDER IMPLICATIONS: Statistically significant associations in the qualitative trend synthesis
Published: 2022

20. Knowledge modeling and incident analysis for special cargo

Author: Reshadat, V. (Vahideh), Kolkman, T. (Tess), Zervanou, K. (Kalliopi), Zhang, Y. (Yingqian), Akçay, A. (Alp), Snijder, C. (Carlijn), McDonnell, R. (Ryan), Schorer, K. (Karel), Wichers, C. (Casper), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Jong, E. (Eelco) de, Reshadat, V. (Vahideh), Kolkman, T. (Tess), Zervanou, K. (Kalliopi), Zhang, Y. (Yingqian), Akçay, A. (Alp), Snijder, C. (Carlijn), McDonnell, R. (Ryan), Schorer, K. (Karel), Wichers, C. (Casper), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), and Jong, E. (Eelco) de
Abstract: The airfreight industry of shipping goods with special handling needs, also known as special cargo, suffers from nontransparent shipping processes, resulting in inefficiency. The LARA project (Lane Analysis and Route Advisor) aims at addressing these limitations and bringing innovation in special cargo route planning so as to improve operational deficiencies and customer services. In this chapter, we discuss the special cargo domain knowledge elicitation and modeling into an ontology. We also present research into cargo incidents, namely, automatic classification of incidents in free-text reports and experiments in detecting significant features associated with specific cargo incident types. Our work mainly addresses two of the main technical priority areas defined by the European Big Data Value (BDV) Strategic Research and Innovation Agenda, namely, the application of data analytics to improve data understanding and providing optimized architectures for analytics of data-at-rest and data-in-motion, the overall goal is to develop technologies contributing to the data value chain in the logistics sector. It addresses the horizontal concerns Data Analytics, Data Processing Architectures, and Data Management of the BDV Reference Model. It also addresses the vertical dimension Big Data Types and Semantics.
Published: 2022
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21. Beeinflusst NoL-Monitoring den Opioidbedarf bei Da-Vinci-Prostatektomien?

Author: Niebhagen, F., Golde, C., Koch, T., Hübler, M., Niebhagen, F., Golde, C., Koch, T., and Hübler, M.
Abstract: Hintergrund Die Gabe von Opioiden zur Schmerzunterdrückung spielt eine zentrale Rolle in der modernen Anästhesiologie. Messungen von Hypnosetiefe und Muskelrelaxierung sind im Gegensatz zur Schmerzmessung seit Jahren etabliert. Seit Kurzem ist das PMD200 („Pain Monitoring System“; Fa. Medasense Biometrics™ Ltd., Ramat-Gan, Israel) verfügbar. Dieser Schmerzmonitor misst nichtinvasiv und errechnet einen dimensionslosen Schmerzindex („nociceptor level“, NoL). Die Validität und Zuverlässigkeit des Verfahrens sind Gegenstand von klinischen Studien. Fragestellung Reduziert die Verwendung des PMD200 die Gabe von Analgetika während einer Da-Vinci-Prostatektomie? Material und Methoden In die Studie wurden 50 Patienten aufgenommen. Nach gewichtsadaptierter Sufentanilgabe zur Narkoseinduktion und einem 10 µg Bolus vor Hautschnitt erfolgte die intraoperative Analgesie durch subjektive Entscheidung (CONT) oder aufgrund eines erhöhten NoL-Index (INT). Die statistische Auswertung erfolgte durch Mann-Whitney-U-, Kolmogorow-Smirnow-Test und Levene-Statistik. Ergebnisse In der INT-Gruppe war die Anzahl der Sufentanilboli/h nicht signifikant geringer als in der CONT-Gruppe (p = 0,065). Die Varianz der Sufentanilgaben unterschied sich signifikant (p = 0,033). In der CONT-Gruppe war die Applikation normal verteilt (p = 0,2), in der INT-Gruppe hingegen nicht (p = 0,003). Diskussion Eine mögliche Interpretation der Daten ist, dass die Schmerzmittelgabe in der INT-Gruppe individualisierter erfolgte, d. h., es wurden nichterforderliche Schmerzmittelgaben vermieden, und gleichzeitig detektierte das NoL-Monitoring einzelne Patienten mit deutlich erhöhtem Schmerzmittelbedarf. Diese Schlussfolgerung ist nur unter der Voraussetzung zulässig, dass das PMD200 auch tatsächlich die Entität Schmerz misst., Background Administration of opioids to suppress pain plays a major role in modern anesthesia. Measuring depth of hypnosis and neuromuscular recovery are already well established, and devices for pain monitoring are available. Nonetheless pain monitoring is rare in clinical practice. Recently, the pain monitoring device PMD200 (Medasense Biometrics™ , Israel) was introduced. It non-invasively measures heart rate, heart rate variability, skin resistance, resistance variability, temperature and movement to calculate a nociception level (NoL) index. The NoL index range starts at zero, which is equivalent to being painless, and goes up to a value of 100. The validity and reliability of NoL monitoring is the content of current studies. Objective We tested the hypothesis if the use of the PMD200 significantly reduces opioid consumption during da Vinci prostatectomy. Material and methods A total of 50 male patients were included in this randomized, single blinded study. Exclusion criteria were arrhythmia because the pain monitoring device requires a sinus rhythm for reliable results. Patients received a weight-adjusted sufentanil bolus (0.3 µg/kg ideal body weight) during induction of anesthesia. Additionally, they received 10 µg of sufentanil before skin incision. Both groups received total intravenous anesthesia with propofol and continuous muscle relaxation through cis-atracurium. In the control group (CONT; n = 26), a standardized sufentanil bolus of 10 µg were administered by common criteria (heart rate/blood pressure increase, lacrimation, gut feeling) at the anesthesiologistʼs discretion. In the intervention group (INT; n = 24), patients received the standardized sufentanil bolus when the NoL index was above 25 for 2 min, which corresponds to the manufacturerʼs recommendation. The NoL index and bolus administrations were recorded for every patient. In the control group, the display of the pain monitor showing the NoL index was not visible for the anesthesiologist. P
Published: 2022

22. Association between night-time surgery and occurrence of intraoperative adverse events and postoperative pulmonary complications

