Thermo-economic investigation on the hydrogen production through the stored solar energy in a salinity gradient solar pond: A comparative study by employing APC and ORC with zeotropic mixture

In this paper, a salinity gradient solar pond (SGSP) is used to harness the solar energy for hydrogen production through two cycles. The first cycle includes an absorption power cycle (APC), a proton exchange membrane (PEM) electrolyzer, and a thermoelectric generator (TEG) unit; in the second one, an organic Rankine cycle (ORC) with the zeotropic mixture is used instead of APC. The cycles are analyzed through the thermoeconomic vantage point to discover the effect of key decision variables on the cycles’ performance. Finally, NSGA-II is used to optimize both cycles. The results indicate that employing ORC with zeotropic mixture leads to a better performance in comparison to utilizing APC. For the base mode, unit cost product (UCP), exergy, and energy efficiency when APC is employed are 59.9 $/GJ, 23.73%, and 3.84%, respectively. These amounts are 47.27 $/GJ, 29.48%, and 5.86% if ORC with the zeotropic mixture is utilized. The APC and ORC generators have the highest exergy destruction rate which is equal to 6.18 and 10.91 kW. In both cycles, the highest investment cost is related to the turbine and is 0.8275 $/h and 0.976 $/h for the first and second cycles, respectively. In the optimum state the energy efficiency, exergy efficiency, UCP, and H₂ production rate of the system enhances 42.44%, 27.54%,15.95%, and 38.24% when ORC with the zeotropic mixture is used. The maximum H₂ production is 0.47 kg/h, and is obtained when the mass fraction of R142b, LCZ temperature, pumps pressure ratio, generator bubble point temperature are 0.603, 364.35 K, 2.12, 337.67 K, respectively.     

A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

Improved optical and structural properties of cadmium sulfide nanostructures for optoelectronic applications

In this study, the effect of ZnO seed layer on the growth of uniform CdS nanostructures was investigated using chemical bath deposition technique. Besides, the influence of molar concentration of reagents on the surface morphology, structural and optoelectrical properties of the deposited CdS thin films were examined. The CdS nanostructures were grown on bare glass and ZnO/glass substrates with different reagent molar concentrations. The results indicated an improvement in the homogeneity and uniformity of the grown CdS nanostructures on ZnO seed layer which can be due to the low lattice mismatch between ZnO and CdS structures. The CdS/ZnO samples were optimized by changing the molar concentration of reagents. A three–dimensional intersecting vertical nanosheet morphology with hexagonal structure was obtained when modified chemical concentration of 0.5 M was applied. The XRD pattern of CdS nanosheets indicated the hexagonal phase of CdS which were strongly orientated along (002) plane. The elevated intensity of dominant peak related to this sample confirmed the improved crystal quality of this CdS nanostructure comparing to the other samples. The UV–Vis spectrum demonstrated a high absorption coefficient for CdS intersecting nanosheets which might be due to the high specific surface area and light trapping behavior of this sample. The photoluminescence study also showed an improvement in optical properties of optimized CdS nanostructures. In order to study the optoelectrical properties of CdS nanostructures, metal–semiconductor–metal photodetectors were fabricated with different CdS samples and their current–voltage characteristics were analyzed. The results indicated an enhancement in photosensitivity, responsivity, and speed of photodetectors based on optimized CdS nanostructures.     

Optical pattern recognition using two-channel joint transform correlation

A new joint transform correlation (JTC) technique, named two-channel JTC (TJTC), is proposed in this paper for optical pattern recognition applications. The TJTC technique independently evaluates the autocorrelation and crosscorrelation values of the reference and the target images and employs a modified decision algorithm. In addition, optical threshold operation and fringe-adjusted filter are incorporated in the proposed technique to enhance the correlation output and to improve the discrimination performance. The proposed technique shows better recognition performance compared to existing JTC techniques. Computer simulation are presented to investigate the salient features of the proposed TJTC technique with noise-free as well as noisy input scenes. The text was submitted by the authors in English.     

Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

Neural Computing and Applications - Cryptography often involves substituting (and converting) the secret information into dummy data so that it could reach the desired destination without leakage....     

Bathymetry and siltation rate for Dokan Reservoir,Iraq

The Dokan Reservoir dam is a concrete cylindrical arch with gravity abutments, located on the Lesser Zab River about 60 km from the city of Sulaimani in north‐eastern Iraq. A bathymetric survey was conducted in November 2014 for a period of 10 days, using an echo sounder of 200‐kHz single beam. The survey results indicated an annual average sediment deposition of 3.8 million m³. Thirty‐two sediment samples were collected from the reservoir bed. The ratio of gravel, sand, silt and clay was 15:14:48:23, respectively. The reservoir bed is covered mainly with silt. The sediments are composed of silty clay (77.6%), silty sandy clay (10%), sandy gravely silty clay (1.2%) and gravely sandy silty clay (1%).     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.