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The objective of this study was to determine the tolerance and effects of a fructooligosaccharide (FOS)-containing enteral formula on fecal microbiota, nutritional status, biologic and immunologic outcomes of pediatric patients with cancer. A prospective, randomized, double-blinded, controlled trial was conducted at Children's Hospital, Shanghai Medical University. Sixty-seven hospitalized patients (1-12 years old) diagnosed with cancer (stage 1-3) and undergoing chemotherapy met study inclusion criteria. Patients received at least 400 mL of an assigned formula for 13 to 30 days. The control group received enteral formula without FOS (control); the study group received the same formula with FOS (+FOS; 2 g/L). Stool samples on days 0, 3, 13, and 30 were analyzed for bifidobacteria, lactobacilli, and other microbiota. Biochemical markers of nutritional and hematologic status and anthropometrics were also assessed. The mean energy intake provided by the formulas was similar. At day 30 but not day 13, stool lactobacilli counts were significantly ( P α 1-acid glycoprotein in the control at day 13 ( P P = .016). These data suggest that an enteral formula containing dietary FOS produced a mild prebiotic effect at day 30 without any gastrointestinal discomfort in pediatric patients with cancer. Both enteral formulas were well-tolerated and accepted.

To improve the time performance of SMP2 Simulation under Service Oriented Architecture, a hybrid time synchronization method is promoted. The method would reduce the redundancy message exchange via specifying different time advance policy to different SMP2 simulation node. An interaction graph model is introduced based on SMP2 model composition and schedule and the parallelity evaluating algorithm is propounded, based on which a time synchronization policy determination method is detailed. Finally, a simulation experiment is given to make comparison between the hybrid method and the traditional methods.