Author: Cortegiani, A, Gregoretti, C, Neto, A, Hemmes, S, Ball, L, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Melo, M, Putensen, C, Schmid, W, Severgnini, P, Wrigge, H, Gama de Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Mossevelde, V, van Malderen, M, Dylst, D, Melkebeek, J, Beran, M, Hert, S, Baerdemaeker, L, Heyse, B, Limmen, J, Wyffels, P, Jacobs, T, Roels, N, Bruyne, A, Velde, S, Marina, J, Dejana, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Darija, A, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Saric, J, Erceg, G, Dvorscak, M, Branka, M, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Inga, M, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, Kafrawy, S, Abdelwahab, H, Metwally, T, Ahmed, A, Ahmed Mostafa, E, Hasan, W, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Jaber, S, Prades, A, Krassler, J, Merzky, S, Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Bluth, T, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodorak, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, Michele, M, Bona, F, Giacoletto, G, Sardo, E, Sottosanti, L, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Agreta, G, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, Veld, B, Werger, A, Dennesen, P, Severijns, C, Jong, J, Hering, J, Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Jannicke, M, Andersson, E, Amir, S, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Popescu Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Barbka, N, Ana, P, Jovanov, M, Strazisar, B, Jasmina, M, Vesna, N, Voje, M, Grynyuk, A, Kostadinov, I, Alenka, S, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Cesar Aldecoa, A, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Ben, C, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Ana, F, Luzier, E, Almagro, A, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Cortegiani A., Gregoretti C., Neto A. S., Hemmes S. N. T., Ball L., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Melo M. F. V., Putensen C., Schmid W., Severgnini P., Wrigge H., Gama de Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Mossevelde V., van Malderen M. C., Dylst D., Melkebeek J. V., Beran M., Hert S. D., Baerdemaeker L. D., Heyse B., Limmen J. V., Wyffels P., Jacobs T., Roels N., Bruyne A. D., Velde S. V. D., Marina J. Z., Dejana D. O., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Darija A. V., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Saric J. P., Erceg G., Dvorscak M. B., Branka M. S., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Inga M. B., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., Kafrawy S. E., Abdelwahab H. H., Metwally T., Ahmed A. R., Ahmed Mostafa E. S., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Jaber S., Prades A., Krassler J., Merzky S., Abreu M. G. D., Uhlig C., Kiss T., Bundy A., Bluth T., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodorak K., Gkiokas G., Tasoulis M. K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., Michele M. D., Bona F., Giacoletto G., Sardo E., Sottosanti L. G. V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., Bella D. L., Mirabella L., D'antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Parma A. L. D., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Agreta G. G., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., Veld B. I. '., Werger A., Dennesen P., Severijns C., Jong J. D., Hering J., Beek R. V., Ivars S., Jammer I. B., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Jannicke M. O., Andersson E., Amir S. K., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., Castro M. D. L. G., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Popescu Garotescu I., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. T., Gherghina I., Costea D., Cindea I., Copotoiu S. M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Barbka N. S., Ana P. G., Jovanov M., Strazisar B., Jasmina M. B., Vesna N. J., Voje M., Grynyuk A., Kostadinov I., Alenka S. V., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., Larriba A. D. A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., Andres Ibanez J. A. D., Pastor E., Soro M., Ferrando C., Defez M., Cesar Aldecoa A. S., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Ben C. B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Ana F. B., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., Tze Y., Cortegiani, A, Gregoretti, C, Neto, A, Hemmes, S, Ball, L, Canet, J, Hiesmayr, M, Hollmann, M, Mills, G, Melo, M, Putensen, C, Schmid, W, Severgnini, P, Wrigge, H, Gama de Abreu, M, Schultz, M, Pelosi, P, Kroell, W, Metzler, H, Struber, G, Wegscheider, T, Gombotz, H, Urbanek, B, Kahn, D, Momeni, M, Pospiech, A, Lois, F, Forget, P, Grosu, I, Poelaert, J, Mossevelde, V, van Malderen, M, Dylst, D, Melkebeek, J, Beran, M, Hert, S, Baerdemaeker, L, Heyse, B, Limmen, J, Wyffels, P, Jacobs, T, Roels, N, Bruyne, A, Velde, S, Marina, J, Dejana, D, Pernar, S, Zunic, J, Miskovic, P, Zilic, A, Kvolik, S, Ivic, D, Darija, A, Skiljic, S, Vinkovic, H, Oputric, I, Juricic, K, Frkovic, V, Kopic, J, Mirkovic, I, Saric, J, Erceg, G, Dvorscak, M, Branka, M, Pavicic, A, Goranovic, T, Maldini, B, Radocaj, T, Gavranovic, Z, Inga, M, Sehovic, M, Stourac, P, Harazim, H, Smekalova, O, Kosinova, M, Kolacek, T, Hudacek, K, Drab, M, Brujevic, J, Vitkova, K, Jirmanova, K, Volfova, I, Dzurnakova, P, Liskova, K, Dudas, R, Filipsky, R, Kafrawy, S, Abdelwahab, H, Metwally, T, Ahmed, A, Ahmed