User evaluations of design complexity: the impact of visual perceptions for effective online health communication.

PubMed

Lazard, Allison; Mackert, Michael

2014-10-01

This paper highlights the influential role of design complexity for users' first impressions of health websites. An experimental design was utilized to investigate whether a website's level of design complexity impacts user evaluations. An online questionnaire measured the hypothesized impact of design complexity on predictors of message effectiveness. Findings reveal that increased design complexity was positively associated with higher levels of perceived design esthetics, attitude toward the website, perceived message comprehensibility, perceived ease of use, perceived usefulness, perceived message quality, perceived informativeness, and perceived visual informativeness. This research gives further evidence that design complexity should be considered an influential variable for health communicators to effectively reach their audiences, as it embodies the critical first step for message evaluation via electronic platforms. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Adolescents’ Attitudes toward Anti-marijuana Ads, Usage Intentions, and Actual Marijuana Usage

PubMed Central

Alvaro, Eusebio M.; Crano, William D.; Siegel, Jason T.; Hohman, Zachary; Johnson, Ian; Nakawaki, Brandon

2015-01-01

The association of adolescents’ appraisals of the anti-marijuana television ads used in the National Youth Anti-drug Media Campaign with future marijuana use was investigated. The 12 to 18 year old respondents (N = 2993) were first classified as users, resolute nonusers, or vulnerable nonusers (Crano, Siegel, Alvaro, Lac, & Hemovich, 2008). Usage status and the covariates of gender, age, and attitudes toward marijuana were used to predict attitudes toward the ads (Aad) in the first phase of a multi-level linear analysis. All covariates were significantly associated with Aad, as was usage status: resolute nonusers evaluated the ads significantly more positively than vulnerable nonusers and users (all p < .001), who did not differ. In the second phase, the covariates along with Aad and respondents’ usage status predicted intentions and actual usage one year after initial measurement. The lagged analysis disclosed negative associations between Aad and usage intentions, and between Aad and actual marijuana use (both p < .05); however, this association held only for users (p < .01), not vulnerable or resolute nonusers. Users reporting more positive attitudes towards the ads were less likely to report intention to use marijuana and to continue marijuana use at 1-year follow-up. These findings may inform designers of persuasion-based prevention campaigns, guiding pre-implementation efforts in the design of ads that targeted groups find appealing and thus, influential. PMID:23528197

Adolescents' attitudes toward antimarijuana ads, usage intentions, and actual marijuana usage.

PubMed

Alvaro, Eusebio M; Crano, William D; Siegel, Jason T; Hohman, Zachary; Johnson, Ian; Nakawaki, Brandon

2013-12-01

The association of adolescents' appraisals of the antimarijuana TV ads used in the National Youth Antidrug Media Campaign with future marijuana use was investigated. The 12- to 18-year-old respondents (N = 2,993) were first classified as users, resolute nonusers, or vulnerable nonusers (Crano, Siegel, Alvaro, Lac, & Hemovich, 2008). Usage status and the covariates of gender, age, and attitudes toward marijuana were used to predict attitudes toward the ads (Aad) in the first phase of a multilevel linear analysis. All covariates were significantly associated with Aad, as was usage status: Resolute nonusers evaluated the ads significantly more positively than vulnerable nonusers and users (all ps < .001), who did not differ. In the second phase, the covariates along with Aad and respondents' usage status predicted intentions and actual usage 1 year after initial measurement. The lagged analysis disclosed negative associations between Aad and usage intentions and between Aad and actual marijuana use (both ps < .05); however, this association held only for users (p < .01), not vulnerable or resolute nonusers. Users who reported more positive attitudes toward the ads were less likely to report intention to use marijuana and to continue marijuana use at 1-year follow-up. These findings may inform designers of persuasion-based prevention campaigns, guiding preimplementation efforts in the design of ads that targeted groups find appealing and thus, influential. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Discovering the influential users oriented to viral marketing based on online social networks

NASA Astrophysics Data System (ADS)

Zhu, Zhiguo

2013-08-01

The target of viral marketing on the platform of popular online social networks is to rapidly propagate marketing information at lower cost and increase sales, in which a key problem is how to precisely discover the most influential users in the process of information diffusion. A novel method is proposed in this paper for helping companies to identify such users as seeds to maximize information diffusion in the viral marketing. Firstly, the user trust network oriented to viral marketing and users’ combined interest degree in the network including isolated users are extensively defined. Next, we construct a model considering the time factor to simulate the process of information diffusion in viral marketing and propose a dynamic algorithm description. Finally, experiments are conducted with a real dataset extracted from the famous SNS website Epinions. The experimental results indicate that the proposed algorithm has better scalability and is less time-consuming. Compared with the classical model, the proposed algorithm achieved a better performance than does the classical method on the two aspects of network coverage rate and time-consumption in our four sub-datasets.

Social network analysis in identifying influential webloggers: A preliminary study

NASA Astrophysics Data System (ADS)

Hasmuni, Noraini; Sulaiman, Nor Intan Saniah; Zaibidi, Nerda Zura

2014-12-01

In recent years, second generation of internet-based services such as weblog has become an effective communication tool to publish information on the Web. Weblogs have unique characteristics that deserve users' attention. Some of webloggers have seen weblogs as appropriate medium to initiate and expand business. These webloggers or also known as direct profit-oriented webloggers (DPOWs) communicate and share knowledge with each other through social interaction. However, survivability is the main issue among DPOW. Frequent communication with influential webloggers is one of the way to keep survive as DPOW. This paper aims to understand the network structure and identify influential webloggers within the network. Proper understanding of the network structure can assist us in knowing how the information is exchanged among members and enhance survivability among DPOW. 30 DPOW were involved in this study. Degree centrality and betweenness centrality measurement in Social Network Analysis (SNA) were used to examine the strength relation and identify influential webloggers within the network. Thus, webloggers with the highest value of these measurements are considered as the most influential webloggers in the network.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Finding the top influential bloggers based on productivity and popularity features

NASA Astrophysics Data System (ADS)

Khan, Hikmat Ullah; Daud, Ali

2017-07-01

A blog acts as a platform of virtual communication to share comments or views about products, events and social issues. Like other social web activities, blogging actions spread to a large number of people. Users influence others in many ways, such as buying a product, having a particular political or social opinion or initiating new activity. Finding the top influential bloggers is an active research domain as it helps us in various fields, such as online marketing, e-commerce, product search and e-advertisements. There exist various models to find the influential bloggers, but they consider limited features using non-modular approach. This paper proposes a new model, Popularity and Productivity Model (PPM), based on a modular approach to find the top influential bloggers. It consists of popularity and productivity modules which exploit various features. We discuss the role of each proposed and existing features and evaluate the proposed model against the standard baseline models using datasets from the real-world blogs. The analysis using standard performance evaluation measures verifies that both productivity and popularity modules play a vital role to find influential bloggers in blogging community in an effective manner.

It's better to give than to receive: the role of social support, trust, and participation on health-related social networking sites.

PubMed

Hether, Heather J; Murphy, Sheila T; Valente, Thomas W

2014-12-01

Nearly 60% of American adults and 80% of Internet users have sought health information online. Moreover, Internet users are no longer solely passive consumers of online health content; they are active producers as well. Social media, such as social networking sites, are increasingly being used as online venues for the exchange of health-related information and advice. However, little is known about how participation on health-related social networking sites affects users. Research has shown that women participate more on social networking sites and social networks are more influential among same-sex members. Therefore, this study examined how participation on a social networking site about pregnancy influenced members' health-related attitudes and behaviors. The authors surveyed 114 pregnant members of 8 popular pregnancy-related sites. Analyses revealed that time spent on the sites was less predictive of health-related outcomes than more qualitative assessments such as trust in the sites. Furthermore, providing support was associated with the most outcomes, including seeking more information from additional sources and following recommendations posted on the sites. The implications of these findings, as well as directions for future research, are discussed.

They Came, They Liked, They Commented: Social Influence on Facebook News Channels.

PubMed

Winter, Stephan; Brückner, Caroline; Krämer, Nicole C

2015-08-01

Due to the increasing importance of social networking sites as sources of information, news media organizations have set up Facebook channels in which they publish news stories or links to articles. This research investigated how journalistic texts are perceived in this new context and how reactions of other users change the influence of the main articles. In an online experiment (N=197), a Facebook posting of a reputable news site and the corresponding article were shown. The type of user comments and the number of likes were systematically varied. Negative comments diminished the persuasive influence of the article, while there were no strengthening effects of positive comments. When readers perceived the topic as personally relevant, comments including relevant arguments were more influential than comments with subjective opinions, which can be explained by higher levels of elaboration. However, against expectations of bandwagon perceptions, a high number of likes did not lead to conformity effects, which suggests that exemplifying comments are more influential than statistical user representations. Results are discussed with regard to effects of news media content and the mechanisms of social influence in Web 2.0.

Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart.

PubMed

Tóth, Gergely; Bodai, Zsolt; Héberger, Károly

2013-10-01

Coefficient of determination (R (2)) and its leave-one-out cross-validated analogue (denoted by Q (2) or R cv (2) ) are the most frequantly published values to characterize the predictive performance of models. In this article we use R (2) and Q (2) in a reversed aspect to determine uncommon points, i.e. influential points in any data sets. The term (1 - Q (2))/(1 - R (2)) corresponds to the ratio of predictive residual sum of squares and the residual sum of squares. The ratio correlates to the number of influential points in experimental and random data sets. We propose an (approximate) F test on (1 - Q (2))/(1 - R (2)) term to quickly pre-estimate the presence of influential points in training sets of models. The test is founded upon the routinely calculated Q (2) and R (2) values and warns the model builders to verify the training set, to perform influence analysis or even to change to robust modeling.

Anger Is More Influential than Joy: Sentiment Correlation in Weibo

PubMed Central

Fan, Rui; Zhao, Jichang; Chen, Yan; Xu, Ke

2014-01-01

Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than five years. Connected by online social ties, different users might share similar affective states. We find that the correlation of anger among users is significantly higher than that of joy. While the correlation of sadness is surprisingly low. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends possess more significant sentiment correlation with their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks. PMID:25333778

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

Factors that contribute to social media influence within an Internal Medicine Twitter learning community

PubMed Central

Desai, Tejas; Patwardhan, Manish; Coore, Hunter

2014-01-01

Medical societies, faculty, and trainees use Twitter to learn from and educate other social media users. These social media communities bring together individuals with various levels of experience. It is not known if experienced individuals are also the most influential members. We hypothesize that participants with the greatest experience would be the most influential members of a Twitter community. We analyzed the 2013 Association of Program Directors in Internal Medicine Twitter community. We measured the number of tweets authored by each participant and the number of amplified tweets (re-tweets). We developed a multivariate linear regression model to identify any relationship to social media influence, measured by the PageRank. Faculty (from academic institutions) comprised 19% of the 132 participants in the learning community (p < 0.0001). Faculty authored 49% of all 867 tweets (p < 0.0001). Their tweets were the most likely to be amplified (52%, p < 0.01). Faculty had the greatest influence amongst all participants (mean 1.99, p < 0.0001). Being a faculty member had no predictive effect on influence (β = 0.068, p = 0.6). The only factors that predicted influence (higher PageRank) were the number of tweets authored (p < 0.0001) and number of tweets amplified (p < 0.0001) The status of “faculty member” did not confer a greater influence. Any participant who was able to author the greatest number of tweets or have more of his/her tweets amplified could wield a greater influence on the participants, regardless of his/her authority. PMID:25110581

Factors that contribute to social media influence within an Internal Medicine Twitter learning community.

PubMed

Desai, Tejas; Patwardhan, Manish; Coore, Hunter

2014-01-01

Medical societies, faculty, and trainees use Twitter to learn from and educate other social media users. These social media communities bring together individuals with various levels of experience. It is not known if experienced individuals are also the most influential members. We hypothesize that participants with the greatest experience would be the most influential members of a Twitter community. We analyzed the 2013 Association of Program Directors in Internal Medicine Twitter community. We measured the number of tweets authored by each participant and the number of amplified tweets (re-tweets). We developed a multivariate linear regression model to identify any relationship to social media influence, measured by the PageRank. Faculty (from academic institutions) comprised 19% of the 132 participants in the learning community (p < 0.0001). Faculty authored 49% of all 867 tweets (p < 0.0001). Their tweets were the most likely to be amplified (52%, p < 0.01). Faculty had the greatest influence amongst all participants (mean 1.99, p < 0.0001). Being a faculty member had no predictive effect on influence (β = 0.068, p = 0.6). The only factors that predicted influence (higher PageRank) were the number of tweets authored (p < 0.0001) and number of tweets amplified (p < 0.0001) The status of "faculty member" did not confer a greater influence. Any participant who was able to author the greatest number of tweets or have more of his/her tweets amplified could wield a greater influence on the participants, regardless of his/her authority.

Searching for superspreaders of information in real-world social media.

PubMed

Pei, Sen; Muchnik, Lev; Andrade, José S; Zheng, Zhiming; Makse, Hernán A

2014-07-03

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

NASA Astrophysics Data System (ADS)

Pei, Sen; Muchnik, Lev; Andrade, José S., Jr.; Zheng, Zhiming; Makse, Hernán A.

2014-07-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for ``viral'' information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

PubMed Central

Pei, Sen; Muchnik, Lev; Andrade, Jr., José S.; Zheng, Zhiming; Makse, Hernán A.

2014-01-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications. PMID:24989148

Detecting influential observations in nonlinear regression modeling of groundwater flow

USGS Publications Warehouse

Yager, Richard M.

1998-01-01

Nonlinear regression is used to estimate optimal parameter values in models of groundwater flow to ensure that differences between predicted and observed heads and flows do not result from nonoptimal parameter values. Parameter estimates can be affected, however, by observations that disproportionately influence the regression, such as outliers that exert undue leverage on the objective function. Certain statistics developed for linear regression can be used to detect influential observations in nonlinear regression if the models are approximately linear. This paper discusses the application of Cook's D, which measures the effect of omitting a single observation on a set of estimated parameter values, and the statistical parameter DFBETAS, which quantifies the influence of an observation on each parameter. The influence statistics were used to (1) identify the influential observations in the calibration of a three-dimensional, groundwater flow model of a fractured-rock aquifer through nonlinear regression, and (2) quantify the effect of omitting influential observations on the set of estimated parameter values. Comparison of the spatial distribution of Cook's D with plots of model sensitivity shows that influential observations correspond to areas where the model heads are most sensitive to certain parameters, and where predicted groundwater flow rates are largest. Five of the six discharge observations were identified as influential, indicating that reliable measurements of groundwater flow rates are valuable data in model calibration. DFBETAS are computed and examined for an alternative model of the aquifer system to identify a parameterization error in the model design that resulted in overestimation of the effect of anisotropy on horizontal hydraulic conductivity.

Serum and Plasma Metabolomic Biomarkers for Lung Cancer.

PubMed

Kumar, Nishith; Shahjaman, Md; Mollah, Md Nurul Haque; Islam, S M Shahinul; Hoque, Md Aminul

2017-01-01

In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.

"Follow" Me: Networked Professional Learning for Teachers

ERIC Educational Resources Information Center

Holmes, Kathryn; Preston, Greg; Shaw, Kylie; Buchanan, Rachel

2013-01-01

Effective professional learning for teachers is fundamental for any school system aiming to make transformative and sustainable change to teacher practice. This paper investigates the efficacy of Twitter as a medium for teachers to participate in professional learning by analysing the tweets of 30 influential users of the popular medium. We find…

Parameter screening: the use of a dummy parameter to identify non-influential parameters in a global sensitivity analysis

NASA Astrophysics Data System (ADS)

Khorashadi Zadeh, Farkhondeh; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2017-04-01

Parameter estimation is a major concern in hydrological modeling, which may limit the use of complex simulators with a large number of parameters. To support the selection of parameters to include in or exclude from the calibration process, Global Sensitivity Analysis (GSA) is widely applied in modeling practices. Based on the results of GSA, the influential and the non-influential parameters are identified (i.e. parameters screening). Nevertheless, the choice of the screening threshold below which parameters are considered non-influential is a critical issue, which has recently received more attention in GSA literature. In theory, the sensitivity index of a non-influential parameter has a value of zero. However, since numerical approximations, rather than analytical solutions, are utilized in GSA methods to calculate the sensitivity indices, small but non-zero indices may be obtained for the indices of non-influential parameters. In order to assess the threshold that identifies non-influential parameters in GSA methods, we propose to calculate the sensitivity index of a "dummy parameter". This dummy parameter has no influence on the model output, but will have a non-zero sensitivity index, representing the error due to the numerical approximation. Hence, the parameters whose indices are above the sensitivity index of the dummy parameter can be classified as influential, whereas the parameters whose indices are below this index are within the range of the numerical error and should be considered as non-influential. To demonstrated the effectiveness of the proposed "dummy parameter approach", 26 parameters of a Soil and Water Assessment Tool (SWAT) model are selected to be analyzed and screened, using the variance-based Sobol' and moment-independent PAWN methods. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. Moreover, the calculation does not even require additional model evaluations for the Sobol' method. A formal statistical test validates these parameter screening results. Based on the dummy parameter screening, 11 model parameters are identified as influential. Therefore, it can be denoted that the "dummy parameter approach" can facilitate the parameter screening process and provide guidance for GSA users to define a screening-threshold, with only limited additional resources. Key words: Parameter screening, Global sensitivity analysis, Dummy parameter, Variance-based method, Moment-independent method

Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events

NASA Astrophysics Data System (ADS)

Guan, Wanqiu; Gao, Haoyu; Yang, Mingmin; Li, Yuan; Ma, Haixin; Qian, Weining; Cao, Zhigang; Yang, Xiaoguang

2014-02-01

The spread and resonance of users’ opinions on Sina Weibo, the most popular micro-blogging website in China, are tremendously influential, having significantly affected the processes of many real-world hot social events. We select 21 hot events that were widely discussed on Sina Weibo in 2011, and do some statistical analyses. Our main findings are that (i) male users are more likely to be involved, (ii) messages that contain pictures and those posted by verified users are more likely to be reposted, while those with URLs are less likely, (iii) the gender factor, for most events, presents no significant difference in reposting likelihood.

Schizophrenic Patients' Perceptions of Stress, Expressed Emotion, and Sensitivity To Criticism

PubMed Central

Cutting, Linda P.; Aakre, Jennifer M.; Docherty, Nancy M.

2006-01-01

This study was designed to get an “insider's view” of expressed emotion (EE) from the perspective of schizophrenic patients. Thirty-two patient and “influential other” pairs participated in the study. Patients' perceptions of EE attitudes in influential others were examined to determine whether they corresponded with actual EE ratings. Patients also rated how “stressed” they felt when interacting with their influential others, and patients' general sensitivity to criticism (STC) was assessed. As predicted, patients' perceptions of critical attitudes were related to actual EE ratings of criticism, although patients' perceptions of emotional overinvolvement (EOI) were not related to EOI ratings. Patients reported feeling more stressed when interacting with high-EE influential others, supporting an “EE as stressor” hypothesis. Finally, patients' STC influenced the level of stress they reported. PMID:16731686

What predicts recovery orientation in county departments of mental health? A pilot study.

PubMed

Brown, Timothy T; Mahoney, Christine B; Adams, Neal; Felton, Mistique; Pareja, Candy

2010-09-01

In this pilot study we examined the determinants of recovery orientation among employees and influential stakeholders in a sample of 12 county departments of mental health in California. A two-level hierarchical linear model with random intercepts was estimated. Analyses show that recovery orientation has a U-shaped relationship with the age of staff/influential stakeholders and is negatively related to the difference between the desired level of adhocracy and the current level of adhocracy. Recovery orientation is positively related to the education level of staff/influential stakeholders, satisfying transformational leadership outcomes, and larger mental health budgets per capita. Policy implications are discussed.

Information fusion-based approach for studying influence on Twitter using belief theory.

PubMed

Azaza, Lobna; Kirgizov, Sergey; Savonnet, Marinette; Leclercq, Éric; Gastineau, Nicolas; Faiz, Rim

2016-01-01

Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter . We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets , mentions , and replies ). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.

Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Cappello, Franck

For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adaptmore » to dynamic prediction errors based on local runtime data and can automatically tune detection ranges for guaranteeing low false alarms. Experiments show that our detector can detect 80-99.99% of SDCs with a false alarm rate less that 1% of iterations for most cases. The memory cost and detection overhead are reduced to 15% and 6.3%, respectively, for a large majority of applications.« less

Influential Factors for Mobile Learning Acceptance among Chinese Users

ERIC Educational Resources Information Center

Hao, Shuang; Dennen, Vanessa P.; Mei, Li

2017-01-01

This study examines the factors that influence mobile learning adoption among Chinese university students. China's higher education market is large and mobile device ownership is considered a status symbol. Combined, these two factors suggest mobile learning could have a big impact in China. From the literature, we identified three major areas…

Smokeless Tobacco Use in Adolescents: The Cardiovascular Health in Children (CHIC II) Study.

ERIC Educational Resources Information Center

Lewis, Paul C.; Harrell, Joanne S.; Deng, Shibing; Bradley, Chyrise

1999-01-01

Examined age, gender, ethnicity, self-esteem, physical activity, parental smoking, and socioeconomic status as predictors of smokeless tobacco use among middle-school students. Student surveys indicated that males, Hispanics, and older students were more likely to be current smokeless-tobacco users. Other influential factors were low self-esteem…

CulSim: A simulator of emergence and resilience of cultural diversity

NASA Astrophysics Data System (ADS)

Ulloa, Roberto

CulSim is an agent-based computer simulation software that allows further exploration of influential and recent models of emergence of cultural groups grounded in sociological theories. CulSim provides a collection of tools to analyze resilience of cultural diversity when events affect agents, institutions or global parameters of the simulations; upon combination, events can be used to approximate historical circumstances. The software provides a graphical and text-based user interface, and so makes this agent-based modeling methodology accessible to a variety of users from different research fields.

Finding Influential Spreaders from Human Activity beyond Network Location.

PubMed

Min, Byungjoon; Liljeros, Fredrik; Makse, Hernán A

2015-01-01

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

Finding influential nodes for integration in brain networks using optimal percolation theory.

PubMed

Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

2018-06-11

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Clinical Prediction Making: Examining Influential Factors Related to Clinician Predictions of Recidivism among Juvenile Offenders

ERIC Educational Resources Information Center

Calley, Nancy G.; Richardson, Emily M.

2011-01-01

This study examined factors influencing clinician predictions of recidivism for juvenile offenders, including youth age at initial juvenile justice system involvement, youth age at discharge, program completion status, clinician perception of strength of the therapeutic relationship, and clinician perception of youth commitment to treatment.…

A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.

PubMed

Akay, Altug; Dragomir, Andrei; Erlandsson, Björn-Erik

2015-01-01

A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.

Linkage disequilibrium among commonly genotyped SNP and variants detected from bull sequence

USDA-ARS?s Scientific Manuscript database

Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNP genotyped by commercial assays. A number of variants detected from sequencing influential sires are likely to be causal, but noticable improvements in prediction accuracy using imputed sequen...

Milieu matters: Evidence that ongoing lifestyle activities influence health behaviors

PubMed Central

Lowe, Rob; Norman, Paul

2017-01-01

Health behaviors occur within a milieu of lifestyle activities that could conflict with health actions. We examined whether cognitions about, and performance of, other lifestyle activities augment the prediction of health behaviors, and whether these lifestyle factors are especially influential among individuals with low health behavior engagement. Participants (N = 211) completed measures of past behavior and cognitions relating to five health behaviors (e.g., smoking, getting drunk) and 23 lifestyle activities (e.g., reading, socializing), as well as personality variables. All behaviors were measured again at two weeks. Data were analyzed using neural network and cluster analyses. The neural network accurately predicted health behaviors at follow-up (R2 = .71). As hypothesized, lifestyle cognitions and activities independently predicted health behaviors over and above behavior-specific cognitions and previous behavior. Additionally, lifestyle activities and poor self-regulatory capability were more influential among people exhibiting unhealthy behaviors. Considering ongoing lifestyle activities can enhance prediction and understanding of health behaviors and offer new targets for health behavior interventions. PMID:28662120

Dutch Economic Textbooks in the 1970s: Raising the Status of a New Secondary School Type by Means of Mathematical Abstraction

ERIC Educational Resources Information Center

Gorter, Gerrit F.; Amsing, Hilda T. A.; Dekker, Jeroen J. H.

2016-01-01

Essential Economics, the influential economics education textbook written by Arnold Heertje for use in Dutch secondary schools in the 1970s, was characterized by a previously unknown and internationally exceptional degree of abstraction. Its users justified this degree of abstraction by arguing that it fulfilled the needs of mental schooling (in…

Re-Imagining Internet Scholarship: Academic Uses and Abuses of the Influential Internet Social Network, Facebook

ERIC Educational Resources Information Center

Nam, Kyoung-Ah; Fry, Gerald W.

2012-01-01

Since its inception at Harvard in 2004, the social network, Facebook, has grown dramatically and spread across the globe. It will soon have 1 billion users and is now operative in over 75 languages. A large percentage of undergraduates are now active on Facebook. Much of the recent literature on Facebook focuses on business applications and how it…

Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Nikolić, Vlastimir; Petković, Dalibor; Lazov, Lyubomir; Milovančević, Miloš

2016-07-01

Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R2 = 0.9653). The worst prediction was observed for dross area per unit length (R2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.

Accounting for discovery bias in genomic prediction

USDA-ARS?s Scientific Manuscript database

Our objective was to evaluate an approach to mitigating discovery bias in genomic prediction. Accuracy may be improved by placing greater emphasis on regions of the genome expected to be more influential on a trait. Methods emphasizing regions result in a phenomenon known as “discovery bias” if info...

The Impact of Demographic Characteristics on Awareness and Usage of Support Groups.

PubMed

Coleman, Linda Jane; Shah, Nehal; Jain, Sanjay

2015-01-01

There are support groups established for one's emotional and/or physical health; as a result, marketing has appeared in regards to the needs, benefits, and hesitations regarding these groups. This study addresses several types of individuals and situations that lend themselves to using support groups. The authors conducted a study designed to examine demographic characteristics as they relate to a person's decision to go to support groups for health conditions. Looking at the demographics of users and the types of support groups, the authors discuss diverse opportunities for support groups and their organizations to promote communication, improve marketing strategies, and create influential users.

Cerebrospinal Fluid Pressure: Revisiting Factors Influencing Optic Nerve Head Biomechanics

PubMed Central

Hua, Yi; Voorhees, Andrew P.; Sigal, Ian A.

2018-01-01

Purpose To model the sensitivity of the optic nerve head (ONH) biomechanical environment to acute variations in IOP, cerebrospinal fluid pressure (CSFP), and central retinal artery blood pressure (BP). Methods We extended a previously published numerical model of the ONH to include 24 factors representing tissue anatomy and mechanical properties, all three pressures, and constraints on the optic nerve (CON). A total of 8340 models were studied to predict factor influences on 98 responses in a two-step process: a fractional factorial screening analysis to identify the 16 most influential factors, followed by a response surface methodology to predict factor effects in detail. Results The six most influential factors were, in order: IOP, CON, moduli of the sclera, lamina cribrosa (LC) and dura, and CSFP. IOP and CSFP affected different aspects of ONH biomechanics. The strongest influence of CSFP, more than twice that of IOP, was on the rotation of the peripapillary sclera. CSFP had similar influence on LC stretch and compression to moduli of sclera and LC. On some ONHs, CSFP caused large retrolamina deformations and subarachnoid expansion. CON had a strong influence on LC displacement. BP overall influence was 633 times smaller than that of IOP. Conclusions Models predict that IOP and CSFP are the top and sixth most influential factors on ONH biomechanics. Different IOP and CSFP effects suggest that translaminar pressure difference may not be a good parameter to predict biomechanics-related glaucomatous neuropathy. CON may drastically affect the responses relating to gross ONH geometry and should be determined experimentally. PMID:29332130

Research on gender differences in online health communities.

PubMed

Liu, Xuan; Sun, Min; Li, Jia

2018-03-01

With the growing concern about health issues and the emergence of online communities based on user-generated content (UGC), more and more people are participating in online health communities (OHCs) to exchange opinions and health information. This paper aims to examine whether and how male and female users behave differently in OHCs. Using data from a leading diabetes community in China (Tianmijiayuan), we incorporate three different techniques: topic modeling analysis, sentiment analysis and friendship network analysis to investigate gender differences in chronic online health communities. The results indicated that (1) Male users' posting content was usually more professional and included more medical terms. Comparatively speaking, female users were more inclined to seek emotional support in the health communities. (2) Female users expressed more negative emotions than male users did, especially anxiety and sadness. (3) In addition, male users were more centered and influential in the friendship network than were women. Through these analyses, our research revealed the behavioral characteristics and needs for different gender users in online health communities. Gaining a deeper understanding of gender differences in OHCs can serve as guidance to better meet the information needs, emotional needs and relationship needs of male and female patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

PubMed Central

Puniya, Bhanwar Lal; Allen, Laura; Hochfelder, Colleen; Majumder, Mahbubul; Helikar, Tomáš

2016-01-01

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results. PMID:26904540

Neurotree: a collaborative, graphical database of the academic genealogy of neuroscience.

PubMed

David, Stephen V; Hayden, Benjamin Y

2012-01-01

Neurotree is an online database that documents the lineage of academic mentorship in neuroscience. Modeled on the tree format typically used to describe biological genealogies, the Neurotree web site provides a concise summary of the intellectual history of neuroscience and relationships between individuals in the current neuroscience community. The contents of the database are entirely crowd-sourced: any internet user can add information about researchers and the connections between them. As of July 2012, Neurotree has collected information from 10,000 users about 35,000 researchers and 50,000 mentor relationships, and continues to grow. The present report serves to highlight the utility of Neurotree as a resource for academic research and to summarize some basic analysis of its data. The tree structure of the database permits a variety of graphical analyses. We find that the connectivity and graphical distance between researchers entered into Neurotree early has stabilized and thus appears to be mostly complete. The connectivity of more recent entries continues to mature. A ranking of researcher fecundity based on their mentorship reveals a sustained period of influential researchers from 1850-1950, with the most influential individuals active at the later end of that period. Finally, a clustering analysis reveals that some subfields of neuroscience are reflected in tightly interconnected mentor-trainee groups.

Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience

PubMed Central

David, Stephen V.; Hayden, Benjamin Y.

2012-01-01

Neurotree is an online database that documents the lineage of academic mentorship in neuroscience. Modeled on the tree format typically used to describe biological genealogies, the Neurotree web site provides a concise summary of the intellectual history of neuroscience and relationships between individuals in the current neuroscience community. The contents of the database are entirely crowd-sourced: any internet user can add information about researchers and the connections between them. As of July 2012, Neurotree has collected information from 10,000 users about 35,000 researchers and 50,000 mentor relationships, and continues to grow. The present report serves to highlight the utility of Neurotree as a resource for academic research and to summarize some basic analysis of its data. The tree structure of the database permits a variety of graphical analyses. We find that the connectivity and graphical distance between researchers entered into Neurotree early has stabilized and thus appears to be mostly complete. The connectivity of more recent entries continues to mature. A ranking of researcher fecundity based on their mentorship reveals a sustained period of influential researchers from 1850–1950, with the most influential individuals active at the later end of that period. Finally, a clustering analysis reveals that some subfields of neuroscience are reflected in tightly interconnected mentor-trainee groups. PMID:23071595

Perceived norms and alcohol use among first-year college student-athletes’ different types of friends

PubMed Central

Massengale, Kelley E. C.; Ma, Alice; Rulison, Kelly L.; Milroy, Jeffrey J.; Wyrick, David L.

2017-01-01

Objective To describe first-year college student-athletes’ friendship contexts and test whether their perceptions of alcohol use and approval by different types of friends are associated with their own alcohol use. Participants First-year student-athletes (N=2,622) from 47 colleges and universities participating in National Collegiate Athletic Association (NCAA) sports during February–March 2013. Methods Student-athletes completed online surveys during the baseline assessment of an alcohol and other drug prevention program evaluation. Analyses tested whether perceptions of friends’ alcohol use (descriptive norms) and perceptions of friends’ approval of alcohol use (injunctive norms) predicted their alcohol use. Results Both use and approval perceptions by upperclassmen, same-team, and most influential friends significantly predicted alcohol use. By contrast, only perceived use by first-year, non-team, and less influential friends significantly predicted alcohol use. Conclusions Athletics departments’ alcohol policies and prevention programming for first-year student-athletes should address the potential influence of different types of friends on alcohol use. PMID:27610821

Perceived norms and alcohol use among first-year college student-athletes' different types of friends.

PubMed

Massengale, Kelley E C; Ma, Alice; Rulison, Kelly L; Milroy, Jeffrey J; Wyrick, David L

2017-01-01

To describe first-year college student-athletes' friendship contexts and test whether their perceptions of alcohol use and approval by different types of friends are associated with their own alcohol use. First-year student-athletes (N = 2,622) from 47 colleges and universities participating in National Collegiate Athletic Association (NCAA) sports during February-March 2013. Student-athletes completed online surveys during the baseline assessment of an alcohol and other drug prevention program evaluation. Analyses tested whether perceptions of friends' alcohol use (descriptive norms) and perceptions of friends' approval of alcohol use (injunctive norms) predicted their alcohol use. Both use and approval perceptions by upperclassmen, same-team, and most influential friends significantly predicted alcohol use. By contrast, only perceived use by first-year, nonteam, and less influential friends significantly predicted alcohol use. Athletics departments' alcohol policies and prevention programming for first-year student-athletes should address the potential influence of different types of friends on alcohol use.

Influence analysis of Github repositories.

PubMed

Hu, Yan; Zhang, Jun; Bai, Xiaomei; Yu, Shuo; Yang, Zhuo

2016-01-01

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

Influences of satisfaction with telecare and family trust in older Taiwanese people.

PubMed

Tsai, Chung-Hung; Kuo, Yu-Ming; Uei, Shu-Lin

2014-01-27

The level of trust given towards telecare by the family members of older people using the service is extremely important. Family trust may be an influential factor in deciding whether to use such services. This study focuses on older people's satisfaction with telecare and examines their family's trust in telecare services. Influences on intention to continue using telecare services are also explored. A questionnaire-based survey on 60 communities dwelling older people who had been receiving telecare services in the past two years was employed. This study developed a satisfaction and trust scale based on previous studies. Our results show that older people's satisfaction with telecare services and families' trust were influential in decided whether to continue to use of telecare services. These findings can help medical institutions to better insight into the user experience of telecare to help them provide future services that better comply with clients' desires and requirements.

A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes

NASA Astrophysics Data System (ADS)

Agha Mohammad Ali Kermani, Mehrdad; Fatemi Ardestani, Seyed Farshad; Aliahmadi, Alireza; Barzinpour, Farnaz

2017-01-01

Influence maximization deals with identification of the most influential nodes in a social network given an influence model. In this paper, a game theoretic framework is developed that models a competitive influence maximization problem. A novel competitive influence model is additionally proposed that incorporates user heterogeneity, message content, and network structure. The proposed game-theoretic model is solved using Nash Equilibrium in a real-world dataset. It is shown that none of the well-known strategies are stable and at least one player has the incentive to deviate from the proposed strategy. Moreover, violation of Nash equilibrium strategy by each player leads to their reduced payoff. Contrary to previous works, our results demonstrate that graph topology, as well as the nodes' sociability and initial tendency measures have an effect on the determination of the influential node in the network.

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

PubMed

Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

2014-04-01

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluating the influential priority of the factors on insurance loss of public transit

PubMed Central

Su, Yongmin; Chen, Xinqiang

2018-01-01

Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest. PMID:29298337

Evaluating the influential priority of the factors on insurance loss of public transit.

PubMed

Zhang, Wenhui; Su, Yongmin; Ke, Ruimin; Chen, Xinqiang

2018-01-01

Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

Determinants of Internet use as a preferred source of information on personal health.

PubMed

Lemire, Marc; Paré, Guy; Sicotte, Claude; Harvey, Charmian

2008-11-01

To understand the personal, social and cultural factors likely to explain recourse to the Internet as a preferred source of personal health information. A cross-sectional survey was conducted among a population of 2923 Internet users visiting a firmly established website that offers information on personal health. Multiple regression analysis was performed to identify the determinants of site use. The analysis template comprised four classes of determinants likely to explain Internet use: beliefs, intentions, user satisfaction and socio-demographic characteristics. Seven-point Likert scales were used. An analysis of the psychometric qualities of the variables provided compelling evidence of the construct's validity and reliability. A confirmatory factor analysis confirmed the correspondence with the factors predicted by the theoretical model. The regression analysis explained 35% of the variance in Internet use. Use was directly associated with five factors: perceived usefulness, importance given to written media in searches for health information, concern for personal health, importance given to the opinions of physicians and other health professionals, and the trust placed in the information available on the site itself. This study confirms the importance of the credibility of information on the frequency of Internet use as a preferred source of information on personal health. It also shows the potentially influential role of the Internet in the development of personal knowledge of health issues.

Boys to Men: Entertainment Media. Messages about Masculinity: A National Poll of Children, Focus Groups, and Content Analysis of Entertainment Media.

ERIC Educational Resources Information Center

Heintz-Knowles, Katharine; Li-Vollmer, Meredith; Chen, Perry; Harris, Tarana; Haufler, Adrienne; Lapp, Joan; Miller, Patti

Boys are especially active users of media, and researchers have suggested that the cumulative impact of media, such as television, movies, and music videos, may make them some of the most influential forces in boys' lives. This report presents the findings of a national poll of 1,200 young people (ages 10 to 17) and focus groups in which boys…

Social-aware data dissemination in opportunistic mobile social networks

NASA Astrophysics Data System (ADS)

Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Han, Xiaowei

Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

'A good method of quitting smoking' or 'just an alternative to smoking'? Comparative evaluations of e-cigarette and traditional cigarette usage by dual users.

PubMed

Vandrevala, Tushna; Coyle, Adrian; Walker, Victoria; Cabrera Torres, Joshelyn; Ordoña, Izobel; Rahman, Panna

2017-01-01

The development of e-cigarettes was initially hailed as a resource in facilitating a reduction in or cessation of cigarette smoking. Many users of e-cigarettes are 'dual users', smoking traditional cigarettes and e-cigarettes. The present qualitative study examines the factors that a group of 20 dual users considered to have been influential in their decisions to use e-cigarettes and their comparative evaluations of e-cigarettes and traditional cigarettes. Health concerns were not found to be sole motivators. Participants pointed to financial and contextual considerations, particularly peer influence on uptake and continued usage of e-cigarettes. E-cigarettes were evaluated as comparable to cigarettes in some ways but not in other important respects such as sensation and satisfaction. Different social evaluations of cigarette and e-cigarette usage were discerned which influenced how participants identified as smokers, 'vapers' or neither. Findings are discussed in relation to social representations, identity and implications for continued e-cigarette usage among dual users.

Acceptability of picture archiving and communication system (PACS) among hospital healthcare personnel based on a unified theory of acceptance and use of technology

PubMed Central

Ahmadi, Maryam; Mehrabi, Nahid; Sheikhtaheri, Abbas; Sadeghi, Mojtaba

2017-01-01

Background and aim The picture archiving and communication system (PACS) is a healthcare system technology which manages medical images and integrates equipment through a network. There are some theories about the use and acceptance of technology by people to describe the behavior and attitudes of end users towards information technologies. We investigated the influential factors on users’ acceptance of PACS in the military hospitals of Tehran. Methods In this applied analytical and cross-sectional study, 151 healthcare employees of military hospitals who had experience in using the PACS system were investigated. Participants were selected by census. The following variables were considered: performance expectancy, efforts expectancy, social influence, facilitating conditions and behavioral intention. Data were gathered using a questionnaire. Its validity and reliability were approved by a panel of experts and was piloted with 30 hospital healthcare staff (Cronbach’s alpha =0.91). Spearman correlation coefficient and multiple linear regression analysis were used in analyzing the data. Results Expected performance, efforts expectancy, social impact and facilitating conditions had a significant relationship with behavioral intention. The multiple regression analysis indicated that only performance expectancy can predict the user’s behavioral intentions to use PACS technology. Conclusion Performance and effort expectancies are quite influential in accepting the use of PACS in hospitals. All healthcare personnel should become aware that using such technology is necessary in a hospital. Knowing the influencing factors that affect the acceptance of using new technology can help in improving its use, especially in a healthcare system. This can improve the offered healthcare services’ quality. PMID:29038717

The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application

PubMed Central

Lund, Jennifer L.; Richardson, David B.; Stürmer, Til

2016-01-01

Better understanding of biases related to selective prescribing of, and adherence to, preventive treatments has led to improvements in the design and analysis of pharmacoepidemiologic studies. One influential development has been the “active comparator, new user” study design, which seeks to emulate the design of a head-to-head randomized controlled trial. In this review, we first discuss biases that may affect pharmacoepidemiologic studies and describe their direction and magnitude in a variety of settings. We then present the historical foundations of the active comparator, new user study design and explain how this design conceptually mitigates biases leading to a paradigm shift in pharmacoepidemiology. We offer practical guidance on the implementation of the study design using administrative databases. Finally, we provide an empirical example in which the active comparator, new user study design addresses biases that have previously impeded pharmacoepidemiologic studies. PMID:26954351

Exploring relationship between human mobility and social ties: Physical distance is not dead

NASA Astrophysics Data System (ADS)

Jin, Bo; Liao, Binbing; Yuan, Ning; Wang, Wenjun

2015-06-01

Partly due to the difficulty of the access to a worldwide dataset that simultaneously captures the location history and social networks, our understanding of the relationship between human mobility and the social ties has been limited. However, this topic is essential for a deeper study from human dynamics and social networks aspects. In this paper, we examine the location history data and social networks data of 712 email users and 399 offline events users from a map-editing based social network website. Based on these data, we expand all our experiment both from individual aspect and community aspect. We find that the physical distance is still the most influential factor to social ties among the nine representative human mobility features extracted from our GPS trajectory dataset, although Internet revolution has made long-distance communication dramatically faster, easier and cheaper than ever before, and in turn, partly expand the physical scope of social networks. Furthermore, we find that to a certain extent, the proximity of South-North direction is more influential than East-West direction to social ties. To the our best of our knowledge, this difference between South-North and East-West is the first time to be raised and quantitatively supported by a large dataset. We believe our findings on the interplay of human mobility and social ties offer a new perspective to this field of study.

A blue carbon soil database: Tidal wetland stocks for the US National Greenhouse Gas Inventory

NASA Astrophysics Data System (ADS)

Feagin, R. A.; Eriksson, M.; Hinson, A.; Najjar, R. G.; Kroeger, K. D.; Herrmann, M.; Holmquist, J. R.; Windham-Myers, L.; MacDonald, G. M.; Brown, L. N.; Bianchi, T. S.

