Showing papers by "Zhejiang Gongshang University published in 2014"

Joint Mode Selection and Resource Allocation for Device-to-Device Communications

Abstract: Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

Toward efficient and privacy-preserving computing in big data era

Abstract: Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as "3V") challenges. However, in addition to the 3V challenges, the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, "veracity," in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining's efficiency and privacy requirements in the big data era.

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

Abstract: Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naive Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

How transformational leadership influences follower helping behavior: The role of trust and prosocial motivation

Abstract: Summary We proposed and tested a moderated mediation model that jointly examines affect-based and cognition-based trust as the mediators and prosocial motivation as the moderator in relationships between transformational leadership and followers’ helping behavior towards coworkers. Data were collected from 348 sales and servicing employees and their supervisors in four private retail companies and five private manufacturing companies located in Southeast China. The results showed that both affect-based trust and cognition-based trust mediated the relationship between transformational leadership and followers’ helping behavior towards coworkers. Furthermore, moderated mediation analyses showed that affect-based trust mediated the relationship between transformational leadership and followers’ helping behavior towards coworkers only among employees with high prosocial motivation, whereas cognition-based trust mediated this relationship among only those with low prosocial motivation. Implications for the theory and practice of leadership are then discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges

Abstract: Cyber-physical systems (CPSs) integrate the computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. CPS was identified as one of the eight research priority areas in the August 2007 report of the President's Council of Advisors on Science and Technology, as CPS will be the core component of many critical infrastructures and industrial control systems in the near future. However, a variety of random failures and cyber attacks exist in CPS, which greatly restrict their growth. Fortunately, an intrusion detection mechanism could take effect for protecting CPS. When a misbehavior is found by the intrusion detector, the appropriate action can be taken immediately so that any harm to the system will be minimized. As CPSs are yet to be defined universally, the application of the instruction detection mechanism remain open presently. As a result, the effort will be made to discuss how to appropriately apply the intrusion detection mechanism to CPS in this paper. By examining the unique properties of CPS, it intends to define the specific requirements first. Then, the design outline of the intrusion detection mechanism in CPS is introduced in terms of the layers of system and specific detection techniques. Finally, some significant research problems are identified for enlightening the subsequent studies.

Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model

Abstract: This study explores users’ continuance intention in online social networks by synthesizing Bhattacherjee’s IS continuance theory with flow theory, social capital theory, and the unified theory of acceptance and use of technology (UTAUT) to consider the special hedonic, social and utilitarian factors in the online social network environment The integrated model was empirically tested with 320 online social network users in China The results indicated that continuance intention was explained substantially by all hypothesized antecedents including perceived enjoyment, perceived usefulness, usage satisfaction, effort expectancy, social influence, tie strength, shared norms and trust Based on the research findings, we offer discussions of both theoretical and practical implications

Self-assembly of triangular and hexagonal molecular necklaces.

Abstract: The formation of catenated systems can be simplified greatly if one or more rings are generated via self-assembly. Herein we exploit the orthogonality of coordination-driven self-assembly and crown-ether host–guest complexation to obtain a [4]molecular necklace and a [7]molecular necklace based on a well-developed recognition motif of 1,2-bis(pyridinium)ethane/dibenzo[24]crown-8. By adapting the bis(pyridinium) motif into the backbone of a donor building block, the resulting semirigid dipyridyl species can serve both as a structural element in the formation of metallacycles and as a site for subsequent host–guest chemistry. The pseudolinear nature of the donor precursor lends itself to the formation of triangular and hexagonal central metallacycles based on the complementary acceptor unit used. This exemplary system organizes up to 18 molecules from three unique species in solution to afford a single supramolecular ensemble.

Exogenous abscisic acid application decreases cadmium accumulation in Arabidopsis plants, which is associated with the inhibition of IRT1-mediated cadmium uptake.

