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Cross-modal retrieval aims to find the similarity between different modal data, while the hash retrieval method improves retrieval efficiency. This paper proposes a cross-modal hash retrieval method based on multiple similarity matrices. This paper proposes an unsupervised cross-modal hash retrieval method based on multiple similarity matrices. This paper uses a weighted combination method to construct fusion features through hash features and original features. Based on the three features, the auxiliary similarity matrix of each of the three features is established. Finally, the fusion matrix is constructed by a weighted combination of the similarity matrix of the original features and the hash features. These four different matrices include similarity matrices with varying forms of features and similarity matrices with varying construction methods, which concentrate the similarity information of other modalities. The loss function between different similarity matrices and the loss function between different modalities are calculated through these four different matrices. Considering that most models have a single method for extracting text features, this paper uses text self-attention to strengthen the effect of text feature extraction so that the final performance of this paper is effectively improved. In order to verify the impact of this article, the results are tested on the Wikipedia, MIRFlickr, and NUS-WIDE datasets, and the results prove that the effect of this article has certain advantages compared with the latest methods.

The presence of chloride ions can facilitate the COD removal efficiency due to the involvement of active chlorine species in the electro-oxidation process, but few attentions have been paid to the negative effect of the electro-generated oxychlorides on electro-oxidation performance. In this study, the effects of oxychlorides were investigated as functions of current density and phenol concentration using DSA anodes in terms of the evaluation of the COD removal performance and the biological toxicity. The results show that oxychlorides formed in the electro-oxidation system could result in the over-evaluation of the COD removal performance. Increasing current density (15-50 mA cm

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

Writing has always been one of the difficulties in teaching Chinese as a foreign language. With the advent of the information society, modern information technology, represented by networking, digitalization, multimedia, and intelligence, has become a creative tool to expand human capabilities, changing people’s traditional way of life, study, and work. Guided by the theory of integration of information technology and curriculum, combined with the characteristics of Chinese writing, this paper puts forward that the goal of integration of information technology and Chinese writing is to change the traditional teaching structure, create an ideal environment for students to learn Chinese and improve their Chinese information literacy. A practical teaching platform for college Chinese writing is constructed, which is designed from the user role and based on user access control. The design includes the roles of graduate students, foreign teachers, administrators, and users. Each user has different operation rights, module rights, and management rights. According to a small amount of online information and the results of a previous course, we can make a good prediction. The results show that this method is effective, and we can predict students’ academic performance through their online behavior.

The vehicle-to-infrastructure communication is a kind of communication mode of the vehicular ad-hoc network (VANET). It is paramount to allocate roadside units (RSUs) appropriately to assure the quality of communication service. Sarubbi et al. have proposed a greedy heuristic allocation algorithm Delta-r. It examines the Δ ρ2 ρ1 -deployment constraint, a metric for evaluating the performance of VANET, with relative contact time. Unfortunately, the relative contact time of vehicles at urban cells may present `false high', which would play an opposite effect on making a decision. In order to solve the problem, a new allocation algorithm Delta-uc is presented, which examines the Δ ρ2 ρ1 -deployment constraint with a useful contribution. It gets rid of the negative effect of `extra' contact time by means of retaining merely the contributing relative duration of a vehicle at an urban cell. Furthermore, an effective recombination operator is introduced basing on algorithm Delta-uc, and a parthenogenetic algorithm UCPGA is presented to solve the allocation problem. Experimental comparisons were performed on the real mobility trace of Cologne, Germany. The experimental results demonstrate that in many Δ ρ2 ρ1 -deployments, the number of RSUs produced by the Delta-uc and UCPGA algorithms are, respectively, fewer than those produced by algorithms Delta-r and DeltaGA.

