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11. A Clinical Teaching Blended Learning Program to Enhance Registered Nurse Preceptors’ Teaching Competencies: Pretest and Posttest Study

Author

Wu, Xi Vivien, Chi, Yuchen, Panneer Selvam, Umadevi, Devi, M Kamala, Wang, Wenru, Chan, Yah Shih, Wee, Fong Chi, Zhao, Shengdong, Sehgal, Vibhor, and Ang, Neo Kim Emily

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundClinical nursing education provides opportunities for students to learn in multiple patient care settings, receive appropriate guidance, and foster the development of clinical competence and professionalism. Nurse preceptors guide students to integrate theory into practice, teach clinical skills, assess clinical competencies, and enhance problem-solving and critical thinking skills. Previous research has indicated that the teaching competencies of nurse preceptors can be transferred to students’ clinical learning to enhance their clinical competencies. ObjectiveThis study aimed to develop a clinical teaching blended learning (CTBL) program with the aid of web-based clinical pedagogy (WCP) and case-based learning for nurse preceptors and to examine the effectiveness of the CTBL program on nurse preceptors’ clinical teaching competencies, self-efficacies, attitudes toward web-based learning, and blended learning outcomes. MethodsA quasi-experimental single-group pretest and posttest design was adopted. A total of 150 nurse preceptors participated in the CTBL program, which was conducted from September 2019 to December 2019. A set of questionnaires, including the clinical teaching competence inventory, preceptor self-efficacy questionnaire, attitudes toward web-based continuing learning survey, and e-learning experience questionnaire, was used to assess the outcomes before and after the CTBL program. ResultsCompared with the baseline, the participants had significantly higher total mean scores and subdomain scores for clinical teaching competence (mean 129.95, SD 16.38; P

Published
2020
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18. Multiple shape factor effects of nanofluids on marangoni mixed convection flow through porous medium

Author

Revathi Devi M, Narsu Sivakumar, Samad Noeiaghdam, and Unai Fernandez-Gamiz

Subjects
Nanofluid, Viscous dissipation, Mixed convection, MHD, Irreversibility, Technology
Abstract

This study explores the effects of multiple shape factors on nanofluids within Marangoni mixed convection flows through porous media. Nanofluids, containing nanoparticles in a base fluid, show unique thermal properties, making them valuable in cooling systems, energy storage, and medical devices. Marangoni mixed convection, driven by surface tension gradients, adds complexity to fluid dynamics and heat transfer in porous media. Analytical and numerical analyses examine how various particle geometries, nanoparticle volume fractions, and porous medium configurations influence transport phenomena. Entropy analysis calculates thermodynamic irreversibilities. Findings reveal relationships between shape factors, heat transfer rates, velocity profiles, temperature distribution, and entropy generation rates, offering insights for optimizing nanofluid-based systems. The study also investigates magnetic forces, heat source/sink effects, and viscous dissipation on thermal energy transfer in Graphene oxide/water and Molybdenum disulfide/water nanofluids. Using similarity transformations, Partial Differential Equations are converted into Ordinary Differential Equations, solved with HAM and NDSolver by using the Wolfram tools. Results graphically depict velocity, temperature, entropy, and skin friction values, providing practical insights for engineering applications.

Published
2024
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25. Supernova Model Discrimination with Hyper-Kamiokande

