Riedel, Michael C; Yanes, Julio A; Ray, Kimberly L; Eickhoff, Simon B; Fox, Peter T; Sutherland, Matthew T; Laird, Angela R
Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks. © 2018 Wiley Periodicals, Inc.
Dragutinovic, N. Brookhuis, K.A. Hagenzieker, M.P. & Marchau, V.A.W.J.
In this study, a meta-analytic approach was used to analyse effects of Advanced Cruise Control (ACC) on driving behaviour reported in seven driving simulator studies. The effects of ACC on three consistent outcome measures, namely, driving speed, headway and driver workload have been analysed. The
Cheung, Mike W. L.; Chan, Wai
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
Dore, Marina Pereira; Farias, Cássia; Hamacher, Cláudia
The exploration and production of oil and gas reserves often result to drill cutting accumulations on the seafloor adjacent to drill locations. In this study, the detection of drilling influence on marine sediments was performed by meta-analytical comparison between data from pre- and post-drilling surveys undertaken in offshore Campos Basin, southeast of Brazil. Besides this overall appraisal on the geochemical variables, a multivariate assessment, considering only the post-drilling data, was performed. Among the variables, fines content, carbonates, total organic carbon, barium, chromium, copper, iron, manganese, nickel, lead, vanadium, zinc, and total petroleum hydrocarbons, only barium, copper, and hydrocarbons were related to drilling impacts. In relation to the point of discharge, relative elevated levels in the post-drilling campaigns were observed preferentially up to 500 m in the northeast and southwest directions, associated to the Brazil Current-predominant direction. Other distributed concentrations in the surroundings seem to indicate the dilution and dispersion of drilling waste promoted by meteoceanographic factors.
Sarah R Braun
Full Text Available OBJECTIVE: Despite numerous investigations, the question whether all bona fide treatments of depression are equally efficacious in adults has not been sufficiently answered. METHOD: We applied two different meta-analytical techniques (conventional meta-analysis and mixed treatment comparisons. Overall, 53 studies with 3,965 patients, which directly compared two or more bona fide psychotherapies in a randomized trial, were included. Meta-analyses were conducted regarding five different types of outcome measures. Additionally, the influence of possible moderators was examined. RESULTS: Direct comparisons of cognitive behavior therapy, behavior activation therapy, psychodynamic therapy, interpersonal therapy, and supportive therapies versus all other respective treatments indicated that at the end of treatment all treatments but supportive therapies were equally efficacious whereas there was some evidence that supportive therapies were somewhat less efficacious than all other treatments according to patient self-ratings and clinical significance. At follow-up no significant differences were present. Age, gender, comorbid mental disorders, and length of therapy session were found to moderate efficacy. Cognitive behavior therapy was superior in studies where therapy sessions lasted 90 minutes or longer, behavior activation therapy was more efficacious when therapy sessions lasted less than 90 minutes. Mixed treatment comparisons indicated no statistically significant differences in treatment efficacy but some interesting trends. CONCLUSIONS: This study suggests that there might be differential effects of bona fide psychotherapies which should be examined in detail.
It remains unclear whether and to what extent the default network subregions involved in episodic memory (EM) and semantic memory (SM) processes overlap or are separated from one another. This study addresses this issue through a controlled meta-analysis of functional neuroimaging studies involving healthy participants. Various EM and SM task paradigms differ widely in the extent of default network involvement. Therefore, the issue at hand cannot be properly addressed without some control for this factor. In this regard, this study employs a two-stage analysis: a preliminary meta-analysis to select EM and SM task paradigms that recruit relatively extensive default network regions and a main analysis to compare the selected task paradigms. Based on a within-EM comparison, the default network contributed more to recollection/familiarity effects than to old/new effects, and based on a within-SM comparison, it contributed more to word/pseudoword effects than to semantic/phonological effects. According to a direct comparison of recollection/familiarity and word/pseudoword effects, each involving a range of default network regions, there were more overlaps than separations in default network subregions involved in these two effects. More specifically, overlaps included the bilateral posterior cingulate/retrosplenial cortex, left inferior parietal lobule, and left anteromedial prefrontal regions, whereas separations included only the hippocampal formation and the parahippocampal cortex region, which was unique to recollection/familiarity effects. These results indicate that EM and SM retrieval processes involving strong memory signals recruit extensive and largely overlapping default network regions and differ mainly in distinct contributions of hippocampus and parahippocampal regions to EM retrieval. Copyright © 2015 Elsevier Ltd. All rights reserved.