The deformation of blades under complex loads of multiple working conditions will reduce the energy conversion efficiency. To reduce the deviation of the blade shape in practical working conditions, a combination and optimization method of blade design schemes under multiple working conditions, based on the entropy weight vague sets, is proposed. The sensitivity of each working condition index is analyzed based on the information entropy, and the satisfaction degree of the design scheme based on the design requirements and experiences is described with the vague set. The matching degree of different design schemes for multiple working conditions is quantified according to the scoring function. The combination and optimization of the design scheme are verified by numerical simulation analysis. The results show that the proposed design scheme has a smaller blade shape deviation than the traditional design scheme under multiple working conditions.

Catalytic upgrading of anisole and guaiacol in an atmospheric dielectric barrier discharge (DBD) plasma reactor was studied. Compared to thermal upgrading in a fixed bed, the DBD plasma method can convert the model compound to benzene and toluene with high selectivity under relatively low temperature. The synergistic effect between H2 plasma and Ni-Mo/SiO2 catalyst is crucial to the high deoxygenation rate. The primary reactions occurring during the DBD upgrading process include demethylation, methyl transfer, and hydrogenolysis. XRD spectra indicate that phase change should not be the primary reason for catalyst deactivation; GC–MS, NH3-TPD, and regeneration experiments show that carbon deposition is the cause for deactivation. NH3-TPD analysis reveals that a combination of medium and strong acid sites plays an important role in the hydrogenolysis of phenols to benzenes. The present work shows DBD catalytic upgrading as a promising route for the C O bond hydrogenolysis reaction.

Mutation profiles together with prior knowledge such as interactions between genes/proteins provide abundant critical information for the identification of driver modules, which is very important for analyzing mutational heterogeneity in human cancers. Due to the negative effects of inevitable false positive interactions in the PPI network, subcellular localization data are exerted to filter out them firstly, and somatic mutation profiles are used to weight the retained interactions. Five novel recombination operators are introduced basing on the vertex degrees and the edge weights in the PPI network, and a parthenogenetic algorithm is devised for solving the presented identification model which takes into account network connectivity, mutual exclusivity, coverage, and hops between genes within a module. Extensive experimental results indicate that compared with two state-of-the-art computational methods Hotnet2 and MEXCOwalk, the proposed method exhibits competitive performance in most cases in terms of recovering known cancer genes, providing modules that have satisfied coverage and mutual exclusivity, and are enriched for mutations in specific cancer types. Many identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in the literature. In addition, the proposed method does identify many cancer related genes missed by methods Hotnet2 and MEXCOwalk, including some recognized genes covering many types of cancers but having low mutation frequency.

Layered double hydroxides (LDHs), also known as hydrotalcite-like anionic clays, are very convenient precursors with a tunable flexibility toward multifunctional nanomaterials, especially in energy storage. Typical methods to improve lithium storage are to introduce additional or self-generating carbonaceous supports to LDH-derived transition metal oxides as anode nanomaterials which can host lithium mainly though a conversion mechanism. Here, we describe a preparation of mesoporous spinel ferrite composite (MgFe 2 O 4 /ZnFe 2 O 4 ) for lithium storage, which is assisted by a combined conversion and alloying mechanism. The composite is derived by a thermal decomposition of a scalablely produced single-resource precursor of ternary Mg 2+ Zn 2+ Fe 3+ -layered double hydroxide (Mg 2+ Zn 2+ Fe 3+ -LDH), and subsequent selective etching. Electrochemical test shows that the electrode delivers an exceptional electrochemical performance, i.e., a reversible capacity of 1190 mA h g −1 after 100 cycles at 100 mA g −1 , and, in particular, a reversible capacity of 981 mA h g −1 at 500 mA g −1 after 330 cycles, as well as a reversible capacity of 541 mA h g −1 at 2000 mA g −1 after 1000 cycles. The high electrochemical performance could be attributed to the following features: the combined alloying and conversion mechanisms of ZnFe 2 O 4 , synergistic MgFe 2 O 4 , and slight-content MgO as a non-active matrix, as well as an appropriate specific area and mesoporous size distribution. Our results show that the cation-tunable LDH precursor-derived synthesis route might be an alternative to prepare multiple-component composites of spinel ferrites and transition metal oxides.

