Gade Terry (manwren39)

The experimental results show the effectiveness and efficiency of the proposed algorithm for tracking the moving optima in dynamic environments.Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some problems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.Exposure to radioactivity inside homes potentially poses severe health risks which can be exacerbated by the interaction between radioactive particles and fine indoor particles; in particular, the presence of α particles are a key risk factor. Hence, in this study, particle radioactivity was concurrently measured in the family rooms and basements of 26 homes to assess its concentrations and identify its sources, both indoors and outdoors, across two seasons. The levels of radon, air ions, and particle radioactivity, which included short- and long-lived α-activity (SLA and LLA, respectively), varied greatly but were substantially higher in the basements. Also, particle radioactivity-as well as PM2.5 and sulfur concentrations-were lower during the heating season. SLA was associated with radon, which was consistently of indoor origin, whereas LLA was more strongly related to the sulfur measured in indoor PM2.5, which is a proxy of outdoor infiltration. A regression model adjusted for sulfur and SLA also indicated a predominance of outdoor sources, likely due to the short residence time of indoor particles. Our results suggest that radiation in homes originates from both the decay of indoor radon and the infiltration of outdoor radioactivity. Ovarian masses are common findings in general gynecology. However giant ovarian mass is a rare finding. In the literature, a few cases of giant ovarian mass have been mentioned sporadically, especially in elderly patients. We report a 68-year-old postmenopausal woman with a giant right ovarian mass measuring 38 × 31 × 29 cm. She presented to our department with complaints of massive abdominal distention which started gradually 6 months ago. The patient also complained of difficulty in breathing and ambulation. There were no other gastrointestinal, urinary, or gynecological symptoms CA-125 marker was slightly elevated. Because of elevation of serum CA-125 levels, ovarian malignancy was included in differential diagnosis. We performed an ovarian cystectomy without any significant complication. On histopathological examination, the mass was confirmed as benign serous cystadenoma of the right ovary. Giant ovarian mass is a rare finding in general gynecology. Physicians must maintain heightened awareness and index of suspicion when approaching a woman with pain in any region of the abdomen or pelvis. Further investigation with abdominal and pelvic ultrasonography and magnetic resonance imaging or computed tomography is necessary. Benign lesions can be found even in patients presenting with giant masses and higher CA-125 than normal levels. Giant ovarian mass is a rare finding in general gynecology. Physicians must maint