2015-12-01

Coastal wetlands contain large reservoirs of carbon, and in 2015 the US National Greenhouse Gas Inventory began the work of placing blue carbon within the national regulatory context. The potential value of a wetland carbon stock, in relation to its location, soon could be influential in determining governmental policy and management activities, or in stimulating market-based CO2 sequestration projects. To meet the national need for high-resolution maps, a blue carbon stock database was developed linking National Wetlands Inventory datasets with the USDA Soil Survey Geographic Database. Users of the database can identify the economic potential for carbon conservation or restoration projects within specific estuarine basins, states, wetland types, physical parameters, and land management activities. The database is geared towards both national-level assessments and local-level inquiries. Spatial analysis of the stocks show high variance within individual estuarine basins, largely dependent on geomorphic position on the landscape, though there are continental scale trends to the carbon distribution as well. Future plans including linking this database with a sedimentary accretion database to predict carbon flux in US tidal wetlands.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Varun, Sajja; Reddy, Kalakada Bhargav Bal; Vardhan Reddy, R. R. Vishnu

2016-09-01

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Radiosurgery alone for 5 or more brain metastases: expert opinion survey.

PubMed

Knisely, Jonathan P S; Yamamoto, Masaaki; Gross, Cary P; Castrucci, William A; Jokura, Hidefumi; Chiang, Veronica L S

2010-12-01

Oligometastatic brain metastases may be treated with stereotactic radiosurgery (SRS) alone, but no consensus exists as to when SRS alone would be appropriate. A survey was conducted at 2 radiosurgery meetings to determine which factors SRS practitioners emphasize in recommending SRS alone, and what physician characteristics are associated with recommending SRS alone for ≥ 5 metastases. All physicians attending the 8th Biennial Congress and Exhibition of the International Stereotactic Radiosurgery Society in June 2007 and the 18th Annual Meeting of the Japanese Society of Stereotactic Radiosurgery in July 2009 were asked to complete a questionnaire ranking 14 clinical factors on a 5-point Likert-type scale (ranging from 1 = not important to 5 = very important) to determine how much each factor might influence a decision to recommend SRS alone for brain metastases. Results were condensed into a single dichotomous outcome variable of "influential" (4-5) versus "not influential" (1-3). Respondents were also asked to complete the statement: "In general, a reasonable number of brain metastases treatable by SRS alone would be, at most, ___." The characteristics of physicians willing to recommend SRS alone for ≥ 5 metastases were assessed. Chi-square was used for univariate analysis, and logistic regression for multivariate analysis. The final study sample included 95 Gamma Knife and LINAC-using respondents (54% Gamma Knife users) in San Francisco and 54 in Sendai (48% Gamma Knife users). More than 70% at each meeting had ≥ 5 years experience with SRS. Sixty-five percent in San Francisco and 83% in Sendai treated ≥ 30 cases annually with SRS. The highest number of metastases considered reasonable to treat with SRS alone in both surveys was 50. In San Francisco, the mean and median numbers of metastases considered reasonable to treat with SRS alone were 6.7 and 5, while in Sendai they were 11 and 10. In the San Francisco sample, the clinical factors identified to be most influential in decision making were Karnofsky Performance Scale score (78%), presence/absence of mass effect (76%), and systemic disease control (63%). In Sendai, the most influential factors were the size of the metastases (78%), the Karnofsky Performance Scale score (70%), and metastasis location (68%). In San Francisco, 55% of respondents considered treating ≥ 5 metastases and 22% considered treating ≥ 10 metastases "reasonable." In Sendai, 83% of respondents considered treating ≥ 5 metastases and 57% considered treating ≥ 10 metastases "reasonable." In both groups, private practitioners, neurosurgeons, and Gamma Knife users were statistically significantly more likely to treat ≥ 5 metastases with SRS alone. Although there is no clear consensus for how many metastases are reasonable to treat with SRS alone, more than half of the radiosurgeons at 2 international meetings were willing to extend the use of SRS as an initial treatment for ≥ 5 brain metastases. Given the substantial variation in clinicians' approaches to SRS use, further research is required to identify patient characteristics associated with optimal SRS outcomes.

Verbal Processing Speed and Executive Functioning in Long-Term Cochlear Implant Users

PubMed Central

Pisoni, David B.; Kronenberger, William G.

2015-01-01

Purpose The purpose of this study was to report how verbal rehearsal speed (VRS), a form of covert speech used to maintain verbal information in working memory, and another verbal processing speed measure, perceptual encoding speed, are related to 3 domains of executive function (EF) at risk in cochlear implant (CI) users: verbal working memory, fluency-speed, and inhibition-concentration. Method EF, speech perception, and language outcome measures were obtained from 55 prelingually deaf, long-term CI users and matched controls with normal hearing (NH controls). Correlational analyses were used to assess relations between VRS (articulation rate), perceptual encoding speed (digit and color naming), and the outcomes in each sample. Results CI users displayed slower verbal processing speeds than NH controls. Verbal rehearsal speed was related to 2 EF domains in the NH sample but was unrelated to EF outcomes in CI users. Perceptual encoding speed was related to all EF domains in both groups. Conclusions Verbal rehearsal speed may be less influential for EF quality in CI users than for NH controls, whereas rapid automatized labeling skills and EF are closely related in both groups. CI users may develop processing strategies in EF tasks that differ from the covert speech strategies routinely employed by NH individuals. PMID:25320961

A study for Chinese Otaku while web shopping

NASA Astrophysics Data System (ADS)

Chang, Che-Chang; Chen, Fang-Tzu

2014-10-01

With the increasing popularity of online shopping, ranging from various communication networks to e-commerce. Same as the rest of world customers, Chinese share their concern about "inadequate information from website" and "trust for internet purchases". This study, uses adequacy of web information and trust for variables in order to ensure the factors for influential to online purchases. Internet users are well educated and younger. Thus, this is necessary to determine whether the Otaku' characteristics have interfered effect for online purchases on this study.

Enhanced ergonomics approaches for product design: a user experience ecosystem perspective and case studies.

PubMed

Xu, Wei

2014-01-01

This paper first discusses the major inefficiencies faced in current human factors and ergonomics (HFE) approaches: (1) delivering an optimal end-to-end user experience (UX) to users of a solution across its solution lifecycle stages; (2) strategically influencing the product business and technology capability roadmaps from a UX perspective and (3) proactively identifying new market opportunities and influencing the platform architecture capabilities on which the UX of end products relies. In response to these challenges, three case studies are presented to demonstrate how enhanced ergonomics design approaches have effectively addressed the challenges faced in current HFE approaches. Then, the enhanced ergonomics design approaches are conceptualised by a user-experience ecosystem (UXE) framework, from a UX ecosystem perspective. Finally, evidence supporting the UXE, the advantage and the formalised process for executing UXE and methodological considerations are discussed. Practitioner Summary: This paper presents enhanced ergonomics approaches to product design via three case studies to effectively address current HFE challenges by leveraging a systematic end-to-end UX approach, UX roadmaps and emerging UX associated with prioritised user needs and usages. Thus, HFE professionals can be more strategic, creative and influential.

‘A good method of quitting smoking’ or ‘just an alternative to smoking’? Comparative evaluations of e-cigarette and traditional cigarette usage by dual users

PubMed Central

Vandrevala, Tushna; Coyle, Adrian; Walker, Victoria; Cabrera Torres, Joshelyn; Ordoña, Izobel; Rahman, Panna

2017-01-01

The development of e-cigarettes was initially hailed as a resource in facilitating a reduction in or cessation of cigarette smoking. Many users of e-cigarettes are ‘dual users’, smoking traditional cigarettes and e-cigarettes. The present qualitative study examines the factors that a group of 20 dual users considered to have been influential in their decisions to use e-cigarettes and their comparative evaluations of e-cigarettes and traditional cigarettes. Health concerns were not found to be sole motivators. Participants pointed to financial and contextual considerations, particularly peer influence on uptake and continued usage of e-cigarettes. E-cigarettes were evaluated as comparable to cigarettes in some ways but not in other important respects such as sensation and satisfaction. Different social evaluations of cigarette and e-cigarette usage were discerned which influenced how participants identified as smokers, ‘vapers’ or neither. Findings are discussed in relation to social representations, identity and implications for continued e-cigarette usage among dual users. PMID:28680694

Alcohol and drug abusers' reasons for seeking treatment.

PubMed

Cunningham, J A; Sobell, L C; Sobell, M B; Gaskin, J

1994-01-01

Clients at two different treatment facilities were asked at assessment how influential each of 10 possible reasons were in their decision to change their alcohol or drug use. Clients at both facilities most often endorsed "weighing the pros and cons of drinking or drug use" and a "warning from spouse." Client's reasons for seeking treatment were also examined in relation to treatment compliance. Three reasons--"weighing the pros and cons," "hitting rock bottom," and experiencing a "major lifestyle change"--were predictive of treatment compliance. Clients who rated any of these reasons as influential were more likely to enter and complete treatment. Although more research is needed, knowledge of clients' reasons for seeking treatment might be useful in treatment matching.

Modeling the heterogeneity of human dynamics based on the measurements of influential users in Sina Microblog

NASA Astrophysics Data System (ADS)

Wang, Chenxu; Guan, Xiaohong; Qin, Tao; Yang, Tao

2015-06-01

Online social network has become an indispensable communication tool in the information age. The development of microblog also provides us a great opportunity to study human dynamics that play a crucial role in the design of efficient communication systems. In this paper we study the characteristics of the tweeting behavior based on the data collected from Sina Microblog. The user activity level is measured to characterize how often a user posts a tweet. We find that the user activity level follows a bimodal distribution. That is, the microblog users tend to be either active or inactive. The inter-tweeting time distribution is then measured at both the aggregate and individual levels. We find that the inter-tweeting time follows a piecewise power law distribution of two tails. Furthermore, the exponents of the two tails have different correlations with the user activity level. These findings demonstrate that the dynamics of the tweeting behavior are heterogeneous in different time scales. We then develop a dynamic model co-driven by the memory and the interest mechanism to characterize the heterogeneity. The numerical simulations validate the model and verify that the short time interval tweeting behavior is driven by the memory mechanism while the long time interval behavior by the interest mechanism.

Asymmetries of Influence: Differential Effects of Body Postures on Perceptions of Emotional Facial Expressions

PubMed Central

Mondloch, Catherine J.; Nelson, Nicole L.; Horner, Matthew

2013-01-01

The accuracy and speed with which emotional facial expressions are identified is influenced by body postures. Two influential models predict that these congruency effects will be largest when the emotion displayed in the face is similar to that displayed in the body: the emotional seed model and the dimensional model. These models differ in whether similarity is based on physical characteristics or underlying dimensions of valence and arousal. Using a 3-alternative forced-choice task in which stimuli were presented briefly (Exp 1a) or for an unlimited time (Exp 1b) we provide evidence that congruency effects are more complex than either model predicts; the effects are asymmetrical and cannot be accounted for by similarity alone. Fearful postures are especially influential when paired with facial expressions, but not when presented in a flanker task (Exp 2). We suggest refinements to each model that may account for our results and suggest that additional studies be conducted prior to drawing strong theoretical conclusions. PMID:24039996

Determinants of the Intention to Pump Breast Milk on a University Campus.

PubMed

Bai, Yeon K; Dinour, Lauren M; Pope, Gina A

2016-09-01

The number of young mothers in the workforce and in schools of higher education has steadily increased. In order to maintain a breastfeeding relationship with their children, these mothers need to pump or express breast milk multiple times a day while at work or school. This study examines the factors associated with the intention to pump breast milk at one university campus. Between January and February 2015, an online survey invitation was sent out to all female employees and students at one university. The survey, based on the Theory of Planned Behavior, assessed intentions to pump breast milk on campus. The intention to pump breast milk was examined between employees and students separately. Within these 2 groups, behavioral performers (women who pump or have pumped breast milk while on campus) were compared to nonperformers. Using multiple regression analysis, the most influential predictors of the intention to pump (ie, attitudes, subjective norm, perceived behavioral control, and underlying beliefs) were identified. A total of 218 women participated in the study (62 employees and 156 students, a 71.7% survey completion rate). Among university employees, the most influential factor that predicted pumping intention among performers was attitude toward pumping (β = 0.36, P = .03). Among student performers, the most influential factor to predict pumping intention was the subjective norm (β = 0.31, P = .02). For student nonperformers, perceived behavioral control (β = 0.54, P < .001) was the most influential factor. Important determinants of the intention to pump on campus included relieving discomfort from engorgement, availability of milk storage, experiencing other people's approval of pumping breast milk, and the inconvenience of carrying pump equipment. Continued efforts are needed to create a supportive culture for breastfeeding in the campus community as well as to provide pump loan and milk storage options for both employee and student mothers. © 2016 by the American College of Nurse-Midwives.

The Role of Memory Consolidation in Generalisation of New Linguistic Information

ERIC Educational Resources Information Center

Tamminen, Jakke; Davis, Matthew H.; Merkx, Marjolein; Rastle, Kathleen

2012-01-01

Accounts of memory that postulate complementary learning systems (CLS) have become increasingly influential in the field of language learning. These accounts predict that generalisation of newly learnt linguistic information to untrained contexts requires offline memory consolidation. Such generalisation should not be observed immediately after…

Stochastic does not equal ad hoc. [theories of lunar origin

NASA Technical Reports Server (NTRS)

Hartmann, W. K.

1984-01-01

Some classes of influential events in solar system history are class-predictable but not event-predictable. Theories of lunar origin should not ignore class-predictable stochastic events. Impacts and close encounters with large objects during planet formation are class-predictable. These stochastic events, such as large impacts that triggered ejection of Earth-mantle material into a circum-Earth cloud, should not be rejected as ad hoc. A way to deal with such events scientifically is to investigate their consequences; if it can be shown that they might produce the Moon, they become viable concepts in theories of lunar origin.

From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities

PubMed Central

Ali, Kathina; Cunningham, John Alastair; Griffiths, Kathleen Margaret

2015-01-01

Background Understanding how people participate in and contribute to online health communities (OHCs) is useful knowledge in multiple domains. It is helpful for community managers in developing strategies for building community, for organizations in disseminating information about health interventions, and for researchers in understanding the social dynamics of peer support. Objective We sought to determine if any patterns were apparent in the nature of user participation across online health communities. Methods The current study involved a systematic review of all studies that have investigated the nature of participation in an online health community and have provided a quantifiable method for categorizing a person based on their participation style. A systematic search yielded 20 papers. Results Participatory styles were classified as either multidimensional (based on multiple metrics) or unidimensional (based on one metric). With respect to the multidimensional category, a total of 41 different participation styles were identified ranging from Influential Users who were leaders on the board to Topic-Focused Responders who focused on a specific topic and tended to respond to rather than initiate posts. However, there was little overlap in participation styles identified both across OHCs for different health conditions and within OHCs for specific health conditions. Five of the 41 styles emerged in more than one study (Hubs, Authorities, Facilitators, Prime Givers, and Discussants), but the remainder were reported in only one study. The focus of the unidimensional studies was on level of engagement and particularly on high-engaged users. Eight different metrics were used to evaluate level of engagement with the greatest focus on frequency of posts. Conclusions With the exception of high-engaged users based on high post frequency, the current review found little evidence for consistent participatory styles across different health communities. However, this area of research is in its infancy, with most of the studies included in the review being published in the last 2 years. Nevertheless, the review delivers a nomenclature for OHC participation styles and metrics and discusses important methodological issues that will provide a basis for future comparative research in the area. Further studies are required to systematically investigate a range of participatory styles, to investigate their association with different types of online health communities and to determine the contribution of different participatory styles within and across online health communities. PMID:26627369

From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities.

PubMed

Carron-Arthur, Bradley; Ali, Kathina; Cunningham, John Alastair; Griffiths, Kathleen Margaret

2015-12-01

Understanding how people participate in and contribute to online health communities (OHCs) is useful knowledge in multiple domains. It is helpful for community managers in developing strategies for building community, for organizations in disseminating information about health interventions, and for researchers in understanding the social dynamics of peer support. We sought to determine if any patterns were apparent in the nature of user participation across online health communities. The current study involved a systematic review of all studies that have investigated the nature of participation in an online health community and have provided a quantifiable method for categorizing a person based on their participation style. A systematic search yielded 20 papers. Participatory styles were classified as either multidimensional (based on multiple metrics) or unidimensional (based on one metric). With respect to the multidimensional category, a total of 41 different participation styles were identified ranging from Influential Users who were leaders on the board to Topic-Focused Responders who focused on a specific topic and tended to respond to rather than initiate posts. However, there was little overlap in participation styles identified both across OHCs for different health conditions and within OHCs for specific health conditions. Five of the 41 styles emerged in more than one study (Hubs, Authorities, Facilitators, Prime Givers, and Discussants), but the remainder were reported in only one study. The focus of the unidimensional studies was on level of engagement and particularly on high-engaged users. Eight different metrics were used to evaluate level of engagement with the greatest focus on frequency of posts. With the exception of high-engaged users based on high post frequency, the current review found little evidence for consistent participatory styles across different health communities. However, this area of research is in its infancy, with most of the studies included in the review being published in the last 2 years. Nevertheless, the review delivers a nomenclature for OHC participation styles and metrics and discusses important methodological issues that will provide a basis for future comparative research in the area. Further studies are required to systematically investigate a range of participatory styles, to investigate their association with different types of online health communities and to determine the contribution of different participatory styles within and across online health communities.

Measures of node centrality in mobile social networks

NASA Astrophysics Data System (ADS)

Gao, Zhenxiang; Shi, Yan; Chen, Shanzhi

2015-02-01

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

A physiologically-inspired model reproducing the speech intelligibility benefit in cochlear implant listeners with residual acoustic hearing.

PubMed

Zamaninezhad, Ladan; Hohmann, Volker; Büchner, Andreas; Schädler, Marc René; Jürgens, Tim

2017-02-01

This study introduces a speech intelligibility model for cochlear implant users with ipsilateral preserved acoustic hearing that aims at simulating the observed speech-in-noise intelligibility benefit when receiving simultaneous electric and acoustic stimulation (EA-benefit). The model simulates the auditory nerve spiking in response to electric and/or acoustic stimulation. The temporally and spatially integrated spiking patterns were used as the final internal representation of noisy speech. Speech reception thresholds (SRTs) in stationary noise were predicted for a sentence test using an automatic speech recognition framework. The model was employed to systematically investigate the effect of three physiologically relevant model factors on simulated SRTs: (1) the spatial spread of the electric field which co-varies with the number of electrically stimulated auditory nerves, (2) the "internal" noise simulating the deprivation of auditory system, and (3) the upper bound frequency limit of acoustic hearing. The model results show that the simulated SRTs increase monotonically with increasing spatial spread for fixed internal noise, and also increase with increasing the internal noise strength for a fixed spatial spread. The predicted EA-benefit does not follow such a systematic trend and depends on the specific combination of the model parameters. Beyond 300 Hz, the upper bound limit for preserved acoustic hearing is less influential on speech intelligibility of EA-listeners in stationary noise. The proposed model-predicted EA-benefits are within the range of EA-benefits shown by 18 out of 21 actual cochlear implant listeners with preserved acoustic hearing. Copyright © 2016 Elsevier B.V. All rights reserved.

The Timing of Verb Selection in Japanese Sentence Production

ERIC Educational Resources Information Center

Momma, Shota; Slevc, L. Robert; Phillips, Colin

2016-01-01

Many influential models of sentence production (e.g., Bock & Levelt, 1994; Kempen & Hoenkamp, 1987; Levelt, 1989) emphasize the central role of verbs in structural encoding, and thus predict that verbs should be selected early in sentence formulation, possibly even before the phonological encoding of the first constituent (Ferreira, 2000).…

DIAGNOSTIC STUDY ON FINE PARTICULATE MATTER PREDICTIONS OF CMAQ IN THE SOUTHEASTERN U.S.

EPA Science Inventory

In this study, the authors use the process analysis tool embedded in CMAQ to examine major processes that govern the fate of key pollutants, identify the most influential processes that contribute to model errors, and guide the diagnostic and sensitivity studies aimed at improvin...

Exploring the factors that influence the decision to adopt and engage with an integrated assistive telehealth and telecare service in Cambridgeshire, UK: a nested qualitative study of patient 'users' and 'non-users'.

PubMed

Cook, Erica J; Randhawa, Gurch; Sharp, Chloe; Ali, Nasreen; Guppy, Andy; Barton, Garry; Bateman, Andrew; Crawford-White, Jane

2016-04-19

There is a political drive in the UK to use assistive technologies such as telehealth and telecare as an innovative and efficient approach to healthcare delivery. However, the success of implementation of such services remains dependent on the ability to engage the wider population to adopt these services. It has been widely acknowledged that low acceptance of technology, forms a key barrier to adoption although findings been mixed. Further, it remains unclear what, if any barriers exist between patients and how these compare to those who have declined or withdrawn from using these technologies. This research aims to address this gap focusing on the UK based Cambridgeshire Community Services Assistive Telehealth and Telecare service, an integrated model of telehealth and telecare. Qualitative semi-structured interviews were conducted between 1st February 2014 and 1st December 2014, to explore the views and experiences of 'users' and 'non-users' using this service. 'Users' were defined as patients who used the service (N = 28) with 'non-users' defined as either referred patients who had declined the service before allocation (N = 3) or had withdrawn after using the ATT service (N = 9). Data were analysed using the Framework Method. This study revealed that there are a range of barriers and facilitators that impact on the decision to adopt and continue to engage with this type of service. Having a positive attitude and a perceived need that could be met by the ATT equipment were influential factors in the decision to adopt and engage in using the service. Engagement of the service centred on 'usability', 'usefulness of equipment', and 'threat to identity and independence'. The paper described the influential role of referrers in decision-making and the need to engage with such agencies on a strategic level. The findings also revealed that reassurance from the onset was paramount to continued engagement, particularly in older patients who appeared to have more negative feelings towards technology. In addition, there is a clear need for continued product development and innovation to not only increase usability and functionality of equipment but also to motivate other sections of the population who could benefit from such services. Uncovering these factors has important policy implications in how services can improve access and patient support through the application of assistive technology which could in turn reduce unnecessary cost and burden on overstretched health services.

SWOT analysis and revelation in traditional Chinese medicine internationalization.

PubMed

Tang, Haitao; Huang, Wenlong; Ma, Jimei; Liu, Li

2018-01-01

Traditional Chinese medicine (TCM) is currently the best-preserved and most influential traditional medical system with the largest number of users worldwide. In recent years, the trend of TCM adoption has increased greatly, but the process of TCM internationalization has suffered from a series of setbacks for both internal and external reasons. Thus, the process of TCM internationalization faces formidable challenges, although it also has favourable opportunities. Using SWOT analysis, this paper investigates the strengths, weaknesses, opportunities and threats for TCM. These findings can serve as references for TCM enterprises with global ambitions.

Multimedia Information Retrieval Literature Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak C.; Bohn, Shawn J.; Payne, Deborah A.

This survey paper highlights some of the recent, influential work in multimedia information retrieval (MIR). MIR is a branch area of multimedia (MM). The young and fast-growing area has received strong industrial and academic support in the United States and around the world (see Section 7 for a list of major conferences and journals of the community). The term "information retrieval" may be misleading to those with different computer science or information technology backgrounds. As shown in our discussion later, it indeed includes topics from user interaction, data analytics, machine learning, feature extraction, information visualization, and more.

A novel framework to alleviate the sparsity problem in context-aware recommender systems

NASA Astrophysics Data System (ADS)

Yu, Penghua; Lin, Lanfen; Wang, Jing

2017-04-01

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.

Spreading dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pei, Sen; Makse, Hernán A.

2013-12-01

Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.

Employee Perceptions of Workplace Health Promotion Programs: Comparison of a Tailored, Semi-Tailored, and Standardized Approach.

PubMed

Street, Tamara D; Lacey, Sarah J

2018-04-28

In the design of workplace health promotion programs (WHPPs), employee perceptions represent an integral variable which is predicted to translate into rate of user engagement (i.e., participation) and program loyalty. This study evaluated employee perceptions of three workplace health programs promoting nutritional consumption and physical activity. Programs included: (1) an individually tailored consultation with an exercise physiologist and dietitian; (2) a semi-tailored 12-week SMS health message program; and (3) a standardized group workshop delivered by an expert. Participating employees from a transport company completed program evaluation surveys rating the overall program, affect, and utility of: consultations ( n = 19); SMS program ( n = 234); and workshops ( n = 86). Overall, participants’ affect and utility evaluations were positive for all programs, with the greatest satisfaction being reported in the tailored individual consultation and standardized group workshop conditions. Furthermore, mode of delivery and the physical presence of an expert health practitioner was more influential than the degree to which the information was tailored to the individual. Thus, the synergy in ratings between individually tailored consultations and standardized group workshops indicates that low-cost delivery health programs may be as appealing to employees as tailored, and comparatively high-cost, program options.

Knights, knaves, pawns and queens: attitudes to behaviour in postwar Britain

PubMed Central

Welshman, John

2007-01-01

The choice agenda is currently one of the most prominent in public policy. One of its main architects, Julian Le Grand, has used the metaphors of knights, knaves, pawns and queens to characterise changing attitudes to questions of motivation and behaviour among public servants and service users. He has said, for example, that, in the immediate postwar period, public servants were perceived as public‐spirited altruists (or knights), whereas service users were seen as passive (or pawns). It was only in the mid‐1980s that public servants came to be seen as essentially self‐interested (knaves) and service users came to be regarded as consumers (queens). However, this highly influential model has undergone remarkably little critical scrutiny to date. This article explores the debate over transmitted deprivation in the 1970s to provide a historically grounded piece of analysis to explore the accuracy and utility of these metaphors. It challenges Le Grand's arguments in three respects. Firstly, a concern with behaviour and agency went much broader than social security fraud. Secondly, the metaphor of pawns is inadequate for characterising attitudes towards the poor and service users. Finally, Le Grand's periodisation of the postwar era also has serious flaws. PMID:17234865

Predicting Influential Factors of Secondary Career and Technical Education Teachers' Intent to Stay in the Profession

ERIC Educational Resources Information Center

Dainty, Julie D.

2012-01-01

Retaining highly qualified career and technical education teachers is important in maintaining and growing quality secondary career and technical education programs. Therefore, the purpose of this study was to identify factors contributing to teacher retention specifically in the area of career and technical education (CTE) and determine…

What Predicts Teachers' Acceptance of Students with Special Educational Needs in Kindergarten?

ERIC Educational Resources Information Center

Lee, Frances Lai Mui; Tracey, Danielle; Barker, Katrina; Fan, Jesmond C. M.; Yeung, Alexander Seeshing

2014-01-01

Despite attempts of educators and policy makers in promoting inclusive education through training and provision of extra resources, it remains unclear what is the most influential factor that may reduce teachers' resistance to and increase their advocacy of inclusive education. Teachers who have been trained in special education are usually…

The Factor Content of Bilateral Trade: An Empirical Test.

ERIC Educational Resources Information Center

Choi, Yong-Seok; Krishna, Pravin

2004-01-01

The factor proportions model of international trade is one of the most influential theories in international economics. Its central standing in this field has appropriately prompted, particularly recently, intense empirical scrutiny. A substantial and growing body of empirical work has tested the predictions of the theory on the net factor content…

Influential role of black carbon in the soil-air partitioning of polychlorinated biphenyls (PCBs) in the Indus River Basin, Pakistan.

PubMed

Ali, Usman; Syed, Jabir Hussain; Mahmood, Adeel; Li, Jun; Zhang, Gan; Jones, Kevin C; Malik, Riffat Naseem

2015-09-01

Levels of polychlorinated biphenyls (PCBs) were assessed in surface soils and passive air samples from the Indus River Basin, and the influential role of black carbon (BC) in the soil-air partitioning process was examined. ∑26-PCBs ranged between 0.002-3.03 pg m(-3) and 0.26-1.89 ng g(-1) for passive air and soil samples, respectively. Lower chlorinated (tri- and tetra-) PCBs were abundant in both air (83.9%) and soil (92.1%) samples. Soil-air partitioning of PCBs was investigated through octanol-air partition coefficients (KOA) and black carbon-air partition coefficients (KBC-A). The results of the paired-t test revealed that both models showed statistically significant agreement between measured and predicted model values for the PCB congeners. Ratios of fBCKBC-AδOCT/fOMKOA>5 explicitly suggested the influential role of black carbon in the retention and soil-air partitioning of PCBs. Lower chlorinated PCBs were strongly adsorbed and retained by black carbon during soil-air partitioning because of their dominance at the sampling sites and planarity effect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using the NIATx Model to Implement User-Centered Design of Technology for Older Adults.

PubMed

Gustafson, David H; Maus, Adam; Judkins, Julianne; Dinauer, Susan; Isham, Andrew; Johnson, Roberta; Landucci, Gina; Atwood, Amy K

2016-01-14

What models can effectively guide the creation of eHealth and mHealth technologies? This paper describes the use of the NIATx model as a framework for the user-centered design of a new technology for older adults. The NIATx model is a simple framework of process improvement based on the following principles derived from an analysis of decades of research from various industries about why some projects fail and others succeed: (1) Understand and involve the customer; (2) fix key problems; (3) pick an influential change leader; (4) get ideas from outside the field; (5) use rapid-cycle testing. This paper describes the use of these principles in technology development, the strengths and challenges of using this approach in this context, and lessons learned from the process. Overall, the NIATx model enabled us to produce a user-focused technology that the anecdotal evidence available so far suggests is engaging and useful to older adults. The first and fourth principles were especially important in developing the technology; the fourth proved the most challenging to use.

Using the NIATx Model to Implement User-Centered Design of Technology for Older Adults

PubMed Central

Maus, Adam; Judkins, Julianne; Dinauer, Susan; Isham, Andrew; Johnson, Roberta; Landucci, Gina; Atwood, Amy K

2016-01-01

What models can effectively guide the creation of eHealth and mHealth technologies? This paper describes the use of the NIATx model as a framework for the user-centered design of a new technology for older adults. The NIATx model is a simple framework of process improvement based on the following principles derived from an analysis of decades of research from various industries about why some projects fail and others succeed: (1) Understand and involve the customer; (2) fix key problems; (3) pick an influential change leader; (4) get ideas from outside the field; (5) use rapid-cycle testing. This paper describes the use of these principles in technology development, the strengths and challenges of using this approach in this context, and lessons learned from the process. Overall, the NIATx model enabled us to produce a user-focused technology that the anecdotal evidence available so far suggests is engaging and useful to older adults. The first and fourth principles were especially important in developing the technology; the fourth proved the most challenging to use. PMID:27025985

Innovation for an Inclusive Future

NASA Astrophysics Data System (ADS)

Springett, Mark; Rice, Mark; Carmichael, Alex; Griffiths, Richard

This workshop will focus on setting the agenda for research, practice and policy in support of inclusive design for third generation computer-based products. The next generation of technology represents an unprecedented opportunity to improve the quality of life for groups of users who have previously faced exclusion, such as those with impairments and older citizens. At the same time it risks creating a greater digital divide and further exclusion. How we approach design for this new generation will determine whether or not the third wave will provide positive advances towards an inclusive digital world. We therefore need to put forward both a rationale for inclusive design and provide pointers towards technical development and design practice in support of inclusion. It is our belief that there is not only a strong moral case for design for inclusion but also significant commercial incentive, which may be key to persuading influential players to focus on inclusion. Therefore one of our key objectives is to describe and promote the advantages of designing ‘in from the edges’ of the user population rather than designing for a notional ‘average’ user.

Quantifying the effects of on-the-fly changes of seating configuration on the stability of a manual wheelchair.

PubMed

Thomas, Louise; Borisoff, Jaimie; Sparrey, Carolyn J

2017-07-01

In general, manual wheelchairs are designed with a fixed frame, which is not optimal for every situation. Adjustable on the fly seating allow users to rapidly adapt their wheelchair configuration to suit different tasks. These changes move the center of gravity (CoG) of the system, altering the wheelchair stability and maneuverability. To assess these changes, a computer simulation of a manual wheelchair was created with adjustable seat, backrest, rear axle position and user position, and validated with experimental testing. The stability of the wheelchair was most affected by the position of the rear axle, but adjustments to the backrest and seat angles also result in stability improvements that could be used when wheeling in the community. These findings describe the most influential parameters for wheelchair stability and maneuverability, as well as provide quantitative guidelines for the use of manual wheelchairs with on the fly adjustable seats.

Some predictions of Rafael Lorente de Nó 80 years later.

PubMed

Larriva-Sahd, Jorge A

2014-01-01

Rafael Lorente de Nó, the youngest of Santiago Ramón y Cajal disciples, was one of the last Century's more influential researches in neuroscience. This assay highlights two fundamental contributions of Rafael Lorente de Nó to neurobiology: the intrinsic organization of the mammalian cerebral cortex and the basic physiology of the neuron processes.

Does Skepticism Predict News Media Literacy: A Study on Turkish Young Adults

ERIC Educational Resources Information Center

Kartal, Osman Yilmaz; Yazgan, Akan Deniz; Kincal, Remzi Y.

2017-01-01

The 2010's are when information and informatics age coexist, information overload has been transformed into a mass engineering tool, "imposing bombardment" has become the norm. The most influential tool of this cultural-industrial act is news media. Efforts to educate young adults, who are most active in touch with information, in view…

Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks

ERIC Educational Resources Information Center

Smith, J. David; Minda, John Paul; Washburn, David A.

2004-01-01

In influential research, R. N. Shepard, C. I. Hovland, and H. M. Jenkins (1961) surveyed humans' categorization abilities using tasks based in rules, exclusive-or (XOR) relations, and exemplar memorization. Humans' performance was poorly predicted by cue-conditioning or stimulus-generalization theories, causing Shepard et al. to describe it in…

Predictive Utility and Causal Influence of the Writing Self-Efficacy Beliefs of Elementary Students.

ERIC Educational Resources Information Center

Pajares, Frank; Valiante, Gio

According to self-efficacy theorists, people's judgments of what they can accomplish are influential arbiters in human agency and, as such, powerful determinants of their behavior. In large part, this is because these self-efficacy beliefs are said to act as mediators between other acknowledged influences on behavior, such as skill, ability,…

Social Influence and Selection Processes as Predictors of Normative Perceptions and Alcohol Use across the Transition to College

ERIC Educational Resources Information Center

Abar, Caitlin C.; Maggs, Jennifer L.

2010-01-01

Research indicates that social influences impact college students' alcohol consumption; however, how selection processes may serve as an influential factor predicting alcohol use in this population has not been widely addressed. A model of influence and selection processes contributing to alcohol use across the transition to college was examined…

60 years ago, Francis Crick changed the logic of biology

PubMed Central

2017-01-01

In September 1957, Francis Crick gave a lecture in which he outlined key ideas about gene function, in particular what he called the central dogma. These ideas still frame how we understand life. This essay explores the concepts he developed in this influential lecture, including his prediction that we would study evolution by comparing sequences. PMID:28922352

Surrogate screening models for the low physical activity criterion of frailty.

PubMed

Eckel, Sandrah P; Bandeen-Roche, Karen; Chaves, Paulo H M; Fried, Linda P; Louis, Thomas A

2011-06-01

Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized, semiquantitative questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women's Health and Aging Study (WHAS). Using data on men and women ages 65 and older from the CHS, we applied logistic regression models to rank activities by "relative influence" in predicting low physical activity.We considered subsets of the most influential activities as inputs to potential surrogate models (logistic regressions). We evaluated predictive accuracy and predictive validity using the area under receiver operating characteristic curves and assessed criterion validity using proportional hazards models relating frailty status (defined using the surrogate) to mortality. Walking for exercise and moderately strenuous household chores were highly influential for both genders. Women required fewer activities than men for accurate classification. The WHAS model (8 CHS activities) was an effective surrogate, but a surrogate using 6 activities (walking, chores, gardening, general exercise, mowing and golfing) was also highly predictive. We recommend a 6 activity questionnaire to assess physical activity for men and women. If efficiency is essential and the study involves only women, fewer activities can be included.

Analysis of emergency physicians' Twitter accounts.

PubMed

Lulic, Ileana; Kovic, Ivor

2013-05-01

Twitter is one of the fastest growing social media networks for communication between users via short messages. Technology proficient physicians have demonstrated enthusiasm in adopting social media for their work. To identify and create the largest directory of emergency physicians on Twitter, analyse their user accounts and reveal details behind their connections. Several web search tools were used to identify emergency physicians on Twitter with biographies completely or partially written in English. NodeXL software was used to calculate emergency physicians' Twitter network metrics and create visualisation graphs. The authors found 672 Twitter accounts of self-identified emergency physicians. Protected accounts were excluded from the study, leaving 632 for further analysis. Most emergency physicians were located in USA (55.4%), had created their accounts in 2009 (43.4%), used their full personal name (77.5%) and provided a custom profile picture (92.2%). Based on at least one published tweet in the last 15 days, there were 345 (54.6%) active users on 31 December 2011. Active users mostly used mobile devices based on the Apple operating system to publish tweets (69.2%). Visualisation of emergency physicians' Twitter network revealed many users with no connections with their colleagues, and a small group of most influential users who were highly interconnected. Only a small proportion of registered emergency physicians use Twitter. Among them exists a smaller inner network of emergency physicians with strong social bonds that is using Twitter's full potentials for professional development.

Exploring the influence of service user involvement on health and social care services for cancer.

PubMed

Attree, Pamela; Morris, Sara; Payne, Sheila; Vaughan, Suzanne; Hinder, Susan

2011-03-01

Service user involvement in health and social care is a key policy driver in the UK. In cancer care it is central to developing services which are effective, responsive and accessible to patients. Cancer network partnership groups are set up to enable joint working between service users and health care professionals and to drive service improvements. The aim of this study was to explore the influence of the cancer network partnership groups' service user involvement activities on cancer care. This was a qualitative study involving documentary analysis and in-depth case studies of a sample of partnership groups. Five partnership groups were purposively selected as case studies from Macmillan regions across the UK; documents were collated from a further five groups. Forty people, including core group members and key stakeholders in cancer services, were interviewed. The evidence from this study suggests that cancer network partnership groups are at their most influential at 'grass roots' level - contributing to patient information resources, enhancing access to services, and improving care environments. While such improvements are undoubtedly important to patients, the groups' aim is to influence strategic changes, for example in cancer care commissioning or macro-level policy decision-making. The evolution of open, participatory relationships between service users and professionals, and recognition of the value of experiential knowledge are seen as key factors in influencing cancer care. The provision of dedicated resources to strengthen service user involvement activities is also vital. © 2010 Blackwell Publishing Ltd.

A New Approach to Predict user Mobility Using Semantic Analysis and Machine Learning.

PubMed

Fernandes, Roshan; D'Souza G L, Rio

2017-10-19

Mobility prediction is a technique in which the future location of a user is identified in a given network. Mobility prediction provides solutions to many day-to-day life problems. It helps in seamless handovers in wireless networks to provide better location based services and to recalculate paths in Mobile Ad hoc Networks (MANET). In the present study, a framework is presented which predicts user mobility in presence and absence of mobility history. Naïve Bayesian classification algorithm and Markov Model are used to predict user future location when user mobility history is available. An attempt is made to predict user future location by using Short Message Service (SMS) and instantaneous Geological coordinates in the absence of mobility patterns. The proposed technique compares the performance metrics with commonly used Markov Chain model. From the experimental results it is evident that the techniques used in this work gives better results when considering both spatial and temporal information. The proposed method predicts user's future location in the absence of mobility history quite fairly. The proposed work is applied to predict the mobility of medical rescue vehicles and social security systems.

Validity of Teacher Ratings in Selecting Influential Aggressive Adolescents for a Targeted Preventive Intervention

PubMed Central

Henry, David B.; Miller-Johnson, Shari; Simon, Thomas R.; Schoeny, Michael E.

2009-01-01

This study describes a method for using teacher nominations and ratings to identify socially influential, aggressive middle school students for participation in a targeted violence prevention intervention. The teacher nomination method is compared with peer nominations of aggression and influence to obtain validity evidence. Participants were urban, predominantly African American and Latino sixth-grade students who were involved in a pilot study for a large multi-site violence prevention project. Convergent validity was suggested by the high correlation of teacher ratings of peer influence and peer nominations of social influence. The teacher ratings of influence demonstrated acceptable sensitivity and specificity when predicting peer nominations of influence among the most aggressive children. Results are discussed m terms of the application of teacher nominations and ratings in large trials and full implementation of targeted prevention programs. PMID:16378226

Characteristics of Patients With Existing Advance Directives: Evaluating Motivations Around Advance Care Planning.

PubMed

Genewick, Joanne E; Lipski, Dorothy M; Schupack, Katherine M; Buffington, Angela L H

2018-04-01

Although 80% of patients endorse an advance directive (AD), less than 35% of American adults have a documented AD. Much research has been done on barriers to creating ADs; however, there is a paucity of research addressing motivations for creating ADs. Previous research has identified 4 categories of influence for engaging in advance care planning (ACP). This study aimed to quantify the influence of these 4 motivating categories in creating an AD. Participants included 238 adults with documented ADs. Participants completed an 11-item questionnaire addressing 1 of the 4 hypothesized categories of influence in addressing ACP: concern for self; concern for others; expectations about the impact of ACP; and anecdotes, stories, and experiences. Principle component analysis yielded 2 factors representing dignity and personal control (intrinsic factors) and societal and familial influence (extrinsic factors). Intrinsic factors were the primary and most influential motivating factors among participants. A regression analysis of individual motivating factors showed that prior to age 50, the desire to provide guidance about personal preferences for end-of-life care significantly predicted the creation of an AD, whereas after age 50, the urging of family members significantly predicted the creation of an AD. Results indicated that intrinsic factors were the most influential motivator among participants of all ages. Extrinsic factors appeared to be less influential in the decision to create an AD. Motivating factors were also found to vary by age. These results may help physicians be more targeted in discussions surrounding ADs, thus saving time, which physicians identify as the main barrier in engaging in such discussions, while meeting patients' wishes for their physicians to bring up the topic of ADs.

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study.

PubMed

Ghomi, Haniyeh; Bagheri, Morteza; Fu, Liping; Miranda-Moreno, Luis F

2016-11-16

The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques. This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs. The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males. The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.

Treatment choices for depression: Young people’s response to a traditional e-health versus a Health 2.0 website

PubMed Central

Scanlan, Faye; Jorm, Anthony; Reavley, Nicola; Meyer, Denny; Bhar, Sunil

2017-01-01

Objective This exploratory experimental study compared young people’s credibility appraisals and behavioural intentions following exposure to depression treatment information on a Health 2.0 website versus a traditional website. The traditional website listed evidence-based treatment recommendations for depression as judged by field experts. The Health 2.0 website contained information about how helpful each treatment was, as aggregated from feedback from young people with lived experience of depression. Method Participants (n = 279) were provided with a vignette asking them to imagine that they had just received a diagnosis of depression and they had gone online to find information to guide their treatment choices. They were randomly allocated to view either the traditional or the Health 2.0 website, and were asked to rate the credibility of the depression treatment information provided. They were also asked to indicate the extent to which they would be likely to act on the advice of the website. Results Participants in the traditional website condition rated their website as significantly more influential than did participants presented with the Health 2.0 website. This difference in treatment influence was fully accounted for the participants’ perception of credibility of the information provided by the websites. Conclusion The traditional website was rated as significantly more credible and influential than the Health 2.0 website. Treatment decisions appeared to be based on the extent to which online information appears credible. In conclusion, health-related content was perceived by users as more credible when endorsed by experts than by other users, and perceived message credibility appears to be a powerful determinant of behavioural intentions within the e-health setting.

Predicting Virtual World User Population Fluctuations with Deep Learning

PubMed Central

Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds. PMID:27936009

Predicting Virtual World User Population Fluctuations with Deep Learning.