Mostafa, E, Hasan, W, Yassin, H, Magdy, M, Abdelhady, M, Mahran, M, Herodes, E, Kivik, P, Oganjan, J, Aun, A, Sormus, A, Sarapuu, K, Mall, M, Karjagin, J, Futier, E, Petit, A, Gerard, A, Marret, E, Solier, M, Jaber, S, Prades, A, Krassler, J, Merzky, S, Abreu, M, Uhlig, C, Kiss, T, Bundy, A, Bluth, T, Gueldner, A, Spieth, P, Scharffenberg, M, Thiem, D, Koch, T, Treschan, T, Schaefer, M, Bastin, B, Geib, J, Weiss, M, Kienbaum, P, Pannen, B, Gottschalk, A, Konrad, M, Westerheide, D, Schwerdtfeger, B, Simon, P, Reske, A, Nestler, C, Valsamidis, D, Stroumpoulis, K, Antholopoulos, G, Andreou, A, Karapanos, D, Theodorak, K, Gkiokas, G, Tasoulis, M, Sidiropoulou, T, Zafeiropoulou, F, Florou, P, Pandazi, A, Tsaousi, G, Nouris, C, Pourzitaki, C, Bystritski, D, Pizov, R, Eden, A, Pesce, C, Campanile, A, Marrella, A, Grasso, S, Michele, M, Bona, F, Giacoletto, G, Sardo, E, Sottosanti, L, Solca, M, Volta, C, Spadaro, S, Verri, M, Ragazzi, R, Zoppellari, R, Cinnella, G, Raimondo, P, Bella, D, Mirabella, L, D'Antini, D, Molin, A, Brunetti, I, Gratarola, A, Pellerano, G, Sileo, R, Pezzatto, S, Montagnani, L, Pasin, L, Landoni, G, Zangrillo, A, Beretta, L, Parma, A, Tarzia, V, Dossi, R, Sassone, M, Sances, D, Tredici, S, Spano, G, Castellani, G, Delunas, L, Peradze, S, Venturino, M, Arpino, I, Sher, S, Tommasino, C, Rapido, F, Morelli, P, Vargas, M, Servillo, G, Raineri, S, Montalto, F, Russotto, V, Giarratano, A, Baciarello, M, Generali, M, Cerati, G, Leykin, Y, Bressan, F, Bartolini, V, Zamidei, L, Brazzi, L, Liperi, C, Sales, G, Pistidda, L, Brugnoni, E, Musella, G, Bacuzzi, A, Muhardri, D, Agreta, G, Sada, F, Bytyqi, A, Karbonskiene, A, Aukstakalniene, R, Teberaite, Z, Salciute, E, Tikuisis, R, Miliauskas, P, Jurate, S, Kontrimaviciute, E, Tomkute, G, Xuereb, J, Bezzina, M, Borg, F, Wiersma, I, Binnekade, J, Bos, L, Boer, C, Duvekot, A, Veld, B, Werger, A, Dennesen, P, Severijns, C, Jong, J, Hering, J, Beek, R, Ivars, S, Jammer, I, Breidablik, A, Hodt, K, Fjellanger, F, Avalos, M, Jannicke, M, Andersson, E, Amir, S, Molina, R, Wutai, S, Morais, E, Tareco, G, Ferreira, D, Amaral, J, Castro, M, Cadilha, S, Appleton, S, Parente, S, Correia, M, Martins, D, Monteirosa, A, Ricardo, A, Rodrigues, S, Horhota, L, Grintescu, I, Mirea, L, Corneci, D, Negoita, S, Dutu, M, Popescu Garotescu, I, Filipescu, D, Prodan, A, Droc, G, Fota, R, Popescu, M, Tomescu, D, Petcu, A, Tudoroiu, M, Moise, A, Guran, C, Gherghina, I, Costea, D, Cindea, I, Copotoiu, S, Copotoiu, R, Barsan, V, Tolcser, Z, Riciu, M, Moldovan, S, Veres, M, Gritsan, A, Kapkan, T, Gritsan, G, Korolkov, O, Kulikov, A, Lubnin, A, Ovezov, A, Prokoshev, P, Lugovoy, A, Anipchenko, N, Babayants, A, Komissarova, I, Zalina, K, Likhvantsev, V, Fedorov, S, Lazukic, A, Pejakovic, J, Mihajlovic, D, Kusnierikova, Z, Zelinkova, M, Bruncakova, K, Polakovicova, L, Sobona, V, Barbka, N, Ana, P, Jovanov, M, Strazisar, B, Jasmina, M, Vesna, N, Voje, M, Grynyuk, A, Kostadinov, I, Alenka, S, Moral, V, Unzueta, M, Puigbo, C, Fava, J, Moret, E, Nunez, M, Sendra, M, Brunelli, A, Rodenas, F, Monedero, P, Martinez, F, Temino, M, Simon, A, Larriba, A, Lisi, A, Perez, G, Martinez, R, Granell, M, Vivo, J, Ruiz, C, Andres Ibanez, J, Pastor, E, Soro, M, Ferrando, C, Defez, M, Cesar Aldecoa, A, Perez, R, Rico, J, Jawad, M, Saeed, Y, Gillberg, L, Bengisun, Z, Kazbek, B, Coskunfirat, N, Boztug, N, Sanli, S, Yilmaz, M, Hadimioglu, N, Senturk, N, Camci, E, Kucukgoncu, S, Sungur, Z, Sivrikoz, N, Ozgen, S, Toraman, F, Selvi, O, Senturk, O, Yildiz, M, Kuvaki, B, Gunenc, F, Kucukguclu, S, Ozbilgin, S, Maral, J, Canli, S, Arun, O, Saltali, A, Aydogan, E, Akgun, F, Sanlikarip, C, Karaman, F, Mazur, A, Vorotyntsev, S, Rousseau, G, Barrett, C, Stancombe, L, Shelley, B, Scholes, H, Limb, J, Rafi, A, Wayman, L, Deane, J, Rogerson, D, Williams, J, Yates, S, Rogers, E, Pulletz, M, Moreton, S, Jones, S, Venkatesh, S, Burton, M, Brown, L, Goodall, C, Rucklidge, M, Fuller, D, Nadolski, M, Kusre, S, Lundberg, M, Everett, L, Nutt, H, Zuleika, M, Carvalho, P, Clements, D, Ben, C, Watt, P, Raymode, P, Pearse, R, Mohr, O, Raj, A, Creary, T, Chishti, A, Bell, A, Higham, C, Cain, A, Gibb, S, Mowat, S, Franklin, D, West, C, Minto, G, Boyd, N, Calton, E, Walker, R, Mackenzie, F, Ellison, B, Roberts, H, Chikungwa, M, Jackson, C, Donovan, A, Foot, J, Homan, E, Montgomery, J, Portch, D, Mercer, P, Palmer, J, Paddle, J, Fouracres, A, Datson, A, Andrew, A, Welch, L, Rose, A, Varma, S, Simeson, K, Rambhatla, M, Susarla, J, Marri, S, Kodaganallur, K, Das, A, Algarsamy, S, Colley, J, Davies, S, Szewczyk, M, Smith, T, Ana, F, Luzier, E, Almagro, A, Fernando, L, Sulemanji, D, Sprung, J, Weingarten, T, Kor, D, Scavonetto, F, Tze, Y, Cortegiani A., Gregoretti C., Neto A. S., Hemmes S. N. T., Ball L., Canet J., Hiesmayr M., Hollmann M. W., Mills G. H., Melo M. F. V., Putensen C., Schmid W., Severgnini P., Wrigge H., Gama de Abreu M., Schultz M. J., Pelosi P., Kroell W., Metzler H., Struber G., Wegscheider T., Gombotz H., Urbanek B., Kahn D., Momeni M., Pospiech A., Lois F., Forget P., Grosu I., Poelaert J., Mossevelde V., van Malderen M. C., Dylst D., Melkebeek J. V., Beran M., Hert S. D., Baerdemaeker L. D., Heyse B., Limmen J. V., Wyffels P., Jacobs T., Roels N., Bruyne A. D., Velde S. V. D., Marina J. Z., Dejana D. O., Pernar S., Zunic J., Miskovic P., Zilic A., Kvolik S., Ivic D., Darija A. V., Skiljic S., Vinkovic H., Oputric I., Juricic K., Frkovic V., Kopic J., Mirkovic I., Saric J. P., Erceg G., Dvorscak M. B., Branka M. S., Pavicic A. M., Goranovic T., Maldini B., Radocaj T., Gavranovic Z., Inga M. B., Sehovic M., Stourac P., Harazim H., Smekalova O., Kosinova M., Kolacek T., Hudacek K., Drab M., Brujevic J., Vitkova K., Jirmanova K., Volfova I., Dzurnakova P., Liskova K., Dudas R., Filipsky R., Kafrawy S. E., Abdelwahab H. H., Metwally T., Ahmed A. R., Ahmed Mostafa E. S., Hasan W. F., Ahmed A. G., Yassin H., Magdy M., Abdelhady M., Mahran M., Herodes E., Kivik P., Oganjan J., Aun A., Sormus A., Sarapuu K., Mall M., Karjagin J., Futier E., Petit A., Gerard A., Marret E., Solier M., Jaber S., Prades A., Krassler J., Merzky S., Abreu M. G. D., Uhlig C., Kiss T., Bundy A., Bluth T., Gueldner A., Spieth P., Scharffenberg M., Thiem D. T., Koch T., Treschan T., Schaefer M., Bastin B., Geib J., Weiss M., Kienbaum P., Pannen B., Gottschalk A., Konrad M., Westerheide D., Schwerdtfeger B., Simon P., Reske A., Nestler C., Valsamidis D., Stroumpoulis K., Antholopoulos G., Andreou A., Karapanos D., Theodorak K., Gkiokas G., Tasoulis M. K., Sidiropoulou T., Zafeiropoulou F., Florou P., Pandazi A., Tsaousi G., Nouris C., Pourzitaki C., Bystritski D., Pizov R., Eden A., Pesce C. V., Campanile A., Marrella A., Grasso S., Michele M. D., Bona F., Giacoletto