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847 results on '"Gonçalves G"'

1. Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

Author

Gallitelli, L., Girard, P., Andriolo, U., Liro, M., Suaria, G., Martin, C., Lusher, A.L., Hancke, K., Blettler, MCM, Garcia-Garin, O., Napper, I.E., Corbari, L., Cózar, A., Morales-Caselles, C., González-Fernández, D., Gasperi, J., Giarrizzo, T., Cesarini, G., De, K., Constant, M., Koutalakis, P., Gonçalves, G., Sharma, P., Gundogdu, S., Kumar, R., Garello, N.A., Camargo, A.L.G., Topouzelis, K., Galgani, F., Royer, S.J., Zaimes, G.N., Rotta, F., Lavender, S., Nava, V., Castro-Jiménez, J., Mani, T., Crosti, R., Azevedo-Santos, V.M., Bessa, F., Tramoy, R., Costa, M.F., Corbau, C., Montanari, A., Battisti, C., and Scalici, M.

Published
2024
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2. Multispectral Vineyard Segmentation: A Deep Learning approach

Author

Barros, T., Conde, P., Gonçalves, G., Premebida, C., Monteiro, M., Ferreira, C. S. S., and Nunes, U. J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available at https://github.com/Cybonic/DL_vineyard_segmentation_study.git, Comment: Accepted in Computer and Electronics in Agriculture journal

Published
2021
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3. Women in academia: a warning on selection bias in gender studies from the astronomical perspective

Author

Dantas, M. L. L., Cameron, E., de Souza, Rafael S., da Silva, A. R., Chies-Santos, A. L., Heneka, C., Coelho, P. R. T., Ederoclite, A., Beloto, I. S., Branco, V., Camargo, Morgan S., de Oliveira, V. M. Carvalho, de Sá-Freitas, C., Gonçalves, G., Pacheco, T. A., and Rebollido, Isabel

Subjects
Physics - Physics and Society, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Popular Physics
Abstract

The recent paper by AlShebli et al. (2020) investigates the impact of mentorship in young scientists. Among their conclusions, they state that female prot\'eg\'es benefit more from male than female mentorship. We herein expose a critical flaw in their methodological design that is a common issue in Astronomy, namely "selection biases". An effect that if not treated properly may lead to unwarranted causality claims. In their analysis, selection biases seem to be present in the response rate of their survey (8.35%), the choice of database, success criterion, and the overlook of the numerous drawbacks female researchers face in academia. We discuss these issues and their implications -- one of them being the potential increase in obstacles for women in academia. Finally, we reinforce the dangers of not considering selection bias effects in studies aimed at retrieving causal relations., Comment: Submitted to Nature Communications on Nov. 27 -- comments are welcome

Published
2020

4. Recurrent neural network approach for cyclic job shop scheduling problem

Author

Kechadi, M-Tahar, Low, Kok Seng, and Goncalves, G.

Subjects
Computer Science - Artificial Intelligence
Abstract

While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system model and the efficiency of neural networks for such a class of problems., Comment: Journal of Manufacturing Systems, Volume 32, Issue 4, October 2013, Pages 689-699

Published
2019
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8. POLÍTICAS PÚBLICAS EM ESPORTE E LAZER: CENÁRIOS E DIRECIONAMENTOS

Author

IGNÁCIO, M. C., primary, BOEHL, W. R., additional, GONÇALVES, G. O., additional, MORESCO, D. N., additional, BRITES, K. J. S., additional, GUTIERRES, G. S. J. G., additional, FARIAS, A. S., additional, PERES, A. L. X., additional, MOURA, H. S., additional, ADOLFO, J. R., additional, IZIDORO JÚNIOR, C. A. R., additional, and SANTOS, F. B., additional

Published
2023
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13. Empagliflozin in the treatment of heart failure with reduced ejection fraction in addition to background therapies and therapeutic combinations (EMPEROR-Reduced): a post-hoc analysis of a randomised, double-blind trial