PubMed

Kim, Young Bin; Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

Risk factors for Apgar score using artificial neural networks.

PubMed

Ibrahim, Doaa; Frize, Monique; Walker, Robin C

2006-01-01

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

A predictive model to allocate frequent service users of community-based mental health services to different packages of care.

PubMed

Grigoletti, Laura; Amaddeo, Francesco; Grassi, Aldrigo; Boldrini, Massimo; Chiappelli, Marco; Percudani, Mauro; Catapano, Francesco; Fiorillo, Andrea; Perris, Francesco; Bacigalupi, Maurizio; Albanese, Paolo; Simonetti, Simona; De Agostini, Paola; Tansella, Michele

2010-01-01

To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifying the costs of these packages. Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.

PubMed

Wang, Xi; Zhao, Kang; Street, Nick

2017-04-24

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. ©Xi Wang, Kang Zhao, Nick Street. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2017.

Vegetation monitoring to detect and predict vegetation change: Connecting historical and future shrub/steppe data in Yellowstone National Park

Treesearch

Geneva Chong; David Barnett; Benjamin Chemel; Roy Renkin; Pamela Sikkink

2011-01-01

A 2002 National Research Council (NRC) evaluation of ungulate management practices in Yellowstone specifically concluded that previous (1957 to present) vegetation monitoring efforts were insufficient to determine whether climate or ungulates were more influential on shrub/steppe dynamics on the northern ungulate winter range. The NRC further recommended that the...

Do Changes in Tympanic Temperature Predict Changes in Affective Valence during High-Intensity Exercise?

ERIC Educational Resources Information Center

Legrand, Fabien D.; Joly, Philippe M.; Bertucci, William M.

2015-01-01

Purpose: Increased core (brain or body) temperature that accompanies exercise has been posited to play an influential role in affective responses to exercise. However, findings in support of this hypothesis have been equivocal, and most of the performed studies have been done in relation to anxiety. The aim of the present study was to investigate…

The Relation between the Time Mothers and Children Spent Together and the Children's Trait Emotional Intelligence

ERIC Educational Resources Information Center

Alegre, Albert

2012-01-01

Background: Parenting practices have been shown to predict children's emotional intelligence. The time that mothers and children spend in joint activity is an important aspect of the parent-child relationship, and it has been found to be influential in different domains of children's development. However, it has not been investigated in relation…

An Investigation of the Most Influential Factors Predicting Nursing Students' Knowledge and Attitude toward Older Persons

ERIC Educational Resources Information Center

Little, Elaine B.

2017-01-01

The aging population with complex health needs is growing. Nursing programs are challenged to educate student nurses competent and willing to meet this specific population's needs. Research on ageism supports the presence of aging bias. Possible negative attitudes towards older persons by nursing students is a concern for nurse educators. Nursing…

Increasing organizational energy conservation behaviors: Comparing the theory of planned behavior and reasons theory for identifying specific motivational factors to target for change

NASA Astrophysics Data System (ADS)

Finlinson, Scott Michael

Social scientists frequently assess factors thought to underlie behavior for the purpose of designing behavioral change interventions. Researchers commonly identify these factors by examining relationships between specific variables and the focal behaviors being investigated. Variables with the strongest relationships to the focal behavior are then assumed to be the most influential determinants of that behavior, and therefore often become the targets for change in a behavioral change intervention. In the current proposal, multiple methods are used to compare the effectiveness of two theoretical frameworks for identifying influential motivational factors. Assessing the relative influence of all factors and sets of factors for driving behavior should clarify which framework and methodology is the most promising for identifying effective change targets. Results indicated each methodology adequately predicted the three focal behaviors examined. However, the reasons theory approach was superior for predicting factor influence ratings compared to the TpB approach. While common method variance contamination had minimal impact on the results or conclusions derived from the present study's findings, there were substantial differences in conclusions depending on the questionnaire design used to collect the data. Examples of applied uses of the present study are discussed.

Employee Perceptions of Workplace Health Promotion Programs: Comparison of a Tailored, Semi-Tailored, and Standardized Approach

PubMed Central

Street, Tamara D.; Lacey, Sarah J.

2018-01-01

In the design of workplace health promotion programs (WHPPs), employee perceptions represent an integral variable which is predicted to translate into rate of user engagement (i.e., participation) and program loyalty. This study evaluated employee perceptions of three workplace health programs promoting nutritional consumption and physical activity. Programs included: (1) an individually tailored consultation with an exercise physiologist and dietitian; (2) a semi-tailored 12-week SMS health message program; and (3) a standardized group workshop delivered by an expert. Participating employees from a transport company completed program evaluation surveys rating the overall program, affect, and utility of: consultations (n = 19); SMS program (n = 234); and workshops (n = 86). Overall, participants’ affect and utility evaluations were positive for all programs, with the greatest satisfaction being reported in the tailored individual consultation and standardized group workshop conditions. Furthermore, mode of delivery and the physical presence of an expert health practitioner was more influential than the degree to which the information was tailored to the individual. Thus, the synergy in ratings between individually tailored consultations and standardized group workshops indicates that low-cost delivery health programs may be as appealing to employees as tailored, and comparatively high-cost, program options. PMID:29710785

On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure.

PubMed

Volkova, Svitlana; Bachrach, Yoram

2015-12-01

Social media services such as Twitter and Facebook are virtual environments where people express their thoughts, emotions, and opinions and where they reveal themselves to their peers. We analyze a sample of 123,000 Twitter users and 25 million of their tweets to investigate the relation between the opinions and emotions that users express and their predicted psychodemographic traits. We show that the emotions that we express on online social networks reveal deep insights about ourselves. Our methodology is based on building machine learning models for inferring coarse-grained emotions and psychodemographic profiles from user-generated content. We examine several user attributes, including gender, income, political views, age, education, optimism, and life satisfaction. We correlate these predicted demographics with the emotional profiles emanating from user tweets, as captured by Ekman's emotion classification. We find that some users tend to express significantly more joy and significantly less sadness in their tweets, such as those predicted to be in a relationship, with children, or with a higher than average annual income or educational level. Users predicted to be women tend to be more opinionated, whereas those predicted to be men tend to be more neutral. Finally, users predicted to be younger and liberal tend to project more negative opinions and emotions. We discuss the implications of our findings to online privacy concerns and self-disclosure behavior.

Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling

NASA Astrophysics Data System (ADS)

Sutton, Jonathan E.; Guo, Wei; Katsoulakis, Markos A.; Vlachos, Dionisios G.

2016-04-01

Kinetic models based on first principles are becoming common place in heterogeneous catalysis because of their ability to interpret experimental data, identify the rate-controlling step, guide experiments and predict novel materials. To overcome the tremendous computational cost of estimating parameters of complex networks on metal catalysts, approximate quantum mechanical calculations are employed that render models potentially inaccurate. Here, by introducing correlative global sensitivity analysis and uncertainty quantification, we show that neglecting correlations in the energies of species and reactions can lead to an incorrect identification of influential parameters and key reaction intermediates and reactions. We rationalize why models often underpredict reaction rates and show that, despite the uncertainty being large, the method can, in conjunction with experimental data, identify influential missing reaction pathways and provide insights into the catalyst active site and the kinetic reliability of a model. The method is demonstrated in ethanol steam reforming for hydrogen production for fuel cells.

Prey risk allocation in a grazing ecosystem.

PubMed

Gude, Justin A; Garrott, Robert A; Borkowski, John J; King, Fred

2006-02-01

Understanding the behaviorally mediated indirect effects of predators in ecosystems requires knowledge of predator-prey behavioral interactions. In predator-ungulate-plant systems, empirical research quantifying how predators affect ungulate group sizes and distribution, in the context of other influential variables, is particularly needed. The risk allocation hypothesis proposes that prey behavioral responses to predation risk depend on background frequencies of exposure to risk, and it can be used to make predictions about predator-ungulate-plant interactions. We determined non-predation variables that affect elk (Cervus elaphus) group sizes and distribution on a winter range in the Greater Yellowstone Ecosystem (GYE) using logistic and log-linear regression on surveys of 513 1-km2 areas conducted over two years. Employing model selection techniques, we evaluated risk allocation and other a priori hypotheses of elk group size and distributional responses to wolf (Canis lupus) predation risk while accounting for influential non-wolf-predation variables. We found little evidence that wolves affect elk group sizes, which were strongly influenced by habitat type and hunting by humans. Following predictions from the risk allocation hypothesis, wolves likely created a more dynamic elk distribution in areas that they frequently hunted, as elk tended to move following wolf encounters in those areas. This response should dilute elk foraging pressure on plant communities in areas where they are frequently hunted by wolves. We predict that this should decrease the spatial heterogeneity of elk impacts on grasslands in areas that wolves frequently hunt. We also predict that this should decrease browsing pressure on heavily browsed woody plant stands in certain areas, which is supported by recent research in the GYE.

A review of predictive coding algorithms.

PubMed

Spratling, M W

2017-03-01

Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term "predictive coding". This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology. Copyright © 2016 Elsevier Inc. All rights reserved.

ANOPP2 User's Manual: Version 1.2

NASA Technical Reports Server (NTRS)

Lopes, L. V.; Burley, C. L.

2016-01-01

This manual documents the Aircraft NOise Prediction Program 2 (ANOPP2). ANOPP2 is a toolkit that includes a framework, noise prediction methods, and peripheral software to aid a user in predicting and understanding aircraft noise. This manual includes an explanation of the overall design and structure of ANOPP2, including a brief introduction to aircraft noise prediction and the ANOPP2 background, philosophy, and architecture. The concept of nested acoustic data surfaces and its application to a mixed-fidelity noise prediction are presented. The structure and usage of ANOPP2, which includes the communication between the user, the ANOPP2 framework, and noise prediction methods, are presented for two scenarios: wind-tunnel and flight. These scenarios serve to provide the user with guidance and documentation references for performing a noise prediction using ANOPP2.

Using an innovative multiple regression procedure in a cancer population (Part II): fever, depressive affect, and mobility problems clarify an influential symptom pair (pain-fatigue/weakness) and cluster (pain-fatigue/weakness-sleep problems).

PubMed

Francoeur, Richard B

2015-01-01

Most patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. However, only combinations where symptoms are mutually influential hold potential for identifying patient subgroups at greater risk, and in some contexts, interventions with "cross-over" (multisymptom) effects. Improved methods to detect and interpret interactions among symptoms, signs, or biomarkers are needed to reveal these influential pairs and clusters. I recently created sequential residual centering (SRC) to reduce multicollinearity in moderated regression, which enhances sensitivity to detect these interactions. I applied SRC to moderated regressions of single-item symptoms that interact to predict outcomes from 268 palliative radiation outpatients. I investigated: 1) the hypothesis that the interaction, pain × fatigue/weakness × sleep problems, predicts depressive affect only when fever presents, and 2) an exploratory analysis, when fever is absent, that the interaction, pain × fatigue/weakness × sleep problems × depressive affect, predicts mobility problems. In the fever context, three-way interactions (and derivative terms) of the four symptoms (pain, fatigue/weakness, fever, sleep problems) are tested individually and simultaneously; in the non-fever context, a single four-way interaction (and derivative terms) is tested. Fever interacts separately with fatigue/weakness and sleep problems; these comoderators each magnify the pain-depressive affect relationship along the upper or full range of pain values. In non-fever contexts, fatigue/weakness, sleep problems, and depressive affect comagnify the relationship between pain and mobility problems. Different mechanisms contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters.

Retweets as a Predictor of Relationships among Users on Social Media.

PubMed

Tsugawa, Sho; Kito, Kosuke

2017-01-01

Link prediction is the problem of detecting missing links or predicting future link formation in a network. Application of link prediction to social media, such as Twitter and Facebook, is useful both for developing novel services and for sociological analyses. While most existing research on link prediction uses only the social network topology for the prediction, in social media, records of user activities such as posting, replying, and reposting are available. These records are expected to reflect user interest, and so incorporating them should improve link prediction. However, research into link prediction using the records of user activities is still in its infancy, and the effectiveness of such records for link prediction has not been fully explored. In this study, we focus in particular on records of reposting as a promising source that could be useful for link prediction, and investigate their effectiveness for link prediction on the popular social media platform Twitter. Our results show that (1) the prediction accuracy of techniques using reposting records is higher than that of popular topology-based techniques such as common neighbors and resource allocation for actively retweeting users, (2) the accuracy of link prediction techniques that use network topology alone can be improved by incorporating reposting records.

Retweets as a Predictor of Relationships among Users on Social Media

PubMed Central

Kito, Kosuke

2017-01-01

Link prediction is the problem of detecting missing links or predicting future link formation in a network. Application of link prediction to social media, such as Twitter and Facebook, is useful both for developing novel services and for sociological analyses. While most existing research on link prediction uses only the social network topology for the prediction, in social media, records of user activities such as posting, replying, and reposting are available. These records are expected to reflect user interest, and so incorporating them should improve link prediction. However, research into link prediction using the records of user activities is still in its infancy, and the effectiveness of such records for link prediction has not been fully explored. In this study, we focus in particular on records of reposting as a promising source that could be useful for link prediction, and investigate their effectiveness for link prediction on the popular social media platform Twitter. Our results show that (1) the prediction accuracy of techniques using reposting records is higher than that of popular topology-based techniques such as common neighbors and resource allocation for actively retweeting users, (2) the accuracy of link prediction techniques that use network topology alone can be improved by incorporating reposting records. PMID:28107489

A hybrid framework for quantifying the influence of data in hydrological model calibration

NASA Astrophysics Data System (ADS)

Wright, David P.; Thyer, Mark; Westra, Seth; McInerney, David

2018-06-01

Influence diagnostics aim to identify a small number of influential data points that have a disproportionate impact on the model parameters and/or predictions. The key issues with current influence diagnostic techniques are that the regression-theory approaches do not provide hydrologically relevant influence metrics, while the case-deletion approaches are computationally expensive to calculate. The main objective of this study is to introduce a new two-stage hybrid framework that overcomes these challenges, by delivering hydrologically relevant influence metrics in a computationally efficient manner. Stage one uses computationally efficient regression-theory influence diagnostics to identify the most influential points based on Cook's distance. Stage two then uses case-deletion influence diagnostics to quantify the influence of points using hydrologically relevant metrics. To illustrate the application of the hybrid framework, we conducted three experiments on 11 hydro-climatologically diverse Australian catchments using the GR4J hydrological model. The first experiment investigated how many data points from stage one need to be retained in order to reliably identify those points that have the hightest influence on hydrologically relevant metrics. We found that a choice of 30-50 is suitable for hydrological applications similar to those explored in this study (30 points identified the most influential data 98% of the time and reduced the required recalibrations by 99% for a 10 year calibration period). The second experiment found little evidence of a change in the magnitude of influence with increasing calibration period length from 1, 2, 5 to 10 years. Even for 10 years the impact of influential points can still be high (>30% influence on maximum predicted flows). The third experiment compared the standard least squares (SLS) objective function with the weighted least squares (WLS) objective function on a 10 year calibration period. In two out of three flow metrics there was evidence that SLS, with the assumption of homoscedastic residual error, identified data points with higher influence (largest changes of 40%, 10%, and 44% for the maximum, mean, and low flows, respectively) than WLS, with the assumption of heteroscedastic residual errors (largest changes of 26%, 6%, and 6% for the maximum, mean, and low flows, respectively). The hybrid framework complements existing model diagnostic tools and can be applied to a wide range of hydrological modelling scenarios.

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

An information propagation model considering incomplete reading behavior in microblog

NASA Astrophysics Data System (ADS)

Su, Qiang; Huang, Jiajia; Zhao, Xiande

2015-02-01

Microblog is one of the most popular communication channels on the Internet, and has already become the third largest source of news and public opinions in China. Although researchers have studied the information propagation in microblog using the epidemic models, previous studies have not considered the incomplete reading behavior among microblog users. Therefore, the model cannot fit the real situations well. In this paper, we proposed an improved model entitled Microblog-Susceptible-Infected-Removed (Mb-SIR) for information propagation by explicitly considering the user's incomplete reading behavior. We also tested the effectiveness of the model using real data from Sina Microblog. We demonstrate that the new proposed model is more accurate in describing the information propagation in microblog. In addition, we also investigate the effects of the critical model parameters, e.g., reading rate, spreading rate, and removed rate through numerical simulations. The simulation results show that, compared with other parameters, reading rate plays the most influential role in the information propagation performance in microblog.

PC-SEAPAK user's guide, version 4.0

NASA Technical Reports Server (NTRS)

Mcclain, Charles R.; Fu, Gary; Darzi, Michael; Firestone, James K.

1992-01-01

PC-SEAPAK is designed to provide a complete and affordable capability for processing and analysis of NOAA Advanced Very High Resolution Radiometer (AVHRR) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. Since the release of version 3.0 over a year ago, significant revisions were made to the AVHRR and CZCS programs and to the statistical data analysis module, and a number of new programs were added. This new version has 114 procedures listed in its menus. The package continues to emphasize user-friendliness and interactive data analysis. Additionally, because the scientific goals of the ocean color research being conducted have shifted to larger space and time scales, batch processing capabilities were enhanced, allowing large quantities of data to be easily ingested and analyzed. The development of PC-SEAPAK was paralled by two other activities that were influential and assistive: the global CZCS processing effort at GSFC and the continued development of VAX-SEAPAK. SEAPAK incorporates the instrument calibration and support all levels of data available from the CZCS archive.

Contrasting cue-density effects in causal and prediction judgments.

PubMed

Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

2011-02-01

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

Investigation of Roadway Geometric and Traffic Flow Factors for Vehicle Crashes Using Spatiotemporal Interaction

NASA Astrophysics Data System (ADS)

Gill, G.; Sakrani, T.; Cheng, W.; Zhou, J.

2017-09-01

Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management processes are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and project prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors which impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link various influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better fitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the vehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal model which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study utilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables and the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and road surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of pavement, and AADT were found to be correlated with vehicle crashes.

Treatment Techniques and Outcomes in Multidimensional Family Therapy for Adolescent Behavior Problems

PubMed Central

Hogue, Aaron; Dauber, Sarah; Samuolis, Jessica; Liddle, Howard A.

2010-01-01

The link between treatment techniques and long-term treatment outcome was examined in an empirically supported family-based treatment for adolescent drug abuse. Observational ratings of therapist interventions were used to predict outcomes at 6 and 12 months posttreatment for 63 families receiving multidimensional family therapy. Greater use of in-session family-focused techniques predicted reduction in internalizing symptoms and improvement in family cohesion. Greater use of family-focused techniques also predicted reduced externalizing symptoms and family conflict, but only when adolescent focus was also high. In addition, greater use of adolescent-focused techniques predicted improvement in family cohesion and family conflict. Results suggest that both individual and multiperson interventions can exert an influential role in family-based therapy for clinically referred adolescents. PMID:17176187

Testing the Agreement/Tense Omission Model: Why the Data on Children's Use of Non-Nominative 3psg Subjects Count against the ATOM

ERIC Educational Resources Information Center

Pine, Julian M.; Rowland, Caroline F.; Lieven, Elena V. M.; Theakston, Anna L.

2005-01-01

One of the most influential recent accounts of pronoun case-marking errors in young children's speech is Schutze & Wexler's (1996) Agreement/Tense Omission Model (ATOM). The ATOM predicts that the rate of agreeing verbs with non-nominative subjects will be so low that such errors can be reasonably disregarded as noise in the data. The present…

Learning to Predict Social Influence in Complex Networks

DTIC Science & Technology

2012-03-29

03/2010 – 17/03/2012 Abstract: First, we addressed the problem of analyzing information diffusion process in a social network using two kinds...algorithm which avoids the inner loop optimization during the search. We tested the performance using the structures of four real world networks, and...result of information diffusion that starts from the node. 2 We use “infected” and “activated” interchangeably. Efficient Discovery of Influential

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

2015-10-01

Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

PubMed

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

PubMed Central

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496

Evolution of cooperation in a heterogeneous population with influential individuals

NASA Astrophysics Data System (ADS)

Zhuang, Qian; Wang, Dong; Fan, Ying; Di, Zengru

2012-02-01

Influential individuals are introduced and integrated with the public goods game (PGG) to investigate their influence on the emergence and evolution of cooperation. In the model, some influential individuals whose behaviors can be controlled by us are introduced into a homogeneous population on a square lattice. The influential individuals can play three kinds of roles: I. exemplar, II. supervisor with the power to punish defectors, and III. supervisor with the power to reward cooperative co-players. It is found that the existence of influential individuals who play Role I turns out to be detrimental to cooperation and that the larger the number of influential individuals is, the more difficult it is for cooperation to be maintained. For those playing supervisory roles, both punishment and reward are found to be effective ways for the influential individuals to promote and stabilize cooperative behavior. By comparing the critical costs and the mean payoffs for a low multiplication factor under the role of punishment and the role of reward, it is found that reward is a more effective intervention measure than punishment for influential individuals seeking to improve cooperation and that reward leads to a higher mean payoff.

ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership.

PubMed

Wu, Hongchen; Wang, Xinjun

2016-01-01

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users' sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users' decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens' sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants' partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness.

Predicting groundwater recharge for varying land cover and climate conditions - a global meta-study

NASA Astrophysics Data System (ADS)

Mohan, Chinchu; Western, Andrew W.; Wei, Yongping; Saft, Margarita

2018-05-01

Groundwater recharge is one of the important factors determining the groundwater development potential of an area. Even though recharge plays a key role in controlling groundwater system dynamics, much uncertainty remains regarding the relationships between groundwater recharge and its governing factors at a large scale. Therefore, this study aims to identify the most influential factors of groundwater recharge, and to develop an empirical model to estimate diffuse rainfall recharge at a global scale. Recharge estimates reported in the literature from various parts of the world (715 sites) were compiled and used in model building and testing exercises. Unlike conventional recharge estimates from water balance, this study used a multimodel inference approach and information theory to explain the relationship between groundwater recharge and influential factors, and to predict groundwater recharge at 0.5° resolution. The results show that meteorological factors (precipitation and potential evapotranspiration) and vegetation factors (land use and land cover) had the most predictive power for recharge. According to the model, long-term global average annual recharge (1981-2014) was 134 mm yr-1 with a prediction error ranging from -8 to 10 mm yr-1 for 97.2 % of cases. The recharge estimates presented in this study are unique and more reliable than the existing global groundwater recharge estimates because of the extensive validation carried out using both independent local estimates collated from the literature and national statistics from the Food and Agriculture Organization (FAO). In a water-scarce future driven by increased anthropogenic development, the results from this study will aid in making informed decisions about groundwater potential at a large scale.

Determinants for Sustained Use of an Activity Tracker: Observational Study

PubMed Central

Moons, Jonas; Kerkhof, Peter; Wiekens, Carina; De Groot, Martijn

2017-01-01

Background A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. Objective The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. Methods A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. Results The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. Conclusions This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers. PMID:29084709

INFLUENCE NETWORK AGENT EFFECTIVENESS IN PROMOTING COUPLES’ HIV COUNSELING AND TESTING IN KIGALI, RWANDA

PubMed Central

Wall, Kristin; Karita, Etienne; Nizam, Azhar; Bekan, Brigitte; Sardar, Gurkiran; Casanova, Debbie; Joseph, Dvora; De Clercq, Freya; Kestelyn, Evelyne; Bayingana, Roger; Tichacek, Amanda; Allen, Susan

2013-01-01

Objective To identify predictors of promotion of couples’ voluntary counseling and testing (CVCT) in Kigali, Rwanda Design Analysis of CVCT promotional agent (influential network leaders, INLs; influential network agents, INAs), and couple/invitation-level predictors of CVCT uptake. Methods Number of invitations and couples tested were evaluated by INL, INA, and couple/contextual factors. Multivariable logistic regression accounting for two-level clustering analyzed factors predictive of couples’ testing. Results 26 INLs recruited and mentored 118 INAs who delivered 24,991 invitations. 4,513 couples sought CVCT services after invitation. INAs distributed an average of 212 invitations resulting in an average of 38 couples tested/agent. Characteristics predictive of CVCT in multivariate analyses included the invitee and INA being socially acquainted (aOR=1.4;95%CI:1.2–1.6); invitations delivered after public endorsement (aOR=1.3;95%CI:1.1–1.5); and presence of a mobile testing unit (aOR=1.4;95%CI:1.0–2.0). In stratified analyses, predictors significant among cohabiting couples included invitation delivery to the couple (aOR=1.2;95%CI:1.0–1.4) in the home (aOR=1.3;95%CI:1.1–1.4), while among non-cohabiting couples predictors included invitations given by unemployed INAs (aOR=1.7;95%CI:1.1–2.7). Cohabiting couples with older men were more likely to test, while younger age was associated with testing among men in non-cohabiting unions. Conclusions Invitations distributed by influential people were successful in prompting couples to seek joint HIV testing, particularly if the invitation was given in the home to someone known to the INA, and accompanied by a public endorsement of CVCT. Mobile units also increased the number of couples tested. Country-specific strategies to promote CVCT programs are needed to reduce HIV transmission among those at highest risk for HIV in sub-Saharan Africa. PMID:22008653

Accurate and scalable social recommendation using mixed-membership stochastic block models.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

2016-12-13

With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

Predicting personality traits related to consumer behavior using SNS analysis

NASA Astrophysics Data System (ADS)

Baik, Jongbum; Lee, Kangbok; Lee, Soowon; Kim, Yongbum; Choi, Jayoung

2016-07-01

Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits-Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem-that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.

Peer Standing and Substance Use in Early-Adolescent Grade-Level Networks: A Short-Term Longitudinal Study

PubMed Central

Killeya-Jones, Ley A.; Nakajima, Ryo; Costanzo, Philip R.

2009-01-01

Two competing hypotheses were tested concerning the associations between current alcohol and cigarette use and measures of individual, group and network peer standing in an ethnically-diverse sample of 156 male and female adolescents sampled at two time points in the seventh grade. Findings lent greater support to the person hypothesis, with early regular substance users enjoying elevated standing amongst their peers and maintaining this standing regardless of their maintenance of or desistance from current use later in the school year. In the fall semester, users (n=20, 13%) had greater social impact, were described by their peers as more popular, and were more central to the peer network than abstainers (i.e., those who did not report current use). Conversely, in the spring semester, there were no differences between users (n=22, 13%) and abstainers in peer ratings of popularity or social impact. Notably, the spring semester users group retained fewer than half of the users from the fall semester. Further, students who had reported current use in the fall, as a group, retained their positions of elevated peer standing in the spring, compared to all other students, and continued to be rated by their peers as more popular and as having greater social impact. We discuss the findings in terms of the benefit of employing simultaneous systemic and individual measures of peer standing or group prominence, which in the case of peer-based prevention programs, can help clarify the truly influential from the “pretenders” in the case of diffusion of risk-related behaviors. PMID:17013672

Estimation of the Driving Style Based on the Users' Activity and Environment Influence.

PubMed

Sysoev, Mikhail; Kos, Andrej; Guna, Jože; Pogačnik, Matevž

2017-10-21

New models and methods have been designed to predict the influence of the user's environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers' activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users' activity. The driving style was predicted from the user's environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user's environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts.

Modular Engine Noise Component Prediction System (MCP) Program Users' Guide

NASA Technical Reports Server (NTRS)

Golub, Robert A. (Technical Monitor); Herkes, William H.; Reed, David H.

2004-01-01

This is a user's manual for Modular Engine Noise Component Prediction System (MCP). This computer code allows the user to predict turbofan engine noise estimates. The program is based on an empirical procedure that has evolved over many years at The Boeing Company. The data used to develop the procedure include both full-scale engine data and small-scale model data, and include testing done by Boeing, by the engine manufacturers, and by NASA. In order to generate a noise estimate, the user specifies the appropriate engine properties (including both geometry and performance parameters), the microphone locations, the atmospheric conditions, and certain data processing options. The version of the program described here allows the user to predict three components: inlet-radiated fan noise, aft-radiated fan noise, and jet noise. MCP predicts one-third octave band noise levels over the frequency range of 50 to 10,000 Hertz. It also calculates overall sound pressure levels and certain subjective noise metrics (e.g., perceived noise levels).

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

Smart home design and operation preferences of Americans and Koreans.

PubMed

Jeong, Kyeong-Ah; Salvendy, Gavriel; Proctor, Robert W

2010-05-01

The purpose of the present study was to generate both culture-specific and universal design and operational guidelines for smart homes. Questionnaire surveys were performed in the USA and South Korea to collect data on preferences for various aspects of the design and operation of smart homes. The factors that the survey participants considered most important were derived through factor analyses of the survey data and the responses of Americans and Koreans were compared to generate culture-specific guidelines. The five factors derived were: 1) environmental connection and control; 2) smart devices (appliances) and their control; 3) physical safety and security concerns; 4) comfort and relaxation issues; 5) control restriction issues. The two cultures showed different preference structures with statistical significance for all five factors. Prediction capability of the derived factors was also examined through multiple regressions for buying intention, interest, self-vision of living, moving intention, living satisfaction and perceived time and effort savings. 'Environmental connection and control' and 'smart devices (appliances) and their control' seemed to be the most influential factors for Americans and Koreans, respectively. STATEMENT OF RELEVANCE: Analysis of a survey of design and operational preferences for smart homes yielded five factors on which US and South Korean respondents differed. These factors form the basis for culture-specific guidelines, which, along with universal guidelines, should be followed in design of user-centred smart homes.

Net-generation attributes and seductive properties of the internet as predictors of online activities and internet addiction.

PubMed

Leung, Louis

2004-06-01

Born between 1977 and 1997, Net-generation is the first generation to grow up surrounded by home computers, video games, and the Internet. As children of the Baby Boomers, the Internet is the medium of choice for the Net-geners. Based on the assumption that Net-generation has unique characteristics, this study examined (1) how Net-geners addicted to the Internet differ from the non-addicted and (2) how these attributes, together with the seductive properties of the Internet, are related to Internet addiction. Data were gathered from a probability sample of 699 Net-geners between the ages of 16 and 24. Results show that Net-geners addicted to the Internet tend to be young female students. Being emotionally open on the Net and a heavy user of ICQ were most influential in predicting Net-geners' problematic use of the Internet. Addicted Net-geners are also strongly linked to the pleasure of being able to control the simulated world in online games. The finding reinforces previous research that "dependents" of the Internet spend most of their time in the synchronous communication environment engaging in interactive online games, chat rooms, and ICQ for pleasure-seeking or escape, while "non-dependents" use information-gathering functions available on the Internet. Furthermore, Internet addicts tend to watch television significantly less, indicating a displacement effect on traditional media use for the Net-generation.

Unsupervised user similarity mining in GSM sensor networks.

PubMed

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.

Study on the factors affecting the quality of public bus transportation service in Bali Province using factor analysis

NASA Astrophysics Data System (ADS)

Susilawati, M.; Nilakusmawati, D. P. E.

2017-06-01

The volume of mobility flows are increasing day by day and the condition of the number of people using private transport modes contribute to traffic congestion. With the limited capacity of the road, one of the alternatives solution to reduce congestion is to optimize the use of public transport. The purposes of this study are to determine the factors that influence user’s satisfaction on the quality of public bus transportation service and determine variables that became identifier on the dominant factor affecting user’s satisfaction. The study was conducted for the public bus transportation between districts in the province of Bali, which is among the eight regencies and one municipality, using a questionnaire as a data collection instrument. Service variables determinant of user’s satisfaction in this study, described in 25 questions, which were analyzed using factor analysis. The results showed there were six factors that explain the satisfaction of users of public transport in Bali, with a total diversity of data that can be parsed by 61.436%. These factors are: Safety and comfort, Responsiveness, Capacity, Tangible, Safety, Reliability. The dominant factor affecting public transport user satisfaction is the safety and comfort, with the most influential variable is feeling concerned about the personal safety of users when on the bus.

Probabilistic Seeking Prediction in P2P VoD Systems

NASA Astrophysics Data System (ADS)

Wang, Weiwei; Xu, Tianyin; Gao, Yang; Lu, Sanglu

In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user's next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership

PubMed Central

Wu, Hongchen; Wang, Xinjun

2016-01-01

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users’ sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users’ decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens’ sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants’ partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness. PMID:26950064

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

Thomas Steven Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Modeling long-term human activeness using recurrent neural networks for biometric data.

PubMed

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin

2017-05-18

With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user. The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold. A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data. This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Don't Judge a Book by its Cover: Examiner Expectancy Effects Predict Neuropsychological Performance for Individuals Judged as Chronic Cannabis Users.

PubMed

Sodos, Louise M; Hirst, Rayna B; Watson, Jessica; Vaughn, Dylan

2018-01-12

The experimenter expectancy effect confound remains largely unexplored in neuropsychological research and has never been investigated among cannabis users. This study investigated whether examiner expectancies of cannabis user status affected examinees' neuropsychological performance. Participants included 41 cannabis users and 20 non-users. Before testing, examiners who were blind to participant user status privately rated whether they believed the examinee was a cannabis user or non-user. Examiners then administered a battery of neuropsychological and performance validity measures. Multiple regression analyses compared performance between examinees judged as cannabis users (n = 37) and those judged as non-users (n = 24). Examiners' judgments of cannabis users were 75% accurate; judgments of non-users were at chance. After controlling for age, gender, and actual user status, examiner judgments of cannabis user status predicted performance on two measures (California Verbal Learning Test-II, and Trail Making Test B; p < .05), as individuals judged as cannabis users obtained lower scores than those judged as non-users. Examiners' judgments of cannabis user status predicted performance even after controlling for actual user status, indicating vulnerability to examiner expectancy effects. These findings have important implications for both research and clinical settings, as scores may partially reflect examiners' expectations regarding cannabis effects rather than participants' cognitive abilities. These results demonstrate the need for expectancy effect research in the neuropsychological assessment of all populations, not just cannabis users. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Systematic Review on Irrational Use of Medicines in China and Vietnam

PubMed Central

Mao, Wenhui; Vu, Huyen; Xie, Zening; Chen, Wen; Tang, Shenglan

2015-01-01

Background Irrational use of medicines has been an issue concerned all over the world and the outlooks in developing countries are more severe. This study aimed to assess the different patterns of irrational use of medicines and its influential factors in China and Vietnam. Methods A systematic review was performed on both published and grey literatures in English, Chinese and Vietnamese languages between 1993 and 2013 based on the WHO framework. Quality assessment was conducted on the basis of the Critical Appraisal Skills Programme. Key indicators were analyzed to compare the irrational use of medicines in two countries. Results A total of 67 published works about China and 29 about Vietnam were included, the majority of which were cross-sectional prescription studies in both China and Vietnam. Irrational use of medicines was found in both the countries but issues with polypharmacy as well as overuse of antibiotics were more severe in Vietnam while overuse of injections was unique to China. Various patterns of irrational use were also indicated between urban and rural areas, and among different levels of hospitals. Rarely does literature focus on the analysis of influential factors of irrational use of medicines. While lack of proper knowledge from both providers and patients were the most recognized influential factors in both countries, economic incentives from pharmaceutical companies in China, and weak control and regulation over prescriptions in Vietnam were the main factors attributed to this issue. Conclusion Severe irrational use of medicines has been abundantly evidenced in both China and Vietnam, highlighting the importance of policy interventions on the issue. However, limited evidence on the appropriateness or its compliance (conformity) to guidelines of prescription has been found. In addition, convincing evidence on the underlying explanation of this issue is lacking, although economic incentives, health insurance coverage, and knowledge of service providers and users have been implied to be factors influencing irrational drug use. PMID:25793497

GM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors.

PubMed

Ren, Jingzheng

2018-01-01

Anaerobic digestion process has been recognized as a promising way for waste treatment and energy recovery in a sustainable way. Modelling of anaerobic digestion system is significantly important for effectively and accurately controlling, adjusting, and predicting the system for higher methane yield. The GM(1,N) approach which does not need the mechanism or a large number of samples was employed to model the anaerobic digestion system to predict methane yield. In order to illustrate the proposed model, an illustrative case about anaerobic digestion of municipal solid waste for methane yield was studied, and the results demonstrate that GM(1,N) model can effectively simulate anaerobic digestion system at the cases of poor information with less computational expense. Copyright © 2017 Elsevier Ltd. All rights reserved.

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

PubMed

Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho

2016-01-23

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.

Predicting Active Users' Personality Based on Micro-Blogging Behaviors

PubMed Central

Hao, Bibo; Guan, Zengda; Zhu, Tingshao

2014-01-01

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors. PMID:24465462

Identifying influential spreaders in complex networks based on kshell hybrid method

NASA Astrophysics Data System (ADS)

Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath

2018-06-01

Influential spreaders are the key players in maximizing or controlling the spreading in a complex network. Identifying the influential spreaders using kshell decomposition method has become very popular in the recent time. In the literature, the core nodes i.e. with the largest kshell index of a network are considered as the most influential spreaders. We have studied the kshell method and spreading dynamics of nodes using Susceptible-Infected-Recovered (SIR) epidemic model to understand the behavior of influential spreaders in terms of its topological location in the network. From the study, we have found that every node in the core area is not the most influential spreader. Even a strategically placed lower shell node can also be a most influential spreader. Moreover, the core area can also be situated at the periphery of the network. The existing indexing methods are only designed to identify the most influential spreaders from core nodes and not from lower shells. In this work, we propose a kshell hybrid method to identify highly influential spreaders not only from the core but also from lower shells. The proposed method comprises the parameters such as kshell power, node's degree, contact distance, and many levels of neighbors' influence potential. The proposed method is evaluated using nine real world network datasets. In terms of the spreading dynamics, the experimental results show the superiority of the proposed method over the other existing indexing methods such as the kshell method, the neighborhood coreness centrality, the mixed degree decomposition, etc. Furthermore, the proposed method can also be applied to large-scale networks by considering the three levels of neighbors' influence potential.

Understanding Adulthood. A Review and Analysis of the Works of Three Leading Authorities on the Stages and Crises in Adult Development. California Personnel and Guidance Association Monograph Number 15.

ERIC Educational Resources Information Center

Gerstein, Martin; Papen-Daniel, Michele

Adult development theorists believe that the changes that occur during the adult years are predictable and age linked. Their theories explain how change is resolved by the majority of the adult population. Three persons whose research has been influential in the field of adult development during the 1970s are Erik Erikson, Daniel Levinson, and…

Illness beliefs predict self-care behaviours in patients with diabetic foot ulcers: a prospective study.

PubMed

Vedhara, Kavita; Dawe, Karen; Wetherell, Mark A; Miles, Jeremy N V; Cullum, Nicky; Dayan, Colin; Drake, Nicola; Price, Patricia; Tarlton, John; Weinman, John; Day, Andrew; Campbell, Rona

2014-10-01

Patients' illness beliefs are known to be influential determinants of self-care behaviours in many chronic conditions. In a prospective observational study we examined their role in predicting foot self-care behaviours in patients with diabetic foot ulcers. Patients (n=169) were recruited from outpatient podiatry clinics. Clinical and demographic factors, illness beliefs and foot self-care behaviours were assessed as baseline (week 0). Foot self-care behaviours were assessed again 6, 12 and 24 weeks later. Linear regressions examined the contribution of beliefs at baseline to subsequent foot self-care behaviours, controlling for past behaviour (i.e., foot self-care at baseline) and clinical and demographic factors that may affect foot self-care (i.e., age and ulcer size). Our models accounted for between 42 and 58% of the variance in foot self-care behaviours. Even after controlling for past foot-care behaviours, age and ulcer size; patients' beliefs regarding the symptoms associated with ulceration, their understanding of ulceration and their perceived personal control over ulceration emerged as independent determinants of foot self-care. Patients' beliefs are important determinants of foot-care practices. They may, therefore, also be influential in determining ulcer outcomes. Interventions aimed at modifying illness beliefs may offer a means for promoting self-care and improving ulcer outcomes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies.

PubMed

Kim, Young Bin; Kim, Jun Gi; Kim, Wook; Im, Jae Ho; Kim, Tae Hyeong; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.

Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies

PubMed Central

Kim, Young Bin; Kim, Jun Gi; Kim, Wook; Im, Jae Ho; Kim, Tae Hyeong; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method. PMID:27533113

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

PubMed Central

Wang, Xi; Street, Nick

2017-01-01

Background Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users’ participations and predict user churn for user retention efforts. Objective This study aimed to analyze OHC users’ Web-based interactions, reveal which types of social support activities are related to users’ participation, and predict whether and when a user will churn from the OHC. Methods We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users’ continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Results Users’ behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Conclusions Detecting different types of social support activities via text mining contributes to better understanding and prediction of users’ participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. PMID:28438725

Topicality and impact in social media: diverse messages, focused messengers.

PubMed

Weng, Lilian; Menczer, Filippo

2015-01-01

We have a limited understanding of the factors that make people influential and topics popular in social media. Are users who comment on a variety of matters more likely to achieve high influence than those who stay focused? Do general subjects tend to be more popular than specific ones? Questions like these demand a way to detect the topics hidden behind messages associated with an individual or a keyword, and a gauge of similarity among these topics. Here we develop such an approach to identify clusters of similar hashtags in Twitter by detecting communities in the hashtag co-occurrence network. Then the topical diversity of a user's interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags implies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact.

How Could Contact Lens Wearers Be at Risk of Acanthamoeba Infection? A Review

PubMed Central

Ibrahim, Youhanna W.; Boase, David L.; Cree, Ian A.

2010-01-01

Contact lens wear is highly influential on the incidence of ulcerative keratitis worldwide, particularly in developed countries. The association between Acanthamoeba keratitis and contact lens wear is firmly established; it may account for up to 95% of the reported cases. Before the popularisation of soft contact lens wear, Acanthamoeba keratitis was extremely rare. In 2000 it was estimated that the number of contact lens wearers worldwide was about 80 million, out of whom 33 million were in the United States and 90% of them wore hydrogel soft lenses. Contact lens-related problems depend on many factors, such as lens material, wearing modality, lens hygiene, type of lens-caring solution, the degree of compliance of the lens user with lens wear and care procedures, lens overwear, sleeping in lenses, rate of changing lenses, and lens case hygiene. This paper is a thorough review of the literature aiming to highlight the role of one of the main risk factors of infectious keratitis, contact lens wear, and also to show the responsibility of lens users in aggravating this risk.

Perceived Attitudes of Schizophrenic Inpatients in Relation to Rehospitalization.

ERIC Educational Resources Information Center

Baker, Brian; And Others

1987-01-01

Schizophrenic inpatients who were ready for discharge completed the Influential Relationships Questionnaire, measuring their perceptions of three characteristic attitudes (care, overprotection, and criticism) demonstrated by two influential people in the patients' lives. Readmitted patients rated the second-most influential person higher on the…

Unsupervised User Similarity Mining in GSM Sensor Networks

PubMed Central

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining. PMID:23576905

Predicting individual fusional range from optometric data

NASA Astrophysics Data System (ADS)

Endrikhovski, Serguei; Jin, Elaine; Miller, Michael E.; Ford, Robert W.

