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The article presents a new comparative analysis of the dielectric properties of polyvinyl alcohol (PVA) and polyethylene (PE) at different temperatures and their other physical properties. The characteristic peaks were observed in the FTIR spectra of the polymers. The amounts of carbon and oxygen on PVA’s surface differed from PE. It was hypothesized that these properties could be affected by the increase in temperature and difference in the chemical structures of the polymers. We investigated the dielectric properties of these polymers between 20 °C and 100 °C. Our results revealed that the dielectric properties of these polymers were not the same. The actual permittivity values of PVA at low temperatures were almost constant for all frequency values and increased at higher temperatures with the decrease in frequency. The same pattern concerning the increase of imaginary permittivity of this polymer was observed at low frequencies. The increase of the real and imaginary permittivity of the PVA accompanied the increase in temperature. The actual permittivity of PE was almost constant with the frequency at different temperatures. However, the imaginary permittivity of this polymer showed an increase at low frequencies. The increase in temperature accompanies the increase of the PE’s imaginary permittivity. As expected, the capacitance, like permittivity, changed differently for the polymers. Moreover, an increase in temperature had more effect on the conductivity of PVA than that of PE. The difference in these polymers’ dielectric properties could be due to their chemical structures. The results of this article can be used for further applications of PVA and PE in science and engineering.

This study presents a new application of TOPSIS for the optimization of transcription factors characteristics. This application is essential as it can help compare the characteristics of these proteins and determine the optimized output of their comparison with this decision-making method. The hypothesis in this article was that according to the previous study of the Cdx transcription factors, as the Cdx2 transcription factor showed more robust characteristics than Cdx1 and Cdx4, the TOPSIS method would show a better rank position of these first proteins in comparison with the two other ones. Moreover, the engrailed repressor domain EnRCdx1 used in the plasmid showed the reduction of the pax3 gene expression in comparison with the induced regulation of the gene expression with the production of the Cdx1, Cdx2, and Cdx4 transcription factors using the corresponding plasmids, the worst rank position with TOPSIS was expected for this repressor domain. The results obtained with this ranking method showed that the rank positions of the transcription factors and the repressor domain corresponded to their compared properties. Moreover, the change in the weight values of the candidates showed the modification of their distances from the best and worst alternatives and closeness coefficients. However, as expected, the candidates’ rank positions were unchanged, and the Cdx2 transcription factor was still the best candidate. The results of this article can be used in computer engineering to improve biological applications of these proteins.

The present study focuses on a new application of TOPSIS to predict and optimize graphene oxide’s characteristics. Although this carbon-based material has been investigated previously, its optimization with this method using an automated decision-making process has not been performed yet. The major problem in the design and analysis of this nanomaterial is the lack of information on comparing its characteristics, which has led to the use of diverse methods that have not been appropriately compared. Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. Several characteristics of graphene oxide, such as its antibiofilm activity, hemocompatibility, activity with ferrous ions in hydrogen peroxide, rheological properties, and the cost of its preparation, have been considered in its analysis with TOPSIS. The results of this study revealed that the consideration of the criteria of this nanomaterial as profit or cost criteria would impact the distances of candidates from the alternatives. Moreover, the ranks of the candidates changed when the rheological properties were considered differently in the data analysis. This investigation can help improve the use of this nanomaterial in academic and industrial investigations.

This paper applies a novel automated decision-making process with TOPSIS to analyze ions and organic materials in drinking water. The hypothesis was that the modified TOPSIS algorithm with the Łukasiewicz fuzzy disjunction would be appropriate to optimize the drinking water samples. The maximum output values were set to one to apply the fuzzy disjunction. The concentrations of ions and organic materials in the drinking water samples were considered from the values for naturally occurring chemicals that would be of health significance. Materials with positive effects on the body were considered profit criteria, whereas other ones with negative impacts on human health were considered cost criteria. The analysis of samples with unmodified TOPSIS showed that profit criteria having high concentrations and cost criteria having low concentrations had the dominant effects on the candidates’ ranking. The modified TOPSIS showed that the candidates’ ranking in the second analysis series was the same as in the first. However, the value of 1.0 for the fourth candidate’s concentration of nitrite, which resulted from the fuzzy disjunction in the algorithm of the modified TOPSIS, was attributed to the confusion of the drinking water and undrinkable water categories. The optimization results for drinking water samples could be applied in science and engineering based on the concentrations of their ions and organic materials with the automated decision-making process for their distinction from undrinkable water.

