Recently, mesenchymal stem/stromal cells (MSCs) due to their pro-angiogenic, anti-apoptotic, and immunoregulatory competencies along with fewer ethical issues are presented as a rational strategy for regenerative medicine. Current reports have signified that the pleiotropic effects of MSCs are not related to their differentiation potentials, but rather are exerted through the release of soluble paracrine molecules. Being nano-sized, non-toxic, biocompatible, barely immunogenic, and owning targeting capability and organotropism, exosomes are considered nanocarriers for their possible use in diagnosis and therapy. Exosomes convey functional molecules such as long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs), proteins (e.g., chemokine and cytokine), and lipids from MSCs to the target cells. They participate in intercellular interaction procedures and enable the repair of damaged or diseased tissues and organs. Findings have evidenced that exosomes alone are liable for the beneficial influences of MSCs in a myriad of experimental models, suggesting that MSC- exosomes can be utilized to establish a novel cell-free therapeutic strategy for the treatment of varied human disorders, encompassing myocardial infarction (MI), CNS-related disorders, musculoskeletal disorders (e.g. arthritis), kidney diseases, liver diseases, lung diseases, as well as cutaneous wounds. Importantly, compared with MSCs, MSC- exosomes serve more steady entities and reduced safety risks concerning the injection of live cells, such as microvasculature occlusion risk. In the current review, we will discuss the therapeutic potential of MSC- exosomes as an innovative approach in the context of regenerative medicine and highlight the recent knowledge on MSC- exosomes in translational medicine, focusing on in vivo researches.

A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe-B-Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe-B-Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs.

The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

Greenhouse gas (GHG) pollution is considered one of the challenging concerns in industrial plants, and to emit the appropriate designation in nitrogen oxide reduction, it is required to implement proper numerical simulation procedures. In this study, ANSYS Fluent® software is used as dynamic software to solve heat and mass flow transfer numerically by considering non-structured networks for complex geometries. Dry nitrogen oxide burners have an additional thermocouple to provide an extra fuel pathway to combine with air. Then, standard K-ε is used in the numerical simulations to calculate thermal efficiency in combustion processes for turbulent flow regimes. It can cause the removal of 50% of nitrogen oxide into the atmosphere. Furthermore, by the increase of temperature, nitrogen oxide concentration has been increased in the system. After 1975 K, Fuel has been changed to dry fuel, and therefore nitrogen oxide concentration increased because the steam can provide a relatively non-combustible compound increase than fuel. On the other hand, regarding the water volume increase at inlet steam, nitrogen oxide volume percentage has been decreased dramatically, especially in the first periods of water volume increase. Consequently, when the steam percentage is increased instead of water, nitrogen oxide reduction is increased. Moreover, our simulation results have a proper match with Gibbs energy equilibrium.

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