Model counting is the problem of computing the number of models or satisfying assignments for a given propositional formula, and is # P -complete. Owing to its inherent complexity, approximate model counting is an alternative in practice. Model counting using the extension rule is an exact method, and is considered as an alternative to resolution-based methods for model counting. Based on the exact method, we propose two approximate model counting algorithms, and prove the time complexity of the algorithms. Experimental results show that they have good performance in the accuracy and efficiency.

With the rapid development of high-throughput sequencing technologies, huge volumes of generated cancer genomics data make it into reality to understand the carcinogenic pathogenesis from the molecular level. It is believed that the study of commonalities among different cancers is one of the significant problems for understanding cancers, and will be beneficial for personalized therapy and precision medicine in cancer treatment. The ComMDP method is a useful one for solving this problem. However, when there is a substantially difference among the number of samples, the method of accumulating the absolute weight value of every cancer, employed by the ComMDP method, may give rise to missing some driver pathways. In this paper, two mathematical models CDP-V and CDP-H, replacing the absolute weight values with relative ones, are presented by using variance and harmonic mean, respectively. By devising a sort of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm is proposed for solving these two models. Extensive experiments were performed on both simulated and real cancer data. The experimental results show that given several types of cancer, the gene sets identified based on the presented models and algorithm not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples in each cancer. In addition, some biologically meaningful gene sets, which are missed by the ComMDP one, are indeed detected. Hence the identified methods based on the presented models and algorithm may become useful complementary tools for identifying cancer pathways.

Background Haplotype assembly, reconstructing haplotypes from sequence data, is one of the major computational problems in bioinformatics. Most of the current methodologies for haplotype assembly are designed for diploid individuals. In recent years, genomes having more than two sets of homologous chromosomes have attracted many research groups that are interested in the genomics of disease, phylogenetics, botany and evolution. However, there is still a lack of methods for reconstructing polyploid haplotypes. Results In this work, the minimum error correction with genotype information (MEC/GI) model, an important combinatorial model for haplotyping a single individual, is used to study the triploid individual haplotype reconstruction problem. A fast and accurate enumeration-based algorithm enumeration haplotyping triploid with least difference (EHTLD) is proposed for solving the MEC/GI model. The EHTLD algorithm tries to reconstruct the three haplotypes according to the order of single nucleotide polymorphism (SNP) loci along them. When reconstructing a given SNP site, the EHTLD algorithm enumerates three kinds of SNP values in terms of the corresponding site’s genotype value, and chooses the one, which leads to the minimum difference between the reconstructed haplotypes and the sequenced fragments covering that SNP site, to fill the SNP loci being reconstructed. Conclusion Extensive experimental comparisons were performed between the EHTLD algorithm and the well known HapCompass and HapTree. Compared with algorithms HapCompass and HapTree, the EHTLD algorithm can reconstruct more accurate haplotypes, which were proven by a number of experiments.

Chemical-looping combustion (CLC), which is a promising technique that includes an inherent separation of CO2 may reduce the generation of dioxins in municipal solid waste (MSW) disposal because in a CLC system, no free oxygen is available for incineration process. Polyvinylchloride (PVC) and kitchen garbage are the main chlorine substances in MSW. The reaction between PVC pyrolysis gas and a calcium (Ca)-based oxygen carrier was investigated in a two-stage reactor in this study. The thermal decomposition and reduction/oxidation cycle behaviors of the oxygen carrier were investigated by analyzing methane (CH4) using a thermogravimetric analyzer (TGA). The characteristics of CaSO4/Fe2O3 oxygen carrier were determined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that the addition of Fe2O3 into CaSO4 can enhance the reaction rate, and also that the CaSO4/Fe2O3 oxygen carrier showed a good heat stability at the temperature of 900 degrees C. The reduction/oxidation cycles confirmed that the decomposition of the CaSO4/Fe2O3 oxygen carrier is usually accompanied by some side reactions. These side reactions could cause the loss of their regeneration ability. The CaSO4/Fe2O3 oxygen carrier successfully reacted with PVC pyrolysis gas in a two-stage reactor and the complete reaction ratio of m(PVC) to m(oxygen carrier) is 8. The research documented herein provides a useful reference for the utilization of MSWs.

