Earticle

In MANET, establishes the network no depends on the infrastructure and communicates. The topology is exchanged following the various conditions. Also the routing path is disconnected due to node mobility and direction of node. To solve these problems, we proposed the Load Tolerance Density-distribution (LTD) algorithm on dynamic density in mobile ad hoc networks. The proposed algorithm measures the dynamic density and load balance then it calculates the relationship between the cluster head nodes by the homogenous routing (passion) distributed procedure and provides the distributed routing path density in mobile ad hoc networks. In the simulation, we compare the LTD algorithm and the WCA algorithm and the Max-min algorithm. As the simulation results, the proposed algorithm improves the packet delivery ratio of various conditions in the clustering routing.

In order to explore the impact of enterprise heterogeneity on technology diffusion, this paper puts forward the hypothesis that the impact factors of enterprise heterogeneity on technology diffusion, using structural equation methods to build the structural equation model of the influence factors to technology diffusion. The results show that the enterprise's own nature, the access to the enterprise technological innovation, the enterprise technology exchange activities and the cooperation degree of the enterprises have a significant effect on the diffusion of technological innovation, which proves that the enterprise heterogeneity has a significant effect on the diffusion of technological innovation.

The patent pool partner selection is an important multi-attribute decision-making (MADM) problem, and especially that based on intuitionistic fuzzy (IF) is becoming an extremely active research field. The aim of this article is to present a new method for patent pool partner selection with no weight information in IF based on bidirectional projection (BP). In the proposed model, we substitute BP for traditional one-way projection, and develop a new ranking alternatives rule on the basis of close degree; we construct a non-linear programming model combing the BP with intuitionistic fuzzy entropy (IFE) to determine the attribute weights, and obtain the optimum alternative; an example analysis is given to illustrate the rationality and scientificity of the proposed method; Finally, we conclude with recommendations for future work.

This paper provides a macromodel in Pspice (Personal Simulation Program with Integrated Circuit Emphasis) for a trench power MOSFET rated at 70V over a temperature range of -55°C to 175°C. The Pspice macromodel was built using device parameters extracted through experiment. The static behavior of the trench power MOSFET is simulated and compared to the measured data to show the accuracy of the Pspice model. The temperature dependent behavior was simulated and analyzed.

This experiment was conducted to study the effect of different water and fertilizer inputs on the yield to improve the paddy yield in blackland in the cold region. A water-fertilizer coupling mathematical model between the yield and factors including N fertilizer, K fertilizer, P fertilizer and irrigation water(W)amount under controlled irrigation was established based on quadratic D-416 optimized saturation design and test-pit experiment. According to the results, the sequence on effects of water and fertilizer on the yield: W > N > K > P, and as the above inputs increasing, the yield increased at an earlier stage but declined later; for the effects caused by reciprocal action of two factors, the yield increased at an earlier stage but started to decline when certain threshold was reached, the sequence on effects caused by reciprocal action of two factors on the yield: N&K > N&W > K&W > N&P > P&W >K&P; under a yield target range of 10500~12000kg/km3 and a confidence interval of 95%, an optimized water and fertilizer adaptation program was 129.10~150.79 kg/hm2，K2O：81.15~105.47 kg/hm2，P2O5：43.19~71.71 kg/hm2, the soil water content at late tillering stage was 70.07%~72.57% of saturated water content.

The purpose of this study is to examine the vacant technology areas by conducting patent analysis of financial technology (FinTech) related to mobile devices. To this end, this study investigated the technological scheme and industry trends first and then derived the technology classification system. Subsequently, key patent was selected from the effective data extracted from this study, and then vacant technology areas of FinTech were examined. It is expected that this study will be used as reference data in order to check patent trends of key technology in FinTech related to mobile devices and to choose the direction of technological development of FinTech.

This paper takes raw milk as research object, and using mid-infrared spectrum analysis method rapidly tests dry matter in raw milk through establishing quantitative analysis model. First of all, researchers collect 84 kinds of raw milk which is different areas and varieties in Heilongjiang province, and classify 84 samples into calibration sets and validation sets by standard chemical testing and spectral scanning on raw milk, and calibration sets contain 64 samples, and validation sets contain 20 samples. Then, respectively selecting SPA and GA characteristic wavelength after treating by smoothing denoising method which window size is 11 points, and establishing PLS model and model demonstration. Eventually, researchers draw the conclusion that SPA wavelength selection method which value of m_max is 10 and GA wavelength selection method which value of Evaluat is 10d can effectively improve validation set model precision, but precision of the latter is higher and characteristic wavelength point reduces from 352 to 10, The calibrating determination coefficient R2 of model is 0.8092155, root mean square error (RMSEC ) is 0.1206172. Validation set determination coefficient R2 is 0.8620867, the root mean square error (RMSEC) is 0.0950656, and relative standard deviation RSD<3%. These prove that the method for rapid testing of raw milk components is feasible.

