Search

Your search keyword '"Palacios, Adriana"' showing total 178 results
Start Over Author "Palacios, Adriana"
178 results on '"Palacios, Adriana"'

1. An FPGA Smart Camera Implementation of Segmentation Models for Drone Wildfire Imagery

Author

Guarduño, Eduardo, Ciprian-Sanchez, Jorge Francisco, Vazquez-Garcia, Valente, Gonzalez-Mendoza, Miguel, Rodriguez-Hernandez, Gerardo, Palacios, Adriana, Rossi-Tisson, Lucile, Ochoa-Ruiz, Gilberto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Calvo, Hiram, editor, Martínez-Villaseñor, Lourdes, editor, and Ponce, Hiram, editor

Published
2024
Full Text
View/download PDF

2. Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach

Author

Pérez-Guerrero, Carmina, Ciprián-Sánchez, Jorge Francisco, Palacios, Adriana, Ochoa-Ruiz, Gilberto, Gonzalez-Mendoza, Miguel, Foroughi, Vahid, Pastor, Elsa, and Rodriguez-Hernandez, Gerardo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Among the different kinds of fire accidents that can occur during industrial activities that involve hazardous materials, jet fires are one of the lesser-known types. This is because they are often involved in a process that generates a sequence of other accidents of greater magnitude, known as domino effect. Flame impingement usually causes domino effects, and jet fires present specific features that can significantly increase the probability of this happening. These features become relevant from a risk analysis perspective, making their proper characterization a crucial task. Deep Learning approaches have become extensively used for tasks such as jet fire characterization; however, these methods are heavily dependent on the amount of data and the quality of the labels. Data acquisition of jet fires involve expensive experiments, especially so if infrared imagery is used. Therefore, this paper proposes the use of Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras. The obtained results are compared with some previous experiments, and it is shown that similar results were obtained., Comment: Pre-print submitted to Engineering Science and Technology, an International Journal

Published
2022

5. Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods

Author

Pérez-Guerrero, Carmina, Palacios, Adriana, Ochoa-Ruiz, Gilberto, Mata, Christian, Casal, Joaquim, Gonzalez-Mendoza, Miguel, and Falcón-Morales, Luis Eduardo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as the domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments. A comparison is made between traditional image processing methods and some state-of-the-art deep learning models. It is found that the best approach is a deep learning architecture known as UNet, along with its two improvements, Attention UNet and UNet++. The models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between Attention UNet and UNet++. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.

Published
2022

7. Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones

Author

Pérez-Guerrero, Carmina, Palacios, Adriana, Ochoa-Ruiz, Gilberto, Mata, Christian, Gonzalez-Mendoza, Miguel, and Falcón-Morales, Luis Eduardo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Risk assessment is relevant in any workplace, however there is a degree of unpredictability when dealing with flammable or hazardous materials so that detection of fire accidents by itself may not be enough. An example of this is the impingement of jet fires, where the heat fluxes of the flame could reach nearby equipment and dramatically increase the probability of a domino effect with catastrophic results. Because of this, the characterization of such fire accidents is important from a risk management point of view. One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches and given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are also explored. Additionally, different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert's criteria. The Hausdorff Distance and Adjusted Random Index were the metrics with the highest correlation and the best results were obtained from the UNet architecture with a Weighted Cross-Entropy Loss. These results can be used in future research to extract more geometric information from the segmentation masks or could even be implemented on other types of fire accidents.

Published
2021
Full Text
View/download PDF

11. Intravenous aviptadil and remdesivir for treatment of COVID-19-associated hypoxaemic respiratory failure in the USA (TESICO): a randomised, placebo-controlled trial