2005-03-01

A model was developed to predict the range of disparities that can be fused by an individual user from optometric measurements. This model uses parameters, such as dissociated phoria and fusional reserves, to calculate an individual user"s fusional range (i.e., the disparities that can be fused on stereoscopic displays) when the user views a stereoscopic stimulus from various distances. This model is validated by comparing its output with data from a study in which the individual fusional range of a group of users was quantified while they viewed a stereoscopic display from distances of 0.5, 1.0, and 2.0 meters. Overall, the model provides good data predictions for the majority of the subjects and can be generalized for other viewing conditions. The model may, therefore, be used within a customized stereoscopic system, which would render stereoscopic information in a way that accounts for the individual differences in fusional range. Because the comfort of an individual user also depends on the user"s ability to fuse stereo images, such a system may, consequently, improve the comfort level and viewing experience for people with different stereoscopic fusional capabilities.

Deletion Diagnostics for the Generalised Linear Mixed Model with independent random effects

PubMed Central

Ganguli, B.; Roy, S. Sen; Naskar, M.; Malloy, E. J.; Eisen, E. A.

2015-01-01

The Generalised Linear Mixed Model (GLMM) is widely used for modelling environmental data. However, such data are prone to influential observations which can distort the estimated exposure-response curve particularly in regions of high exposure. Deletion diagnostics for iterative estimation schemes commonly derive the deleted estimates based on a single iteration of the full system holding certain pivotal quantities such as the information matrix to be constant. In this paper, we present an approximate formula for the deleted estimates and Cook’s distance for the GLMM which does not assume that the estimates of variance parameters are unaffected by deletion. The procedure allows the user to calculate standardised DFBETAs for mean as well as variance parameters. In certain cases, such as when using the GLMM as a device for smoothing, such residuals for the variance parameters are interesting in their own right. In general, the procedure leads to deleted estimates of mean parameters which are corrected for the effect of deletion on variance components as estimation of the two sets of parameters is interdependent. The probabilistic behaviour of these residuals is investigated and a simulation based procedure suggested for their standardisation. The method is used to identify influential individuals in an occupational cohort exposed to silica. The results show that failure to conduct post model fitting diagnostics for variance components can lead to erroneous conclusions about the fitted curve and unstable confidence intervals. PMID:26626135

Use of Online Sources of Information by Dental Practitioners: Findings from The Dental Practice-Based Research Network

PubMed Central

Funkhouser, Ellen; Agee, Bonita S.; Gordan, Valeria V.; Rindal, D. Brad; Fellows, Jeffrey L.; Qvist, Vibeke; McClelland, Jocelyn; Gilbert, Gregg H.

2013-01-01

Objectives Estimate the proportion of dental practitioners who use online sources of information for practice guidance. Methods From a survey of 657 dental practitioners in The Dental Practice Based Research Network, four indicators of online use for practice guidance were calculated: read journals online, obtained continuing education (CDE) through online sources, rated an online source as most influential, and reported frequently using an online source for guidance. Demographics, journals read, and use of various sources of information for practice guidance in terms of frequency and influence were ascertained for each. Results Overall, 21% (n=138) were classified into one of the four indicators of online use: 14% (n=89) rated an online source as most influential and 13% (n=87) reported frequently using an online source for guidance; few practitioners (5%, n=34) read journals online, fewer (3%, n=17) obtained CDE through online sources. Use of online information sources varied considerably by region and practice characteristics. In general, the 4 indicators represented practitioners with as many differences as similarities to each other and to offline users. Conclusion A relatively small proportion of dental practitioners use information from online sources for practice guidance. Variation exists regarding practitioners’ use of online source resources and how they rate the value of offline information sources for practice guidance. PMID:22994848

Is Exposure to Tobacco Advertising, Promotion and Sponsorship Associated with Initiation of Tobacco Use among Current Tobacco Users in Youth in India?

PubMed

Sardana, Mohini; Goel, Sonu; Gupta, Madhu; Sardana, Veera; Singh, B S

2015-01-01

The rise in consumption of tobacco products among youth is a public health concern in India. Several studies have shown that advertisements promoting tobacco products influence decisions and behaviour of youth towards smoking. To ascertain which method of Tobacco Advertising, Promotion and Sponsorship (TAPS) was more influential for initiating tobacco use in youth in India. The secondary data of youth (15-24 years) from nationally representative Global Adult Tobacco Survey (GATS) conducted in 2009-2010 was analyzed. Odds ratio and p-value were used to know the association between TAPS and initiation of use of tobacco products among youth. Logistic regression was used to determine the most significant means of TAPS altering the youth's behaviour towards tobacco products. Out of 13,383 youths, 1,982 (14.7%) used smokeless forms of tobacco and 860 (6.38%) used smoke forms. Logistic regression reveals that promotional activities mainly through cinemas (p<0.05) and providing free samples of tobacco products (p<=.001) were most influential means of initiating consumption of tobacco products among youth. The smoking in youth is associated with watching advertisements particularly in cinema and promotional activities like distribution of free samples, coupons and sales on the price of tobacco products. Stronger legislative measures should be enforced to curb promotional advertisements in cinemas and distribution of free samples.

Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 User Guide

EPA Science Inventory

User Guide to describe the complete functionality of the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 online tool. The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility tool (SeqAPASS; https://seqa...

Strong claims and weak evidence: reassessing the predictive validity of the IAT.

PubMed

Blanton, Hart; Jaccard, James; Klick, Jonathan; Mellers, Barbara; Mitchell, Gregory; Tetlock, Philip E

2009-05-01

The authors reanalyzed data from 2 influential studies-A. R. McConnell and J. M. Leibold and J. C. Ziegert and P. J. Hanges-that explore links between implicit bias and discriminatory behavior and that have been invoked to support strong claims about the predictive validity of the Implicit Association Test. In both of these studies, the inclusion of race Implicit Association Test scores in regression models reduced prediction errors by only tiny amounts, and Implicit Association Test scores did not permit prediction of individual-level behaviors. Furthermore, the results were not robust when the impact of rater reliability, statistical specifications, and/or outliers were taken into account, and reanalysis of A. R. McConnell & J. M. Leibold (2001) revealed a pattern of behavior consistent with a pro-Black behavioral bias, rather than the anti-Black bias suggested in the original study. (c) 2009 APA, all rights reserved.

Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Communal and Agentic Interpersonal and Intergroup Motives Predict Preferences for Status Versus Power.

PubMed

Locke, Kenneth D; Heller, Sonja

2017-01-01

Seven studies involving 1,343 participants showed how circumplex models of social motives can help explain individual differences in preferences for status (having others' admiration) versus power (controlling valuable resources). Studies 1 to 3 and 7 concerned interpersonal motives in workplace contexts, and found that stronger communal motives (to have mutual trust, support, and cooperation) predicted being more attracted to status (but not power) and achieving more workplace status, while stronger agentic motives (to be firm, decisive, and influential) predicted being more attracted to and achieving more workplace power, and experiencing a stronger connection between workplace power and job satisfaction. Studies 4 to 6 found similar effects for intergroup motives: Stronger communal motives predicted wanting one's ingroup (e.g., country) to have status-but not power-relative to other groups. Finally, most people preferred status over power, and this was especially true for women, which was partially explained by women having stronger communal motives.

QoS prediction for web services based on user-trust propagation model

NASA Astrophysics Data System (ADS)

Thinh, Le-Van; Tu, Truong-Dinh

2017-10-01

There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user's mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

PubMed Central

Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N.; Barahona, Mauricio

2014-01-01

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks. PMID:25297320

Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

ERIC Educational Resources Information Center

Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

1999-01-01

A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

Influence of family members on utilization of maternal health care services among teen and adult pregnant women in Kathmandu, Nepal: a cross sectional study.

PubMed

Upadhyay, Priti; Liabsuetrakul, Tippawan; Shrestha, Amir Babu; Pradhan, Neelam

2014-12-23

In some developing countries a woman's decision to utilize maternal health care services is not made by the woman herself but by other family members. The perception of family members regarding who is the most influential person for making the decision to utilize these services is inconclusive. Hence, this study aimed to determine the perceived influential person on utilization of antenatal care (ANC) and delivery care services among teen, young adult and adult pregnant women from the perspective of the woman themselves, their husband and their mother-in-law, identify the factors associated with the woman being the most influential person, and assess the level of agreement between the woman's and her husband's response to the woman being the most influential person. A cross-sectional study was conducted at Paropakar Maternity and Women's Hospital and Tribhuvan University Teaching Hospital. Purposive sampling technique was used to select 315 women of which 105 were from each age group and their accompanied husbands (n = 315) and mothers-in-law (n = 315). The proportion of perceived influential person and mean priority score of the perceived influence with its 95% confidence interval was calculated. The factors associated with the woman perceived as the most influential person were analyzed by multivariate logistic regression model. The agreement was analyzed using kappa statistic. Among teens and young adults and their husband and mother-in-law, the woman's husband was perceived as the most influential person. Among adults, the most influential person for ANC was the woman herself but for delivery care was the woman's husband. A woman of adult age, having a non-indigenous ethnicity or who was not referred was more likely to perceive herself as the most influential person in the decision to utilize delivery care. A fair to poor level of agreement was found on the perception of the most influential person for ANC and delivery care utilization. Both women and their husbands influenced the decision to utilize ANC and delivery care but husbands were more influential, especially in teens and young adults. Thus, husband's involvement is crucial as a strategy to improve maternal health care utilization in Nepal.

PREDICTING ATTENUATION OF VIRUSES DURING PERCOLATION IN SOILS: 2. USER'S GUIDE TO THE VIRULO 1.0 COMPUTER MODEL

EPA Science Inventory

In the EPA document Predicting Attenuation of Viruses During Percolation in Soils 1. Probabilistic Model the conceptual, theoretical, and mathematical foundations for a predictive screening model were presented. In this current volume we present a User's Guide for the computer mo...

Axial charges of octet and decuplet baryons in a perturbative chiral quark model

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Samart, D.; Khosonthongkee, K.; Limphirat, A.; Xu, K.; Yan, Y.

2018-05-01

Using the perturbative chiral quark model (PCQM), we investigate and predict in this work axial charges gAB of octet and decuplet N , Σ , Ξ , Δ , Σ*, and Ξ* baryons, considering both the ground and excited states in the quark propagator. The PCQM predictions are in good agreement with the experimental data, lattice-QCD values, and other approaches. In addition, the study reveals that the meson cloud is influential in the PCQM, contributing around 30% to the total values of gAB, and the meson cloud contribution to gAB stems mainly from the diagrams with the ground-state quark propagator while the excited intermediate quark states reduce gAB by 10-20%.

The accuracy of new wheelchair users' predictions about their future wheelchair use.

PubMed

Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

2012-06-01

This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

Type A and hardiness.

PubMed

Kobasa, S C; Maddi, S R; Zola, M A

1983-03-01

The study examined the relationship between the Type A behavior pattern and personality hardiness and predicted an interaction between the two that would be influential for illness onset. Type A and hardiness were found to be conceptually different and empirically independent factors. Under high stressful life events, male executives who were high in Type A and low in hardiness tended toward higher general illness scores than any other executives. Type A and hardiness emerge from this study as bases for extrinsic and intrinsic motivation, respectively.

Polymorphism of the transcription factor 7-like 2 Gene (TCF7L2) interacts with obesity on type-2 diabetes in the PREDIMED Study emphasizing the heterogeneity of genetic variants in type-2 diabetes risk prediction: time for...

USDA-ARS?s Scientific Manuscript database

Nutrigenetic studies analyzing gene-diet interactions of the TCF7L2-rs7903146 C > T polymorphism on type-2 diabetes (T2D) have shown controversial results. A reason contributing to this may be the additional modulation by obesity. Moreover, TCF7L2-rs7903146 is one of the most influential variants in...

Accurate and scalable social recommendation using mixed-membership stochastic block models

PubMed Central

Godoy-Lorite, Antonia; Moore, Cristopher

2016-01-01

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

Husbands’ and Wives’ Physical Activity and Depressive Symptoms: Longitudinal Findings from the Cardiovascular Health Study

PubMed Central

Monin, Joan K.; Levy, Becca; Chen, Baibing; Fried, Terri; Stahl, Sarah T.; Schulz, Richard; Doyle, Margaret; Kershaw, Trace

2015-01-01

Background When examining older adults’ health behaviors and psychological health it is important to consider the social context. Purpose To examine in older adult marriages whether each spouse’s physical activity predicted changes in their own (actor effects) and their partner’s (partner effects) depressive symptoms. Gender differences were also examined. Method Each spouse within 1,260 married couples (at baseline) in the Cardiovascular Health Study completed self-report measures at wave 1 (1989–1990), wave 3 (1992–1993), and wave 7 (1996–1997). Dyadic path analyses were performed. Results Husbands’ physical activity significantly predicted own decreased depressive symptoms (actor effect). For both spouses, own physical activity did not significantly predict the spouse’s depressive symptoms (partner effects). However, husbands’ physical activity and depressive symptoms predicted wives’ physical activity and depressive symptoms (partner effects), respectively. Depressive symptoms did not predict physical activity. Conclusion Findings suggest that husbands’ physical activity is particularly influential for older married couples’ psychological health. PMID:25868508

Can language prime culture in Hispanics? The differential impact of self-construals in predicting intention to use a condom.

PubMed

Lechuga, Julia; Wiebe, John S

2009-12-01

The highly influential theory of planned behavior suggests that norms and attitudes predict an important antecedent of behavior: intention. Cross-cultural research suggests that culturally influenced self-construals can be primed and differentially affect behaviors that are influenced by norms and attitudes. The purpose of this experiment was twofold: (1) To investigate whether language functions as a prime for culture in Hispanics, and (2) if so, if norms and attitudes differentially predict condom use intention. Fluent English-Spanish bilingual participants (N = 145) of Mexican descent were randomly assigned to answer questionnaires in English and Spanish. Subjective norms and private evaluations towards condom use were assessed and their relative strength in predicting condom use intention was evaluated. Results suggest that language can prime culture and affect the relative accessibility of culture-relevant norms and self-construals in Hispanics. Moreover, consistent with our expectations, norms and attitudes differentially predicted condom use intention.

Can language prime culture in Hispanics? The differential impact of self-construals in predicting intention to use a condom

PubMed Central

Lechuga, Julia; Wiebe, John S.

2012-01-01

The highly influential theory of planned behavior suggests that norms and attitudes predict an important antecedent of behavior: intention. Cross-cultural research suggests that culturally influenced self-construals can be primed and differentially affect behaviors that are influenced by norms and attitudes. The purpose of this experiment was twofold: (1) To investigate whether language functions as a prime for culture in Hispanics, and (2) if so, if norms and attitudes differentially predict condom use intention. Fluent English–Spanish bilingual participants (N = 145) of Mexican descent were randomly assigned to answer questionnaires in English and Spanish. Subjective norms and private evaluations towards condom use were assessed and their relative strength in predicting condom use intention was evaluated. Results suggest that language can prime culture and affect the relative accessibility of culture-relevant norms and self-construals in Hispanics. Moreover, consistent with our expectations, norms and attitudes differentially predicted condom use intention. PMID:22029664

Does message framing predict willingness to participate in a hypothetical HIV vaccine trial: an application of Prospect Theory.

PubMed

Evangeli, Michael; Kafaar, Zuhayr; Kagee, Ashraf; Swartz, Leslie; Bullemor-Day, Philippa

2013-01-01

It is vital that enough participants are willing to participate in clinical trials to test HIV vaccines adequately. It is, therefore, necessary to explore what affects peoples' willingness to participate (WTP) in such trials. Studies have only examined individual factors associated with WTP and not the effect of messages about trial participation on potential participants (e.g., whether losses or gains are emphasized, or whether the outcome is certain or uncertain). This study explores whether the effects of message framing on WTP in a hypothetical HIV vaccine trial are consistent with Prospect Theory. This theory suggests that people are fundamentally risk averse and that (1) under conditions of low risk and high certainty, gain-framed messages will be influential (2) under conditions of high risk and low certainty, loss-framed messages will be influential. This cross-sectional study recruited 283 HIV-negative students from a South African university who were given a questionnaire that contained matched certain gain-framed, certain loss-framed, uncertain gain-framed, and uncertain loss-framed statements based on common barriers and facilitators of WTP. Participants were asked to rate how likely each statement was to result in their participation in a hypothetical preventative HIV vaccine trial. Consistent with Prospect Theory predictions, for certain outcomes, gain-framed messages were more likely to result in WTP than loss-framed messages. Inconsistent with predictions, loss-framed message were not more likely to be related to WTP for uncertain outcomes than gain-framed messages. Older students were less likely to express their WTP across the different message frames. Recruitment for HIV vaccine trials should pay attention to how messages about the trial are presented to potential participants.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams.

PubMed

DeRolph, Christopher R; Nelson, Stacy A C; Kwak, Thomas J; Hain, Ernie F

2015-01-01

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. We predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistance and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams

USGS Publications Warehouse

DeRolph, Christopher R.; Nelson, S.; Kwak, Thomas J.; Hain, Ernie F.

2015-01-01

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. We predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistance and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams

DOE PAGES

DeRolph, Christopher R.; Nelson, Stacy A. C.; Kwak, Thomas J.; ...

2014-12-09

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. In this paper, we predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistancemore » and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Finally and additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients.« less

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

PubMed

Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W

2017-05-01

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors. © 2016, The American College of Clinical Pharmacology.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams

PubMed Central

DeRolph, Christopher R; Nelson, Stacy A C; Kwak, Thomas J; Hain, Ernie F

2015-01-01

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. We predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistance and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients. PMID:25628872

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

A Human-Centered Smart Home System with Wearable-Sensor Behavior Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ji, Jianting; Liu, Ting; Shen, Chao

Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extractedmore » from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.« less

Neural activity predicts attitude change in cognitive dissonance.

PubMed

van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

2009-11-01

When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

Technology transfer for DOE's office of buildings and community systems: assessment and strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, M.A.; Jones, D.W.; Kolb, J.O.

1986-07-01

The uninterrupted availability of oil supplies over the past several years and the moderation of energy price increases has sent signals to consumers and decision-makers in the buildings industry that the ''energy crisis'' is over. As a result, efforts to promote energy-conserving technologies must emphasize benefits other than BTU savings. The improved ambience of daylit spaces and the lower first costs associated with installing down-sized HVAC systems in ''tight'' buildings are examples of benefits which are likely to more influential than estimates of energy saved. Successful technology transfer requires that an R and D product have intrinsic value and thatmore » these values be effectively communicated to potential users. Active technology transfer programs are more effective than passive ones. Transfer activities should involve more than simply making information available to those who seek it. Information should be tailored to meet the needs of specific user groups and disseminated through those channels which users normally employ. In addition to information dissemination, successful technology transfer involves the management of intellectual property, including patented inventions, copyrights, technical data, and rights to future inventions. When the public can best benefit from an invention through commercialization of a new product, the exclusivity necessary to protect the investment from copiers should be provided. Most federal technology transfer programs concentrate on information exchange and largely avoid intellectual property transfers.« less

Dysprosium sorption by polymeric composite bead: robust parametric optimization using Taguchi method.

PubMed

Yadav, Kartikey K; Dasgupta, Kinshuk; Singh, Dhruva K; Varshney, Lalit; Singh, Harvinderpal

2015-03-06

Polyethersulfone-based beads encapsulating di-2-ethylhexyl phosphoric acid have been synthesized and evaluated for the recovery of rare earth values from the aqueous media. Percentage recovery and the sorption behavior of Dy(III) have been investigated under wide range of experimental parameters using these beads. Taguchi method utilizing L-18 orthogonal array has been adopted to identify the most influential process parameters responsible for higher degree of recovery with enhanced sorption of Dy(III) from chloride medium. Analysis of variance indicated that the feed concentration of Dy(III) is the most influential factor for equilibrium sorption capacity, whereas aqueous phase acidity influences the percentage recovery most. The presence of polyvinyl alcohol and multiwalled carbon nanotube modified the internal structure of the composite beads and resulted in uniform distribution of organic extractant inside polymeric matrix. The experiment performed under optimum process conditions as predicted by Taguchi method resulted in enhanced Dy(III) recovery and sorption capacity by polymeric beads with minimum standard deviation. Copyright © 2015 Elsevier B.V. All rights reserved.

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

Content Analysis of the 20 Most Influential Articles in "PIQ"

ERIC Educational Resources Information Center

Cho, Yonjoo; Park, Sunyoung

2012-01-01

The purpose of this study is to examine key research themes in human performance technology (HPT) through content analysis of the 20 most influential articles identified in Cho, Jo, Park, Kang, and Chen (2011). Three questions guiding this inquiry are: (1) What are the key themes of the 20 most influential articles in "PIQ", (2) What information…

Finding Waldo: Learning about Users from their Interactions.

PubMed

Brown, Eli T; Ottley, Alvitta; Zhao, Helen; Quan Lin; Souvenir, Richard; Endert, Alex; Chang, Remco

2014-12-01

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems.

Drought Predictability and Prediction in a Changing Climate: Assessing Current Predictive Knowledge and Capabilities, User Requirements and Research Priorities

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2011-01-01

Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.

Predicting Positive and Negative Relationships in Large Social Networks.

PubMed

Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

2015-01-01

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Prediction of advertisement preference by fusing EEG response and sentiment analysis.

PubMed

Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

2017-08-01

This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Influential Aspects of the Smart City

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marinovici, Maria C.; Kirkham, Harold; Widergren, Steven E.

2016-01-05

Using millions of sensors in everyday objects, smart cities will generate petabytes of data, and it will be delivered to multiple users via networks. Multi-disciplinary inter-operability is essential. We propose system engineering management, with multidisciplinary teams as an effective way to deliver real change. Their goal is to develop intelligent and integrated services through the use of digital technologies and open collaboration. We also caution that the process cannot be entirely planned ahead of time, it must be allowed to evolve. New technology will change the game (where does a 3-D printer fit into a smart city?). Municipal planning meansmore » central planning – not known for its sensitivity to reality. A successful smart city will include lots of feedback mechanisms for the citizenry.« less

Effects of influent fractionation, kinetics, stoichiometry and mass transfer on CH4, H2 and CO2 production for (plant-wide) modeling of anaerobic digesters.

PubMed

Solon, Kimberly; Flores-Alsina, Xavier; Gernaey, Krist V; Jeppsson, Ulf

2015-01-01

This paper examines the importance of influent fractionation, kinetic, stoichiometric and mass transfer parameter uncertainties when modeling biogas production in wastewater treatment plants. The anaerobic digestion model no. 1 implemented in the plant-wide context provided by the benchmark simulation model no. 2 is used to quantify the generation of CH₄, H₂and CO₂. A comprehensive global sensitivity analysis based on (i) standardized regression coefficients (SRC) and (ii) Morris' screening's (MS's) elementary effects reveals the set of parameters that influence the biogas production uncertainty the most. This analysis is repeated for (i) different temperature regimes and (ii) different solids retention times (SRTs) in the anaerobic digester. Results show that both SRC and MS are good measures of sensitivity unless the anaerobic digester is operating at low SRT and mesophilic conditions. In the latter situation, and due to the intrinsic nonlinearities of the system, SRC fails in decomposing the variance of the model predictions (R² < 0.7) making MS a more reliable method. At high SRT, influent fractionations are the most influential parameters for predictions of CH₄and CO₂emissions. Nevertheless, when the anaerobic digester volume is decreased (for the same load), the role of acetate degraders gains more importance under mesophilic conditions, while lipids and fatty acid metabolism is more influential under thermophilic conditions. The paper ends with a critical discussion of the results and their implications during model calibration and validation exercises.

Stability analyses of the mass abrasive projectile high-speed penetrating into a concrete target Part III: Terminal ballistic trajectory analyses

NASA Astrophysics Data System (ADS)

Wu, H.; Chen, X. W.; Fang, Q.; Kong, X. Z.; He, L. L.

2015-08-01

During the high-speed penetration of projectiles into concrete targets (the impact velocity ranges from 1.0 to 1.5 km/s), important factors such as the incident oblique and attacking angles, as well as the asymmetric abrasions of the projectile nose induced by the target-projectile interactions, may lead to obvious deviation of the terminal ballistic trajectory and reduction of the penetration efficiency. Based on the engineering model for the mass loss and nose-blunting of ogive-nosed projectiles established, by using the Differential Area Force Law (DAFL) method and semi-empirical resistance function, a finite differential approach was programmed (PENTRA2D) for predicting the terminal ballistic trajectory of mass abrasive high-speed projectiles penetrating into concrete targets. It accounts for the free-surface effects on the drag force acting on the projectile, which are attributed to the oblique and attacking angles, as well as the asymmetric nose abrasion of the projectile. Its validation on the prediction of curvilinear trajectories of non-normal high-speed penetrators into concrete targets is verified by comparison with available test data. Relevant parametric influential analyses show that the most influential factor for the stability of terminal ballistic trajectories is the attacking angle, followed by the oblique angle, the discrepancy of asymmetric nose abrasion, and the location of mass center of projectile. The terminal ballistic trajectory deviations are aggravated as the above four parameters increase.

Librarian instruction-delivery modality preferences for professional continuing education

PubMed Central

Lynn, Valerie A.; Bose, Arpita; Boehmer, Susan J.

2010-01-01

Objectives: Attending professional continuing education (CE) is an important component of librarianship. This research study identified librarians' preferences in delivery modalities of instruction for professional CE. The study also identified influential factors associated with attending CE classes. Methods: Five instruction-delivery modalities and six influential factors were identified for inclusion in an online survey. The survey completed by members of the American Library Association (ALA), Special Libraries Association (SLA), and Medical Library Association (MLA) provided the data for analysis of librarian preferences and influential factors. Results: The majority of respondents were MLA members, followed by ALA and SLA members. Librarians from all three library associations preferred the face-to-face instructional modality. The most influential factor associated with the decision to attend a professional CE class was cost. Conclusions: All five instruction-delivery modalities present useful structures for imparting professional CE. As librarians' experience with different modalities increases and as technology improves, preferences in instruction delivery may shift. But at present, face-to-face remains the most preferred modality. Based on the results of this study, cost was the most influential factor associated with attending a CE class. This may change as additional influential factors are identified and analyzed in future studies. PMID:20098656

Librarian instruction-delivery modality preferences for professional continuing education.

PubMed

Lynn, Valerie A; Bose, Arpita; Boehmer, Susan J

2010-01-01

Attending professional continuing education (CE) is an important component of librarianship. This research study identified librarians' preferences in delivery modalities of instruction for professional CE. The study also identified influential factors associated with attending CE classes. Five instruction-delivery modalities and six influential factors were identified for inclusion in an online survey. The survey completed by members of the American Library Association (ALA), Special Libraries Association (SLA), and Medical Library Association (MLA) provided the data for analysis of librarian preferences and influential factors. The majority of respondents were MLA members, followed by ALA and SLA members. Librarians from all three library associations preferred the face-to-face instructional modality. The most influential factor associated with the decision to attend a professional CE class was cost. All five instruction-delivery modalities present useful structures for imparting professional CE. As librarians' experience with different modalities increases and as technology improves, preferences in instruction delivery may shift. But at present, face-to-face remains the most preferred modality. Based on the results of this study, cost was the most influential factor associated with attending a CE class. This may change as additional influential factors are identified and analyzed in future studies.

Influential sources affecting Bangkok adolescent body image perceptions.

PubMed

Thianthai, Chulanee

2006-01-01

The study of body image-related problems in non-Western countries is still very limited. Thus, this study aims to identify the main influential sources and show how they affect the body image perceptions of Bangkok adolescents. The researcher recruited 400 Thai male and female adolescents in Bangkok, attending high school to freshmen level, ranging from 16-19 years, to participate in this study. Survey questionnaires were distributed to every student and follow-up interviews conducted with 40 students. The findings showed that there are eight main influential sources respectively ranked from the most influential to the least influential: magazines, television, peer group, familial, fashion trend, the opposite gender, self-realization and health knowledge. Similar to those studies conducted in Western countries, more than half of the total percentage was the influence of mass media and peer groups. Bangkok adolescents also internalized Western ideal beauty through these mass media channels. Alike studies conducted in the West, there was similarities in the process of how these influential sources affect Bangkok adolescent body image perception, with the exception of familial source. In conclusion, taking the approach of identifying the main influential sources and understanding how they affect adolescent body image perceptions can help prevent adolescents from having unhealthy views and taking risky measures toward their bodies. More studies conducted in non-Western countries are needed in order to build a cultural sensitive program, catered to the body image problems occurring in adolescents within that particular society.

Canonical microcircuits for predictive coding

PubMed Central

Bastos, Andre M.; Usrey, W. Martin; Adams, Rick A.; Mangun, George R.; Fries, Pascal; Friston, Karl J.

2013-01-01

Summary This review considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference – paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate. PMID:23177956

Assessing task-technology fit in a PACS upgrade: do users' and developers' appraisals converge?

PubMed

Lepanto, Luigi; Sicotte, Claude; Lehoux, Pascale

2011-12-01

The purpose of this study was to measure users' perceived benefits of a picture archiving and communication system (PACS) upgrade, and compare their responses to those predicted by developers. The Task-Technology Fit (TTF) model served as the theoretical framework to study the relation between TTF, utilization, and perceived benefits. A self-administered survey was distributed to radiologists working in a university hospital undergoing a PACS upgrade. Four variables were measured: impact, utilization, TTF, and perceived net benefits. The radiologists were divided into subgroups according to their utilization profiles. Analysis of variance was performed and the hypotheses were tested with regression analysis. Interviews were conducted with developers involved in the PACS upgrade who were asked to predict impact and TTF. Users identified only a moderate fit between the PACS enhancements and their tasks, while developers predicted a high level of TTF. The combination of a moderate fit and an underestimation of the potential impact of changes in the PACS led to a low score for perceived net benefits. Results varied significantly among user subgroups. Globally, the data support the hypotheses that TTF predicts utilization and perceived net benefits, but not that utilization predicts perceived net benefits. TTF is a valid tool to assess perceived benefits, but it is important to take into account the characteristics of users. In the context of a technology that is rapidly evolving, there needs to be an alignment of what users perceive as a good fit and the functionality developers incorporate into their products.

A Comparison of Selected Statistical Techniques to Model Soil Cation Exchange Capacity

NASA Astrophysics Data System (ADS)

Khaledian, Yones; Brevik, Eric C.; Pereira, Paulo; Cerdà, Artemi; Fattah, Mohammed A.; Tazikeh, Hossein

2017-04-01

Cation exchange capacity (CEC) measures the soil's ability to hold positively charged ions and is an important indicator of soil quality (Khaledian et al., 2016). However, other soil properties are more commonly determined and reported, such as texture, pH, organic matter and biology. We attempted to predict CEC using different advanced statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R2= 0.82 and Root Mean Squared Error (RMSE) =6.32) and ANNs (R2= 0.82 and RMSE=5.53) were the best models to estimate CEC, PSO (R2= 0.80 and RMSE=5.54) and PCR (R2= 0.70 and RMSE=6.48) also worked well and the overall results were very similar to each other. Clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set, while the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available. References Khaledian, Y., Kiani, F., Ebrahimi, S., Brevik, E.C., Aitkenhead-Peterson, J. 2016. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degradation and Development. doi: 10.1002/ldr.2541.

Putting reward in art: A tentative prediction error account of visual art

PubMed Central

Van de Cruys, Sander; Wagemans, Johan

2011-01-01

The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations. PMID:23145260

Models of Affective Decision Making: How Do Feelings Predict Choice?

PubMed

Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

2016-06-01

Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

Interfacial Healing and Transport Phenomena Modeling ff Biopolymers

NASA Astrophysics Data System (ADS)

Lebron, Karla

This research focuses on the characterization of bioplastics joined using ultrasonic welding and modeling of temperature distributions and interfacial healing. Polylactic acid (PLA), which is typically derived from starch-rich crops such as corn, was studied. While the measurement of activation energy for interfacial healing at weld interfaces of PLA films has been reported, here, this information is used to predict the weld strength of rigid PLA samples welded by ultrasonics. A characterization of the mechanical properties was completed with a tensile test to determine the effects of amplitude, melt velocity and collapse distance on weld strength. From previous interfacial healing activation energy measurements based on an impulse welding method, it was also possible to predict weld strength. It was found that the most influential parameters were weld time, collapse distance and weld velocity. In general, the model predicted weld strength reasonably well with r2 values between 0.77 and 0.78.

Redundancy and reduction: Speakers manage syntactic information density

PubMed Central

Florian Jaeger, T.

2010-01-01

A principle of efficient language production based on information theoretic considerations is proposed: Uniform Information Density predicts that language production is affected by a preference to distribute information uniformly across the linguistic signal. This prediction is tested against data from syntactic reduction. A single multilevel logit model analysis of naturally distributed data from a corpus of spontaneous speech is used to assess the effect of information density on complementizer that-mentioning, while simultaneously evaluating the predictions of several influential alternative accounts: availability, ambiguity avoidance, and dependency processing accounts. Information density emerges as an important predictor of speakers’ preferences during production. As information is defined in terms of probabilities, it follows that production is probability-sensitive, in that speakers’ preferences are affected by the contextual probability of syntactic structures. The merits of a corpus-based approach to the study of language production are discussed as well. PMID:20434141

When Advocacy Obscures Accuracy Online: Digital Pandemics of Public Health Misinformation Through an Antifluoride Case Study

PubMed Central

Getman, Rebekah; Saraf, Avinash; Zhang, Lily H.; Kalenderian, Elsbeth

2015-01-01

Objectives. In an antifluoridation case study, we explored digital pandemics and the social spread of scientifically inaccurate health information across the Web, and we considered the potential health effects. Methods. Using the social networking site Facebook and the open source applications Netvizz and Gephi, we analyzed the connectedness of antifluoride networks as a measure of social influence, the social diffusion of information based on conversations about a sample scientific publication as a measure of spread, and the engagement and sentiment about the publication as a measure of attitudes and behaviors. Results. Our study sample was significantly more connected than was the social networking site overall (P < .001). Social diffusion was evident; users were forced to navigate multiple pages or never reached the sample publication being discussed 60% and 12% of the time, respectively. Users had a 1 in 2 chance of encountering negative and nonempirical content about fluoride unrelated to the sample publication. Conclusions. Network sociology may be as influential as the information content and scientific validity of a particular health topic discussed using social media. Public health must employ social strategies for improved communication management. PMID:25602893

The Facebook Influence Model: A Concept Mapping Approach

PubMed Central

Kota, Rajitha; Schoohs, Shari; Whitehill, Jennifer M.

2013-01-01

Abstract Facebook is a popular social media Web site that has been hypothesized to exert potential influence over users' attitudes, intentions, or behaviors. The purpose of this study was to develop a conceptual framework to explain influential aspects of Facebook. This mixed methods study applied concept mapping methodology, a validated five-step method to visually represent complex topics. The five steps comprise preparation, brainstorming, sort and rank, analysis, and interpretation. College student participants were identified using purposeful sampling. The 80 participants had a mean age of 20.5 years, and included 36% males. A total of 169 statements were generated during brainstorming, and sorted into between 6 and 22 groups. The final concept map included 13 clusters. Interpretation data led to grouping of clusters into four final domains, including connection, comparison, identification, and Facebook as an experience. The Facebook Influence Concept Map illustrates key constructs that contribute to influence, incorporating perspectives of older adolescent Facebook users. While Facebook provides a novel lens through which to consider behavioral influence, it can best be considered in the context of existing behavioral theory. The concept map may be used toward development of potential future intervention efforts. PMID:23621717

Ambient Persuasive Technology Needs Little Cognitive Effort: The Differential Effects of Cognitive Load on Lighting Feedback versus Factual Feedback

NASA Astrophysics Data System (ADS)

Ham, Jaap; Midden, Cees

Persuasive technology can influence behavior or attitudes by for example providing interactive factual feedback about energy conservation. However, people often lack motivation or cognitive capacity to consciously process such relative complex information (e.g., numerical consumption feedback). Extending recent research that indicates that ambient persuasive technology can persuade the user without receiving the user's conscious attention, we argue here that Ambient Persuasive Technology can be effective while needing only little cognitive resources, and in general can be more influential than more focal forms of persuasive technology. In an experimental study, some participants received energy consumption feedback by means of a light changing color (more green=lower energy consumption, vs. more red=higher energy consumption) and others by means of numbers indicating kWh consumption. Results indicated that ambient feedback led to more conservation than factual feedback. Also, as expected, only for participants processing factual feedback, additional cognitive load lead to slower processing of that feedback. This research sheds light on fundamental characteristics of Ambient Persuasive Technology and Persuasive Lighting, and suggests that it can have important advantages over more focal persuasive technologies without losing its persuasive potential.

The Facebook influence model: a concept mapping approach.

PubMed

Moreno, Megan A; Kota, Rajitha; Schoohs, Shari; Whitehill, Jennifer M

2013-07-01

Facebook is a popular social media Web site that has been hypothesized to exert potential influence over users' attitudes, intentions, or behaviors. The purpose of this study was to develop a conceptual framework to explain influential aspects of Facebook. This mixed methods study applied concept mapping methodology, a validated five-step method to visually represent complex topics. The five steps comprise preparation, brainstorming, sort and rank, analysis, and interpretation. College student participants were identified using purposeful sampling. The 80 participants had a mean age of 20.5 years, and included 36% males. A total of 169 statements were generated during brainstorming, and sorted into between 6 and 22 groups. The final concept map included 13 clusters. Interpretation data led to grouping of clusters into four final domains, including connection, comparison, identification, and Facebook as an experience. The Facebook Influence Concept Map illustrates key constructs that contribute to influence, incorporating perspectives of older adolescent Facebook users. While Facebook provides a novel lens through which to consider behavioral influence, it can best be considered in the context of existing behavioral theory. The concept map may be used toward development of potential future intervention efforts.

Mechanistic and "natural" body metaphors and their effects on attitudes to hormonal contraception.

PubMed

Walker, Susan

2012-01-01

A small, self-selected convenience sample of male and female contraceptive users in the United Kingdom (n = 34) were interviewed between 2006 and 2008 concerning their feelings about the body and their contraceptive attitudes and experiences. The interviewees were a sub-sample of respondents (n = 188) who completed a paper-based questionnaire on similar topics, who were recruited through a poster placed in a family planning clinic, web-based advertisements on workplace and university websites, and through direct approaches to social groups. The bodily metaphors used when discussing contraception were analyzed using an interpretative phenomenological analytical approach facilitated by Atlas.ti software. The dominant bodily metaphor was mechanistic (i.e.,"body as machine"). A subordinate but influential bodily metaphor was the "natural" body, which had connotations of connection to nature and a quasi-sacred bodily order. Interviewees drew upon this "natural" metaphorical image in the context of discussing their anxieties about hormonal contraception. Drawing upon a "natural," non-mechanistic body image in the context of contraceptive decision-making contributed to reluctance to use a hormonal form of contraception. This research suggests that clinicians could improve communication and advice about contraception by recognizing that some users may draw upon non-mechanistic body imagery.

Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area.

PubMed

Su, Zhiguo; Dai, Tianjiao; Tang, Yushi; Tao, Yile; Huang, Bei; Mu, Qinglin; Wen, Donghui

2018-06-01

Coastal ecosystem structures and functions are changing under natural and anthropogenic influences. In this study, surface sediment samples were collected from disturbed zone (DZ), near estuary zone (NEZ), and far estuary zone (FEZ) of Hangzhou Bay, one of the most seriously polluted bays in China. The bacterial community structures and predicted functions varied significantly in different zones. Firmicutes were found most abundantly in DZ, highlighting the impacts of anthropogenic activities. Sediment total phosphorus was most influential on the bacterial community structures. Predicted by PICRUSt analysis, DZ significantly exceeded FEZ and NEZ in the subcategory of Xenobiotics Biodegradation and Metabolism; and DZ enriched all the nitrate reduction related genes, except nrfA gene. Seawater salinity and inorganic nitrogen, respectively as the representative natural and anthropogenic factor, performed exact-oppositely in nitrogen metabolism functions. The changes of bacterial community compositions and predicted functions provide a new insight into human-induced pollution impacts on coastal ecosystem. Copyright © 2018 Elsevier Ltd. All rights reserved.

Assessing Bleeding Risk in Patients Taking Anticoagulants

PubMed Central

Shoeb, Marwa; Fang, Margaret C.

2013-01-01

Anticoagulant medications are commonly used for the prevention and treatment of thromboembolism. Although highly effective, they are also associated with significant bleeding risks. Numerous individual clinical factors have been linked to an increased risk of hemorrhage, including older age, anemia, and renal disease. To help quantify hemorrhage risk for individual patients, a number of clinical risk prediction tools have been developed. These risk prediction tools differ in how they were derived and how they identify and weight individual risk factors. At present, their ability to effective predict anticoagulant-associated hemorrhage remains modest. Use of risk prediction tools to estimate bleeding in clinical practice is most influential when applied to patients at the lower spectrum of thromboembolic risk, when the risk of hemorrhage will more strongly affect clinical decisions about anticoagulation. Using risk tools may also help counsel and inform patients about their potential risk for hemorrhage while on anticoagulants, and can identify patients who might benefit from more careful management of anticoagulation. PMID:23479259

Virtues, Vices, and Political Influence in the U.S. Senate.

PubMed

ten Brinke, Leanne; Liu, Christopher C; Keltner, Dacher; Srivastava, Sameer B

2016-01-01

What qualities make a political leader more influential or less influential? Philosophers, political scientists, and psychologists have puzzled over this question, positing two opposing routes to political power--one driven by human virtues, such as courage and wisdom, and the other driven by vices, such as Machiavellianism and psychopathy. By coding nonverbal behaviors displayed in political speeches, we assessed the virtues and vices of 151 U.S. senators. We found that virtuous senators became more influential after they assumed leadership roles, whereas senators who displayed behaviors consistent with vices--particularly psychopathy--became no more influential or even less influential after they assumed leadership roles. Our results inform a long-standing debate about the role of morality and ethics in leadership and have important implications for electing effective government officials. Citizens would be wise to consider a candidate's virtue in casting their votes, which might increase the likelihood that elected officials will have genuine concern for their constituents and simultaneously promote cooperation and progress in government. © The Author(s) 2015.

Family Disruption and Intergenerational Reproduction: Comparing the Influences of Married Parents, Divorced Parents, and Stepparents.

PubMed

Kalmijn, Matthijs

2015-06-01

The transmission of individual characteristics and behaviors across generations has frequently been studied in the social sciences. For a growing number of children, however, the biological father was present in the household for only part of the time; and for many children, stepfathers were present. What are the implications of these changes for the process of intergenerational transmission? To answer this question, this article compares intergenerational transmission among married, divorced, and stepparents. Two forms of reproduction are studied: educational attainment and church attendance. For education, divorced fathers were as influential as married fathers, whereas stepfathers were less influential. For church attendance, married fathers were most influential, divorced fathers were least influential, and stepfathers were in between. Divorced mothers, in contrast, appeared to be more influential than married mothers. These findings lend negative support for the social capital hypothesis and positive support for notions of value socialization. The strong role of the divorced father for educational transmission is consistent with genetic processes and hypotheses about early advantages.