In view of existing structure heterogeneous and semantic heterogeneous, a multi-source data integration approach based on ontology is proposed. In this approach, constructed domain ontologies are used to describe data sources, and semantic integration for multi-source data is realized by using an information integration toolkit named KARMA to map multiple datasets to RDF data. By analyzing Food and Agriculture Organization (FAO)’s indicator datasets and World Health Organization (WHO)’s datasets, main concepts are extracted to construct indicator ontology, food security ontology, health ontology and human security ontology. KARMA models are created for mapping FAO datasets and WHO datasets to RDF data. Based on constructed ontology and published RDF data, an application system named Food Security Indicators Management System (FSIMS) is developed to implement food security data query, statistical analysis and comparison functions. FSIMS has positive effects on food security risk management; it also demonstrates the applicability of the proposed semantic integration method, and the validity of constructed domain ontologies and published RDF data.

This paper explored the impact of secondary education level and national culture on the entrepreneurial activities. Specifically, we examine how higher education and one dimension of national culture (individualist-collectivist orientation) are related to the ratio of opportunity-entrepreneurship in a country. The research model is developed as: high education and individualism are positive determinants of the entrepreneurial activity, but only when the motivation for starting new ventures is opportunity or improvement and not when people do it for necessity. Our variables were developed with GEM data, World Economic Forum's Global Competitiveness Report and Hofstede's cultural determinants for 57 countries for the years 2009, 2010 and 2011, and we test our hypothesis empirically. In addition to our testing independent variables, we added 3more control variables, economic freedom, and perception of corruption and property rights, in order to specify our findings. Our analysis proves that higher education and individualism are determinants for superior opportunity entrepreneurial activity. Education levels and individualism, however, do not show the interaction effect. Our results show that, after controlling for other entrepreneurship determinants variables borrowed from the institutional theory of entrepreneurship, education at the tertiary level i.e., university, college, etc., positively affects the choice of individuals to pursue opportunity driven entrepreneurship. Moreover, individualism is also found to positively affect entrepreneurs' choice. We hope this research contribute in expanding our understanding on global entrepreneurship activities.

The lyrics play important roles in emotion expression of songs, as well as the emotion words of the lyrics reveal the emotion theme of songs. By extracting the lyrics’ emotion information, this paper uses songs’ emotion themes as the recommended standard. We get all kinds of emotion information extraction coefficient combining emotion gene sequences of lyrics which named DNALS (DNA for Lyrics of Songs), and then put forward the DNALS recommendation algorithm based on emotion analysis. By analyzing the emotion information of user’s historical music data, we recommend to the user a list of songs with similar themes emotion, so as to help the users to find songs for mood.

As the number of Web users and the amount of information produced keep growing rapidly, information systems need to be scaled up constantly. Usually scaling methods of information systems suffer from being chosen by experience, this paper studied on quantitative optimal selection of vertical/horizontal scaling methods. According to investigation and statistics, we assumed the cost growth formula of the key performance parameters reasonably. Then five key parameters of systems were chosen to construct Kiviat graphs and the areas of Kiviat graphs were computed to evaluate system performance. Next, the quantitative function relation between the performance of information system and the cost of system scaling was established, and then three optimal selection algorithms for system scaling were proposed. Finally, some case studies demonstrate that algorithms are feasible and have a great significance for guiding system scaling.

In this study, the Enneagram test was done for the technical high school students, and the personality types of those students were categorized as the following three types: head, breast, and belly, which are three main types of the Enneagram test. Through the discussion between expert course tutors and an Enneagram professional, respective effective teaching-learning methods were chosen for each type of student groups, and the three groups began to study the robot programming course. Students were advised to study with VPL and TPL, and the results of their learning were examined using robots. The results showed that learners of respective personality types preferred VPL and TPL differently. Learners' achievement was found remarkably different according to their preference of VPL and TPL (programming tool). When not considering the Enneagram personality type factor, it could happen that part of personality type learners could not get desirable results.

This paper has improved the accuracy of recommendation by an improved collaborative filtering algorithm based on conventional computing method. By considering the preferences of different user and the similarity of their respective preferences, it proposed a similarity formula based on logarithm. Experimental results show that compared to traditional collaborative filtering algorithms, the new recommendation algorithm dramatically increased the recommendation accuracy, with better personalized recommendation effect.

