Surface modification and physical properties of various UHMWPE‐fiber‐reinforced modified epoxy composites

Two surface modification methods—plasma surface treatment and chemical agent treatment—were used to investigate their effects on the surface properties of ultrahigh‐molecular‐weight polyethylene (UHMWPE) fibers. In the analyses, performed using electron spectroscopy for chemical analysis, changes in weight, and scanning electron microscope observations, demonstrated that the two fiber‐surface‐modified composites formed between UHMWPE fiber and epoxy matrix exhibited improved interfacial adhesion and slight improvements in tensile strengths, but notable decreases in elongation, relative to those properties of the composites reinforced with the untreated UHMWPE fibers. In addition, three kinds of epoxy resins—neat DGEBA, polyurethane‐crosslinked DGEBA, and BHHBP‐DGEBA—were used as resin matrices to examine the tensile and elongation properties of their UHMWPE fiber‐reinforced composites. From stress/strain measurements and scanning electron microscope observations, the resin matrix improved the tensile strength apparently, but did not affect the elongation. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 655–665, 2007     

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.     

Risk of benzene-induced leukemia: a sensitivity analysis of the pliofilm cohort with additional follow-up and new exposure estimates

This report updates the risk assessment by Crump and Allen (1984) for benzene-induced leukemia that was used by OSHA (1987) to support its reduction of the permissible exposure limit (PEL) to 1 ppm and that also was the basis for EPA's (1985) interim "unit risk" for benzene. The present study derives new risk estimates using data from follow-up through 1987 (whereas the earlier assessment only had follow-up available through 1978), and using new exposure estimates for this cohort developed by Paustenbach et al. (1992) that account for a number of factors that were unknown or not fully evaluated in earlier exposure assessments. There was a significant excess of acute myelocytic or acute monocytic leukemia (AMML, the only forms of acute nonlymphatic leukemia observed) in this cohort, and this end point also exhibited a strong dose-response trend. AMML was the only hematopoietic or lymphatic cancer that was clearly linked to benzene exposure. However, quantitative estimates of risk based on modeling either AMML or all leukemia differed by only 20%. Differences between the two Pliofilm plant locations in the occurrence of AMML were not statistically significant (.12 < or = p < or = .21) after differences in levels of benzene exposure were taken into account. The Paustenbach et al. exposures predicted a quadratic dose response, based on a measure of exposure that weighted intensity of exposure more heavily than duration of exposure. The best-fitting quadratic models predicted an additional lifetime risk of a benzene-related death from 45 yr of exposure to 1 ppm of between 0.020 and 0.036 per thousand. Statistical confidence intervals (90%) on these estimates were barely wide enough to include risk estimates based on linear dose response models. These linear models predicted risks of between 1.6 and 3.1 per thousand.     

Interaction of transport resistances with biochemical reaction in packed-bed solid-state fermentors: effect of temperature gradients

Subdermal superficial liposuction, first presented by the authors at the ISAPS Congress at Zurich in 1989, is performed with thin three-hole Mercedes cannulas (diameter ranges from 1.8 to 2 mm) to treat small and secondary adiposities and to allow better skin retraction. Suction of the subdermal layer of fat reduces the thickness and consistency of the superficial fat and enhances the possibility of skin retraction. In cases where there is a large adiposity of the abdomen, arms, or inner thighs, there is a conspicuous volume of fat whose weight tends to overstretch and to carry the overlying skin downward. In these cases we need to reduce the large fat volume to permit effective skin retraction. Therefore, we apply the principles of traditional liposuction with those of subdermal superficial liposuction to aspirate large amounts of fat from all the adipose layers. We call this technique Massive All Layer Liposuction (MALL). The amount of skin shrinkage after this "defatting" procedure is remarkable and the clinical results are very good. The MALL technique can be applied to other areas as well. In our experience this new liposuction technique has dramatically reduced the indications of abdominoplasties and dermolipectomies of inner thighs and arms.     

Epidemiology and ethics: the perspective of the Third World

The relation of epidemiology and ethics is discussed from a Third World perspective, including the following issues: the structure and dynamics of a Third World Society; the nature of the relationship between the First World "islanders" in the Third World and their counterparts in the First World, and between the "islanders" and their fellow citizens; the inner dynamics of Third World researchers; the different knowledge systems; and the role of institutions in the Third World and the First World.     

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     

Evolution of gypsy-group retrotransposons: phylogenetic analysis of domains included in the pol-polyprotein

Transposons of gypsy group are assigned to LTR-containing retrotransposons present in the genomes of invertebrates, fungi, and plants. In this work, a theoretical analysis of the potential products of ORFs of these retrotranposons was conducted. Alignments were obtained and trees of similarity were constructed for domains of the POL region. On the basis of the obtained data, two hypothetically monophyletic subgroups of transposons were distinguished within the framework of the gypsy group, settling the genomes of taxonomically related organisms (the subgroup of "true" gypsy of insects and the subgroup of gypsy-like transposons of plants and fungi). A number of peculiarities of the topology of these trees hypothetically indicate cases of genetic conversion and recombination of domains accompanying the evolution of this group. The amino acid substitution fixation rate was evaluated on the basis of comparison of sequences of the protein products of ORFs. Estimates of the time of divergence of subgroups of gypsy-group transposons are significantly less than estimates of the times of divergence of their host species. One explanation for this discrepancy might be the hypothesis of settlement by transposons of the genomes of isolated host species.     

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.