G., Sardo E., Sottosanti L. G. V., Solca M., Volta C. A., Spadaro S., Verri M., Ragazzi R., Zoppellari R., Cinnella G., Raimondo P., Bella D. L., Mirabella L., D'antini D., Molin A., Brunetti I., Gratarola A., Pellerano G., Sileo R., Pezzatto S., Montagnani L., Pasin L., Landoni G., Zangrillo A., Beretta L., Parma A. L. D., Tarzia V., Dossi R., Sassone M. E., Sances D., Tredici S., Spano G., Castellani G., Delunas L., Peradze S., Venturino M., Arpino I., Sher S., Tommasino C., Rapido F., Morelli P., Vargas M., Servillo G., Raineri S. M., Montalto F., Russotto V., Giarratano A., Baciarello M., Generali M., Cerati G., Leykin Y., Bressan F., Bartolini V., Zamidei L., Brazzi L., Liperi C., Sales G., Pistidda L., Brugnoni E., Musella G., Bacuzzi A., Muhardri D., Agreta G. G., Sada F., Bytyqi A., Karbonskiene A., Aukstakalniene R., Teberaite Z., Salciute E., Tikuisis R., Miliauskas P., Jurate S., Kontrimaviciute E., Tomkute G., Xuereb J., Bezzina M., Borg F. J., Hemmes S., Schultz M., Hollmann M., Wiersma I., Binnekade J., Bos L., Boer C., Duvekot A., Veld B. I. '., Werger A., Dennesen P., Severijns C., Jong J. D., Hering J., Beek R. V., Ivars S., Jammer I. B., Breidablik A., Hodt K. S., Fjellanger F., Avalos M. V., Jannicke M. O., Andersson E., Amir S. K., Molina R., Wutai S., Morais E., Tareco G., Ferreira D., Amaral J., Castro M. D. L. G., Cadilha S., Appleton S., Parente S., Correia M., Martins D., Monteirosa A., Ricardo A., Rodrigues S., Horhota L., Grintescu I. M., Mirea L., Grintescu I. C., Corneci D., Negoita S., Dutu M., Popescu Garotescu I., Filipescu D., Prodan A. B., Droc G., Fota R., Popescu M., Tomescu D., Petcu A. M., Tudoroiu M. I., Moise A., Guran C. T., Gherghina I., Costea D., Cindea I., Copotoiu S. M., Copotoiu R., Barsan V., Tolcser Z., Riciu M., Moldovan S. G., Veres M., Gritsan A., Kapkan T., Gritsan G., Korolkov O., Kulikov A., Lubnin A., Ovezov A., Prokoshev P., Lugovoy A., Anipchenko N., Babayants A., Komissarova I., Zalina K., Likhvantsev V., Fedorov S., Lazukic A., Pejakovic J., Mihajlovic D., Kusnierikova Z., Zelinkova M., Bruncakova K., Polakovicova L., Sobona V., Barbka N. S., Ana P. G., Jovanov M., Strazisar B., Jasmina M. B., Vesna N. J., Voje M., Grynyuk A., Kostadinov I., Alenka S. V., Moral V., Unzueta M. C., Puigbo C., Fava J., Moret E., Nunez M. R., Sendra M., Brunelli A., Rodenas F., Monedero P., Martinez F. H., Temino M. J. Y., Simon A. M., Larriba A. D. A., Lisi A., Perez G., Martinez R., Granell M., Vivo J. T., Ruiz C. S., Andres Ibanez J. A. D., Pastor E., Soro M., Ferrando C., Defez M., Cesar Aldecoa A. S., Perez R., Rico J., Jawad M., Saeed Y., Gillberg L., Bengisun Z. K., Kazbek B. K., Coskunfirat N., Boztug N., Sanli S., Yilmaz M., Hadimioglu N., Senturk N. M., Camci E., Kucukgoncu S., Sungur Z., Sivrikoz N., Ozgen S. U., Toraman F., Selvi O., Senturk O., Yildiz M., Kuvaki B., Gunenc F., Kucukguclu S., Ozbilgin S., Maral J., Canli S., Arun O., Saltali A., Aydogan E., Akgun F. N., Sanlikarip C., Karaman F. M., Mazur A., Vorotyntsev S., Rousseau G., Barrett C., Stancombe L., Shelley B., Scholes H., Limb J., Rafi A., Wayman L., Deane J., Rogerson D., Williams J., Yates S., Rogers E., Pulletz M., Moreton S., Jones S., Venkatesh S., Burton M., Brown L., Goodall C., Rucklidge M., Fuller D., Nadolski M., Kusre S., Lundberg M., Everett L., Nutt H., Zuleika M., Carvalho P., Clements D., Ben C. B., Watt P., Raymode P., Pearse R., Mohr O., Raj A., Creary T., Chishti A., Bell A., Higham C., Cain A., Gibb S., Mowat S., Franklin D., West C., Minto G., Boyd N., Mills G., Calton E., Walker R., Mackenzie F., Ellison B., Roberts H., Chikungwa M., Jackson C., Donovan A., Foot J., Homan E., Montgomery J., Portch D., Mercer P., Palmer J., Paddle J., Fouracres A., Datson A., Andrew A., Welch L., Rose A., Varma S., Simeson K., Rambhatla M., Susarla J., Marri S., Kodaganallur K., Das A., Algarsamy S., Colley J., Davies S., Szewczyk M., Smith T., Ana F. B., Luzier E., Almagro A., Melo M. V., Fernando L., Sulemanji D., Sprung J., Weingarten T., Kor D., Scavonetto F., and Tze Y.
Abstract: Background: The aim of this post hoc analysis of a large cohort study was to evaluate the association between night-time surgery and the occurrence of intraoperative adverse events (AEs) and postoperative pulmonary complications (PPCs). Methods: LAS VEGAS (Local Assessment of Ventilatory Management During General Anesthesia for Surgery) was a prospective international 1-week study that enrolled adult patients undergoing surgical procedures with general anaesthesia and mechanical ventilation in 146 hospitals across 29 countries. Surgeries were defined as occurring during ‘daytime’ when induction of anaesthesia was between 8:00 AM and 7:59 PM, and as ‘night-time’ when induction was between 8:00 PM and 7:59 AM. Results: Of 9861 included patients, 555 (5.6%) underwent surgery during night-time. The proportion of patients who developed intraoperative AEs was higher during night-time surgery in unmatched (43.6% vs 34.1%; P<0.001) and propensity-matched analyses (43.7% vs 36.8%; P=0.029). PPCs also occurred more often in patients who underwent night-time surgery (14% vs 10%; P=0.004) in an unmatched cohort analysis, although not in a propensity-matched analysis (13.8% vs 11.8%; P=0.39). In a multivariable regression model, including patient characteristics and types of surgery and anaesthesia, night-time surgery was independently associated with a higher incidence of intraoperative AEs (odds ratio: 1.44; 95% confidence interval: 1.09–1.90; P=0.01), but not with a higher incidence of PPCs (odds ratio: 1.32; 95% confidence interval: 0.89–1.90; P=0.15). Conclusions: Intraoperative adverse events and postoperative pulmonary complications occurred more often in patients undergoing night-time surgery. Imbalances in patients’ clinical characteristics, types of surgery, and intraoperative management at night-time partially explained the higher incidence of postoperative pulmonary complications, but not the higher incidence of adverse events. Clinical trial registration: NCT0160122
Published: 2019

