Separation membranes with higher molecular weight cut-offs are needed to separate ions and small molecules from a mixed feed. The molecular sieving phenomenon can be utilized to separate smaller species with well-defined dimensions from a mixture. Here, the formation of freestanding polyimine nanofilms with thicknesses down to ≈14 nm synthesized via self-assembly of pre-synthesized imine oligomers is reported. Nanofilms are fabricated at the water–xylene interface followed by reversible condensation of polymerization according to the Pieranski theory. Polyimine nanofilm composite membranes are made via transferring the freestanding nanofilm onto ultrafiltration supports. High water permeance of 49.5 L m-2 h−1 bar−1 is achieved with a complete rejection of brilliant blue-R (BBR; molecular weight = 825 g mol−1) and no more than 10% rejection of monovalent and divalent salts. However, for a mixed feed of BBR dye and monovalent salt, the salt rejection is increased to ≈18%. Membranes are also capable of separating small dyes (e.g., methyl orange; MO; molecular weight = 327 g mol−1) from a mixed feed of BBR and MO. Considering a thickness of ≈14 nm and its separation efficiency, the present membrane has significance in separation processes. 相似文献
Rates of coke formation during steam pyrolysis of naphtha have been investigated in a jet-stirred reactor both for sodium silicate coated and uncoated Inconel 600 surfaces in the temperature range of 1078–1108 K. Coke formation rates were significantly reduced on sodium silicate coated plates due to the passivation of the metal surface. However, the coking rates gradually increased with successive decokings of the coated surface. 相似文献
We consider a system comprising a finite number of nodes, with infinite packet buffers, that use unslotted ALOHA with Code
Division Multiple Access (CDMA) to share a channel for transmitting packetised data. We propose a simple model for packet
transmission and retransmission at each node, and show that saturation throughput in this model yields a sufficient condition
for the stability of the packet buffers; we interpret this as the capacity of the access method. We calculate and compare
the capacities of CDMA‐ALOHA (with and without code sharing) and TDMA‐ALOHA; we also consider carrier sensing and collision
detection versions of these protocols. In each case, saturation throughput can be obtained via analysis of a continuous time
Markov chain. Our results show how saturation throughput degrades with code‐sharing. Finally, we also present some simulation
results for mean packet delay. Our work is motivated by optical CDMA in which “chips” can be optically generated, and hence
the achievable chip rate can exceed the achievable TDMA bit rate which is limited by electronics. Code sharing may be useful
in the optical CDMA context as it reduces the number of optical correlators at the receivers. Our throughput results help
to quantify by how much the CDMA chip rate should exceed the TDMA bit rate so that CDMA‐ALOHA yields better capacity than
TDMA‐ALOHA.
This revised version was published online in June 2006 with corrections to the Cover Date. 相似文献
Theoretical results for identifying unnecessary inferences are discussed in the context of the use of a completion-procedure-based approach toward automated reasoning. The notion of a general superposition is introduced and it is proved that in a completion procedure, once a general superposition is considered, all its instances are unnecessary inferences and, thus, do not have to be considered. It is also shown that this result can be combined with another criterion, called the prime superposition criterion, proposed by Kapur, Musser, and Narendran, thus implying that prime and general superpositions are sufficient. These results should be applicable to other approaches toward automated reasoning, too. These criteria can be effectively implemented, and their implementation has resulted in automatically proving instances of Jacobson's theorem (also known as the ring commutativity problems) usingRRL (Rewrite Rule Laboratory), a theorem prover based on rewriting techniques and completion.A preliminary version of this paper appeared in a paper entitled Consider only general superpositions in completion procedures in theProceedings of the Third International Conference on Rewriting Techniques and Applications, Chapel Hill, NC, April, 1989, Lecture Notes in Computer Science, Vol. 355, Springer-Verlag, Berlin, pp. 513–527. Part of the work of Hantao Zhang was done at the Rensselaer Polytechnic Institute, New York, and he was partially supported by National Science Foundation Grant No. CCR-8408461; also affiliated with Institute of Programming and Logics at SUNY, Albany, NY, and RPI. Deepak Kapur was partially supported by National Science Foundation Grantr Nos. CCR-8408461 and CCR-8906678. 相似文献
The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise... 相似文献
Better prediction ability is the main objective of any regression-based model. Large margin Distribution Machine for Regression (LDMR) is an efficient approach where it tries to reduce both loss functions, i.e. ε-insensitive and quadratic loss to diminish the effects of outliers. However, still, it has a significant drawback, i.e. high computational complexity. To achieve the improved generalization of the regression model with less computational cost, we propose an enhanced form of LDMR named as Least Squares Large margin Distribution Machine-based Regression (LS-LDMR) by transforming the inequality conditions alleviate to equality conditions. The elucidation is attained by handling a system of linear equations where we need to measure the inverse of the matrix only. Hence, there is no need to solve the large size of the quadratic programming problem, unlike in the case of other regression-based algorithms as SVR, Twin SVR, and LDMR. The numerical experiment has been performed on the benchmark real-life datasets along with synthetically generated datasets by using the linear and Gaussian kernel. All the experiments of presented LS-LDMR are analyzed with standard SVR, Twin SVR, primal least squares Twin SVR (PLSTSVR), ε-Huber SVR (ε-HSVR), ε-support vector quantile regression (ε-SVQR), minimum deviation regression (MDR), and LDMR, which shows the effectiveness and usability of LS-LDMR. This approach is also statistically validated and verified in terms of various metrics.