The web service developed by telecommunication domain is ineffective since they are demonstrated by syntactic description rather than semantic. The motivation of the research is to have semantic description with existing web services, and provides discovery, composition and invocation of web services automatically. The objective is to identify the discovery and composition concerns and devise a compositional approach that covers all concerns. So a new prototype named Semantic Web Service Engine for Telecommunication which automatically discover and composite a web service was proposed, enables semantic through upper ontology and maps Web Service Description Language to Ontology Web Language-Semantic. For composition, a genetic algorithm was proposed which can solve problems with great distinctiveness. This approach automatically discovers and generates the required composite semantic web services and considers all identified concerns concurrently, improves the accuracy for the service discovery and unifies the semantic representation of telecommunications without human intervention.

Because of the roll eccentricity in backup rolls, the exit thickness deviates periodically. Each strip thickness defect on exit is related to a roll of the finishing mills, allowing the implementation of an optimal policy for the substitution and maintenance of the rolls, thereby maintaining the required quality level of the strip. This policy allows the minimization of roll changes and the concentration of several changes at the same time, reducing production costs. Roll eccentricity is one of the most important factors which affect the thickness quality of rolled strips and, consequently, any action directed to removing roll eccentricity or its effects will greatly improve thickness quality and reduce production costs. In this paper, roll eccentricity signal is identified by cooperation of quadrature detector and robust tracking differentiator, which are adopted by compensation loop. The identification and compensation part of the loop are combined, and the rolling process knowledge as well as the need of additional sensors for a good compensation is minimised. The algorithm is proved applicable to eccentricity compensation of modern rolling mill by simulation and field experiment, and can improve the thickness quality.

In recent years, with the rapid development of E-business and network economy, technological innovation has become the driving force for the survival and development of enterprises. In this paper, the author analyzes complex adaptive system as multi -agent integrated modeling and simulation method, using simulation platform to create a virtual enterprise knowledge transfer asymmetric evolutionary game simulation model, and then make analysis of asymmetric cooperation and competition game evolution of knowledge transfer behavior. At the same time, we construct the structural equation and carries on the empirical analysis, the result shows that employee knowledge sharing self-efficacy has an indirect positive effect on innovation activity, and the indirect coefficient is 0.283; employee knowledge sharing willingness has positive effect on market performance, and the direct coefficient is 0.012. According to the results of path analysis of structural model, it can be known that innovation activity has a mediating effect, and indirect effect is produced through innovation activity. Therefore, the process of knowledge sharing and learning is an important way for enterprises to improve technological capability.

About half of the products and services have been developed based on the technology transfer. Although the institution has been established for support technology transfer in many countries, technology transfer has not been cultivated. Because of the lack of knowledge about the technology broker, the technology transfer has been limited. Therefore, in this research, the new method for diagnosing the technology transfer network is drawn. The method is comprised of phase 1(extracting the technology broker), phase 2(drawing the technology expert). The method in this research provides TTB(Technology Transfer Broker), TTH(Technology Transfer Structure Hole) and TTD(Technology donor). In addition, to reflect the characteristics and constraints of each organization, the method recommends the optimized path of the network from TTD to TTB and the optimized technical path in organization of TTB. By diagnosing the technology transfer network, the method provide chance to grasp the opportunity that can lead to technology innovation. Further, the method support TTR(Technology Transfer Recipient) to understand the connectable point of TTB within the technology transfer network.

As a popular registry for Web services, UDDI provides a mature mechanism for service registration and search, but it does not support semantic operations on Web services, so its service discovery capability is not satisfactory. OWL-S overcomes UDDI’s shortcomings. Using OWL-S, service advertisements and requests based on capability description could be built in a uniform way, and the semantic matching between them could also be achieved. By means of these two technologies, a system was developed which is compatible with UDDI and supports the publication and discovery of semantic Web services. The matching performed by the system is divided into two parts: pre-matching and post-matching. The pre-matching is generally performed only in the course of service publication, so the time for service discovery is reduced. Experimental results show that the performance of the system is reliable. Furthermore, the system can produce query results ordered by priority according to the requester’s expectations.

With Fujian Province as object of study, panel data from 1981 to 2013 is selected in this paper. Besides, VAR model is established. Co-integration test, Granger causality and impulse response function are used for quantitative analysis on correlation between the three main marine industries and economic growth in Fujian Province. In the paper, the long-term stable relation between the three main marine industries and economic growth is revealed, and the causal relations between the primary marine industry and economic growth, between the secondary marine industry and economic growth and between tertiary marine industry and economic growth are verified. The government should give priority to the secondary marine industry and effective support to the tertiary marine industry when adjusting the industrial structure and optimizing policies.

