Research on cloud manufacturing service recommendation based on graph neural network.

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes' vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems.

A genetic algorithm (GA) cannot always avoid premature convergence, and multi-population is usually used to overcome this limitation by dividing the population into several sub-populations (sub-population number) with the same number of individuals (sub-population size). In previous research, the questions of how a network structure composed of sub-populations affects the propagation rate of advantageous genes among sub-populations and how it affects the performance of GA have always been ignored. Therefore, we first propose a multi-population GA with an ER network (MPGA-ER). Then, by using the flexible job shop scheduling problem (FJSP) as an example and considering the total individual number (TIN), we study how the sub-population number and size and the propagation rate of advantageous genes affect the performance of MPGA-ER, wherein the performance is evaluated by the average optimal value and success rate based on TIN. The simulation results indicate the following regarding the performance of MPGA-ER: (i) performance shows considerable improvement compared with that of traditional GA; (ii) for an increase in the sub-population number for a certain TIN, the performance first increases slowly, and then decreases rapidly; (iii) for an increase in the sub-population size for a certain TIN, the performance of MPGA-ER first increases rapidly and then tends to remain stable; and (iv) with an increase in the propagation rate of advantageous genes, the performance first increases rapidly and then decreases slowly. Finally, we use a parameter-optimized MPGA-ER to solve for more FJSP instances and demonstrate its effectiveness by comparing it with that of other algorithms proposed in other studies.

Different Performances of Different Intelligent Algorithms for Solving FJSP: A Perspective of Structure.

There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1-S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP.

[The interaction of beta-cyclodextrin with etoposide studied by fluorescence spectroscopy].

The fluorescence spectroscopy and UV spectroscopy have been used to monitor the inclusion phenomena of VP-16 with beta-cyclodextrin (beta-CD), together with studies concerning the effects of reaction time, temperature and concentration on this behavior. The results show that the fluorescence intensity increased when VP-16 and beta-CD forming a 1 : 1 inclusion complex, which indicate that beta-CD has fluorescence sensitizing effect on the VP-16. At 22 degrees C, the inclusion constant was 2.63 x 10(5) L x mol(-1) at pH 7.0. VP-16 has maximum emission wavelength at 316 nm under the optimum conditions. According to this, the quantitative micro-detection method of VP-16 by fluorescence spectrometry was established. The linear regression equation was y = 1.107 89 x 10(70 x + 95.898 1, with a correlation coefficient of 0.999 9. The detection limit was 2.094 x 10(-7) mol x L(-1).