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The escalating global mortality and morbidity rates associated with gastrointestinal (GI) bleeding, compounded by the complexities and limitations of traditional endoscopic methods, underscore the urgent need for a critical review of current methodologies used for addressing this condition. With an estimated 300,000 annual deaths worldwide, the demand for innovative diagnostic and therapeutic strategies is paramount. The introduction of Video Capsule Endoscopy (VCE) has marked a significant advancement, offering a comprehensive, non-invasive visualization of the digestive tract that is pivotal for detecting bleeding sources unattainable by traditional methods. Despite its benefits, the efficacy of VCE is hindered by diagnostic challenges, including time-consuming analysis and susceptibility to human error. This backdrop sets the stage for exploring Machine Learning (ML) applications in automating GI bleeding detection within capsule endoscopy, aiming to enhance diagnostic accuracy, reduce manual labor, and improve patient outcomes. Through an exhaustive analysis of 113 papers published between 2008 and 2023, this review assesses the current state of ML methodologies in bleeding detection, highlighting their effectiveness, challenges, and prospective directions. It contributes an in-depth examination of AI techniques in VCE frame analysis, offering insights into open-source datasets, mathematical performance metrics, and technique categorization. The paper sets a foundation for future research to overcome existing challenges, advancing gastrointestinal diagnostics through interdisciplinary collaboration and innovation in ML applications.

Decay and dispersal of the tilted Bipolar Magnetic Regions (BMRs) on the solar surface are observed to produce large-scale poloidal field, which acts as the seed for the toroidal field and, thus, the next sunspot cycle. However, various properties of BMR, namely, the tilt, time delay between successive emergences, location, and flux, all have irregular variations. Previous studies show that these variations can lead to changes in the polar field. In this study, we first demonstrate that our 3D kinematic dynamo model, STABLE, reproduces the robust feature of the surface flux transport (SFT) model, namely the variation of the generated dipole moment with the latitude of the BMR position. Using STABLE in both SFT and dynamo modes, we perform simulations by varying the individual properties of BMR and keeping their distributions the same in all the cycles as inspired by the observations. We find that randomness due to the distribution in either the time delay or the BMR latitude produces negligible variation in the polar field and the solar cycle. However, randomness due to BMR flux distribution produces substantial effects, while the scatter in the tilt around Joy law produces the largest variation. Our comparative analyses suggest that the scatter of BMR tilt around Joy law is the major cause of variation in the solar cycle. Furthermore, our simulations also show that the magnetic field-dependent time delay of BMR emergence produces more realistic features of the magnetic cycle, consistent with observation., Comment: 11 Pages, 10 figures, Under review in MNRAS Journal

One of the intriguing mechanisms of the Sun is the formation of the bipolar magnetic regions (BMRs) in the solar convection zone which are observed as regions of concentrated magnetic fields of opposite polarity on photosphere. These BMRs are tilted with respect to the equatorial line, which statistically increases with latitude. The thin flux tube model, employing the rise of magnetically buoyant flux loops and their twist by Coriolis force, is a popular paradigm for explaining the formation of tilted BMRs. In this study, we assess the validity of the thin flux tube model by analyzing the tracked BMR data obtained through the Automatic Tracking Algorithm for BMRs (AutoTAB). Our observations reveal that the tracked BMRs exhibit the expected collective behaviors. We find that the polarity separation of BMRs increases over their normalized lifetime, supporting the assumption of a rising flux tube from the CZ. Moreover, we observe an increasing trend of the tilt with the flux of the BMR, suggesting that rising flux tubes associated with lower flux regions are primarily influenced by drag force and Coriolis force, while in higher flux regions, magnetic buoyancy dominates. Furthermore, we observe Joy's law dependence for emerging BMRs from their first detection, indicating that at least a portion of the tilt observed in BMRs can be attributed to the Coriolis force. Notably, lower flux regions exhibit a higher amount of fluctuations associated with their tilt measurement compared to stronger flux regions, suggesting that lower flux regions are more susceptible to turbulent convection., Comment: 16 pages, 10 figures. Under consideration in ApJ. Comments are welcome

The study on the generating function approach to entropy become popular as it generates several well-known entropy measures discussed in the literature. In this work, we define the weighted cumulative residual entropy generating function (WCREGF) and study its properties. We then introduce the dynamic weighted cumulative residual entropy generating function (DWCREGF). It is shown that the DWCREGF determines the distribution uniquely. We study some characterization results using the relationship between the DWCREGF and the hazard rate and/or the mean residual life function. Using a characterization based on DWCREGF, we develop a new goodness fit test for Rayleigh distribution. A Monte Carlo simulation study is conducted to evaluate the proposed test. Finally, the test is illustrated using two real data sets., Comment: arXiv admin note: text overlap with arXiv:2211.05484

The tilt of the bipolar magnetic region (BMR) is crucial in the Babcock-Leighton process for the generation of the poloidal magnetic field in the Sun. We extend the work of Jha et al. (2020) and analyze the recently reported tracked BMR catalogue based on AutoTAB Sreedevi et al. (2023) from Michelson Doppler Imager (1996-2011) and Helioseismic and Magnetic Imager (2010-2018). Using the tracked information of BMRs based on AutoTAB, we confirm that the distribution of bmax reported by Jha et al. (2020) is not because of the BMRs are picked multiple times at the different phases of their evolution instead it is also present if we consider each BMRs only once. Moreover, we find that the slope of Joy's law initially increases slowly with the increase of bmax. However, when bmax>2.5 kG, gamma_0 decreases. The decrease of observed $gamma_0$ with bmax provides a hint to a nonlinear tilt quenching in the Babcock-Leighton process., Comment: 4 Pages, 2 Figures; IAUS 365 Proceedings Series

Bipolar Magnetic Regions (BMRs) provide crucial information about solar magnetism. They exhibit varying morphology and magnetic properties throughout their lifetime, and studying these properties can provide valuable insights into the workings of the solar dynamo. The majority of previous studies have counted every detected BMR as a new one and have not been able to study the full life history of each BMRs. To address this issue, we have developed an Automatic Tracking Algorithm (AutoTAB) for BMRs, that tracks the BMRs for their entire lifetime or throughout their disk passage. AutoTAB uses the binary maps of detected BMRs to automatically track the regions. This is done by differentially rotating the binary maps of the detected regions and checking for overlaps between them. In this first article of this project, we provide a detailed description of the working of the algorithm and evaluate its strengths and weaknesses by comparing it with existing algorithms. AutoTAB excels in tracking even for the small BMRs (with flux $\sim 10^{20}$ Mx) and it has successfully tracked 9152 BMRs over the last two solar cycles (1996--2020), providing a comprehensive dataset that depicts the evolution of various properties for each BMR. The tracked BMRs exhibit the well-known latitudinal-time distribution and 11 year cyclic variation except for small BMRs which appear at all phases of the solar cycle and show weak latitudinal dependency. Finally, we discuss the possibility of adapting our algorithm to other datasets and expanding the technique to track other solar features in the future., Comment: 14 pages including 9 figures; Re-submitted in ApJS; Comments are welcome