Author

Collaboration, Hyper-Kamiokande, Abe, K., Adrich, P., Aihara, H., Akutsu, R., Alekseev, I., Ali, A., Ameli, F., Anghel, I., Anthony, L. H. V., Antonova, M., Araya, A., Asaoka, Y., Ashida, Y., Aushev, V., Ballester, F., Bandac, I., Barbi, M., Barker, G. J., Barr, G., Batkiewicz-Kwasniak, M., Bellato, M., Berardi, V., Bergevin, M., Bernard, L., Bernardini, E., Berns, L., Bhadra, S., Bian, J., Blanchet, A., Blaszczyk, F. d. M., Blondel, A., Boiano, A., Bolognesi, S., Bonavera, L., Booth, N., Borjabad, S., Boschi, T., Bose, D., Boyd, S . B., Bozza, C., Bravar, A., Bravo-Berguño, D., Bronner, C., Brown, L., Bubak, A., Buchowicz, A., Avanzini, M. Buizza, Cafagna, F. S., Calabria, N. F., Calvo-Mozota, J. M., Cao, S., Cartwright, S. L., Carroll, A., Catanesi, M. G., Cebriàn, S., Chabera, M., Chakraborty, S., Checchia, C., Choi, J. H., Choubey, S., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cowan, G., Cuen-Rochin, S., Danilov, M., Lopez, G. Daz, De la Fuente, E., de Perio, P., De Rosa, G., Dealtry, T., Densham, C. J., Dergacheva, A., Deshmukh, N., Devi, M. M., Di Lodovico, F., Di Meo, P., Di Palma, I., Doyle, T. A., Drakopoulou, E., Drapier, O., Dumarchez, J., Dunne, P., Dziewiecki, M., Eklund, L., Hedri, S. El, Ellis, J., Emery, S., Esmaili, A., Esteve, R., Evangelisti, A., Feely, M., Fedotov, S., Feng, J., Fernandez, P., Fernández-Martinez, E., Ferrario, P., Ferrazzi, B., Feusels, T., Finch, A., Finley, C., Fiorentini, A., Fiorillo, G., Fitton, M., Frankiewicz, K., Friend, M., Fujii, Y., Fukuda, Y., Galinski, G., Gao, J., Garde, C., Garfagnini, A., Garode, S., Gialanella, L., Giganti, C., Gomez-Cadenas, J. J., Gonin, M., González-Nuevo, J., Gorin, A., Gornea, R., Gousy-Leblanc, V., Gramegna, F., Grassi, M., Grella, G., Guigue, M., Gumplinger, P., Hadley, D. R., Harada, M., Hartfiel, B., Hartz, M., Hassani, S., Hastings, N. C., Hayato, Y., Hernando-Morata, J. A., Herrero, V., Hill, J., Hiraide, K., Hirota, S., Holin, A., Horiuchi, S., Hoshina, K., Hultqvist, K., Iacob, F., Ichikawa, A. K., Ibnsalih, W. Idrissi, Iijima, T., Ikeda, M., Inomoto, M., Inoue, K., Insler, J., Ioannisian, A., Ishida, T., Ishidoshiro, K., Ishino, H., Ishitsuka, M., Ito, H., Ito, S., Itow, Y., Iwamoto, K., Izmaylov, A., Izumi, N., Izumiyama, S., Jakkapu, M., Jamieson, B., Jang, H. I., Jang, J. S., Jenkins, S. J., Jeon, S. H., Jiang, M., Jo, H. S., Jonsson, P., Joo, K. K., Kajita, T., Kakuno, H., Kameda, J., Kano, Y., Kalaczynski, P., Karlen, D., Kasperek, J., Kataoka, Y., Kato, A., Katori, T., Kazarian, N., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikec, M., Kim, J. H., Kim, J. Y., Kim, S. B., Kim, S. Y., King, S., Kinoshita, T., Kisiel, J., Klekotko, A., Kobayashi, T., Koch, L., Koga, M., Koerich, L., Kolev, N., Konaka, A., Kormos, L. L., Koshio, Y., Korzenev, A., Kotsar, Y., Kouzakov, K. A., Kowalik, K. L., Kravchuk, L., Kryukov, A. P., Kudenko, Y., Kumita, T., Kurjata, R., Kutter, T., Kuze, M., Kwak, K., La Commara, M., Labarga, L., Lagoda, J., James, M. Lamers, Lamoureux, M., Laveder, M., Lavitola, L., Lawe, M., Learned, J. G., Lee, J., Leitner, R., Lezaun, V., Lim, I. T., Lindner, T., Litchfield, R. P., Long, K. R., Longhin, A., Loverre, P., Lu, X., Ludovici, L., Maekawa, Y., Magaletti, L., Magar, K., Mahn, K., Makida, Y., Malek, M., Malinský, M., Marchi, T., Maret, L., Mariani, C., Marinelli, A., Martens, K., Marti, Ll., Martin, J. F., Martin, D., Marzec, J., Matsubara, T., Matsumoto, R., Matsuno, S., Matusiak, M., Mazzucato, E., McCarthy, M., McCauley, N., McElwee, J., McGrew, C., Mefodiev, A., Medhi, A., Mehta, P., Mellet, L., Menjo, H., Mermod, P., Metelko, C., Mezzetto, M., Migenda, J., Migliozzi, P., Mijakowski, P., Miki, S., Miller, E. W., Minakata, H., Minamino, A., Mine, S., Mineev, O., Mitra, A., Miura, M., Moharana, R., Mollo, C. M., Mondal, T., Mongelli, M., Monrabal, F., Moon, D. H., Moon, C. S., Mora, F. J., Moriyama, S., Mueller, Th. A., Munteanu, L., Murase, K., Nagao, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakai, S., Nakajima, Y., Nakamura, K., Nakamura, KI., Nakamura, H., Nakano, Y., Nakaya, T., Nakayama, S., Nakayoshi, K., Machado, L. Nascimento, Naseby, C. E. R., Navarro-Garcia, B., Needham, M., Nicholls, T., Niewczas, K., Nishimura, Y., Noah, E., Nova, F., Nugent, J. C., Nunokawa, H., Obrebski, W., Ochoa-Ricoux, J. P., O'Connor, E., Ogawa, N., Ogitsu, T., Ohta, K., Okamoto, K., O'Keeffe, H. M., Okumura, K., Onishchuk, Y., Orozco-Luna, F., Oshlianskyi, A., Ospina, N., Ostrowski, M., O'Sullivan, E., O'Sullivan, L., Ovsiannikova, T., Oyama, Y., Ozaki, H., Pac, M. Y., Paganini, P., Palladino, V., Paolone, V., Pari, M., Parsa, S., Pasternak, J., Pastore, C., Pastuszak, G., Patel, D. A., Pavin, M., Payne, D., Peña-Garay, C., Pidcott, C., Guerra, E. Pinzon, Playfer, S., Pointon, B. W., Popov, A., Popov, B., Porwit, K., Posiadala-Zezula, M., Poutissou, J. -M., Pozimski, J., Pronost, G., Prouse, N. W., Przewlocki, P., Quilain, B., Quiroga, A. A., Radicioni, E., Radics, B., Rajda, P. J., Renner, J., Rescigno, M., Retiere, F., Ricciardi, G., Riccio, C., Richards, B., Rondio, E., Rose, H. J., Roskovec, B., Roth, S., Rott, C., Rountree, S. D., Rubbia, A., Ruggeri, A. C., Ruggles, C., Russo, S., Rychter, A., Ryu, D., Sakashita, K., Samani, S., Sánchez, F., Sánchez, M. L., Sanchez, M. C., Sano, S., Santos, J. D., Santucci, G., Sarmah, P., Sashima, I., Sato, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Seo, J. W., Seo, S. H., Sgalaberna, D., Shaikhiev, A., Shan, Z., Shaykina, A., Shimizu, I., Shin, C. D., Shinoki, M., Shiozawa, M., Sinnis, G., Skrobova, N., Skwarczynski, K., Smy, M. B., Sobczyk, J., Sobel, H. W., Soler, F. J. P., Sonoda, Y., Spina, R., Spisso, B., Spradlin, P., Stankevich, K. L., Stawarz, L., Stellacci, S. M., Stopa, K., Studenikin, A. I., Gómez, S. L. Suárez, Suganuma, T., Suvorov, S., Suwa, Y., Suzuki, A. T., Suzuki, S. Y., Suzuki, Y., Svirida, D., Svoboda, R., Taani, M., Tada, M., Takeda, A., Takemoto, Y., Takenaka, A., Taketa, A., Takeuchi, Y., Takhistov, V., Tanaka, H., Tanaka, H. A., Tanaka, H. I., Tanaka, M., Tashiro, T., Thiesse, M., Thompson, L. F., Toledo, J., Tomatani-Sánchez, A. K., Tortone, G., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M. R., Valder, S., Valentino, V., Vasseur, G., Vijayvargi, A., Vilela, C., Vinning, W. G. S., Vivolo, D., Vladisavljevic, T., Vogelaar, R. B., Vyalkov, M. M., Wachala, T., Walker, J., Wark, D., Wascko, M. O., Wendell, R. A., Wilkes, R. J., Wilking, M. J., Wilson, J. R., Wronka, S., Xia, J., Xie, Z., Xin, T., Yamaguchi, Y., Yamamoto, K., Yanagisawa, C., Yano, T., Yen, S., Yershov, N., Yeum, D. N., Yokoyama, M., Yonenaga, M., Yoo, J., Yu, I., Yu, M., Zakrzewski, T., Zaldivar, B., Zalipska, J., Zaremba, K., Zarnecki, G., Ziembicki, M., Zietara, K., Zito, M., and Zsoldos, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants -- neutron stars and black holes -- are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations towards a precise reproduction of the explosion mechanism observed in nature., Comment: 21 pages, 7 figures. Article based on thesis published as arXiv:2002.01649. v2: added references and some explanations in response to reviewer comments

