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Gastric cancer is the fourth leading cause of cancer-related mortality and one of the most commonly diagnosed malignancies worldwide. Gastric adenocarcinoma (GAC) accounts for the majority of gastric cancer cases. Circular RNAs (circRNAs) have been shown to be associated with carcinogenesis and cancer progression. This research aims to investigate GAC-associated circRNAs and the underlying mechanisms of circRNA-miRNA-mRNA networks in the development and progression of GAC. Differentially expressed miRNAs and mRNAs (DEMs and DEGs) were identified in Gene Expression Omnibus (GEO) microarray datasets using the R package Limma. A microarray meta-analysis was performed to identify potential GAC-associated circRNAs with high statistical power, resulting in 13 up-regulated and 19 down-regulated circRNAs. CircRNA-miRNA-mRNA networks were constructed by combining predicted and experimentally validated databases and considering differentially expressed miRNAs and mRNAs. The constructed ceRNA networks revealed the potential regulatory effect of hsa_circ_0002019 and hsa_circ_0074736 on key survival-related genes. The expression levels of these two circRNAs were measured in plasma samples from GAC patients and healthy controls using SYBR Green-based real-time PCR. Axon guidance, cellular senescence, AGE-RAGE signaling pathway in diabetic complications, and AMPK signaling pathway were among the major significant (P-value

This paper proposes using sodium-cooled fast reactor technologies for use in hydrogen vapor methane (SMR) modification. Using three independent energy rings in the Russian BN-600 fast reactor, steam is generated in one of the steam-generating cycles with a pressure of 13.1 MPa and a temperature of 505 °C. The reactor's second energy cycles can increase the gas-steam mixture's temperature to the required amount for efficient correction. The 620 ton/hr 540 °C steam generated in this cycle is sufficient to supply a high-temperature synthesis current source (700 °C), which raises the steam-gas mixture's temperature in the reactor. The proposed technology provides a high rate of hydrogen production (approximately 144.5 ton/hr of standard H2), also up to 25% of the original natural gas, in line with existing SMR technology for preparing and heating steam and gas mixtures will be saved. Also, exergy analysis results show that the plant's efficiency reaches 78.5% using HTR heat for combined hydrogen and power generation.

The tendency to renewables is one of the consequences of changing attitudes towards energy issues. As a result, solar energy, which is the leader among renewable energies based on availability and potential, plays a crucial role in full filing global needs. Significant problems with the solar thermal power plants (STPP) are the operation time, which is limited by daylight and is approximately half of the power plants with fossil fuels, and the capital cost. Exergy analysis survey of STPP hybrid with PCM storage carried out using Engineering Equation Solver (EES) program with genetic algorithm (GA) for three different scenarios, based on eight decision variables, which led us to decrease final product cost (electricity) in optimized scenario up to 30% compare to base case scenario from 28.99 $/kWh to 20.27 $/kWh for the case study. Also, in the optimal third scenario of this plant, the inner carbon dioxide gas cycle produces 1200 kW power with a thermal efficiency of 59% and also 1000 m3/h water with an exergy efficiency of 23.4% and 79.70 kg/h with an overall exergy efficiency of 34% is produced in the tetrageneration plant.

A light water nuclear Reactor has been exergy analyzed, and the rate of irreversible exergy loss and exergy destruction is calculated for each of its components. The ratio of these losses compared to the total input exergy loss is calculated, which shows that most irreversible losses occur in the reactors, turbines, steam generators, respectively, as well as in the downstream operations. The main aim of this paper is to optimize the power plant using an innovative firefly algorithm and then to propose a novel strategy to improve the overall performance of the plant. As shown in the results, the exergy destruction rate of the plant decreased by 1.18% using the firefly method, and the exergy efficiency of the plant reached 29.3% comparing to the operational amount of 28.99%. Also, the results of the firefly optimization process compared to the Genetic algorithm and gravitational search algorithm to study the accuracy of the model for exergy analysis fitness problems in the power plants and the results of this comparison has shown that the results are nearly similar in the mentioned methods. However, the firefly is faster and more accurate in limited iterations.