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We summarize the status of a computer simulator for microlens planet surveys. The simulator generates synthetic light curves of microlensing events observed with specified networks of telescopes over specified periods of time. Particular attention is paid to models for sky brightness and seeing, calibrated by fitting to data from the OGLE survey and RoboNet observations in 2011. Time intervals during which events are observable are identified by accounting for positions of the Sun and the Moon, and other restrictions on telescope pointing. Simulated observations are then generated for an algorithm that adjusts target priorities in real time with the aim of maximizing planet detection zone area summed over all the available events. The exoplanet detection capability of observations was compared for several telescopes.

We report the first unambiguous detection and mass measurement of an isolated stellar-mass black hole (BH). We used the Hubble Space Telescope (HST) to carry out precise astrometry of the source star of the long-duration (t_E~270 days), high-magnification microlensing event MOA-2011-BLG-191/OGLE-2011-BLG-0462 (hereafter designated as MOA-11-191/OGLE-11-462), in the direction of the Galactic bulge. HST imaging, conducted at eight epochs over an interval of six years, reveals a clear relativistic astrometric deflection of the background star's apparent position. Ground-based photometry of MOA-11-191/OGLE-11-462 shows a parallactic signature of the effect of the Earth's motion on the microlensing light curve. Combining the HST astrometry with the ground-based light curve and the derived parallax, we obtain a lens mass of 7.1 +/- 1.3 Msun and a distance of 1.58 +/- 0.18 kpc. We show that the lens emits no detectable light, which, along with having a mass higher than is possible for a white dwarf or neutron star, confirms its BH nature. Our analysis also provides an absolute proper motion for the BH. The proper motion is offset from the mean motion of Galactic-disk stars at similar distances by an amount corresponding to a transverse space velocity of ~45 km/s, suggesting that the BH received a 'natal kick' from its supernova explosion. Previous mass determinations for stellar-mass BHs have come from radial-velocity measurements of Galactic X-ray binaries, and from gravitational radiation emitted by merging BHs in binary systems in external galaxies. Our mass measurement is the first for an isolated stellar-mass BH using any technique.

We report the discovery of Qatar-3b, Qatar-4b, and Qatar-5b, three new transiting planets identified by the Qatar Exoplanet Survey (QES). The three planets belong to the hot Jupiter family, with orbital periods of \\(P_{Q3b}\\)=2.50792 days, \\(P_{Q4b}\\)=1.80539 days, and \\(P_{Q5b}\\)=2.87923 days. Follow-up spectroscopic observations reveal the masses of the planets to be \\(M_{Q3b}\\)=4.31\\(\\pm0.47\\) \\(M_{\\rm J}\\), \\(M_{Q4b}\\)=6.10\\( \\pm0.54\\) \\(M_{\\rm J}\\), and \\(M_{Q5b}\\) = 4.32\\( \\pm0.18\\) \\(M_{\\rm J}\\), while model fits to the transit light curves yield radii of \\(R_{Q3b}\\) = 1.096\\( \\pm0.14\\) \\(R_{\\rm J}\\), \\(R_{Q4b}\\) = 1.135\\( \\pm0.11\\) \\(R_{\\rm J}\\), and \\(R_{Q5b}\\) = 1.107\\( \\pm0.064\\) \\(R_{\\rm J}\\). The host stars are low-mass main sequence stars with masses and radii \\(M_{Q3}\\) = 1.145\\( \\pm0.064\\) \\(M_{\\odot}\\), \\(M_{Q4}\\) = 0.896\\( \\pm0.048\\) \\(M_{\\odot}\\), \\(M_{Q5}\\) = 1.128\\( \\pm0.056\\) \\(M_{\\odot}\\) and \\(R_{Q3}\\) = 1.272\\( \\pm0.14\\) \\(R_{\\odot}\\), \\(R_{Q4}\\) = 0.849\\(\\pm0.063\\) \\(R_{\\odot}\\) and \\(R_{Q5}\\) = 1.076\\(\\pm0.051\\) \\(R_{\\odot}\\) for Qatar-3, 4 and 5 respectively. The V magnitudes of the three host stars are \\(V_{Q3}\\)=12.88, \\(V_{Q4}\\)=13.60, and \\(V_{Q5}\\)=12.82. All three new planets can be classified as heavy hot Jupiters (M > 4 \\(M_{J}\\)).

