The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles of patients. There are two possible vein structures in each image: a cross-section (circular) and a tube (non-circular) structure. This proposed system identifies dilated (varicocele) veins of these structures in ultrasound images in three stages: preprocessing, processing, and detection and measurement. These three stages are applied in three different color modes: Grayscale, Red-Green-Blue (RGB), and Hue, Saturation, and Value (HSV). In the preprocessing stage, the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation. Then, the processing stage employs different filters to perform image denoising. Finally, edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist. Overall implementation results showed the proposed system is faster and more effective than the previous work.

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This study deals with an integrated solar combined cycle systems (ISCCS) simulation. The ISCCS presents a cogeneration of the Parabolic Trough Solar collector (PTC) system and the Rankine power cycle. The PTC system is employed to ensure stable thermal energy which is required to operate a steam turbine (with three stages), which is designed to produce enough electrical power (medium scale). The performance of the ISCCS is simulated and evaluated by using the TRNSYS software. This system is being considered for installation in Irbid, Jordan, and a comparison study is being conducted to determine the performance of the ISCCSmedium scale during the winter and summer seasons. The results show that the ISCRCS system's efficiency is 14.3% in winter (January 1) and 21.2% in summer (August 1), with 4.48 MW of electrical power produced in winter (January 1) and 4.05 MW produced in summer (August 1).

As The effect of iron oxide nanoparticles (IONPs) on the anaerobic co-digestion (AD) of olive mill wastewater and chicken manure was investigated. In mesophilic conditions, biogas yield, methane (CH4) content, the removal efficiency of TS, VS., acidification and hydrolysis percentage, and contaminant removal efficiency were investigated. Supplementing AD with IONPs at a concentration of 20 mg/g VS. > IONPs and INOPs >30 mg/g VS. causes an inhibitor impact on biogas, methane generation, and hydrolysis. Furthermore, implantation with 20–30 mg of IONPs/kg VS. has induced an equivalent favorable impact, with hydrolysis percentages reaching roughly 7.2%–15.1% compared to the control test, in addition to a 1.3%–4.2% enhancement in methane generation yield. The maximum acidification concentration after five days of the incubation of 1,084, 9,463, and 760 g/L was attained with IONPs dosages of 25, 30, and 20 mg/g VS., respectively, compared to 713 g/L obtained with the control test. The results have illustrated that supplementing AD with a specific concentration of IONPs (20–30 mg/g VS.) has a significant effect and enhances the inhibitor removal efficiency, most possibly due to the small surface area of IONP particles. The resultant increase in the active surface area enhances the enzyme diffusion within the substrate. This study provides new data specifying the enhancement of iron oxide nanoparticles (IONPs) and identifies the impact of IONP doses at various concentrations on the AD of olive mill wastewater and chicken waste.

<p>This work investigates the problems of extending the sensors network lifetime in smart cities. The limited capacity of the sensors’ batteries, and the difficulty of replacing the sensors’ batteries in hard-to-reach areas are some of the main challenges that contribute in reducing the lifetimes of the networks. The direction of this study is to use renewable energy as an energy source for collecting data from various infrastructures that are distributed throughout these cities. We present a model for data collection based on combining energy harvesting (EH) with the cluster head rotation feature, which results in flexible and sustainable networks that can be used in smart cities. Simulation results depict the performance of the proposed model with and without EH technology. The metrics used to compare the performance of the proposed model with and without EH technology include the consumed energy by sensors, number of live and dead sensors, and energy variance. The results show that the network lifetime increases when EH technology is used.</p>