Influence Ranking Research Articles

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. Finally, we demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

THE IMPACT OF OCCUPANT BEHAVIOUR ON RESIDENTIAL GREENHOUSE GAS EMISSIONS REDUCTION

In 2011–12, greenhouse gas emissions from the Australian residential sector were 101.6 Mt and are expected to grow by 38% by 2050. In order to reduce these emissions, much emphasis has been placed on increasing the energy efficiency of buildings and appliances. Occupant behaviour, however, is probably the single most significant factor which determines energy use and emissions. This paper describes research undertaken to rank the most common occupant behaviours, based upon their impact on greenhouse gas emissions associated with residential energy use, in an architect-designed house in Australia. The occupant behaviours investigated were changing: the heating and cooling temperature set points, window openings, external blind use and lighting use. Simulations were carried out using Primero and EnergyPlus software. Based on the simulation results of greenhouse gas emissions, the following ranking of overall influence (from most influential to the least) has been determined: external blind use was one of the most effective measures to reduce emissions. Cooling set point temperature was similarly important with the magnitude of impact depending on the set point e.g. a 2°C increase had an impact comparable to the use of external blinds. The impact of the heating set point temperature was also dependent on the set point and overall slightly lower compared to the cooling set point temperature. Lighting use was the least influential parameter in the context of this study.

INFLUENCE OF COMPOSITE ELASTIC MODULUS AND LATERAL LOAD PATTERN ON DEFLECTION OF ANTI-SLIDE PILE HEAD

This paper develops a new method to directly determine the composite elastic modulus of the reinforced con­crete cantilever pile rather than adopt theoretical or empirical approaches. On the basis of the theory of material mecha­nism, the load-deflection equation was deduced to create the relationship between the elastic modulus and the ratio of load-deflection. The numerical modelling tests based on ANSYS separation modelling technology were carried out to determine the composite elastic modulus under different reinforcement ratio, concrete strength grade and distribution pat­tern of driving force. The results can be used to create the quantitative relationship between the composite elastic modulus and reinforcement ratio as well as concrete strength grade. Comparison amongst various lateral load pattern were made to show that the deflection of pile head is obviously affected by the lateral load pattern, and the corresponding equations of the deflection of the pile head under different lateral load pattern were proposed, which can reflect the ranking of the great influence on the deflection of the pile head, rectangular, trapezoidal and triangular lateral load pattern in order.

Influence Maximization in MicroBlog Based on a New User Influence Ranking Method

Influence Maximization in MicroBlog Based on a New User Influence Ranking Method

Modeling influence diffusion to uncover influence centrality and community structure in social networks

Node centrality and vertex similarity in net- work graph topology are two of the most fundamental and significant notions for network analysis. Defining mean- ingful and quantitatively precise measures of them, how- ever, is nontrivial but an important challenge. In this paper, we base our centrality and similarity measures on the idea of influence of a node and exploit the implicit knowledge of influence-based connectivity encoded in the network graph topology. We arrive at a novel influence diffusion model, which builds egocentric influence rings and generates an influence vector for each node. It captures not only the total influence but also its distribution that each node spreads through the network. A Shared-Influence-Neighbor (SIN) similarity defined in this influence space gives rise to a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Using this influence diffu- sion model, we propose a novel influence centrality for influence analysis and an Influence-Guided Spherical K-means (IGSK) algorithm for community detection. Our approach not only differentiates the influence ranking in a more detailed manner but also effectively finds communi- ties in both undirected/directed and unweighted/weighted networks. Furthermore, it can be easily adapted to the identification of overlapping communities and individual roles in each community. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks.

