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- Aguilar, J., et al.
(författare)
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Search for Leptonic CP Violation with the ESSnuSBplus Project
- 2024
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Ingår i: Letters in High Energy Physics. - : Andromeda Publishing And Academic Services LTD. - 2632-2714.
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Tidskriftsartikel (refereegranskat)abstract
- ESSνSB is a design study for a next-generation long-baseline neutrino experiment that aims at the precise measurement of the CP-violating phase, δCP, in the leptonic sector at the second oscillation maximum. The conceptual design report published from the first phase of the project showed that after 10 years of data taking, more than 70% of the possible δCP range will be covered with 5σ C.L. to reject the no-CP-violation hypothesis. The expected value of δCP precision is smaller than 8◦ for all δCP values. The next phase of the project, the ESSνSB+, aims at using the intense muon flux produced together with neutrinos to measure the neutrino-nucleus cross-section, the dominant term of the systematic uncertainty, in the energy range of 0.2–0.6 GeV, using a Low Energy neutrinos from STORed Muons (LEnuSTORM) and a Low Energy Monitored Neutrino Beam (LEMNB) facilities.
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- Jasmine Pemeena Priyadarsini, M., et al.
(författare)
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Lung Diseases Detection Using Various Deep Learning Algorithms
- 2023
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Ingår i: Journal of Healthcare Engineering. - : Hindawi Publishing Corporation. - 2040-2295 .- 2040-2309. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
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- Becker, Joel, et al.
(författare)
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Resource profile and user guide of the Polygenic Index Repository
- 2021
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Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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- Hariharan, S.V, et al.
(författare)
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Trade in Services in the New Century
- 2019
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Bok (refereegranskat)abstract
- Concerns have been raised about the potential adverse effects of liberalizing trade in services under the GATS framework on equity, costs, distribution, availability of services and sovereignty of governments in defining their national objectives. These concerns have been voiced in poor countries in case of such social service sectors as health and education. Critics have stressed the need for an environment that is conducive to both efficiency and social development.
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