The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. The main hypotheses are that the change in the weights of alternatives could affect the ranking of algorithms. The obtained data confirmed this hypothesis for their ranking. Moreover, adding labor as a cost criterion to the list of criteria did not affect this ranking. This was because candidate 3 had better fuzzy membership degrees than the two other candidates concerning their criteria. This work showed the importance of the value of the fuzzy membership degrees of the cost criterion of the algorithms in their ranks. The values of the fuzzy membership degrees of the algorithms used for proteomic analysis could determine their priority according to their score differences. One of the advantages of this study was that the studied methods could be compared according to their characteristics. Another advantage was that the obtained results could be related to the new ones after improving these methods. The results of this work could be applied in engineering, where the analysis of proteins would be performed with these methods.

The present study focuses on the comparative analysis of superparamagnetic iron oxide nanoparticles (SPIONs) characteristics with the TOPSIS method. The prediction of the characteristics of SPIONs is required for better manufacturing of these nanoparticles. Although the characteristics of these nanoparticles have been investigated, no research has been done on their comparison in order to determine which one of their surface functionalities would be more appropriate for their diverse applications. The objective of this study was to analyze the characteristics of SPIONs without or with surface charge with a prediction model and TOPSIS in order to determine the best nanoparticles. Moreover, the effect of inappropriate consideration of their cost criterion on their ranks was explored with the modified TOPSIS. This analysis showed that the characteristics of SPIONs such as antibiofilm activity, hemocompatibility, activity with hydrogen peroxide, rheological properties, and the labour of their chemical synthesis could affect their ranking. Neutral SPIONs, negatively charged SPIONs, and positively charged SPIONs were ranked as the first, second, and third candidates, respectively. However, the improvement of the activity of positively charged SPIONs with hydrogen peroxide showed an increase to 0.3 instead of 0.2, which resulted in a better rank of these nanoparticles in comparison with that of the same nanoparticles in the first analysis series. One of the advantages of this study was to determine the impact of the characteristics of SPIONs on their ranking for their manufacturing. The other advantage was getting the information for further comparative study of these nanoparticles with the others. The results of this work can be used in manufacturing engineering and materials science.

This paper focuses on studying the rheological properties of polydimethylsiloxane (PDMS). This polymer has been used to fabricate membranes and filters in engineering. The analysis of the rheological properties of this polymer is required for a further investigation of its mechanical behavior. In this study, the rheological behavior of PDMS is reported at different temperatures. This polymer showed steady shear viscosity during a short duration. However, this behavior changed with time and increased more with increasing temperature. The impact of the temperature increase was also observed when the shear viscosity of PDMS increased with shear strain. The increase of torque with shear strain and time was observed at different temperatures. Shear stress increased linearly with the shear rate at 20 °C and 40 °C. As expected, the deformation of the polymer required less shear stress with the increase of temperature. However, the change of shear stress with the shear rate at 60 °C was not linear, and the slope of the curve increased more at high shear rates. The results of this investigation can provide the required information for a better fabrication of membranes and filters with this polymer.

This paper aims to present a new application of TOPSIS with an automated decision-making process for the analysis of drinking water. For this purpose, the algorithm was modified with a fuzzy disjunction, and the maximal output values were set to one. The properties of drinking water, such as total dissolved solids, hardness, electrical conductivity, and cost, were the criteria analyzed in this study. These criteria were analyzed with unmodified and modified algorithms. Therefore, the modified TOPSIS was also used to optimize the parameters of the candidates. The appearance of the value of 1.0 in the algorithm’s output was due to the confusion of an individual’s categories of drinking water and undrinkable water. The advantage of this investigation was that, for the first time, it allowed automated decision-making to detect the drinking water in different samples and analyze them according to their characteristics. This would be important in developing new technologies for detecting and analyzing drinking water in the environment. The results of this paper can be applied in materials sciences and engineering.

