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1. Science Requirements and Detector Concepts for the Electron-Ion Collider

Author

Abdul Khalek, R., Accardi, A., Adam, J., Adamiak, D., Akers, W., Albaladejo, M., Al-bataineh, A., Alexeev, M. G., Ameli, F., Antonioli, P., Armesto, N., Armstrong, W. R., Arratia, M., Arrington, J., Asaturyan, A., Asai, M., Aschenauer, E. C., Aune, S., Avagyan, H., Ayerbe Gayoso, C., Azmoun, B., Bacchetta, A., Baker, M. D., Barbosa, F., Barion, L., Barish, K. N., Barry, P. C., Battaglieri, M., Bazilevsky, A., Behera, N. K., Benmokhtar, F., Berdnikov, V. V., Bernauer, J. C., Bertone, V., Bhattacharya, S., Bissolotti, C., Boer, D., Boglione, M., Bondì, M., Boora, P., Borsa, I., Bossù, F., Bozzi, G., Brandenburg, J. D., Brei, N., Bressan, A., Brooks, W. K., Bufalino, S., Bukhari, M. H. S., Burkert, V., Buttimore, N. H., Camsonne, A., Celentano, A., Celiberto, F. G., Chang, W., Chatterjee, C., Chen, K., Chetry, T., Chiarusi, T., Chien, Y. -T., Chiosso, M., Chu, X., Chudakov, E., Cicala, G., Cisbani, E., Cloet, I. C., Cocuzza, C., Cole, P. L., Colella, D., Collins, J. L., Constantinou, M., Contalbrigo, M., Contin, G., Corliss, R., Cosyn, W., Courtoy, A., Crafts, J., Cruz-Torres, R., Cuevas, R. C., D'Alesio, U., Dalla Torre, S., Das, D., Dasgupta, S. S., Da Silva, C., Deconinck, W., Defurne, M., Degraw, W., Dehmelt, K., Del Dotto, A., Delcarro, F., Deshpande, A., Detmold, W., De Vita, R., Diefenthaler, M., Dilks, C., Dixit, D. U., Dulat, S., Dumitru, A., Dupré, R., Durham, J. M., Echevarria, M. G., El Fassi, L., Elia, D., Ent, R., Esha, R., Ethier, J. J., Evdokimov, O., Eyser, K. O., Fanelli, C., Fatemi, R., Fazio, S., Fernandez-Ramirez, C., Finger, M., Fitzgerald, D., Flore, C., Frederico, T., Friščić, I., Fucini, S., Furletov, S., Furletova, Y., Gal, C., Gamberg, L., Gao, H., Garg, P., Gaskell, D., Gates, K., Gay Ducati, M. B., Gericke, M., Gil Da Silveira, G., Girod, F. -X., Glazier, D. I., Gnanvo, K., Goncalves, V. P., Gonella, L., Gonzalez Hernandez, J. O., Goto, Y., Grancagnolo, F., Greiner, L. C., Guryn, W., Guzey, V., Hatta, Y., Hattawy, M., Hauenstein, F., He, X., Hemmick, T. K., Hen, O., Heyes, G., Higinbotham, D. W., Hiller Blin, A. N., Hobbs, T. J., Hohlmann, M., Horn, T., Hou, T. -J., Huang, J., Huang, Q., Huber, G. M., Hyde, C. E., Iakovidis, G., Ilieva, Y., Jacak, B. V., Jacobs, P. M., Jadhav, M., Janoska, Z., Jentsch, A., Jezo, T., Jing, X., Jones, P. G., Joo, K., Joosten, S., Kafka, V., Kalantarians, N., Kalicy, G., Kang, D., Kang, Z. B., Kauder, K., Kay, S. J. D., Keppel, C. E., Kim, J., Kiselev, A., Klasen, M., Klein, S., Klest, H. T., Korchak, O., Kostina, A., Kotko, P., Kovchegov, Y. V., Krelina, M., Kuleshov, S., Kumano, S., Kumar, K. S., Kumar, R., Kumar, L., Kumerički, K., Kusina, A., Kutak, K., Lai, Y. S., Lalwani, K., Lappi, T., Lauret, J., Lavinsky, M., Lawrence, D., Lednicky, D., Lee, C., Lee, K., Lee, S. H., Levorato, S., Li, H., Li, S., Li, W., Li, X., W. B., Li, Ligonzo, T., Liu, H., Liu, M. X., Liu, X., Liuti, S., Liyanage, N., Lorcé, C., Lu, Z., Lucero, G., Lukow, N. S., Lunghi, E., Majka, R., Makris, Y., Mandjavidze, I., Mantry, S., Mäntysaari, H., Marhauser, F., Markowitz, P., Marsicano, L., Mastroserio, A., Mathieu, V., Mehtar-Tani, Y., Melnitchouk, W., Mendez, L., Metz, A., Meziani, Z. -E., Mezrag, C., Mihovilovič, M., Milner, R., Mirazita, M., Mkrtchyan, H., Mkrtchyan, A., Mochalov, V., Moiseev, V., Mondal, M. M., Morreale, A., Morrison, D., Motyka, L., Moutarde, H., Muñoz