23. Advances in the HCl gas-phase electrolysis employing an oxygen-depolarized cathode

Author: Bechtel, S, Bechtel, S, Crothers, AR, Weber, AZ, Kunz, U, Turek, T, Vidaković-Koch, T, Sundmacher, K, Bechtel, S, Bechtel, S, Crothers, AR, Weber, AZ, Kunz, U, Turek, T, Vidaković-Koch, T, and Sundmacher, K
Abstract: The electrolysis of HCl to form Cl2 is an integral part of the production of polycarbonates and polyurethanes. In recent years, the direct gas-phase electrolysis was shown to be significantly more efficient than the current state-of-the-art process based on the oxidation of hydrochloric acid. Still, three phenomena significantly limit the performance and industrial applicability of this process and have so far only been investigated theoretically. Firstly, a limiting behavior in the HCl oxidation reaction was observed, which seems to be of purely kinetic origin. Secondly, also in the full-cell employing an oxygen depolarized cathode, a limiting behavior was detected, which however appears to have a different origin. Lastly, the performance of the oxygen reduction is significantly reduced in comparison to classical H2 PEM fuel cells. The present work utilizes a combined experimental and theoretical approach to confirm that the HCl oxidation is purely reaction limited while the limiting behavior in the full-cell system employing an oxygen depolarized cathode is caused by flooding at low reactor temperatures and, lastly, that the reduced performance of the oxygen reduction reaction is a consequence of significant HCl crossover that can be mitigated by means of increased cathode humidification. These insights are furthermore used to operate the HCl gas phase electrolyzer employing an oxygen depolarized cathode at current densities of more than 5000 A/m2 for the first time, while also substituting the previously employed platinum based cathode catalyst with RhxSy, decreasing the impact of HCl crossover and allowing for the lowest so far measured cell potentials over a wide interval of current densities.
Published: 2021

24. Taste variation in environmental features of bicycle routes

Author: Koch, T. (Thomas), Dugundji, E.R. (Elenna), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: In this paper we look at route choice modeling based on observational GPS traces collected by bicyclists in Amsterdam and surroundings. We consider factors influencing bicycle route choice such as distance and environmental factors such as cycle-way infrastructure, land-use environment, tree cover and the effect of noise emitting roads using data from a noise emission model. We estimate a route choice model, comparing multinomial logit, mixed logit and mixed path size logit specifications. Our results show that cyclists have a highly stochastic behavior that are likely to prefer detours to drive over cycle-way infrastructure, near greener landuse and near water, and on less busy roads. Models such as mixed logit that can estimate the stochasticity of cyclists perform best to capture this behavior.
Published: 2021
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25. Short-term forecasting of off-street parking occupancy

Author: Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Leeuwen, M. (Marco) van, Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Leeuwen, M. (Marco) van, and Dugundji, E.R. (Elenna)
Abstract: Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.
Published: 2021
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26. An evaluation of first-come, first-served scheduling in a geometrically-constrained wet bulk terminal

Author: Heisen, H. (Heleen), Lei, J. (Joachim) van der, Zuidema, J. (Joost), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Heisen, H. (Heleen), Lei, J. (Joachim) van der, Zuidema, J. (Joost), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: In 2022, a new sea lock at IJmuiden is expected to open, permitting an increase in marine traffic of larger ships from the sea to the port. In the interest of facilitating operations, we evaluate the impact of the current first-come, first-served (FCFS) admittance policy in the context of berth allocation for a wet bulk terminal in the port. Four model types are constructed: optimal FCFS; no FCFS with fixed arrival times; 48-h arrival time relaxation; and complete arrival time relaxation. Comparison of the model types is done by means of a rolling time window: of each day within the time frame, a schedule was created for the following 2 weeks, after which the objective value was calculated. When comparing the average of all objective values, it was found that the optimal FCFS model already shows an improvement compared to the historical situation. Between the FCFS and the no FCFS model, there are no considerable differences, because the vessels are constrained to be scheduled on/after their arrival time at the port. When relaxation is allowed, a considerable efficiency gain is possible, especially if larger ships arrive at the port.
Published: 2021
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27. Applying transfer learning and various ANN architectures to predict transportation mode choice in Amsterdam

Author: Buijs, R.J.W. (Ruurd), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Buijs, R.J.W. (Ruurd), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: For long, statistical models have been used for transportation mode choice analysis, due to their ability to extract economic information from the model parameters. Recently, the application of Artificial Neural Nets to predict transportation mode choice is gaining ground, partly due to efforts that have led to an improved interpretability of this class of models. In this development, various innovations have been suggested concerning Neural Net architecture and hyperparameter tuning. Building on this, this paper investigates 3 similar Neural Net architectures to be applied to data from an Amsterdam case study. This data has been collected in 3 waves. Between the first and second collection period, the public transportation network in Amsterdam changed. A transfer learning approach is suggested to improve models that were trained on a single wave of data. Based on the test loss of the models from the transfer learning experiments, we conclude that this is a promising technique to use in this context, since it has shown to improve model performance.
Published: 2021
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28. Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Author: Koch, T. (Thomas), Dugundji, E.R. (Elenna), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research.
Published: 2021
Full Text: View/download PDF

29. The impact of a new public transport line on parking behavior

Author: Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: To reduce congestion problems in urban environments, policy makers around the world recognize the importance of public transport quality improvement, P&R facilities near peripheral public transport stops, and parking price incentives. This paper proposes a logit model to study the short-term and long-term impact of a new subway line in Amsterdam on the parking behavior. Three groups of travelers are defined in this research: (a) travelers inside Amsterdam, (b) travelers from Amsterdam to outside Amsterdam, and (c) travelers from outside to inside Amsterdam. From the model it is found that in the short term the subway line resulted in an increase in parking near the city center of Amsterdam, especially caused by commuters traveling from outside Amsterdam. However, one year later, the parking demand has dropped significantly which is possibly an effect from increased parking tariffs. Further, before the opening of the public transport line, higher parking tariffs lead to more parking near destination. Experiments with parking tariff cross-variable models reveal that parking tariffs consist of two underlying bi-modal distributions, which are the location of origin and destination with respect to Amsterdam, and whether the time period is during summer or autumn. Parking tariffs affect the parking behavior from and to Amsterdam. Another finding is that during the autumn parking tariffs significantly affect the parking behavior in the short-term. This model can be extended further with more specific location variables, continuing the parking tariffs research, and the addition of more trip, spatial and personal attributes.
Published: 2021
Full Text: View/download PDF