Author

Tierney, John, Vogel, Susan E., McNay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Segal Raim, Sharo, Shaw-Saliba, Katy, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Delph, Yvette, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Harrigan, Rachel, Carlsen, Amy, Carter, Anita, DuChene, Alain, Eckroth, Kate, Frase, Alex, Harrison, Merrie, Meger, Sue, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Thompson, Greg, Walski, Jamie, Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Bedoya, Gabriela, Gupta, Lopa, Overbey, Jessica R., Padillia, Maria L., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Smith, Peter K., Vekstein, Andrew M., Ko, Emily R., Al-Hegelan, Mashael S., McGowan, Lauren M., Motta, Mary, Howell, Shauna, Bent, Francine, Kalager, Rachel, Chan, Emmanuel, Aloor, Heather L., Griffin, S. Michelle, Covington, Anna, McLendon-Arvik, Beth, Bussadori, Barbara, Miller-Bell, Mary, Sampey, Cathy, Gaver, Vincent, Hollister, Beth A., Giangiacomo, Dana M., Pauley, Alena, Patel, Aashay, Classon, Chris, Frazier, Madison, Osborne, Robyn, Conlon, Debbi H., Joshi, Marybeth, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samantha, Wang, Samantha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacios, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Bender, William, Miller, Jeffrey, Nugent, Katherine, Farrington, Woodrow, Baio, Kim T., McBride, Mary K., Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Haley, Elizabeth, Rogers, Susan, Tyler, Derrick, Perin, Emerson, Costello, Briana, Postalian, Alexander, Sohail, Rizwan, Hinsu, Punit, Watson, Carolyn, Kappenman, Casey, Chen, James, Walker, Kim, Fink, Melyssa, Phillip, Gabrielle, Mahon, Kim, Sturgis, Lydia, Maher, Patrick, Rogers, Linda, Ng, Nicole, Marshall, Jason, Bassily-Marcus, Adel, Cohen, Ivy, Ramoo, Shamini, Malhotra, Aryan, Kessler, Jonathan, Goetz, Rebekah, Badhwar, Vinay, Hayanga, Jeremiah, Giblin Sutton, Lisa, Williams, Roger, Berry Bartolo, Elizabeth, Walker, Dmitry, Bunner, Robin, Glaze, Chad, Aucremanne, Tanja, Bishop, James, Kelley, Macey, Peterson, Autumn, Sauerborn, Erica, Reckart, Robin, Miller, Brittany, Mittel, Aaron, Darmanian, Anita, Rosen, Amanda, Madahar, Purnema, Schicchi, John, Gosek, Katarzyna, Dzierba, Amy, Wahab, Romina, Eng, Connie, Al-Saadi, Mukhtar, Zahiruddin, Faisal, Syed, Mohi, George, Michael, Patel, Varsha, Onwunyi, Chisom, Barroso da Costa, Rosa, North, Crystal, Ringwood, Nancy, Fitzgerald, Laura, Muzikansky, Ariela, Morse, Richard, Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Hou, Peter, Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, De Souza, Leslie, Talmor, Daniel, Shapiro, Nathan, Hibbert, Kathryn, Brait, Kelsey, Kone, Mamary, Hendey, Gregory, Kangelaris, Kirsten N., Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Timothy, Kelly, Zhou, Hanjing, Hughes, Alyssa, Garcia, Rebekah, Torres, Adrian, Hernandez-Almaraz, Maria Elena, Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan, Chang, Steven Y., Vargas, Julia, Moss, Marc, McKeehan, Jeffrey, Higgins, Carrie, Johnson, Emily, Slaughter, Suzanne, Wyles, David, Hiller, Terra, Oakes, Judy, Garcia, Ana, Gravitz, Stephanie, Lyle, Carolynn, Swanson, Diandra, Gong, Michelle Ng., Richardson, Lynnne D., Chen, Jen-Ting, Moskowitz, Ari, Mohamed, Amira, Lopez, Brenda, Amosu, Omowunmi, Tzehaie, Hiwet, Boujid, Sabah, Bixby, Billie, Lopez, Anitza A., Durley, JaVon, Gilson, Boris, Hite, R. Duncan, Wang, Henry, Wiedemann, Hebert P., Mehkri, Omar, Ashok, Kiran, King, Alexander, Brennan, Connery, Exline, Matthew C., Englert, Joshua A., Karow, Sarah, Schwartz, Elizabeth, So, Preston, So, Madison, Krol, Olivia F., Briceno Parra, Genesis I., Mills, Emmanuel Nii Lantei, Oh, Minn, Pena, Jose, Martínez, Jesús Alejandro, Jackman, Susan E., Bayoumi, Emad, Pascual, Ethan, Caudill, Antonina, Chen, Po-En, Richardson, Tabia, Clapham, Gregg J., Herrera, Lisa, Ojukwu, Cristabelle, Fine, Devin, Gomez, Millie J., Choi-Kuaea, Yunhee, Weissberg, Gwendolyn, Isip, Katherine, Mattison, Brittany, Tran, Dana, Emilov Dukov, Jennifer, Chung, Paul, Kang, Bo Ran, Escobar, Lauren, Tran, Trung, Baig, Saba, Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Gundel, Stephanie, Fuentes, Megan, Newton, Maranda, Peterson, Emily, Jiang, Kelsey, Files, D. Clark, Miller, Chadwick, Lematty, Caitlin, Rasberry, April, Warden, Ashley, Bledsoe, Joseph, Knowlton, Kirk, Knox, Daniel B., Klippel, Carolyn, Armbruster, Brent P., Applegate, Darrin, Imel, Karah, Fergus, Melissa, Rahmati, Kasra, Jensen, Hannah, Aston, Valerie T., Jeppson, Joshua, Marshall, J. Hunter, Lumpkin, Jenna, Smith, Cassie, Burke, Tyler, Gray, Andrew, Paine, Robert, Callahan, Sean, Yamane, Misty, Waddoups, Lindsey, Rice, Todd W., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Musick, Sarah, Miller, Karen, Semler, Matthew W., Popielski, Laura, Kambo, Amy, Viens, Kimberly, Turner, Melissa, Vjecha, Michael J., Denyer, Rachel, Khosla, Rahul, Rajendran, Bindu, Gonzales, Melissa, Moriarty, Theresa, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Pittman, David, Vasudeva, Shikha S., Anholm, James D., Specht, Lennard, Rodriguez, Aimee, Ngo, Han, Duong, Lien, Previte, Matthew, Raben, Dorthe, Nielsen, Charlotte B., Friis Larsen, Jakob, Peters, Lars, Matthews, Gail, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina, Dharan, Nila, Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Abdelghany, Mazin, Baseler, Beth, Teitelbaum, Marc, Holley, H. Preston, Jankelevich, Shirley, Adams, Amy, Becker, Nancy, Doleny, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Vanderpuye, Sharon, Yeon, Lindsey, Frye, Leanna, Rudzinski, Erin, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonalad, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, CK, Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Sandrus, Jen, Manchard, Jon, Giri, Jiwan, Kopka, Stacy, Chang, Weizhong, Sherman, Brad T., Rupert, Adam W., Highbarger, Helene, Baseler, Michael, Lallemand, Perrine, Rehman, Tauseef, Imamichi, Tom, Laverdure, Sylvain, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Hazen, Allison, Badralmaa, Yunden, Highbarger, Jeroen, McCormack, Ashley, Gerry, Norman P., Smith, Kenneth, Patel, Bhakti, Domeraski, Nadia, Hoover, Marie L., DuChateau, Nadine, Flosi, Adam, Nelson, Rich, Stojanovic, Jelena, Wenner, Christine, Brown, Samuel M, Barkauskas, Christina E, Grund, Birgit, Sharma, Shweta, Phillips, Andrew N, Leither, Lindsay, Peltan, Ithan D, Lanspa, Michael, Gilstrap, Daniel L, Mourad, Ahmad, Lane, Kathleen, Beitler, Jeremy R, Serra, Alexis L, Garcia, Ivan, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley, Harris, Estelle S, Middleton, Elizabeth A, Barrios, Macy A G, Mathews, Kusum S, Goel, Neha N, Acquah, Samuel, Mosier, Jarrod, Hypes, Cameron, Salvagio Campbell, Elizabeth, Khan, Akram, Hough, Catherine L, Wilson, Jennifer G, Levitt, Joseph E, Duggal, Abhijit, Dugar, Siddharth, Goodwin, Andrew J, Terry, Charles, Chen, Peter, Torbati, Sam, Iyer, Nithya, Sandkovsky, Uriel S, Johnson, Nicholas J, Robinson, Bryce R H, Matthay, Michael A, Aggarwal, Neil R, Douglas, Ivor S, Casey, Jonathan D, Hache-Marliere, Manuel, Georges Youssef, J, Nkemdirim, William, Leshnower, Brad, Awan, Omar, Pannu, Sonal, O'Mahony, Darragh Shane, Manian, Prasad, Awori Hayanga, J W, Wortmann, Glenn W, Tomazini, Bruno M, Miller, Robert F, Jensen, Jens-Ulrik, Murray, Daniel D, Bickell, Nina A, Zatakia, Jigna, Burris, Sarah, Higgs, Elizabeth S, Natarajan, Ven, Dewar, Robin L, Schechner, Adam, Kang, Nayon, Arenas-Pinto, Alejandro, Hudson, Fleur, Ginde, Adit A, Self, Wesley H, Rogers, Angela J, Oldmixon, Cathryn F, Morin, Haley, Sanchez, Adriana, Weintrob, Amy C, Cavalcanti, Alexandre Biasi, Davis-Karim, Anne, Engen, Nicole, Denning, Eileen, Taylor Thompson, B, Gelijns, Annetine C, Kan, Virginia, Davey, Victoria J, Lundgren, Jens D, Babiker, Abdel G, Neaton, James D, and Lane, H Clifford

