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The poor stability and aqueous-quenching of fluorescence of perovskite nanocrystals (NCs) hinder their application in bio-detection and bio-imaging. Herein, through the synergistic effects of polymer surface ligand and silica encapsulation, highly stable and enhanced aqueous fluorescent CsPbBr3-mPEG@SiO2 NCs were synthesized and used as a novel "on–off–on" fluorescent probe for highly sensitive and selective detection of mercury ions (Hg2+) and glutathione (GSH) in aqueous solutions. The effects of the methoxypolyethylene glycol amine (mPEG-NH2) ligand and silica encapsulation on the stability and aqueous fluorescence of the CsPbBr3 NCs were studied. It indicated that the aqueous fluorescence of perovskite NCs was increased by 2.59 times. The water stability was also greatly improved, with the NCs maintaining 73% of their original fluorescence after storage for 30 days in water. X-ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FT-IR) analyses further demonstrated that the NCs were successfully passivated by mPEG-NH2 and silica. The fluorescence of the CsPbBr3-mPEG@SiO2 nanocrystals was effectively quenched by Hg2+ which is attributed to the electron transfer process between NCs and Hg2+. Then, through the interaction between Hg2+ and GSH, the restoration of fluorescence for CsPbBr3-mPEG@SiO2 was realized. The "on–off–on" fluorescent probe can be used for the detection of Hg2+ and GSH with a low detection limit of 0.08 nM and 0.19 μM, respectively. It also shows a fast response time and high accuracy for practical sample detection. The simple and sensitive fluorescent probe of CsPbBr3-mPEG@SiO2 shows great potential in environmental and biological sensing. [ABSTRACT FROM AUTHOR]

The rapidly increasing and widespread use of graphene oxide (GO) as catalyst supports, requires further understanding of its chemical stability in advanced oxidation processes (AOPs). In this study, UV/H 2 O 2 and UV/persulfate (UV/PS) processes were selected to test the chemical instability of GO in terms of their performance in producing highly reactive hydroxyl radicals ( OH) and sulfate radicals (SO 4 − ), respectively. The degradation intermediates were characterized using UV–visible absorption spectra (UV–vis), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), Raman spectroscopy, and matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Experimental data indicate that UV/PS process was more effective in enhancing GO degradation than the UV/H 2 O 2 system. The overall oxygen-containing functionalities (e.g. C O, C O and O C O groups) dramatically declined. After radical attack, sheet-like GO was destructed into lots of flakes and some low-molecular-weight molecules were detected. The results suggest GO is most vulnerable against SO 4 − radical attack, which deserves special attention while GO acts as a catalyst support or even as a catalyst itself. Therefore, stability of GO and its derivatives should be carefully assessed before they are applied to SO 4 − -based AOPs. [ABSTRACT FROM AUTHOR]

Recently, the novel SNP rs12704290 in GRM3 was identified in a genome-wide association study on schizophrenia susceptibility. Our study was to investigate the association of 29 selected SNPs (including rs12704290) with schizophrenia and to evaluate any possible relationship between them and cognition related to schizophrenia. The SNPs were analyzed in 1115 unrelated schizophrenic patients and 2289 healthy controls. The results showed significant associations between these SNPs and schizophrenia as well as with changes in cognition. [ABSTRACT FROM AUTHOR]

Recently, the H2AFZ gene was reported in relation to schizophrenia in Japanese males. A two-stage case-control study was designed to investigate the association of the H2AFZ gene with schizophrenia and its relationship with cognitive function in Han Chinese patients with schizophrenia. This study included a testing set with 1115 patients and 2289 controls and a validation set with 1843 patients and 3155 controls. A total of 10 single-nucleotide polymorphisms (SNPs) in the H2AFZ gene were genotyped, and both independent data sets were analyzed in association with SNP and gender. The rs2276939 SNP was found to be significantly associated with schizophrenia, particularly in males. A similar pattern was observed in our two-stage study on conducting further imputation and haplotype association analyses. In addition, two of the SNPs (rs61203457 and rs2276939) and cognitive functioning were found to interact significantly when processing the perseverative error in the Wisconsin Card Sorting Test. Our findings suggest that the H2AFZ gene may confer a risk for schizophrenia and contribute to the impairment of executive function in Han Chinese patients with schizophrenia. These findings augment our current state of knowledge regarding the risk of schizophrenia and the impairment of cognitive performance in patients with this disorder. [ABSTRACT FROM AUTHOR]