Published
2021
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26. Multichannel Approach for Sentiment Analysis Using Stack of Neural Network with Lexicon Based Padding and Attention Mechanism

Author

Kota Venkateswara Rao and Shyamala Devi M.

Subjects
attention mechanism, bi-lstm, convolution neural network, lexicon, multichannel approach, padding, sentiment analysis, stack of neural network, Computer software, QA76.75-76.765
Abstract

Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.

Published
2023
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27. The Hyper-Kamiokande Experiment -- Snowmass LOI

Author

Collaboration, Hyper-Kamiokande, Abe, K., Adrich, P., Aihara, H., Akutsu, R., Alekseev, I., Ali, A., Ameli, F., Anthony, L. H. V., Araya, A., Asaoka, Y., Aushev, V., Bandac, I., Barbi, M., Barr, G., Batkiewicz-Kwasniak, M., Bellato, M., Berardi, V., Bernard, L., Bernardini, E., Berns, L., Bhadra, S., Bian, J., Blanchet, A., Blondel, A., Boiano, A., Bolognesi, S., Bonavera, L., Borjabad, S., Boschi, T., Bose, D., Boyd, S . B., Bozza, C., Bravar, A., Bronner, C., Bubak, A., Buchowicz, A., Avanzini, M. Buizza, Cafagna, F. S., Calabria, N. F., Calvo-Mozota, J. M., Cao, S., Catanesi, M. G., Chakraborty, S., Choi, J. H., Choubey, S., Cicerchia, M., Coleman, J., Collazuol, G., Cuen-Rochin, S., Danilov, M., De la Fuente, E., de Perio, P., De Rosa, G., Dealtry, T., Densham, C. J., Dergacheva, A., Deshmukh, N., Devi, M. M., Di Lodovico, F., Di Meo, P., Di Palma, I., Doyle, T. A., Drakopoulou, E., Drapier, O., Dumarchez, J., Eklund, L., Hedri, S. El, Ellis, J., Emery, S., Esmaili, A., Fedotov, S., Feng, J., Fernández-Martinez, E., Ferrario, P., Ferrazzi, B., Finch, A., Finley, C., Fiorillo, G., Fitton, M., Friend, M., Fujii, Y., Fukuda, Y., Galinski, G., Gao, J., Garde, C., Garfagnini, A., Garode, S., Gialanella, L., Giganti, C., Gomez-Cadenas, J. J., Gonin, M., González-Nuevo, J., Gorin, A., Gornea, R., Gramegna, F., Grassi, M., Grella, G., Guigue, M., Hadley, D. R., Harada, M., Hartz, M., Hassani, S., Hastings, N. C., Hayato, Y., Hiraide, K., Hoshina, K., Hultqvist, K., Iacob, F., Ichikawa, A. K., Ibnsalih, W. Idrissi, Ikeda, M., Inomoto, M., Ioannisian, A., Ishida, T., Ishidoshiro, K., Ishino, H., Ishitsuka, M., Ito, H., Ito, S., Itow, Y., Iwamoto, K., Izumi, N., Izumiyama, S., Jakkapu, M., Jamieson, B., Jang, J. S., Jo, H. S., Jonsson, P., Joo, K. K., Kajita, T., Kakuno, H., Kameda, J., Kano, Y., Karlen, D., Kataoka, Y., Kato, A., Katori, T., Kazarian, N., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kim, J. Y., Kim, S. B., King, S., Kinoshita, T., Kisiel, J., Klekotko, A., Kobayashi, T., Koerich, L., Kolev, N., Konaka, A., Kormos, L. L., Koshio, Y., Kotsar, Y., Kouzakov, K. A., Kowalik, K. L., Kravchuk, L., Kryukov, A. P., Kudenko, Y., Kumita, T., Kurjata, R., Kutter, T., Kuze, M., Kwak, K., La