Optimization of the Water-Cooled Structure for the Divertor Plates in EAST Based on an Orthogonal Theory**supported by National Basic Research Program of China (973 Program) (No. 2013GB102000)

An orthogonal experimental scheme was designed for optimizing a water-cooled structure of the divertor plate. There were three influencing factors: the radius R of the water-cooled pipe, and the pipe spacing L1 and L3. The influence rule of different factors on the cooling effect and thermal stress of the plate were studied, for which the influence rank was respectively R > L1 > L3 and L3 > R > L1. The highest temperature value decreased when R and L1 increased, and the maximum thermal stress value dropped when R, L1 and L3 increased. The final optimized results can be summarized as: R equals 6 mm or 7 mm, L1 equals 19 mm, and L3 equals 20 mm. Compared with the initial design, the highest temperature value had a small decline, and the maximum thermal stress value dropped by 19% to 24%. So it was not ideal to improve the cooling effect by optimizing the geometry sizes of the water-cooled structure, even worse than increasing the flow speed, but it was very effective for dropping the maximum thermal stress value. The orthogonal experimental method reduces the number of experiments by 80%, and thus it is feasible and effective to optimize the water-cooled structure of the divertor plate with the orthogonal theory.

Major construction risk factors considered by general contractors in Qatar

Purpose – The purpose of this paper is to identify, explore, rank the relative importance and determine the prevalent allocation response trends of the major construction risk factors considered by general contractors operating in the State of Qatar. Design/methodology/approach – A structured questionnaire survey comprising 37 potential risk factors was distributed to a statistically representative sample of contractors. The influence ranks of the factors explored were determined using the “Relative Importance Index (RII)” technique, whereas the prevalent trend of contractors’ attitudes toward risk allocation of each factor investigated was quantified and expressed as a percentage, based on the number of respondents who selected a specific option, in relation to the total number of respondents. Findings – The results obtained indicate that risks related to the “client” group are perceived as most critical, followed by the “consultant”, “contractor” and “exogenous” group-related factors, respectively. The outcomes further show that the “transfer” option is the contractors’ prevalent response to “client” and “consultant”-related risks, while the “retention” decision is the principal pattern linked to “contractor” and “exogenous” group-related risk factors. Research limitations/implications – The dominant respondents’ perception that the crucial construction risks are related to clients and consultants suggests that these two parties have an essential role in controlling the negative ramifications of the associated factors. Practical implications – The findings suggest that increasing designers’ awareness of the significant effect of applying the constructability concept can considerably help reducing the risks concomitant of the construction operation. Policy makers may contribute, moreover, in alleviating the risk of incompetent technical staff and operatives’ employment by controlling the migration of inexperienced and unskilled construction workforce into the State. Originality/value – Given the knowledge gap for the major construction risk factors considered by general contractors in Qatar, the results reported in this study can provide clients, industry practitioners and policy makers with guidance to effectively manage the significant risks determined, which can further assist in achieving a reasonable level of competitiveness and cost-effective operation.

Factors Influencing Construction Ergonomic Performance in India

Many construction industry sectors have been experiencing chronic problems, such as poor management of workforce, improper working conditions, tools, and working methods. Many researchers have identified these problems as factors that affect construction productivity and overall ergonomic performance of the workers. Keeping in view the necessity of addressing problems as stated, a questionnaire-based survey was conducted to study the critical issues related to the workers involved in man-machine interaction at the construction sites. This research has identified the quantified RIIs, determined the influence ranks of 30 factors affecting construction ergonomic performance in India. These factors were classified under the following three primary classifications: (a) human/labor related factors; (b) tasks-related factors; and (c) equipment/tools-related factors based on various age groups and occupations of the construction workers. Industry practitioners and researchers can use the primary outcomes of this study in developing systems to enhance and improve health and safety of the construction workers for effective management of construction labor workforce and to achieve a competitive level of quality and a cost-effective project.

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

The New Media Influence Ranking of Provincial Satellite TV Based on Fuzzy Math Method

With the rapid development of various media forms in modern society and the rise of various means of communication, the media pattern has already had dramatic changes. Increasingly convenient means of communication have brought a large amount of information for the mass public and the society. To help the public better analyze the current situation and trends in the media industry, we have proposed different rating systems for different segments of the media industry, and then we give each system corresponding emphasis and calculate its assessment index, and finally use the ranking list to present an objective analysis of the results. A set of precise rating system has been established based on the method of fuzzy mathematics and analytical hierarchy process in this paper. The target field of broadcasting and television is ranked accurately according to the ranking system combined with the advice of the senior in the media industry and researcher. The result of the ranking can make a reference to the current situation and trend of broadcasting and television industry and make a long-term guide for future planning and development of the industry.

