Exoplanets form from circumstellar protoplanetary disks whose fundamental properties (notably their extent, composition, mass, temperature, and lifetime) depend on the host star properties, such as their mass and luminosity. B stars are among the most massive stars and their protoplanetary disks test extreme conditions for exoplanet formation. This paper investigates the frequency of giant planet companions around young B stars (median age of 16 Myr) in the Scorpius-Centaurus (Sco-Cen) association, the closest association containing a large population of B stars. We systematically searched for massive exoplanets with the high-contrast direct imaging instrument SPHERE using the data from the BEAST survey, which targets a homogeneous sample of young B stars from the wide Sco-Cen association. We derived accurate detection limits in the case of non-detections. We found evidence in previous papers for two substellar companions around 42 stars. The masses of these companions are straddling the sim 13 Jupiter mass deuterium burning limit, but their mass ratio with respect to their host star is close to that of Jupiter. We derived a frequency of such massive planetary-mass companions around B stars of $11_ $<!PCT!>, accounting for the survey sensitivity. The discoveries of substellar companions b and B happened after only a few stars in the survey had been observed, raising the possibility that massive Jovian planets might be common around B stars. However, our statistical analysis shows that the occurrence rate of such planets is similar around B stars and around solar-type stars of a similar age, while B-star companions exhibit low mass ratios and a larger semi-major axis.

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.

This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A comprehensive analysis of recent studies reveals promising results in using ML techniques for diagnosing and monitoring SCD. The review covers various ML algorithms, including Multilayer Perceptron, Support Vector Machine, Random Forest, Logistic Regression, Long short-term memory, Extreme Learning Machines, Convolutional Neural Networks, and Transfer Learning methods. Despite significant advances, challenges such as limited dataset sizes, interpretability concerns, and risks of overfitting are identified in studies. Future research directions entail addressing these limitations by harnessing larger and more representative datasets, enhancing model interpretability, and exploring advanced ML techniques like deep learning. Overall, this review underscores the transformative potential of ML in increasing the diagnosis, monitoring and define prognosis of sickle cell disease while also highlighting the need for further investigation in the field.