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Main observation and conclusion: Three diamines (1,8‐diaminooctane/p‐phenylenediamine/biphenylenediamine) with different alkyl groups were used to synthesize the functionalized graphene networks. Pd as the common metal catalyst was used to explore the effects of alkyl types of diamines on anti‐stacking, metal anchoring ability of functional graphene and hence the catalytic performance. The results were discussed by characterization of the material and its catalytic properties. All catalysts showed excellent catalytic performance on the Suzuki cross‐coupling reaction with a high yield of up to 100%. The p‐phenylenediamine and biphenylenediamine functionalized graphene with larger specific surface area was beneficial to improve the stability of the graphene catalyst and maintain good catalytic activity after 5 cycles. The Pd nanoparticles in 1,8‐octanediamine materials have smaller size and a smaller loading with the same or even better catalytic performance than p‐phenylenediamine, biphenylenediamine materials, due to the stronger electron donating ability of N of octanediamine than that of p‐phenylenediamine. [ABSTRACT FROM AUTHOR]

The authors consider a class of discrete resource allocation problems which are hard due to the combinatorial explosion of the feasible allocation search space. In addition, if no closed-form expressions are available for the cost function of interest, one needs to evaluate or (for stochastic environments) estimate the cost function through direct online observation or through simulation. For the deterministic version of this class of problems, the authors derive necessary and sufficient conditions for a globally optimal solution and present an online algorithm which they show to yield a global optimum. For the stochastic version, they show that an appropriately modified algorithm, analyzed as a Markov process, converges in probability to the global optimum. An important feature of this algorithm is that it is driven by ordinal estimates of a cost function, i.e., simple comparisons of estimates, rather than their cardinal values. They can therefore exploit the fast convergence properties of ordinal comparisons, as well as eliminate the need for "step size" parameters whose selection is always difficult in optimization schemes. An application to a stochastic discrete resource allocation problem is included, illustrating the main features of their approach. Index Terms - Discrete-event systems, resource allocation, stochastic optimization.

This paper considers nonhomogeneous M(t)/M(t)/1 queues which can model systems such as communications networks. For such systems, bounds on moment-generating functions and on the tail distribution of the queue process are obtained. These bounds are useful for characterizing the quality of service a system can provide to its users. An approach utilizing the theory of differential equations is adopted. The bounds given in this paper are tighter than those previously available. In fact, the bounds can be made arbitrarily tight given sufficient computational effort. Index Terms - Performance bound, quality of service, queueing system.

We consider a single-server loss system in random environment. The environment is determined by a finite-state Markov process. When the environment is in state i, the arrival process is Poisson with rate [[Lambda].sub.i]. and the service time is exponential with rate [[Mu].sub.i]. i = 1, ..., m. We show that the blocking probability of this system is bounded from below (above) by that of the same system with a more (less) regular arrival and service pattern. This result supports Ross's conjecture [16] that the blocking probability is smaller when the arrival process is more regular and suggests its validity in scenarios with dependent arrival and service processes. Such structural properties are useful in obtaining bounds and approximations for system performance. We then fix the marginal service (arrival) process and search for an arrival (service) process with the same long-run rate that minimizes the blocking probability. It is shown that the optimal solution is the one such that the arrival and service rates are proportional. This result is in contrary with the case of independent arrival and service processes, where the system performance reaches its minimum when the arrival is replaced by Poisson, and it provides insight into the understanding of the effect of nonstationarity on system performance.

In this paper we present an algorithm for derivative estimation of discrete-event dynamic systems (DED's). This algorithm is applicable to derivative estimation with respect to most structural parameters in DED's. It extends the applicability of existing perturbation analysis techniques (including infinitesimal perturbation analysis) on structural parameters. Furthermore, it offers an alternative approach to derivative estimation in DED's which sheds additional light on discrete-event simulation. Finally, unifying relationships with existing techniques are discussed.

Perturbation analysis is an efficient method for performance analysis of discrete event dynamic systems. It yields gradient information from only one sample path observation. Over the last two decades, various perturbation analysis techniques have been developed to handle a large class of problems. Coupling is a method of generating multiple random samples. It has wide range of applications in applied probability. This paper is concerned with perturbation analysis via coupling. This approach offers a great versatility of the form of gradient estimators, which is potentially useful for variance reduction and for efficient implementation. Several known perturbation analysis techniques can be reviewed as special ways of coupling. The coupling method is further applied to gradient estimation of Markov chains. The method is used not only in deriving a gradient estimator but also in its implementation. It is proved that the estimator is strongly consistent. Finally, different coupling schemes are compared using an illustrative example. Index Terms--Coupling method, discrete event dynamic systems, Markov chain, perturbation analysis, simulation.

In [1], stochastic discrete resource allocation problems were considered which are hard due to the combinatorial explosion of the feasible allocation search space, as well as the absence of closed-form expressions for the cost function of interest. An ordinal optimization algorithm for solving a class of such problems was then shown to converge in probability to the global optimum. In this paper, we show that this result can be strengthened to almost sure convergence, under some additional mild conditions, and we determine the associated rate of convergence. In the case of regenerative systems, we further show that the algorithm converges exponentially fast. Index Terms--Convergence, ordinal optimization, resource allocation, stochastic optimization.