Key characteristic thermal events during the pyrolysis and CO2-gasification of six representative combustible solid wastes (CSWs), namely, poplar, paper, polyethylene (PE), rubber, dacron and rice, were investigated using a thermogravimetric analyser (TGA) in order to observe and contrast the thermal behaviour of these materials during pyrolysis and gasification and as a means to reveal their thermal conversion mechanisms. The TGA experiments were carried out in N-2 and CO2 atmospheres, respectively, and from room temperature to 1193 K at various heating rates ranging from 5 to 30 K min(-1). Three generic characteristic thermal events, namely, pyrolysis of the raw CSW (stage I), gasification of the char of incomplete carbonisation (stage II), and gasification of the fixed carbon (stage III), were identified during the CO2 gasification processes. It was observed that the poplar and paper samples went through all the three key events, however, the PE and rice samples only incurred stage I, while the rubber and dacron samples only showed the stages I and III events. In addition, the pyrolysis process (stage I) of all six CSW samples during the CO2-gasification was similar to that in N-2 but, as expected, the reaction was shifted to occur at slightly higher temperatures in CO2. The activation energy of the CO2-gasification reactions in the different stages of conversion was estimated from the TGA data using the Friedman iso-conversional method. The activation energy for the pyrolysis of poplar, PE and dacron remained relatively constant while that for paper, rice and rubber increased with increasing the degree of conversion in stage I. (C) 2014 Elsevier Ltd. All rights reserved.

Corn stalks char from fast pyrolysis was activated by physical and chemical activation process in a fluidized bed reactor. The structure and morphology of the carbons were characterized by N-2 adsorption and SEM. Effects of activation time and activation agents on the structure of activation carbon were investigated. The physically activated carbons with CO2 have BET specific surface area up to 880 m(2)/g, and exhibit microporous structure. The chemically activated carbons with H3PO4 have BET specific surface area up to 600 m(2)/g, and exhibit mesoporous structure. The surface morphology shows that physically activated carbons exhibit fibrous like structure in nature with long ridges, resembling parallel lines. Whereas chemically activated carbons have cross-interconnected smooth open pores without the fibrous like structure. (C) 2014 Elsevier Ltd. All rights reserved.

Thermal decompositions of three components of biomass (cellulose, hemicellulose and lignin) were studied using nonisothermal thermogravimetric analysis (TGA) under both oxidative and inert atmospheres at a heating rate of 10 K min(-1). The multi-Gaussian-distributed activation energy model (DAEM)-reaction model was first developed to describe thermal decomposition behaviors of three components. Results showed that the presence of CO₂ enhanced the thermal decompositions of three components in high temperature range, but made little difference in low temperature range. Decomposition behaviors under CO₂ were analyzed by the two-stage reaction mechanism corresponding to the pyrolysis process of original materials and the gasification process of char. During gasification stage, CO₂ was reduced to CO, which provided a possibility of a reduction in greenhouse gas emissions. In addition, more CO was produced, and therefore the thermal value of gas was improved. The findings imply that CO₂ gasification technology of biomass has great research significance.