Support vector regression (SVR) has been regarded as a state-of-the-art method for approximation and regression. The importance of kernel function, which is so-called admissible support vector kernel (SV kernel) in SVR, has motivated many studies on its composition. The Gaussian kernel (RBF) is regarded as a "best" choice of SV kernel used by non-expert in SVR, whereas there is no evidence, except for its superior performance on some practical applications, to prove the statement. Its well-known that reproducing kernel (R.K) is also a SV kernel which possesses many important properties, e.g. positive definiteness, reproducing property and composing complex R.K by simpler ones. However, there are a limited number of R.Ks with explicit forms and consequently few quantitative comparison studies in practice. In this paper, two R.Ks, i.e. SV kernels, composed by the sum and product of a translation invariant kernel in a Sobolev space are proposed. An exploratory study on the performance of SVR based general R.K is presented through a systematic comparison to that of RBF using multiple criteria and synthetic problems. The results show that the R.K is an equivalent or even better SV kernel than RBF for the problems with more input variables (more than 5, especially more than 10) and higher nonlinearity., {"references":["V. Vapnik, The Nature of Statistical Learning Theory. New York:\nSpringer-Verlag, 1995.","G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard, \"Support vector regression\nfrom simulation data and few experimental samples,\" Information\nSciences, vol. 178, pp. 3813-3827, 2008.","J.-B. Gao, S. R. Gunn, and C. J. Harris, \"Mean field method for the\nsupport vector machine regression,\" Neurocomputing, vol. 50, pp. 391-\n405, 2003.","M. A. Mohandes, T. O. Halawani, S. Rehman, and A. A. Hussain, \"Support\nvector machines for wind speed prediction,\" Renewable Energy,\nvol. 29, no. 6, pp. 939-947, 2004.","W.-W. He, Z.-Z. Wang, and H. Jiang, \"Model optimizing and feature\nselecting for support vector regression in time series forecasting,\"\nNeurocomputing, vol. 73, no. 3, pp. 600-611, 2008.","F. Pan, P. Zhu, and Y. Zhang, \"Metamodel-based lightweight design of\nb-pillar with twb structure via support vector regression,\" Computers\nand Structures, vol. 88, pp. 36-44, 2010.","C. J. Burges, \"A tutorial on support vector machines for pattern recognition,\"\nData Mining and Knowledge Discovery, vol. 2, pp. 121-167,\n1998.","A. J. Smola and B. Sch¨olkopf, \"A tutorial on support vector regression,\"\nStatistics and Computing, vol. 14, no. 3, pp. 199-222, 2004.","J. Mercer, Ed., Functions of positive and negative type and their\nconnection with the theory of integral equations, ser. Philosophical\nTransactions of the Royal Society, London, 1909, vol. A, 209.\n[10] B. E. Boser, I. M. Guyon, and V. Vapnik, \"A training algorithm for optimal\nmargin classifiers,\" in Proceedings of the 5th Annual ACM Workshop\non Computational Learning Theory, D. Haussler, Ed. Pittsburgh, PA:\nACM Press, 1992, pp. 144-152.\n[11] B. Schp¨olkopf, \"The kernel trick for distances,\" Neural Information\nProcess. Systems (NIPS), vol. 13, 2000.\n[12] B. Sch¨olkopf, K.-K. Sung, C. J. Burges, F. Girosi, P. Niyogi, T. Poggio,\nand V. Vapnik, \"Comparing support vector machines with gaussian\nkernels to radial basis function classifiers,\" IEEE Transactions on Signal\nProcessing, vol. 45, pp. 2758-2765, 1997.\n[13] D. Anguita and G. Bozza, \"The effect of quantization on support vector\nmachines with gaussian kernel,\" in Proceedings of International Joint\nConference on Neural Networks, Montreal, Canada, 2005, pp. 681-684.\n[14] X.-Y. Zhang and Y.-C. Liu, \"Performance analysis of support vector\nmachines with gauss kernel,\" Computer Engineering, vol. 29, no. 8, pp.\n22-25, 2003.\n[15] Y. Tan and J. Wang, \"A support vector machine with a hybrid kernel\nand minimal vapnik-chervonenkis dimension,\" IEEE Transactions on\nKnowledge and Data Engineering, vol. 16, pp. 385-395, 2004.\n[16] J.-X. Liu, J. Li, and Y.-J. Tan, \"An empirical assessment on the\nrobustness of support vector regression with different kernels,\" in\nProceedings of the 4th International Conference on Machine Learning\nand Cybernetics, vol. 7, Guangzhou, China, 2005.\n[17] R. Opfer, \"Multiscale kernels,\" Advances in Computational Mathematics,\nvol. 25, pp. 357-380, 2006.\n[18] L. Zhang, W.-D. Zhou, and L.-C. Jiao, \"Wavelet support vector machine,\"\nIEEE Transactions on Systems, Man and Cybernetics - Part B:\nCybernetics, vol. 34, pp. 34-39, 2004.\n[19] X.-G. Zhang, D. Gao, X.-G. Zhang, and S.-J. Ren, \"Robust wavelat\nsupport machines for regression estimation,\" International Journal Information\nTechnology, vol. 11, no. 9, pp. 35-46, 2005.\n[20] A. J. Smola, B. Sch¨olkopf, and K.-R. M¨uller, \"The connection between\nregularization operators and support vector kernels,\" Neural Networks,\nvol. 11, pp. 637-649, 1998.\n[21] G. Wahba, \"Support vector machines,reproducing kernel hilbert spaces\nand randomized gacv,\" in Advances in Kernel Methods - Support Vector\nLearning, B. Sch?lkopf, C. J. Burges, and A. J. Smola, Eds. Cambridge,\nEngland: MIT Press, 1999, pp. 69-88.\n[22] L.-M. Ma and Z.-M. Wu, \"Kernel based approximation in sobolev spaces\nwith radial basis functions,\" Applied Mathematics and Computation, vol.\n215, pp. 2229-2237, 2009.\n[23] N. Aronszajn, \"Theory of reproducing kernels,\" Transactions of the\nAmerican Mathematical Society, vol. 68, no. 3, pp. 337-404, 1950.\n[24] J. Gao, C. J. Harris, and S. R. Gunn, \"Support vector kernel based on\nframes in function hilbert spaces,\" Neural Computation, vol. 13, pp.\n1975-1994, 2001.\n[25] R. Schaback, \"A unified theory of radial basis functions native hilbert\nspaces for radial basis functions ii,\" Journal of Computational and\nApplied Mathematics, vol. 121, pp. 165-177, 2000.\n[26] R. Schaback and H. Wendland, \"Approximation by positive definite\nkernels,\" in Advanced Problems in Constructive Approximation, M. D.\nBuhmann and D. H. Mache, Eds. Birkh?user, Basel: Verlag, 2002, pp.\n203-221.\n[27] A. J. Smola, B. Sch¨olkopf, and G. R¨atsch, \"Linear programs for\nautomatic accuracy control in regression,\" in Proceedings of the 9th\ninternational conference on artificial neural networks, vol. 2, Edinburgh,\nUK., 1999.\n[28] O. L. Mangasarian and D. R. Musicant, \"Large scale kernel regression\nvia linear programming,\" Machine Learning, vol. 46, no. 1-3, pp. 255-\n269, 2002.\n[29] A. J. Smola, B. Sch¨olkopf, and K.-R. Mller, \"General cost functions for\nsupport vector regression,\" in Proceedings of Ninth Australian Conf. on\nNeural Networks, Brisbane, Australia, University of Queensland, 1998,\npp. 79-83.\n[30] S. Bochner, Lectures on Fourier Integral. Princeton, New Jersey:\nPrinceton University Press, 1959.\n[31] A. J. Smola, Z. L. O' vri, and R. C. Williamson, \"Regularization with dotproduct\nkernels,\" in Advances in Neural Information Processing Systems,\nT. K. Leen, T. G. Dietterich, and V. Tresp, Eds., vol. 13. MIT Press,\n2001, pp. 308-314.\n[32] G. Wahba, Spline Models for Observational Data, SIAM, Philadephia,\n1990.\n[33] J. St¨ockler, \"Multivariate bernoulli splines and the periodic interpolation\nproblem,\" Constr. Approx., vol. 7, pp. 105-120, 1991.\n[34] A. Berlinet and C. Thomas-Agnan, Reproducing Kernel Hilbert Spaces\nin Probability and Statistics. Boston, Dordrecht, London: Kluwer\nAcademic Publishers Group, 2003.\n[35] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector\nMachines and Other Kernel-based Learning Methods. Cambridge, U.K:\nCambridge University Press, 2000.\n[36] B. Sch¨olkopf, R. Herbrich, A. J. Smola, and R. C. Williamson, \"A\ngeneralized representer theorem,\" Tech. Rep. NeuroCOLT Technical\nReport 2000-81, 2000.\n[37] W. Zhang, \"The construction of reproducing kernel and some approximating\nproblems in the reproducing kernel spaces,\" Ph.D. dissertation,\nNational University of Defense Technology, 2005.\n[38] R. A. Adams, Sobolev Spaces. New York: Academic Press, 1975.\n[39] R. Jin, W. Chen, and T. W. Simpson, \"Comparative studies of metamodeling\ntechniques under multiple modeling criteria,\" in Proceedings of the\n8th AIIA/NASA/USAF/ISSMO Symposium on Multidisciplinary Analysis\nand Optimization, Long Beach, CA, 2000.\n[40] Y.-J. Park, \"Application of genetic algorithms in response surface\noptimization problems,\" Doctor of Philosophy, Arizona State University,\nDecember 2003.\n[41] S. S. Chaudhry and W. Luo, \"Application of genetic algorithms in production\nand operations management: A review,\" International Journal\nof Production Research, vol. 43, pp. 4083-4101, 2005.\n[42] C. R. Houck, J. A. Joines, and M. G. Kay, \"A genetic algorithm for\nfunction optimization: A matlab implementation,\" Tech. Rep. NCSU-IE\nTR 95-09, 1995.\n[43] W. Zhang, H. Wu, J. Liu, Y.-F. Zhu, and Q. Li, \"A study of exploratory\nanalysis experimental design supporting robust decision-making,\" Journal\nof System Simulation, vol. 21, no. 14, pp. 4461-4466, 2009.\n[44] J. P. C. Kleijnen and R. G. Sargent, \"A methodology for fitting and\nvalidating metamodels in simulation,\" European Journal of Operational\nResearch, vol. 120, no. 1, pp. 14-29, 2000."]}

