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To achieve a better balance between the refractive index and Abbe number, the thiol-functionalized polyhedral oligomeric silsesquioxane (POSS–SH) was incorporated as both nanoreinforcement and cros...

A series of novel coronene-based fluorescent materials have been synthesized through well-defined protocol from 1,2-dimethoxybenzene, with the key steps including Suzuki–Miyaura coupling and oxidative cyclodehydrogenation with ferric chloride. Fluorescence spectra investigations proved that they have emissions in the range λ 460-470 nm with excitation wavelength at λ 373 nm. It is found that different chromophores from substituents can significantly enhance the fluorescence intensities, which paves a new way of application of these materials in various fields.

1,1′-Bichrysene-2,2′-diol and its thiophene analogs, 6,6′-biphenanthro-[1,2- b]thiophene-7,7′-diols, as a series of novel π-expanded chrysene-/phenanthro[1,2- b]thiophene-based axially chiral molecules are synthesized from 1,1′-bi-2-naphthols with key steps including a Suzuki coupling, a Wittig reaction, and acid-mediated cyclization.

Scientific papers are an important form for researchers to summarize and display their research results. Information mining and analysis of scientific papers can help to form a comprehensive understanding of the subject. Aiming at the ignorance of contextual semantic information in current topic mining and the uncertainty of screening rules in association evolution research, this paper proposes a topic mining evolution model based on the BERT-LDA model. First, the model combines the contextual semantic information learned by the BERT model with the word vectors of the LDA model to mine deep semantic topics. Then construct topic filtering rules to eliminate invalid associations between topics. Finally, the relationship between themes is analyzed through the theme evolution, and the complex relationship between the themes such as fusion, diffusion, emergence, and disappearance is displayed. The experimental results show that, compared with the traditional LDA model, the topic mining evolution model based on BERTLDA can accurately mine topics with deep semantics and effectively analyze the development trend of scientific and technological paper topics.

Supplemental material, 3.SI-Proof-An_SJ for Novel π-expanded chrysene-based axially chiral molecules: 1,1′-bichrysene-2,2′-diols and thiophene analogs by Shujie An, Guofeng Tang, Yaling Zhong, Li Ma and Qiancai Liu in Journal of Chemical Research

In this paper, we propose a lattice-based HIBE scheme in the standard model with faster trapdoor delegation. It is proven secure under the Learning With Errors assumption. Inspired by Canetti et al.’s transformation (Eurocrypt’03), an HIBE can be converted into a forward-secure public-key encryption (FS-PKE) scheme, and the efficiency of key update relies on the efficiency of trapdoor delegation. For applications, our HIBE with faster delegation can be used to generate a lattice-based FS-PKE with faster key update. Furthermore, we also obtain a lattice-based forward-secure signature (FSS) scheme combining HIBE-like key-update technique with Zhang et al.’s short signature construction in the standard model (Crypto’16).

Item classification is an issue among inventory managers who want to achieve key-point management of items with different emphases, thus prompting managers and researchers to pursue efficient and effective classification algorithms. In processing multiple-criteria inventory classifications, back-propagation neural network (BPNN) shows its superiority in balancing items’ multiple competing attributes. However, because the training processes and the final results of BPNN rely on the initial connection weights and thresholds, finding reasonable values of the two parameters is a challenge. This paper introduces biogeography-based optimization (BBO) and an adaptive mutation particle swarm optimizer (AMPSO) to BPNN to optimize the training parameters, i.e., the global initial connection weights and thresholds, of BPNN. On the basis of this, two hybrid classification algorithms—BBO-BPNN and AMPSO-BPNN—are presented. Real-life data from three cases are adopted to verify the effectiveness and feasibility of the two proposed hybrid algorithms. Experimental results demonstrate that BBO-BPNN and AMPSO-BPNN show higher classification accuracy than other hybrid models, i.e., BPNN, PSO-BPNN, DE-BPNN, and GA-BPNN, in the multiple-criteria inventory classification problem.