Published
2023
Full Text
View/download PDF

26. Historical analysis of accidents in the Saudi Arabian chemical industry.

Author

Palacios, Adriana, Palacios‐Rosas, Erika, and Abdul‐Aziz‐Al‐Mughanam, Tawfiq

Subjects
CHEMICAL industry, HISTORICAL analysis, EXPLOSIONS, CHEMICAL industry accidents, PETROLEUM chemicals industry, HAZARDOUS substances
Abstract

The chemical industry oversees the transformation of raw materials into products through unit operations that require appropriate organization to avoid accidents. Hence, it is important to do analyses to identify any possible mistakes, substances involved, or common sources of accidents in the industry to avoid such errors and design better safety measures to create a safer space for the chemical industry, which is hugely important and boasts a worldwide presence. This document presents an analysis of chemical industry‐related accidents in Saudi Arabia, namely fires, explosions, and toxic clouds which occurred in the chemical and petrochemical industries and while transporting hazardous materials in the last 46 years. Three databases—one for each type of accident—were created with information collected from articles, newspapers, videos, and papers. 'Explosion', 'fire', and 'toxic clouds' were the key words used for the research, focusing on accident taking place in Saudi Arabia. Once the information had been collected, the accidents were filtered, checked, and moved to a fourth general database. It is shown that 54.0% of all related accidents were fires, 25.4% toxic clouds, and 20.6% were explosions. The provinces with the most registered accidents were Riyadh (15), Jeddah (10), and Jubail (6). [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

27. Historical Analysis of the Types of Accidents in the North American Region.

Author

Palacios, Adriana, Palacios-Rosas, Erika, Alejandro Hernandez-Canepa, Victor, and Abdul-Aziz-Al-Mughanam, Tawfiq

Abstract

Accidents can occur for several reasons in the chemical and petrochemical industries, whether during the transportation of hazardous materials or at industrial facilities. Frequently, the reasons for these accidents are physical effects that can damage the equipment. If equipment is damaged and a fire occurs, it can provoke a domino effect, increasing the severity of the primary event, as mentioned by Mercedes and colleagues (Gómez-Mares et al., 2008), where databases focused on European countries, such as the Major Hazard Incident Data Service (MHIDAS), are used to analyse the types of accidents that occurred due not only to fires, explosions, and gas emissions, but also different equipment malfunctions or the transportation of hazardous materials, with the total number of accidents being recorded. In the present work, an in-depth analysis of the different types of accidents that occurred in the chemical and petrochemical industries between the 90s and recent years in the North American region (i.e., the United States, Mexico, and Canada), has been carried out. The database used for the present research work was the International Disaster Database (EM-DAT Query Tool, 2020). On this platform, different types of accidents, such as those caused naturally, technologically, and by more complex disasters, can be investigated. In the present research article, a total of 340 accidents which occurred over the last 3 decades in the United States, Mexico, and Canada, have been analysed. The total number of accidents per decade for each country was collected and their types, causes, consequences, and number of deaths were analysed. Finally, safety measures have also been suggested through the comparisons obtained for the three countries. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