Social relevance: toward understanding the impact of the individual in an information cascade

NASA Astrophysics Data System (ADS)

Hall, Robert T.; White, Joshua S.; Fields, Jeremy

2016-05-01

Information Cascades (IC) through a social network occur due to the decision of users to disseminate content. We define this decision process as User Diffusion (UD). IC models typically describe an information cascade by treating a user as a node within a social graph, where a node's reception of an idea is represented by some activation state. The probability of activation then becomes a function of a node's connectedness to other activated nodes as well as, potentially, the history of activation attempts. We enrich this Coarse-Grained User Diffusion (CGUD) model by applying actor type logics to the nodes of the graph. The resulting Fine-Grained User Diffusion (FGUD) model utilizes prior research in actor typing to generate a predictive model regarding the future influence a user will have on an Information Cascade. Furthermore, we introduce a measure of Information Resonance that is used to aid in predictions regarding user behavior.

User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

PubMed

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

2018-01-01

Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

CHALLENGES FOR IMPLEMENTING A PTSD PREVENTIVE GENOMIC SEQUENCING PROGRAM IN THE U.S. MILITARY

PubMed Central

Lázaro-Muñoz, Gabriel; Juengst, Eric T.

2015-01-01

There is growing interest in using the quickly developing field of genomics to contribute to military readiness and effectiveness. Specifically, influential military advisory panels have recommended that the U.S. military apply genomics to help treat, prevent, or minimize the risk for post-traumatic stress disorder (PTSD) among service members. This article highlights some important scientific, legal, and ethical challenges regarding the development and deployment of a preventive genomic sequencing (PGS) program to predict the risk of PTSD among military service members. PMID:26401056

Revisiting Wittgenstein on Köhler and Gestalt psychology.

PubMed

Benjafield, John G

2008-01-01

In an article in this journal, Nicholas Pastore rejected Ludwig Wittgenstein's critique of Wolfgang Köhler and Gestalt psychology. Pastore appears not to have appreciated Wittgenstein's argument that Köhler mistook conceptual questions for factual ones. A simi-lar confusion seems to underlie at least some aspects of contemporary neuroscience. Be that as it may, Wittgenstein has had minimal influence on the research practices of psychologists while Köhler remains influential. This outcome would not have surprised Wittgenstein, who predicted that scientists would not see his work as relevant to theirs.

Fifty Years of Mountain Passes: A Perspective on Dan Janzen's Classic Article.

PubMed

Sheldon, Kimberly S; Huey, Raymond B; Kaspari, Michael; Sanders, Nathan J

2018-05-01

In 1967, Dan Janzen published "Why Mountain Passes Are Higher in the Tropics" in The American Naturalist. Janzen's seminal article has captured the attention of generations of biologists and continues to inspire theoretical and empirical work. The underlying assumptions and derived predictions are broadly synthetic and widely applicable. Consequently, Janzen's "seasonality hypothesis" has proven relevant to physiology, climate change, ecology, and evolution. To celebrate the fiftieth anniversary of this highly influential article, we highlight the past, present, and future of this work and include a unique historical perspective from Janzen himself.

An analysis for high speed propeller-nacelle aerodynamic performance prediction. Volume 2: User's manual

NASA Technical Reports Server (NTRS)

Egolf, T. Alan; Anderson, Olof L.; Edwards, David E.; Landgrebe, Anton J.

1988-01-01

A user's manual for the computer program developed for the prediction of propeller-nacelle aerodynamic performance reported in, An Analysis for High Speed Propeller-Nacelle Aerodynamic Performance Prediction: Volume 1 -- Theory and Application, is presented. The manual describes the computer program mode of operation requirements, input structure, input data requirements and the program output. In addition, it provides the user with documentation of the internal program structure and the software used in the computer program as it relates to the theory presented in Volume 1. Sample input data setups are provided along with selected printout of the program output for one of the sample setups.

An Evaluation and Redesign of the Conflict Prediction and Trial Planning Planview Graphical User Interface

NASA Technical Reports Server (NTRS)

Laudeman, Irene V.; Brasil, Connie L.; Stassart, Philippe

1998-01-01

The Planview Graphical User Interface (PGUI) is the primary display of air traffic for the Conflict Prediction and Trial Planning, function of the Center TRACON Automation System. The PGUI displays air traffic information that assists the user in making decisions related to conflict detection, conflict resolution, and traffic flow management. The intent of this document is to outline the human factors issues related to the design of the conflict prediction and trial planning portions of the PGUI, document all human factors related design changes made to the PGUI from December 1996 to September 1997, and outline future plans for the ongoing PGUI design.

Toward the Attribution of Web Behavior

DTIC Science & Technology

2012-07-01

den Poel, “Predicting website audience demo- graphics for Web advertising targeting using multi-website clickstream data,” Fundamenta Informaticae ...and M. Sydow, “Effective prediction of web user behaviour with user-level models,” Fundamenta Informaticae , vol. 89, no. 2, pp. 189–206, 2008. [24] J

Stimulus-response learning in long-term cocaine users: acquired equivalence and probabilistic category learning.

PubMed

Vadhan, Nehal P; Myers, Catherine E; Rubin, Eric; Shohamy, Daphna; Foltin, Richard W; Gluck, Mark A

2008-01-11

The purpose of this study was to examine stimulus-response (S-R) learning in active cocaine users. Twenty-two cocaine-dependent participants (20 males and 2 females) and 21 non-drug using control participants (19 males and 2 females) who were similar in age and education were administered two computerized learning tasks. The Acquired Equivalence task initially requires learning of simple antecedent-consequent discriminations, but later requires generalization of this learning when the stimuli are presented in novel recombinations. The Weather Prediction task requires the prediction of a dichotomous outcome based on different stimuli combinations when the stimuli predict the outcome only probabilistically. On the Acquired Equivalence task, cocaine users made significantly more errors than control participants when required to learn new discriminations while maintaining previously learned discriminations, but performed similarly to controls when required to generalize this learning. No group differences were seen on the Weather Prediction task. Cocaine users' learning of stimulus discriminations under conflicting response demands was impaired, but their ability to generalize this learning once they achieved criterion was intact. This performance pattern is consistent with other laboratory studies of long-term cocaine users that demonstrated that established learning interfered with new learning on incremental learning tasks, relative to healthy controls, and may reflect altered dopamine transmission in the basal ganglia of long-term cocaine users.

Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

2011-01-01

Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

Evaluation of protein-ligand affinity prediction using steered molecular dynamics simulations.

PubMed

Okimoto, Noriaki; Suenaga, Atsushi; Taiji, Makoto

2017-11-01

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.

Using artificial intelligence to predict permeability from petrographic data

NASA Astrophysics Data System (ADS)

Ali, Maqsood; Chawathé, Adwait

2000-10-01

Petrographic data collected during thin section analysis can be invaluable for understanding the factors that control permeability distribution. Reliable prediction of permeability is important for reservoir characterization. The petrographic elements (mineralogy, porosity types, cements and clays, and pore morphology) interact with each other uniquely to generate a specific permeability distribution. It is difficult to quantify accurately this interaction and its consequent effect on permeability, emphasizing the non-linear nature of the process. To capture these non-linear interactions, neural networks were used to predict permeability from petrographic data. The neural net was used as a multivariate correlative tool because of its ability to learn the non-linear relationships between multiple input and output variables. The study was conducted on the upper Queen formation called the Shattuck Member (Permian age). The Shattuck Member is composed of very fine-grained arkosic sandstone. The core samples were available from the Sulimar Queen and South Lucky Lake fields located in Chaves County, New Mexico. Nineteen petrographic elements were collected for each permeability value using a combined minipermeameter-petrographic technique. In order to reduce noise and overfitting the permeability model, these petrographic elements were screened, and their control (ranking) with respect to permeability was determined using fuzzy logic. Since the fuzzy logic algorithm provides unbiased ranking, it was used to reduce the dimensionality of the input variables. Based on the fuzzy logic ranking, only the most influential petrographic elements were selected as inputs for permeability prediction. The neural net was trained and tested using data from Well 1-16 in the Sulimar Queen field. Relying on the ranking obtained from the fuzzy logic analysis, the net was trained using the most influential three, five, and ten petrographic elements. A fast algorithm (the scaled conjugate gradient method) was used to optimize the network weight matrix. The net was then successfully used to predict the permeability in the nearby South Lucky Lake field, also in the Shattuck Member. This study underscored various important aspects of using neural networks as non-linear estimators. The neural network learnt the complex relationships between petrographic control and permeability. By predicting permeability in a remotely-located, yet geologically similar field, the generalizing capability of the neural network was also demonstrated. In old fields, where conventional petrographic analysis was routine, this technique may be used to supplement core permeability estimates.

Dynamic characteristics of tweeting and tweet topics

NASA Astrophysics Data System (ADS)

Kwon, Hyun Woong; Choi, M. Y.; Kim, Ho Sung; Lee, Keumsook

2012-02-01

Twitter, having more than 200 million world users and more than 4 million Korean users, is still growing fast. Because Twitter users can `tweet' about any topic within the 140-character limit, and other users who follow the users and see the tweets can `retweet' them, Twitter is regarded as a new medium of transferring and sharing information. Nevertheless, the propensities of Twitter users to tweet or to retweet still remain unclear. In order to investigate these propensities, we propose a simple model for the dynamics of the total number of tweets about specific topics. We then observe that the topics can be categorized into three kinds according to predictability and sustainability: predictable events, unpredictable events, and sustainable events. Comparing model results with real data, we infer the tweet propensities motivated by external causes as well as retweet propensities.

Regression Simulation Model. Appendix X. Users Manual,

DTIC Science & Technology

1981-03-01

change as the prediction equations become refined. Whereas no notice will be provided when the changes are made, the programs will be modified such that...NATIONAL BUREAU Of STANDARDS 1963 A ___,_ __ _ __ _ . APPENDIX X ( R4/ EGRESSION IMULATION ’jDEL. Ape’A ’) 7 USERS MANUA submitted to The Great River...regression analysis and to establish a prediction equation (model). The prediction equation contains the partial regression coefficients (B-weights) which

Examining Marijuana User and Non-User Prototypes in Formative Research for Prevention Campaigns

ERIC Educational Resources Information Center

Comello, Maria Leonora G.; Slater, Michael D.

2010-01-01

We report on research--both quantitative and qualitative--conducted to explore perceptions of prototypes of marijuana users, as well as the extent to which self-prototype congruence predicted marijuana use intention. Results of a survey of undergraduates (N = 139) showed that prototypes of users and non-users differed in terms of key attributes,…

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE PAGES

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

2015-05-15

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

Aggregation Trade Offs in Family Based Recommendations

NASA Astrophysics Data System (ADS)

Berkovsky, Shlomo; Freyne, Jill; Coombe, Mac

Personalized information access tools are frequently based on collaborative filtering recommendation algorithms. Collaborative filtering recommender systems typically suffer from a data sparsity problem, where systems do not have sufficient user data to generate accurate and reliable predictions. Prior research suggested using group-based user data in the collaborative filtering recommendation process to generate group-based predictions and partially resolve the sparsity problem. Although group recommendations are less accurate than personalized recommendations, they are more accurate than general non-personalized recommendations, which are the natural fall back when personalized recommendations cannot be generated. In this work we present initial results of a study that exploits the browsing logs of real families of users gathered in an eHealth portal. The browsing logs allowed us to experimentally compare the accuracy of two group-based recommendation strategies: aggregated group models and aggregated predictions. Our results showed that aggregating individual models into group models resulted in more accurate predictions than aggregating individual predictions into group predictions.

Satisfaction Formation Processes in Library Users: Understanding Multisource Effects

ERIC Educational Resources Information Center

Shi, Xi; Holahan, Patricia J.; Jurkat, M. Peter

2004-01-01

This study explores whether disconfirmation theory can explain satisfaction formation processes in library users. Both library users' needs and expectations are investigated as disconfirmation standards. Overall library user satisfaction is predicted to be a function of two independent sources--satisfaction with the information product received…

Measuring pedestrian volumes and conflicts. Volume IV, Pedestrian/vehicle accident prediction model : a users manual

DOT National Transportation Integrated Search

1988-03-01

Users of this manual are expected to be researchers who are attempting to develop models that can be used to predict occurrence of pedestrian accidents in a particular city. The manual presents guidelines in the development of such models. A group-...

The microcomputer scientific software series 4: testing prediction accuracy.

Treesearch

H. Michael Rauscher

1986-01-01

A computer program, ATEST, is described in this combination user's guide / programmer's manual. ATEST provides users with an efficient and convenient tool to test the accuracy of predictors. As input ATEST requires observed-predicted data pairs. The output reports the two components of accuracy, bias and precision.

Distance telescopes: a survey of user success.

PubMed

Lowe, J B; Rubinstein, M P

2000-05-01

The distance telescope has a historical reputation for causing difficulties in prescribing and adaptation. Hence, we considered that a retrospective survey of patients at Nottingham Low Vision Clinic might elucidate specific attributes that influence an individual patient's success in using a distance telescope. From 142 patients prescribed distance telescopes since the Clinic's inception, 133 apparently remained users and were mailed a preliminary three-question enquiry about usage of their distance telescopes. The 87 respondents were followed up with questionnaire 2, requesting explicit information about usage, namely frequency, degree of ease or difficulty, and purpose. Older patients required higher magnification (p < 0.025). Seventeen of 74 respondents to questionnaire 2 had various adaptational problems, which are discussed; 57 of 74 patients found their distance telescopes easy to use, and 49 of 57 were frequent users. Thus, ease and frequency are linked (p < 0.05). People tended to use their distance telescopes outdoors and indoors with similar frequency (p > or = 0.29). Adaptation was found to be unrelated to visual acuity, binocularity/monocularity, ocular pathology, or restricted mobility; magnification seemed to be influential, although not significantly. Aging did not significantly impede adaptation. We infer that the universal criterion for selecting treatable patients seems to be personality type. We conclude that adaptation to a device is dependent upon active recognition of its benefits, paralleled with a tolerance of its constraints, which combine to make usage easy and regular on at least one common task.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering.

PubMed

Kuang, Li; Yu, Long; Huang, Lan; Wang, Yin; Ma, Pengju; Li, Chuanbin; Zhu, Yujia

2018-05-14

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

PubMed Central

Huang, Lan; Wang, Yin; Ma, Pengju; Li, Chuanbin; Zhu, Yujia

2018-01-01

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness. PMID:29757995

Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations

NASA Astrophysics Data System (ADS)

Yoo, Kyung-Hyan; Gretzel, Ulrike

Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding the influence of source characteristics, which is abundant in the context of humanhuman relationships, can provide an important framework for identifying potential influence factors. This chapter reviews the existing literature on source characteristics in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.

Probabilistic Modeling and Evaluation of Surf Zone Injury Occurrence along the Delaware Coast

NASA Astrophysics Data System (ADS)

Doelp, M.; Puleo, J. A.

2017-12-01

Beebe Healthcare in Lewes, DE collected along the DE coast surf zone injury (SZI) data for seven summer seasons from 2010 through 2016. Data include, but are not limited to, time of injury, gender, age, and activity. Over 2000 injuries were recorded over the seven year period, including 116 spinal injuries and three fatalities. These injuries are predominantly wave related incidents including wading (41%), bodysurfing (26%), and body-boarding (20%). Despite the large number of injuries, beach associated hazards do not receive the same level of awareness that rip currents receive. Injury population statistics revealed those between the ages of 11 and 15 years old suffered the greatest proportion of injuries (18.8%). Male water users were twice as likely to sustain injury as their female counterparts. Also, non-locals were roughly six times more likely to sustain injury than locals. In 2016, five or more injuries occurred for 18.5% of the days sampled, and no injuries occurred for 31.4% of the sample days. The episodic nature of injury occurrence and population statistics indicate the importance of environmental conditions and human behavior on surf zone injuries. Higher order statistics are necessary to effectively assess SZI cause and likelihood of occurrence on a particular day. A Bayesian network using Netica software (Norsys) was constructed to model SZI and predict changes in injury likelihood on an hourly basis. The network incorporates environmental data collected by weather stations, NDBC buoy #44009, USACE buoy at Bethany Beach, and by researcher personnel on the beach. The Bayesian model includes prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential variables to injury likelihood are population, water temperature, nearshore wave height, beach slope, and the day of the week. Forecasting during the 2017 summer season will test model ability to predict injury likelihood.

Most influential FEMS publications.

PubMed

Prosser, James I; Cole, Jeff A; Nielsen, Jens; Bavoil, Patrik M; Häggblom, Max M

2014-05-01

A selection of influential FEMS publications to celebrate the 40th anniversary of FEMS. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Psychological and behavioral consequences of job loss: a covariance structure analysis using Weiner's (1985) attribution model.

PubMed

Prussia, G E; Kinicki, A J; Bracker, J S

1993-06-01

B. Weiner's (1985) attribution model of achievement motivation and emotion was used as a theoretical foundation to examine the mediating processes between involuntary job loss and employment status. Seventy-nine manufacturing employees were surveyed 1 month prior to permanent displacement, and finding another job was assessed 18 months later. Covariance structure analysis was used to evaluate goodness of fit and to compare the model to alternative measurement and structural representations. Discriminant validity analyses indicated that the causal dimensions underlying the model were not independent. Model predictions were supported in that internal and stable attributions for job loss negatively influenced finding another job through expectations for re-employment. These predictions held up even after controlling for influential unmeasured variables. Practical and theoretical implications are discussed.

Large Eddy Simulation of a Film Cooling Flow Injected from an Inclined Discrete Cylindrical Hole into a Crossflow with Zero-Pressure Gradient Turbulent Boundary Layer

NASA Technical Reports Server (NTRS)

Johnson, Perry L.; Shyam, Vikram

2012-01-01

A Large Eddy Simulation (LES) is performed of a high blowing ratio (M = 1.7) film cooling flow with density ratio of unity. Mean results are compared with experimental data to show the degree of fidelity achieved in the simulation. While the trends in the LES prediction are a noticeable improvement over Reynolds-Averaged Navier-Stokes (RANS) predictions, there is still a lack a spreading on the underside of the lifted jet. This is likely due to the inability of the LES to capture the full range of influential eddies on the underside of the jet due to their smaller structure. The unsteady structures in the turbulent coolant jet are also explored and related to turbulent mixing characteristics

To give or not to give: children's and adolescents' sharing and moral negotiations in economic decision situations.

PubMed

Gummerum, Michaela; Keller, Monika; Takezawa, Masanori; Mata, Jutta

2008-01-01

This study interconnects developmental psychology of fair and moral behavior with economic game theory. One hundred eighty-nine 9- to 17-year-old students shared a sum of money as individuals and groups with another anonymous group (dictator game). Individual allocations did not differ by age but did by gender and were predicted by participants' preferences for fair allocations. Group decision making followed a majority process. Level of moral reasoning did not predict individual offers, but group members with a higher moral reasoning ability were more influential during group negotiations and in influencing group outcomes. The youngest participants justified offers more frequently by referring to simple distribution principles. Older participants employed more complex reasons to justify deviations from allocation principles.

Attention and Conscious Perception in the Hypothesis Testing Brain

PubMed Central

Hohwy, Jakob

2012-01-01

Conscious perception and attention are difficult to study, partly because their relation to each other is not fully understood. Rather than conceiving and studying them in isolation from each other it may be useful to locate them in an independently motivated, general framework, from which a principled account of how they relate can then emerge. Accordingly, these mental phenomena are here reviewed through the prism of the increasingly influential predictive coding framework. On this framework, conscious perception can be seen as the upshot of prediction error minimization and attention as the optimization of precision expectations during such perceptual inference. This approach maps on well to a range of standard characteristics of conscious perception and attention, and can be used to interpret a range of empirical findings on their relation to each other. PMID:22485102

The challenge of predicting problematic chemicals using a decision analysis tool: Triclosan as a case study.

PubMed

Perez, Angela L; Gauthier, Alison M; Ferracini, Tyler; Cowan, Dallas M; Kingsbury, Tony; Panko, Julie

2017-01-01

Manufacturers lack a reliable means for determining whether a chemical will be targeted for deselection from their supply chain. In this analysis, 3 methods for determining whether a specific chemical (triclosan) would meet the criteria necessary for being targeted for deselection are presented. The methods included a list-based approach, use of a commercially available chemical assessment software tool run in 2 modes, and a public interest evaluation. Our results indicated that triclosan was included on only 6 of the lists reviewed, none of which were particularly influential in chemical selection decisions. The results from the chemical assessment tool evaluations indicated that human and ecological toxicity for triclosan is low and received scores indicating that the chemical would be considered of low concern. However, triclosan's peak public interest tracked several years in advance of increased regulatory scrutiny of this chemical suggesting that public pressure may have been influential in deselection decisions. Key data gaps and toxicity endpoints not yet regulated such as endocrine disruption potential or phototoxicity, but that are important to estimate the trajectory for deselection of a chemical, are discussed. Integr Environ Assess Manag 2017;13:198-207. © 2016 SETAC. © 2016 SETAC.

The effect of speaking rate on serial-order sound-level errors in normal healthy controls and persons with aphasia.

PubMed

Fossett, Tepanta R D; McNeil, Malcolm R; Pratt, Sheila R; Tompkins, Connie A; Shuster, Linda I

Although many speech errors can be generated at either a linguistic or motoric level of production, phonetically well-formed sound-level serial-order errors are generally assumed to result from disruption of phonologic encoding (PE) processes. An influential model of PE (Dell, 1986; Dell, Burger & Svec, 1997) predicts that speaking rate should affect the relative proportion of these serial-order sound errors (anticipations, perseverations, exchanges). These predictions have been extended to, and have special relevance for persons with aphasia (PWA) because of the increased frequency with which speech errors occur and because their localization within the functional linguistic architecture may help in diagnosis and treatment. Supporting evidence regarding the effect of speaking rate on phonological encoding has been provided by studies using young normal language (NL) speakers and computer simulations. Limited data exist for older NL users and no group data exist for PWA. This study tested the phonologic encoding properties of Dell's model of speech production (Dell, 1986; Dell,et al., 1997), which predicts that increasing speaking rate affects the relative proportion of serial-order sound errors (i.e., anticipations, perseverations, and exchanges). The effects of speech rate on the error ratios of anticipation/exchange (AE), anticipation/perseveration (AP) and vocal reaction time (VRT) were examined in 16 normal healthy controls (NHC) and 16 PWA without concomitant motor speech disorders. The participants were recorded performing a phonologically challenging (tongue twister) speech production task at their typical and two faster speaking rates. A significant effect of increased rate was obtained for the AP but not the AE ratio. Significant effects of group and rate were obtained for VRT. Although the significant effect of rate for the AP ratio provided evidence that changes in speaking rate did affect PE, the results failed to support the model derived predictions regarding the direction of change for error type proportions. The current findings argued for an alternative concept of the role of activation and decay in influencing types of serial-order sound errors. Rather than a slow activation decay rate (Dell, 1986), the results of the current study were more compatible with an alternative explanation of rapid activation decay or slow build-up of residual activation.

Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages.

PubMed

Rus, Holly M; Cameron, Linda D

2016-10-01

Social media provides unprecedented opportunities for enhancing health communication and health care, including self-management of chronic conditions such as diabetes. Creating messages that engage users is critical for enhancing message impact and dissemination. This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement. The Common-Sense Model of Illness Self-Regulation and established health communication techniques guided content analyses of 500 Facebook posts. Each post was coded for message features predicted to engage users and numbers of likes, shares, and comments during the week following posting. Multi-level, negative binomial regressions revealed that specific features predicted different forms of engagement. Imagery emerged as a strong predictor; messages with images had higher rates of liking and sharing relative to messages without images. Diabetes consequence information and positive identity predicted higher sharing while negative affect, social support, and crowdsourcing predicted higher commenting. Negative affect, crowdsourcing, and use of external links predicted lower sharing while positive identity predicted lower commenting. The presence of imagery weakened or reversed the positive relationships of several message features with engagement. Diabetes control information and negative affect predicted more likes in text-only messages, but fewer likes when these messages included illustrative imagery. Similar patterns of imagery's attenuating effects emerged for the positive relationships of consequence information, control information, and positive identity with shares and for positive relationships of negative affect and social support with comments. These findings hold promise for guiding communication design in health-related social media.

National Centers for Environmental Prediction

Science.gov Websites

Ensemble Users Meetings 7th NCEP/NWS Ensemble User Workshop 13-15 June 2016 6th NCEP/NWS Ensemble User Workshop 25 - 27 March 2014 5th NCEP/NWS Ensemble User Workshop 10 - 12 May, 2011 4th NCEP/NWS Ensemble User Workshop 13 - 15 May, 2008 3rd NCEP/NWS Ensemble User Workshop 31 Oct - 2 Nov, 2006 2nd NCEP/NWS

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

DOE PAGES

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; ...

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

Enhancing user experience by using multi-sensor data fusion to predict phone's luminance

NASA Astrophysics Data System (ADS)

Marhoubi, Asmaa H.

2017-09-01

The movement of a phone in an environment with different brightness, makes the luminance prediction challenging. The ambient light sensor takes time to modify the brightness of the screen based on the environment it is placed in. This causes an unsatisfactory user experience and delays in adjustment of the screen brightness. In this research, a method is proposed for enhancing the prediction of luminance using accelerometer, gyroscope and speed measurement technique. The speed of the phone is identified using Sum-of-Sine parameters. The lux values are then fused with the accelerometer and gyroscope data to present more accurate luminance values for the ALS based on the movement of the phone. An investigation is made during the movement of the user in a standard lighting environment. This enhances the user experience and improves the screen brightness precision. The accuracy has given an R-Square value of up to 0.97.

Ethical issues in using Twitter for population-level depression monitoring: a qualitative study.

PubMed

Mikal, Jude; Hurst, Samantha; Conway, Mike

2016-04-14

Recently, significant research effort has focused on using Twitter (and other social media) to investigate mental health at the population-level. While there has been influential work in developing ethical guidelines for Internet discussion forum-based research in public health, there is currently limited work focused on addressing ethical problems in Twitter-based public health research, and less still that considers these issues from users' own perspectives. In this work, we aim to investigate public attitudes towards utilizing public domain Twitter data for population-level mental health monitoring using a qualitative methodology. The study explores user perspectives in a series of five, 2-h focus group interviews. Following a semi-structured protocol, 26 Twitter users with and without a diagnosed history of depression discussed general Twitter use, along with privacy expectations, and ethical issues in using social media for health monitoring, with a particular focus on mental health monitoring. Transcripts were then transcribed, redacted, and coded using a constant comparative approach. While participants expressed a wide range of opinions, there was an overall trend towards a relatively positive view of using public domain Twitter data as a resource for population level mental health monitoring, provided that results are appropriately aggregated. Results are divided into five sections: (1) a profile of respondents' Twitter use patterns and use variability; (2) users' privacy expectations, including expectations regarding data reach and permanence; (3) attitudes towards social media based population-level health monitoring in general, and attitudes towards mental health monitoring in particular; (4) attitudes towards individual versus population-level health monitoring; and (5) users' own recommendations for the appropriate regulation of population-level mental health monitoring. Focus group data reveal a wide range of attitudes towards the use of public-domain social media "big data" in population health research, from enthusiasm, through acceptance, to opposition. Study results highlight new perspectives in the discussion of ethical use of public data, particularly with respect to consent, privacy, and oversight.

Rapid identifying high-influence nodes in complex networks

NASA Astrophysics Data System (ADS)

Song, Bo; Jiang, Guo-Ping; Song, Yu-Rong; Xia, Ling-Ling

2015-10-01

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods. Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).

Ranking influential spreaders is an ill-defined problem

NASA Astrophysics Data System (ADS)

Gu, Jain; Lee, Sungmin; Saramäki, Jari; Holme, Petter

2017-06-01

Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem —methods for identifying influential spreaders output a ranking of the nodes. In this work, we show that such a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Therefore, the set of n most important nodes to vaccinate does not need to have any node in common with the set of n + 1 most important nodes. We propose a method for quantifying the extent and impact of this phenomenon. By this method, we show that it is a common phenomenon in both empirical and model networks.

Question popularity analysis and prediction in community question answering services.

PubMed

Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan; Zhang, Yu

2014-01-01

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users' interest so as to improve the users' experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository.

CONSUME: users guide.

Treesearch

R.D. Ottmar; M.F. Burns; J.N. Hall; A.D. Hanson

1993-01-01

CONSUME is a user-friendly computer program designed for resource managers with some working knowledge of IBM-PC applications. The software predicts the amount of fuel consumption on logged units based on weather data, the amount and fuel moisture of fuels, and a number of other factors. Using these predictions, the resource manager can accurately determine when and...

Research on potential user identification model for electric energy substitution

NASA Astrophysics Data System (ADS)

Xia, Huaijian; Chen, Meiling; Lin, Haiying; Yang, Shuo; Miao, Bo; Zhu, Xinzhi

2018-01-01

The implementation of energy substitution plays an important role in promoting the development of energy conservation and emission reduction in china. Energy service management platform of alternative energy users based on the data in the enterprise production value, product output, coal and other energy consumption as a potential evaluation index, using principal component analysis model to simplify the formation of characteristic index, comprehensive index contains the original variables, and using fuzzy clustering model for the same industry user’s flexible classification. The comprehensive index number and user clustering classification based on constructed particle optimization neural network classification model based on the user, user can replace electric potential prediction. The results of an example show that the model can effectively predict the potential of users’ energy potential.

Studying User Income through Language, Behaviour and Affect in Social Media.

PubMed

Preoţiuc-Pietro, Daniel; Volkova, Svitlana; Lampos, Vasileios; Bachrach, Yoram; Aletras, Nikolaos

2015-01-01

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Studying User Income through Language, Behaviour and Affect in Social Media

PubMed Central

Preoţiuc-Pietro, Daniel; Volkova, Svitlana; Lampos, Vasileios; Bachrach, Yoram; Aletras, Nikolaos

2015-01-01

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions. PMID:26394145

An Effective News Recommendation Method for Microblog User

PubMed Central

Gu, Wanrong; Dong, Shoubin; Zeng, Zhizhao; He, Jinchao

2014-01-01

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data. PMID:24983011

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

PubMed

Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

2015-11-01

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Prediction value of anti-Mullerian hormone (AMH) serum levels and antral follicle count (AFC) in hormonal contraceptive (HC) users and non-HC users undergoing IVF-PGD treatment.

PubMed

Bas-Lando, Maayan; Rabinowitz, Ron; Farkash, Rivka; Algur, Nurit; Rubinstein, Esther; Schonberger, Oshrat; Eldar-Geva, Talia

2017-10-01

Use of hormone contraceptives (HC) is very popular in the reproductive age and, therefore, evaluation of ovarian reserve would be a useful tool to accurately evaluate the reproductive potential in HC users. We conducted a retrospective cohort study of 41 HC users compared to 57 non-HC users undergoing IVF-preimplantation genetic diagnosis (PGD) aiming to evaluate the effect of HC on the levels of anti-Mullerian hormone (AMH), small (2-5 mm), large (6-10 mm) and total antral follicle count (AFC) and the ability of these markers to predict IVF outcome. Significant differences in large AFC (p = 0.04) and ovarian volume (p < 0.0001) were seen, however, there were no significant differences in small and total AFC or in serum AMH and FSH levels. Oocyte number significantly correlated with AMH and total AFC in HC users (p < 0.001) while in non-HC users these correlations were weaker. In HC users, the significant predictors of achieving <6 and >18 oocytes were AFC (ROC-AUC; 0.958, p = 0.001 and 0.883, p = 0.001) and AMH (ROC-AUC-0.858, p = 0.01 and 0.878, p = 0.001), respectively. The predictive values were less significant in non-HC users. These findings are important in women treated for PGD, in ovum donors and for assessing the fertility prognosis in women using HC and wishing to postpone pregnancy.

Predicting age groups of Twitter users based on language and metadata features

PubMed Central

Morgan-Lopez, Antonio A.; Chew, Robert F.; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research. PMID:28850620

Predicting age groups of Twitter users based on language and metadata features.

PubMed

Morgan-Lopez, Antonio A; Kim, Annice E; Chew, Robert F; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

White matter tract covariance patterns predict age-declining cognitive abilities.

PubMed

Gazes, Yunglin; Bowman, F DuBois; Razlighi, Qolamreza R; O'Shea, Deirdre; Stern, Yaakov; Habeck, Christian

2016-01-15

Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations. In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis. A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number. Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and fluid reasoning but not vocabulary. Other measures of brain health will need to be explored to reveal the major influences on the vocabulary ability. Copyright © 2015 Elsevier Inc. All rights reserved.

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.

PubMed

Agarwal, Vibhu; Zhang, Liangliang; Zhu, Josh; Fang, Shiyuan; Cheng, Tim; Hong, Chloe; Shah, Nigam H

2016-09-21

By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user's future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. We inferred presence within the geofence of a medical facility from location and duration information from users' search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model's utility. We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate. Results from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers.

Predicting Adherence to Use of Remote Health Monitoring Systems in a Cohort of Patients with Chronic Heart Failure

PubMed Central

Evangelista, Lorraine S.; Ghasemzadeh, Hassan; Lee, Jung-Ah; Fallahzadeh, Ramin; Sarrafzadeh, Majid; Moser, Debra K.

2017-01-01

Background It is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF). Objective The study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF. Methods This pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8±6.1, range 58–83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration. Results Baseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively. Conclusion Our preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies. PMID:27886024

Correlates of Prescription Drug Market Involvement among Young Adults

PubMed Central

Vuolo, Mike; Kelly, Brian C.; Wells, Brooke E.; Parsons, Jeffrey T.

2014-01-01

Background While a significant minority of prescription drug misusers report purchasing prescription drugs, little is known about prescription drug selling. We build upon past research on illicit drug markets, which increasingly recognizes networks and nightlife as influential, by examining prescription drug market involvement. Methods We use data from 404 young adult prescription drug misusers sampled from nightlife scenes. Using logistic regression, we examine recent selling of and being approached to sell prescription drugs, predicted using demographics, misuse, prescription access, and nightlife scene involvement. Results Those from the wealthiest parental class and heterosexuals had higher odds (OR=6.8) of selling. Higher sedative and stimulant misuse (ORs=1.03), having a stimulant prescription (OR=4.14), and having sold other illegal drugs (OR=6.73) increased the odds of selling. College bar scene involvement increased the odds of selling (OR=2.73) and being approached to sell (OR=2.09). Males (OR=1.93), stimulant users (OR=1.03), and sedative prescription holders (OR=2.11) had higher odds of being approached. Discussion College bar scene involvement was the only site associated with selling and being approached; such participation may provide a network for prescription drug markets. There were also differences between actual selling and being approached. Males were more likely to be approached, but not more likely to sell than females, while the opposite held for those in the wealthiest parental class relative to lower socioeconomic statuses. Given that misuse and prescriptions of sedatives and stimulants were associated with prescription drug market involvement, painkiller misusers may be less likely to sell their drugs given the associated physiological dependence. PMID:25175544

Social media and flu: Media Twitter accounts as agenda setters.

PubMed

Yun, Gi Woong; Morin, David; Park, Sanghee; Joa, Claire Youngnyo; Labbe, Brett; Lim, Jongsoo; Lee, Sooyoung; Hyun, Daewon

2016-07-01

This paper has two objectives. First, it categorizes the Twitter handles tweeted flu related information based on the amount of replies and mentions within the Twitter network. The collected Twitter accounts are categorized as media, health related individuals, organizations, government, individuals with no background with media or medical field, in order to test the relationship between centrality measures of the accounts and their categories. The second objective is to examine the relationship between the importance of the Twitter accounts in the network, centrality measures, and specific characteristics of each account, including the number of tweets and followers as well as the number of accounts followed and liked. Using Twitter search network API, tweets with "flu" keyword were collected and tabulated. Network centralities were calculated with network analysis tool, NodeXL. The collected Twitters accounts were content analyzed and categorized by multiple coders. When the media or organizational Twitter accounts were present in the list of important Twitter accounts, they were highly effective disseminating flu-related information. Also, they were more likely to stay active one year after the data collection period compared to other influential individual accounts. Health campaigns are recommended to focus on recruiting influential Twitter accounts and encouraging them to retweet or mention in order to produce better results in disseminating information. Although some individual social media users were valuable assets in terms of spreading information about flu, media and organization handles were more reliable information distributors. Thus, health information practitioners are advised to design health campaigns better utilizing media and organizations rather than individuals to achieve consistent and efficient campaign outcomes. Published by Elsevier Ireland Ltd.

Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Visweswara Sathanur, Arun; Halappanavar, Mahantesh; Shi, Yi

In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. To model the spread of influence in such a scenario, we consider the problem of identification of influential entities in a complex network when nodal activation can happen through two different mechanisms. The first mode of activation is due mechanisms intrinsic to the node. The second mechanism is through the influence of connected neighbors. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarilymore » because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. We provide small motivating examples to provide an intuitive understanding of the effect of including the intrinsic activation mechanism. We sketch a proof of the submodularity of the influence function under the new formulation and demonstrate the same with larger graphs. We then show by means of additional experiments on a real-world twitter dataset how the formulation can be applied to real-world social media datasets. Finally we derive a computationally efficient centrality metric that takes into account, both the mechanisms of activation and provides for an accurate as well as computationally efficient alternative approach to the problem of identifying influencers under intrinsic activation.« less

Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

PubMed Central

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction.

PubMed

Salvatore, M; Shu, N; Elofsson, A

2018-01-01

SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/. © 2017 The Protein Society.

Identifying influential spreaders in complex networks based on gravity formula

NASA Astrophysics Data System (ADS)

Ma, Ling-ling; Ma, Chuang; Zhang, Hai-Feng; Wang, Bing-Hong

2016-06-01

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.

Conceptual Web Users' Actions Prediction for Ontology-Based Browsing Recommendations

NASA Astrophysics Data System (ADS)

Robal, Tarmo; Kalja, Ahto

The Internet consists of thousands of web sites with different kinds of structures. However, users are browsing the web according to their informational expectations towards the web site searched, having an implicit conceptual model of the domain in their minds. Nevertheless, people tend to repeat themselves and have partially shared conceptual views while surfing the web, finding some areas of web sites more interesting than others. Herein, we take advantage of the latter and provide a model and a study on predicting users' actions based on the web ontology concepts and their relations.

Toward Predicting Social Support Needs in Online Health Social Networks.

PubMed

Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

2017-08-02

While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs. ©Min-Je Choi, Sung-Hee Kim, Sukwon Lee, Bum Chul Kwon, Ji Soo Yi, Jaegul Choo, Jina Huh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.08.2017.

Synthesis of User Needs for Arctic Sea Ice Predictions

NASA Astrophysics Data System (ADS)

Wiggins, H. V.; Turner-Bogren, E. J.; Sheffield Guy, L.

2017-12-01

Forecasting Arctic sea ice on sub-seasonal to seasonal scales in a changing Arctic is of interest to a diverse range of stakeholders. However, sea ice forecasting is still challenging due to high variability in weather and ocean conditions and limits to prediction capabilities; the science needs for observations and modeling are extensive. At a time of challenged science funding, one way to prioritize sea ice prediction efforts is to examine the information needs of various stakeholder groups. This poster will present a summary and synthesis of existing surveys, reports, and other literature that examines user needs for sea ice predictions. The synthesis will include lessons learned from the Sea Ice Prediction Network (a collaborative, multi-agency-funded project focused on seasonal Arctic sea ice predictions), the Sea Ice for Walrus Outlook (a resource for Alaska Native subsistence hunters and coastal communities, that provides reports on weather and sea ice conditions), and other efforts. The poster will specifically compare the scales and variables of sea ice forecasts currently available, as compared to what information is requested by various user groups.

Combining multiple earthquake models in real time for earthquake early warning

USGS Publications Warehouse

Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

iPat: intelligent prediction and association tool for genomic research.

PubMed

Chen, Chunpeng James; Zhang, Zhiwu

2018-06-01

The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. zhiwu.zhang@wsu.edu.

Do clients' problem-solving appraisals predict career counseling outcomes or vice versa? A reanalysis of Heppner, et al.

PubMed

Lee, Dong-Gwi; Park, Hyun-Joo; Heppner, Mary J

2009-12-01

Using Heppner, et al.'s data from 2004, this study tested career counseling clients in the United States on problem-solving appraisal scores and career-related variables. A cross-lagged panel design with structural equation modeling was used. Results supported the link between clients' precounseling problem-solving appraisal scores and career outcome. This finding held for career decision-making, but not for vocational identity. The study provided further support for Heppner, et al.'s findings, highlighting the influential role of clients' problem-solving appraisals in advancing their career decision-making processes.

Predicting Foreign Language Usage from English-Only Social Media Posts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkova, Svitlana; Ranshous, Stephen M.; Phillips, Lawrence A.

Social media is known for its multicultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 23 thousand multilingual users speaking 11 other languages besides English. We rely on this multilingual corpus to build predictive models for a novel task – inferring non- English languages that users speak exclusively from their English tweets. We contrast the predictive power of different linguistic signals and report that lexical content and syntactic structuremore » of English tweets are the most predictive of non-English languages that users speak on Twitter. By analyzing cross-lingual transfer – the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 11 languages.« less

Consuming energy drinks at the age of 14 predicted legal and illegal substance use at 16.

PubMed

Barrense-Dias, Yara; Berchtold, André; Akre, Christina; Surís, Joan-Carles

2016-11-01

This study examined whether consuming energy drinks at the age of 14 predicted substance use at 16. We followed 621 youths from an area of Switzerland who completed a longitudinal online survey in both 2012 and 2014 when they were 14 and 16 years of age. At 14, participants, who were divided into nonenergy drink users (n = 262), occasional users (n = 183) and regular users (n = 176), reported demographic, health-related and substance use data. Substance use at 16 was assessed through logistic regression using nonusers as the reference group and controlling for significant variables at 14. At the bivariate level, energy drink consumption was associated with substance use at both 14 and 16. Energy drink consumers were also more likely to be male, older, less academic, sleep less on schooldays and live in an urban area. In the multivariate analysis, smokers, alcohol misusers and cannabis users at the age of 16 were significantly more likely to have been regular energy drink users at the age of 14. Consuming energy drinks at 14 years of age predicted using legal and illegal substances at 16. Health providers should screen young adolescents for energy drink use and closely monitor weekly users. ©2016 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Who runs public health? A mixed-methods study combining qualitative and network analyses.

PubMed

Oliver, Kathryn; de Vocht, Frank; Money, Annemarie; Everett, Martin

2013-09-01

Persistent health inequalities encourage researchers to identify new ways of understanding the policy process. Informal relationships are implicated in finding evidence and making decisions for public health policy (PHP), but few studies use specialized methods to identify key actors in the policy process. We combined network and qualitative data to identify the most influential individuals in PHP in a UK conurbation and describe their strategies to influence policy. Network data were collected by asking for nominations of powerful and influential people in PHP (n = 152, response rate 80%), and 23 semi-structured interviews were analysed using a framework approach. The most influential PHP makers in this conurbation were mid-level managers in the National Health Service and local government, characterized by managerial skills: controlling policy processes through gate keeping key organizations, providing policy content and managing selected experts and executives to lead on policies. Public health professionals and academics are indirectly connected to policy via managers. The most powerful individuals in public health are managers, not usually considered targets for research. As we show, they are highly influential through all stages of the policy process. This study shows the importance of understanding the daily activities of influential policy individuals.