30. Forecasting public transport ridership: Management of information systems using CNN and LSTM architectures

Author: Khalil, S. (Sergey), Amrit, C. (Chintan), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Khalil, S. (Sergey), Amrit, C. (Chintan), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings.
Published: 2021
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31. Predicting lessee switch behavior using logit models

Author: Feilzer, (), J.-W. (Jan-Willem), Stroosnier, D. (Daan), Dugundji, E.R. (Elenna), Koch, T. (Thomas), Feilzer, (), J.-W. (Jan-Willem), Stroosnier, D. (Daan), Dugundji, E.R. (Elenna), and Koch, T. (Thomas)
Abstract: Modeling the mode choice by an individual is a challenging task. In this paper, vehicle choice of lessees is discussed. Prediction of vehicle choice occurs by fitting three different logit models: standard, nested and cross-nested multinomial logistic regression. Both nested and cross-nested logit relax error term distribution assumptions and therefore allow for correlations across alternative vehicle choices. It is shown that allowing for correlation across alternatives is the proper way of modeling lessees' vehicle choice.
Published: 2021
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32. Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Author: Koch, T. (Thomas), Dugundji, E.R. (Elenna), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research.
Published: 2021

33. Short-Term Forecasting of Air Cargo Demand from a European Airport Hub to the United States during COVID-19

Author: Verhoeven, B. (Britt), Hout, N.K. (Nimo), Devaraj, A. (Archika), Zwitzer, H. (Hans), Crapts, T. (Terry), Ion, A. (Andrei), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Verhoeven, B. (Britt), Hout, N.K. (Nimo), Devaraj, A. (Archika), Zwitzer, H. (Hans), Crapts, T. (Terry), Ion, A. (Andrei), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Air cargo is mostly transported on passenger flights. During the COVID-19 outbreak, there have been worldwide restrictions on passenger transportation. Therefore, airlines experienced a capacity problem for air cargo. Better insight of air cargo demand during COVID-19 could contribute to the better arrangement of capacity by accordingly adapting flight schedules for cargo. The aim of this research was to make short-term predictions of air cargo demand between a major European airport hub and the United States during the COVID-19 pandemic. This was done for the month of May in 2020 by making 14-day predictions. The same was done for the year 2019 to observe whether the models performed well in the absence of the pandemic. The data set was compiled using data provided by a major commercial airline and exogenous features, such as stock market indices, foreign currency exchange rates and healthcare related predictions during COVID-19. To make the predictions, two classes of machine learning models for time series were compared: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). In the year 2020, the best performing model among the ARIMA-based models is the Seasonal ARIMA including the exogenous feature Schedule . During the year 2019 the Seasonal ARIMA model without exogenous features generates the most accurate predictions. Among the LSTM models, the multivariate LSTM models outperform the univariate LSTM models in both years. Nonetheless, the ARIMA-based models are more accurate than the multivariate LSTM model in this research.
Published: 2021

34. Long-Term Forecasting of Off-Street Parking Occupancy for Smart Cities

Author: Fokker, E.S. (Elisabeth), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Recent developments in the field of parking can be enhanced with smart city alternatives. One of these alternatives is the monitoring of parking sensor data. In this paper, this data is used to propose a Decision Support System (DSS) that supports the decision-making of the municipality of Amsterdam on parking. The DSS provides insight into the six months ahead parking occupancy for 57 off-street parking locations in Amsterdam. An effect analysis has been conducted into factors that influence the off-street parking occupancy, and five forecast models are compared to predict the parking occupancy. For the effect analysis, weather and event variables are highlighted. It is observed that the most influential factors on parking occupancy are sunshine, temperature, relative humidity and event factor ’match’, that indicates whether or not a soccer match is taking place. The forecasting algorithms compared are Seasonal Naive Model as a benchmark approach, Box-Jenkins Seasonal Autoregressive Integrated Moving Average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and Long Short-Term Memory neural network. Based on the effect analysis study, the exogenous regressors of the SARIMAX model are included per parking location. This model also outperforms the other algorithms according to the lowest Root Mean Squared Error. Especially the event factor is important for the parking occupancy forecasts. Future studies can focus on the addition of more event variables, the extension into an online model based on real-time parking sensor data and the effect analysis on changes, such as public transit networks on parking.
Published: 2021

35. Determining the effect of lower public transit frequencies in COVID-19 timetables on perceived door-to-door travel times using Pareto optimal range queries

Author: Koch, T. (Thomas), Dugundji, E.R. (Elenna), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Using modern transit routing algorithms it became feasible to compute all fastest travel options between all traffic zones in larger cities, allowing for an in-depth analysis how frequencies affect journeys between different origins, destinations and departure times. We use the rooftop method to calculate a realistic model of how a public transit user may perceive travel time, taking into account waiting time and/or adaption time to fit appointments in someone's schedule. The higher the frequencies, the lower those waiting times will be and vice versa. The rooftop method calculates a travel impedance for any given moment in the travel time. Furthermore, as few journeys will start and end at a transit stop, some walk component is often also involved. We sample 6693 addresses for 799 zones to compute travel times door-to-door in Amsterdam and surrounding area, explicitly including walk access and egress time to and from transit. In this study we focus on the transit timetables before, during, and in the current phase of the COVID-19 pandemic in order to investigate the effect of changed schedules on accessibility and mobility by public transit. This is particularly relevant for services that have been reduced and may remain reduced for the near future moving ahead. We expect this application of methods outlined in this paper to be of interest to public authorities and transit providers in making difficult decisions during COVID-19.
Published: 2021

36. Using neural nets to predict transportation mode choice: Amsterdam network change analysis

Author: Buijs, R.J.W. (Ruurd), Koch, T. (Thomas), Dugundji, E.R. (Elenna), Buijs, R.J.W. (Ruurd), Koch, T. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: In the Amsterdam metropolitan area, the opening of a new metro line along the north–south axis of the city has introduced a significant change in the region’s public transportation network. Mode choice analysis can help in assessment of changes in traveler behavior that occurred after the opening of the new metro line. As it is known that artificial neural nets excel at complex classification problems, this paper aims to investigate an approach where the traveler’s transportation mode is predicted through a neural net, trained on choice sets and user specific attributes inferred from the data. The method shows promising results. It is shown that such models perform better when it is asked to predict the choice of mode for trips which take place on the same underlying transportation network as the data with which the model is trained. This difference in performance is observed to be especially high for trips from and to certain areas that were impacted by the introduction of the north–south line, indicating possible changes in behavioural patterns, entailing interesting possible directions for further research.
Published: 2021
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37. Breast cancer rate after oophorectomy: A Prospective Danish Cohort Study