32. Intravenous aviptadil and remdesivir for treatment of COVID-19-associated hypoxaemic respiratory failure in the USA (TESICO): a randomised, placebo-controlled trial

Author

Brown, Samuel M, primary, Barkauskas, Christina E, additional, Grund, Birgit, additional, Sharma, Shweta, additional, Phillips, Andrew N, additional, Leither, Lindsay, additional, Peltan, Ithan D, additional, Lanspa, Michael, additional, Gilstrap, Daniel L, additional, Mourad, Ahmad, additional, Lane, Kathleen, additional, Beitler, Jeremy R, additional, Serra, Alexis L, additional, Garcia, Ivan, additional, Almasri, Eyad, additional, Fayed, Mohamed, additional, Hubel, Kinsley, additional, Harris, Estelle S, additional, Middleton, Elizabeth A, additional, Barrios, Macy A G, additional, Mathews, Kusum S, additional, Goel, Neha N, additional, Acquah, Samuel, additional, Mosier, Jarrod, additional, Hypes, Cameron, additional, Salvagio Campbell, Elizabeth, additional, Khan, Akram, additional, Hough, Catherine L, additional, Wilson, Jennifer G, additional, Levitt, Joseph E, additional, Duggal, Abhijit, additional, Dugar, Siddharth, additional, Goodwin, Andrew J, additional, Terry, Charles, additional, Chen, Peter, additional, Torbati, Sam, additional, Iyer, Nithya, additional, Sandkovsky, Uriel S, additional, Johnson, Nicholas J, additional, Robinson, Bryce R H, additional, Matthay, Michael A, additional, Aggarwal, Neil R, additional, Douglas, Ivor S, additional, Casey, Jonathan D, additional, Hache-Marliere, Manuel, additional, Georges Youssef, J, additional, Nkemdirim, William, additional, Leshnower, Brad, additional, Awan, Omar, additional, Pannu, Sonal, additional, O'Mahony, Darragh Shane, additional, Manian, Prasad, additional, Awori Hayanga, J W, additional, Wortmann, Glenn W, additional, Tomazini, Bruno M, additional, Miller, Robert F, additional, Jensen, Jens-Ulrik, additional, Murray, Daniel D, additional, Bickell, Nina A, additional, Zatakia, Jigna, additional, Burris, Sarah, additional, Higgs, Elizabeth S, additional, Natarajan, Ven, additional, Dewar, Robin L, additional, Schechner, Adam, additional, Kang, Nayon, additional, Arenas-Pinto, Alejandro, additional, Hudson, Fleur, additional, Ginde, Adit A, additional, Self, Wesley H, additional, Rogers, Angela J, additional, Oldmixon, Cathryn F, additional, Morin, Haley, additional, Sanchez, Adriana, additional, Weintrob, Amy C, additional, Cavalcanti, Alexandre Biasi, additional, Davis-Karim, Anne, additional, Engen, Nicole, additional, Denning, Eileen, additional, Taylor Thompson, B, additional, Gelijns, Annetine C, additional, Kan, Virginia, additional, Davey, Victoria J, additional, Lundgren, Jens D, additional, Babiker, Abdel G, additional, Neaton, James D, additional, Lane, H Clifford, additional, Tierney, John, additional, Vogel, Susan E., additional, McNay, Laura A., additional, Cahill, Kelly, additional, Crew, Page, additional, Sardana, Ratna, additional, Segal Raim, Sharo, additional, Shaw-Saliba, Katy, additional, Atri, Negin, additional, Miller, Mark, additional, Vallee, David, additional, Chung, Lucy, additional, Delph, Yvette, additional, Adam, Stacey J., additional, Read, Sarah, additional, Draghia-Akli, Ruxandra, additional, Harrigan, Rachel, additional, Carlsen, Amy, additional, Carter, Anita, additional, DuChene, Alain, additional, Eckroth, Kate, additional, Frase, Alex, additional, Harrison, Merrie, additional, Meger, Sue, additional, Quan, Kien, additional, Quan, Siu Fun, additional, Reilly, Cavan, additional, Thompson, Greg, additional, Walski, Jamie, additional, Moskowitz, Alan J., additional, Bagiella, Emilia, additional, Moquete, Ellen, additional, O'Sullivan, Karen, additional, Marks, Mary E., additional, Accardi, Evan, additional, Kinzel, Emily, additional, Bedoya, Gabriela, additional, Gupta, Lopa, additional, Overbey, Jessica R., additional, Padillia, Maria L., additional, Santos, Milerva, additional, Gillinov, Marc A., additional, Miller, Marissa A., additional, Taddei-Peters, Wendy C., additional, Fenton, Kathleen, additional, Smith, Peter K., additional, Vekstein, Andrew M., additional, Ko, Emily R., additional, Al-Hegelan, Mashael S., additional, McGowan, Lauren M., additional, Motta, Mary, additional, Howell, Shauna, additional, Bent, Francine, additional, Kalager, Rachel, additional, Chan, Emmanuel, additional, Aloor, Heather L., additional, Griffin, S. Michelle, additional, Covington, Anna, additional, McLendon-Arvik, Beth, additional, Bussadori, Barbara, additional, Miller-Bell, Mary, additional, Sampey, Cathy, additional, Gaver, Vincent, additional, Hollister, Beth A., additional, Giangiacomo, Dana M., additional, Pauley, Alena, additional, Patel, Aashay, additional, Classon, Chris, additional, Frazier, Madison, additional, Osborne, Robyn, additional, Conlon, Debbi H., additional, Joshi, Marybeth, additional, Gottlieb, Robert