Working without a Crystal Ball: Predicting Web Trends for Web Services Librarians

ERIC Educational Resources Information Center

Ovadia, Steven

2008-01-01

User-centered design is a principle stating that electronic resources, like library Web sites, should be built around the needs of the users. This article interviews Web developers of library and non-library-related Web sites, determining how they assess user needs and how they decide to adapt certain technologies for users. According to the…

I’ll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application

PubMed Central

Lin, Zhiyuan; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. These primary intents are associated with how long each life lasts and how likely the user is to re-engage for a new life. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). We discuss implications of the multiple life paradigm and propose a novel prediction task of predicting the number of lives of a user. Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement. PMID:29780978

Using Mid Infrared Spectroscopy to Predict the Decomposability of Soil Organic Matter Stored in Arctic Tundra Soils

NASA Astrophysics Data System (ADS)

Matamala, R.; Fan, Z.; Jastrow, J. D.; Liang, C.; Calderon, F.; Michaelson, G.; Ping, C. L.; Mishra, U.; Hofmann, S. M.

2016-12-01

The large amounts of organic matter stored in permafrost-region soils are preserved in a relatively undecomposed state by the cold and wet environmental conditions limiting decomposer activity. With pending climate changes and the potential for warming of Arctic soils, there is a need to better understand the amount and potential susceptibility to mineralization of the carbon stored in the soils of this region. Studies have suggested that soil C:N ratio or other indicators based on the molecular composition of soil organic matter could be good predictors of potential decomposability. In this study, we investigated the capability of Fourier-transform mid infrared spectroscopy (MidIR) spectroscopy to predict the evolution of carbon dioxide (CO2) produced by Arctic tundra soils during a 60-day laboratory incubation. Soils collected from four tundra sites on the Coastal Plain, and Arctic Foothills of the North Slope of Alaska were separated into active-layer organic, active-layer mineral, and upper permafrost and incubated at 1, 4, 8 and 16 °C. Carbon dioxide production was measured throughout the incubations. Total soil organic carbon (SOC) and total nitrogen (TN) concentrations, salt (0.5 M K2SO4) extractable organic matter (SEOM), and MidIR spectra of the soils were measured before and after incubation. Multivariate partial least squares (PLS) modeling was used to predict cumulative CO2 production, decay rates, and the other measurements. MidIR reliably estimated SOC and TN and SEOM concentrations. The MidIR prediction models of CO2 production were very good for active-layer mineral and upper permafrost soils and good for the active-layer organic soils. SEOM was also a very good predictor of CO2 produced during the incubations. Analysis of the standardized beta coefficients from the PLS models of CO2 production for the three soil layers indicated a small number (9) of influential spectral bands. Of these, bands associated with O-H and N-H stretch, carbonates, and ester C-O appeared to be most important for predicting CO2 production for both active-layer mineral and upper permafrost soils. Further analysis of these influential bands and their relationships to SEOM in soil will be explored. Our results show that the MidIR spectra contains valuable information that can be related to decomposability of soils.

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

PubMed Central

Marto, Aminaton; Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. PMID:25147856

From action intentions to action effects: how does the sense of agency come about?

PubMed Central

Chambon, Valérian; Sidarus, Nura; Haggard, Patrick

2014-01-01

Sense of agency refers to the feeling of controlling an external event through one’s own action. On one influential view, agency depends on how predictable the consequences of one’s action are, getting stronger as the match between predicted and actual effect of an action gets closer. Thus, sense of agency arises when external events that follow our action are consistent with predictions of action effects made by the motor system while we perform or simply intend to perform an action. According to this view, agency is inferred retrospectively, after an action has been performed and its consequences are known. In contrast, little is known about whether and how internal processes involved in the selection of actions may influence subjective sense of control, in advance of the action itself, and irrespective of effect predictability. In this article, we review several classes of behavioral and neuroimaging data suggesting that earlier processes, linked to fluency of action selection, prospectively contribute to sense of agency. These findings have important implications for better understanding human volition and abnormalities of action experience. PMID:24860486

Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woods, Jason; Winkler, Jon

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to themore » interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.« less

A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

PubMed

Marto, Aminaton; Hajihassani, Mohsen; Armaghani, Danial Jahed; Mohamad, Edy Tonnizam; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs

DOE PAGES

Woods, Jason; Winkler, Jon

2018-01-31

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to themore » interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.« less

Identifying influential nodes in complex networks: A node information dimension approach

NASA Astrophysics Data System (ADS)

Bian, Tian; Deng, Yong

2018-04-01

In the field of complex networks, how to identify influential nodes is a significant issue in analyzing the structure of a network. In the existing method proposed to identify influential nodes based on the local dimension, the global structure information in complex networks is not taken into consideration. In this paper, a node information dimension is proposed by synthesizing the local dimensions at different topological distance scales. A case study of the Netscience network is used to illustrate the efficiency and practicability of the proposed method.

Spelling is Just a Click Away - A User-Centered Brain-Computer Interface Including Auto-Calibration and Predictive Text Entry.

PubMed

Kaufmann, Tobias; Völker, Stefan; Gunesch, Laura; Kübler, Andrea

2012-01-01

Brain-computer interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communication channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP-BCIs can be handled independently by laymen without expert support, which is inevitable for establishing BCIs in end-user's daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character-matrix. N = 19 BCI novices handled a user-centered ERP-BCI application on their own without expert support. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and, importantly, did not reduce accuracy. In sum, this study demonstrates feasibility of auto-calibrating ERP-BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix.

Can We Predict Patient Wait Time?

PubMed

Pianykh, Oleg S; Rosenthal, Daniel I

2015-10-01

The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Features of effective medical knowledge resources to support point of care learning: a focus group study.

PubMed

Cook, David A; Sorensen, Kristi J; Hersh, William; Berger, Richard A; Wilkinson, John M

2013-01-01

Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field.

Features of Effective Medical Knowledge Resources to Support Point of Care Learning: A Focus Group Study

PubMed Central

Cook, David A.; Sorensen, Kristi J.; Hersh, William; Berger, Richard A.; Wilkinson, John M.

2013-01-01

Objective Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. Methods We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. Results We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. Conclusion The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field. PMID:24282535

Analyzing big data in social media: Text and network analyses of an eating disorder forum.

PubMed

Moessner, Markus; Feldhege, Johannes; Wolf, Markus; Bauer, Stephanie

2018-05-10

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders. Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit. Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses. This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication. © 2018 Wiley Periodicals, Inc.

The impact of online reviews on exhibitor behaviour: evidence from movie industry

NASA Astrophysics Data System (ADS)

Wang, H.; Guo, K.

2017-11-01

This paper explores the effects of online reviews in the empirical context of movie industry. Quality uncertainty and hedonic consumption render movies a particularly interesting category to study quality signals and social influence. Online consumer chatters about a movie are readily available in popular movie-related websites. These data, coupled with movie-specific sales and advertising information accessible online or from commercial sources, provide unique opportunity to explore the implication of word-of-mouth (WOM) for consumer behaviour and business strategy. We thus examine how online reviews impact on business strategies, in this case, exhibitor behaviour, through an empirical analysis of movie industry. First, online reviews are grouped into user reviews and critic reviews. We then use them respectively in two simultaneous equation systems, which regard online reviews, box office revenue and the number of screens as endogenous variables. Next, a three-stage least-square procedure is employed to estimate these two systems using a sample of 141 movies in the U.S. market. The results indicate that the online user reviews' rating is positively associated with the number of screens, confirming that the online review is influential on exhibitor behaviour. The number of online reviews indirectly influences the number of screens through the box office revenue.

Testing Video and Social Media for Engaging Users of the U.S. Climate Resilience Toolkit

NASA Astrophysics Data System (ADS)

Green, C. J.; Gardiner, N.; Niepold, F., III; Esposito, C.

2015-12-01

We developed a custom video production stye and a method for analyzing social media behavior so that we may deliberately build and track audience growth for decision-support tools and case studies within the U.S. Climate Resilience Toolkit. The new style of video focuses quickly on decision processes; its 30s format is well-suited for deployment through social media. We measured both traffic and engagement with video using Google Analytics. Each video included an embedded tag, allowing us to measure viewers' behavior: whether or not they entered the toolkit website; the duration of their session on the website; and the number pages they visited in that session. Results showed that video promotion was more effective on Facebook than Twitter. Facebook links generated twice the number of visits to the toolkit. Videos also increased Facebook interaction overall. Because most Facebook users are return visitors, this campaign did not substantially draw new site visitors. We continue to research and apply these methods in a targeted engagement and outreach campaign that utilizes the theory of social diffusion and social influence strategies to grow our audience of "influential" decision-makers and people within their social networks. Our goal is to increase access and use of the U.S. Climate Resilience Toolkit.

Physical Outdoor Activity versus Indoor Activity: Their Influence on Environmental Behaviors.

PubMed

Fang, Wei-Ta; Ng, Eric; Chang, Mei-Chuan

2017-07-17

There are strong evidences linking physical outdoor activity and health benefits; however, little is known about the impact on environmental behaviors. Thus, this study aims to close this gap by investigating the influence of physical outdoor activity on environmental behaviors. A total of 416 surveys were distributed to students in eight public primary schools located near the Hsinchu Science and Industrial Park in Taiwan. Findings from the analysis revealed that subjective norms had a more influential effect on environmental behaviors for participants who engaged in physical activity at outdoor parks. In contrast, descriptive norms had a direct predictive impact on environmental behaviors for participants whose main physical activity venue was at the indoor after-school centers. Research results also highlighted attitude as the strongest predictive variable influence on environmental behaviors for children who engaged in physical indoor and outdoor activities.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

NASA Astrophysics Data System (ADS)

Navares, Ricardo; Aznarte, José Luis

2017-04-01

In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features.

PubMed

Navares, Ricardo; Aznarte, José Luis

2017-04-01

In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

Using a digital storytelling assignment to teach public health advocacy.

PubMed

de Castro, A B; Levesque, Salem

2018-03-01

The need and expectation for advocacy is central to public health nursing practice. Advocacy efforts that effectively call attention to population health threats and promote the well-being of communities rely on strategies that deliver influential messaging. The digital story is a lay method to capture meaningful, impactful stories that can be used to advocate for public health concerns. Readily available, user-friendly digital technologies allow engagement in digital media production to create digital stories. This paper describes how digital story making can be utilized as an academic assignment to teach public health advocacy within an undergraduate nursing curriculum. Providing nursing students this artistic outlet can facilitate meeting academic learning goals, while also equipping them with creative skills that can be applied in future professional practice. Nursing educators can take advantage of institutional resources and campus culture to support the use of novel digital media assignments that facilitate application of advocacy concepts. © 2017 Wiley Periodicals, Inc.

The effect of sequential exposure of color conditions on time and accuracy of graphic symbol location.

PubMed

Alant, Erna; Kolatsis, Anna; Lilienfeld, Margi

2010-03-01

An important aspect in AAC concerns the user's ability to locate an aided visual symbol on a communication display in order to facilitate meaningful interaction with partners. Recent studies have suggested that the use of different colored symbols may be influential in the visual search process, and that this, in turn will influence the speed and accuracy of symbol location. This study examined the role of color on rate and accuracy of identifying symbols on an 8-location overlay through the use of 3 color conditions (same, mixed and unique). Sixty typically developing preschool children were exposed to two different sequential exposures (Set 1 and Set 2). Participants searched for a target stimulus (either meaningful symbols or arbitrary forms) in a stimuli array. Findings indicated that the sequential exposures (orderings) impacted both time and accuracy for both types of symbols within specific instances.

Connecting Athletes’ Self-Perceptions and Metaperceptions of Competence: a Structural Equation Modeling Approach

PubMed Central

Cecchini, Jose A.; Fernández-Rio, Javier; Méndez-Giménez, Antonio

2015-01-01

This study explored the relationships between athletes’ competence self-perceptions and metaperceptions. Two hundred and fifty one student-athletes (14.26 ± 1.89 years), members of twenty different teams (basketball, soccer) completed a questionnaire which included the Perception of Success Questionnaire, the Competence subscale of the Intrinsic Motivation Inventory, and modified versions of both questionnaires to assess athletes’ metaperceptions. Structural equation modelling analysis revealed that athletes’ task and ego metaperceptions positively predicted task and ego self-perceptions, respectively. Competence metaperceptions were strong predictors of competence self-perceptions, confirming the atypical metaperception formation in outcome-dependent contexts such as sport. Task and ego metaperceptions positively predicted athletes’ competence metaperceptions. How coaches value their athletes’ competence is more influential on what the athletes think of themselves than their own self-perceptions. Athletes’ ego and task metaperceptions influenced their competence metaperceptions (how coaches rate their competence). Therefore, athletes build their competence metaperceptions using all information available from their coaches. Finally, only task-self perfections positively predicted athletes’ competence self-perceptions. PMID:26240662

Weight Shame, Social Connection, and Depressive Symptoms in Late Adolescence

PubMed Central

Brewis, Alexandra A.; Bruening, Meg

2018-01-01

Child and adolescent obesity is increasingly the focus of interventions, because it predicts serious disease morbidity later in life. However, social environments that permit weight-related stigma and body shame may make weight control and loss more difficult. Rarely do youth obesity interventions address these complexities. Drawing on repeated measures in a large sample (N = 1443) of first-year (freshman), campus-resident university students across a nine-month period, we model how weight-related shame predicts depressive symptom levels, how being overweight (assessed by anthropometric measures) shapes that risk, and how social connection (openness to friendship) might mediate/moderate. Body shame directly, clearly, and repeatedly predicts depression symptom levels across the whole school year for all students, but overweight youth have significantly elevated risk. Social connections mediate earlier in the school year, and in all phases moderate, body shame effects on depression. Youth obesity interventions would be well-served recognizing and incorporating the influential roles of social-environmental factors like weight stigma and friendship in program design. PMID:29723962

Constraints on decision making: implications from genetics, personality, and addiction.

PubMed

Baker, Travis E; Stockwell, Tim; Holroyd, Clay B

2013-09-01

An influential neurocomputational theory of the biological mechanisms of decision making, the "basal ganglia go/no-go model," holds that individual variability in decision making is determined by differences in the makeup of a striatal system for approach and avoidance learning. The model has been tested empirically with the probabilistic selection task (PST), which determines whether individuals learn better from positive or negative feedback. In accordance with the model, in the present study we examined whether an individual's ability to learn from positive and negative reinforcement can be predicted by genetic factors related to the midbrain dopamine system. We also asked whether psychiatric and personality factors related to substance dependence and dopamine affect PST performance. Although we found characteristics that predicted individual differences in approach versus avoidance learning, these observations were qualified by additional findings that appear inconsistent with the predictions of the go/no-go model. These results highlight a need for future research to validate the PST as a measure of basal ganglia reward learning.

Weight Shame, Social Connection, and Depressive Symptoms in Late Adolescence.

PubMed

Brewis, Alexandra A; Bruening, Meg

2018-05-01

Child and adolescent obesity is increasingly the focus of interventions, because it predicts serious disease morbidity later in life. However, social environments that permit weight-related stigma and body shame may make weight control and loss more difficult. Rarely do youth obesity interventions address these complexities. Drawing on repeated measures in a large sample ( N = 1443) of first-year (freshman), campus-resident university students across a nine-month period, we model how weight-related shame predicts depressive symptom levels, how being overweight (assessed by anthropometric measures) shapes that risk, and how social connection (openness to friendship) might mediate/moderate. Body shame directly, clearly, and repeatedly predicts depression symptom levels across the whole school year for all students, but overweight youth have significantly elevated risk. Social connections mediate earlier in the school year, and in all phases moderate, body shame effects on depression. Youth obesity interventions would be well-served recognizing and incorporating the influential roles of social-environmental factors like weight stigma and friendship in program design.

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results.

PubMed

De-Arteaga, Maria; Eggel, Ivan; Kahn, Charles E; Müller, Henning

2015-10-01

Log files of information retrieval systems that record user behavior have been used to improve the outcomes of retrieval systems, understand user behavior, and predict events. In this article, a log file of the ARRS GoldMiner search engine containing 222,005 consecutive queries is analyzed. Time stamps are available for each query, as well as masked IP addresses, which enables to identify queries from the same person. This article describes the ways in which physicians (or Internet searchers interested in medical images) search and proposes potential improvements by suggesting query modifications. For example, many queries contain only few terms and therefore are not specific; others contain spelling mistakes or non-medical terms that likely lead to poor or empty results. One of the goals of this report is to predict the number of results a query will have since such a model allows search engines to automatically propose query modifications in order to avoid result lists that are empty or too large. This prediction is made based on characteristics of the query terms themselves. Prediction of empty results has an accuracy above 88%, and thus can be used to automatically modify the query to avoid empty result sets for a user. The semantic analysis and data of reformulations done by users in the past can aid the development of better search systems, particularly to improve results for novice users. Therefore, this paper gives important ideas to better understand how people search and how to use this knowledge to improve the performance of specialized medical search engines.

Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015

USGS Publications Warehouse

Sando, Steven K.; McCarthy, Peter M.

2018-05-10

This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both.Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described.Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency-analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.

Influential Observations in Principal Factor Analysis.

ERIC Educational Resources Information Center

Tanaka, Yutaka; Odaka, Yoshimasa

1989-01-01

A method is proposed for detecting influential observations in iterative principal factor analysis. Theoretical influence functions are derived for two components of the common variance decomposition. The major mathematical tool is the influence function derived by Tanaka (1988). (SLD)

Helping Resource Managers Understand Hydroclimatic Variability and Forecasts: A Case Study in Research Equity

NASA Astrophysics Data System (ADS)

Hartmann, H. C.; Pagano, T. C.; Sorooshian, S.; Bales, R.

2002-12-01

Expectations for hydroclimatic research are evolving as changes in the contract between science and society require researchers to provide "usable science" that can improve resource management policies and practices. However, decision makers have a broad range of abilities to access, interpret, and apply scientific research. "High-end users" have technical capabilities and operational flexibility capable of readily exploiting new information and products. "Low-end users" have fewer resources and are less likely to change their decision making processes without clear demonstration of benefits by influential early adopters (i.e., high-end users). Should research programs aim for efficiency, targeting high-end users? Should they aim for impact, targeting decisions with high economic value or great influence (e.g., state or national agencies)? Or should they focus on equity, whereby outcomes benefit groups across a range of capabilities? In this case study, we focus on hydroclimatic variability and forecasts. Agencies and individuals responsible for resource management decisions have varying perspectives about hydroclimatic variability and opportunities for using forecasts to improve decision outcomes. Improper interpretation of forecasts is widespread and many individuals find it difficult to place forecasts in an appropriate regional historical context. In addressing these issues, we attempted to mitigate traditional inequities in the scope, communication, and accessibility of hydroclimatic research results. High-end users were important in prioritizing information needs, while low-end users were important in determining how information should be communicated. For example, high-end users expressed hesitancy to use seasonal forecasts in the absence of quantitative performance evaluations. Our subsequently developed forecast evaluation framework and research products, however, were guided by the need for a continuum of evaluation measures and interpretive materials to enable low-end users to increase their understanding of probabilistic forecasts, credibility concepts, and implications for decision making. We also developed an interactive forecast assessment tool accessible over the Internet, to support resource decisions by individuals as well as agencies. The tool provides tutorials for guiding forecast interpretation, including quizzes that allow users to test their forecast interpretation skills. Users can monitor recent and historical observations for selected regions, communicated using terminology consistent with available forecast products. The tool also allows users to evaluate forecast performance for the regions, seasons, forecast lead times, and performance criteria relevant to their specific decision making situations. Using consistent product formats, the evaluation component allows individuals to use results at the level they are capable of understanding, while offering opportunity to shift to more sophisticated criteria. Recognizing that many individuals lack Internet access, the forecast assessment webtool design also includes capabilities for customized report generation so extension agents or other trusted information intermediaries can provide material to decision makers at meetings or site visits.

A user opinion and metadata mining scheme for predicting box office performance of movies in the social network environment

NASA Astrophysics Data System (ADS)

Kim, Daehoon; Kim, Daeyong; Hwang, Eenjun; Choi, Hong-Gu

2013-12-01

With the rapid proliferation of social network services (SNS), it has become common for people to express their thoughts or opinions on various subjects, such as political events, movies, or commercial products, using short comments. Though the comments reflect personal opinion or preferences, collectively, these represent public opinion or trends. Mining public opinion or trends from a collection of user comments made on SNS could be very useful for many applications. One interesting application is to predict the box office performance of a new movie from user comments made on the movie's trailer. Such a prediction is, nevertheless, a very complicated task because many factors can have an influence on it. In this paper, we propose a scheme for mining public opinion from a collection of user comments, easily available on social networks, on the trailer of a new movie. Next, we predict whether the movie will be a box office hit, based on public opinion and other properties such as the leading actors, director, and their past works. Through various experiments, we show that our scheme can produce satisfactory results.

MollDE: a homology modeling framework you can click with.

PubMed

Canutescu, Adrian A; Dunbrack, Roland L

2005-06-15

Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. http://dunbrack.fccc.edu/molide/molide.php rl_dunbrack@fccc.edu.

Mining User Dwell Time for Personalized Web Search Re-Ranking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Songhua; Jiang, Hao; Lau, Francis

We propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer conceptword level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search enginesmore » and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method. In this paper, we propose a new personalized webpage ranking algorithmthrough mining dwell times of a user. We introduce a quantitative model to derive concept word level user dwell times from the observed document level user dwell times. Once we have inferred a user's interest over the set of concept words the user has encountered in previous readings, we can then predict the user's potential dwell time over a new document. Such predicted user dwell time allows us to carry out personalized webpage re-ranking. To explore the effectiveness of our algorithm, we measured the performance of our algorithm under two conditions - one with a relatively limited amount of user dwell time data and the other with a doubled amount. Both evaluation cases put our algorithm for generating personalized webpage rankings to satisfy a user's personal preference ahead of those by Google, Yahoo!, and Bing, as well as a recent personalized webpage ranking algorithm.« less

Broadband Fan Noise Prediction System for Turbofan Engines. Volume 2; BFaNS User's Manual and Developer's Guide

NASA Technical Reports Server (NTRS)

Morin, Bruce L.

2010-01-01

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the second volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running BFaNS. It concludes with technical documentation of the BFaNS computer program.

Broadband Fan Noise Prediction System for Turbofan Engines. Volume 1; Setup_BFaNS User's Manual and Developer's Guide

NASA Technical Reports Server (NTRS)

Morin, Bruce L.

2010-01-01

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the first volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User's Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running Setup_BFaNS. It concludes with technical documentation of the Setup_BFaNS computer program.

Distress tolerance interacts with circumstances, motivation, and readiness to predict substance abuse treatment retention.

PubMed

Ali, Bina; Green, Kerry M; Daughters, Stacey B; Lejuez, C W

2017-10-01

Our understanding of the conditions that influence substance abuse treatment retention in urban African American substance users is limited. This study examined the interacting effect of circumstances, motivation, and readiness (CMR) with distress tolerance to predict substance abuse treatment retention in a sample of urban African American treatment-seeking substance users. Data were collected from 81 African American substance users entering residential substance abuse treatment facility in an urban setting. Participants completed self-reported measures on CMR and distress tolerance. In addition, participants were assessed on psychiatric comorbidities, substance use severity, number of previous treatments, and demographic characteristics. Data on substance abuse treatment retention were obtained using administrative records of the treatment center. Logistic regression analysis found that the interaction of CMR and distress tolerance was significant in predicting substance abuse treatment retention. Higher score on CMR was significantly associated with increased likelihood of treatment retention in substance users with higher distress tolerance, but not in substance users with lower distress tolerance. Findings of the study indicate that at higher level of distress tolerance, favorable external circumstances, higher internal motivation, and greater readiness to treatment are important indicators of substance abuse treatment retention. The study highlights the need for assessing CMR and distress tolerance levels among substance users entering treatment, and providing targeted interventions to increase substance abuse treatment retention and subsequent recovery from substance abuse among urban African American substance users. Copyright © 2017 Elsevier Ltd. All rights reserved.

Examining the applicability of the IMB model in predicting condom use among sexually active secondary school students in Mbarara, Uganda

PubMed Central

Ybarra, Michele L.; Korchmaros, Josephine; Kiwanuka, Julius; Bangsberg, David R.; Bull, Sheana

2012-01-01

We tested the applicability of the IMB model in predicting condom use among sexually active secondary school students in Mbarara, Uganda. Three hundred and ninety adolescents across five secondary schools completed a self-report survey about their health and sexual experiences. Based upon results from structural equation modeling, the IMB model partially predicts condom use. Condom use was directly predicted by HIV prevention information and behavioral skills regarding having and using condoms. It was indirectly predicted (through behavioral skills regarding having and using condoms) by behavioral intentions regarding using condoms and talking to one‘s partner about safer sex. Aspects of one‘s first sexual experience (i.e., age at first sex, having discussed using condoms with first sex partner, willingness at first sex) are hugely influential of current condom use; this is especially true for discussing condoms with one‘s first partner. Findings highlight the importance of providing clear and comprehensive condom use training in HIV prevention programs aimed at Ugandan adolescents. They also underscore the importance of targeting abstinent youth before they become sexually active to positively affect their HIV preventive behavior at their first sexual experience. PMID:22350827

The rhythms of predictive coding? Pre-stimulus phase modulates the influence of shape perception on luminance judgments

PubMed Central

Han, Biao; VanRullen, Rufin

2017-01-01

Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated the temporal dynamics of these interactions. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. The choice of 3D-shape or random-lines as the brighter disk was used to assess the influence of feedback signals on sensory processing in each trial (i.e., as a measure of post-stimulus predictive coding efficiency). Independently of the spatial response (left/right), we found that this choice fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5 Hz oscillation in contralateral frontal electrodes and a ~16 Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas. PMID:28262824

Subliminal action priming modulates the perceived intensity of sensory action consequences.

PubMed

Stenner, Max-Philipp; Bauer, Markus; Sidarus, Nura; Heinze, Hans-Jochen; Haggard, Patrick; Dolan, Raymond J

2014-02-01

The sense of control over the consequences of one's actions depends on predictions about these consequences. According to an influential computational model, consistency between predicted and observed action consequences attenuates perceived stimulus intensity, which might provide a marker of agentic control. An important assumption of this model is that these predictions are generated within the motor system. However, previous studies of sensory attenuation have typically confounded motor-specific perceptual modulation with perceptual effects of stimulus predictability that are not specific to motor action. As a result, these studies cannot unambiguously attribute sensory attenuation to a motor locus. We present a psychophysical experiment on auditory attenuation that avoids this pitfall. Subliminal masked priming of motor actions with compatible prime-target pairs has previously been shown to modulate both reaction times and the explicit feeling of control over action consequences. Here, we demonstrate reduced perceived loudness of tones caused by compatibly primed actions. Importantly, this modulation results from a manipulation of motor processing and is not confounded by stimulus predictability. We discuss our results with respect to theoretical models of the mechanisms underlying sensory attenuation and subliminal motor priming. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Subliminal action priming modulates the perceived intensity of sensory action consequences☆

PubMed Central

Stenner, Max-Philipp; Bauer, Markus; Sidarus, Nura; Heinze, Hans-Jochen; Haggard, Patrick; Dolan, Raymond J.

2014-01-01

The sense of control over the consequences of one’s actions depends on predictions about these consequences. According to an influential computational model, consistency between predicted and observed action consequences attenuates perceived stimulus intensity, which might provide a marker of agentic control. An important assumption of this model is that these predictions are generated within the motor system. However, previous studies of sensory attenuation have typically confounded motor-specific perceptual modulation with perceptual effects of stimulus predictability that are not specific to motor action. As a result, these studies cannot unambiguously attribute sensory attenuation to a motor locus. We present a psychophysical experiment on auditory attenuation that avoids this pitfall. Subliminal masked priming of motor actions with compatible prime–target pairs has previously been shown to modulate both reaction times and the explicit feeling of control over action consequences. Here, we demonstrate reduced perceived loudness of tones caused by compatibly primed actions. Importantly, this modulation results from a manipulation of motor processing and is not confounded by stimulus predictability. We discuss our results with respect to theoretical models of the mechanisms underlying sensory attenuation and subliminal motor priming. PMID:24333539

Marital stress and children's externalizing behavior as predictors of mothers' and fathers' parenting.

PubMed

Elam, Kit K; Chassin, Laurie; Eisenberg, Nancy; Spinrad, Tracy L

2017-10-01

Previous research suggests that mothers' and fathers' parenting may be differentially influenced by marital and child factors within the family. Some research indicates that marital stress is more influential in fathers' than mothers' parenting, whereas other research shows that children's difficult behavior preferentially affects mothers' parenting. The present study examined marital stress and children's externalizing behavior in middle childhood as predictors of mothers' versus fathers' consistency, monitoring, and support and care in early adolescence, and the subsequent associations of these parenting behaviors with externalizing behavior 1.5 years later. Pathways were examined within a longitudinal mediation model testing for moderation by parent gender (N = 276 mothers, N = 229 fathers). Children's externalizing behavior in middle childhood was found to more strongly inversely predict mothers' versus fathers' monitoring in early adolescence. In contrast, marital stress more strongly predicted low monitoring for fathers than for mothers. Regardless of parent gender, marital stress predicted lower levels of parental consistency, and children's externalizing behavior predicted lower levels of parental support. Mothers' monitoring and fathers' support in early adolescence predicted lower levels of externalizing behavior 1.5 years later. The results are discussed with respect to family transactions relative to parent gender and implications for intervention.

The ‘who’ and ‘what’ of #diabetes on Twitter

PubMed Central

Beguerisse-Díaz, Mariano; McLennan, Amy K.; Garduño-Hernández, Guillermo; Barahona, Mauricio; Ulijaszek, Stanley J.

2017-01-01

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all of the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions. (1) What themes arise in these tweets? (2) Who are the most influential users? (3) Which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal ‘hub’ and ‘authority’ scores. Whereas the hub landscape is diffuse and fluid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design. PMID:29942579

How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

PubMed

Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Leader Influence, the Professional Practice Environment, and Nurse Engagement in Essential Nursing Practice.

PubMed

Ducharme, Maria P; Bernhardt, Jean M; Padula, Cynthia A; Adams, Jeffrey M

The purpose of this study was to examine relationships between leaders' perceived influence over professional practice environments (PPEs) and clinical nurses' reported engagement in essential professional nursing practice. There is little empirical evidence identifying impact of nurse leader influence or why nursing leaders are not perceived, nor do they perceive themselves, as influential in healthcare decision making. A nonexperimental method of prediction was used to examine relationships between engagement in professional practice, measured by Essentials of Magnetism II (EOMII) tool, and nurse leaders' perceived influence, measured by Leadership Influence over Professional Practice Environment Scale (LIPPES). A convenience sample of 30 nurse leaders and 169 clinical nurses, employed in a 247-bed acute care Magnet® hospital, participated. Findings indicated that leaders perceived their influence presence from "often" to "always," with mean scores of 3.02 to 3.70 on a 4-point Likert scale, with the lowest subscale as "access to resources" for which a significant relationship was found with clinical nurses' reported presence of adequate staffing (P < .004). Clinical nurses reported more positive perceptions in adequacy of staffing on the EOMII when nurse leaders perceived themselves to be more influential, as measured by the LIPPES, in collegial administrative approach (P = .014), authority (P = .001), access to resources (P = .004), and leadership expectations of staff (P = .039). Relationships were seen in the outcome measure of the EOMII scale, nurse-assessed quality of patient care (NAQC), where nurse leaders' perception of their authority (P = .003) and access to resources (P = .022) positively impacted and was predictive of NAQC. Findings support assertion that nurse leaders are integral in enhancing PPEs and their influence links structures necessary for an environment that supports outcomes.

A user credit assessment model based on clustering ensemble for broadband network new media service supervision

NASA Astrophysics Data System (ADS)

Liu, Fang; Cao, San-xing; Lu, Rui

2012-04-01

This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design

PubMed Central

2018-01-01

Background Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way that will enhance their uptake in routine clinical practice remains challenging; however, mobile apps show promise in this respect. To increase the likelihood that an app-delivered clinical prediction tool can be successfully incorporated into clinical practice, it is important to involve end users in the app design process. Objective The aim of the study was to maximize patient engagement in an app designed to improve treatment allocation for depression. Methods An iterative, user-centered design process was employed. Qualitative data were collected via 2 focus groups with a community sample (n=17) and 7 semistructured interviews with people with depressive symptoms. The results of the focus groups and interviews were used by the computer engineering team to modify subsequent protoypes of the app. Results Iterative development resulted in 3 prototypes and a final app. The areas requiring the most substantial changes following end-user input were related to the iconography used and the way that feedback was provided. In particular, communicating risk of future depressive symptoms proved difficult; these messages were consistently misinterpreted and negatively viewed and were ultimately removed. All participants felt positively about seeing their results summarized after completion of the clinical prediction tool, but there was a need for a personalized treatment recommendation made in conjunction with a consultation with a health professional. Conclusions User-centered design led to valuable improvements in the content and design of an app designed to improve allocation of and engagement in depression treatment. Iterative design allowed us to develop a tool that allows users to feel hope, engage in self-reflection, and motivate them to treatment. The tool is currently being evaluated in a randomized controlled trial. PMID:29685864

TFaNS-Tone Fan Noise Design/Prediction System: Users' Manual TFaNS Version 1.5

NASA Technical Reports Server (NTRS)

Topol, David A.; Huff, Dennis L. (Technical Monitor)

2003-01-01

TFaNS is the Tone Fan Noise Design/Prediction System developed by Pratt & Whitney under contract to NASA Glenn. The purpose of this system is to predict tone noise emanating from a fan stage including the effects of reflection and transmission by the rotor and stator and by the duct inlet and nozzle. The first version of this design system was developed under a previous NASA contract. Several improvements have been made to TFaNS. This users' manual shows how to run this new system. TFaNS consists of the codes that compute the acoustic properties (reflection and transmission coefficients) of the various elements and writes them to files, CUP3D Fan Noise Coupling Code that reads these files, solves the coupling problem, and outputs the desired noise predictions, and AWAKEN CFD/Measured Wake Postprocessor which reformats CFD wake predictions and/or measured wake data so they can be used by the system. This report provides information on code input and file structure essential for potential users of TFaNS.

Utilizing sensory prediction errors for movement intention decoding: A new methodology

PubMed Central

Nakamura, Keigo; Ando, Hideyuki

2018-01-01

We propose a new methodology for decoding movement intentions of humans. This methodology is motivated by the well-documented ability of the brain to predict sensory outcomes of self-generated and imagined actions using so-called forward models. We propose to subliminally stimulate the sensory modality corresponding to a user’s intended movement, and decode a user’s movement intention from his electroencephalography (EEG), by decoding for prediction errors—whether the sensory prediction corresponding to a user’s intended movement matches the subliminal sensory stimulation we induce. We tested our proposal in a binary wheelchair turning task in which users thought of turning their wheelchair either left or right. We stimulated their vestibular system subliminally, toward either the left or the right direction, using a galvanic vestibular stimulator and show that the decoding for prediction errors from the EEG can radically improve movement intention decoding performance. We observed an 87.2% median single-trial decoding accuracy across tested participants, with zero user training, within 96 ms of the stimulation, and with no additional cognitive load on the users because the stimulation was subliminal. PMID:29750195

TFaNS Tone Fan Noise Design/Prediction System. Volume 2; User's Manual; 1.4

NASA Technical Reports Server (NTRS)

Topol, David A.; Eversman, Walter

1999-01-01

TFaNS is the Tone Fan Noise Design/Prediction System developed by Pratt & Whitney under contract to NASA Lewis (presently NASA Glenn). The purpose of this system is to predict tone noise emanating from a fan stage including the effects of reflection and transmission by the rotor and stator and by the duct inlet and nozzle. These effects have been added to an existing annular duct/isolated stator noise prediction capability. TFaNS consists of: the codes that compute the acoustic properties (reflection and transmission coefficients) of the various elements and write them to files. CUP3D: Fan Noise Coupling Code that reads these files, solves the coupling problem, and outputs the desired noise predictions. AWAKEN: CFD/Measured Wake Postprocessor which reformats CFD wake predictions and/or measured wake data so it can be used by the system. This volume of the report provides information on code input and file structure essential for potential users of TFANS. This report is divided into three volumes: Volume 1. System Description, CUP3D Technical Documentation, and Manual for Code Developers; Volume 2. User's Manual, TFANS Vers. 1.4; Volume 3. Evaluation of System Codes.

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning

PubMed Central

Gillingham, Philip

2016-01-01

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can ‘learn’, it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the ‘black box’ of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services. PMID:27559213

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the 'Black Box' of Machine Learning.

PubMed

Gillingham, Philip

2016-06-01

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can 'learn', it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the 'black box' of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services.

A synopsis of recent influential papers published in psychiatric journals (2010-2011) from the Arab world.

PubMed

Okasha, Tarek; Elkholy, Hussien

2012-06-01

Six recent and influential papers that have appeared in the three leading psychiatry journals from the Arab region are summarized in this review. The first paper examined the prevalence of eating disorders (EDs) in rural and urban secondary school girls in Sharkia; more EDs were found among urban than rural population. The second study reported the high prevalence of Post Traumatic Stress Disorder (PTSD) in primary school children in Iraq in context of the present situation in Iraq dominated by violence creating a traumatizing atmosphere for the population, especially children. The third paper reported that substance dependent patients manifest elevated traits of impulsivity; emotionally driven impulsivity in particular predicted substance related problems. The fourth study reported significant cognitive impairments at illness onset in a large sample of patients with a first psychotic episode. The fifth paper, investigated the cultural imprint on symptom profile of mood disorders. Culture effect on mood disorder was more prominent in depression than in mania. The last article examined the relations between social circumstances, medical morbidity, locus of control and depression in elderly patients suffering from medical conditions. Overall, the papers describe a wide spectrum of research initiatives in the Arab World that are likely to have implications for global mental health. Copyright © 2012. Published by Elsevier B.V.

Reliability analysis of a robotic system using hybridized technique

NASA Astrophysics Data System (ADS)

Kumar, Naveen; Komal; Lather, J. S.

2017-09-01

In this manuscript, the reliability of a robotic system has been analyzed using the available data (containing vagueness, uncertainty, etc). Quantification of involved uncertainties is done through data fuzzification using triangular fuzzy numbers with known spreads as suggested by system experts. With fuzzified data, if the existing fuzzy lambda-tau (FLT) technique is employed, then the computed reliability parameters have wide range of predictions. Therefore, decision-maker cannot suggest any specific and influential managerial strategy to prevent unexpected failures and consequently to improve complex system performance. To overcome this problem, the present study utilizes a hybridized technique. With this technique, fuzzy set theory is utilized to quantify uncertainties, fault tree is utilized for the system modeling, lambda-tau method is utilized to formulate mathematical expressions for failure/repair rates of the system, and genetic algorithm is utilized to solve established nonlinear programming problem. Different reliability parameters of a robotic system are computed and the results are compared with the existing technique. The components of the robotic system follow exponential distribution, i.e., constant. Sensitivity analysis is also performed and impact on system mean time between failures (MTBF) is addressed by varying other reliability parameters. Based on analysis some influential suggestions are given to improve the system performance.

Who are mobile app users from healthy lifestyle websites? Analysis of patterns of app use and user characteristics.

PubMed

Elavsky, Steriani; Smahel, David; Machackova, Hana

2017-12-01

The use of online communities and websites for health information has proliferated along with the use of mobile apps for managing health behaviors such as diet and exercise. The scarce evidence available to date suggests that users of these websites and apps differ in significant ways from non-users but most data come from US- and UK-based populations. In this study, we recruited users of nutrition, weight management, and fitness-oriented websites in the Czech Republic to better understand who uses mobile apps and who does not, including user sociodemographic and psychological profiles. Respondents aged 13-39 provided information on app use through an online survey (n = 669; M age = 24.06, SD = 5.23; 84% female). Among users interested in health topics, respondents using apps for managing nutrition, weight, and fitness (n = 403, 60%) were more often female, reported more frequent smartphone use, and more expert phone skills. In logistic regression models, controlling for sociodemographics, web, and phone activity, mHealth app use was predicted by levels of excessive exercise (OR 1.346, 95% CI 1.061-1.707, p < .01). Among app users, we found differences in types of apps used by gender, age, and weight status. Controlling for sociodemographics and web and phone use, drive for thinness predicted the frequency of use of apps for healthy eating (β = 0.14, p < .05), keeping a diet (β = 0.27, p < .001), and losing weight (β = 0.33, p < .001), whereas excessive exercise predicted the use of apps for keeping a diet (β = 0.18, p < .01), losing weight (β = 0.12, p < .05), and managing sport/exercise (β = 0.28, p < .001). Sensation seeking was negatively associated with the frequency of use of apps for maintaining weight (β = - 0.13, p < .05). These data unveil the user characteristics of mHealth app users from nutrition, weight management, and fitness websites, helping inform subsequent design of mHealth apps and mobile intervention strategies.

Evaluation of User Acceptance of Mixed Reality Technology

ERIC Educational Resources Information Center

Yusoff, Rasimah Che Mohd; Zaman, Halimah Badioze; Ahmad, Azlina

2011-01-01

This study investigates users' perception and acceptance of mixed reality (MR) technology. Acceptance of new information technologies has been important research area since 1990s. It is important to understand the reasons why people accept information technologies, as this can help to improve design, evaluation and prediction how users will…

Aircraft noise prediction program user's manual

NASA Technical Reports Server (NTRS)

Gillian, R. E.

1982-01-01

The Aircraft Noise Prediction Program (ANOPP) predicts aircraft noise with the best methods available. This manual is designed to give the user an understanding of the capabilities of ANOPP and to show how to formulate problems and obtain solutions by using these capabilities. Sections within the manual document basic ANOPP concepts, ANOPP usage, ANOPP functional modules, ANOPP control statement procedure library, and ANOPP permanent data base. appendixes to the manual include information on preparing job decks for the operating systems in use, error diagnostics and recovery techniques, and a glossary of ANOPP terms.

Customer satisfaction at US Army Corps of Engineers-administered lakes: a compilation of two years of performance data

Treesearch

Robert C. Burns; Alan R. Graefe; John P. Titre

1998-01-01

The purpose of this paper was to demonstrate the application of a model which can be used to predict the overall customer satisfaction levels of water-based recreationists. Data were collected from two distinctly different user groups; boat ramp users and campground users. Results indicated that each user group had different satisfaction attributes that impacted their...

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

FLAPS (Fatigue Life Analysis Programs): Computer Programs to Predict Cyclic Life Using the Total Strain Version of Strainrange Partitioning and Other Life Prediction Methods. Users' Manual and Example Problems, Version 1.0

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R. (Technical Monitor)

2003-01-01

This manual presents computer programs FLAPS for characterizing and predicting fatigue and creep-fatigue resistance of metallic materials in the high-temperature, long-life regime for isothermal and nonisothermal fatigue. The programs use the Total Strain version of Strainrange Partitioning (TS-SRP), and several other life prediction methods described in this manual. The user should be thoroughly familiar with the TS-SRP and these life prediction methods before attempting to use any of these programs. Improper understanding can lead to incorrect use of the method and erroneous life predictions. An extensive database has also been developed in a parallel effort. The database is probably the largest source of high-temperature, creep-fatigue test data available in the public domain and can be used with other life-prediction methods as well. This users' manual, software, and database are all in the public domain and can be obtained by contacting the author. The Compact Disk (CD) accompanying this manual contains an executable file for the FLAPS program, two datasets required for the example problems in the manual, and the creep-fatigue data in a format compatible with these programs.