Author

Packer, M, Anker, S, Butler, J, Filippatos, G, Pocock, S, Zannad, F, Ferreira, JP, Brueckmann, M, George, J, Jamal, W, Welty, FK, Palmer, M, Clayton, T, Parhofer, KG, Pedersen, TR, Greenberg, B, Konstam, MA, Lees, KR, Carson, P, Doehner, W, Miller, A, Haas, M, Pehrson, S, Komajda, M, Anand, I, Teerlink, J, Rabinstein, A, Steiner, T, Kamel, H, Tsivgoulis, G, Lewis, J, Freston, J, Kaplowitz, N, Mann, J, Petrie, J, Perrone, S, Nicholls, S, Janssens, S, Bocchi, E, Giannetti, N, Verma, S, Zhang, J, Spinar, J, Seronde, M-F, Boehm, M, Merkely, B, Chopra, V, Senni, M, Taddi, S, Tsutsui, H, Choi, D-J, Chuquiure, E, La Rocca, HPB, Ponikowski, P, Juanatey, JRG, Squire, I, Januzzi, J, Pina, I, Bernstein, R, Cheung, A, Green, J, Kaul, S, Lam, C, Lip, G, Marx, N, McCullough, P, Mehta, C, Rosenstock, J, Sattar, N, Scirica, B, Shah, S, Wanner, C, Aizenberg, D, Cartasegna, L, Colombo Berra, F, Colombo, H, Fernandez Moutin, M, Glenny, J, Alvarez Lorio, C, Anauch, D, Campos, R, Facta, A, Fernandez, A, Ahuad Guerrero, R, Lobo Márquez, L, Leon de la Fuente, RA, Mansilla, M, Hominal, M, Hasbani, E, Najenson, M, Moises Azize, G, Luquez, H, Guzman, L, Sessa, H, Amuchástegui, M, Salomone, O, Perna, E, Piskorz, D, Sicer, M, Perez de Arenaza, D, Zaidman, C, Nani, S, Poy, C, Resk, J, Villarreal, R, Majul, C, Smith Casabella, T, Sassone, S, Liberman, A, Carnero, G, Caccavo, A, Berli, M, Budassi, N, Bono, J, Alvarisqueta, A, Amerena, J, Kostner, K, Hamilton, A, Begg, A, Beltrame, J, Colquhoun, D, Gordon, G, Sverdlov, A, Vaddadi, G, Wong, J, Coller, J, Prior, D, Friart, A, Leone, A, Vervoort, G, Timmermans, P, Troisfontaines, P, Franssen, C, Sarens, T, Vandekerckhove, H, Van De Borne, P, Chenot, F, De Sutter, J, De Vuyst, E, Debonnaire, P, Dupont, M, Pereira Dutra, O, Canani, LH, Vieira Moreira, MdC, de Souza, W, Backes, LM, Maia, L, De Souza Paolino, B, Manenti, ER, Saporito, W, Villaça Guimarães Filho, F, Franco Hirakawa, T, Saliba, LA, Neuenschwander, FC, de Freitas Zerbini, CA, Gonçalves, G, Gonçalves Mello, Y, Ascenção de Souza, J, Beck da Silva Neto, L, Bocchi, EA, Da Silveira, J, de Moura Xavier Moraes Junior, JB, de Souza Neto, JD, Hernandes, M, Finimundi, HC, Sampaio, CR, Vasconcellos, E, Neves Mancuso, FJ, Noya Rabelo, MM, Rodrigues Bacci, M, Santos, F, Vidotti, M, Simões, MV, Gomes, FL, Vieira Nascimento, C, Precoma, D, Helfenstein Fonseca, FA, Ribas Fortes, JA, Leães, PE, Campos de Albuquerque, D, Kerr Saraiva, JF, Rassi, S, Alves da Costa, FA, Reis, G, Zieroth, S, Dion, D, Savard, D, Bourgeois, R, Constance, C, Anderson, K, Leblanc, M-H, Yung, D, Swiggum, E, Pliamm, L, Pesant, Y, Tyrrell, B, Huynh, T, Spiegelman, J, Lavoie, J-P, Hartleib, M, Bhargava, R, Straatman, L, Virani, S, Costa-Vitali, A, Hill, L, Heffernan, M, Khaykin, Y, Ricci, J, Senaratne, M, Zhai, A, Lubelsky, B, Toma, M, Yao, L, McKelvie, R, Noronha, L, Babapulle, M, Pandey, A, Curnew, G, Lavoie, A, Berlingieri, J, Kouz, S, Lonn, E, Chehayeb, R, Zheng, Y, Sun, Y, Cui, H, Fan, Z, Han, X, Jiang, X, Tang, Q, Zhou, J, Zheng, Z, Zhang, X, Zhang, N, Zhang, Y, Shen, A, Yu, J, Ye, J, Yao, Y, Yan, J