L., additional, Mack, Michael, additional, Berhe, Mezgebe, additional, Haley, Clinton, additional, Dishner, Emma, additional, Bettacchi, Christopher, additional, Golden, Kevin, additional, Duhaime, Erin, additional, Ryan, Madison, additional, Tallmadge, Catherine, additional, Estrada, Lorie, additional, Jones, Felecia, additional, Villa, Samantha, additional, Wang, Samantha, additional, Robert, Raven, additional, Coleman, Tanquinisha, additional, Clariday, Laura, additional, Baker, Rebecca, additional, Hurutado-Rodriguez, Mariana, additional, Iram, Nazia, additional, Fresnedo, Michelle, additional, Davis, Allyson, additional, Leonard, Kiara, additional, Ramierez, Noelia, additional, Thammavong, Jon, additional, Duque, Krizia, additional, Turner, Emma, additional, Fisher, Tammy, additional, Robinson, Dianna, additional, Ransom, Desirae, additional, Maldonado, Nicholas, additional, Lusk, Erica, additional, Killian, Aaron, additional, Palacios, Adriana, additional, Solis, Edilia, additional, Jerrow, Janet, additional, Watts, Matthew, additional, Whitacre, Heather, additional, Cothran, Elizabeth, additional, Bender, William, additional, Miller, Jeffrey, additional, Nugent, Katherine, additional, Farrington, Woodrow, additional, Baio, Kim T., additional, McBride, Mary K., additional, Fielding, Michele, additional, Mathewson, Sonya, additional, Porte, Kristina, additional, Haley, Elizabeth, additional, Rogers, Susan, additional, Tyler, Derrick, additional, Perin, Emerson, additional, Costello, Briana, additional, Postalian, Alexander, additional, Sohail, Rizwan, additional, Hinsu, Punit, additional, Watson, Carolyn, additional, Kappenman, Casey, additional, Chen, James, additional, Walker, Kim, additional, Fink, Melyssa, additional, Phillip, Gabrielle, additional, Mahon, Kim, additional, Sturgis, Lydia, additional, Maher, Patrick, additional, Rogers, Linda, additional, Ng, Nicole, additional, Marshall, Jason, additional, Bassily-Marcus, Adel, additional, Cohen, Ivy, additional, Ramoo, Shamini, additional, Malhotra, Aryan, additional, Kessler, Jonathan, additional, Goetz, Rebekah, additional, Badhwar, Vinay, additional, Hayanga, Jeremiah, additional, Giblin Sutton, Lisa, additional, Williams, Roger, additional, Berry Bartolo, Elizabeth, additional, Walker, Dmitry, additional, Bunner, Robin, additional, Glaze, Chad, additional, Aucremanne, Tanja, additional, Bishop, James, additional, Kelley, Macey, additional, Peterson, Autumn, additional, Sauerborn, Erica, additional, Reckart, Robin, additional, Miller, Brittany, additional, Mittel, Aaron, additional, Darmanian, Anita, additional, Rosen, Amanda, additional, Madahar, Purnema, additional, Schicchi, John, additional, Gosek, Katarzyna, additional, Dzierba, Amy, additional, Wahab, Romina, additional, Eng, Connie, additional, Al-Saadi, Mukhtar, additional, Zahiruddin, Faisal, additional, Syed, Mohi, additional, George, Michael, additional, Patel, Varsha, additional, Onwunyi, Chisom, additional, Barroso da Costa, Rosa, additional, North, Crystal, additional, Ringwood, Nancy, additional, Fitzgerald, Laura, additional, Muzikansky, Ariela, additional, Morse, Richard, additional, Brower, Roy G., additional, Reineck, Lora A., additional, Bienstock, Karen, additional, Hou, Peter, additional, Steingrub, Jay S., additional, Tidswell, Mark A., additional, Kozikowski, Lori-Ann, additional, Kardos, Cynthia, additional, De Souza, Leslie, additional, Talmor, Daniel, additional, Shapiro, Nathan, additional, Hibbert, Kathryn, additional, Brait, Kelsey, additional, Kone, Mamary, additional, Hendey, Gregory, additional, Kangelaris, Kirsten N., additional, Ashktorab, Kimia, additional, Gropper, Rachel, additional, Agrawal, Anika, additional, Timothy, Kelly, additional, Zhou, Hanjing, additional, Hughes, Alyssa, additional, Garcia, Rebekah, additional, Torres, Adrian, additional, Hernandez-Almaraz, Maria Elena, additional, Vojnik, Rosemary, additional, Perez, Cynthia, additional, McDowell, Jordan, additional, Chang, Steven Y., additional, Vargas, Julia, additional, Moss, Marc, additional, McKeehan, Jeffrey, additional, Higgins, Carrie, additional, Johnson, Emily, additional, Slaughter, Suzanne, additional, Wyles, David, additional, Hiller, Terra, additional, Oakes, Judy, additional, Garcia, Ana, additional, Gravitz, Stephanie, additional, Lyle, Carolynn, additional, Swanson, Diandra, additional, Gong, Michelle Ng., additional, Richardson, Lynnne D., additional, Chen, Jen-Ting, additional, Moskowitz, Ari, additional, Mohamed, Amira, additional, Lopez, Brenda, additional, Amosu, Omowunmi, additional, Tzehaie, Hiwet, additional, Boujid, Sabah, additional, Bixby, Billie, additional, Lopez, Anitza A., additional, Durley, JaVon, additional, Gilson, Boris, additional, Hite, R. Duncan, additional, Wang, Henry, additional, Wiedemann, Hebert P., additional, Mehkri, Omar, additional, Ashok, Kiran, additional, King, Alexander, additional, Brennan, Connery, additional, Exline, Matthew C., additional, Englert, Joshua A., additional, Karow, Sarah, additional, Schwartz, Elizabeth, additional, So, Preston, additional, So, Madison, additional, Krol, Olivia F., additional, Briceno Parra, Genesis