The growing emergence of antibiotic-resistant bacteria in the food industry needs to be controlled with effective antimicrobials. In this study, bacteriocin MN047 A (BMA) was found to have antibacterial activity against multidrug-resistant bacteria. It was produced by Lactobacillus crustorum MN047, which was first isolated from koumiss, a traditional fermented dairy product from Xinjiang Autonomous Region, China. It was purified by ammonium sulfate precipitation, ion-exchange chromatography, and reversed-phase chromatography. It had a low molecular mass of 1,770.89 Da according to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and the sequence was identified as QLPWQILGIVAGMFQA by liquid chromatography-tandem mass spectrometry analysis and MASCOT searching. It was proteinaceous in nature: the bacteriocin was digested by protease but not by α-amylase or lipase. It showed broad pH toleration (pH 2-11), good thermostability, and good storage stability. It had a broad inhibitory spectrum, including both gram-positive and gram-negative bacteria. Growth curve and time-kill kinetics indicated that it was bactericidal to the indicator strains, and this finding was verified by scanning electron microscope and transmission electron microscope after treatment with BMA. As well, BMA halted the growth of Staphylococcus aureus and Escherichia coli in the G1 and G2/M phases according to cell-cycle analysis by flow cytometry, indicating that BMA had comprehensive inhibitory effects against foodborne pathogens.

Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a computer-assisted clinical decision support system. Since this data contains individuals’ privacy, they must be appropriately anonymized before to be released. However, the existing anonymization method based on l-diversity for hierarchical data may cause a serious similarity attack, and cannot protect data privacy very well. In this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute values uniformly (a categorical attribute value can be converted into a numerical attribute value according to its frequency of occurrences), and then transform the value levels to sensitivity levels. The privacy model ( α l e v h , k)-anonymity for hierarchical data with multi-level sensitivity is proposed. Furthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment results demonstrate that our approach is obviously superior to existing anonymous approach in hierarchical data in terms of utility and security.

The minimum error correction (MEC) model is one of the important computational models for single individual single nucleotide polymorphism (SNP) haplotyping. Due to the NP-hardness of the model, Qian et al. presented a particle swarm optimization (PSO) algorithm to solve it, and the particle code length is equal to the number of SNP fragments. However, there are hundreds and thousands of SNP fragments in practical applications. The PSO algorithm based on this kind of long particle code cannot obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm PGA-MEC based on parthenogenetic algorithm (PGA) is presented to solve the model. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. The reconstruction rate of PGA-MEC algorithm is higher than that of PSO algorithm and the running time of PGA-MEC algorithm is shorter than that of PSO algorithm, which are proved by a number of experiments.

Circular and sectorial blanks are often cut from silicon steel sheet to make stators and rotors of electric generators. This paper presents an algorithm to generate optimal multi-section cutting patterns for the blanks. A cutting pattern includes one or more sections, each of which consists of strips of the same direction and length. Blanks in a strip are of the same size. The algorithm employs dynamic programming to generate cutting patterns, and linear programming to determine the cutting scheme. Cutting stock data from a factory that produces electric generators are analyzed, and the results indicate that the algorithm is efficient in improving material usage.

This work deals with the pyrolysis kinetics of cellulose, hemicellulose and lignin. Experiments were carried out in a thermogravimetric analyzer (TGA Q5000) in the inert atmosphere of nitrogen at a heating rate of 10 K min−1. The single Gaussian distributed activation energy model (DAEM) was utilized to study the pyrolysis kinetics. Kinetic parameters such as the pre-exponential factor (k0), mean activation energy (E0) and standard variance (σ) were computed by a pattern search algorithm. It was found that the calculated kinetic parameters using the single Gaussian-DAEM-reaction model could reproduce the differential thermogravimetric (DTG) curves of cellulose very well. However, there existed obvious deviations in the whole range of temperatures between the calculated and experimental data for hemicellulose and lignin. In order to describe the thermal decompositions of hemicellulose and lignin more accurately, a novel double Gaussian-DAEM-reaction model consisting of two parallel partial reactions was developed to describe the pyrolysis processes. Calculated results of the fitting procedure using the double Gaussian-DAEM-reaction model showed good agreement with experimental DTG data of hemicellulose and lignin.

Background Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. Results Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. Conclusions We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.