Cavitation phenomenon can cause deterioration of the hydraulic performance, damage by pitting, material erosion, structure vibration and noise in fluid machinery, turbo-machinery, ship propellers and in many other applications. Therefore, it is important to detect Inception of cavitation phenomenon. An experimental study has been carried out in order to investigate the noise radiated by various cavitating sources to determine the validity of noise measurements for detecting the onset of cavitation. Measurements have been made measuring the noise radiated by a number of configurations in a water tunnel at various operating condition to determine the onset of cavitation. The measurements have been conducted over a frequency range of 31. 5 Hz to 31. 5 kHz in one-third octave bands. The onset of cavitation was measured visually through a Perspex side of the working section of the water tunnel. Moreover, a theoretical estimate of the pressure radiated from the cavitation nuclei at their critical radii and their frequency was presented. Tests indicated that, generally, at the point of visual Inception there was a marked rise of the sound pressure level in the high-frequency noise, whilst the low-frequency noise increased as the cavitation developed. This finding was supported by the theoretical estimate of the pulsating frequency of cavitation nuclei. The results illustrated that the visual observations of Inception confirm the noise measurements.

Cardiovascular diseases are the world's principal reason for death, accounting it about 17.9 million people per year, as reported by World Health Organization(WHO). Arrhythmia is often a heart disease that is interpreted by a variation in the linearity of the heartbeat. The goal of this study would be to develop a new deep learning technique to accurately interpret arrhythmia utilizing a one-second segment. This paper introduces a novel method for automatic GAN-based arrhythmia classification. The input ECG signal is derived from the fusion of well known Physionet dataset from MIT-BIH and some Hospital ECG databases. The ECG segment over time is used to detect 15 different classes of arrhythmias. The GAN network uses an attention-based generator to learn local essential features and to maintain data integrity for both time and frequency domains. Among these, the highest accuracy obtained is 98\%. It can be inferred from the results that the proposed approach is smart enough to make meaningful predictions and produces excellent performance on the related metrics.

Cervical Spondylosis is a recurring spinal syndrome in which the spine progressively tightens and that can eventually become fully rigid. Early diagnosis is really an efficient way of improving the recovery rate and reducing costs. Due to the difficult and comprehensive procedure for recognizing cervical spondylosis in the initial stages, this area is untreated. Strong correlations of the vertebrae make the automatic detection procedure challenging. These minor variations in the X-ray image make visual interpretation a challenging task involving skilled explorers. Even after this, the problem still remains untreated and also the feasibility of even an automatic detection framework has still not been addressed for this application. Thus, the Deep learning-based method was used to predict some potential relevance of Cervical Spondylosis has. The proposed system can be used to detect the onset of cervical spondylosis in the early stages using deep learning techniques.

The ever-increasing growth of amoral content in cyberspace has caused the idea of detecting such contents in images, then creating an alarm or limiters in different age ranges around the world. This content not only detects harmful visual items, but also it detects instances of abusing individuals. This study has represented a new approach to detect images with amoral content. The suggested model includes two deep models based on Inception blocks and attention and residual based convolutional layer which acts with two different approaches: the first model is a five-class recognition model and the second one is an estimator model which maps the image into levels of inappropriateness between 0 and 1. Combining these two models by an aggregator based on first-order rules leads to representing a model which improved 2.1% detection precision in not suitable for work (NSFW) dataset and 2.3% on TI-UNRAM dataset in comparison with other state-of-the-art models. Our approach also shows promising results to model culturally based definition of NSFW, especially in Iran.

Stepped ogee spillways are one of the most widely used types of dams that are used in most dam construction projects, including small and large dams. The Inception point of aeration on these spillways is an important place in determining the range of single-phase and two-phase flow, which characterize the areas at risk of cavitation. In this paper, the effect of roughness on the location of the Inception point of flow aeration (IPFA) on the stepped Ogee spillways was investigated. For this purpose, the surface of the steps of a laboratory model was covered with gravel with specific granulation. The result indicated that by roughing the surface of steps, the displacement of IPFA moves towards the crest (upstream) and the length of non-aerated area on the stepped spillways is decreased by about 15 percent. The results declared that there is a direct exponentially relation between flow rate and displacement IPFA. At low flow rates, most of the flow turbulence is due to the roughness created by the geometry of the steps, hence the role of surface roughness is negligible, while with increasing flow rate, its role in increasing the flow turbulence increases, and its effect on displacement of IPFA becomes obvious. At a given flow, the length of the non-aerated is decreased with increasing roughness.In this study, the effect of surface roughness of steps on the displacement of IFPA was investigated experimentally. To this end number of laboratory experiments were programmed. To investigate the objective of this study, a stepped ogee spillway in which its horizontal part of steps was covered by gravel with given grain size. The results declared that three factors including the flow rate, the roughness caused by steps dimension (ks), and the roughness of steps surface (ns) are effective in the displacement of IPFA. In this study, the change in the size of the steps and the longitudinal slope of the stepped chute on the displacement of IPFA has not been investigated because it has already been studied by other researchers. There is a direct exponential relationship between the discharge and the IPFA (length of the non-aerated area on the stepped ogee spillway). As the flow rate increases, the location of this point is transferred downstream exponentially. With the increase of flow, the role of roughness in IPFA displacement became clearer and the reason is the increase of its role in creating and increasing flow turbulence. On average, surface roughness can be about 15% effective in reducing the displacement of IPFA.

With the rising incidence of metabolic syndrome and progressive kidney disease, efforts to halt this progression have become the mainstay of therapies in the era of modern nephrology. The necessity of one versus two kidneys has occupied the minds of leading scientists and philosophers throughout the ages and has laid the foundation for our understanding of progressive kidney disease. This review focuses on the major discoveries of the leading thinkers who with their paradigm shifting ideas and skepticism pushed the boundaries of our knowledge and shaped the theory of hyperfiltration.