I., additional, Mills, Emmanuel Nii Lantei, additional, Oh, Minn, additional, Pena, Jose, additional, Martínez, Jesús Alejandro, additional, Jackman, Susan E., additional, Bayoumi, Emad, additional, Pascual, Ethan, additional, Caudill, Antonina, additional, Chen, Po-En, additional, Richardson, Tabia, additional, Clapham, Gregg J., additional, Herrera, Lisa, additional, Ojukwu, Cristabelle, additional, Fine, Devin, additional, Gomez, Millie J., additional, Choi-Kuaea, Yunhee, additional, Weissberg, Gwendolyn, additional, Isip, Katherine, additional, Mattison, Brittany, additional, Tran, Dana, additional, Emilov Dukov, Jennifer, additional, Chung, Paul, additional, Kang, Bo Ran, additional, Escobar, Lauren, additional, Tran, Trung, additional, Baig, Saba, additional, Wallick, Julie A., additional, Duven, Alexandria M., additional, Fletcher, Dakota D., additional, Gundel, Stephanie, additional, Fuentes, Megan, additional, Newton, Maranda, additional, Peterson, Emily, additional, Jiang, Kelsey, additional, Files, D. Clark, additional, Miller, Chadwick, additional, Lematty, Caitlin, additional, Rasberry, April, additional, Warden, Ashley, additional, Bledsoe, Joseph, additional, Knowlton, Kirk, additional, Knox, Daniel B., additional, Klippel, Carolyn, additional, Armbruster, Brent P., additional, Applegate, Darrin, additional, Imel, Karah, additional, Fergus, Melissa, additional, Rahmati, Kasra, additional, Jensen, Hannah, additional, Aston, Valerie T., additional, Jeppson, Joshua, additional, Marshall, J. Hunter, additional, Lumpkin, Jenna, additional, Smith, Cassie, additional, Burke, Tyler, additional, Gray, Andrew, additional, Paine, Robert, additional, Callahan, Sean, additional, Yamane, Misty, additional, Waddoups, Lindsey, additional, Rice, Todd W., additional, Johnson, Jakea, additional, Gray, Christopher, additional, Hays, Margaret, additional, Roth, Megan, additional, Musick, Sarah, additional, Miller, Karen, additional, Semler, Matthew W., additional, Popielski, Laura, additional, Kambo, Amy, additional, Viens, Kimberly, additional, Turner, Melissa, additional, Vjecha, Michael J., additional, Denyer, Rachel, additional, Khosla, Rahul, additional, Rajendran, Bindu, additional, Gonzales, Melissa, additional, Moriarty, Theresa, additional, Biswas, Kousick, additional, Harrington, Cristin, additional, Garcia, Amanda, additional, Bremer, Tammy, additional, Burke, Tara, additional, Koker, Brittany, additional, Pittman, David, additional, Vasudeva, Shikha S., additional, Anholm, James D., additional, Specht, Lennard, additional, Rodriguez, Aimee, additional, Ngo, Han, additional, Duong, Lien, additional, Previte, Matthew, additional, Raben, Dorthe, additional, Nielsen, Charlotte B., additional, Friis Larsen, Jakob, additional, Peters, Lars, additional, Matthews, Gail, additional, Kelleher, Anthony, additional, Polizzotto, Mark, additional, Carey, Catherine, additional, Chang, Christina, additional, Dharan, Nila, additional, Hough, Sally, additional, Virachit, Sophie, additional, Davidson, Sarah, additional, Bice, Daniel J., additional, Ognenovska, Katherine, additional, Cabrera, Gesalit, additional, Flynn, Ruth, additional, Abdelghany, Mazin, additional, Baseler, Beth, additional, Teitelbaum, Marc, additional, Holley, H. Preston, additional, Jankelevich, Shirley, additional, Adams, Amy, additional, Becker, Nancy, additional, Doleny, Suzanne, additional, Hissey, Debbie, additional, Simpson, Shelly, additional, Kim, Mi Ha, additional, Beeler, Joy, additional, Harmon, Liam, additional, Vanderpuye, Sharon, additional, Yeon, Lindsey, additional, Frye, Leanna, additional, Rudzinski, Erin, additional, Buehn, Molly, additional, Eccard-Koons, Vanessa, additional, Frary, Sadie, additional, MacDonalad, Leah, additional, Cash, Jennifer, additional, Hoopengardner, Lisa, additional, Linton, Jessica, additional, Nelson, Michaela, additional, Spinelli-Nadzam, Mary, additional, Proffitt, Calvin, additional, Lee, Christopher, additional, Engel, Theresa, additional, Fontaine, Laura, additional, Osborne, CK, additional, Hohn, Matt, additional, Galcik, Michael, additional, Thompson, DeeDee, additional, Sandrus, Jen, additional, Manchard, Jon, additional, Giri, Jiwan, additional, Kopka, Stacy, additional, Chang, Weizhong, additional, Sherman, Brad T., additional, Rupert, Adam W., additional, Highbarger, Helene, additional, Baseler, Michael, additional, Lallemand, Perrine, additional, Rehman, Tauseef, additional, Imamichi, Tom, additional, Laverdure, Sylvain, additional, Paudel, Sharada, additional, Cook, Kyndal, additional, Haupt, Kendra, additional, Hazen, Allison, additional, Badralmaa, Yunden, additional, Highbarger, Jeroen, additional, McCormack, Ashley, additional, Gerry, Norman P., additional, Smith, Kenneth, additional, Patel, Bhakti, additional, Domeraski, Nadia, additional, Hoover, Marie L., additional, DuChateau, Nadine, additional, Flosi, Adam, additional, Nelson, Rich, additional, Stojanovic, Jelena, additional, and Wenner, Christine, additional

