Establishment of a calibration curve for an isocentric cobalt unit using Monte Carlo simulation

Measurement of dose distribution in patients during radiotherapy is impossible. The Monte Carlo simulation is an alternative method for dose calculations. In routine radiotherapy, the source-to-surface distance(SSD)method is not practical for an isocentric unit because it requires numerous values of tissue–air ratios and inverse square law. Therefore, this method is time consuming. In this paper, the curves of relative depth doses were obtained for three different SSDs using the MCNP4C Monte Carlo simulation and approximated with a single curve called calibration curve. This curve was compared to the curve obtained by published data, differing in approximately 5% in the worst case. It was also observed that the obtained results were more accurate for distances between-5 and 10 cm from source-to-axis distance.     

Rheology of fluids measured by correlation force spectroscopy

We describe a method, correlation force spectrometry (CFS), which characterizes fluids through measurement of the correlations between the thermally stimulated vibrations of two closely spaced micrometer-scale cantilevers in fluid. We discuss a major application: measurement of the rheological properties of fluids at high frequency and high spatial resolution. Use of CFS as a rheometer is validated by comparison between experimental data and finite element modeling of the deterministic ring-down of cantilevers using the known viscosity of fluids. The data can also be accurately fitted using a harmonic oscillator model, which can be used for rapid rheometric measurements after calibration. The method is non-invasive, uses a very small amount of fluid, and has no actively moving parts. It can also be used to analyze the rheology of complex fluids. We use CFS to show that (non-Newtonian) aqueous polyethylene oxide solution can be modeled approximately by incorporating an elastic spring between the cantilevers.     

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.     

Real-time separation of aerosolized Staphylococcus epidermidis and polystyrene latex particles with similar size distributions

For rapid and effective detection of airborne microorganisms, it is preferable to remove dust particles during the air sampling process because they can reduce the detection accuracy of measurements. In this study, a methodology of real-time separation ofaerosolized Staphylococcus epidermidis (S. epidermidis) andpolystyrene latex (PSL) particles of similar size was investigated. These two species represent biological and non-biological particles, respectively. Due to their different relative permittivities, they grasp different numbers of air ions under corona discharge. After these charged particles enter a mobility analyzer with airflow, in which an electric field is applied perpendicular to the airflow, the S. epidermidis and PSL particles separate, due to the difference in their electric mobilities, and exit through two different outlets. Purities and recoveries for S. epidermidis and PSLat their respective outlets were determined with measurements of aerosol number concentrations and ATP bioluminescence intensities at the inlet and two outlets. The results were that purities for PSL and S. epidermidis were 70% and 80%, respectively. This methodology provides a rapid and simple way to increase the detection accuracy of bacterial agents in air.

Copyright © 2017 American Association for Aerosol Research     

Sn-adopted fullerene $$(\hbox {C}_{60})$$ nanocage as acceptable catalyst for silicon monoxide oxidation

In recent years, the discovery of metal catalysts for the oxidation of silicon monoxide (SiO) has become extremely important. In first step, the Sn adoption of fullerene (\(\hbox {C}_{60})\) was investigated and then activation of surface of \(\hbox {Sn-C}_{60}\) via \(\hbox {O}_{2}\) molecule was examined. In second step, the SiO oxidation on surface of \(\hbox {Sn-C}_{60}\) via Langmuir Hinshelwood (LH) and Eley Rideal (ER) mechanisms was investigated. Results show that \(\hbox {O}_{2}\hbox {-Sn-C}_{60}\) can oxidize the SiO molecule via \(\hbox {Sn-C}_{60}\hbox {-O-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60}\hbox {-O-O}^{*}\hbox {-SiO} \rightarrow \hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}_{2}\) and \(\hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60} + \hbox {SiO}_{2}\) reactions. Results show that SiO oxidation via the LH mechanism has lower energy barrier than ER mechanism. Finally, \(\hbox {Sn-C}_{60}\) is an acceptable catalyst with high performance for SiO oxidation in normal temperature.     