, Xu, X, Wang, Z, Ma, J, Li, Y, Li, S, Lu, S, Kong, X, Song, Y, Yang, G, Yao, Z, Pan, Y, Guo, X, Sun, Z, Dong, Y, Zhu, J, Peng, D, Yuan, Z, Lin, J, Yin, Y, Jerabek, O, Burianova, H, Fiala, T, Hubac, J, Ludka, O, Monhart, Z, Vodnansky, P, Zeman, K, Foldyna, D, Krupicka, J, Podpera, I, Busak, L, Radvan, M, Vomacka, Z, Prosecky, R, Cifkova, R, Durdil, V, Vesely, J, Vaclavik, J, Cervinka, P, Linhart, A, Brabec, T, Miklik, R, Bourhaial, H, Olbrich, H-G, Genth-Zotz, S, Kemala, E, Lemke, B, Böhm, M, Schellong, S, Rieker, W, Heitzer, T, Ince, H, Faghih, M, Birkenfeld, A, Begemann, A, Ghanem, A, Ujeyl, A, von Haehling, S, Dorsel, T, Bauersachs, J, Prull, M, Weidemann, F, Darius, H, Nickenig, G, Wilke, A, Sauter, J, Rauch-Kroehnert, U, Frey, N, Schulze, CP, König, W, Maier, L, Menzel, F, Proskynitopoulos, N, Ebert, H-H, Sarnighausen, H-E, Düngen, H-D, Licka, M, Stellbrink, C, Winkelmann, B, Menck, N, López-Sendón, JL, de la Fuente Galán, L, Delgado Jiménez, JF, Manito Lorite, N, Pérez de Juan Romero, M, Galve Basilio, E, Cereto Castro, F, González Juanatey, JR, Gómez, JJ, Sanmartín Fernández, M, Garcia-Moll Marimon, X, Pascual Figal, D, Bover Freire, R, Bonnefoy Cudraz, E, Jobbe Duval, A, Tomasevic, D, Habib, G, Isnard, R, Picard, F, Khanoyan, P, Dubois-Rande, J-L, Galinier, M, Roubille, F, Alexandre, J, Babuty, D, Delarche, N, Berneau, J-B, Girerd, N, Saxena, M, Rosano, G, Yousef, Z, Clifford, C, Arden, C, Bakhai, A, Boos, C, Jenkins, G, Travill, C, Price, D, Koenyves, L, Lakatos, F, Matoltsy, A, Noori, E, Zilahi, Z, Andrassy, P, Kancz, S, Simon, G, Sydo, T, Vorobcsuk, A, Kiss, RG, Toth, K, Szakal, I, Nagy, L, Barany, T, Nagy, A, Szolnoki, E, Chopra, VK, Mandal, S, Rastogi, V, Shah, B, Mullasari, A, Shankar, J, Mehta, V, Oomman, A, Kaul, U, Komarlu, S, Kahali, D, Bhagwat, A, Vijan, V, Ghaisas, NK, Mehta, A, Kashyap, J, Kothari, Y, TaddeI, S, Scherillo, M, Zacà, V, Genovese, S, Salvioni, A, Fucili, A, Fedele, F, Cosmi, F, Volpe, M, Mazzone, C, Esposito, G, Doi, M, Yamamoto, H, Sakagami, S, Oishi, S, Yasaka, Y, Tsuboi, H, Fujino, Y, Matsuoka, S, Watanabe, Y, Himi, T, Ide, T, Ichikawa, M, Kijima, Y, Koga, T, Yuda, S, Fukui, K, Kubota, T, Manita, M, Fujinaga, H, Matsumura, T, Fukumoto, Y, Kato, R, Kawai, Y, Hiasa, G, Kazatani, Y, Mori, M, Ogimoto, A, Inoko, M, Oguri, M, Kinoshita, M, Okuhara, K, Watanabe, N, Ono, Y, Otomo, K, Sato, Y, Matsunaga, T, Takaishi, A, Miyagi, N, Uehara, H, Takaishi, H, Urata, H, Kataoka, T, Matsubara, H, Matsumoto, T, Suzuki, T, Takahashi, N, Imamaki, M, Yoshitama, T, Saito, T, Sekino, H, Furutani, Y, Koda, M, Shinozaki, T, Hirabayashi, K, Tsunoda, R, Yonezawa, K, Hori, H, Yagi, M, Arikawa, M, Hashizume, T, Ishiki, R, Koizumi, T, Nakayama, K, Taguchi, S, Nanasato, M, Yoshida, Y, Tsujiyama, S, Nakamura, T, Oku, K, Shimizu, M, Suwa, M, Momiyama, Y, Sugiyama, H, Kobayashi, K, Inoue, S, Kadokami, T, Maeno, K, Kawamitsu, K, Maruyama, Y, Nakata, A, Shibata, T, Wada, A, Cho, H-J, Na, JO, Yoo, B-S, Choi, J-O, Hong, SK, Shin, J-H, Cho, M-C, Han, SH, Jeong, J-O, Kim, J-J, Kang, SM, Kim, D-S, Kim, MH, Llamas Esperon, G, Illescas Díaz, J, Fajardo