Camacho, C., Murgia, F., Murray, M. J., Musico, P., Nadel-Turonski, P., Nadolsky, P. M., Nam, J., Newman, P. R., Neyret, D., Nguyen, D., Nocera, E. R., Noferini, F., Noto, F., Nunes, A. S., Okorokov, V. A., Olness, F., Osborn, J. D., Page, B. S., Park, S., Parker, A., Paschke, K., Pasquini, B., Paukkunen, H., Paul, S., Pecar, C., Pegg, I. L., Pellegrino, C., Peng, C., Pentchev, L., Perrino, R., Petriello, F., Petti, R., Pilloni, A., Pinkenburg, C., Pire, B., Pisano, C., Pitonyak, D., Poblaguev, A. A., Polakovic, T., Posik, M., Potekhin, M., Preghenella, R., Preins, S., Prokudin, A., Pujahari, P., Purschke, M. L., Pybus, J. R., Radici, M., Rajput-Ghoshal, R., Reimer, P. E., Rinaldi, M., Ringer, F., Roberts, C. D., Rodini, S., Rojo, J., Romanov, D., Rossi, P., Santopinto, E., Sarsour, M., Sassot, R., Sato, N., Schenke, B., Schmidke, W. B., Schmidt, I., Schmidt, A., Schmookler, B., Schnell, G., Schweitzer, P., Schwiening, J., Scimemi, I., Scopetta, S., Segovia, J., Seidl, R., Sekula, S., Semenov-Tian-Shanskiy, K., Shao, D. Y., Sherrill, N., Sichtermann, E., Siddikov, M., Signori, A., Singh, B. K., Širca, S., Slifer, K., Slominski, W., Sokhan, D., Sondheim, W. E., Song, Y., Soto, O., Spiesberger, H., Stasto, A. M., Stepanov, P., Sterman, G., Stevens, J. R., Stewart, I. W., Strakovsky, I., Strikman, M., Sturm, M., Stutzman, M. L., Sullivan, M., Surrow, B., Svihra, P., Syritsyn, S., Szczepaniak, A., Sznajder, P., Szumila-Vance, H., Szymanowski, L., Tadepalli, A. S., Tapia Takaki, J. D., Tassielli, G. F., Terry, J., Tessarotto, F., Tezgin, K., Tomasek, L., Torales Acosta, F., Tribedy, P., Tricoli, A., Triloki, Null, Tripathi, S., Trotta, R. L., Tsai, O. D., Tu, Z., Tuvè, C., Ullrich, T., Ungaro, M., Urciuoli, G. M., Valentini, A., Vancura, P., Vandenbroucke, M., Van Hulse, C., Varner, G., Venugopalan, R., Vitev, I., Vladimirov, A., Volpe, G., Vossen, A., Voutier, E., Wagner, J., Wallon, S., Wang, H., Wang, Q., Wang, X., Wei, S. Y., Weiss, C., Wenaus, T., Wennlöf, H., Wickramaarachchi, N., Wikramanayake, A., Winney, D., Wong, C. P., Woody, C., Xia, L., Xiao, B. W., Xie, J., Xing, H., Q. H., Xu, Zhang, J., Zhang, S., Zhang, Z., Zhao, Z. W., Zhao, Y. X., Zheng, L., Zhou, Y., Zurita, P., Abdul Khalek, R., Accardi, A., Adam, J., Adamiak, D., Akers, W., Albaladejo, M., Al-bataineh, A., Alexeev, M. G., Ameli, F., Antonioli, P., Armesto, N., Armstrong, W. R., Arratia, M., Arrington, J., Asaturyan, A., Asai, M., Aschenauer, E. C., Aune, S., Avagyan, H., Ayerbe Gayoso, C., Azmoun, B., Bacchetta, A., Baker, M. D., Barbosa, F., Barion, L., Barish, K. N., Barry, P. C., Battaglieri, M., Bazilevsky, A., Behera, N. K., Benmokhtar, F., Berdnikov, V. V., Bernauer, J. C., Bertone, V., Bhattacharya, S., Bissolotti, C., Boer, D., Boglione, M., Bondì, M., Boora, P., Borsa, I., Bossù, F., Bozzi, G., Brandenburg, J. D., Brei, N., Bressan, A., Brooks, W. K., Bufalino, S., Bukhari, M. H. S., Burkert, V., Buttimore, N. H., Camsonne, A., Celentano, A., Celiberto, F. G., Chang, W., Chatterjee, C., Chen, K., Chetry, T., Chiarusi, T., Chien, Y. -T., Chiosso, M., Chu, X., Chudakov, E., Cicala, G., Cisbani, E., Cloet, I. C., Cocuzza, C., Cole, P. L., Colella, D., Collins, J. L., Constantinou, M., Contalbrigo, M., Contin, G., Corliss, R., Cosyn, W., Courtoy, A., Crafts, J., Cruz-Torres, R., Cuevas, R. C., D'Alesio, U., Dalla Torre, S., Das, D., Dasgupta, S. S., Da Silva, C., Deconinck, W., Defurne, M., Degraw, W., Dehmelt, K., Del Dotto, A., Delcarro, F., Deshpande, A., Detmold, W., De Vita, R., Diefenthaler, M., Dilks, C., Dixit, D. U., Dulat, S., Dumitru, A., Dupré, R., Durham, J. M., Echevarria, M. G., El Fassi, L., Elia, D., Ent, R., Esha, R., Ethier, J. J., Evdokimov, O., Eyser, K. O., Fanelli, C., Fatemi, R., Fazio, S., Fernandez-Ramirez, C., Finger, M., Fitzgerald, D., Flore, C., Frederico, T., Friščić, I., Fucini, S., Furletov, S., Furletova, Y., Gal, C., Gamberg, L., Gao, H., Garg, P., Gaskell, D., Gates, K., Gay Ducati, M. B., Gericke, M., Gil Da