Author: Koch, T, Jorgensen, JT, Christensen, J, Dehlendorff, C, Priskorn, L, Simonsen, MK, Duun-Henriksen, AK, Andersen, ZJ, Juul, A, Brauner, EV, Hickey, M, Koch, T, Jorgensen, JT, Christensen, J, Dehlendorff, C, Priskorn, L, Simonsen, MK, Duun-Henriksen, AK, Andersen, ZJ, Juul, A, Brauner, EV, and Hickey, M
Abstract: The association between oophorectomy and risk of breast cancer in the general population is uncertain. The aim of our study was to determine the breast cancer rate in women from the general population after oophorectomy (performed before/after menopause), and whether this varies by use of hormone replacement therapy (HRT), hysterectomy, body mass index (BMI) and shift work. The study included 24 409 female nurses (aged ≥45 years) participating in the Danish Nurse Cohort. Nurses were followed from cohort entry until date of breast cancer, death, emigration or end of follow-up at 31 December 2018, whichever came first. Poisson regression with log-transformed person-years as the offset examined the association between oophorectomy and breast cancer (all ages and stratified by menopausal status at time of oophorectomy). The potential modifying effect of HRT use, hysterectomy, BMI and shift work on the associations was estimated. During 502 463 person-years of follow-up, 1975 (8.1%) nurses were diagnosed with breast cancer. Bilateral oophorectomy was associated with a reduced breast cancer rate compared to nurses with preserved ovaries, adjusted rate ratio (95% confidence interval): 0.79 (0.64; 0.99). Similar associations (magnitude and direction) were detected for unilateral oophorectomy and when stratifying according to menopausal status at time of oophorectomy, but without statistical significance. Unilateral and bilateral oophorectomy is associated with a reduced breast cancer rate in women from the general population. This association is not modified by use of HRT, hysterectomy, BMI or shift work.
Published: 2021

38. Sex-dependent associations between maternal prenatal stressful life events, BMI trajectories and obesity risk in offspring: The Raine Study

Author: Brauner, E, Lim, Y-H, Koch, T, Mori, TA, Beilin, L, Doherty, DA, Juul, A, Hart, R, Hickey, M, Brauner, E, Lim, Y-H, Koch, T, Mori, TA, Beilin, L, Doherty, DA, Juul, A, Hart, R, and Hickey, M
Abstract: BACKGROUND: There is a high and growing prevalence of childhood obesity which increases the risk of adult obesity and adverse physical and mental health outcomes in adulthood. Experimental and clinical data suggest that the early life environment, particularly prenatal stress, may program development of obesity in the offspring. But few studies have assessed the associations between prenatal maternal stress and rapid (ascending) weight gain, which is the strongest predictor of adult obesity and metabolic disease. Experimental data indicate that the associations may be sex dependent, but the sex-dependent association between prenatal stress and growth in the human offspring during childhood and adolescence is largely unexplored. The aim of this study is to investigate the association between prenatal exposure to stressful life events and childhood obesity in the offspring and whether maternal smoking during pregnancy and breastfeeding mediate this. METHOD: Participants from a large prospective population-based Australian pregnancy cohort study (The Raine Study, n=2868) were closely and frequently followed from prenatal life (18 weeks gestation) through to adolescence. Maternal stressful life events were prospectively recorded at 18 and 34 weeks and childhood BMI (categorized into six z-score trajectories) was measured from 3 to age 14 years. We studied the prospective association between maternal exposure to stressful life events and BMI z-score trajectories in 2056 offspring (1082 boys). Mothers prospectively reported stressful life events at 18- and 34-weeks' gestation using a standardized and validated 10-point questionnaire. Age- and gender-specific z-scores for BMI were obtained from height and weight at age 3, 5, 8, 10 and 14 years using standardized methods. Latent class group analysis identified six distinct trajectory classes of BMI z-score. Multinomial logistic regression was used to examine the associations between maternal stressful life events and gender
Published: 2021

39. Advances in the HCl gas-phase electrolysis employing an oxygen-depolarized cathode

Author: Bechtel, S, Bechtel, S, Crothers, AR, Weber, AZ, Kunz, U, Turek, T, Vidaković-Koch, T, Sundmacher, K, Bechtel, S, Bechtel, S, Crothers, AR, Weber, AZ, Kunz, U, Turek, T, Vidaković-Koch, T, and Sundmacher, K
Abstract: The electrolysis of HCl to form Cl2 is an integral part of the production of polycarbonates and polyurethanes. In recent years, the direct gas-phase electrolysis was shown to be significantly more efficient than the current state-of-the-art process based on the oxidation of hydrochloric acid. Still, three phenomena significantly limit the performance and industrial applicability of this process and have so far only been investigated theoretically. Firstly, a limiting behavior in the HCl oxidation reaction was observed, which seems to be of purely kinetic origin. Secondly, also in the full-cell employing an oxygen depolarized cathode, a limiting behavior was detected, which however appears to have a different origin. Lastly, the performance of the oxygen reduction is significantly reduced in comparison to classical H2 PEM fuel cells. The present work utilizes a combined experimental and theoretical approach to confirm that the HCl oxidation is purely reaction limited while the limiting behavior in the full-cell system employing an oxygen depolarized cathode is caused by flooding at low reactor temperatures and, lastly, that the reduced performance of the oxygen reduction reaction is a consequence of significant HCl crossover that can be mitigated by means of increased cathode humidification. These insights are furthermore used to operate the HCl gas phase electrolyzer employing an oxygen depolarized cathode at current densities of more than 5000 A/m2 for the first time, while also substituting the previously employed platinum based cathode catalyst with RhxSy, decreasing the impact of HCl crossover and allowing for the lowest so far measured cell potentials over a wide interval of current densities.
Published: 2021

40. An interdisciplinary outpatient therapy program for children and adolescents with headache - real world data

Author: Social Determinants of Health, Sobe, H.; Richter, M.; Berner, R.; von der Hagen, M.; Hähner, A.; Röder, I.; Koch, T.; Sabatowski, R.; Klimova, A.; Gossrau, G., Social Determinants of Health, and Sobe, H.; Richter, M.; Berner, R.; von der Hagen, M.; Hähner, A.; Röder, I.; Koch, T.; Sabatowski, R.; Klimova, A.; Gossrau, G.
Published: 2021