Commara, M., Labarga, L., Lagoda, J., Lamoureux, M., Laveder, M., Lavitola, L., Lee, J., Leitner, R., Lezaun, V., Lim, I. T., Lindner, T., Litchfield, R. P., Long, K. R., Longhin, A., Loverre, P., Lu, X., Ludovici, L., Maekawa, Y., Magaletti, L., Magar, K., Makida, Y., Malek, M., Malinský, M., Marchi, T., Mariani, C., Marinelli, A., Martens, K., Marti, Ll., Martin, J. F., Martin, D., Marzec, J., Matsubara, T., Matsumoto, R., McCauley, N., Medhi, A., Mehta, P., Mellet, L., Menjo, H., Mezzetto, M., Migenda, J., Migliozzi, P., Miki, S., Minamino, A., Mine, S., Mineev, O., Mitra, A., Miura, M., Moharana, R., Mollo, C. M., Mondal, T., Mongelli, M., Monrabal, F., Moon, D. H., Moon, C. S., Moriyama, S., Mueller, T., Nagao, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakai, S., Nakajima, Y., Nakamura, K., Nakamura, KI., Nakamura, H., Nakano, Y., Nakaya, T., Nakayama, S., Nakayoshi, K., Machado, L. Nascimento, Naseby, C. E. R., Navarro-Garcia, B., Needham, M., Niewczas, K., Nishimura, Y., Nova, F., Nugent, J. C., Nunokawa, H., Obrebski, W., Ochoa-Ricoux, J. P., O'Connor, E., Ogawa, N., Ogitsu, T., Okamoto, K., O'Keeffe, H. M., Okumura, K., Onishchuk, Y., Orozco-Luna, F., Oshlianskyi, A., Ospina, N., Ostrowski, M., O'Sullivan, E., Oyama, Y., Ozaki, H., Pac, M. Y., Paganini, P., Palladino, V., Pari, M., Pasternak, J., Pastore, C., Pastuszak, G., Patel, D. A., Pavin, M., Payne, D., Peña-Garay, C., Pidcott, C., Playfer, S., Pointon, B. W., Popov, A., Popov, B., Porwit, K., Posiadala-Zezula, M., Pronost, G., Prouse, N. W., Quilain, B., Quiroga, A. A., Radicioni, E., Radics, B., Rajda, P. J., Rescigno, M., Ricciardi, G., Richards, B., Rondio, E., Roskovec, B., Roth, S., Rott, C., Rubbia, A., Ruggeri, A. C., Russo, S., Rychter, A., Ryu, D., Sakashita, K., Samani, S., Sánchez, F., Sánchez, M. L., Sano, S., Santos, J. D., Santucci, G., Sarmah, P., Sato, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Seo, J. W., Sgalaberna, D., Shaykina, A., Shimizu, I., Shin, C. D., Shinoki, M., Shiozawa, M., Skrobova, N., Skwarczynski, K., Smy, M. B., Sobczyk, J., Sobel, H. W., Soler, F. J. P., Sonoda, Y., Spina, R., Spisso, B., Spradlin, P., Stankevich, K. L., Stawarz, L., Stellacci, S. M., Studenikin, A. I., Gómez, S. L. Suárez, Suganuma, T., Suvorov, S., Suwa, Y., Suzuki, A. T., Suzuki, S., Suzuki, Y., Svirida, D., Taani, M., Tada, M., Takeda, A., Takemoto, Y., Takenaka, A., Taketa, A., Takeuchi, Y., Tanaka, H., Tanaka, H. I., Tanaka, M., Tashiro, T., Thiesse, M., Thompson, L. F., Tomatani-Sánchez, A. K., Tortone, G., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M. R., Valder, S., Valentino, V., Vasseur, G., Vijayvargi, A., Vinning, W. G. S., Vivolo, D., Vogelaar, R. B., Vyalkov, M. M., Wachala, T., Walker, J., Wark, D., Wascko, M. O., Wendell, R. A., Wilson, J. R., Wronka, S., Xia, J., Yamaguchi, Y., Yamamoto, K., Yano, T., Yershov, N., Yokoyama, M., Yoo, J., Yu, I., Zakrzewski, T., Zaldivar, B., Zalipska, J., Zaremba, K., Zarnecki, G., Ziembicki, M., Zietara, K., Zito, M., and Zsoldos, S.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