Silveira, G., Girod, F. -X., Glazier, D. I., Gnanvo, K., Goncalves, V. P., Gonella, L., Gonzalez Hernandez, J. O., Goto, Y., Grancagnolo, F., Greiner, L. C., Guryn, W., Guzey, V., Hatta, Y., Hattawy, M., Hauenstein, F., He, X., Hemmick, T. K., Hen, O., Heyes, G., Higinbotham, D. W., Hiller Blin, A. N., Hobbs, T. J., Hohlmann, M., Horn, T., Hou, T. -J., Huang, J., Huang, Q., Huber, G. M., Hyde, C. E., Iakovidis, G., Ilieva, Y., Jacak, B. V., Jacobs, P. M., Jadhav, M., Janoska, Z., Jentsch, A., Jezo, T., Jing, X., Jones, P. G., Joo, K., Joosten, S., Kafka, V., Kalantarians, N., Kalicy, G., Kang, D., Kang, Z. B., Kauder, K., Kay, S. J. D., Keppel, C. E., Kim, J., Kiselev, A., Klasen, M., Klein, S., Klest, H. T., Korchak, O., Kostina, A., Kotko, P., Kovchegov, Y. V., Krelina, M., Kuleshov, S., Kumano, S., Kumar, K. S., Kumar, R., Kumar, L., Kumerički, K., Kusina, A., Kutak, K., Lai, Y. S., Lalwani, K., Lappi, T., Lauret, J., Lavinsky, M., Lawrence, D., Lednicky, D., Lee, C., Lee, K., Lee, S. H., Levorato, S., Li, H., Li, S., Li, W., Li, X., Li, W. B., Ligonzo, T., Liu, H., Liu, M. X., Liu, X., Liuti, S., Liyanage, N., Lorcé, C., Lu, Z., Lucero, G., Lukow, N. S., Lunghi, E., Majka, R., Makris, Y., Mandjavidze, I., Mantry, S., Mäntysaari, H., Marhauser, F., Markowitz, P., Marsicano, L., Mastroserio, A., Mathieu, V., Mehtar-Tani, Y., Melnitchouk, W., Mendez, L., Metz, A., Meziani, Z. -E., Mezrag, C., Mihovilovič, M., Milner, R., Mirazita, M., Mkrtchyan, H., Mkrtchyan, A., Mochalov, V., Moiseev, V., Mondal, M. M., Morreale, A., Morrison, D., Motyka, L., Moutarde, H., Muñoz Camacho, C., Murgia, F., Murray, M. J., Musico, P., Nadel-Turonski, P., Nadolsky, P. M., Nam, J., Newman, P. R., Neyret, D., Nguyen, D., Nocera, E. R., Noferini, F., Noto, F., Nunes, A. S., Okorokov, V. A., Olness, F., Osborn, J. D., Page, B. S., Park, S., Parker, A., Paschke, K., Pasquini, B., Paukkunen, H., Paul, S., Pecar, C., Pegg, I. L., Pellegrino, C., Peng, C., Pentchev, L., Perrino, R., Petriello, F., Petti, R., Pilloni, A., Pinkenburg, C., Pire, B., Pisano, C., Pitonyak, D., Poblaguev, A. A., Polakovic, T., Posik, M., Potekhin, M., Preghenella, R., Preins, S., Prokudin, A., Pujahari, P., Purschke, M. L., Pybus, J. R., Radici, M., Rajput-Ghoshal, R., Reimer, P. E., Rinaldi, M., Ringer, F., Roberts, C. D., Rodini, S., Rojo, J., Romanov, D., Rossi, P., Santopinto, E., Sarsour, M., Sassot, R., Sato, N., Schenke, B., Schmidke, W. B., Schmidt, I., Schmidt, A., Schmookler, B., Schnell, G., Schweitzer, P., Schwiening, J., Scimemi, I., Scopetta, S., Segovia, J., Seidl, R., Sekula, S., Semenov-Tian-Shanskiy, K., Shao, D. Y., Sherrill, N., Sichtermann, E., Siddikov, M., Signori, A., Singh, B. K., Širca, S., Slifer, K., Slominski, W., Sokhan, D., Sondheim, W. E., Song, Y., Soto, O., Spiesberger, H., Stasto, A. M., Stepanov, P., Sterman, G., Stevens, J. R., Stewart, I. W., Strakovsky, I., Strikman, M., Sturm, M., Stutzman, M. L., Sullivan, M., Surrow, B., Svihra, P., Syritsyn, S., Szczepaniak, A., Sznajder, P., Szumila-Vance, H., Szymanowski, L., Tadepalli, A. S., Tapia Takaki, J. D., Tassielli, G. F., Terry, J., Tessarotto, F., Tezgin, K., Tomasek, L., Torales Acosta, F., Tribedy, P., Tricoli, A., Triloki, Null, Tripathi, S., Trotta, R. L., Tsai, O. D., Tu, Z., Tuvè, C., Ullrich, T., Ungaro, M., Urciuoli, G. M., Valentini, A., Vancura, P., Vandenbroucke, M., Van Hulse, C., Varner, G., Venugopalan, R., Vitev, I., Vladimirov, A., Volpe, G., Vossen, A., Voutier, E., Wagner, J., Wallon, S., Wang, H., Wang, Q., Wang, X., Wei, S. Y., Weiss, C., Wenaus, T., Wennlöf, H., Wickramaarachchi, N., Wikramanayake, A., Winney, D., Wong, C. P., Woody, C., Xia, L., Xiao, B. W., Xie, J., Xing, H., Xu, Q. H., Zhang, J., Zhang, S., Zhang, Z., Zhao, Z. W., Zhao, Y. X., Zheng, L., Zhou, Y., and Zurita, P.