Variations in the persistence of health expenditures and the implications for the design of capitation payments in Taiwan.

PubMed

Ku, Li-Jung Elizabeth; Chiou, Meng-Jiun; Liu, Li-Fan

2015-07-01

The National Health Insurance (NHI) system in Taiwan launched a trial capitation provider payment programme in 2011, with the capitation formula based on patients' average NHI expenditure in the previous year. This study seeks to examine the concentration and persistence of health care expenditure among the elderly, and to assess the performance of the current capitation formula in predicting future high-cost users. This study analysed NHI expenditures for a nationally representative sample of people aged 65 years and over who took part in Taiwan's National Health Interview Survey, 2005. Expenditure concentration was assessed by the proportion of NHI expenditures attributable to four groups by expenditure percentile. Four transition probability matrixes examined changes in a person's position in the expenditure percentiles and generalized estimation equation models were estimated to identify significant predictors of a patient being in the top 10% of users. Between 2005 and 2009, the top 10% of users on average accounted for 55% of total NHI expenditures. Of the top 10% in 2005, 39% retained this position in 2006. However, expenditure persistence was the highest (77%) among the bottom 50% of users. NHI expenditure percentiles in both the baseline year and the prior year, and chronic conditions all significantly predicted future high expenditures. The model including chronic conditions performed better in predicting the top 10% of users (c-statistics increased from 0.772 to 0.904) than the model without. Given the increase in predictive ability, adding chronic conditions and baseline health care use data to Taiwan's capitation payment formula would correctly identify more high users. © The Author(s) 2015.

Mathematical model to predict drivers' reaction speeds.

PubMed

Long, Benjamin L; Gillespie, A Isabella; Tanaka, Martin L

2012-02-01

Mental distractions and physical impairments can increase the risk of accidents by affecting a driver's ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers' performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers' reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers' reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions.

Human-centric predictive model of task difficulty for human-in-the-loop control tasks

PubMed Central

Majewicz Fey, Ann

2018-01-01

Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control. PMID:29621301

Predicting use of case management support services for adolescents and adults living in community following brain injury: A longitudinal Canadian database study with implications for life care planning.

PubMed

Baptiste, B; Dawson, D R; Streiner, D

2015-01-01

To determine factors associated with case management (CM) service use in people with traumatic brain injury (TBI), using a published model for service use. A retrospective cohort, with nested case-control design. Correlational and logistic regression analyses of questionnaires from a longitudinal community data base. Questionnaires of 203 users of CM services and 273 non-users, complete for all outcome and predictor variables. Individuals with TBI, 15 years of age and older. Out of a dataset of 1,960 questionnaires, 476 met the inclusion criteria. Eight predictor variables and one outcome variable (use or non-use of the service). Predictor variables considered the framework of the Behaviour Model of Health Service Use (BMHSU); specifically, pre-disposing, need and enabling factor groups as these relate to health service use and access. Analyses revealed significant differences between users and non-users of CM services. In particular, users were significantly younger than non-users as the older the person the less likely to use the service. Also, users had less education and more severe activity limitations and lower community integration. Persons living alone are less likely to use case management. Funding groups also significantly impact users. This study advances an empirical understanding of equity of access to health services usage in the practice of CM for persons living with TBI as a fairly new area of research, and considers direct relevance to Life Care Planning (LCP). Many life care planers are CM and the genesis of LCP is CM. The findings relate to health service use and access, rather than health outcomes. These findings may assist with development of a modified model for prediction of use to advance future cost of care predictions.

Predicting Facebook users' online privacy protection: risk, trust, norm focus theory, and the theory of planned behavior.

PubMed

Saeri, Alexander K; Ogilvie, Claudette; La Macchia, Stephen T; Smith, Joanne R; Louis, Winnifred R

2014-01-01

The present research adopts an extended theory of the planned behavior model that included descriptive norms, risk, and trust to investigate online privacy protection in Facebook users. Facebook users (N = 119) completed a questionnaire assessing their attitude, subjective injunctive norm, subjective descriptive norm, perceived behavioral control, implicit perceived risk, trust of other Facebook users, and intentions toward protecting their privacy online. Behavior was measured indirectly 2 weeks after the study. The data show partial support for the theory of planned behavior and strong support for the independence of subjective injunctive and descriptive norms. Risk also uniquely predicted intentions over and above the theory of planned behavior, but there were no unique effects of trust on intentions, nor of risk or trust on behavior. Implications are discussed.

Predicting clinical image delivery time by monitoring PACS queue behavior.

PubMed

King, Nelson E; Documet, Jorge; Liu, Brent

2006-01-01

The expectation of rapid image retrieval from PACS users contributes to increased information technology (IT) infrastructure investments to increase performance as well as continuing demands upon PACS administrators to respond to "slow" system performance. The ability to provide predicted delivery times to a PACS user may curb user expectations for "fastest" response especially during peak hours. This, in turn, could result in a PACS infrastructure tailored to more realistic performance demands. A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. We investigate the contents of a stand-alone architecture PACS RetrieveSend queue and identified parameters and behaviors that enable a more accurate prediction of delivery time. A prediction algorithm for studies delayed in a stand-alone PACS queue can be extendible to other potential bottlenecks such as long-term storage archives. Implications of a queue monitor in other PACS architectures are also discussed.

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

PubMed Central

Lee, Jurim; Park, Nuri; Choo, Jaegul; Kim, Jong-Hyun; Kim, Chang Hun

2017-01-01

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016. PMID:28498843

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

PubMed

Kim, Young Bin; Lee, Jurim; Park, Nuri; Choo, Jaegul; Kim, Jong-Hyun; Kim, Chang Hun

2017-01-01

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

[Development of a predictive program for microbial growth under various temperature conditions].

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi; Kimura, Bon; Fujii, Tateo

2006-12-01

A predictive program for microbial growth under various temperature conditions was developed with a mathematical model. The model was a new logistic model recently developed by us. The program predicts Escherichia coli growth in broth, Staphylococcus aureus growth and its enterotoxin production in milk, and Vibrio parahaemolyticus growth in broth at various temperature patterns. The program, which was built with Microsoft Excel (Visual Basic Application), is user-friendly; users can easily input the temperature history of a test food and obtain the prediction instantly on the computer screen. The predicted growth and toxin production can be important indices to determine whether a food is microbiologically safe or not. This program should be a useful tool to confirm the microbial safety of commercial foods.

An inventory of nitrous oxide emissions from agriculture in the UK using the IPCC methodology: emission estimate, uncertainty and sensitivity analysis

NASA Astrophysics Data System (ADS)

Brown, L.; Armstrong Brown, S.; Jarvis, S. C.; Syed, B.; Goulding, K. W. T.; Phillips, V. R.; Sneath, R. W.; Pain, B. F.

Nitrous oxide emission from UK agriculture was estimated, using the IPCC default values of all emission factors and parameters, to be 87 Gg N 2O-N in both 1990 and 1995. This estimate was shown, however, to have an overall uncertainty of 62%. The largest component of the emission (54%) was from the direct (soil) sector. Two of the three emission factors applied within the soil sector, EF1 (direct emission from soil) and EF3 PRP (emission from pasture range and paddock) were amongst the most influential on the total estimate, producing a ±31 and +11% to -17% change in emissions, respectively, when varied through the IPCC range from the default value. The indirect sector (from leached N and deposited ammonia) contributed 29% of the total emission, and had the largest uncertainty (126%). The factors determining the fraction of N leached (Frac LEACH) and emissions from it (EF5), were the two most influential. These parameters are poorly specified and there is great potential to improve the emission estimate for this component. Use of mathematical models (NCYCLE and SUNDIAL) to predict Frac LEACH suggested that the IPCC default value for this parameter may be too high for most situations in the UK. Comparison with other UK-derived inventories suggests that the IPCC methodology may overestimate emission. Although the IPCC approach includes additional components to the other inventories (most notably emission from indirect sources), estimates for the common components (i.e. fertiliser and animals), and emission factors used, are higher than those of other inventories. Whilst it is recognised that the IPCC approach is generalised in order to allow widespread applicability, sufficient data are available to specify at least two of the most influential parameters, i.e. EF1 and Frac LEACH, more accurately, and so provide an improved estimate of nitrous oxide emissions from UK agriculture.

User perspectives on the usability of a regional health information exchange

PubMed Central

Ho, Yun-Xian; Cala, Cather Marie; Blakemore, Dana; Chen, Qingxia; Frisse, Mark E; Johnson, Kevin B

2011-01-01

Objective We assessed the usability of a health information exchange (HIE) in a densely populated metropolitan region. This grant-funded HIE had been deployed rapidly to address the imminent needs of the patient population and the need to draw wider participation from regional entities. Design We conducted a cross-sectional survey of individuals given access to the HIE at participating organizations and examined some of the usability and usage factors related to the technology acceptance model. Measurements We probed user perceptions using the Questionnaire for User Interaction Satisfaction, an author-generated Trust scale, and user characteristic questions (eg, age, weekly system usage time). Results Overall, users viewed the system favorably (ratings for all usability items were greater than neutral (one-sample Wilcoxon test, p<0.0014, Bonferroni-corrected for 35 tests). System usage was regressed on usability, trust, and demographic and user characteristic factors. Three usability factors were positively predictive of system usage: overall reactions (p<0 0.01), learning (p<0.05), and system functionality (p<0.01). Although trust is an important component in collaborative relationships, we did not find that user trust of other participating healthcare entities was significantly predictive of usage. An analysis of respondents' comments revealed ways to improve the HIE. Conclusion We used a rapid deployment model to develop an HIE and found that perceptions of system usability were positive. We also found that system usage was predicted well by some aspects of usability. Results from this study suggest that a rapid development approach may serve as a viable model for developing usable HIEs serving communities with limited resources. PMID:21622933

Contextual mediation of perceptions in hauntings and poltergeist-like experiences.

PubMed

Lange, R; Houran, J; Harte, T M; Havens, R A

1996-06-01

The content of perceived apparitions, e.g., bereavement hallucinations, cannot be explained entirely in terms of electromagnetically induced neurochemical processes. It was shown that contextual variables influential in hallucinatory and hypnotic states also structured reported haunting experiences. As predicted, high congruency was found between the experiential content and the nature of the contextual variables. Further, the number of contextual variables involved in an experience was related to the type of experience and the state or arousal preceding the experience. Based on these findings we argue that a more complete explanation of haunting experiences should take into account both electromagnetically induced neurochemical processes and factors related to contextual mediation.

Graphical methods for the sensitivity analysis in discriminant analysis

DOE PAGES

Kim, Youngil; Anderson-Cook, Christine M.; Dae-Heung, Jang

2015-09-30

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern ofmore » the change.« less

An examination of the social, behavioral, and cognitive influences of infamous individuals on media consumers.

PubMed

Matusitz, Jonathan; Breen, Gerald-Mark

2011-01-01

This article presents a substantial extant and predictive statement on social cognitive theory (SCT), a well-known interpersonal communication theory coined by Bandura (1986) and researched by prominent scholars in the social sciences. An important rationale behind conducting this analysis is that it provides several groundbreaking and unique applications of SCT through the exploration of infamous celebrities (i.e., Michael Jackson, Keith Richards, Robert Downey, Jr., and sexually perverted religious leaders) published in global media outlets. The objective is to demonstrate the socially influential effects that these notorious individuals pose on media consumers and interested parties, in line with theoretical assumptions posited by SCT.

Looking Closer at the Effects of Framing on Risky Choice: An Item Response Theory Analysis.

PubMed

Sickar; Highhouse

1998-07-01

Item response theory (IRT) methodology allowed an in-depth examination of several issues that would be difficult to explore using traditional methodology. IRT models were estimated for 4 risky-choice items, answered by students under either a gain or loss frame. Results supported the typical framing finding of risk-aversion for gains and risk-seeking for losses but also suggested that a latent construct we label preference for risk was influential in predicting risky choice. Also, the Asian Disease item, most often used in framing research, was found to have anomalous statistical properties when compared to other framing items. Copyright 1998 Academic Press.

Finding a balance: health promotion challenges of military women.

PubMed

Agazio, Janice Griffin; Buckley, Kathleen M

2010-09-01

In this study, we explored what may determine, or predict, United States military women's health promotion behaviors. Using a descriptive correlational design grounded in Pender's Health Promotion model, 491 military women completed instruments measuring their demographic variables, perception of health, definition of health, self-efficacy, and interpersonal influences to determine the significant factors affecting participation in health promotion activities. The outcome indicated that self-efficacy and interpersonal influences were the most influential in determining health promotion. This research illuminates some of the challenges working women face in meeting health promotion activities and how best to support their ability to participate in healthy behaviors.

Tailoring Configuration to User’s Tasks under Uncertainty

DTIC Science & Technology

2008-04-28

CARISMA is the problem being solved. CARISMA applies microeconom- ics and game theory to make runtime decisions about allocating scarce resources among...scarce resources, these applications are running on be- half of one user. Thus, our problem has no game theoretic aspects. 2.2 Task Oriented...prediction tool [15] is based on the RPS tool and allows prediction of bandwidth online . There is additional evidence (see, for example [49

D-MATRIX: A web tool for constructing weight matrix of conserved DNA motifs

PubMed Central

Sen, Naresh; Mishra, Manoj; Khan, Feroz; Meena, Abha; Sharma, Ashok

2009-01-01

Despite considerable efforts to date, DNA motif prediction in whole genome remains a challenge for researchers. Currently the genome wide motif prediction tools required either direct pattern sequence (for single motif) or weight matrix (for multiple motifs). Although there are known motif pattern databases and tools for genome level prediction but no tool for weight matrix construction. Considering this, we developed a D-MATRIX tool which predicts the different types of weight matrix based on user defined aligned motif sequence set and motif width. For retrieval of known motif sequences user can access the commonly used databases such as TFD, RegulonDB, DBTBS, Transfac. D­MATRIX program uses a simple statistical approach for weight matrix construction, which can be converted into different file formats according to user requirement. It provides the possibility to identify the conserved motifs in the co­regulated genes or whole genome. As example, we successfully constructed the weight matrix of LexA transcription factor binding site with the help of known sos­box cis­regulatory elements in Deinococcus radiodurans genome. The algorithm is implemented in C-Sharp and wrapped in ASP.Net to maintain a user friendly web interface. D­MATRIX tool is accessible through the CIMAP domain network. Availability http://203.190.147.116/dmatrix/ PMID:19759861

D-MATRIX: a web tool for constructing weight matrix of conserved DNA motifs.

PubMed

Sen, Naresh; Mishra, Manoj; Khan, Feroz; Meena, Abha; Sharma, Ashok

2009-07-27

Despite considerable efforts to date, DNA motif prediction in whole genome remains a challenge for researchers. Currently the genome wide motif prediction tools required either direct pattern sequence (for single motif) or weight matrix (for multiple motifs). Although there are known motif pattern databases and tools for genome level prediction but no tool for weight matrix construction. Considering this, we developed a D-MATRIX tool which predicts the different types of weight matrix based on user defined aligned motif sequence set and motif width. For retrieval of known motif sequences user can access the commonly used databases such as TFD, RegulonDB, DBTBS, Transfac. D-MATRIX program uses a simple statistical approach for weight matrix construction, which can be converted into different file formats according to user requirement. It provides the possibility to identify the conserved motifs in the co-regulated genes or whole genome. As example, we successfully constructed the weight matrix of LexA transcription factor binding site with the help of known sos-box cis-regulatory elements in Deinococcus radiodurans genome. The algorithm is implemented in C-Sharp and wrapped in ASP.Net to maintain a user friendly web interface. D-MATRIX tool is accessible through the CIMAP domain network. http://203.190.147.116/dmatrix/

Empirical Study of User Preferences Based on Rating Data of Movies

PubMed Central

Zhao, YingSi; Shen, Bo

2016-01-01

User preference plays a prominent role in many fields, including electronic commerce, social opinion, and Internet search engines. Particularly in recommender systems, it directly influences the accuracy of the recommendation. Though many methods have been presented, most of these have only focused on how to improve the recommendation results. In this paper, we introduce an empirical study of user preferences based on a set of rating data about movies. We develop a simple statistical method to investigate the characteristics of user preferences. We find that the movies have potential characteristics of closure, which results in the formation of numerous cliques with a power-law size distribution. We also find that a user related to a small clique always has similar opinions on the movies in this clique. Then, we suggest a user preference model, which can eliminate the predictions that are considered to be impracticable. Numerical results show that the model can reflect user preference with remarkable accuracy when data elimination is allowed, and random factors in the rating data make prediction error inevitable. In further research, we will investigate many other rating data sets to examine the universality of our findings. PMID:26735847

ITrace: An implicit trust inference method for trust-aware collaborative filtering

NASA Astrophysics Data System (ADS)

He, Xu; Liu, Bin; Chen, Kejia

2018-04-01

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error.

Empirical Study of User Preferences Based on Rating Data of Movies.

PubMed

Zhao, YingSi; Shen, Bo

2016-01-01

User preference plays a prominent role in many fields, including electronic commerce, social opinion, and Internet search engines. Particularly in recommender systems, it directly influences the accuracy of the recommendation. Though many methods have been presented, most of these have only focused on how to improve the recommendation results. In this paper, we introduce an empirical study of user preferences based on a set of rating data about movies. We develop a simple statistical method to investigate the characteristics of user preferences. We find that the movies have potential characteristics of closure, which results in the formation of numerous cliques with a power-law size distribution. We also find that a user related to a small clique always has similar opinions on the movies in this clique. Then, we suggest a user preference model, which can eliminate the predictions that are considered to be impracticable. Numerical results show that the model can reflect user preference with remarkable accuracy when data elimination is allowed, and random factors in the rating data make prediction error inevitable. In further research, we will investigate many other rating data sets to examine the universality of our findings.

The study on variation of influential regions in China from a perspective of asymmetry economic information flow

NASA Astrophysics Data System (ADS)

Yang, Chunxia; Tang, Minxuan; Cao, Yongjian; Chen, Yanhua; Deng, Qiangqiang

2015-10-01

Based on the annual GDP (Gross Domestic Product) in 27 Chinese provinces and autonomous regions, the asymmetric economic information flows between different regions are calculated by the symbolic transfer entropy method and corresponding economic information flow networks are built over two periods, one is before the reform and opening up policy, the other is after that. By analyzing such networks, the obtained results are as follows. First, before the policy, balanced development strategy weakens or cuts off the ties between adjacent areas, resulting in a slow regional economic development, does not conform to the law of scientific development. Second, with introducing market mechanisms and promoting the reform and opening up policy, increasing economic activities have gradually shifted from coast to inland of China over Period II. Last but not least, there has a dramatic alternation of the influential centers that Jilin, Beijing and Jiangsu become new influential centers. Especially, at Beijing-Tianjin-Hebei metropolis circle Beijing becomes an influential center after the policy.

Factors related to sexual behaviors and sexual education programs for Asian-American adolescents.

PubMed

Lee, Young-Me; Florez, Elizabeth; Tariman, Joseph; McCarter, Sarah; Riesche, Laren

2015-08-01

To understand the influential factors related to sexual behaviors among Asian-American adolescents and to evaluate common factors across successful sexual education programs for this population. Despite a rapid increase in cases of STIs/HIV among Asian-American populations, there remains a need for a comprehensive understanding of the influential factors related to risky sexual behaviors for this population. An integrative literature review was conducted. Peer-reviewed articles and government resources were analyzed. Five influential factors were identified: family-centered cultural values, parental relationship, acculturation, gender roles, and lack of knowledge and information about sex and STIs. Only two sexual educational programs met the inclusion criteria and provided evidence towards effectiveness: Safer Choices and Seattle Social Development Project. The findings of this study indicate an urgent need for culturally sensitive sexual education programs that incorporate the identified influential factors, especially cultural values in order to reduce risky sexual behaviors among Asian-American adolescents. Copyright © 2015 Elsevier Inc. All rights reserved.

Experiences of Discrimination among Chinese American Adolescents and the Consequences for Socioemotional and Academic Development

PubMed Central

Benner, Aprile D.; Kim, Su Yeong

2009-01-01

This longitudinal study examined the influences of discrimination on socioemotional adjustment and academic performance for a sample of 444 Chinese American adolescents. Using autoregressive and cross-lagged techniques, results indicate that discrimination in early adolescence predicted depressive symptoms, alienation, school engagement, and grades in middle adolescence, but early socioemotional adjustment and academic performance did not predict later experiences of discrimination. Further, our investigation of whether earlier or contemporaneous experiences of discrimination influenced developmental outcomes in middle adolescence indicated differential effects, with contemporaneous experiences of discrimination affecting socioemotional adjustment, while earlier discrimination was more influential for academic performance. Finally, we found a persistent negative effect of acculturation on the link between discrimination and adolescents’ developmental outcomes, such that those adolescents who were more acculturated (in this case, higher in American orientation) experienced more deleterious effects of discrimination on both socioemotional and academic outcomes. PMID:19899924

Phylogenies support out-of-equilibrium models of biodiversity.

PubMed

Manceau, Marc; Lambert, Amaury; Morlon, Hélène

2015-04-01

There is a long tradition in ecology of studying models of biodiversity at equilibrium. These models, including the influential Neutral Theory of Biodiversity, have been successful at predicting major macroecological patterns, such as species abundance distributions. But they have failed to predict macroevolutionary patterns, such as those captured in phylogenetic trees. Here, we develop a model of biodiversity in which all individuals have identical demographic rates, metacommunity size is allowed to vary stochastically according to population dynamics, and speciation arises naturally from the accumulation of point mutations. We show that this model generates phylogenies matching those observed in nature if the metacommunity is out of equilibrium. We develop a likelihood inference framework that allows fitting our model to empirical phylogenies, and apply this framework to various mammalian families. Our results corroborate the hypothesis that biodiversity dynamics are out of equilibrium. © 2015 John Wiley & Sons Ltd/CNRS.

Spatial analysis of the invasion of lionfish in the western Atlantic and Caribbean.

PubMed

Johnston, Matthew W; Purkis, Samuel J

2011-06-01

Pterois volitans and Pterois miles, two sub-species of lionfish, have become the first non-native, invasive marine fish established along the United States Atlantic coast and Caribbean. The route and timing of the invasion is poorly understood, however historical sightings and captures have been robustly documented since their introduction. Herein we analyze these records based on spatial location, dates of arrival, and prevailing physical factors at the capture sights. Using a cellular automata model, we examine the relationship between depth, salinity, temperature, and current, finding the latter as the most influential parameter for transport of lionfish to new areas. The model output is a synthetic validated reproduction of the lionfish invasion, upon which predictive simulations in other locations can be based. This predictive model is simple, highly adaptable, relies entirely on publicly available data, and is applicable to other species. Copyright © 2011 Elsevier Ltd. All rights reserved.

Physical Outdoor Activity versus Indoor Activity: Their Influence on Environmental Behaviors

PubMed Central

Fang, Wei-Ta; Ng, Eric; Chang, Mei-Chuan

2017-01-01

There are strong evidences linking physical outdoor activity and health benefits; however, little is known about the impact on environmental behaviors. Thus, this study aims to close this gap by investigating the influence of physical outdoor activity on environmental behaviors. A total of 416 surveys were distributed to students in eight public primary schools located near the Hsinchu Science and Industrial Park in Taiwan. Findings from the analysis revealed that subjective norms had a more influential effect on environmental behaviors for participants who engaged in physical activity at outdoor parks. In contrast, descriptive norms had a direct predictive impact on environmental behaviors for participants whose main physical activity venue was at the indoor after-school centers. Research results also highlighted attitude as the strongest predictive variable influence on environmental behaviors for children who engaged in physical indoor and outdoor activities. PMID:28714934

Models of Affective Decision Making

PubMed Central

Charpentier, Caroline J.; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P.; Sharot, Tali

2016-01-01

Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. PMID:27071751

Threat in dreams: an adaptation?

PubMed

Malcolm-Smith, Susan; Solms, Mark; Turnbull, Oliver; Tredoux, Colin

2008-12-01

Revonsuo's influential Threat Simulation Theory (TST) predicts that people exposed to survival threats will have more threat dreams, and evince enhanced responses to dream threats, compared to those living in relatively safe conditions. Participants in a high crime area (South Africa: n=208) differed significantly from participants in a low crime area (Wales, UK: n=116) in having greater recent exposure to a life-threatening event (chi([1,N=186])(2)=14.84, p<.00012). Contrary to TST's predictions, the SA participants reported significantly fewer threat dreams (chi([1,N=287])(2)=6.11, p<.0134), and did not differ from the Welsh participants in responses to dream threats (Fisher's Exact test, p=.2478). Overall, the incidence of threat in dreams was extremely low-less than 20% of dreams featured realistic survival threats. Escape from dream threats occurred in less than 2% of dreams. We conclude that this evidence contradicts key aspects of TST.

Is problematic mobile phone use explained by chronotype and personality?

PubMed

Demirhan, Eda; Randler, Christoph; Horzum, Mehmet Barış

2016-01-01

In this study, the relationships among problematic mobile phone use, age, gender, personality and chronotype of Turkish university students were examined. The study included 902 university students (73% female, 27% male) and their participation in the study was anonymous and voluntary. Data were collected from each participant by assessing a demographic questionnaire, Composite Scale of Morningness (CSM) as a measure of chronotype, the Big Five Inventory (BIG-5) for personality assessment and Mobile Phone Problem Usage Scale (MPPUS). The most important result was that CSM scores were the best predictor for problematic mobile phone usage, and as a consequence, evening-oriented university students scored higher on the MPPUS. This result remained, even when compared with the most influential personality predictor, conscientiousness. In addition, while extraversion positively predicted, emotional stable and chronotype negatively predicted problematic mobile phone use. Lastly, age and gender were not predictors of problematic mobile phone use.

Modeling pedestrian gap crossing index under mixed traffic condition.

PubMed

Naser, Mohamed M; Zulkiple, Adnan; Al Bargi, Walid A; Khalifa, Nasradeen A; Daniel, Basil David

2017-12-01

There are a variety of challenges faced by pedestrians when they walk along and attempt to cross a road, as the most recorded accidents occur during this time. Pedestrians of all types, including both sexes with numerous aging groups, are always subjected to risk and are characterized as the most exposed road users. The increased demand for better traffic management strategies to reduce the risks at intersections, improve quality traffic management, traffic volume, and longer cycle time has further increased concerns over the past decade. This paper aims to develop a sustainable pedestrian gap crossing index model based on traffic flow density. It focusses on the gaps accepted by pedestrians and their decision for street crossing, where (Log-Gap) logarithm of accepted gaps was used to optimize the result of a model for gap crossing behavior. Through a review of extant literature, 15 influential variables were extracted for further empirical analysis. Subsequently, data from the observation at an uncontrolled mid-block in Jalan Ampang in Kuala Lumpur, Malaysia was gathered and Multiple Linear Regression (MLR) and Binary Logit Model (BLM) techniques were employed to analyze the results. From the results, different pedestrian behavioral characteristics were considered for a minimum gap size model, out of which only a few (four) variables could explain the pedestrian road crossing behavior while the remaining variables have an insignificant effect. Among the different variables, age, rolling gap, vehicle type, and crossing were the most influential variables. The study concludes that pedestrians' decision to cross the street depends on the pedestrian age, rolling gap, vehicle type, and size of traffic gap before crossing. The inferences from these models will be useful to increase pedestrian safety and performance evaluation of uncontrolled midblock road crossings in developing countries. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Navigating HIV prevention policy and Islam in Malaysia: contention, compatibility or reconciliation? Findings from in-depth interviews among key stakeholders.

PubMed

Barmania, Sima; Aljunid, Syed Mohamed

2016-07-07

Malaysia is a multicultural society, predominantly composed of a Muslim majority population, where Islam is influential. Malaysia has a concentrated HIV epidemic amongst high risk groups, such as, Intravenous Drug Users (IVDU), sex workers, transgender women and Men who have sex with Men (MSM). The objective of this study is to understand how Islam shapes HIV prevention strategies in Malaysia by interviewing the three key stakeholder groups identified as being influential, namely the Ministry of Health, Religious leaders and People living with HIV. Thirty-Five in depth semi structured interviews were undertaken with religious leaders, Ministry of Health and People living with HIV in the last half of 2013 using purposive sampling. Interviews adhered to a topic guide, were audiotaped, and transcribed verbatim and analyzed using a framework analysis. Themes including the importance of Islam to health, stakeholder relationships and opinions on HIV prevention emerged. Islam was seen to play a pivotal role in shaping strategies relating to HIV prevention in Malaysia both directly and indirectly. Stakeholders often held different approaches to HIV prevention, which had to be sensitively considered, with some favouring promotion of Islamic principles, whilst others steering towards a more public health centred approach. The study suggests that Islam indeed plays an important role in shaping health policies and strategies related to HIV prevention in Malaysia. Certainly, stakeholders do hold differing viewpoints, such as stances of what constitutes the right approach to HIV prevention. However there are also areas of broad consensus, such as the importance in Islamic tradition to prevent harm and disease, which can be crafted into existing and future HIV prevention strategies in Malaysia, as well as the wider Muslim world.

[Current situation related to antiretroviral therapy and related influential factors on HIV infected injection drug users in the methadone maintenance treatment clinics].

PubMed

Cheng, Xiao-Qing; Pang, Lin; Cao, Xiao-Bin; Wang, Chang-He; Luo, Wei; Zhang, Bo; Wang, Hua; Li, Rong-Jian; Rou, Ke-Ming; Wu, Zun-You

2013-08-01

To find out the current coverage of antiretroviral therapy (ART) among HIV positive subjects and to identify the major influential factors associated with the participation in ART among them. 291 HIV positive subjects from 6 methadone maintenance treatment (MMT) clinics in Guangxi and Yunnan province were surveyed by questionnaires. 217 males (74.6%) and 74 females (25.4%) were under investigation, with the average age of 38.4 +/- 5.9. Most of them received less than senior high school education, married and unemployed. Results from the single factor logistic regression analysis showed that: working status, living alone, self-reported history of drinking alcohol in the last month, negative attitude towards MMT among family members,poor self-reported compliance to MMT in the last month,lack of incentives in the MMT clinics, reluctance on disclosure of their own HIV status, good self-perception on their health status, lack of communication on ART related topics among family members in the last 6 months, lack of correct attitude and knowledge on ART etc. appeared as the main factors that influencing the participation in ART program among the patients. Data from the multivariate logistic regression analysis showed that factors as: living alone, unwilling to tell others about the status of HIV infection, poor self-perception on HIV infection, lack of discussion of ART related topics within family members in the last 6 months and poor awareness towards ART among the family members etc., were associated with the low participation rate of ART. Conclusion Strengthening the publicity and education programs on HIV positive patients and their family members at the MMT clinics seemed to be effective in extending the ART coverage. Attention should also be paid to increase the family support to the patients.

Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia

PubMed Central

Segkouli, Sofia; Tzovaras, Dimitrios; Tsakiris, Thanos; Tsolaki, Magda; Karagiannidis, Charalampos

2015-01-01

Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessibility for older adults. The main aim of this study is to develop and test virtual user models (VUM) simulating mild cognitive impairment (MCI) through novel specific modules, embodied at cognitive models and defined by estimations of cognitive parameters. Well-established MCI detection tests assessed users' cognition, elaborated their ability to perform multitasks, and monitored the performance of infotainment related tasks to provide more accurate simulation results on existing conceptual frameworks and enhanced predictive validity in interfaces' design supported by increased tasks' complexity to capture a more detailed profile of users' capabilities and limitations. The final outcome is a more robust cognitive prediction model, accurately fitted to human data to be used for more reliable interfaces' evaluation through simulation on the basis of virtual models of MCI users. PMID:26339282

Personalized Vehicle Energy Efficiency & Range Predictor/MyGreenCar

DOE Office of Scientific and Technical Information (OSTI.GOV)

SAXENA, SAMVEG

MyGreenCar provides users with the ability to predict the range capabilities, fuel economy, and operating costs for any vehicle for their individual driving patterns. Users launce the MyGreeCar mobile app on their smartphones to collect their driving patterns over any duration (e.g. serval days, weeks, months, etc) using a phones's locational capabilities. Using vehicle powertrain models for any user-specified vehicle type, MyGreenCar, calculates the component-level energy and power interactions for the chosen vehicle to predict several important quantities, including: 1. For Evs: Alleviating range anxiety 2. Comparing fuel economy, operating costs, and payback time across models and types.

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

PubMed

van Zundert, G C P; Rodrigues, J P G L M; Trellet, M; Schmitz, C; Kastritis, P L; Karaca, E; Melquiond, A S J; van Dijk, M; de Vries, S J; Bonvin, A M J J

2016-02-22

The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Internet Competency Predicts Practical Hearing Aid Knowledge and Skills in First-Time Hearing Aid Users.

PubMed

Maidment, David; Brassington, William; Wharrad, Heather; Ferguson, Melanie

2016-10-01

The purpose of the study was to assess whether Internet competency predicted practical hearing aid knowledge and handling skills in first-time hearing aid users. The design was a prospective, randomized controlled trial of a multimedia educational intervention consisting of interactive video tutorials (or reusable learning objects [RLOs]). RLOs were delivered through DVD for TV or PC, and online. Internet competency was measured at the hearing aid fitting appointment, whereas hearing aid knowledge and practical handling skills were assessed 6 weeks postfitting. Internet competency predicted practical hearing aid knowledge and handling skills, controlling for age, hearing sensitivity, educational status, and gender for the group that received the RLOs. Internet competency was inversely related to the number of times the RLOs were watched. Associations between Internet competency and practical hearing aid knowledge, handling skills, and watching the RLOs fewer times may have arisen because of improved self-efficacy. Therefore, first-time hearing aid users who are more competent Internet users may be better equipped to apply newly learned information to effectively manage their hearing loss.

Improved Personalized Recommendation Based on Causal Association Rule and Collaborative Filtering

ERIC Educational Resources Information Center

Lei, Wu; Qing, Fang; Zhou, Jin

2016-01-01

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

Modeling erosion under future climates with the WEPP model

Treesearch

Timothy Bayley; William Elliot; Mark A. Nearing; D. Phillp Guertin; Thomas Johnson; David Goodrich; Dennis Flanagan

2010-01-01

The Water Erosion Prediction Project Climate Assessment Tool (WEPPCAT) was developed to be an easy-to-use, web-based erosion model that allows users to adjust climate inputs for user-specified climate scenarios. WEPPCAT allows the user to modify monthly mean climate parameters, including maximum and minimum temperatures, number of wet days, precipitation, and...

Working Memory and Impulsivity Predict Marijuana-Related Problems Among Frequent Users

PubMed Central

Day, Anne M.; Metrik, Jane; Spillane, Nichea S.; Kahler, Christopher W.

2012-01-01

Background Although marijuana is the most commonly used illicit substance in the US, only a small portion of users go on to develop dependence, suggesting that there are substantial individual differences in vulnerability to marijuana-related problems among users. Deficits in working memory and high trait impulsivity are two factors that may place marijuana users at increased risk for experiencing related problems. Methods Using baseline data from an experimental study that recruited 104 frequent marijuana users (M=71.86% of prior 60 days, SD=22%), we examined the associations of working memory and trait impulsivity with marijuana-related problems. Results Lower working memory, as measured by Trail Making Test B, but not short-term memory capacity, predicted more marijuana-related problems. Higher trait impulsivity scores were independently associated with greater number of problems. Conclusions Results suggest that marijuana users with reduced executive cognitive ability are more susceptible to developing problems related to their use. Trait impulsivity and executive working memory appear to be independent risk factors for experiencing marijuana-related problems. PMID:23312340

Smart Meter Driven Segmentation: What Your Consumption Says About You

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, A; Rajagopal, R

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploitedmore » for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.« less

A mathematical model of vowel identification by users of cochlear implants

PubMed Central

Sagi, Elad; Meyer, Ted A.; Kaiser, Adam R.; Teoh, Su Wooi; Svirsky, Mario A.

2010-01-01

A simple mathematical model is presented that predicts vowel identification by cochlear implant users based on these listeners’ resolving power for the mean locations of first, second, and∕or third formant energies along the implanted electrode array. This psychophysically based model provides hypotheses about the mechanism cochlear implant users employ to encode and process the input auditory signal to extract information relevant for identifying steady-state vowels. Using one free parameter, the model predicts most of the patterns of vowel confusions made by users of different cochlear implant devices and stimulation strategies, and who show widely different levels of speech perception (from near chance to near perfect). Furthermore, the model can predict results from the literature, such as Skinner, et al. [(1995). Ann. Otol. Rhinol. Laryngol. 104, 307–311] frequency mapping study, and the general trend in the vowel results of Zeng and Galvin’s [(1999). Ear Hear. 20, 60–74] studies of output electrical dynamic range reduction. The implementation of the model presented here is specific to vowel identification by cochlear implant users, but the framework of the model is more general. Computational models such as the one presented here can be useful for advancing knowledge about speech perception in hearing impaired populations, and for providing a guide for clinical research and clinical practice. PMID:20136228

PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems

PubMed Central

Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

2016-01-01

PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems. PMID:27174940

On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

NASA Astrophysics Data System (ADS)

Soares, Marta Bruno; Dessai, Suraje

2014-05-01

The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on the use of seasonal forecasts. The potential to use decadal predictions across European sectors was also noted although these are currently not used due to the limitations of the science and the experimental nature of existing predictions. Despite the limited use of these types of climate predictions there is a general understanding that information on the uncertainty of such predictions is a fundamental component of S2DCP although approaches for dealing with such uncertainty also tend to differ across organisations. Perceived barriers to the uptake of these types of climate predictions are mainly associated with low skill and reliability of the models but also with other factors such as relevance, usability, and accessibility of S2DCP by end-users. Potential solutions to overcome such barriers include the potential to explore existing 'windows of opportunity' in Europe, improve current understanding of users' needs, and increase accessibility and awareness of users to available S2DCP in Europe. This paper will present findings from our analysis and consider some of the broader issues raised by the emergence of S2DCP for climate services in Europe.

Different factors influence self-reports and third-party reports of anger by adults with intellectual disabilities.

PubMed

Rose, John; Willner, Paul; Shead, Jennifer; Jahoda, Andrew; Gillespie, David; Townson, Julia; Lammie, Claire; Woodgate, Christopher; Stenfert Kroese, Biza; Felce, David; MacMahon, Pamela; Rose, Nikki; Stimpson, Aimee; Nuttall, Jacqueline; Hood, Kerenza

2013-09-01

Many people with intellectual disabilities display high levels of anger, and cognitive-behavioural anger management interventions are used routinely. However, for these methods to be used optimally, a better understanding is needed of different forms of anger assessment. The aim of this study was to investigate the relationship of a range of measures to self- and carer reports of anger expression, including instruments used to assess mental health and challenging behaviour. Adults with intellectual disabilities, who had been identified as having problems with anger control, their key-workers and home carers all rated the service users' trait anger, using parallel versions of the same instrument (the Provocation Inventory). In addition, service users completed a battery of mental health assessments (the Glasgow Depression Scale, Glasgow Anxiety Scale and Rosenberg Self-Esteem Scale), and both groups of carers completed a battery of challenging behaviour measures (the Hyperactivity and Irritability domains of the Aberrant Behavior Checklist and the Modified Overt Anger Scale). Participants had high levels of mental health problems (depression: 34%; anxiety: 73%) and severe challenging behaviour (26%). Hierarchical linear regression analysis was used to explore the extent to which anger ratings by the three groups of respondents were predicted by demographic factors, mental health measures and challenging behaviour measures. Older service users rated themselves as less angry and were also rated as less angry by home carers, but not by key-workers. More intellectually able service users were rated as more angry by both sets of carers, but not by the service users themselves. Significantly, mental health status (but not challenging behaviour) predicted service users' self-ratings of anger, whereas challenging behaviour (but not mental health status) predicted carers' ratings of service users' anger. Service users and their carers appear to use different information when rating the service users' anger. Service users' self-ratings reflect their internal emotional state and mental health, as reflected by their ratings of anxiety and depression, whereas staff rate service users' anger on the basis of overt behaviours, as measured by challenging behaviour scales. © 2013 John Wiley & Sons Ltd.

DIANA-microT web server: elucidating microRNA functions through target prediction.

PubMed

Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

2009-07-01

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Commentary: Building Web Research Strategies for Teachers and Students

ERIC Educational Resources Information Center

Maloy, Robert W.

2016-01-01

This paper presents web research strategies for teachers and students to use in building Dramatic Event, Historical Biography, and Influential Literature wiki pages for history/social studies learning. Dramatic Events refer to milestone or turning point moments in history. Historical Biographies and Influential Literature pages feature…

The ACAPS and SESPRS surveys to identify the most influential innovators and innovations in plastic surgery: no line on the horizon.

PubMed

Hultman, Charles Scott; Friedstat, Jonathan S

2014-01-01

Who and what have been the most influential innovators and innovations in plastic surgery? This historical paper attempts to determine our most important contributors and contributions. We conducted an anonymous, 7-question, web-based survey of all members of the American Council of Academic Plastic Surgeons (ACAPS) and the Southeastern Society of Plastic and Reconstructive Surgeons (SESPRS). We asked respondents to list their top 5 most influential surgeons, the most important publications or bodies of work, and the most important innovations in plastic surgery, past and present. Of the 86 nominees from ACAPS, the 15 most influential surgeons of the past century were Tessier, Buncke, Murray, Millard, Gillies, Mathes, Jurkiewicz, Taylor, Converse, Blair, Kleinert, Edgerton, McCraw, Peacock, and Brown, in that order. The most 10 influential surgeons of the current era are Rohrich, McCarthy, Wei, Lee, Siemionow, Allen, Coleman, Guyuron, Serletti, and Nahai. Of the 112 nominees from SESPRS, the 15 most influential surgeons of the past century were Gillies, Millard, Tessier, Buncke, Murray, Jurkiewicz, Hartrampf, Mathes, Taylor, Bostwick, McCraw, Furlow, Converse, Peacock, and Blair, in that order. The 10 most influential surgeons of the current era are Rohrich, Nahai, Wei, McCarthy, Coleman, MacKinnon, McGrath, Rubin, Guyuron, and Hammond. Pooled from both lists, the 10 most influential publications or bodies of work were Hartrampf's TRAM flap, Millard's cleft lip repair, McCraw/Mathes/Nahai's myocutaneous flaps, Furlow's cleft palate repair, Tessier's cleft classification and craniofacial repairs, Ramirez's components separation, Buncke's replantation/toe-to-thumb transfer, McCarthy's mandibular distraction osteogenesis, Taylor's free flap and angiosome concepts, and Murray's kidney transplant. The top 10 innovations of the 20th century were myocutaneous flaps, microsurgery, craniofacial surgery, skin grafts, transplantation, liposuction, bioimplants, distraction osteogenesis, angiosome anatomy, and rigid fixation. The 10 most important, current innovations are hand/face transplantation, fat grafting, stem cells, neurotoxins and soft-tissue fillers, biologic scaffolds, information technology, tissue engineering and regenerative medicine, negative pressure wound therapy, perforator flaps, and noninvasive imaging. Plastic surgery includes a rich history of both incremental and disruptive innovation, which has endowed our discipline with a competitive advantage over other medical and surgical subspecialties. Based upon our past success in managing change, there may be no limit, or no line on the horizon, as to what is possible, provided that we pursue innovation in a systematic way that combines creativity and discipline.