Campos, P, Almeida Alvarado, J, Bazzoni Ruiz, A, Echeverri Rico, J, Lopez Alcocer, I, Valle Molina, L, Hernandez Herrera, C, Calvo Vargas, C, Padilla Padilla, FG, Rodriguez Briones, I, Chuquiure Valenzuela, EJJR, Aguilera Real, ME, Carrillo Calvillo, J, Alpizar Salazar, M, Cervantes Escárcega, JL, Velasco Sanchez, R, Al - Windy, N, van Heerebeek, L, Bellersen, L, Brunner-La Rocca, H-P, Post, J, Linssen, GCM, van de Wetering, M, Peters, R, van Stralen, R, Groutars, R, Smits, P, Yilmaz, A, Kok, WEM, Van der Meer, P, Dijkmans, P, Troquay, R, van Alem, AP, Van de Wal, R, Handoko, L, Westendorp, ICD, van Bergen, PFMM, Rensing, BJWM, Hoogslag, P, Kietselaer, B, Kragten, JA, den Hartog, FR, Alings, A, Danilowicz-Szymanowicz, L, Raczak, G, Piesiewicz, W, Zmuda, W, Kus, W, Podolec, P, Musial, W, Drelich, G, Kania, G, Miekus, P, Mazur, S, Janik, A, Spyra, J, Peruga, J, Balsam, P, Krakowiak, B, Szachniewicz, J, Ginel, M, Grzybowski, J, Chrustowski, W, Wojewoda, P, Kalinka, A, Zurakowski, A, Koc, R, Debinski, M, Fil, W, Kujawiak, M, Forys, J, Kasprzak, M, Krol, M, Michalski, P, Mirek-Bryniarska, E, Radwan, K, Skonieczny, G, Stania, K, Skoczylas, G, Madej, A, Jurowiecki, J, Firek, B, Wozakowska-Kaplon, B, Cymerman, K, Neutel, J, Adams, K, Balfour, P, Deswal, A, Djamson, A, Duncan, P, Hong, M, Murray, C, Rinde-Hoffman, D, Woodhouse, S, MacNevin, R, Rama, B, Broome-Webster, C, Kindsvater, S, Abramov, D, Barettella, M, Pinney, S, Herre, J, Cohen, A, Vora, K, Challappa, K, West, S, Baum, S, Cox, J, Jani, S, Karim, A, Akhtar, A, Quintana, O, Paukman, L, Goldberg, R, Bhatti, Z, Budoff, M, Bush, E, Potler, A, Delgado, R, Ellis, B, Dy, J, Fialkow, J, Sangrigoli, R, Ferdinand, K, East, C, Falkowski, S, Donahoe, S, Ebrahimi, R, Kline, G, Harris, B, Khouzam, R, Jaffrani, N, Jarmukli, N, Kazemi, N, Koren, M, Friedman, K, Herzog, W, Silva Enciso, J, Cheung, D, Grover-McKay, M, Hauptman, P, Mikhalkova, D, Hegde, V, Hodsden, J, Khouri, S, McGrew, F, Littlefield, R, Bradley, P, McLaurin, B, Lupovitch, S, Labin, I, Rao, V, Leithe, M, Lesko, M, Lewis, N, Lombardo, D, Mahal, S, Malhotra, V, Dauber, I, Banerjee, A, Needell, J, Miller, G, Paladino, L, Munuswamy, K, Nanna, M, McMillan, E, Mumma, M, Napoli, M, Nelson, W, O'Brien, T, Adlakha, A, Onwuanyi, A, Serota, H, Schmedtje, J, Paraschos, A, Potu, R, Sai-Sudhakar, C, Saltzberg, M, Sauer, A, Shah, P, Skopicki, H, Bui, H, Carr, K, Stevens, G, Tahirkheli, N, Tallaj, J, Yousuf, K, Trichon, B, Welker, J, Tolerico, P, Vest, A, Vivo, R, Wang, X, Abadier, R, Dunlap, S, Weintraub, N, Malik, A, Kotha, P, Zaha, V, Kim, G, Uriel, N, Greene, T, Salacata, A, Arora, R, Gazmuri, R, Kobayashi, J, Iteld, B, Vijayakrishnan, R, Dab, R, Mirza, Z, Marques, V, Nallasivan, M, Bensimhon, D, Peart, B, Saint-Jacques, H, Barringhaus, K, Contreras, J, Gupta, A, Koneru, S, Nguyen, V, Verma, Subodh, Dhingra, Nitish K, Butler, Javed, Anker, Stefan D, Ferreira, Joao Pedro, Filippatos, Gerasimos, Januzzi, James L, Lam, Carolyn S P, Sattar, Naveed, Peil, Barbara, Nordaby, Matias, Brueckmann, Martina, Pocock, Stuart J, Zannad, Faiez, and Packer, Milton

