Investigation on the potential of Eulaliopsis binata (babiyo) as a sustainable fiber reinforcement for mortar and concrete

Autor: Kiran Thapa, Suraj Sedai, Jiwan Paudel, Tek Raj Gyawali

Publikováno v: Case Studies in Construction Materials, Vol 20, Iss , Pp e03115- (2024)

The production of synthetic materials emits harmful gases, driving researchers to seek eco-friendly alternatives, leading to increased demand for natural fiber-based composites in various industries. With advantages like cost-effectiveness, renewabil

Externí odkaz: https://doaj.org/article/0562104c33c34180b53e26a3927117ff

Eupatorium Adenophorum Spreng as a green additive in cementitious materials for sustainable infrastructure

Autor: Paudel, Jiwan, Thapa, Kiran, Sedai, Suraj, Gyawali, Tek Raj

Publikováno v: In Journal of Cleaner Production 5 July 2024 461

Uncertainty guided semi-supervised segmentation of retinal layers in OCT images

Autor: Sedai, Suman, Antony, Bhavna, Rai, Ravneet, Jones, Katie, Ishikawa, Hiroshi, Schuman, Joel, Gadi, Wollstein, Garnavi, Rahil

Publikováno v: MICCAI 2019 pp 282-290

Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In t

Externí odkaz: http://arxiv.org/abs/2103.02083

Well-being costs of unpaid care: Gendered evidence from a contextualized time-use survey in India

Autor: Sinha, Aashima, Kumar Sedai, Ashish, Bahadur Rahut, Dil, Sonobe, Tetsushi

Publikováno v: In World Development January 2024 173

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Autor: Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Eshaghi, Arman, Toni, Tina, Salaterski, Marcin, Lunina, Veronika, Ansart, Manon, Durrleman, Stanley, Lu, Pascal, Iddi, Samuel, Li, Dan, Thompson, Wesley K., Donohue, Michael C., Nahon, Aviv, Levy, Yarden, Halbersberg, Dan, Cohen, Mariya, Liao, Huiling, Li, Tengfei, Yu, Kaixian, Zhu, Hongtu, Tamez-Pena, Jose G., Ismail, Aya, Wood, Timothy, Bravo, Hector Corrada, Nguyen, Minh, Sun, Nanbo, Feng, Jiashi, Yeo, B. T. Thomas, Chen, Gang, Qi, Ke, Chen, Shiyang, Qiu, Deqiang, Buciuman, Ionut, Kelner, Alex, Pop, Raluca, Rimocea, Denisa, Ghazi, Mostafa M., Nielsen, Mads, Ourselin, Sebastien, Sorensen, Lauge, Venkatraghavan, Vikram, Liu, Keli, Rabe, Christina, Manser, Paul, Hill, Steven M., Howlett, James, Huang, Zhiyue, Kiddle, Steven, Mukherjee, Sach, Rouanet, Anais, Taschler, Bernd, Tom, Brian D. M., White, Simon R., Faux, Noel, Sedai, Suman, Oriol, Javier de Velasco, Clemente, Edgar E. V., Estrada, Karol, Aksman, Leon, Altmann, Andre, Stonnington, Cynthia M., Wang, Yalin, Wu, Jianfeng, Devadas, Vivek, Fourrier, Clementine, Raket, Lars Lau, Sotiras, Aristeidis, Erus, Guray, Doshi, Jimit, Davatzikos, Christos, Vogel, Jacob, Doyle, Andrew, Tam, Angela, Diaz-Papkovich, Alex, Jammeh, Emmanuel, Koval, Igor, Moore, Paul, Lyons, Terry J., Gallacher, John, Tohka, Jussi, Ciszek, Robert, Jedynak, Bruno, Pandya, Kruti, Bilgel, Murat, Engels, William, Cole, Joseph, Golland, Polina, Klein, Stefan, Alexander, Daniel C.

Publikováno v: Machine Learning for Biomedical Imaging (MELBA), Dec 2021

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk

Externí odkaz: http://arxiv.org/abs/2002.03419