Published
2023
Full Text
View/download PDF

37. Incorporación de chía (Salvia hispánica) en la dieta tradicional del cuy (Cavia porcellus) y su influencia sobre los parámetros productivos y características organolépticas de la carne

Author

Rosales Jaramillo, Cornelio Alejandro, Castro Labanda, Josselyn Priscila, Matute Palacios, Adriana Tamara, Rosales Jaramillo, Cornelio Alejandro, Castro Labanda, Josselyn Priscila, and Matute Palacios, Adriana Tamara

Abstract

Guinea pig meat has several characteristics that are beneficial to human health: low in fat (less than 15%), high in protein (higher than 14%), source of vitamin B12, low in cholesterol and sodium. Currently, functional foods are used, one of them is chia known for its high content of antioxidants, in addition to providing high levels of dietary fiber. The research was conducted at the Irquis farm, belonging to the University of Cuenca, where the incorporation of chia seed to the superfood commonly used by producers in the area was evaluated (T1 control Alfalfa + supplement; T2 alfalfa + supplement + chia 10%; T3 alfalfa + supplement + chia 15%; T4 alfalfa + supplement + chia 20%), using 80 mongrel guinea pigs in fattening age in which productive parameters and organoleptic characteristics of the meat were determined. The addition of 15% chia seed (T2) presented important differences, reaching an accumulated and carcass weight of 1298.6g ± 31.50 and 864.6g ± 21.35 respectively, and a feed conversion of 4.40. The amount of intra-abdominal fat presented an inverse relationship, the greater the addition of chia seed the lower the amount of fat; also presenting a change in the organoleptic characteristics of the meat. It is considered that the addition of 15% of chia seed to the overfeeding of fattening guinea pigs is convenient to reach adequate weights for the market in the expected time with an important economic benefit.

Published
2023

39. Machine learning modelling and evaluation of jet fires from natural gas processing, storage, and transport.

Author

Mashhadimoslem, Hossein, Ghaemi, Ahad, Palacios, Adriana, Almansoori, Ali, and Elkamel, Ali

Subjects
MACHINE learning, NATURAL gas, TUNNEL ventilation, FEEDFORWARD neural networks, STATISTICS, REGRESSION analysis, HEAT flux
Abstract

Jet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi‐layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

42. Influence of wind speed and ammonia concentration on its evaporation rate from aqueous solution spills

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria Química, Corruchaga Guerrero, Agustín, Casal Valls, Oriol, Palacios, Adriana, Casal Fàbrega, Joaquim, Universitat Politècnica de Catalunya. Departament d'Enginyeria Química, Corruchaga Guerrero, Agustín, Casal Valls, Oriol, Palacios, Adriana, and Casal Fàbrega, Joaquim

Abstract

In the event of an accidental spill of an aqueous solution of ammonia, a toxic cloud can be generated, being the ammonia evaporation rate a key issue. In this communication, new experimental data have been obtained at different initial ammonia concentrations and air speeds. The evolution of the evaporation rate, of the ammonia concentration in the solution and of the solution temperature have been studied. Three phases have been clearly identified in the evaporation process, affected by the ammonia concentration, the evaporation rate and the condensation of water. A simple model has also been proposed, based on previous ones from the literature and now including the condensation of water. The proposed model has been compared with experimental data. Although some scattering has been found, it still proves itself useful at predicting the source term in the event of an accidental spill., Peer Reviewed, Postprint (published version)

Published
2022

43. Experimental large-scale jet flames’ geometrical features extraction for risk management using infrared images and deep learning segmentation methods

Author

Universitat Politècnica de Catalunya. Doctorat en Enginyeria de Processos Químics, Universitat Politècnica de Catalunya. Departament d'Enginyeria Química, Universitat Politècnica de Catalunya. CERTEC - Centre d'Estudis del Risc Tecnològic, Pérez Guerrero, Carmina, Palacios, Adriana, Ochoa Ruíz, Gilberto, Foroughi, Vahid, Pastor Ferrer, Elsa, González Mendoza, Miguel, Falcón Morales, Luis Eduardo, Universitat Politècnica de Catalunya. Doctorat en Enginyeria de Processos Químics, Universitat Politècnica de Catalunya. Departament d'Enginyeria Química, Universitat Politècnica de Catalunya. CERTEC - Centre d'Estudis del Risc Tecnològic, Pérez Guerrero, Carmina, Palacios, Adriana, Ochoa Ruíz, Gilberto, Foroughi, Vahid, Pastor Ferrer, Elsa, González Mendoza, Miguel, and Falcón Morales, Luis Eduardo