Published
2022
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20. Advancing river monitoring using image-based techniques: challenges and opportunities

Author

Manfreda, S., Miglino, D., Saddi, K.C., Jomaa, Seifeddine, Eltner, A., Perks, M., Peña-Haro. S., Bogaard, T., van Emmerik, T.H.M., Mariani, S., Maddock, I., Tauro, F., Grimaldi, S., Zeng, Y., Gonçalves, G., Strelnikova, D., Bussettini, M., Marchetti, G., Lastoria, B., Su, Z., Rode, Michael, Manfreda, S., Miglino, D., Saddi, K.C., Jomaa, Seifeddine, Eltner, A., Perks, M., Peña-Haro. S., Bogaard, T., van Emmerik, T.H.M., Mariani, S., Maddock, I., Tauro, F., Grimaldi, S., Zeng, Y., Gonçalves, G., Strelnikova, D., Bussettini, M., Marchetti, G., Lastoria, B., Su, Z., and Rode, Michael

Abstract

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision-making. The integration of remote and proximal sensing technologies, with citizen science, and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to quantify the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build critical mass around relevant challenges to meet the need for innovative solutions to overcome limitations of traditional approaches. The present study reviews showcases and good practices of enhanced hydrological monitoring in different applications, reflecting the strengths and limitations of new approaches.