Abstract: Cadmium (Cd) contamination of agricultural soils is an increasingly serious problem. Measures need to be developed to minimise Cd entering the human food chain from contaminated soils. We report here that, under Cd exposure condition, application with low doses of (0.1–0.5 μM) abscisic acid (ABA) clearly inhibited Cd uptake by roots and decreased Cd level in Arabidopsis wild-type plants (Col-0). Expression of IRT1 in roots was also strongly inhibited by ABA treatment. Decrease in Cd uptake and the inhibition of IRT1 expression were clearly lesser pronounced in an ABA-insensitive double mutant snrk2.2/2.3 than in the Col-0 in response to ABA application. The ABA-decreased Cd uptake was found to correlate with the ABA-inhibited IRT1 expression in the roots of Col-0 plants fed two different levels of iron. Furthermore, the Cd uptake of irt1 mutants was barely affected by ABA application. These results indicated that inhibition of IRT1 expression is involved in the decrease of Cd uptake in response to exogenous ABA application. Interestingly, ABA application increased the iron level in both Col-0 plants and irt1 mutants, suggesting that ABA-increased Fe acquisition does not depend on the IRT1 function, but on the contrary, the ABA-mediated inhibition of IRT1 expression may be due to the elevation of iron level in plants. From our results, we concluded that ABA application might increase iron acquisition, followed by the decrease in Cd uptake by inhibition of IRT1 activity. Thus, for crop production in Cd contaminated soils, developing techniques based on ABA application potentially is a promising approach for reducing Cd accumulation in edible organs in plants.

FINE: A fine-grained privacy-preserving location-based service framework for mobile devices

Abstract: In this paper, we propose a fine-grained privacy- preserving location-based service (LBS) framework, called FINE, for mobile devices. It adopts the data-as-a-service (DaaS) model, where the LBS provider publishes its data to a third party (e.g., cloud server) who executes users' LBS queries. The pro- posed FINE framework employs a ciphertext-policy anonymous attribute-based encryption technique to achieve fine-grained access control, location privacy, confidentiality of the LBS data and its access policy, and accurate LBS query result while without involving any trusted third party. Moreover, the proposed FINE framework also integrates the transformation key and proxy re- encryption to migrate most of computation-intensive tasks from the LBS provider and users to the cloud server. This property keeps mobile devices away from massive resource-consuming operations. Extensive analysis shows that our proposed FINE framework is secure and highly efficient for mobile devices in terms of computation and communication cost.

Effect of Zr Addition on the Low-Temperature SCR Activity and SO2 Tolerance of Fe–Mn/Ti Catalysts

Abstract: Zr is added to the Fe–Mn/Ti catalyst to increase NO conversion and improve SO2 tolerance. It is found that 0.03 is the optimal ratio for Zr/(Ti + Zr). With this ratio, the NO conversion below 150 °C increases, and the SO2 poisoning is alleviated, while the further increase of Zr does not have a positive effect on NO conversion and SO2 tolerance. With Zr additive, more manganese oxides are reduced in the form of MnO2 and Mn2O3 at lower temperature in H2-TPR, and the total amount of H2 consumption rises, indicating better redox properties. It leads to the increase of NO complexes on the catalysts. Despite the small decrease of NH3 adsorption, the reaction via the L–H way is promoted and NO conversion increases. Furthermore, more nitrates and NO2 are formed in the reaction with SO2 on Fe–Mn/Ti–Zr(0.03) compared to Fe–Mn/Ti, so the L–H reaction way is less inhibited by SO2 with Zr additive, and the SO2 tolerance of this catalyst is also improved.

Tantalum and aluminum co-doped iron oxide as a robust photocatalyst for water oxidation

Abstract: Efficient and stable photocatalysts for water oxidation are highly sought after in the field of photoelectrochemical (PEC) water splitting. Herein, a new type of tantalum and aluminum co-doped iron oxide (Ta/Al-Fe2O3) material was fabricated by a simple drop coating method. XPS analysis suggests that Ta and Al were successfully co-doped into Fe2O3 and Ta can greatly influence the chemical environment of Al and O on the surface of catalyst. The resultant optimum (0.25%)Ta/(10%)Al-Fe2O3 film presented excellent enhanced PEC activity and photostability. A 15 times higher photocurrent density as well as two times higher incident-photon-to-current efficiency (IPCE, 430 nm) can be clearly observed relative to (10%)Al-Fe2O3 at 0.35 V vs. Ag/AgCl. The dramatic enhanced PEC and IPCE performance are attributed to mixed effects induced by tantalum doping, such as positive shift of flat band potential (ca. 50 mV), a reduction in anodic overpotential for water oxidation and greatly reduced charge transfer resistance, which eventually facilitate more efficient separation and easier transfer of photogenerated electron–hole pairs. The highly improved visible light activity and film stability indicate that tantalum and aluminum co-doped iron oxide will be a promising semiconductor for water oxidation.

Visual Abstraction and Exploration of Multi-class Scatterplots

Abstract: Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.

