This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

A review of the literature on intellectual capital development was conducted using systemic criteria for the inclusion of relevant studies. The concepts behind the ideas explored in the present study were discussed in respect to the subject matter. Examining the past state of the art in the intellectual capital sector for achieving high levels of innovation performance provided a multidimensional picture of intellectual capital, innovation performance, and dynamic capabilities. The present review was designed to illustrate the correlation between intellectual capital and innovation performance, as well as the role of dynamic capabilities in moderating the relationship between these constructs. Accordingly, we presented an extensive

Sage (Salvia officinalis), belong to Labiatae family is indigenous to Iraq and other Mediterranean areas but now cultivated world- wide, principally for its use as culinary herb. In the present study preliminary screening for the important phytochemical natural product groups indicated the presence of flavonoid, saponin, hyrolysable and condensed tannin groups. The antibacterial  activity of two concentrations 10 mg/ml and 100 mg/ml of chloroform and hydroalcoholic extracts from  Salvia officinalis leaves was evaluated against four strains of gram negative bacteria (Escherichia coli,  Pseudomonas arigenossa,  Klebsiella pneumonia, and  Proteus spp) and two str

The present study aimed to synthesize selenium nanoparticles (SeNPs) using aqueous extract of black currant as a reducing agent. The green synthesized black currant selenium nanoparticles (BCSeNPs) were identified by color change. The characterization of SeNPs was achieved by Ultraviolet-visible (UV–VIS) spectroscopy, scanning electron microscopy (SEM), X–ray diffraction analysis (XRD), and Fourier transform infrared spectroscopy (FTIR). These tests were used to detect: stability, morphology, size, crystalline nature, and functional groups present on the surface of BCSeNPs. The results revealed appearance of the brick-red color indicating the specific color of selenium nanoparticles, and UV-Vis spectroscopy showed band absorbanc

Since the appearance of COVID-19 disease as an epidemic and pandemic disease, many studies are performed to uncover the genetic nature of the newly discovered coronavirus with unique clinical features. The last three human coronavirus outbreaks, SARS-CoV, MERS-CoV and SARS-CoV-2 are caused by Beta-Coronaviruses. Horizontal genetic materials transfer was proven from one coronavirus to the other coronavirus of non-human origin like infectious bronchitis virus (IBV) of avian. Horizontal genetic materials transfer was also from non-corona viruses like astroviruses and equine rhinovirus (ERV-2) or from coronavirus-unrelated viruses, like influenza virus type C. However, SARS-CoV-2 is identical to SARS-CoV and MERS-CoV. Interestingly, Wuhan ci