Published
2024

21. Effects of Sunlight on Psychological Well-Being, Job Satisfaction and Confinement Perception of Workplace: The Case of Shopkeepers and Marketers

Author

Gonçalves, G., Sousa, A., Sousa, C., Jesus, F., Afonso, E., Kacprzyk, Janusz, Series Editor, Arezes, Pedro M., editor, Baptista, João S., editor, Barroso, Mónica P., editor, Carneiro, Paula, editor, Cordeiro, Patrício, editor, Costa, Nélson, editor, Melo, Rui B., editor, Miguel, A. Sérgio, editor, and Perestrelo, Gonçalo, editor

Published
2019
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34. SAÚDE INFANTIL E HEBIÁTRICA: ludicidade, tecnologias e desafios

Author

SILVA, Ana Valeska Siebra, primary, MATTOS, S. M., additional, MOREIRA, Thereza Maria Magalhães, additional, CHAVES, E. M. C., additional, GOMES, I. L. V., additional, CAVALCANTE, A. R. L., additional, SILVA, A. M. L., additional, SARAIVA, A. G. S., additional, SILVA, A. M. C., additional, SILVA, Ana Valeska Siebra, additional, MONTEIRO, A. R. M., additional, MENEZES, A. V. B., additional, SILVA, Anamaria Cavalcante e, additional, FERREIRA JÚNIOR, A. R., additional, MAGALHÃES, B. C., additional, SILVA, B. T., additional, FARIAS, B. O., additional, SOUSA, B. E. V., additional, SILVA, CAMILA BRASILEIRO DE ARAUJO, additional, MENDES, C. F., additional, CARVALHO, C.G.M, additional, SOARES, C. S., additional, CAVALCANTI, C. A. L., additional, RIBEIRO, C. F., additional, VERGARA, Clarice Maria Araújo Chagas, additional, ALVES, D. L. G., additional, ARAUJO, E. M., additional, VERNIER, Eliane Tatsch Neves ou NEVES, additional, CARDOSO, E. R., additional, PINTO,, Francisco José Maia., additional, LOUREIRO NETO, F. M., additional, PAULA, F. W. S., additional, GONÇALVES, G. A. A., additional, OLIVEIRA, I. M. M., additional, CHAGAS, I. C. B., additional, SILVA, J M F L, additional, GUIMARAES, J. M. X., additional, MELO, J. A. L., additional, FREIRE, K. A., additional, CARVALHO, L. A., additional, SILVA, L. G. M., additional, ALENCAR, L.C., additional, RODRIGUES, L. N., additional, GURGEL, L. A., additional, DANTAS, M. B., additional, FERRAZ, M. A. L., additional, CLEMENTINO, M. H. F., additional, VASCONCELOS, Mardenia Gomes Ferreira, additional, PASSAMAI, Maria da Penha Baião, additional, MACHADO, M. F. A. S., additional, GALENO, M. E. N., additional, SILVA, M. R. F. da, additional, ALMEIDA, Nádia Maria Girão Saraiva de, additional, VESCO, N. L., additional, OLIVEIRA, N. G., additional, FERNANDES, P. F. C. B. C., additional, BRAGA NETO, Pedro, additional, SANTOS, R. P., additional, FLORÊNCIO, R. S., additional, CAMELO, R. V., additional, SANTOS, R. S. S., additional, MENESES, R. B., additional, GONZALEZ, Ricardo Hugo, additional, FREITAS, R. J. M., additional, ALBUQUERQUE, R.A.S, additional, OLIVEIRA, R. S., additional, FIGUEIREDO, S. V., additional, MACHADO, S. P., additional, SILVA, STEFFANNY ROCHA DA, additional, REBOUÇAS, T. S., additional, SEVERO, V. R. G., additional, Barbosa Filho, Valter Cordeiro, additional, VIEIRA, V. M. P. F., additional, PESSOA, Vera Lucia Mendes de Paula, additional, and BRAGA, Violante Augusta B., additional