Subjects
DIS, Nuclear and High Energy Physics, EIC, Electron-Ion Collider, Detectors, SIDIS
Abstract

This report describes the physics case, the resulting detector requirements, and the evolving detector concepts for the experimental program at the Electron-Ion Col- lider (EIC). The EIC will be a powerful new high-luminosity facility in the United States with the capability to collide high-energy electron beams with high-energy proton and ion beams, providing access to those regions in the nucleon and nu- clei where their structure is dominated by gluons. Moreover, polarized beams in the EIC will give unprecedented access to the spatial and spin structure of the proton, neutron, and light ions. The studies leading to this document were com- missioned and organized by the EIC User Group with the objective of advancing the state and detail of the physics program and developing detector concepts that meet the emerging requirements in preparation for the realization of the EIC. The effort aims to provide the basis for further development of concepts for experimen- tal equipment best suited for the science needs, including the importance of two complementary detectors and interaction regions. This report consists of three volumes. Volume I is an executive summary of our findings and developed concepts. In Volume II we describe studies of a wide range of physics measurements and the emerging requirements on detector acceptance and performance. Volume III discusses general-purpose detector concepts and the underlying technologies to meet the physics requirements. These considerations will form the basis for a world-class experimental program that aims to increase our understanding of the fundamental structure of all visible matter.

