Precipitation behavior of σ phase in fusion zone of dissimilar stainless steel welds during multi-pass GTAW process

The purpose of this study is to investigate the precipitation characteristics of σ phase in the fusion zone of stainless steel welds at various welding passes during a tungsten are welding (GTAW) process. The morphology, quantity, and chemical composition of the δ-ferrite and σ phase were analyzed using optical microscopy (OM), a ferritscope (FS), a X-ray diffractometer (XRD), scanning electron microscopy (SEM), an electron probe micro-analyzer (EPMA), and a wavelength dispersive spectrometer (WDS), respectively. Massive δ-ferrite was observed in the fusion zone of the first pass welds during welding of dissimilar stainless steels. The σ phase precipitated at the inner δ-ferrite particles and decreased δ-ferrite content during the third pass welding. The σ and δ phases can be stabilized by Si element, which promoted the phase transformation of σ→ϱ+λ₂ in the fusion zone of the third pass welds. It was found that the σ phase was a Fe−Cr−Si intermetallic compound found in the fusion zone of the third pass welds during multi-pass welding.     

Comparison of calcium phosphate cement mixture and pure calcium hydroxide as direct pulp-capping agents

This review reports the different genetic factors that have been identified either as risk factor for Alzheimer's disease (AD) or directly causing the disease. First are reviewed epidemiological data and biological mechanisms about the apoplipoprotein E gene allele epsilon 4 that is a major risk factor for Alzheimer's disease. The second part describes the mutations responsible for early-onset autosomal dominant AD found in three different genes. The gene located on chromosome 21 encodes the amyloid precusor protein (APP). The presenilin 1 and presenilin 2 genes, located on chromosome 14 and 1 respectively, encode not yet known membrane proteins.     

The quantum dynamic capacity formula of a quantum channel

The dynamic capacity theorem characterizes the reliable communication rates of a quantum channel when combined with the noiseless resources of classical communication, quantum communication, and entanglement. In prior work, we proved the converse part of this theorem by making contact with many previous results in the quantum Shannon theory literature. In this work, we prove the theorem with an ??ab initio?? approach, using only the most basic tools in the quantum information theorist??s toolkit: the Alicki-Fannes?? inequality, the chain rule for quantum mutual information, elementary properties of quantum entropy, and the quantum data processing inequality. The result is a simplified proof of the theorem that should be more accessible to those unfamiliar with the quantum Shannon theory literature. We also demonstrate that the ??quantum dynamic capacity formula?? characterizes the Pareto optimal trade-off surface for the full dynamic capacity region. Additivity of this formula reduces the computation of the trade-off surface to a tractable, textbook problem in Pareto trade-off analysis, and we prove that its additivity holds for the quantum Hadamard channels and the quantum erasure channel. We then determine exact expressions for and plot the dynamic capacity region of the quantum dephasing channel, an example from the Hadamard class, and the quantum erasure channel.     

Reverse osmosis separations of polyethylene glycols in dilute aqueous solutions using porous cellulose acetate membranes

Reverse osmosis separations of eight polyethylene glycol (PEG) solutes in the average molecular weight range of 200 to 6750 in single-solute dilute aqueous solutions have been studied using porous cellulose acetate membranes at the operating pressures of 50, 75, and 100 psig. Diffusivity data for the above PEG solutes have also been obtained from experimental data on intrinsic viscosities. From an analysis of all experimental data, numerical values for the parameters representing the polar (?ΔΔG/RT), steric (δ^*ΣE_s), and nonpolar (ω^*Σs^*) forces governing reverse osmosis separations of PEG solutes have been generated. These numerical values are useful for precise characterization of cellulose acetate membranes for whose specifications sodium chloride is not the appropriate reference solute because of its low or practically negligible separation under reverse osmosis operating conditions. This work also illustrates that solute separation in reverse osmosis can predictably increase or decrease with increase in operating pressure depending on experimental conditions.     

Influence of Potassium on Preparation and Performance of PTC Resistors

Different amounts of K₂CO₃ were added to (Ba,Sr)TiO₃-based PTCR (positive temperature coefficient of resistance) ceramics to investigate their influence on the microstructural and electrical properties. Experimental results showed that the incorporation of K acted as an A-site acceptor-type dopant. In addition to enhancing discontinuous grain growth, the increase of K₂CO₃ was found to raise the room-temperature resistivity which was dominated by grain-boundary resistance rather than grain resistance. By adjusting to a suitable amount of donor dopant, the inherent contamination of K in raw material can be compensated to achieve a high-quality PTC resistor.     