Empirical predictions of hypervelocity impact damage to the space station

NASA Technical Reports Server (NTRS)

Rule, W. K.; Hayashida, K. B.

1991-01-01

A family of user-friendly, DOS PC based, Microsoft BASIC programs written to provide spacecraft designers with empirical predictions of space debris damage to orbiting spacecraft is described. The spacecraft wall configuration is assumed to consist of multilayer insulation (MLI) placed between a Whipple style bumper and the pressure wall. Predictions are based on data sets of experimental results obtained from simulating debris impacts on spacecraft using light gas guns on Earth. A module of the program facilitates the creation of the data base of experimental results that are used by the damage prediction modules of the code. The user has the choice of three different prediction modules to predict damage to the bumper, the MLI, and the pressure wall. One prediction module is based on fitting low order polynomials through subsets of the experimental data. Another prediction module fits functions based on nondimensional parameters through the data. The last prediction technique is a unique approach that is based on weighting the experimental data according to the distance from the design point.

The Impact of Personality Factors and Preceding User Comments on the Processing of Research Findings on Deep Brain Stimulation: A Randomized Controlled Experiment in a Simulated Online Forum.

PubMed

Feinkohl, Insa; Flemming, Danny; Cress, Ulrike; Kimmerle, Joachim

2016-03-03

Laypeople frequently discuss medical research findings on Web-based platforms, but little is known about whether they grasp the tentativeness that is inherent in these findings. Potential influential factors involved in understanding medical tentativeness have hardly been assessed to date. The research presented here aimed to examine the effects of personality factors and of other users' previous contributions in a Web-based forum on laypeople's understanding of the tentativeness of medical research findings, using the example of research on deep brain stimulation. We presented 70 university students with an online news article that reported findings on applying deep brain stimulation as a novel therapeutic method for depression, which participants were unfamiliar with. In a randomized controlled experiment, we manipulated the forum such that the article was either accompanied by user comments that addressed the issue of tentativeness, by comments that did not address this issue, or the article was accompanied by no comments at all. Participants were instructed to write their own individual user comments. Their scientific literacy, epistemological beliefs, and academic self-efficacy were measured. The outcomes measured were perceived tentativeness and tentativeness addressed in the participants' own comments. More sophisticated epistemological beliefs enhanced the perception of tentativeness (standardized β=.26, P=.034). Greater scientific literacy (stand. β=.25, P=.025) and greater academic self-efficacy (stand. β=.31, P=.007) were both predictors of a more extensive discussion of tentativeness in participants' comments. When forum posts presented in the experiment addressed the issue of tentativeness, participants' subsequent behavior tended to be consistent with what they had read in the forum, F2,63=3.66; P=.049, ηp(2)=.092. Students' understanding of the tentativeness of research findings on deep brain stimulation in an online forum is influenced by a number of character traits and by the previous comments that were contributed to the forum by other users. There is potential for targeted modification of traits such as scientific literacy, epistemological beliefs, and academic self-efficacy to foster critical thinking in laypeople who take part in online discussions of medical research findings.

Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

PubMed

Heilbron, Micha; Chait, Maria

2017-08-04

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges

PubMed Central

Lee, Jaebeom; Lee, Young-Joo

2018-01-01

Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance. PMID:29747421

Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges.

PubMed

Lee, Jaebeom; Lee, Kyoung-Chan; Lee, Young-Joo

2018-05-09

Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance.

Finding Waldo: Learning about Users from their Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Eli T.; Ottley, Alvitta; Zhao, Helen

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user’s interactions with a system reflect a large amount of the user’s reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user’s task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, wemore » conduct an experiment in which participants perform a visual search task and we apply well-known machine learning algorithms to three encodings of the users interaction data. We achieve, depending on algorithm and encoding, between 62% and 96% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user’s personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time, in some cases, 82% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed- initiative visual analytics systems.« less

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments.

PubMed

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-10-15

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

PubMed Central

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-01-01

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates. PMID:27754456

Psychosocial Outcomes in Long-Term Cochlear Implant Users.

PubMed

Castellanos, Irina; Kronenberger, William G; Pisoni, David B

The objectives of this study were to investigate psychosocial outcomes in a sample of prelingually deaf, early-implanted children, adolescents, and young adults who are long-term cochlear implant (CI) users and to examine the extent to which language and executive functioning predict psychosocial outcomes. Psychosocial outcomes were measured using two well-validated, parent-completed checklists: the Behavior Assessment System for Children and the Conduct Hyperactive Attention Problem Oppositional Symptom. Neurocognitive skills were measured using gold standard, performance-based assessments of language and executive functioning. CI users were at greater risk for clinically significant deficits in areas related to attention, oppositional behavior, hyperactivity-impulsivity, and social-adaptive skills compared with their normal-hearing peers, although the majority of CI users scored within average ranges relative to Behavior Assessment System for Children norms. Regression analyses revealed that language, visual-spatial working memory, and inhibition-concentration skills predicted psychosocial outcomes. Findings suggest that underlying delays and deficits in language and executive functioning may place some CI users at a risk for difficulties in psychosocial adjustment.

Correlates of prescription drug market involvement among young adults.

PubMed

Vuolo, Mike; Kelly, Brian C; Wells, Brooke E; Parsons, Jeffrey T

2014-10-01

While a significant minority of prescription drug misusers report purchasing prescription drugs, little is known about prescription drug selling. We build upon past research on illicit drug markets, which increasingly recognizes networks and nightlife as influential, by examining prescription drug market involvement. We use data from 404 young adult prescription drug misusers sampled from nightlife scenes. Using logistic regression, we examine recent selling of and being approached to sell prescription drugs, predicted using demographics, misuse, prescription access, and nightlife scene involvement. Those from the wealthiest parental class and heterosexuals had higher odds (OR=6.8) of selling. Higher sedative and stimulant misuse (OR=1.03), having a stimulant prescription (OR=4.14), and having sold other illegal drugs (OR=6.73) increased the odds of selling. College bar scene involvement increased the odds of selling (OR=2.73) and being approached to sell (OR=2.09). Males (OR=1.93), stimulant users (OR=1.03), and sedative prescription holders (OR=2.11) had higher odds of being approached. College bar scene involvement was the only site associated with selling and being approached; such participation may provide a network for prescription drug markets. There were also differences between actual selling and being approached. Males were more likely to be approached, but not more likely to sell than females, while the opposite held for those in the wealthiest parental class relative to lower socioeconomic statuses. Given that misuse and prescriptions of sedatives and stimulants were associated with prescription drug market involvement, painkiller misusers may be less likely to sell their drugs given the associated physiological dependence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Predicting item popularity: Analysing local clustering behaviour of users

NASA Astrophysics Data System (ADS)

Liebig, Jessica; Rao, Asha

2016-01-01

Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new bipartite clustering coefficient to predict the popularity of movies and stories in the MovieLens and Digg networks respectively. We show that the clustering behaviour of the first user who rates a new item gives insight into the future popularity of that item. Our method predicts, with a success rate of over 65% for the MovieLens network and over 50% for the Digg network, the future popularity of an item. This is a major improvement on current results.

Generalized role for the cerebellum in encoding internal models: evidence from semantic processing.

PubMed

Moberget, Torgeir; Gullesen, Eva Hilland; Andersson, Stein; Ivry, Richard B; Endestad, Tor

2014-02-19

The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uniformity of function. Consequently, theoretical models of the cerebellum's role in motor control should offer important clues regarding cerebellar contributions to cognition. One such influential theory holds that the cerebellum encodes internal models, neural representations of the context-specific dynamic properties of an object, to facilitate predictive control when manipulating the object. The present study examined whether this theoretical construct can shed light on the contribution of the cerebellum to language processing. We reasoned that the cerebellum might perform a similar coordinative function when the context provided by the initial part of a sentence can be highly predictive of the end of the sentence. Using functional MRI in humans we tested two predictions derived from this hypothesis, building on previous neuroimaging studies of internal models in motor control. First, focal cerebellar activation-reflecting the operation of acquired internal models-should be enhanced when the linguistic context leads terminal words to be predictable. Second, more widespread activation should be observed when such predictions are violated, reflecting the processing of error signals that can be used to update internal models. Both predictions were confirmed, with predictability and prediction violations associated with increased blood oxygenation level-dependent signal in the posterior cerebellum (Crus I/II). Our results provide further evidence for cerebellar involvement in predictive language processing and suggest that the notion of cerebellar internal models may be extended to the language domain.

ReactPRED: a tool to predict and analyze biochemical reactions.

PubMed

Sivakumar, Tadi Venkata; Giri, Varun; Park, Jin Hwan; Kim, Tae Yong; Bhaduri, Anirban

2016-11-15

Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: anirban.b@samsung.com or ty76.kim@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fuels planning: science synthesis and integration; environmental consequences fact sheet 12: Water Erosion Prediction Project (WEPP) Fuel Management (FuMe) tool

Treesearch

William Elliot; David Hall

2005-01-01

The Water Erosion Prediction Project (WEPP) Fuel Management (FuMe) tool was developed to estimate sediment generated by fuel management activities. WEPP FuMe estimates sediment generated for 12 fuel-related conditions from a single input. This fact sheet identifies the intended users and uses, required inputs, what the model does, and tells the user how to obtain the...

Examining ISIS Support and Opposition Networks on Twitter

DTIC Science & Technology

2016-01-01

interactive communities of Twitter users , lexical analysis that can identify key themes and content for large data sets, and social network analysis...Twitter data, we lexically analyzed the content and key themes of users who mostly employ Daesh versus those who mostly use Islamic State in their tweets...As predicted, we found that frequent users of Daesh had content that was highly critical of ISIS, with users using such terms as Terrorist Daesh

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

PubMed

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

"Disproportionately Influential?"

ERIC Educational Resources Information Center

Stuart, Reginald

2010-01-01

This article discusses why the Lumina Foundation is considered so influential in higher education despite its small size and the fact that is is a relatively new foundation. Lumina approaches its 10th anniversary this month with a focused higher education funding mission targeting efforts aimed at expanding access and success beyond high school,…

Qualities of Influential Literacy Teacher Educators

ERIC Educational Resources Information Center

Wold, Linda S.; Young, Janet R.; Risko, Victoria J.

2011-01-01

An online survey of award-winning literacy teachers was conducted to determine the most influential qualities of literacy teacher educators in teacher preparation programs. Sixty-two recipients of literacy awards participated in the study, representing teachers of excellence from all U.S. geographic regions. Using a backward mapping process,…

College Women's Career Self-Efficacy and Educational Environments.

ERIC Educational Resources Information Center

Scheye, Paula A.; Gilroy, Faith D.

1994-01-01

Examined relationship between composition by sex (single-sex versus coeducational) of women's (n=274) high school and college environments and sex of their selected influential teachers and their self-efficacy in traditional or nontraditional careers. Found no main effects for composition by sex of institution, or sex of influential teachers, nor…

A Study of the Educationally Influential Physician.

ERIC Educational Resources Information Center

Kaufman, David M.; Ryan, Kurt; Hodder, Ian

1999-01-01

A survey of 172 family doctors found that they approached educationally influential (EI) physicians they knew through their hospitals; only 20% used e-mail and 40% the Internet for medical information; EI physicians helped extend their knowledge and validate innovations found in the literature; and health care reform was negatively affecting…

Role of Socializing Agents in Female Sport Involvement

ERIC Educational Resources Information Center

Greendorfer, Susan L.

1977-01-01

Research into the socializing of women into sports activities revealed that peers were most influential at all life-cycle stages, family was the most influential during childhood, and coaches and teachers during adolescence; in addition, males were the predominant role models during childhood, and females during adolescence and adult life. (MB)

Identifying the Educationally Influential Physician: A Systematic Review of Approaches

ERIC Educational Resources Information Center

Kronberger, Matthew P.; Bakken, Lori L.

2011-01-01

Introduction: Previous studies have indicated that educationally influential physicians' (EIPs) interactions with peers can lead to practice changes and improved patient outcomes. However, multiple approaches have been used to identify and investigate EIPs' informal or formal influence on practice, which creates study outcomes that are difficult…

A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis Revision 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Narlesky, Joshua Edward; Kelly, Elizabeth J.

2015-09-10

This report documents the new PG calibration regression equation. These calibration equations incorporate new data that have become available since revision 1 of “A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis” was issued [3] The calibration equations are based on a weighted least squares (WLS) approach for the regression. The WLS method gives each data point its proper amount of influence over the parameter estimates. This gives two big advantages, more precise parameter estimates and better and more defensible estimates of uncertainties. The WLS approach makes sense both statistically and experimentally because themore » variances increase with concentration, and there are physical reasons that the higher measurements are less reliable and should be less influential. The new magnesium calibration includes a correction for sodium and separate calibration equation for items with and without chlorine. These additional calibration equations allow for better predictions and smaller uncertainties for sodium in materials with and without chlorine. Chlorine and sodium have separate equations for RICH materials. Again, these equations give better predictions and smaller uncertainties chlorine and sodium for RICH materials.« less

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Predictive modeling of deep-sea fish distribution in the Azores

NASA Astrophysics Data System (ADS)

Parra, Hugo E.; Pham, Christopher K.; Menezes, Gui M.; Rosa, Alexandra; Tempera, Fernando; Morato, Telmo

2017-11-01

Understanding the link between fish and their habitat is essential for an ecosystem approach to fisheries management. However, determining such relationship is challenging, especially for deep-sea species. In this study, we applied generalized additive models (GAMs) to relate presence-absence and relative abundance data of eight economically-important fish species to environmental variables (depth, slope, aspect, substrate type, bottom temperature, salinity and oxygen saturation). We combined 13 years of catch data collected from systematic longline surveys performed across the region. Overall, presence-absence GAMs performed better than abundance models and predictions made for the observed data successfully predicted the occurrence of the eight deep-sea fish species. Depth was the most influential predictor of all fish species occurrence and abundance distributions, whereas other factors were found to be significant for some species but did not show such a clear influence. Our results predicted that despite the extensive Azores EEZ, the habitats available for the studied deep-sea fish species are highly limited and patchy, restricted to seamounts slopes and summits, offshore banks and island slopes. Despite some identified limitations, our GAMs provide an improved knowledge of the spatial distribution of these commercially important fish species in the region.

Based on user interest level of modeling scenarios and browse content

NASA Astrophysics Data System (ADS)

Zhao, Yang

2017-08-01

User interest modeling is the core of personalized service, taking into account the impact of situational information on user preferences, the user behavior days of financial information. This paper proposes a method of user interest modeling based on scenario information, which is obtained by calculating the similarity of the situation. The user's current scene of the approximate scenario set; on the "user - interest items - scenarios" three-dimensional model using the situation pre-filtering method of dimension reduction processing. View the content of the user interested in the theme, the analysis of the page content to get each topic of interest keywords, based on the level of vector space model user interest. The experimental results show that the user interest model based on the scenario information is within 9% of the user's interest prediction, which is effective.

Spherical roller bearing analysis. SKF computer program SPHERBEAN. Volume 2: User's manual

NASA Technical Reports Server (NTRS)

Kleckner, R. J.; Dyba, G. J.

1980-01-01

The user's guide for the SPHERBEAN computer program for prediction of the thermomechanical performance characteristics of high speed lubricated double row spherical roller bearings is presented. The material presented is structured to guide the user in the practical and correct implementation of SPHERBEAN. Input and output, guidelines for program use, and sample executions are detailed.

Comparative ergonomic workflow and user experience analysis of MRI versus fluoroscopy-guided vascular interventions: an iliac angioplasty exemplar case study.

PubMed

Fernández-Gutiérrez, Fabiola; Martínez, Santiago; Rube, Martin A; Cox, Benjamin F; Fatahi, Mahsa; Scott-Brown, Kenneth C; Houston, J Graeme; McLeod, Helen; White, Richard D; French, Karen; Gueorguieva, Mariana; Immel, Erwin; Melzer, Andreas

2015-10-01

A methodological framework is introduced to assess and compare a conventional fluoroscopy protocol for peripheral angioplasty with a new magnetic resonant imaging (MRI)-guided protocol. Different scenarios were considered during interventions on a perfused arterial phantom with regard to time-based and cognitive task analysis, user experience and ergonomics. Three clinicians with different expertise performed a total of 43 simulated common iliac angioplasties (9 fluoroscopic, 34 MRI-guided) in two blocks of sessions. Six different configurations for MRI guidance were tested in the first block. Four of them were evaluated in the second block and compared to the fluoroscopy protocol. Relevant stages' durations were collected, and interventions were audio-visually recorded from different perspectives. A cued retrospective protocol analysis (CRPA) was undertaken, including personal interviews. In addition, ergonomic constraints in the MRI suite were evaluated. Significant differences were found when comparing the performance between MRI configurations versus fluoroscopy. Two configurations [with times of 8.56 (0.64) and 9.48 (1.13) min] led to reduce procedure time for MRI guidance, comparable to fluoroscopy [8.49 (0.75) min]. The CRPA pointed out the main influential factors for clinical procedure performance. The ergonomic analysis quantified musculoskeletal risks for interventional radiologists when utilising MRI. Several alternatives were suggested to prevent potential low-back injuries. This work presents a step towards the implementation of efficient operational protocols for MRI-guided procedures based on an integral and multidisciplinary framework, applicable to the assessment of current vascular protocols. The use of first-user perspective raises the possibility of establishing new forms of clinical training and education.

What are the Best Practices of Using to Twitter in Climate Change Communication?: A Case Study of Two Climate Related Events

NASA Astrophysics Data System (ADS)

McNeal, K.; Luginbuhl, S.; Ngo, A. M.

2017-12-01

Climate change is a major environmental issue that is often discussed throughout the world using social media outlets. One major social media site that is commonly utilized by the public is Twitter, with over 300 million active users. Using a Twitter account and Ncapture we were able to collect tens of thousands of tweets around the COP21 event, a United Nations climate change conference held on Dec. 7-8, 2015 in Paris, and the 2015 Encyclical Release by the Pope, using the hashtags @climate, pope, and COP21. This research aimed to follow and collect tweets about what and who the major influencers on Twitter are concerning these events, and subsequently climate change in general, and what content was most persistent. Specifically, we examined Twitter users with high numbers of followers (>10,000), the number of re-tweets, the frequency of tweets, and the content of the tweet. We have tabulated the top 10 most influential Tweeters among each of the months (August, September, October, November and December) of 2015 leading up to and following the COP21 event, which included an array of Twitter users from NGOs, Politicians, Celebrities, Religious Leaders, Governmental Organizations, among others. We also examined tweets about climate change as they relate to the two events and interpret why these tweets may have persisted in the twitter space. From our observations, we have established some best practices in how to create climate messages that have high reach and longevity. We hope our results assist climate change communicators in understanding the role Twitter plays in regard to climate change discourse and how to most efficiently utilize it for reaching broad audiences and engaging them in the climate conversation.

It is not just memory: propositional thinking influences performance on the autobiographical IAT.

PubMed

Vargo, Elisabeth Julie; Petróczi, Andrea; Shah, Iltaf; Naughton, Declan P

2014-12-01

The autobiographical Implicit Association Test (aIAT) is a variant of the Implicit Association Test reportedly capable of detecting an individual's concealed autobiographical event with very high accuracy. A previous attempt to utilize this measurement technique for the identification of cocaine users rendered an alarming rate of false positives. In this study, we aimed to explore the potential reasons behind the measurement's inaccuracy. Two versions of the cocaine aIAT were devised with different category labels (descriptive 'guilty/innocent' and self-referenced 'as if you were/were not'). Forty-one cocaine abstinent participants (43.9% male; mean age = 28.17 ± 7.36) were randomly assigned to one of the two conditions. Self-declared cocaine abstinence was confirmed for the 12-month period preceding data collection through hair analysis. Participants were also administered bespoke implicit and explicit cocaine user attitude measures, the self-esteem IAT and the Rosenberg self-esteem scale. The category labels which elicited self-referenced knowledge showed low accuracy (19%) compared to the 65% of the 'guilty/innocent' labels proposed by original authors. The self-referenced aIAT version significantly correlated with the self-concept measures. The aIAT outcomes were independent from attitudes toward cocaine users. Category labels play an influential role in determining the test's accuracy, demonstrating that participants' propositional knowledge and self-concept are involved during test performance. The aIAT does not appear to tap directly into an individual's implicit memory when relevant memory is not available. Although the test cannot be recommended for detecting drug use, further research should investigate underlying mechanisms and other potentials of the technique. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The influence of the mass media on the selection of physicians.

PubMed

Trandel-Korenchuk, D M

1998-01-01

The purpose of this study was to examine now media sources influence an individual's reported choice of a physician as compared to personal referral sources and how consumers use the Yellow Pages to search for health care information. A random sample of 762 residents was systematically selected from the Charlotte, North Carolina White Pages and was asked to participate in a 20-item descriptive phone survey designed and tested by the investigator. Five hundred and seventy-eight individuals completed the survey, with a response rate of 75.9%. This study supports previous research suggesting that personal referrals are the most influential sources in selecting health care services. Therefore, satisfying and delighting the physician's/practice's existing client base may be one of the most potent advertising resources at hand. Mass media sources played a relatively minor role in influencing provider selection in this study. Nevertheless, it should not be dismissed in as much as the media may be an important way for physicians to promote "brand recognition," a problem not considered in this study. Finally, approximately 28% of the participants were "Yellow Pages users"; that is, individuals who tended to be heavy users of the Yellow Pages and used it for multiple information-seeking tasks. The findings related to the Yellow Pages suggest that while it may be useful to advertise in the Yellow Pages, a more modest financial allocation to this source may be considered.

The potential distribution of Phlebotomus papatasi (Diptera: Psychodidae) in Libya based on ecological niche model.

PubMed

Abdel-Dayem, M S; Annajar, B B; Hanafi, H A; Obenauer, P J

2012-05-01

The increased cases of cutaneous leishmaniasis vectored by Phlebotomus papatasi (Scopoli) in Libya have driven considerable effort to develop a predictive model for the potential geographical distribution of this disease. We collected adult P. papatasi from 17 sites in Musrata and Yefern regions of Libya using four different attraction traps. Our trap results and literature records describing the distribution of P. papatasi were incorporated into a MaxEnt algorithm prediction model that used 22 environmental variables. The model showed a high performance (AUC = 0.992 and 0.990 for training and test data, respectively). High suitability for P. papatasi was predicted to be largely confined to the coast at altitudes <600 m. Regions south of 300 degrees N latitude were calculated as unsuitable for this species. Jackknife analysis identified precipitation as having the most significant predictive power, while temperature and elevation variables were less influential. The National Leishmaniasis Control Program in Libya may find this information useful in their efforts to control zoonotic cutaneous leishmaniasis. Existing records are strongly biased toward a few geographical regions, and therefore, further sand fly collections are warranted that should include documentation of such factors as soil texture and humidity, land cover, and normalized difference vegetation index (NDVI) data to increase the model's predictive power.

Regional variations in the diversity and predicted metabolic potential of benthic prokaryotes in coastal northern Zhejiang, East China Sea

PubMed Central

Wang, Kai; Ye, Xiansen; Zhang, Huajun; Chen, Heping; Zhang, Demin; Liu, Lian

2016-01-01

Knowledge about the drivers of benthic prokaryotic diversity and metabolic potential in interconnected coastal sediments at regional scales is limited. We collected surface sediments across six zones covering ~200 km in coastal northern Zhejiang, East China Sea and combined 16 S rRNA gene sequencing, community-level metabolic prediction, and sediment physicochemical measurements to investigate variations in prokaryotic diversity and metabolic gene composition with geographic distance and under local environmental conditions. Geographic distance was the most influential factor in prokaryotic β-diversity compared with major environmental drivers, including temperature, sediment texture, acid-volatile sulfide, and water depth, but a large unexplained variation in community composition suggested the potential effects of unmeasured abiotic/biotic factors and stochastic processes. Moreover, prokaryotic assemblages showed a biogeographic provincialism across the zones. The predicted metabolic gene composition similarly shifted as taxonomic composition did. Acid-volatile sulfide was strongly correlated with variation in metabolic gene composition. The enrichments in the relative abundance of sulfate-reducing bacteria and genes relevant with dissimilatory sulfate reduction were observed and predicted, respectively, in the Yushan area. These results provide insights into the relative importance of geographic distance and environmental condition in driving benthic prokaryotic diversity in coastal areas and predict specific biogeochemically-relevant genes for future studies. PMID:27917954

A multi-label, semi-supervised classification approach applied to personality prediction in social media.

PubMed

Lima, Ana Carolina E S; de Castro, Leandro Nunes

2014-10-01

Social media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others. Copyright © 2014 Elsevier Ltd. All rights reserved.

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

NASA Astrophysics Data System (ADS)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

Identifying influential factors of business process performance using dependency analysis

NASA Astrophysics Data System (ADS)

Wetzstein, Branimir; Leitner, Philipp; Rosenberg, Florian; Dustdar, Schahram; Leymann, Frank

2011-02-01

We present a comprehensive framework for identifying influential factors of business process performance. In particular, our approach combines monitoring of process events and Quality of Service (QoS) measurements with dependency analysis to effectively identify influential factors. The framework uses data mining techniques to construct tree structures to represent dependencies of a key performance indicator (KPI) on process and QoS metrics. These dependency trees allow business analysts to determine how process KPIs depend on lower-level process metrics and QoS characteristics of the IT infrastructure. The structure of the dependencies enables a drill-down analysis of single factors of influence to gain a deeper knowledge why certain KPI targets are not met.

Determining the influential depth for surface reflectance of sediment by BRDF measurements.

PubMed

Zhang, H; Voss, K; Reid, R

2003-10-20

We measure the Bi-directional reflectance distribution function (BRDF) of ooid sand layers with three particle size distributions (0.5-1mm, 0.25-0.5mm and 0.125-0.25mm) and layer thicknesses on a reflecting mirror to determine the influential depth in the optical region at wavelengths of 658 nm (red), 570 nm (green) and 457 nm (blue). The hemispherical reflectance (albedo) was used as an indicator of BRDF changes between different layers. Measurements are carried out on both dry and water wetted grains. The results indicate that for both dry and wet and all size distributions, the influential depth is at most 2mm.

Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design.

PubMed

Wachtler, Caroline; Coe, Amy; Davidson, Sandra; Fletcher, Susan; Mendoza, Antonette; Sterling, Leon; Gunn, Jane

2018-04-23

Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way that will enhance their uptake in routine clinical practice remains challenging; however, mobile apps show promise in this respect. To increase the likelihood that an app-delivered clinical prediction tool can be successfully incorporated into clinical practice, it is important to involve end users in the app design process. The aim of the study was to maximize patient engagement in an app designed to improve treatment allocation for depression. An iterative, user-centered design process was employed. Qualitative data were collected via 2 focus groups with a community sample (n=17) and 7 semistructured interviews with people with depressive symptoms. The results of the focus groups and interviews were used by the computer engineering team to modify subsequent protoypes of the app. Iterative development resulted in 3 prototypes and a final app. The areas requiring the most substantial changes following end-user input were related to the iconography used and the way that feedback was provided. In particular, communicating risk of future depressive symptoms proved difficult; these messages were consistently misinterpreted and negatively viewed and were ultimately removed. All participants felt positively about seeing their results summarized after completion of the clinical prediction tool, but there was a need for a personalized treatment recommendation made in conjunction with a consultation with a health professional. User-centered design led to valuable improvements in the content and design of an app designed to improve allocation of and engagement in depression treatment. Iterative design allowed us to develop a tool that allows users to feel hope, engage in self-reflection, and motivate them to treatment. The tool is currently being evaluated in a randomized controlled trial. ©Caroline Wachtler, Amy Coe, Sandra Davidson, Susan Fletcher, Antonette Mendoza, Leon Sterling, Jane Gunn. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.04.2018.

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

NASA Astrophysics Data System (ADS)

Dávalos, J. O.; García, J. C.; Urquiza, G.; Huicochea, A.; De Santiago, O.

2018-05-01

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

Me After You: Partner Influence and Individual Effort Predict Rejection of Self-Aspects and Self-Concept Clarity After Relationship Dissolution.

PubMed

Slotter, Erica B; Emery, Lydia F; Luchies, Laura B

2014-07-01

Individuals in ongoing romantic relationships incorporate attributes from their partner into their own self-concepts. However, little research has investigated what happens to these attributes should the relationship end. Across three studies, the present research sought to examine factors that predicted whether individuals retain or reject attributes from their self-concept that they initially gained during a relationship. We predicted that individuals would be more likely to reject attributes from their self post-dissolution if their ex-partner was influential in them adding those attributes to the self in the first place. However, we expected this effect to be moderated such that individuals who exerted greater, versus lesser, effort in maintaining relevant attributes would retain them as part of the self, regardless of whether the attribute originated from the partner. In addition, in two of our three studies, we explored the roles of partner influence, effort, and attribute rejection on individuals' post-dissolution self-concept clarity. © 2014 by the Society for Personality and Social Psychology, Inc.

Predicting Slag Generation in Sub-Scale Test Motors Using a Neural Network

NASA Technical Reports Server (NTRS)

Wiesenberg, Brent

1999-01-01

Generation of slag (aluminum oxide) is an important issue for the Reusable Solid Rocket Motor (RSRM). Thiokol performed testing to quantify the relationship between raw material variations and slag generation in solid propellants by testing sub-scale motors cast with propellant containing various combinations of aluminum fuel and ammonium perchlorate (AP) oxidizer particle sizes. The test data were analyzed using statistical methods and an artificial neural network. This paper primarily addresses the neural network results with some comparisons to the statistical results. The neural network showed that the particle sizes of both the aluminum and unground AP have a measurable effect on slag generation. The neural network analysis showed that aluminum particle size is the dominant driver in slag generation, about 40% more influential than AP. The network predictions of the amount of slag produced during firing of sub-scale motors were 16% better than the predictions of a statistically derived empirical equation. Another neural network successfully characterized the slag generated during full-scale motor tests. The success is attributable to the ability of neural networks to characterize multiple complex factors including interactions that affect slag generation.

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xin; Baker, Kyri A; Isley, Steven C

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

The dark side of technologies: technostress among users of information and communication technologies.

PubMed

Salanova, Marisa; Llorens, Susana; Cifre, Eva

2013-01-01

This paper tests the structure and the predictors of two psychological experiences of technostress associated with the use of information and communication technologies (ICT), i.e., technostrain (users report feelings of anxiety, fatigue, scepticism and inefficacy beliefs related to the use of technologies) and technoaddiction (users feel bad due to an excessive and compulsive use of these technologies). The study included a sample of 1072 ICT users (N = 675 nonintensive ICT users and N = 397 intensive ICT users). Results from multigroup confirmatory factor analyses among non-intensive and intensive ICT users showed, as expected, the four-factor structure of technostrain in both samples. Secondly, and also as expected, confirmatory factorial analyses revealed that technostress experiences are characterized not only by technostrain but also by an excessive and compulsive use of ICT. Moreover, multiple analyses of variance showed significant differences between non-intensive and intensive ICT users (1) in the dimensions of technostress and (2) in specific job demands and job/personal resources. Finally, linear multiple regression analyses revealed that technostrain is positively predicted by work overload, role ambiguity, emotional overload, mobbing and obstacles hindering ICT use, as well as by lack of autonomy, transformational leadership, social support, ICT use facilitators and mental competences. Work overload, role ambiguity and mobbing, as well as the lack of emotional competences, positively predict technoaddiction. Theoretical and practical implications, in addition to future research, are discussed.

Information Filtering Based on Users' Negative Opinions

NASA Astrophysics Data System (ADS)

Guo, Qiang; Li, Yang; Liu, Jian-Guo

2013-05-01

The process of heat conduction (HC) has recently found application in the information filtering [Zhang et al., Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

[Testing a Model to Predict Problem Gambling in Speculative Game Users].

PubMed

Park, Hyangjin; Kim, Suk Sun

2018-04-01

The purpose of the study was to develop and test a model for predicting problem gambling in speculative game users based on Blaszczynski and Nower's pathways model of problem and pathological gambling. The participants were 262 speculative game users recruited from seven speculative gambling places located in Seoul, Gangwon, and Gyeonggi, Korea. They completed a structured self-report questionnaire comprising measures of problem gambling, negative emotions, attentional impulsivity, motor impulsivity, non-planning impulsivity, gambler's fallacy, and gambling self-efficacy. Structural Equation Modeling was used to test the hypothesized model and to examine the direct and indirect effects on problem gambling in speculative game users using SPSS 22.0 and AMOS 20.0 programs. The hypothetical research model provided a reasonable fit to the data. Negative emotions, motor impulsivity, gambler's fallacy, and gambling self-efficacy had direct effects on problem gambling in speculative game users, while indirect effects were reported for negative emotions, motor impulsivity, and gambler's fallacy. These predictors explained 75.2% problem gambling in speculative game users. The findings suggest that developing intervention programs to reduce negative emotions, motor impulsivity, and gambler's fallacy, and to increase gambling self-efficacy in speculative game users are needed to prevent their problem gambling. © 2018 Korean Society of Nursing Science.

Shilling attack detection for recommender systems based on credibility of group users and rating time series.

PubMed

Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

Computer program documentation: Raw-to-processed SINDA program (RTOPHS) user's guide

NASA Technical Reports Server (NTRS)

Damico, S. J.

1980-01-01

Use of the Raw to Processed SINDA(System Improved Numerical Differencing Analyzer) Program, RTOPHS, which provides a means of making the temperature prediction data on binary HSTFLO and HISTRY units generated by SINDA available to engineers in an easy to use format, is discussed. The program accomplishes this by reading the HISTRY unit and according to user input instructions, the desired times and temperature prediction data are extracted and written to a word addressable drum file.

Micromechanics of metal matrix composites using the Generalized Method of Cells model (GMC) user's guide

NASA Technical Reports Server (NTRS)

Aboudi, Jacob; Pindera, Marek-Jerzy

1992-01-01

A user's guide for the program gmc.f is presented. The program is based on the generalized method of cells model (GMC) which is capable via a micromechanical analysis, of predicting the overall, inelastic behavior of unidirectional, multi-phase composites from the knowledge of the properties of the viscoplastic constituents. In particular, the program is sufficiently general to predict the response of unidirectional composites having variable fiber shapes and arrays.

Prenatal exposure to testosterone interacts with lifetime physical abuse to predict anger rumination and cognitive flexibility among incarcerated methamphetamine users.

PubMed

Herschl, Laura C; Highland, Krista B; McChargue, Dennis E

2012-01-01

The present pilot study hypothesized that degree of exposure to prenatal testosterone interacts with a history of lifetime physical abuse (LPA) to predict the cognitive (anger rumination) and behavioral (intimate partner and interpersonal violence) components of aggression within incarcerated methamphetamine (MA) users. In addition, we hypothesized that the degree of exposure to prenatal testosterone interacts with LPA to predict cognitive flexibility (Stroop Color-Word performance). Male inmate MA users (N = 60) completed neuropsychological and paper/pencil tests. Hand photocopies were also obtained to index prenatal testosterone exposure. Five covariate-adjusted moderation models were tested using anger rumination, intimate partner violence (IPV) perpetration, interpersonal violence perpetration (before and while incarcerated), and Stroop Color-Word T-score as the criteria, prenatal testosterone exposure as the predictor, and LPA as the moderator. Results indicated that, in individuals with a history of LPA, exposure to higher levels of prenatal testosterone exposure predicted greater anger rumination, lower Stroop Color-Word test T-scores, and lower frequencies of IPV perpetration. Findings were not significant in individuals without a history of LPA. This research suggests that biochemical and psychosocial vulnerabilities influence anger rumination and cognitive flexibility, which may render incarcerated MA users at greater risk to relapse or recidivate upon release from prison. Copyright © American Academy of Addiction Psychiatry.

Spelling is Just a Click Away – A User-Centered Brain–Computer Interface Including Auto-Calibration and Predictive Text Entry

PubMed Central

Kaufmann, Tobias; Völker, Stefan; Gunesch, Laura; Kübler, Andrea

2012-01-01

Brain–computer interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communication channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP–BCIs can be handled independently by laymen without expert support, which is inevitable for establishing BCIs in end-user’s daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character-matrix. N = 19 BCI novices handled a user-centered ERP–BCI application on their own without expert support. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and, importantly, did not reduce accuracy. In sum, this study demonstrates feasibility of auto-calibrating ERP–BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix. PMID:22833713

Characteristics of Academically-Influential Children: Achievement Motivation and Social Status

ERIC Educational Resources Information Center

Masland, Lindsay C.; Lease, A. Michele

2016-01-01

The contributions of academic achievement motivation and social status to peer-reported academic influence were explored in a sample of 322 children in grades three through five. Latent moderated structural equation modeling indicated that children who value academics are more likely to be rated by peers as academically influential. Social status…

Rural influentials' perceptions of tourism and its potential for economic development: a qualitative study

Treesearch

Steven W. Burr

1995-01-01

Rural residents' perceptions of tourism and its associated impacts are likely to be important in planning, development, marketing, and operation of existing and future tourism projects. This study examines rural influentials' perceptions of tourism as a tool for economic revitalization in Pennsylvania's rural counties, its present impact, and its...

Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

NASA Astrophysics Data System (ADS)

Yang, Fan; Zhang, Ruisheng; Yang, Zhao; Hu, Rongjing; Li, Mengtian; Yuan, Yongna; Li, Keqin

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

Leaking privacy and shadow profiles in online social networks.

PubMed

Garcia, David

2017-08-01

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

Predicting use of case management support services for adolescents and adults living in community following brain injury: A longitudinal Canadian database study with implications for life care planning

PubMed Central

Baptiste, B.; Dawson, D.R.; Streiner, D.

2015-01-01

Abstract OBJECTIVE: To determine factors associated with case management (CM) service use in people with traumatic brain injury (TBI), using a published model for service use. DESIGN: A retrospective cohort, with nested case-control design. Correlational and logistic regression analyses of questionnaires from a longitudinal community data base. STUDY SAMPLE: Questionnaires of 203 users of CM services and 273 non-users, complete for all outcome and predictor variables. Individuals with TBI, 15 years of age and older. Out of a dataset of 1,960 questionnaires, 476 met the inclusion criteria. METHODOLOGY: Eight predictor variables and one outcome variable (use or non-use of the service). Predictor variables considered the framework of the Behaviour Model of Health Service Use (BMHSU); specifically, pre-disposing, need and enabling factor groups as these relate to health service use and access. RESULTS: Analyses revealed significant differences between users and non-users of CM services. In particular, users were significantly younger than non-users as the older the person the less likely to use the service. Also, users had less education and more severe activity limitations and lower community integration. Persons living alone are less likely to use case management. Funding groups also significantly impact users. CONCLUSIONS: This study advances an empirical understanding of equity of access to health services usage in the practice of CM for persons living with TBI as a fairly new area of research, and considers direct relevance to Life Care Planning (LCP). Many life care planers are CM and the genesis of LCP is CM. The findings relate to health service use and access, rather than health outcomes. These findings may assist with development of a modified model for prediction of use to advance future cost of care predictions. PMID:26409333

Effects of user puff topography, device voltage, and liquid nicotine concentration on electronic cigarette nicotine yield: measurements and model predictions.

PubMed

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat; Shihadeh, Alan

2015-02-01

Some electronic cigarette (ECIG) users attain tobacco cigarette-like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3-5.2 V or 3.0-7.5 W) and e-liquid nicotine concentration (18-36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R (2) = 0.99). Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Effects of User Puff Topography, Device Voltage, and Liquid Nicotine Concentration on Electronic Cigarette Nicotine Yield: Measurements and Model Predictions

PubMed Central

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat

2015-01-01

Introduction: Some electronic cigarette (ECIG) users attain tobacco cigarette–like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Methods: Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3–5.2 V or 3.0–7.5 W) and e-liquid nicotine concentration (18–36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Results: Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R 2 = 0.99). Conclusions: Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. PMID:25187061

Factors influencing the career interest of medical graduates in obstetrics and gynaecology in Hong Kong: a cross-sectional questionnaire survey.

PubMed

Lam, Christy Y Y; Cheung, Charleen S Y; Hui, Annie S Y

2016-04-01

The trend of declining interest of medical graduates in pursuing obstetrics and gynaecology as a career has been observed in many overseas studies. This study aimed to evaluate the career interest of the most recent medical graduates in Hong Kong, especially their level of interest in obstetrics and gynaecology, and to identify key influential factors for career choice and career interest in obstetrics and gynaecology. All medical graduates from the Chinese University of Hong Kong and the University of Hong Kong who attended the pre-internship lectures in June 2015 were invited to participate in this cross-sectional questionnaire survey. The main outcome measures were the level of career interest in obstetrics and gynaecology, the first three choices of specialty as a career, key influential factors for career choice, and key influential factors for career interest in obstetrics and gynaecology. Overall, 73.7% of 323 new medical graduates participated in the study and 233 questionnaires were analysed. The median score (out of 10) for the level of career interest in obstetrics and gynaecology was 3. There were 37 (16.2%) participants in whom obstetrics and gynaecology was among their first three choices, of whom 29 (78.4%) were female. Obstetrics and gynaecology ranked as the eighth most popular career choice. By factor analysis, the strongest key influential factor for career interest in obstetrics and gynaecology was clerkship experience (variance explained 28.9%) and the strongest key influential factor for career choice was working style (variance explained 26.4%). The study confirmed a low level of career interest in obstetrics and gynaecology among medical graduates and a decreasing popularity of the specialty as a career choice. The three key influential factors for career interest in obstetrics and gynaecology and career choice were working style, clerkship experience, and career prospects.

Social Trust Prediction Using Heterogeneous Networks

PubMed Central

HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

2014-01-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method. PMID:24729776

Social Trust Prediction Using Heterogeneous Networks.

PubMed

Huang, Jin; Nie, Feiping; Huang, Heng; Tu, Yi-Cheng; Lei, Yu

2013-11-01

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.

A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease.

PubMed

Cuetos, Fernando; Rodríguez-Ferreiro, Javier; Sage, Karen; Ellis, Andrew W

2012-09-01

In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors. ©2012 The British Psychological Society.

The perception of Malaysian pedestrians toward the use of footbridges.

PubMed

Hasan, Razi; Napiah, Madzlan

2018-04-03

The footbridge is a vital structure in the road network and a cornerstone among crossing facilities. Yet, it suffers from low usage by pedestrians as they try to cross the street on the level. This study aims to analyze the perceptions of Malaysian pedestrians toward the use of footbridges with the consideration of different factors. The study was carried out by collecting data from field observation and questionnaire distribution on the street among the public. The data were statistically analyzed by applying multiple linear regression models and a series of chi-square tests. The study found that the most influential factor cited by pedestrians in decision making regarding using a footbridge is the existence of an escalator. Being in a hurry and the fear of heights were significantly associated with choosing not to use a footbridge. Zebra crossing was chosen as the most favorable type of crossing facility by the majority of respondents. In addition, installation of a fence and barriers was proposed as an effective procedure to prevent jaywalking. To construct new and efficient footbridges in the future, the study suggests consideration of traffic volume, posted speed limit, and the number of lanes, because these are the most influential factors to predict the usage rate. The study encourages decision makers and stakeholders to consider providing escalators for new footbridges to enhance the safety of pedestrians.

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.