436 research outputs found
Values, environmental vulnerabilities and implications on adaptation: evidence from an indigenous community in Rajasthan, India
No full textThis dataset consists of 19 life history interviews and one focus group discussion
undertaken in Sadri, Jalore, and Pali districts of Rajasthan to understand the linkages between the societal values and adaptation decisions of a pastoralist community named Raikas
Ethics Principles for Artificial Intelligence–Based Telemedicine for Public Health
No full textThe use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. Published online ahead of print March 9, 2023:e1-e8. https://doi.org/10.2105/AJPH.2022.307225)
Challenging Masculine Norms: A Psychological Study of Stereotypical Gender Conflicts in Classical Dance and the Positive Transformation through Dance in Anmol Arora\u27s Novel, the Last Dance
No full textIn India, classical dance is often identified as a woman-centered profession. Society often criticizes men who choose classical dance and then name them effeminate and gay. Dance is so far considered as entertainment, but Anmol Arora\u27s The Last Dance mainly explores creative approaches to Indian classical dance in treating the psychological pain of men for being a classical dancer. The novel depicts the impacts of conventional masculine traits in India and the mode of resistance in Indian classical dance through the leading character, Chandrasekar. The protagonist, Chandrasekar, strives to revamp the stereotypes and sustains in his profession despite all the humiliation and psychological pain in his life. Eventually, he breaks stereotypical gender ideas of society on Indian classical dance and attains the pinnacle of success as a male classical dancer with the help of dance. The purpose of this study is to manifest the therapeutic value of Indian classical dance and to understand the diplomatic public notion that dancing is viewed as an "effeminate and suspect activity for a male body" (Migdalek, 2015, p.76) and how Chandrasekar changes this view. Based on the thematic analysis, the Masculinity of Chandrasekar is analysed through the lens of psychology
Ideas for rent: an overview of markets for technology
No full textThis article surveys some of the recent literature on technology markets, and summarizes its main issues and insights. We structure our analysis in three parts: the supply and demand of technology; the factors that condition the formation and growth of technology markets; industry structure and dynamic issues. In addition, we summarize some of the studies that have tried to document the size and growth of these markets. We find that the literature has focused mainly on the supply of technology, but several other aspects of these markets remain under-studied, including the demand for external technology, the role of uncertainty in technology markets, and the dynamic interaction between industry structure and the market for technology. Understanding these will illuminate whether markets for technology will continue to grow or remained confined to pockets of the economy. Copyright 2010 The Author 2010. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.
Metrics for analytics and visualization of big data with applications to activity recognition
No full textActivity recognition systems detect the hidden actions of an agent from sensor measurements made on the agents' actions and the environmental conditions. For such systems, metrics are important for both performance evaluation and visualization purposes. In this thesis, such metrics are developed and illustrated. For human activity recognition datasets, a reporting structure is described to visualize the metrics in a systematic manner. The other contribution of this thesis is to describe a visualization tool for estimating the orientation (attitude) of a rigid body from streaming motion sensor (accelerometer and gyroscope) data. A feedback particle filter (FPF) is implemented algorithmically to solve the estimation problem.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Rohan Arora, accepted the attached license on 2016-04-25 at 10:47.The student, Rohan Arora, submitted this Thesis for approval on 2016-04-25 at 10:48.This Thesis was approved for publication on 2016-04-27 at 15:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #9459 on 2016-07-07 at 14:17:57Made available in DSpace on 2016-07-07T21:18:02Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-04-27Embargo set by: Seth Robbins for item 93308
Lift date: 2018-07-07T21:18:16Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 93308 on 2018-07-08T09:15:30Z
Optical Characterization of Band Gaps for Sulfide-based Chalcogenide and Copper Oxide Thin Films
No full textabstract: The purpose of this research is to optically characterize the band gaps of sulfide-based chalcogenides and copper oxide thin films. The analysis on the copper oxide thin films will view the effects of various annealing temperatures and the analysis of the chalcogenides will view the effects of silver doping on the thin films. Using UV-Vis spectroscopy, parameters such as the absorption coefficient and determined which then provide details on the optical band gaps of these various semiconductors. With a better understanding of the bandgap of these materials, the behavior can be better predicted in fields of nanoionics and photonics
Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset.
No full textINTRODUCTION: The potential for synthetic data to act as a replacement for real data in research has attracted attention in recent months due to the prospect of increasing access to data and overcoming data privacy concerns when sharing data. The field of generative artificial intelligence and synthetic data is still early in its development, with a research gap evidencing that synthetic data can adequately be used to train algorithms that can be used on real data. This study compares the performance of a series machine learning models trained on real data and synthetic data, based on the National Diet and Nutrition Survey (NDNS). METHODS: Features identified to be potentially of relevance by directed acyclic graphs were isolated from the NDNS dataset and used to construct synthetic datasets and impute missing data. Recursive feature elimination identified only four variables needed to predict mean arterial blood pressure: age, sex, weight and height. Bayesian generalised linear regression, random forest and neural network models were constructed based on these four variables to predict blood pressure. Models were trained on the real data training set (n = 2408), a synthetic data training set (n = 2408) and larger synthetic data training set (n = 4816) and a combination of the real and synthetic data training set (n = 4816). The same test set (n = 424) was used for each model. RESULTS: Synthetic datasets demonstrated a high degree of fidelity with the real dataset. There was no significant difference between the performance of models trained on real, synthetic or combined datasets. Mean average error across all models and all training data ranged from 8.12 To 8.33. This indicates that synthetic data was capable of training equally accurate machine learning models as real data. DISCUSSION: Further research is needed on a variety of datasets to confirm the utility of synthetic data to replace the use of potentially identifiable patient data. There is also further urgent research needed into evidencing that synthetic data can truly protect patient privacy against adversarial attempts to re-identify real individuals from the synthetic dataset
Values, environmental vulnerabilities, and implications on adaptation: Evidence from an indigenous Raika community in Rajasthan, India
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