41. Long-Term Forecasting of Off-Street Parking Occupancy for Smart Cities

Author: Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Recent developments in the field of parking can be enhanced with smart city alternatives. One of these alternatives is the monitoring of parking sensor data. In this paper, this data is used to propose a Decision Support System (DSS) that supports the decision-making of the municipality of Amsterdam on parking. The DSS provides insight into the six months ahead parking occupancy for 57 off-street parking locations in Amsterdam. An effect analysis has been conducted into factors that influence the off-street parking occupancy, and five forecast models are compared to predict the parking occupancy. For the effect analysis, weather and event variables are highlighted. It is observed that the most influential factors on parking occupancy are sunshine, temperature, relative humidity and event factor ’match’, that indicates whether or not a soccer match is taking place. The forecasting algorithms compared are Seasonal Naive Model as a benchmark approach, Box-Jenkins Seasonal Autoregressive Integrated Moving Average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and Long Short-Term Memory neural network. Based on the effect analysis study, the exogenous regressors of the SARIMAX model are included per parking location. This model also outperforms the other algorithms according to the lowest Root Mean Squared Error. Especially the event factor is important for the parking occupancy forecasts. Future studies can focus on the addition of more event variables, the extension into an online model based on real-time parking sensor data and the effect analysis on changes, such as public transit networks on parking.
Published: 2021

42. The impact of a new public transport line on parking behavior

Author: Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: To reduce congestion problems in urban environments, policy makers around the world recognize the importance of public transport quality improvement, P&R facilities near peripheral public transport stops, and parking price incentives. This paper proposes a logit model to study the short-term and long-term impact of a new subway line in Amsterdam on the parking behavior. Three groups of travelers are defined in this research: (a) travelers inside Amsterdam, (b) travelers from Amsterdam to outside Amsterdam, and (c) travelers from outside to inside Amsterdam. From the model it is found that in the short term the subway line resulted in an increase in parking near the city center of Amsterdam, especially caused by commuters traveling from outside Amsterdam. However, one year later, the parking demand has dropped significantly which is possibly an effect from increased parking tariffs. Further, before the opening of the public transport line, higher parking tariffs lead to more parking near destination. Experiments with parking tariff cross-variable models reveal that parking tariffs consist of two underlying bi-modal distributions, which are the location of origin and destination with respect to Amsterdam, and whether the time period is during summer or autumn. Parking tariffs affect the parking behavior from and to Amsterdam. Another finding is that during the autumn parking tariffs significantly affect the parking behavior in the short-term. This model can be extended further with more specific location variables, continuing the parking tariffs research, and the addition of more trip, spatial and personal attributes.
Published: 2021
Full Text: View/download PDF

43. Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Author: Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research.
Published: 2021
Full Text: View/download PDF

44. Using neural nets to predict transportation mode choice: Amsterdam network change analysis

Author: Buijs, R.J.W. (Ruurd), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Buijs, R.J.W. (Ruurd), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: In the Amsterdam metropolitan area, the opening of a new metro line along the north–south axis of the city has introduced a significant change in the region’s public transportation network. Mode choice analysis can help in assessment of changes in traveler behavior that occurred after the opening of the new metro line. As it is known that artificial neural nets excel at complex classification problems, this paper aims to investigate an approach where the traveler’s transportation mode is predicted through a neural net, trained on choice sets and user specific attributes inferred from the data. The method shows promising results. It is shown that such models perform better when it is asked to predict the choice of mode for trips which take place on the same underlying transportation network as the data with which the model is trained. This difference in performance is observed to be especially high for trips from and to certain areas that were impacted by the introduction of the north–south line, indicating possible changes in behavioural patterns, entailing interesting possible directions for further research.
Published: 2021
Full Text: View/download PDF

45. Forecasting public transport ridership: Management of information systems using CNN and LSTM architectures

Author: Khalil, S. (Sergey), Amrit, C. (Chintan), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Khalil, S. (Sergey), Amrit, C. (Chintan), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings.
Published: 2021
Full Text: View/download PDF

46. Short-term forecasting of off-street parking occupancy

Author: Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Leeuwen, M. (Marco) van, Dugundji, E.R. (Elenna), Fokker, E.S. (Elisabeth), Koch, T. H. A. (Thomas), Leeuwen, M. (Marco) van, and Dugundji, E.R. (Elenna)
Abstract: Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.
Published: 2021
Full Text: View/download PDF

47. Applying transfer learning and various ANN architectures to predict transportation mode choice in Amsterdam

Author: Buijs, R.J.W. (Ruurd), Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Buijs, R.J.W. (Ruurd), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: For long, statistical models have been used for transportation mode choice analysis, due to their ability to extract economic information from the model parameters. Recently, the application of Artificial Neural Nets to predict transportation mode choice is gaining ground, partly due to efforts that have led to an improved interpretability of this class of models. In this development, various innovations have been suggested concerning Neural Net architecture and hyperparameter tuning. Building on this, this paper investigates 3 similar Neural Net architectures to be applied to data from an Amsterdam case study. This data has been collected in 3 waves. Between the first and second collection period, the public transportation network in Amsterdam changed. A transfer learning approach is suggested to improve models that were trained on a single wave of data. Based on the test loss of the models from the transfer learning experiments, we conclude that this is a promising technique to use in this context, since it has shown to improve model performance.
Published: 2021
Full Text: View/download PDF

48. Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Author: Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research.
Published: 2021

49. Determining the effect of lower public transit frequencies in COVID-19 timetables on perceived door-to-door travel times using Pareto optimal range queries

Author: Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: Using modern transit routing algorithms it became feasible to compute all fastest travel options between all traffic zones in larger cities, allowing for an in-depth analysis how frequencies affect journeys between different origins, destinations and departure times. We use the rooftop method to calculate a realistic model of how a public transit user may perceive travel time, taking into account waiting time and/or adaption time to fit appointments in someone's schedule. The higher the frequencies, the lower those waiting times will be and vice versa. The rooftop method calculates a travel impedance for any given moment in the travel time. Furthermore, as few journeys will start and end at a transit stop, some walk component is often also involved. We sample 6693 addresses for 799 zones to compute travel times door-to-door in Amsterdam and surrounding area, explicitly including walk access and egress time to and from transit. In this study we focus on the transit timetables before, during, and in the current phase of the COVID-19 pandemic in order to investigate the effect of changed schedules on accessibility and mobility by public transit. This is particularly relevant for services that have been reduced and may remain reduced for the near future moving ahead. We expect this application of methods outlined in this paper to be of interest to public authorities and transit providers in making difficult decisions during COVID-19.
Published: 2021

50. Taste variation in environmental features of bicycle routes

Author: Koch, T. H. A. (Thomas), Dugundji, E.R. (Elenna), Koch, T. H. A. (Thomas), and Dugundji, E.R. (Elenna)
Abstract: In this paper we look at route choice modeling based on observational GPS traces collected by bicyclists in Amsterdam and surroundings. We consider factors influencing bicycle route choice such as distance and environmental factors such as cycle-way infrastructure, land-use environment, tree cover and the effect of noise emitting roads using data from a noise emission model. We estimate a route choice model, comparing multinomial logit, mixed logit and mixed path size logit specifications. Our results show that cyclists have a highly stochastic behavior that are likely to prefer detours to drive over cycle-way infrastructure, near greener landuse and near water, and on less busy roads. Models such as mixed logit that can estimate the stochasticity of cyclists perform best to capture this behavior.
Published: 2021
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