Published
2019
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37. Spatial distributions, environmental drivers and co-existence patterns of key cold-water corals in the deep sea of the Azores (NE Atlantic)

Author

European Commission, Ministerio de Economía, Industria y Competitividad (España), Royal Netherlands Institute for Sea Research, Taranto, G.H., González-Irusta, José Manuel, Dominguez-Carrió, C., Pham, C.K., Tempera, F., Ramos, M., Gonçalves, G., Carreiro-Silva, M., Morato, T., European Commission, Ministerio de Economía, Industria y Competitividad (España), Royal Netherlands Institute for Sea Research, Taranto, G.H., González-Irusta, José Manuel, Dominguez-Carrió, C., Pham, C.K., Tempera, F., Ramos, M., Gonçalves, G., Carreiro-Silva, M., and Morato, T.

Abstract

Habitat-forming cold-water corals (CWCs) represent a key component of deep-sea benthic communities and a priority target for conservation. Although research efforts have been mounting to try and identify the drivers of CWC distributions, progress has been limited by the scarcity of ecological data. The present work employs habitat suitability models (HSMs) to estimate spatial distributions, environmental drivers and co-existence patterns of 14 habitat-forming CWCs in the Azores, an area considered a hotspot of coral diversity in the Atlantic. The modelled CWCs showed a strong bathymetric zonation, which seems to be determined by the vertical stratification of water masses in the region. In particular, the modelled CWCs can be clustered in four groups named after the isopycnal (vertical) layers in which Atlantic water masses are organized: species restricted to upper water masses, species extending down from upper water masses, species restricted to intermediate water masses and species extending up from deep water masses. Horizontal patterns further indicate that the Azores Current and different production regimes north and south of the archipelago likely influence the distribution of CWCs in sub-surface waters. Such results have important implications for the regional management of deep-sea benthic communities and, in particular, for the design of representative networks of protected areas. The combined habitat of all modelled species covered only 11%. Given that they all possess the characteristics of benthic foundation organisms and represent indicator taxa of vulnerable marine ecosystems all the modelled species should be viewed as important targets for conservation. The lace coral Errina dabneyi deserves particular attention since this species appears to be endemic to the Azores and has a very limited estimated distribution.

Published
2023

46. Preparedness in a public health emergency: determinants of willingness and readiness to respond in the onset of the COVID-19 pandemic

Author

Leão, T., Duarte, G., and Gonçalves, G.

Subjects
Cross-Sectional Studies, Preparedness, Pandemic, SARS-CoV-2, Short Communication, Public Health, Environmental and Occupational Health, Health services research, COVID-19, Humans, Public Health, General Medicine, Pandemics, Health services management
Abstract

Objectives Healthcare professionals’ high risk of infection and burnout in the first months of the COVID-19 pandemic probably hindered their much-needed preparedness to respond. We aimed to inform how individual and institutional factors contributed for the preparedness to respond during the first months of a public health emergency. Study design Cross-sectional study. Methods We surveyed healthcare workers from a Local Health Unit in Portugal, which comprises primary health care centers and hospital services, including public health units and intensive care units, in the second and third months of the COVID-19 epidemic in Portugal. The 460 answers, completed by 252 participants (about 10% of the healthcare workers), were analyzed using descriptive statistics and multiple logistic regressions. We estimated adjusted odds ratios for the readiness and willingness to respond. Results Readiness to respond was associated with the perception of adequate infrastructures (aOR = 4.04, P

Published
2022

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