Abstract

Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as the domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract the flame’s main geometrical attributes, relevant for fire risk assessments. A comparison is made between traditional image processing methods and some state-of-the-art deep learning models. It is found that the best approach is a deep learning architecture known as UNet, along with its two improvements, Attention UNet and UNet++. The models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between Attention UNet and UNet++. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios., Peer Reviewed, Postprint (published version)

Published
2022

49. Level of knowledge about diagnostic image quality in the fifth year students of the Radiology career of the Peruvian University Cayetano Heredia, from January to February 2021

Author

Carmona Alvarez, Oscar Francisco, Ríos Paredes, Ingrid Margaret, Zapata Palacios, Adriana Sofía, and Ramírez Toscano, Erika Giovana

Subjects
Criterios Anatómicos, Artefactos, Calidad de Imagen, Parámetros Físicos, purl.org/pe-repo/ocde/ford#3.02.12 [http]
Abstract

ANTECEDENTES: Una imagen de calidad diagnóstica es un conjunto de criterios necesarios para ayudar a identificar signos normales y/o patológicos en una imagen, permitiendo un diagnóstico oportuno y eficaz en la evaluación del paciente. El Tecnólogo Médico especializado en Radiología (TMR) es el encargado en la gestión y producción de una imagen de calidad diagnóstica, siendo esta el producto final en la ocupación de un TMR. Sin embargo, algunos TMR recientemente egresados demostraron que no tenían un conocimiento básico al producir estas imágenes, alterando directamente la calidad de imagen y el diagnóstico del paciente. OBJETIVO: Describir el nivel de conocimiento sobre calidad de imagen diagnóstica en los estudiantes de quinto año de la carrera de radiología de la Universidad Peruana Cayetano Heredia en el periodo de enero a febrero del 2021. RESULTADOS: Se evidenció que el nivel de conocimiento sobre los 38 estudiantes fue de un 44.74% lo que representó un nivel bajo de conocimiento sobre calidad de imagen diagnóstica, mientras que el 47.37% obtuvo un nivel medio y el 7.89% obtuvo un nivel alto. CONCLUSIONES: El nivel de conocimiento que describe la calidad de imagen diagnóstica fue medio y bajo con un 47.37% y 44.74% respectivamente. La nota máxima calificada en todo el cuestionario fue de 15. BACKGROUND: An image of diagnostic quality is a set of criteria necessary to help identify normal and / or pathological signs in an image, allowing a timely and effective diagnosis in the evaluation of the patient.The Medical Technologist specialized in Radiology (TMR) is in charge of the management and production of an image of diagnostic quality, this being the final product in the occupation of a TMR. However, some recently graduated TMRs demonstrated that they did not have a basic knowledge when producing these images, directly altering the image quality and the diagnosis of the patient. OBJECTIVE: Describe the level of knowledge about diagnostic image quality in fifth-year students of the radiology career at the Universidad Peruana Cayetano Heredia in the period from January to February 2021. MATERIAL AND METHOD: Observational, cross-sectional study, descriptive design with a quantitative approach. RESULTS: It was evidenced that the level of knowledge about the 38 students was 44.74%, which represented a low level of knowledge about diagnostic image quality, while 47.37% obtained a medium level and 7.89% obtained a high level. CONCLUSIONS: The level of knowledge that describes the quality of the diagnostic image was medium and low with 47.37% and 44.74% respectively. The maximum score scored in the entire questionnaire was 15.

Published
2021

50. Abriendo la puerta a la salud bucal V : Fortalecimiento de la salud comunitaria y del sistema público de salud

Author

Capraro, María Cecilia, Tomas, Leandro Juan, Sparacino, Sandra Elizabeth, Ricciardi, Nicolás, Arce, Débora Magalí, Dappello, María Victoria, Vijandi, Valeria Raquel, Castelli, Patricia Ester, Tomas, Paula Mariela, Mattanó, Claudia Aurora, Schuler, Mónica Patricia, González, Stella Maris, Capraro, María Eugenia, Blotto, Bettina Graciela, Echagüe, Patricia Elizabeth, Palacios, Adriana, Bettiol, María Laura, Capraro, Carlos Gabriel, Domínguez, Gabriel Ernesto, and Barriga Stapich, Emiliano

Subjects
Odontología, Salud comunitaria, Caries Dental, Selladores de Fosas y Fisuras
Abstract

Este proyecto tiene como objetivo revertir una problemática creciente: el aumento de lesiones cariosas en molares de niños de 3 a 11 años. Las actividades planificadas se enmarcan dentro del campo de la Educación para la salud, y apuntan a la concientización de la situación mencionada desde aspectos socioculturales y odontológicos considerando las edades de los destinatarios, como por ejemplo la relevancia del flúor como protección a la caries dental y la aplicación de selladores de fosas y fisuras. Los mismos tienen por objeto impedir la colonización bacteriana, evitando la difusión de los sustratos fermentables que pueden ser metabolizados por las bacterias. Debido a la alta prevalencia de lesiones oclusales y a que el flúor protege, fundamentalmente, las superficies lisas, los selladores son doblemente importantes., Facultad de Odontología

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results