The structure of alkali silicate glasses

Pulsed neutron diffraction has been used to study the structure of three alkali silicate glasses of nominal composition SiO₂ · (M₂O)_0.5, with M = K, K_0.46Li_0.54 and Li. The data are quantitatively consistent with the modified random network model. Lithium is coordinated by two oxygen atoms with an interatomic distance of 1.97 Å. The potassium-oxygen distance is similar to the tetrahedral oxygen-oxygen distance, 2.66 Å, with potassium being coordinated by approximately four oxygen atoms.     

Effects of final-state interactions in pure annihilation decay of B s 0 → π+π −

The hadronic decay of B _s ⁰ → π ⁺ π ^? is analyzed by using “QCD factorization” (QCDF) method and final-state interaction (FSI). First, the B _s ⁰ → π ⁺ π ^? decay is calculated via QCDF method and the annihilation graphs only exist in this method. Hence, the FSI must be seriously considered to solve the B _s ⁰ → π ⁺ π ^? decay and the K ^+(*) K ^?(*) and \(K^{0(*)} K^{\bar 0(*)}\) via the exchange of K ^0(*) and K ^?(*) mesons are chosen for the intermediate states. To estimate the intermediate state amplitudes, the QCDF method is again used. These amplitudes are used in the absorptive part of the diagrams. The experimental branching ratio of B _s ⁰ → π ⁺ π ^? decay is less than 1.2 × 10^?6 and our results according to the QCDF method and FSI are 0.68 × 10^?8 and 1.18 × 10^?6, respectively.     

Homogeneous liquid‐phase microextraction followed by filtration‐based phase separation coupled to high‐performance liquid chromatography

A simple homogenous liquid‐phase microextraction methodology applying octanoic acid as the extraction phase was introduced for determination of chlorobenzenes. In this approach, phase separation phenomenon occurred by changing pH of the solution. The extraction phase was isolated based on filtration of emulsion instead of centrifugation and it was introduced to the high‐performance liquid chromatography instrument as an on‐line procedure. This method was used for extraction and determination of five chlorobenzenes in different samples. Experimental design and response surface methodology were used for the optimization of various parameters influencing the extraction efficiency of the method. Under optimal conditions, chlorobenzenes were effectively extracted, and preconcentration factors of 255–294 were obtained. The calibration curves were investigated in the concentration range of 1–200 μg/L and good linearity was achieved with coefficient of determinations better than 0.997. Limits of detection of 0.1 and 0.3 μg/L and suitable precision with relative standard deviations better than 5.1% (n = 5) were attained. Finally, the proposed method was applied to determine the concentration of chlorobenzenes in different samples and acceptable recoveries were gained.     

Mammalian cell viability in electrospun composite nanofiber structures

Incorporation of mammalian cells into nanofibers (cell electrospinning) and multilayered cell-nanofiber structures (cell layering) via electrospinning are promising techniques for tissue engineering applications. We investigate the viability of 3T3-L1 mouse fibroblasts after incorporation into poly(vinyl alcohol) nanofibers and multilayering with poly(caprolactone) nanofibers and analyze the possible factors that affect cell viability. We observe that cells do not survive cell electrospinning but survive cell layering. Assessing the factors involved in cell electrospinning, we find that dehydration and fiber stretching are the main causes of cell death. In cell layering, the choice of solvent is critical, as residual solvent in the electrospun fibers could be detrimental to the cells.     

Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid

Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

     

Efficient computation of adiabatic populations in multi-mode Jahn-Teller systems through the use of effective vibrational modes

A highly efficient scheme for computing adiabatic electronic populations in multi-mode Jahn-Teller systems is presented. It relies on the transformation to an effective-mode vibrational basis in which the relevant quantities depend on the coordinates of a single mode only. In this way, the generally tedious numerical evaluation of high-dimensional integrals is avoided and replaced by one-dimensional integrations. The effective-mode scheme is applied to a variety of two-mode and three-mode Jahn-Teller systems and gives a typical speedup of about two to three orders of magnitude as compared to the direct evaluation of the adiabatic populations. The gain grows rapidly with the numbers of modes.     

A Simulation-free Nonlinear Model Order-reduction Approach and Comparison Study

In this paper, a new approach to the model order reduction of nonlinear systems is presented. This approach does not need a simulation of the original system, and therefore, it is suitable for large systems. By separating the linear and nonlinear parts of the original nonlinear model, the idea is to consider the nonlinearities of the resulting system as additional inputs. Based on the linear system from the last step, a known order-reduction method can be applied to find the coefficients of the nonlinear and the linear parts of a reduced-order model. Two different methods from linear-order reduction (balancing and truncation and Eitelberg's method with some modification) are used for this purpose, and their advantages and disadvantages are discussed. For comparison with some known methods in order reduction of nonlinear systems, three other methods are discussed briefly. Finally, a technical nonlinear system is reduced, and different methods are compared.     

Recognition of \text {PSL}(2,p) by order and some information on its character degrees where p is a prime

Let \(G\) be a finite group and \(\text {cd}(G)\) be the set of irreducible character degrees of \(G\) . In this paper we prove that if \(p\) is a prime number, then the simple group \(\text {PSL}(2,p)\) is uniquely determined by its order and some information about its character degrees. In fact we prove that if \(G\) is a finite group such that (i) \(|G|=|\text {PSL}(2,p)|\) , (ii) \(p\in \text {cd}(G)\) , (iii) \(\text {cd}(G)\) has an even integer, and (iv) there does not exist any element \(a\in \text {cd}(G)\) such that \(2p\mid a\) , then \(G\cong \text {PSL}(2,p)\) . As a consequence of our result we get that \(\text {PSL}(2,p)\) is uniquely determined by its order and the largest and the second largest character degrees.     

Thermal fluctuations in a hyperscaling-violation background

In this paper, we study the effect of thermal fluctuations on the thermodynamics of a black geometry with hyperscaling violation. These thermal fluctuations in the thermodynamics of this system are produced from quantum corrections of geometry describing this system. We discuss the stability of this system using specific heat and the entire Hessian matrix of the free energy. We will analyze the effects of thermal fluctuations on the stability of this system. We also analyze the effects of thermal fluctuations on the criticality of the hyperscaling-violation background.     

Simulation of Structure Change in Porous Media During Gas–Solid Reactions Using Cellular Automata Model

Transport in Porous Media - In some gas–solid reactions, a new solid substance is produced. The product acts as a shield and prevents the collision between gas and solid reactants which...