Longitudinal test of ePortfolio continuous use: an empirical study on the change of students’ beliefs

The electronic portfolio (ePortfolio) is learner-centred, and its effectiveness depends on the learners’ long-term use. Thus, it is proper to conduct the study from the learners’ perspectives. Currently, most research on the use of the ePortfolio is in the form of cross-sectional studies, and it is difficult to find the reasons for the changes in students’ beliefs with regard to the long-term use of the ePortfolio. Based on the longitudinal test, this study explained the students’ continuous use of ePortfolio and the changes in their beliefs. With 122 samplers in the adoption–continuous stage (t ₁–t ₂) and 117 samplers in the continuous stage (t ₂–t ₃), this study demonstrates that in continuous use stage (t ₂–t ₃), perceived ease of use still influences the users’ perceived usefulness (PU) and attitude towards the ePortfolio. Attribution significantly moderates the users’ beliefs from the adoption stage (t ₁) to the continuous use stage (t ₂). However, the moderating effect of attribution in the continuous use stage (t ₂–t ₃) is insignificant; satisfaction and attribution are the key factors driving the users’ continuous intention towards the ePortfolio rather than PU and attitude. Meanwhile, satisfaction and attribution will change because of the users’ expectation disconfirmation, and it will influence the users to continue using ePortfolio.     

