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The W UMa binaries USNO-A2.0 1350-17365531,V471 Cas,V479 Lac and V560 Lac:light curve solutions and global parameters based on Gaia distances

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Research in Astronomy and Astrophysics 2019年第1期19卷 141-148页

作者： Diana P.Kjurkchieva Velimir A.Popov yordank. eneva Nikola I.Petrov Department of Physics and Astronomy Shumen University Medical University Institute of Astronomy and NAO Bulgarian Academy of Sciences

We present photometric observations in Sloan filters g′, i′of the eclipsing W UMa stars USNOA2.0 1350-17365531, V471 Cas, V479 Lac and V560 Lac. The sinusoidal-like O-C diagram of V471 Cas indicates the presence of a third body with mass 0.12 M_⊙(a red dwarf) at distance 897 R_⊙. The O-C diagram of V479 Lac reveals a period decrease of d P/dt =-1.69 × 10-6d yr-1. The results of the light curve solutions are:(i) the targets are overcontact binaries with small fill-out factors;(ii) their components are F–K stars, comparable in size, whose temperature differences are below 80 K;(iii) all targets undergo partial eclipses and to limit the possible mass ratios we carried out two-step q-search analysis. The target global parameters(luminosities, radii, masses) were obtained on the basis of their Gaia distances and the results of our light curve solutions. The obtained total mass of V560 Lac turns out to be smaller than the lower mass limit for presently known W UMa binaries of 1.0-1.2 M_⊙, i.e. this target is a peculiar overcontact system.

关键词： binaries:close binaries:eclipsing methods:data analysis stars:fundamental parameters stars:individual(USNO-A2.0 1350-17365531,V471 Cas,V479 Lac,V560 Lac)

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A Qualitative Assessment of Medical Diagnosis Capabilities of Three Artificial Intelligence Models: ChatGPT-4o, CodyMD, and Dr. Gupta

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Journal of Biosciences and Medicines 2024年第11期12卷 243-254页

作者： yordank. eneva Bora Dogan Department of Physics and Biophysics Faculty of Pharmacy Medical University of Varna Prof. Dr. Paraskev Stoyanov Varna Bulgaria

Background: Artificial intelligence (AI) has the potential to transform medical diagnostics by enhancing the accuracy and efficiency of diagnostic processes. Its application in clinical practice can greatly support medical professionals by offering improved tools for faster and more precise diagnoses. Understanding AI’s capabilities is essential for its successful integration into medical diagnostics. In this context, evaluating the performance of different AI models in the diagnostic process becomes particularly important. The objective of this study is to qualitatively evaluate the diagnostic performance of three AI models—ChatGPT-4o, CodyMD, and Dr. Gupta—based on patient-reported symptoms. Objectives: The aim of the study is to compare the three AI models in terms of diagnostic accuracy, the level of detail in the provided information, the interaction between the models and patients, and the number of differential diagnoses offered. Results: ChatGPT-4o achieved the highest accuracy, correctly diagnosing 90% of the cases. The model provides basic information and focuses on a single most likely diagnosis. CodyMD and Dr. Gupta achieved 50% accuracy, with CodyMD using an interactive approach and offering differential diagnoses with probability percentages for each. Dr. Gupta provided educational medical information and differential diagnoses without probability estimates. Conclusions: The AI models assessed can assist medical professionals in the diagnostic process, but they require further refinement and optimization. ChatGPT-4o stands out for its high accuracy, though increased patient interaction is needed. CodyMD excels in offering an interactive approach and more detailed responses, but requires improved accuracy. Dr. Gupta provided differential diagnoses, but the information provided is suitable for educational purposes. While these models show potential for clinical use, further research is needed to optimize and validate them in real-world settings.

关键词： Artificial Intelligence Medical Diagnostics ChatGPT-4o CodyMD Dr. Gupta Qualitative Assessment

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