Hydrogels are promising biomaterials for diverse applications that require studying their rheological properties. While some properties of hydrogels have been investigated, their comparative analysis for a deeper understanding of their rheological properties is required to determine their mechanical behavior. Polyvinyl alcohol (PVA) and polyethylene glycol (PEG) are among the hydrogels with diverse applications in engineering. This study aims to provide comparative data on their rheological properties. Both PVA and PEG showed steady shear viscosity as their viscosity did not show a huge change with time. Their shear viscosity increased with shear strain. PEG showed more shear thickening behavior than PVA. While the shear viscosity of PVA reached a plateau, that of PEG continued to increase. This was attributed to the sensitivity of PEG to its deformation because of the junction separations after the application of mechanical force on the polymer. Furthermore, the slow increase in the shear viscosity of both polymers was observed with the increase of the shear rate. This increase was 2.4 % for PVA and 8.7 % PEG, respectively. As these polymers are among the candidates for the preparation of nanocomposites, the results of this study can provide the required information for their applications in engineering.

The present study focuses on the rheological properties of polyethylene glycol (PEG) modified, positively charged, and negatively charged superparamagnetic iron oxide nanoparticles (SPIONs) at different temperatures. We hypothesized that the surface properties of these nanoparticles in the water did not affect their rheological properties. These nanoparticles had not the same surface properties as SPIONs-PEG had not to charge on their surface whereas positively charged and negatively charged ones with amine and carboxyl groups as their surfaces had positive and negative surface charges, respectively. However, their rheological behaviors were not different from each other. The comparative rheological study of SPIONs revealed their pseudo-Newtonian behavior. The viscosity of SPIONs decreased with the increase in temperature. At low shear rates, the shear stress of SPIONs was independent of rate and increased with the increase of rate. Moreover, at high shear rates, the shear stress for PEG-SPIONs was more than those for positively charged and negatively charged SPIONs. These measurements also revealed that at high shear rates, the shear stress of samples decreased with the increase of temperature. The shear stress of samples decreased with the increase of shear strain and the temperature. We also observed that all the samples had the same amount of shear strain at each shear stress, which indicated the exact resistance of SPIONs to deformation. Furthermore, the shear modulus decreased with time for these nanoparticles. These results suggest that these nanoparticles are promising candidates with appropriate properties for fluid processing applications and drug vectors in biomedical applications.

In this article, the antibiofilm activity and physicochemical properties of graphene oxide (GO) nanoribbons, which have been among the most exciting materials, were studied by measuring the ratio of killed to alive bacteria incubated with these nanomaterials. Our objective was to determine the related physicochemical and antibiofilm properties of graphene oxide nanoribbons. We hypothesized that the physicochemical properties of graphene oxide nanoribbons could affect their antibiofilm activity. A combination of spectroscopic and microscopic measurements of the samples allowed us to determine their physicochemical properties affecting the biofilms. Our work includes information on the surface properties of these materials related to their incubation with the biofilms. The Fourier transform infrared spectroscopy showed the vibrations of OH groups of water molecules adsorbed on graphene oxide nanoribbons. The results show the high antibiofilm activity of the graphene oxide nanoribbons. The fluorescence confocal microscopy revealed that 50 % ± 3 % of the total number of bacteria were killed with these nanomaterials. The incubation of graphene oxide nanoribbons with bacterial biofilms resulted in the appearance of the NO2–, NO3– peaks in the negative mode mass spectrum. The attenuation of the O– and OH– peaks were attributed to the interactions of the samples with the biofilms. Our study gives more evidence of the practical value of graphene oxide nanoribbons in killing bacteria related to their surface physical properties and the potential of these nanomaterials for materials science and biomedical applications.