As smartphones rapidly spread in the society, the number of security accidents is also dramatically increasing. Particularly, the rapid growth of FinTech provides users with a convenient payment method. FinTech, which is a combination of ‘Financial’ and ‘Technique’, means a technology that handles financial transactions based on IT technology. Users can make a payment anytime and anywhere by using FinTech and smartphone. Accordingly, since it has become easier to make a payment, security accidents related to financial transaction are increasing. Financial security accidents related to the use of smartphones can take place in various ways such as stealing, hacking and fraud, and the amount of damage and loss is astronomical. Therefore, this study aims to propose FDS (Fraud Detection System) using GPS information provided as default by terminals such as smartphone.

When Cloud Platform for Computer Aided Engineering Simulation (CPCAES) are developed to use Service-Oriented Architecture (SOA), on the one hand, it’s necessary to accurately express the service requirements of users, on the other hand it’s need to semantic of the existing services in the platform, and then to match the ontologies. The existing Ontology Web Language for Services / Universal Description Discovery and Integration (OWL-S / UDDI) algorithm has weak matching precision, and not meet the Computer Aided Engineering (CAE) simulation applications. The other semantic similarity algorithms also have weakness in judgment factor, or do not meet the requirements of CAE simulation applications for matching to service requirement ontology in cloud platform. Such that, the authors present a kind of ontology of service requirement for CAE simulation, model the content of the ontology, including resource requirements, computing requirements, computing job requirements, input, output, etc. The authors give the ontology mapping relationship between Ontology Web Language for Services (OWL-S) and the ontology, give the matching decision rules for the ontologies, propose a matching algorithm for matching the ontologies, and compare with the classic OWL-S / UDDI matching algorithm and a similarity matching algorithm proposed in other paper in the ability for measure the similarity quantity of services, the ability to suit the applications, as well as algorithms recall rate and precision rate. The results show the matching algorithm proposed in this paper is more suitable for service requirement ontology of CPCAES, can use the quantify value for the similarity analysis to the ontologies, and has higher precision rate and higher recall rate, which can reach about 90%. The research work in this paper is used in the second prototype of CPCAES, and the research team is developing the third prototype based on semantic Web Services and SOA framework.

The steam coal supply chain has the problem that the member’s inventory level cannot change with the market demand. The main reason is that market demand information has not been shared in the supply chain members. This paper considers the steam coal supply chain which consists of one coal manufacturer and one fire power plant, with the assumption that the steam coal demand follows a seasonal autoregressive moving average process, including marketing actions that cannot be deduced form the other parameters of the demand process. We compare the optimal inventory level of the steam coal manufacturer and the power plant in two scenarios and analyses the reasons that influence the value of the information sharing in the steam coal supply chain. It demonstrates that the seasonal effect and lead-time have an important impact on optimal inventory policy. This result provides managers of steam coal manufacturers and power plant with insights to establish strategic partnership to promote the performance of the whole supply chain.

Small-and Medium-sized Enterprises (SME) benefit from the tax cut provisions and tax subsidies from SME support policies, and thus are considered to have less motivation to avoid taxes due to the lower tax burden than that of non-SME firms. This study empirically investigates the contrast in the motives for tax avoidance between SME and non-SME firms. Based on the sample consisting of 18,954 audited firms, 18,954 externally audited firms from 2011 to 2013, this study examines book-tax difference (BTD), the difference between accounting profits and taxable income, and estimated corporate tax avoidance (TS), which is the portion of BTD that cannot be explained by total accruals, as a proxy for tax avoidance. (Desai and Dharmapala 2006). The empirical findings are summarized as follows. First, SME hold less motivation for tax avoidance than non-SME firms, as shown by analogous results from both BTD and TS, which are the proxies for corporate tax avoidance. Second, the logistic analysis also demonstrates logistic analysis demonstrates consistent results which also contend that SME tend to hold less motivation for tax avoidance. We conclude that the consistent results can be explained by the smaller tax burden of SME from tax deduction and exemption clauses applied to SME. The present study investigates financial determinants that lead to tax avoidance in SME and empirically test the difference between SME and non-SME firms in tax avoidance. Throughout this study, we intend to propose policy implications for SME subsidies.

Compared with previous studies on artificial neural network, this paper expounds the artificial neural network theory, and gives information transfer function and mathematical models of BP neural network. This paper has selected 100 financial crisis companies and 100 non-financial companies as samples crisis, which are in line with the definition of the financial crisis. They has established the enterprise financial crisis model by using BP neural network algorithm, also made a sample test, and the accuracy rate is up to 80%.