A Novel Influence Measure Algorithm for Social Networks

As the development of communication, social networks are becoming increasingly ubiquity nowadays. The reliable measure of the influence of each individual has a great significance. Better decisions can be made according to the influence ranking in a social network. In this paper, we introduce the time series into PageRank and propose an improved influence measure model. Experiments on the Erdos1 coauthors network and the bloggers network both achieved reliable results.

Mining Influential Communities for Virus Prevention in Cloud-Based Network

In the study of cloud computing and its applications, security has been regarded as one of the greatest problems in the development of cloud computing. Therefore, the study and prevention of computer virus in cloud computing infrastructure becomes practically significant for cloud security. Based on the characteristics of the rapid spread of computer viruses, when a community which is composed by cloud nodes has a higher influence in the cloud-based network, it has a greater potential hazard if there are some viruses lurking in the community. Therefore, it's necessary to find the influential community for virus prevention in cloud infrastructure. To evaluate cross-community influence, this paper proposes a novel method that consists of two components: (1) the Mutual Evaluation Learning (MEL) model for evaluating the pairwise influence between two nodes (clients, servers) and (2) a cross-community influence ranking algorithm (CCIR) designed to quantify the influential strength of each cross-community based on MEL and PageRank. Empirical studies on numerous real networks demonstrate that the proposed method can adapt to different networking scenarios and can reasonably and effectively reflect the distribution of community influence.

Influence of nutrient addition on the bioethanol yield from oil palm trunk sap fermented by Saccharomyces cerevisiae

This paper presents the influence of nutrient addition namely MgSO4, C3H7NO2, (NH4)2SO4 and Na2HPO4 to the bioethanol yield from oil palm trunk saps (OPTS) with fermentation carried out by Saccharomyces cerevisiae. The sugar and ethanol contents in the sample were determined using a high-performance liquid chromatography. Nutrient addition has improved the bioethanol yield markedly, with the average yield ranged from 58.50% to 77.12% compared to about 51.08% without nutrient addition. The highest bioethanol yield (81.89%) was achieved by adding MgSO4. The rank of nutrient influence on improving the bioethanol yield was MgSO4>C3H7NO2>(NH4)2SO4>Na2HPO4

Effects of Transition Metals and Rare Earths on Iron-Carbon Micro Electrolysis Degrading Dyeing Effluent

The principle and methodology of effluent treatment by iron-carbon micro electrolysis were introduced in the paper. Then design of the orthogonal experiments for dyeing effluent treatment was formulated. Discussion of influences of related factors on effluent treatment followed. Results were got after the detailed analysis. Therefore, we deduced the mechanism that the cations of Transition Metal (TM) and rare earth (RE) assist of zero-valent irons catalyzing degradation of dyeing effluent. The mechanism is formed based on the following explanation. Cations of manganese and cobalt easily penetrate Fe0lattices, while Ce4+cations do it difficultly because of their larger radius. Thus Ce4+is weaker than both of Mn2+and Co2+for helping zero-valent irons to improve their activity. Furthermore, because the valence electron structure of Mn2+is more stable than that of Co2+, Mn2+is better for assisting zero-valent iron catalysis of degradation of dyeing effluent than Co2+. Therefore, ranking of influence for zero-valent iron catalysis activity from greatest to smallest is Mn2+, Co2+and Ce4+.

Maximizing the spread of influence ranking in social networks

Information flows in a network where individuals influence each other. In this paper, we study the influence maximization problem of finding a small subset of nodes in a social network that could maximize the spread of influence. We propose a novel information diffusion model CTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the Independent Cascade Model (ICM). Furthermore, we propose a new ranking metric named SpreadRank generalized by the new information propagation model CTMC-ICM. We experimentally demonstrate the new ranking method that can, in general, extract nontrivial nodes as an influential node set that maximizes the spread of information in a social network and is more efficient than a distance-based centrality.

The Micro-Blogging Network Leading Group Recognition Algorithm

Based on the analysis and research of micro-blogging network transmission of information, the transmission of information model is constructed. By studying the network model, a small group of core users of the network information dissemination play a guiding role. To solve the problem that the research of micro-blogging user influence ranking can only ranking order given its influence, but not determine which user play a guiding role in transmission of information, LeadersRank algorithm based on the idea of personalized PageRank algorithm is proposed, and the algorithm is applied to the real micro-blogging data to identify the leading group, the experimental results prove the feasibility and effectiveness of the algorithm.