Application of adaptive neuro-fuzzy inference system in prediction of hydrate formation temperature

A look at the number of publications in the last decades on the prediction of hydrate forming conditions for various gas mixtures obviously indicates the importance of this field from scientific and industrial viewpoints. Yet, the correlations presented in the literature are not accurate enough and also some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design. In this study adaptive neuro-fuzzy inference systems were used to produce a nonlinear model to predict the hydrate formation temperature. The model was trained using 303 input–output patterns collected from reliable sources. The adaptive neuro-fuzzy inference system model enables the user to accurately predict hydrate formation conditions under varying system conditions (i.e., temperature, pressure, and gas composition), without having to do costly experimental measurements. Also, statistical error analysis is used to evaluate the performance and the accuracy of the adaptive neuro-fuzzy inference system for estimating natural gas hydrate formation to guide designers and operators in selecting the best system conditions for their particular applications. It is shown that the results of predictions are in acceptable agreement with experimental data indicating the capability of the adaptive neuro-fuzzy inference system for predicting hydrate formation conditions of natural gases.     

A novel heteropolyacid-doped carbon nanotubes/Nafion nanocomposite membrane for high performance proton-exchange methanol fuel cell applications

A novel proton-exchange polymer composite membrane was synthesized using Nafion^®, tetraethoxysilane-modified carbon nanotubes (CNTs) and phosphotungstic acid-modified carbon nanotubes with the aim of using direct methanol fuel cells (DMFCs). Physicochemical properties of the modified CNTs and fabricated composite membranes were investigated by Fourier transform infrared spectroscopy, field emission scanning electron microscopy, water uptake, thermogravimetric analysis, ion exchange capacity, proton conductivity and methanol permeability tests. It was demonstrated that chemical surface modification of CNTs and introduction of the phosphotungstic acid (PWA) groups effectively improved the performance of DMFC. It was found that the presence of PWA groups on the surface of CNTs led to the formation of strong electrostatic interactions between the PWA groups and clusters of sulfonic acid in Nafion^® macromolecules. Hence, the incorporation of inorganic phosphotungstic super-acid-doped silicon oxide-covered carbon nanotubes (CNT@SiO₂-PWA) into Nafion^® matrices enhanced the proton conductivity of the prepared membranes. Moreover, the methanol permeability was reduced to 2.63 × 10^?7 cm² s^?1 in comparison with the recast Nafion^® membrane (2.25 × 10^?6 cm² s^?1). Enhancing the proton conductivity and reducing the methanol permeability, the selectivity of the prepared nanocomposite membranes was enhanced to a greater value of 330,700 S s cm^?3 as compared to the value of 38,222 S s cm^?3 for recast Nafion^®.     

Para-xylene adsorption separation process using nano-zeolite Ba-X

The liquid phase adsorption process was studied on nano-zeolite Ba-X for separating para-xylene from a feed mixture containing all C₈ aromatics. Nano-zeolite Ba-X with different ratios of SiO₂/Al₂O₃ was synthesized through hydrothermal process and ion-exchanged with barium. The product was characterized by X-ray diffraction, scanning electron microscopy (SEM), nitrogen adsorption and in situ Fourier transform infrared (FTIR) spectroscopy. The adsorption process was carried out in a breakthrough system at temperature range of 120–160 °C under 4–7 atm pressure. The influence of nano-zeolite water content on the separation process was studied. The optimization of adsorption process was also investigated by changing the operation conditions. The adsorption isotherm for all C₈ aromatic isomers and also desorbents indicated the typical Langmuir type. The selectivity factor of adsorbent for para-xylene and the adsorption capacity at saturation of the different adsorbate samples with each component from C₈ aromatic mixture were determined. It was observed that the selectivity of para-xylene increased by barium ion-exchange of cationic sites in nano-zeolite X and the adsorbent selectivity for para-xylene relative to each of meta-xylene, ortho-xylene and ethyl-benzene under the optimum conditions was found to be 7.191, 2.819 and 3.745, in the order given. It was also studied the influence of desorbent type on its selectivity for para-xylene compared to each isomer from the C₈ aromatic mixture.     

Rigorous modeling of gypsum solubility in Na–Ca–Mg–Fe–Al–H–Cl–H2O system at elevated temperatures

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from the open literature in order to construct and evaluate the reliability of presented model. Solubility values predicted by LS-SVM model are in well accordance with the observed values yielding a squared correlation coefficient (R ²) of 0.994. Sensitivity of the model for some important parameters is also checked to ascertain whether the learning process has succeeded. At the end, outlier diagnosis was performed using the method of leverage value statistics to find and eliminate the falsely recorded measurements from assembled dataset. Results obtained from this study indicate that LS-SVM model can successfully be applied in predicting accurate solubility of calcium sulfate dihydrate in Na–Ca–Mg–Fe–Al–H–Cl–H₂O system over temperatures ranging from 283.15 to 371.15 K.