Corrigendum to "Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes".

[This corrects the article DOI: 10.1155/2021/7332776.].

Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization.

This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.

The molecular mechanisms and therapeutic potential of EZH2 in breast cancer.

Due to its high occurrence and mortality rate, breast cancer has been studied from various aspects as one of the cancer field's hot topics in the last decade. Epigenetic alterations are spoused to be highly effective in breast cancer development. Enhancer of zeste homolog 2 (EZH2) is an enzymatic epi-protein that takes part in most vital cell functions by its different action modes. EZH2 is suggested to be dysregulated in specific breast cancer types, particularly in advanced stages. Mounting evidence revealed that EZH2 overexpression or dysfunction affects the pathophysiology of breast cancer. In this review, we discuss biological aspects of the EZH2 molecule with a focus on its newly identified action mechanisms. We also highlight how EZH2 plays an essential role in breast cancer initiation, progression, metastasis, and invasion, which emerged as a worthy target for treating breast cancer in different approaches.

Computational simulation and modelling of uranium extraction using tributylphosphate through membrane extractor.

Non-disperse solvent extraction is an effective technique for the extraction of metal ions from aqueous solution. In this study, uranium extraction using n-dodecane solvent containing tributylphosphate extractant in a membrane contactor was investigated. A 2D mathematical model was developed for the fluid flow and mass transfer in the hollow fibre membrane extractor. The equations of the created model were solved using the finite element method. The uranium concentration distribution in the extractor at different extractant concentrations as well as feed acidity was studied. The results showed that there is reasonable good agreement between experimental uranium extraction and modelling outputs at different extractant concentrations. Increasing extractant concentration from 5 to 30% led to the enhancement of uranium extraction from 2.60 to 34.13%. Also, there was an increase in the uranium extraction with increasing feed acidity in the range of 1-3 M. Furthermore, based on the radial uranium concentration distribution, it was found that the main mass transfer resistance in the system was microporous membrane section. Finally, it was obtained that the uranium extraction efficiency could be improved significantly by increasing porosity-to-tortuosity ratio. It was concluded that the membrane specification plays the most important role as the dominant mass transfer resistant was in the membrane subdomain.

Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm.

Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food.

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.

Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes.

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources.

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses' search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

A paradigm shift in cell-free approach: the emerging role of MSCs-derived exosomes in regenerative medicine.

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.