Tunable induced transparency and Fano-resonance in double cavity optomechanical system
Published on: 7th April, 2021
OCLC Number/Unique Identifier: 9026724935
We analyze optomechanically induced Transparency and asymmetric Fano-line shape Profile in a two-mode cavity system, coupling at weak and strong coupling regimes. The model system consists of one mechanical mode and two optical modes. The transmission shows nonreciprocal behavior. Both the forward transmission and backward reflection for the system are analyzed for both optic-optic and mechanical-optic cavities by considering various system parameters. The output spectra lead to sharp asymmetric Fano-resonance and tunable transparency. Double line-shape profile is observed in the output Spectrum. Our proposal provides a new platform for application in quantum telecommunications and a photonic device like optical Switches.
Senile Cataract
Published on: 2nd February, 2024
Cataracts may be described as the opacity of crystalline lenses present in the eye. The translucent crystalline lens is a part of the human eye. It possesses all the physical characteristics of a biconvex lens. The eye’s lens performs similar functions to a camera’s lens. The lens directs light rays entering the eye to the retina’s sensitive layers. Any factor that increases the absorption of scattering of light by the lens reduces its transparency. The opacity of the lens or its capsule, whether developmental or acquired, is called a cataract. Cataracts vary in degree of density and site and assume various forms. Cataract is the leading cause of reversible visual impairment and blindness globally. There are several classifications of cataracts based on morphologic and/or etiologic criteria. However, in epidemiologic studies, the simplified system of three types based on localization of lens opacities is most commonly used: Nuclear cataract is the most common type, followed by cortical cataract and posterior subcapsular cataract. This most prevalent form of acquired cataract, also known as an “age-related cataract,” affects people of both sexes equally and typically develops after age 50. More than 90% of people experience senile cataracts by age 70. Although one eye is almost always afflicted before the other, the disorder is typically bilateral. In the available literature, there is no mention of any such drug that could reverse the opacity of the lens (cataract) once it occurred and make it clear and transparent again.Only replacement of opaque lenses with artificial transparent Intraocular lens (IOL) is successful treatment of cataracts. This review focuses on senile cataracts and the best possible management of senile cataracts.
Harmonizing Artificial Intelligence Governance; A Model for Regulating a High-risk Categories and Applications in Clinical Pathology: The Evidence and some Concerns
Published on: 18th March, 2024
The Canadian healthcare system, grappling with issues like systemic and intelligently established structural anti-black racism, including indigenous nations; even within Pathology and Laboratory Medicine Communities: and deteriorating outcomes, sees potential in AI to address challenges, though concerns exist regarding exacerbating discriminatory practices. In clinical pathology, AI demonstrates superior diagnostic accuracy compared to pathologists in a study, emphasizing its potential to improve healthcare. However, AI governance is crucial to navigating ethical, legal, and societal concerns. The Royal College of Physicians of Canada acknowledges the transformative impact of AI in healthcare but stresses the need for responsible AI tools co-developed by diverse teams. Despite positive attitudes towards AI in healthcare, concerns about patient safety, privacy, and autonomy highlight the necessity for comprehensive education, engagement, and collaboration. Legal concerns, including liability and regulation, pose challenges, emphasizing the need for a robust regulatory framework. AI application in healthcare is categorized as high-risk, demanding stringent regulation to ensure safety, efficacy, and fairness. A parallel is drawn to drug regulation processes, suggesting a similar approach for AI. The lack of transparency in AI-based decision-making raises ethical questions, necessitating measures to address biases and ensure patient privacy. Social accountability is crucial to prevent AI from exacerbating health disparities and harming marginalized communities. In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into healthcare.
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