Published
2022

2. Employment of nanodiamond photocathodes on MPGD-based HEP detector at the future EIC

Author

Brunbauer, F M, Chatterjee, C, Cicala, G, Cicuttin, A, Ciliberti, P, Crespo, M L, D’Ago, D, Torre, S Dalla, Dasgupta, S, Gregori, M, Ligonzo, T, Levorato, S, Lisowska, M, Menon, G, Tessarotto, F, Ropelewski, L, Triloki, T, Valentini, A, Velardi, L, and Zhao, Y X

Subjects
History, Detectors and Experimental Techniques, Computer Science Applications, Education
Abstract

In high momenta range, the construction of a Ring Imaging CHerenkov (RICH) detector for the particle identification at the future Electron Ion Collider (EIC) is a complicated task. A compact collider setup imposes to construct a RICH with a short radiator length, hence limiting the number of photons. The number of photons can be increase by choosing to work in far UV region. However, as standard fused-silica windows are opaque below 165 nm, therefore, a windowless RICH approach could be a possible choice. In the far UV range, CsI is a widely used photo-cathode (PC) to detect photons, but because of its hygroscopic nature, it is very delicate to handle. Its Quantum Efficiency (QE) degrades in high intensity ion fluxes. These are the key reasons to search a novel, less delicate PC with sensitivity in the far UV region. Hydrogenated nanodiamond films are proposed as an alternative PC material and shown to have promising characteristics. The performance of nanodiamond PC coupled to THGEM-based detectors is the objects of our ongoing R & D.The first phase of these studies includes the characterization of THGEMs coated with nanodiamont PC, the comparison of the effective QE in vacuum and in gaseous atmospheres, the hardness respect to the PC bombardment by ions from the multiplication process. The approach is described in detail as well as all the results obtained so far with these exploratory studies.

Published
2022

8. Case-control study on risk factors for caesarean section: methodological issues

Author

Signorelli, C., Autelitano, M., Bailo, U., Maria Sofia Cattaruzza, Cicala, G., Corpolongo, D., Giunta, R., Grossi, E., Palleschi, B. M., Petrelli, F., Signorelli, C, Autelitano, M, Bailo, U, Cattaruzza, M. S, Cicala, G, Corpolongo, D, Giunta, R, Grossi, E, Palleschi, B. M, and Petrelli, F.

Subjects
Adult, Informed Consent, Cesarean Section, Statistics as Topic, Infant, Newborn, Pilot Projects, Italy, Pregnancy, Research Design, Risk Factors, Case-Control Studies, Surveys and Questionnaires, Humans, Female, Confidentiality
Published
1991

12. Preliminary study on polycrystalline diamond films suitable for radiation detection

Author

Acquafredda, P., Bisceglie, E., Bottalico, D., Brescia, R., Brigida, M., Caliandro, G. A., Capitelli, M., Casamassima, G., Cassano, T., Celiberto, R., Cicala, G., Crismale, V., Giacomo, A., Pascale, O., Favuzzi, C., Ferraro, G., Fusco, P., Gargano, F., Giglietto, N., Giordano, F., Gorse, C., Laporta, V., Longo, S., Francesco Loparco, Marangelli, B., Mazziotta, M. N., Mirizzi, N., Muscarella, M. F., Nitti, M. A., Raino, A., Romeo, A., Senesi, G., Spinelli, P., Valentini, A., Verrone, G., and Ieee

17. Design and manufacturing of a surface plasmon resonance sensor based on inkjet 3D printing for simultaneous measurements of refractive index and temperature

Author

Lorena Saitta, Francesco Arcadio, Giovanni Celano, Nunzio Cennamo, Luigi Zeni, Claudio Tosto, Gianluca Cicala, Saitta, L., Arcadio, F., Celano, G., Cennamo, N., Zeni, L., Tosto, C., and Cicala, G.