Abstract: Enhanced index tracking is a popular strategy in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. In this paper, we propose a multi-objective optimization scheme for the enhanced index tracking problem, which provides the framework of defining the objectives as both maximizing the degree of beating the benchmark index and minimizing the accumulated error of underperforming the benchmark. Transaction costs are limited in the constraints. An immunity-based multi-objective optimization algorithm is presented to search for the solution of the enhanced index tracking problem. Treatment of infeasibility and solution selection are also presented. Our proposed approach is implemented to five data sets drawn from major world markets. The computational results compared with other published results show that our method has superior performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting. [Copyright &y& Elsevier]

Abstract: In this paper, matched synchronous daily trading data, the cointegration theory and the Permanent-transitory (PT) model are employed to analyze the price discovery mechanism and evolvement of the A and the B shares in the Shanghai Stock Exchange and the Shenzhen Stock Exchange markets. The results show that the process of enlarging the scope of investor for the B shares have much improved informational flow between the A shares and the B shares in the Shanghai Stock Exchange than that of the Shenzhen Stock Exchange. At the same time, the results also show that the A shares play a dominant role in price discovery in China''s emerging stock markets. Seven hypotheses were formulated and analyzed. Evidence supporting the domestic investors'' information advantage hypothesis, market segmentation hypothesis, trading cost hypothesis, trading volume ratio hypothesis and negotiable volume ratio hypothesis is found in Shanghai Stock Exchange market. Evidence supporting the investor structure hypothesis, domestic investors'' information advantage hypothesis and trading cost hypothesis is found in the Shenzhen Stock Exchange market. [Copyright &y& Elsevier]

Abstract: This paper addresses a single machine scheduling problem in which the actual job processing times are determined by resource allocation function, its position in a sequence and a rate-modifying activity simultaneously. We discuss two objective functions with two resource allocation functions under the consideration of a rate-modifying activity. We show that the problems are solvable in time for a linear resource allocation function and are solvable in time for a convex resource allocation function. [Copyright &y& Elsevier]

We consider a supply chain system with a risk-neutral manufacturer as the leader and a risk-averse retailer as the follower with uncertain demand. At the beginning of the game, the manufacturer makes efforts on advertising and then the retailer decides its order quantity before demand realization. The retailer's risk aversion is modeled by the Value-at-Risk (CAR) approach with the downside risk constraint. The analysis of equilibrium strategies indicates some characteristics of the game are different from those under risk-neutral assumptions. We find that the manufacturer can effectively prevent the risk-averse retailer from downsizing the order quantity through advertising. In order to explain the difference, we investigate the impacts of risk aversion on the manufacturer's advertising decision and the retailer's ordering decision. We find that the retailer with moderate degree of risk aversion orders a larger volume and receives greater advertising support from the manufacturer. Moreover, the feasible combinations of target profit and downside risk for moderate risk aversion are discussed to derive the relationship of the two parameters. In addition, we make a simple analysis of the situation with two independent retailers who have heterogeneous degrees of risk aversion. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we consider the single machine scheduling problems with exponential sum-of-logarithm-processing-times based learning effect. By the exponential sum-of-logarithm-processing-times based learning effect, we mean that the processing time of a job is defined by an exponent function of the sum of the logarithm of the processing times of the jobs already processed. We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions. [Copyright &y& Elsevier]

Abstract: This paper introduces a new problem called the capacitated plant location problem with customer and supplier matching (CLCSM). The product distribution from plants to customers and the material supply from suppliers to plants are considered together. We merge a distribution trip and a supply trip into one triangular trip for saving allocation cost. Vehicles from plants visit a customer and a supplier for each trip. We provide a heuristic solution procedure based on Lagrangian relaxation. Computational results indicate that the proposed heuristic solution procedure is shown to be efficient yielding optimal or near-optimal solutions for randomly generated instances. [Copyright &y& Elsevier]

Background: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4). Results: The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo. Conclusions: The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription. [ABSTRACT FROM AUTHOR]