Alignment and preliminary outcomes of an ELT-size instrument to a very large telescope: LINC-NIRVANA at LBT

LINC-NIRVANA (LN) is a high resolution, near infrared imager that uses a multiple field-of-view, layer-oriented, multi-conjugate AO system, consisting of four multi-pyramid wavefront sensors (two for each arm of the Large Binocular Telescope, each conjugated to a different altitude). The system employs up to 40 star probes, looking at up to 20 natural guide stars simultaneously. Its final goal is to perform Fizeau interferometric imaging, thereby achieving ELT-like spatial resolution (22.8 m baseline resolution). For this reason, LN is also equipped with a fringe tracker, a beam combiner and a NIR science camera, for a total of more than 250 optical components and an overall size of approximately 6x4x4.5 meters. This paper describes the tradeoffs evaluated in order to achieve the alignment of the system to the telescope. We note that LN is comparable in size to planned ELT instrumentation. The impact of such alignment strategies will be compared and the selected procedure, where the LBT telescope is, in fact, aligned to the instrument, will be described. Furthermore, results coming from early night-time commissioning of the system will be presented.Comment: 8 pages, 6 pages, AO4ELT5 Proceedings, 201

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

The search for a method that utilizes biological information to predict humans’ place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000–130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS’s accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing

Study of Cognition in Type 2 Diabetes with Yoga Asana and Pranayama

ABSTRACT Cognition is the process an organism uses to organize the information. Cognition can be used to assess the functional status of the brain. Our main objective of the study is to see the effect of yoga asana and pranayama on cognition in type2 diabetes by using Mini Mental State Examination. The design of our study is Informal experimental design. We have selected 50 type 2 diabetic subjects aged between 35 to 60 years, who are on oral hypoglycemic agents. Subjects are divided in to two groups; control group includes 25 type 2 diabetic subjects who are not having any significant physical activity and examination group includes 25 type 2 diabetic subjects who are doing specific yoga asana and pranayama daily for 30-45 minutes since 1 year. The Mini-Mental State Examination is a widely used, well-validated screening tool for cognitive impairment. It briefly measures orientation to time and place, immediate recall, short-term verbal memory, calculation, language, and construct ability. Each area tested as a designated point value, with the maximum possible score being 30/30. Cognition is significantly more in examination group than the control group. Data was analyzed by using unpaired t-test and P value is <0.05

Ingot-like class of wavefront sensors for laser guide stars

Context. Full sky coverage adaptive optics (AO) on extremely large telescopes requires the adoption of several laser guide stars as references. With such large apertures, the apparent elongation of the beacons is absolutely significant. With a few exceptions, wavefront sensors (WFSs) designed for natural guide stars can be adapted and used in suboptimal mode in this context. Aims. We analyse and describe the geometrical properties of a class of WFSs that are specifically designed to deal with laser guide stars propagated from a location in the immediate vicinity of the telescope aperture. Methods. We describe, in three dimensions, the loci where the light of the laser guide stars would focus in the focal volume located behind the focal plane where astronomical objects are reimaged. We also describe the properties of several types of optomechanical devices that act as perturbers for this new class of pupil plane sensors, through refraction and reflections. We refer to these as ingot WFSs. Results. We provide the recipes both for the most reasonably complex version of these WFSs, with six pupils and, for the simplest one, only three pupils. Both of them are referred to on the basis of the European Extremely Large Telescope (ELT) case. We outlined elements that are meant to give a qualitative idea of how the sensitivity of this new class of sensors compares to conventional ones. Conclusions. We present a new class of WFSs, based on an extension to the case of elongated sources at a finite distance of the pyramid WFS. We point out which advantages of the pyramid can be retained and how it may be adopted to optimize the sensing procedure

End-to-End simulation framework for astronomical spectrographs: SOXS, CUBES and ANDES

We present our numerical simulation approach for the End-to-End (E2E) model applied to various astronomical spectrographs, such as SOXS (ESO-NTT), CUBES (ESO-VLT), and ANDES (ESO-ELT), covering multiple wavelength regions. The E2E model aim at simulating the expected astronomical observations starting from the radiation of the scientific sources (or calibration sources) up to the raw-frame data produced by the detectors. The comprehensive description includes E2E architecture, computational models, and tools for rendering the simulated frames. Collaboration with Data Reduction Software (DRS) teams is discussed, along with efforts to meet instrument requirements. The contribution to the cross-correlation algorithm for the Active Flexure Compensation (AFC) system of CUBES is detailed.Comment: 19 pages, 17 figures, SPIE Astronomical Telescopes + Instrumentation, Yokohama 2024. arXiv admin note: text overlap with arXiv:2209.07185, arXiv:2012.1268