Subjects
quality monitoring, Control and Systems Engineering, additive manufacturing, quality monitoring, polymers, sensor design, plastic optical fibers, Mechanical Engineering, Plastic optical fiber, plastic optical fibers, Polymer, additive manufacturing, sensor design, polymers, Industrial and Manufacturing Engineering, Software, Computer Science Applications
Abstract

In this work, a surface plasmon resonance (SPR) multiparameter sensor for simultaneous determination of refractive index and temperature was manufactured through a novel and low-cost approach. Monitoring these parameters is useful when biosensors are developed by exploiting SPR phenomena. A polymer planar optical structure was realized via inkjet 3D printing, by using photo-curable resins having tailored refractive index for device’s core and cladding, respectively. The multiparameter sensor was fully designed, manufactured, and experimentally tested to check the numerical analyses run on a preliminary phase. In such a way, a temperature resolution equal to about 0.5°C and a refractive index resolution equal to about 2 × 10−4 RIU (refractive index unit) were obtained. Next, even a quality control analysis of the 3D printed surface was carried out by following a novel approach that relies on the profile monitoring technique, with the aim to evaluate the suitability of the design and the geometric accuracy control. In addition, thanks to the cost analysis performed through a properly model, it was proved that the multiparameter sensor designed, manufactured, and tested satisfies the low-cost requirements, being the estimated cost ~ 23 €, which is an absolutely competitive cost if compared with other traditional sensors. In the end, even the performance of the sensor in terms of bulk sensitivity (equal to about 900nm/RIU) resulted to be higher than similar devices already presented in the state-of-the-art, thus proving the validity of the developed SPR multiparameter sensor both in economic and performance terms. Graphical abstract: [Figure not available: see fulltext.]

Published
2022

18. Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

Author

Gualtiero I. Colombo, Giuseppe Muscogiuri, Marco Guglielmo, Vitanio Palmisano, Mauro Pepi, Giulia Rizzon, Andrea Igoren Guaricci, Annachiara Cavaliere, Andrea Baggiano, Gloria Cicala, Luca Saba, Carlo N. De Cecco, Antonella Loffreno, Mattia Chiesa, Gianluca Pontone, Laura Fusini, Michela Trotta, Daniele Andreini, Mark G. Rabbat, Marco Gatti, Francesca Baessato, Serena Dell'Aversana, Muscogiuri, G, Chiesa, M, Trotta, M, Gatti, M, Palmisano, V, Dell'Aversana, S, Baessato, F, Cavaliere, A, Cicala, G, Loffreno, A, Rizzon, G, Guglielmo, M, Baggiano, A, Fusini, L, Saba, L, Andreini, D, Pepi, M, Rabbat, M, Guaricci, A, De, C, C. N., C, and G., P

Subjects
Male, 0301 basic medicine, Artificial intelligence, medicine.medical_specialty, Computed Tomography Angiography, Convolutional neural network, CAD, Diagnostic accuracy, 030204 cardiovascular system & hematology, Coronary Angiography, Severity of Illness Index, Coronary artery disease, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Predictive Value of Tests, medicine, Humans, CADRADS, Aged, Retrospective Studies, Deep cnn, Plaque characterization, Coronary Artery Disease, Female, Middle Aged, Algorithms, business.industry, Deep learning, Coronary computed tomography angiography, Retrospective cohort study, medicine.disease, Predictive value, 030104 developmental biology, Radiology, Cardiology and Cardiovascular Medicine, business
Abstract

Background and aims Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) Conclusions Deep CNN yielded accurate automated classification of patients with CAD-RADS.

Published
2020

19. Magnetic resonance imaging and artificial intelligence

Author

Laura Fusini, Giuseppe Muscogiuri, Andrea I. Guaricci, Francesca Ricci, Mark G. Rabbat, Gloria Cicala, G Pontone, Marco Guglielmo, A Baggiano, Muscogiuri, G, Guglielmo, M, Baggiano, A, Fusini, L, Ricci, F, Cicala, G, Rabbat, M, Guaricci, A, and Pontone, G

Subjects
Artificial intelligence, Modality (human–computer interaction), Cardiac magnetic resonance, medicine.diagnostic_test, Prognosi, Computer science, business.industry, Myocardial tissue characterization, Magnetic resonance imaging, Tissue characterization, Both ventricles, Clinical Practice, medicine, Segmentation, Function, business, Cardiac imaging, Diagnosi
Abstract

Cardiac magnetic resonance (CMR) is the best-suited cardiac imaging modality to provide reliable and reproducible information regarding cardiac volumes, function, and tissue characterization. In clinical practice, artificial intelligence (AI) in CMR can be applied to the whole process, ranging from sequence reconstruction, image analysis, and reporting of findings. In sequence reconstruction, the application of AI is mainly focused on the speed of reconstruction, while in image analysis and reporting AI is primarily aimed at automating the process of segmentation of both ventricles as well as fibrotic tissue. Within the realm of tissue characterization, AI can also be helpful in the depiction of tissue abnormalities from noncontrast images. Finally, AI can be useful for prognostic stratification.