Global optimization methods for engineering applications: A review

A review of the methods for global optimization reveals that most methods have been developed for unconstrained problems. They need to be extended to general constrained problems because most of the engineering applications have constraints. Some of the methods can be easily extended while others need further work. It is also possible to transform a constrained problem to an unconstrained one by using penalty or augmented Lagrangian methods and solve the problem that way. Some of the global optimization methods find all the local minimum points while others find only a few of them. In any case, all the methods require a very large number of calculations. Therefore, the computational effort to obtain a global solution is generally substantial. The methods for global optimization can be divided into two broad categories: deterministic and stochastic. Some deterministic methods are based on certain assumptions on the cost function that are not easy to check. These methods are not very useful since they are not applicable to general problems. Other deterministic methods are based on certain heuristics which may not lead to the true global solution. Several stochastic methods have been developed as some variation of the pure random search. Some methods are useful for only discrete optimization problems while others can be used for both discrete and continuous problems. Main characteristics of each method are identified and discussed. The selection of a method for a particular application depends on several attributes, such as types of design variables, whether or not all local minima are desired, and availability of gradients of all the functions.Notation Number of equality constraints - () ^T A transpose of a vector - A A hypercubic cell in clustering methods - Distance between two adjacent mesh points - Probability that a uniform sample of sizeN contains at least one point in a subsetA ofS - A(v, x) Aspiration level function - A The set of points with cost function values less thanf(x _G ^* ) +. Same asA _f () - A _f () A set of points at which the cost function value is within off(x _G ^* ) - A () A set of points x with[f(x)] smaller than - A _N The set ofN random points - A _q The set of sample points with the cost function value f _q - _Q The contraction coefficient; –1 _Q 0 - _R The expansion coefficient; _E > 1 - _R The reflection coefficient; 0 < _R 1 - A _x () A set of points that are within the distance from x _G ^* - D Diagonal form of the Hessian matrix - det() Determinant of a matrix - d _j A monotonic function of the number of failed local minimizations - d _t Infinitesimal change in time - d _x Infinitesimal change in design - A small positive constant - (t) A real function called the noise coefficient - ₀ Initial value for(t) - exp() The exponential function - f ^(c) The record; smallest cost function value over X^(C) - [f(x)] Functional for calculating the volume fraction of a subset - Second-order approximation tof(x) - f(x) The cost function - An estimate of the upper bound of global minimum - f ^E The cost function value at x^E - f ^L The cost function value at x^L - f _opt The current best minimum function value - f ^P The cost function value at x ^P - f ^Q The cost function value at x ^Q - f _q A function value used to reduce the random sample - f ^R The cost function value at x ^R - f ^S The cost function value at x^S - f _{T F min} A common minimum cost function value for several trajectories - f _{TF opt} The best current minimum value found so far forf _{TF min} - f ^W The cost function value at x ^W - G Minimum number of points in a cell (A) to be considered full - The gamma function - A factor used to scale the global optimum cost in the zooming method - Minimum distance assumed to exist between two local minimum points - gi(x) Constraints of the optimization problem - H The size of the tabu list - H(x^*) The Hessian matrix of the cost function at x^* - h _j Half side length of a hypercube - h _m Minimum half side lengths of hypercubes in one row - I The unity matrix - ILIM A limit on the number of trials before the temperature is reduced - J The set of active constraints - K Estimate of total number of local minima - k Iteration counter - The number of times a clustering algorithm is executed - L Lipschitz constant, defined in Section 2 - L The number of local searches performed - _i The corresponding pole strengths - log () The natural logarithm - LS Local search procedure - M Number of local minimum points found inL searches - m Total number of constraints - m(t) Mass of a particle as a function of time - m() TheLebesgue measure of thea set - Average cost value for a number of random sample of points inS - N The number of sample points taken from a uniform random distribution - n Number of design variables - n(t) Nonconservative resistance forces - n _c Number of cells;S is divided inton _c cells - NT Number of trajectories - Pi (3.1415926) - P _i ^(j) Hypersphere approximating thej-th cluster at stagei - p(x ⁽ⁱ⁾⁾ Boltzmann-Gibbs distribution; the probability of finding the system in a particular configuration - pg A parameter corresponding to each reduced sample point, defined in (36) - Q An orthogonal matrix used to diagonalize the Hessian matrix - _i (i = 1, K) The relative size of thei-th region of attraction - r _i ^(j) Radius of thej-th hypersp here at stagei - R _x ^* Region of attraction of a local minimum x^* - r _j Radius of a hypersphere - r A critical distance; determines whether a point is linked to a cluster - R ⁿ A set ofn tuples of real numbers - A hyper rectangle set used to approximateS - S The constraint set - A user supplied parameter used to determiner - s The number of failed local minimizations - T The tabu list - t Time - T(x) The tunneling function - T _c (x) The constrained tunneling function - T _i The temperature of a system at a configurationi - TLIMIT A lower limit for the temperature - TR A factor between 0 and 1 used to reduce the temperature - u(x) A unimodal function - V(x) The set of all feasible moves at the current design - v(x) An oscillating small perturbation. - V(y⁽ⁱ⁾) Voronoi cell of the code point y⁽ⁱ⁾ - v^–1 An inverse move - v _k A move; the change from previous to current designs - w(t) Ann-dimensional standard. Wiener process - x Design variable vector of dimensionn - x^# A movable pole used in the tunneling method - x⁽⁰⁾ A starting point for a local search procedure - X^(c) A sequence of feasible points {x⁽¹⁾, x⁽²⁾,,x^(c)} - x(t) Design vector as a function of time - X^* The set of all local minimum points - x^* A local minimum point forf(x) - x^*(i) Poles used in the tunneling method - x _G ^* A global minimum point forf(x) - Transformed design space - The velocity vector of the particle as a function of time - Acceleration vector of the particle as a function of time - x ^C Centroid of the simplex excluding x ^L - x ^c A pole point used in the tunneling method - x ^E An expansion point of x ^R along the direction x ^C x ^R - x ^L The best point of a simplex - x ^P A new trial point - x ^Q A contraction point - x ^R A reflection point; reflection of x ^W on x ^C - x ^S The second worst point of a simplex - x ^W The worst point of a simplex - The reduced sample point with the smallest function value of a full cell - Y The set of code points - y ⁽ⁱ⁾ A code point; a point that represents all the points of thei-th cell - z A random number uniformly distributed in (0,1) - Z ^(c) The set of points x where [f ^(c) – ] is smaller thanf(x) - []₊ Max (0,) - | | Absolute value - The Euclidean norm - f[x(t)] The gradient of the cost function     

Polarization-Dependent Periodic Pulse Oscillation in a Diode-Laser-Pumped and Intracavity Frequency-Doubled Nd:YVO 4 Laser

We demonstrate that two cross-polarized longitudinal modes can have 50% higher conversion efficiency than two parallel-polarized longitudinal modes in a diode-laser-pumped and intracavity frequency-doubled Nd:YVO(4) laser when operated under periodic pulse oscillation. Through simulations of the rate equations for primary frequency intensities and gains, we also verify that this effect can be attributed to gain competition and complementary conversion coefficient between second-harmonic and sum-frequency generations.