In this paper, by assuming a B2C e-business company with several regional distribution centers (DCs), an extension of multi-item joint replenishment-distribution problem (JRD) is raised with stochastic lead-time and demands to investigate their mutual impacts to the JRD system. For the proposed JRD, the objective comprising four types of costs, namely, the ordering cost, the inventory holding cost, the lost sale penalties and the transportation cost is minimized by jointly deciding the basic cycle time, the replenishment frequencies and safety stock factors of all items. A hybrid differential artificial bee colony (DABC) algorithm, which combines the superiorities of the differential evolution (DE) algorithm in global search and the artificial bee colony (ABC) algorithm in fine search, is presented to solve the proposed JRD model. Numerical experiments and parameter sensitivity analyses are conducted. The computational results have testified that DABC is faster than that of DE (16%) and two hybrid ABC-DEs (18% and 9%), more effective than that of genetic algorithm (GA, 0.15%), ABC (0.03%) and ABC-DE1 (0.015%) and robust than that of GA (99%), DE (71%), ABC (0.97%) and two hybrid ABC-DEs (86% and 88%). Most importantly, different uncertainty significance of lead-time and demands to JRD are revealed and analyzed. Furthermore, management insights, such as the magnitudes of the problem-related parameters and the mutual effects of the two stochastic variables to the system cost, are indicated in the replenishment process.

Shanghai Nuclear Engineering Research and Design Institute (SNERDI) has been studying seismic risk analysis for nuclear power plant for a long time, and completed seismic margin analysis for several plants. After Fukushima accident, seismic risk has drawn an increasing attention worldwide, and the regulatory body in China has also required the utilities to conduct a detailed analysis for seismic risk. So, we turned our focus on a more intensive study of seismic probabilistic safety assessment (PSA/PRA) for nuclear power plant in recent years. Since quantification of seismic risk is a key part in Seismic PSA, lots of efforts have been devoted to its research by SNERDI. The quantification tool is the main product of this research, and will be discussed in detail in this paper. First, a brief introduction to Seismic PSA quantification methodology is presented in this paper, including fragility analysis on system or plant level, convolution of seismic hazard curves and fragility curves, and uncertainty analysis as well. To derive more accurate quantification results, the binary decision diagram (BDD) algorithm was introduced into the quantification process, which effectively reduces the deficiency of the conventional method on coping with large probability events and negated logic. Finally, this paper introduced the development of the seismic PSA quantification tool based on the algorithms discussed in this paper. Tests and application have been made for this software based on a specific nuclear power plant seismic PSA model.

Significance determination process (SDP) is one of important application tools in the scheme of risk-informed and performance based regulation. It is used to help the analyst evaluate the risk significance of a particular safety concerned event. By referring to the design and development of SDP system, this paper gives a brief introduction on the design and development of the system. An autonomously developed PSA quantification engine is incorporated into the SDP system, and it helps to resolve quantification issue for event risk evaluation. From qualified screening to risk significance quantification, SDP system is designed to realize the whole process for event risk analysis. SDP system could potentially help analysts from both regulatory agency and NPP licensees to quickly evaluate the risk significance of a particular event.

Zero-suppressed Binary Decision Diagram (ZBDD) algorithm is an advanced method in fault tree analysis, which is developing quickly in recent years and being used in the development of the Probabilistic Safety Assessment (PSA) Quantification Engine. This algorithm converts a fault tree to a ZBDD structure, solves the minimal cut sets and calculates the top node unavailability. The ordering of the basic events and logical gates is the core technique of the ZBDD algorithm, which determines the efficiency of the ZBDD conversion and the size of the ZBDD structure. A variable ordering method based on the structure of the fault tree is developed in this paper, which gives a better basic events order by compressing the fault tree; meanwhile, the method offers a logical gates order. The nodes order derived from this method can accelerate the ZBDD conversion obviously.

Minimal Cut-sets (MCS) post processing is an important part of the Probabilistic Safety Assessment (PSA) analysis. Two alternative post processing methods have been described in this paper based on traditional and Zero-suppressed Binary Decision Diagram (ZBDD) methods. The first post processing method, named the Quick MCS Post Processing Method, obtains MCS results directly from the cut-sets list using a quick sort method according to a certain set of sorting rules. The alternative post processing method, named the ZBDD Based MCS Post Processing Method, obtains MCS results from the ZBDD structure which encodes the MCS, using the ZBDD algorithm according to the post processing rules. Tests show that both of the two methods can derive the accurate MCS post processing results.