Published
2021

20. Microstructured Surface Plasmon Resonance Sensor Based on Inkjet 3D Printing Using Photocurable Resins with Tailored Refractive Index

Author

Lorena Saitta, Luigi Zeni, Gianluca Cicala, Francesco Arcadio, Claudio Tosto, Nunzio Cennamo, Maria Elena Fragalà, Cennamo, N., Saitta, L., Tosto, C., Arcadio, F., Zeni, L., Fragala, M. E., and Cicala, G.

Subjects
Optical fiber, Surface plasmon resonance sensor, Materials science, Polymers and Plastics, Organic chemistry, 3D printing, 02 engineering and technology, 01 natural sciences, Article, law.invention, QD241-441, Planar, sensor, law, Figure of merit, Surface plasmon resonance, Plasmon, plasmonic, business.industry, 010401 analytical chemistry, General Chemistry, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Optoelectronics, 0210 nano-technology, business, additive manufacturing, Refractive index, photocurable resins, Photocurable resin
Abstract

In this work, a novel approach to realize a plasmonic sensor is presented. The proposed optical sensor device is designed, manufactured, and experimentally tested. Two photo-curable resins are used to 3D print a surface plasmon resonance (SPR) sensor. Both numerical and experimental analyses are presented in the paper. The numerical and experimental results confirm that the 3D printed SPR sensor presents performances, in term of figure of merit (FOM), very similar to other SPR sensors made using plastic optical fibers (POFs). For the 3D printed sensor, the measured FOM is 13.6 versus 13.4 for the SPR-POF configuration. The cost analysis shows that the 3D printed SPR sensor can be manufactured at low cost (∼15 €) that is competitive with traditional sensors. The approach presented here allows to realize an innovative SPR sensor showing low-cost, 3D-printing manufacturing free design and the feasibility to be integrated with other optical devices on the same plastic planar support, thus opening undisclosed future for the optical sensor systems.

Published
2021

21. Diamond Films for Electrical and Electronic Circuitry

Author

Alessandro Massaro, G. Cicala, Luciano Velardi, Giorgio S. Senesi, Aime Lay-Ekuakille, Lay-Ekuakille, A., Cicala, G., Massaro, A., Velardi, L., and Senesi, G. S.

Subjects
I-V characteristic, Materials science, Silicon, business.industry, MWPECVD, Doping, chemistry.chemical_element, Diamond, Circuital component, Chemical vapor deposition, engineering.material, Ion source, chemistry, Electrical resistivity and conductivity, visual_art, Electronic component, visual_art.visual_art_medium, engineering, Optoelectronics, diamond film, AFM, business, Instrumentation, Order of magnitude
Abstract

Diamond films grown by microwave plasma enhanced chemical vapor deposition technique exhibit different electrical conductivity. In particular, many factors such as substrate type, more or less hydrogenated surface, aging and doping change the electrical current of the diamond surface. There is an increasing need of diamond film for manifold applications because of its excellent behavior in emerging activities such as high-performing telecommunication systems, high-sensitive detecting pathology sensors within noisy human matrices with or without contrast agent, etc. Moreover, new ideas are still coming out from researchers and scientists to give out benefits to the entire area of research. The major finding is to carried circuit components and wired elements. In this paper we report the I- V characteristics of two polycrystalline diamond (PCD) films grown on intrinsic (i-Si) and p-doped silicon (p-Si) substrates. At 40 V the current of PCD film grown on p-Si is one order of magnitude higher than one on i-Si. The result suggests a possible application of diamond films in circuital elements or in more complex electronic components integrated into different substrates.

Published
2015

22. Reactive microspheres as active fillers for epoxy resins

Author

A. Recca, Antonino Pollicino, Cosimo Carfagna, Gianluca Cicala, G. Costa, Veronica Ambrogi, Carfagna, Cosimo, Ambrogi, Veronica, Cicala, G., Pollicino, A., Recca, A., and Costa, G.