Hyper-Kamiokande is the next generation underground water Cherenkov detector that builds on the highly successful Super-Kamiokande experiment. The detector which has an 8.4~times larger effective volume than its predecessor will be located along the T2K neutrino beamline and utilize an upgraded J-PARC beam with 2.6~times beam power. Hyper-K's low energy threshold combined with the very large fiducial volume make the detector unique, that is expected to acquire an unprecedented exposure of 3.8~Mton$\cdot$year over a period of 20~years of operation. Hyper-Kamiokande combines an extremely diverse science program including nucleon decays, long-baseline neutrino oscillations, atmospheric neutrinos, and neutrinos from astrophysical origins. The scientific scope of this program is highly complementary to liquid-argon detectors for example in sensitivity to nucleon decay channels or supernova detection modes. Hyper-Kamiokande construction has started in early 2020 and the experiment is expected to start operations in 2027. The Hyper-Kamiokande collaboration is presently being formed amongst groups from 19 countries including the United States, whose community has a long history of making significant contributions to the neutrino physics program in Japan. US physicists have played leading roles in the Kamiokande, Super-Kamiokande, EGADS, K2K, and T2K programs., Comment: 6 pages, prepared as Snowmass2021 LOI

Published
2020

32. The DireXeno Experiment -- Measuring Correlated Scintillation Signatures in Liquid Xenon

Author

Itay, R., Szabo, P. Z., Koltman, G., Devi, M. M., Shutman, M., Landsman, H., and Budnik, R.

Subjects
Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract

We present a detector apparatus, DireXeno (DIRectinal Xenon), designed to measure the spatial and temporal properties of scintillation in liquid xenon to very high accuracy. The properties of scintillation are of primary importance for dark matter and neutrinoless double beta decay experiments, however the complicated microphysics involved limits theoretical predictions. We will explore the possibility that scintillation emission exhibits correlation in light emission such as super-radiance, which depends on the type of interaction. Such properties of scintillation light may open a new window for background rejection as well as directionality measurements. We present the technical design and the concepts driving it, and demonstrate that statistical treatment will enable detecting anisotropy of as little as 10% of the photons. We show results from commissioning runs in which the detector operated for over 44 days in stable conditions. The time resolution for individual photons in different PMTs was measured to be $\lesssim1.3$ ns FWHM, corresponding to $\lesssim0.55$ ns (1 $\sigma$).

Published
2019
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35. Lightweight PUF-Based Gate Replacement Technique to Reduce Leakage of Information through Power Profile Analysis

Author

Mohankumar N., Jayakumar M., and Nirmala Devi M.

Subjects
design for security, hardware security, puf, trng, wave dynamic differential logic, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Telecommunication, TK5101-6720
Abstract

The major challenge faced by electronic device designers is to defend the system from attackers and malicious modules called Hardware Trojans and to deliver a secured design. Although there are many cryptographic preventive measures in place adversaries find different ways to attack the device. Differential Power Analysis (DPA) attack is a type of Side Channel Attacks, used by an attacker to analyze the power leakage in the circuit, through which the functionality of the circuit is extracted. To overcome this, a lightweight approach is proposed in this paper using, Wave Dynamic Differential Logic (WDDL) technique, without incurring any additional resource cost and power. The primary objective of WDDL is to make the power consumption constant of an entire circuit by restricting the leakage power. The alternate strategy used by an adversary is to leak the information through reverse engineering. The proposed work avoids this by using a bit sequencer and a modified butterfly PUF based randomizing architecture. A modified version of butterfly PUF is also proposed in this paper, and from various qualitative tests performed it is evident that this PUF can prevent information leakage. This work is validated on ISCAS 85, ISCAS 89 benchmark circuits and the results obtained indicate that the difference in leakage power is found to be very marginal.

Published
2022
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39. Mandibular metastasis of follicular thyroid carcinoma

Author

Devi, M., Regu, P., and Bakyalakshmi, K.

Subjects
Metastasis -- Care and treatment, Thyroid diseases -- Care and treatment, Carcinoma -- Care and treatment, Cancer -- Care and treatment, Radiotherapy, Health
Abstract

Byline: M. Devi, P. Regu, K. Bakyalakshmi The orofacial region may be the first site for the dissemination of malignancies from the remote regions in the jawbones. The most common [...]

Published
2023

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