We summarize the status of a computer simulator for microlens planet surveys. The simulator generates synthetic light curves of microlensing events observed with specified networks of telescopes over specified periods of time. Particular attention is paid to models for sky brightness and seeing, calibrated by fitting to data from the OGLE survey and RoboNet observations in 2011. Time intervals during which events are observable are identified by accounting for positions of the Sun and the Moon, and other restrictions on telescope pointing. Simulated observations are then generated for an algorithm that adjusts target priorities in real time with the aim of maximizing planet detection zone area summed over all the available events. The exoplanet detection capability of observations was compared for several telescopes.

We report the discovery and initial characterization of Qatar-2b, a hot Jupiter transiting a V = 13.3 mag K dwarf in a circular orbit with a short period, P_ b = 1.34 days. The mass and radius of Qatar-2b are M_p = 2.49 M_j and R_p = 1.14 R_j, respectively. Radial-velocity monitoring of Qatar-2 over a span of 153 days revealed the presence of a second companion in an outer orbit. The Systemic Console yielded plausible orbits for the outer companion, with periods on the order of a year and a companion mass of at least several M_j. Thus Qatar-2 joins the short but growing list of systems with a transiting hot Jupiter and an outer companion with a much longer period. This system architecture is in sharp contrast to that found by Kepler for multi-transiting systems, which are dominated by objects smaller than Neptune, usually with tightly spaced orbits that must be nearly coplanar.

Background and Objective: In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. Methods: In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. Results: The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. Conclusion: The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.

The fracture energy of fiber-reinforced concrete (FRC) affects the durability and structural performance of concrete elements. Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy, as the process remains time-intensive and costly. Therefore, machine learning techniques have emerged as powerful alternatives. This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC. For this purpose, 500 data points, including 8 input parameters that affect the fracture energy of FRC, are collected from three-point bending tests and employed to train and evaluate the machine learning techniques. The findings showed that Gaussian process regression (GPR) outperforms all other models in terms of predictive accuracy, achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation. It is then followed by support vector regression (SVR) and extreme gradient boosting regression (XGBR), whereas K-nearest neighbours (KNN) and random forest regression (RFR) show the weakest predictions. The superiority of GPR is further reinforced in a 5-fold cross-validation, where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance. Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy, cementing its claim. The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction, whereas the glass fiber dominates energy absorption amongst the other types of fibers. In addition, increasing the water-to-cement (W/C) ratio from 0.30 to 0.50 yields a significant improvement in fracture energy, which aligns well with the machine learning predictions. Similarly, loading rate positively correlates with fracture energy, highlighting the strain-rate sensitivity of FRC. This work is the missing link to integrate experimental fracture mechanics and computational intelligence, optimally and reasonably predicting and refining the fracture energy of FRC.

In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.

Background and Objective: In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. Methods: In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. Results: The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. Conclusion: The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.

The transit method is considered to be one of the most promising for discovering extrasolar planets. However, the method requires photometric precision of better than ∼ 1%. If we are able to achieve this kind of accuracy, then we are set to discover extrasolar planets. The uniqueness of my experiment will lead to the discovery of transiting planets around the brightest and most important stars quicker than the competitors in the field. The importance of the transit method stems from being able to supply many more planetary parameters than other methods, which plays a crucial role in testing planet formation theories. This thesis is divided into eight chapters. The first chapter provides a general background about transits and their theory. We discuss other methods of extrasolar planet detection, recent developments, future space missions, and what we have learned so far about properties of hot Jupiters. The second chapter details the theory of signals and noise on CCDs followed by the design of the PASS0 experiment. The third chapter reports on the difference imaging data pipeline that we developed and applied to a set of PASS0 data to search for transiting planets. The fourth chapter shows how we apply the PASS0 pipeline to SuperWASP data and improve on the accuracy obtained with their aperture photometry pipeline. The fifth chapter reports on the search for variable stars from the PASS0 and SuperWASP data sets that we consider in this thesis. In the sixth chapter we perform a transit search on the PASS0 and SuperWASP data sets and report the results. In the seventh chapter we use the PASS0 pipeline to process a full season of observing data from 2007 for two recent planet discoveries, WASP-7b and WASP-8b, that have not yet been announced. We analyse their lightcurves and predict their radii. Finally we conclude in the eighth chapter.