Subjects
chemistry.chemical_classification, Toughness, Materials science, Polymers and Plastics, Thermosetting polymer, structure properties, General Chemistry, Polymer, Epoxy, Surfaces, Coatings and Films, microsphere, Rheology, chemistry, visual_art, Materials Chemistry, visual_art.visual_art_medium, Polymer blend, Microparticle, Composite material, Curing (chemistry)
Abstract

Physical properties of epoxy resins filledwith microparticles are presented and discussed. Micropar-ticles were synthesized in the form of micron-sized,crosslinked spherical particles, with an excess of reactiveamino groups on their outer surface, and subsequentlyblended with EPON828-3,3 DDS in different weight per-cents(10and20wt%).Differentialscanningcalorimetryandscanning electronic microscopy were applied to investigatemicrosphere properties such as morphology, shape, size,and size distribution. Electron spectroscopy for chemicalanalysis was applied on particles to relate surface composi-tion and reactivity of microspheres. Rheological, dynamic–mechanical, and mechanical properties of the cured blendswere analyzed and related to the pure resin and to the sameresin modified with PES180. © 2004 Wiley Periodicals, Inc.J Appl Polym Sci 93: 2031–2044, 2004 Key words: adhesion; crosslinking; curing of polymers; res-ins; thermosets INTRODUCTION It is well known that physical properties of thermosetscan be improved by inclusion of a filler, as a secondphase, to the resin batch. For example, toughness isnoticeably increased by mixing reactive rubbers withepoxy resins. The same effect can be reached with theaddition of some reactive thermoplastics.In many cases, the target is viscosity modificationduring processing of thermosets. For this purpose, asuspension of solid particles, as well as of some ther-moplastics, included in the resin can be very useful inbalancing the proper viscosity profile during process-ing.In this article, a novel approach to change the vis-cosity of epoxy resins is presented. The method isbased on the inclusion of reactive microspheres pre-viously prepared by reaction of the epoxy monomerwith the curing agent.Over the last several years, a growing interest in theproduction of polymer particles was recorded.

Published
2004

23. Interaction of the estradiol receptor from calf uterus with its nuclear acceptor sites

Author

V. Sica, U Hibner, G A Puca, G Cicala, E Nola, Puca, Ga, Nola, Ernesto, Hibner, U, Cicala, G, and Sica, V.

Subjects
Muscle Proteins, Receptors, Cell Surface, Biochemistry, chemistry.chemical_compound, Animals, Molecule, Testosterone, Binding site, Receptor, Molecular Biology, Progesterone, Cell Nucleus, Estradiol, Chemistry, Uterus, Cell Biology, Acceptor, Cortisone, Dithiothreitol, Organ Specificity, Ionic strength, Sephadex, Covalent bond, Biophysics, Agarose, Cattle, Female, Protein Binding
Abstract

The specific interaction between 17 beta-estradiol-receptor complex and nuclear acceptors was analyzed by immobilizing various nuclear proteins to CNBr-activated agarose. The specific, high affinity sites identified in a fraction of basic proteins that can be solubilized from purified nuclei of calf uterus (Puca, G.A., Sica, V., and Nola. E (1974) Proc. Natl. Acad. Sci. U.S.A. 71, 979-983) were chromatographed on Sephadex G-100 columns. Elution of the acceptor activity depends on the pH and ionic strength of the buffer used. With 5 mM HCl, however, a peak of acceptor activity with a molecular weight of about 70,000 was partially dissociated from the other basic nuclear proteins. The high affinity binding of the receptor to the acceptor proteins was estradiol-, but not progesterone-, cortisone-, or testosterone-dependent; it was very sensitive to ionic strength and showed a physiological pH optimum. Low affinity binding, such as that seen between receptor and histone, showed no estradiol dependence and little ionic strength and pH sensitivity. Native or heat-denatured DNA strongly modified the receptor-acceptor interaction, reducing the number of binding sites of acceptor for the receptor without changing the high affinity of the interaction. Heating of the acceptor protein before its covalent linkage to agarose considerably increased the affinity of the resulting agarose derivative. Free sulfhydryl groups of the receptor but not of the acceptor molecule play an important role in the acceptor-receptor interaction. When receptor and acceptor preparations were incubated in solution, the resulting complex was included on a Sephadex G-100 column and it eluted from DEAE-cellulose columns at lower ionic strength than the receptor alone. Even though not absolutely specific, these two properties allowed determination of the molecular weight (85,000) of the acceptor protein at neutral pH and more nearly physiological ionic strength. The apparent KD of the acceptor-receptor interaction was determined to be 2 x 10(-10) M at O degrees. Apparently similar, high affinity binding sites for estradiol receptors are also present in nuclei of other tissues.

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