Seismic risk of nuclear power plant has drawn increasing attention after Fukushima accident. An intensive study has been carried out in this paper, including sampling of component and structure fragility based on Monte Carlo method, fragility analysis on system or plant level, convolution of seismic hazard curves and fragility curves. To derive more accurate quantification results, the binary decision diagram (BDD) algorithm was introduced into the quantification process, which effectively reduces the deficiency of the conventional method on coping with large probability events and negated logic. Seismic Probabilistic Safety Analysis (PSA/PRA) quantification software was developed based on algorithms discussed in this paper. Tests and application has been made for this software with a specific nuclear power plant seismic PSA model. The results show that this software is effective on seismic PSA quantification.

A novel chemiluminescence (CL) animal tissue‐based sensor for pyruvic acid is presented in this paper. Pork heart tissue was chopped into small pieces and packed into a mini‐glass column as the recognition element. When pyruvic acid passed through the column, hydrogen peroxide was produced under the catalytic oxidation of oxygen by pyruvic acid oxidase present in the pork heart tissue. This produced hydrogen peroxide could react with luminol in alkaline solution to produce chemiluminescence in the presence of potassium hexacyanoferrate(III). The developed sensor system promises simplicity, fastness, stability, and sensitivity. Under the optimum conditions, CL intensities are proportional to the concentration of pyruvic acid in the range of 0.02–12 µmol/L, with a detection limit of 0.004 µmol/L (3σ). RSD is 2.3% for 0.5 µmol/L pyruvic acid (n=11). The sensor could be stable for 150 min by more than 100 times determination. The proposed method has been applied successfully to the analysis of pyruvi...

This paper studies the coordinating contracts of an application supply chain consisting of one application service provider (ASP) and one application infrastructure provider (AIP). The AIP supplies the computer capacity to the ASP that in turn sells the value-added application services to the market. The market is characterized by price dependent random demand. Revenue sharing contract and Service quantity discount contract are discussed in the paper. And the authors get some managerial insights from the model.

The Eleman Neural Network has been widely used in various fields ranging from temporal version of the Exclusive-OR function to the discovery of syntactic categories in natural language date. However, one of the problems often associated with this type of network is the local minima problem which usually occurs in the process of the learning. To solve this problem, we have proposed an error function which can harmonize the update weights connected to the hidden layer and those connected to the output layer by adding one term to the conventional error function. It can avoid the local minima problem caused by this disharmony. We applied this method to the Boolean Series Prediction Questions problems to demonstrate its validity. The result shows that the proposed method can avoid the local minima problem and largely accelerate the speed of the convergence and get good results for the prediction tasks.

Eleman Neural Network has been widely used in various fields, from classification to the prediction categories in natural language data. However, the local minima problem usually occurs in the process of the learning. To solve this problem and to speed up the process of the convergence, we propose an improved learning method by adding a term in error function which relates to the neuron saturation of the hidden layer for the Eleman Neural Network. The activation functions are adapted to prevent neurons in the hidden layer from getting into deep saturation area. We apply this method to the Boolean Series Prediction Questions to demonstrate its validity. The simulation result shows that the proposed algorithm can avoid the local minima problem, largely accelerate the speed of the convergence and get good results for the simulation tasks.

A new chemiluminescence (CL) method is proposed for the determination of quinine sulphate, which is based on the dichloromethane solvent extraction of the ion-pair complex of tetrachloroaurate (III) with quinine sulphate, and luminol CL detection in a reversed micellar medium formed by the cation surfactant cetyltrimethylammonium bromide in a dichloromethane–cyclohexane (3:7, v[sol ]v)–water (0.35 mol[sol ]L Na2CO3 buffer solution, pH 11.5). The ion-pair complex of tetrachloroaurate (III) with quinine sulphate produced an analytical CL signal when it entered the reversed micellar water pool. In optimum conditions, CL intensities are proportional to the concentrations of the studied drug over the range 0.015–10 µg[sol ]mL, with a detection limit of 1.5 ng[sol ]mL. The relative standard deviation (RSD) is 1.38% for 2.5 µg[sol ]mL quinine sulphate (n = 11). The method has been applied to the determination of the studied drug in biological fluids, with satisfactory results. Copyright © 2005 John Wiley & Sons, Ltd.