Cointegration analysis and influence rank—A network approach to global stock markets

In this paper, cointegration relationships among 26 global stock market indices over the periods of sub-prime and European debt crisis and their influence rank are investigated by constructing and analyzing directed and weighted cointegration networks. The obtained results are shown as follows: the crises have changed cointegration relationships among stock market indices, their cointegration relationship increased after the Lehman Brothers collapse, while the degree of cointegration gradually decreased from the sub-prime to European debt crisis. The influence of US, Japan and China market indices are entirely distinguished over different periods. Before European debt crisis US stock market is a ‘global factor’ which leads the developed and emerging markets, while the influence of US stock market decreased evidently during the European debt crisis. Before sub-prime crisis, there is no significant evidence to show that other stock markets co-move with China stock market, while it becomes more integrated with other markets during the sub-prime and European debt crisis. Among developed and emerging stock markets, the developed stock markets lead the world stock markets before European debt crisis, while due to the shock of sub-prime and European debt crisis, their influences decreased and emerging stock markets replaced them to lead global stock markets.

The models of coalitional process in the formation of governmental cabinets: theoretical cut and review of literature

The article offers a review of literature on the theorization and systematization of models of coalitional process in the formation of governmental cabinets. It was demonstrated that all models of coalitional process can be divided into institutional and non-institutional, but most of them are characterized by game theories and theories of rational choice. At the same time, it was found that the models of coalitional process should be divided into formal and empirical as well as pragmatic and ideological. Nevertheless, it was stated that the isolated models of coalitional process in the formation of governmental cabinets are not exhaustive. Since the real coalitional process should be perceived as a gradation based on the ranking of various influence, which can be determined nationally and regionally.

The Influence of Large Clinical Trials in Orthopedic Trauma

To evaluate the influence of top fracture trials on the practice of orthopedic surgeons. This is a cross-sectional study. We electronically administered the survey to all members of the Canadian Orthopedic Association. We received responses for 222 surveys, of which, 178 surveys were completed. We distributed a survey that evaluated the influence of 7 important fracture studies (6 randomized controlled trials and 1 prospective cohort study) on practice, patient care and the overall advancement of knowledge in the field of orthopedics. This study was approved by our local ethics review board. The primary outcome measure was the perceived general influence and impact of important fracture studies on the perceptions and practice of orthopedic surgeons. The Clavicular Fixation Trial (2007) and Tibial Fracture Trial (SPRINT, 2008) were perceived by surgeons to have the greatest influence on advancing overall knowledge in the field, improving personal practice, and the most influence on improving patient care. On the other hand, the Bone Stimulation in Fractures Trial (2011) and the recombinant human bone morphogenetic protein-2-BESST Trial (2002) had the lowest mean influence ranks. The probability of changing practice was significantly higher (Odds Ratio, 2.89; 95% confidence interval, 2.16-3.88; P < 0.00001) when studies had positive outcomes in comparison with negative outcomes. Despite the complexity and costs associated with clinical trials in orthopedic trauma, the results from this survey suggest that these studies result in a demonstrable perceived influence and impact on the practice of orthopedic surgeons.

Web structure and influence of the Arab universities of the MENA zone (Middle East and North Africa)

Purpose – This article aims to display the structure and reveal the web influence of institutions in the MENA zone, in geographic terms (country) and academic terms (universities), by means of their links. Design/methodology/approach – Using search engines and webcrawlers designed to gather information about web links, in conjunction with visualization techniques and degree indicators based on social network analysis, the authors achieved their objective and found responses to a series of pertinent research questions. Findings – There is no direct relationship between the number of university websites and the number of inlinks. Linking between countries in the MENA zone obeys patterns of vicinity and geopolitics. Arab universities are interlinked following trends governed by territorial proximity. There is a strong endogamic tendency, with universities from a single country citing each other, particularly in the case of Saudi Arabia. The authors present the first ranking of web influence in the MENA zone ...