The commitment of measurements to information mining can be followed back to the work by Bayes in 1763. The business organizations gather information and offer it to the Data Marts. The individuals who run little and medium association needs to set up information warehousing to touch base, best case scenario arrangement. Such datasets contain part of missing qualities, at some point the missing qualities range from 10% to 33%. A portion of the information might be fundamental; to recall such information is a troublesome undertaking and this kind of datasets won't yield better arrangement, to take care of this issue the Expectation Maximization (EM) calculation gauges missing qualities. Utilizing EM Algorithm the outcomes are supplanted in the missing positions of the specific information which serves to exact conclusion. In this paper, point estimators were connected, among which EM calculation gives best gauge. It is watched that the more straightforward models by and large yield the best results.

The purpose of this study was to understand which variable can most effectively benchmark subrogation and use the result to establish a more stable guarantee policy. In this study, credit ratings are calculated from the model to evaluate micro-enterprises using data mining technique for micro-enterprises. The model to evaluate micro-enterprises is obtained from the local credit guarantee foundation. With respect to prediction accuracy of each of the models from the study result, out of decision tree, CHAID was 67.7% and it is the highest model prediction accuracy and prediction of subrogated performance using CHAID model out of decision tree can bring good result of prediction. In addition, because in CHAID model of decision tree the combination grade for which added points are not applied was 79% and this shows predictor importance, it is necessary that the combination grade for which added points are not applied predicts subrogated performance as the optimum predictor and uses the result of prediction for guarantee policy. Suggestions through the study result are like the following. The result of prediction of occurrence of subrogated performance for guaranteed enterprise through evaluation model for Micro enterprisers should be reflected to guarantee supply policy to lower the rate of subrogated performance and through these basic materials that make the tax of government used efficiently are provided. Because of this, suggested is the appropriateness that each of the local foundations can predict subrogated performance through credit evaluation model and use the result of prediction for adjustment of guarantee rate before supplying guarantee, and Korean Federation of Credit Guarantee Foundations should decide whether the model to evaluate credit of Micro enterprisers was reflected or not in audit of supplementation through reguarantee. In addition, likewise Credit Scoring System(CSS) used for individual guarantee should reflect the result of prediction of subrogated performance and decide guarantee limit or whether or not of approval of guarantee. Therefore, the outcome of this study would have great significance as the method to reduce guarantee risk.

Lexical and syntactic analysis, including word segmentation, part-of-speech (POS) tagging, shallow parsing and full parsing, are essential for medical language processing (MLP). However, research on full parsing, even shallow parsing and POS tagging for Chinese electronic medical record (CEMR), has not been carried out because of the lack of annotated corpus on CEMR. In this paper, we built a corpus of 5,024 sentences from CEMR with word segmentation, POS tags and phrase tags, of them, 2,553 are annotated as full parsing trees. Inter-annotator agreement results: Chinese word segmentation (97.56%), POS tagging (93.34%), shallow parsing (96.5%), full parsing (91.22%). A lexical and syntactic analysis system for CEMR is developed and evaluated based on above corpus. Of its components, we proposed a joint model for word segmentation and POS tagging with the transformation-based error-driven model as correction postprocessing to alleviate the problem of error accumulation, the F1-score of word segmentation and POS tagging were 94.39% and 93.2%, respectively. A shallow parsing model under the framework of group learning we proposed was developed, which enriched word features by word embedding from large unlabeled CEMRs and achieved the F1-score of 96.3%. At last, we presented a state-of-art full parser combining the Berkeley parser and the Stanford parser to outperform the best single parser by 3.68%. The evaluation results show a substantial benefit to statistical machine learning models from the annotated CEMR. These works are the foundation for natural language processing (NLP) technologies applied to CEMR.

The successful implementation of remanufacturing supply chain not only needs foundation engineering technology, but also needs the efficient supply chain model to support logistics network. But till now, the optimization problems of supply chain logistics network focus on the determination for the number and location of facilities and logistics distribution between the various facilities. Except for the decisive factors, the optimization problem of remanufacturing supply chain logistics network considers the environmental pollution factors the waste product returns and transportation. The market demand and waste product returns are actually uncertain in remanufacturing supply chain. There is few papers focus on dual uncertain factors although there are lots of studies on the problems. Therefore, based on dual stochastic programming, the optimization model of multi-phase multi-objective remanufacturing supply chain is established with maximum profit in the remanufacturing supply chain, maximum rapid response customer satisfaction and minimum environmental pollution. Dual-layer genetic algorithm mechanism was brought up. The first layer algorithm is responsible for supply chain logistics network structure. The second layer algorithm determines specific distributions for remanufacturing supply chain, based on optimized logistics network structure mechanism in the first layer algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.