Purification and characterization of Plantaricin ZJ5, a new bacteriocin produced by Lactobacillus plantarum ZJ5

Abstract: The aim of this study is to investigate the antimicrobial potential of Lactobacillus plantarum ZJ5, a strain isolated from fermented mustard with a broad range of inhibitory activity against both Gram-positive and Gram-negative bacteria. Here we present the peptide plantaricin ZJ5 (PZJ5), which is an extreme pH and heat-stable. However, it can be digested by pepsin and proteinase K. This peptide has strong activity against Staphylococcus aureus. PZJ5 has been purified using a multi-step process, including ammonium sulfate precipitation, cation-exchange chromatography, hydrophobic interactions and reverse-phase chromatography. The molecular mass of the peptide was found to be 2572.9 Da using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The primary structure of this peptide was determined using amino acid sequencing and DNA sequencing, and these analyses revealed that the DNA sequence translated as a 44-residue precursor containing a 22-amino-acid N-terminal extension that was of the double-glycine type. The bacteriocin sequence exhibited no homology with known bacteriocins when compared with those available in the database, indicating that it was a new class IId bacteriocin. PZJ5 from a food-borne strain may be useful as a promising probiotic candidate.

Internet Traffic Classification Using Constrained Clustering

Abstract: Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence

Characterization and application of bioflocculant prepared by Rhodococcus erythropolis using sludge and livestock wastewater as cheap culture media

Abstract: A new bioflocculant was produced by culturing Rhodococcus erythropolis in a cheap medium. When culture pH was 7.0, inoculum size was 2 % (v/v), Na2HPO4 concentration was 0.5 g L−1, and the ratio of sludge/livestock wastewater was 7:1 (v/v), a maximum flocculating rate of 87.6 % could be achieved. Among 13 different kinds of pretreatments for sludge, the optimal one was the thermal-alkaline pretreatment. Different from a bioflocculant produced in a standard medium, this bioflocculant was effective over a wide pH range from 2 to 12 with flocculating rates higher than 98 %. Approximately, 1.6 g L−1 of crude bioflocculant could be harvested using cold ethanol for extraction. This bioflocculant showed color removal rates up to 80 % when applied to direct and disperse dye solutions, but only 23.0 % for reactive dye solutions. Infrared spectrum showed that the bioflocculant contained functional groups such as –OH, –NH2, and –CONH2. Components in the bioflocculant consisted of 91.2 % of polysaccharides, 7.6 % of proteins, and 1.2 % of DNA. When the bioflocculant and copper sulfate (CuSO4) were used together for decolorization in actual dye wastewater, the optimum decolorization conditions were specified by the response surface methodology as pH 11, bioflocculant dosage of 40 mg/L, and CuSO4 80 mg/L, under which a decolorization rate of 93.9 % could be reached.

Inhibition of Nitrate Transporter 1.1-Controlled Nitrate Uptake Reduces Cadmium Uptake in Arabidopsis

Abstract: Identification of mechanisms that decrease cadmium accumulation in plants is a prerequisite for minimizing dietary uptake of cadmium from contaminated crops. Here, we show that cadmium inhibits nitrate transporter 1.1 (NRT1.1)-mediated nitrate (NO3−) uptake in Arabidopsis (Arabidopsis thaliana) and impairs NO3− homeostasis in roots. In NO3−-containing medium, loss of NRT1.1 function in nrt1.1 mutants leads to decreased levels of cadmium and several other metals in both roots and shoots and results in better biomass production in the presence of cadmium, whereas in NO3−-free medium, no difference is seen between nrt1.1 mutants and wild-type plants. These results suggest that inhibition of NRT1.1 activity reduces cadmium uptake, thus enhancing cadmium tolerance in an NO3− uptake-dependent manner. Furthermore, using a treatment rotation system allowing synchronous uptake of NO3− and nutrient cations and asynchronous uptake of cadmium, the nrt1.1 mutants had similar cadmium levels to wild-type plants but lower levels of nutrient metals, whereas the opposite effect was seen using treatment rotation allowing synchronous uptake of NO3− and cadmium and asynchronous uptake of nutrient cations. We conclude that, although inhibition of NRT1.1-mediated NO3− uptake by cadmium might have negative effects on nitrogen nutrition in plants, it has a positive effect on cadmium detoxification by reducing cadmium entry into roots. NRT1.1 may regulate the uptake of cadmium and other cations by a common mechanism.