BackgroundThe aim of this report is to analyse our results and to underline our criteria for day surgery practice.Patients and methodsOur patients are selected according to ASA criteria. Occasionally, the patients with some risk factors are selected but only when their concomitant diseases are well controlled.ResultsWe have treated 673 patients with the third and early fourth degree haemorrhoids. We have had patients with several minor problems causing no influence on the success of the procedure. Good haemostasis, preventing postoperative bleeding, is the most important factor.ConclusionAmong proctological procedures in haemorrhoid treatment, the PPH technique has firm and well-accepted position even in ambulatory surgery.
In this study, a low cost nano-second pulsed high voltage power system was used to generate plasma on water surface in a water treatment reactor at the expense of electrical energy. E. coli was inoculated as a model bacterial contaminant in the treatment water. The results of the bacterial counts obtained showed a 65% decrease from 244 colony forming units (CFUs) in the control to 89 CFUs in the plasma treated water that is a significant reduction of the bacterial count. In a separate experiment, methylene blue was mixed with the water as a model chemical decontaminant. The significance of an alternative decontamination method, both cost effective and safe will be an answer to the increasing scarcity of clean water all around the world. This method will especially be useful in developing countries.
Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted natural images are used during training to improve robustness against various noise levels. Experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state of the art methods. Extensive experiments showed the proposed method gives promising performance in a wide range of noise levels with a single network.
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