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

The Particle Swarm Optimization (PSO) algorithm is already proved efficient in the rule extraction in intrusion detection. But in practice the most intrusion detection systems often have a high false alarm rate. To solve it, this paper gives a new PSO-based algorithm which has a special fitness function to extract better rules set with lower false alarm rate to detect the attacks. Experiments based on the 1999 KDD cup data show that the algorithm can efficiently lower the false alarm rate.

Based on the changeability of wireless network circumstance, information packets are prone to lose in the process of the wireless transmission. In wireless broadcasting ,any node of multi-nodes request retransmission information packets .This paper presents a novel retransmission approach in wireless broadcasting based on network coding (NCWBR), whose key idea is to combine different lost packets with network coding to achieve retransmission. Theoretical analysis reveals that our approach can ensure the solvability in the received nodes, and have better retransmission performance. Simulation results indicate that compared with existing approach, the approach in this paper can effectively reduce the average number of transmissions and advance the transmission efficiency.

In environments where sensor networks are used to monitor sensitive objects or valuable assets, attackers may use the method of hop-by-hop backtracking to find out the protected objects. This paper proposes a new source protected protocol in WSN, the phantom routing with locational angle (PRLA). In PRLA, inclination angles are introduced and used to direct random walks, which avoids choosing paths harmful to the privacy of source location. Simulation results show that, compared to the phantom single-path routing protocol proposed in the literature, PRLA improves the safety period by up to 50% with minor increase in energy overhead.

A new anonymous communication mechanism DDACM (delay demand-based anonymous communication mechanism) has been proposed in this paper. In DDACM, rerouting path can be setup according to the initiator's delay-demand, which can guarantee the initiator's anonymity. Hops between two peers are used as the metric of choosing rerouting node. Peers can find hops to neighbors through broadcasting TTL packets and record the neighbors with n hops as its n-hops group. Initiator's delay demand is described as DelayD being carried in the rerouting request. Every node receiving the request will randomly choose a neighbor from its 0 to DelayD-hops groups and forward the request with probability Pf. So, the delay between every two forwarders will be limited in DelayD hops and the total delay of rerouting path will be decreased. The anonymity and delay performance of DDACM have analyzed in this paper. Mathematic analysis and simulation results indicate that DDACM can keep almost the same anonymity with the typical anonymous communication system Crowds and decrease the transmit delay obviously.