Influence of dietary long-chain PUFA on premature baboon lung FA and dipalmitoyl PC composition

One of the major survival challenges of premature birth is production of lung surfactant. The lipid component of surfactant, dipalmitoyl PC (DPPC), increases in concentration in the period before normal term birth via a net shift in FA composition away from unsaturates. We investigated the influence of dietary DHA and arachidonic acid (AA) on lung FA composition and DPPC concentration in term and preterm baboons. Pregnant animals/neonates were randomized to one of four groups: breast-fed (B), term formula-fed (T⁻), preterm formulafed (P⁻), and preterm fed formula supplemented with DHA-AA (P⁺). Breast milk contained 0.68%wt DHA and the P⁺ group formula contained 0.61%wt DHA. In the preterm groups (P⁻ and P⁺), pregnant females received a course of antenatal corticosteroids. At the adjusted age of 4 wk, neonate lung tissue was harvested, and FA composition and DPPC were analyzed. Palmitate was ∼28%wt of lung total FA and no significant differences were found among the four treatment groups. In contrast, DPPC in the B group lung tissue was significantly greater than DPPC in the unsupplemented groups, but not compared with the P⁺ group. The B and P⁺ groups were not significantly different in DHA and AA, but were different compared with the unsupplemented (T, P⁻) groups. These results indicate that LCP supplementation increases lung DHA and AA, without compromising overall lung 16∶0 or DPPC. The shift in FA composition toward greater unsaturation in the groups consuming LCP supported improved surfactant lipid concentration in preterm neonate lungs.     

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.     

Cross-national differences in individual knowledge-seeking patterns: a climato-economic contextualization

Electronic Knowledge Repository (EKR) is one of the most commonly deployed knowledge management technologies, yet its success hinges upon employees’ continued use and is further complicated in today’s multinational context. We integrate multiple theoretical linkages into a research model, conceptualizing knowledge-seeking as an instrumental behavior, adopting the technology acceptance model to characterize the individual-level continued EKR knowledge-seeking behavioral model, and drawing on the climato-economic theory to explain cross-national behavioral differences. Using hierarchical linear modeling (HLM), we test the model with data from 1352 randomly sampled knowledge workers across 30 nations. We find that two national-level factors, climate harshness and national wealth, interactively moderate the individual-level relationship between perceived usefulness (PU) and behavioral intention (BI) to continue seeking knowledge from EKR, such that the difference in the strength of this relationship is larger between poor-harsh and poor-temperate nations than between rich-harsh and rich-temperate nations. We find similar cross-level cross-national differences for the link between perceived ease of use (PEOU) and PU but not for the link between PEOU and BI. Implications for research and practice are discussed.     

Understanding User Satisfaction With Instant Messaging: An Empirical Survey Study

The current article examines user satisfaction with instant messaging in building and maintaining social relationships with friends, family members, and others. The research model integrates motivation theory with media capacity theories to explain how the attributes of media capacity (e.g., social presence and media richness) and users' intrinsic and extrinsic motivations toward using instant messaging influence user satisfaction. Data were collected from a sample of 247 Chinese university students via an online survey. The results suggest that perceived enjoyment, perceived social presence, and perceived usefulness are key to user satisfaction. Perceived social presence and perceived media richness are positively associated with perceived enjoyment. It was also found that perceived enjoyment, perceived social presence, and perceived media richness have significant effects on perceived usefulness. Of interest, perceived enjoyment and perceived social presence have stronger effects on user satisfaction than perceived usefulness.     

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     

Automated sampling decision scheme for the AVM system

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic virtual metrology based intelligent sampling decision (ISD) scheme had been previously developed for reducing the sampling rate and sustaining the virtual metrology (VM) accuracy. However, the desired sampling rate of the ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot adaptively increase the default sampling rate in the ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the default sampling rate in ISD, which may result in unnecessary waste. Accordingly, this paper proposes an automated sampling decision (ASD) scheme to adaptively and automatically modify the sampling rate online and in real time for continuous improvement. The ASD scheme can monitor the VM accuracy online as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the ASD scheme can automatically reduce the sampling rate while the VM accuracy performs well.     

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.