Full Text Available Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology.
Chang, Luye; Connelly, Brian S; Geeza, Alexis A
Though most personality researchers now recognize that ratings of the Big Five are not orthogonal, the field has been divided about whether these trait intercorrelations are substantive (i.e., driven by higher order factors) or artifactual (i.e., driven by correlated measurement error). We used a meta-analytic multitrait-multirater study to estimate trait correlations after common method variance was controlled. Our results indicated that common method variance substantially inflates trait correlations, and, once controlled, correlations among the Big Five became relatively modest. We then evaluated whether two different theories of higher order factors could account for the pattern of Big Five trait correlations. Our results did not support Rushton and colleagues' (Rushton & Irwing, 2008; Rushton et al., 2009) proposed general factor of personality, but Digman's (1997) α and β metatraits (relabeled by DeYoung, Peterson, and Higgins (2002) as Stability and Plasticity, respectively) produced viable fit. However, our models showed considerable overlap between Stability and Emotional Stability and between Plasticity and Extraversion, raising the question of whether these metatraits are redundant with their dominant Big Five traits. This pattern of findings was robust when we included only studies whose observers were intimately acquainted with targets. Our results underscore the importance of using a multirater approach to studying personality and the need to separate the causes and outcomes of higher order metatraits from those of the Big Five. We discussed the implications of these findings for the array of research fields in which personality is studied.
The increasing popularity of social networking sites (SNSs) has drawn scholarly attention in recent years, and a large amount of efforts have been made in applying SNSs to health behavior change interventions. However, these interventions showed mixed results, with a large variance of effect sizes in Cohen's d ranging from -1.17 to 1.28. To provide a better understanding of SNS-based interventions' effectiveness, a meta-analysis of 21 studies examining the effects of health interventions using SNS was conducted. Results indicated that health behavior change interventions using SNS are effective in general, but the effects were moderated by health topic, methodological features, and participant features. Theoretical and practical implications of findings are discussed.
Prato, Carlo Giacomo
objective judgments for effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network to generate possible subjective choice sets considered by travelers. Next, “true model estimates” and “postulated predicted routes” are assumed from the simulation...... of a route choice model. Then, objective choice sets are applied for model estimation and results are compared to the “true model estimates”. Last, predictions from the simulation of models estimated with objective choice sets are compared to the “postulated predicted routes”. Meta-analysis allows...
Salsman, John M.; Pustejovsky, James E.; Jim, Heather S.L.; Munoz, Alexis R.; Merluzzi, Thomas V.; George, Login; Park, Crystal L.; Danhauer, Suzanne C.; Sherman, Allen C.; Snyder, Mallory A.; Fitchett, George
Purpose Religion and spirituality (R/S) are patient-centered factors and often resources for managing the emotional sequelae of the cancer experience. Studies investigating the relationship between R/S (e.g., beliefs, experiences, coping) and mental health (e.g., depression, anxiety, well-being) in cancer have used very heterogeneous measures, with correspondingly inconsistent results. A meaningful synthesis of these findings has been lacking; thus, the purpose of this study was to conduct a meta-analysis of the research on R/S and mental health. Methods Four electronic databases were systematically reviewed and 2,073 abstracts met initial selection criteria. Reviewer pairs applied standardized coding schemes to extract correlational indices of the relationship between R/S and mental health. A total of 617 effect sizes from 148 eligible studies were synthesized using meta-analytic generalized estimating equations; subgroup analyses were performed to examine moderators of effects. Results The estimated mean correlation (Fisher z) was 0.19 (95% CI 0.16–0.23), which varied as a function of R/S dimension: affective, z=0.38 (95% CI 0.33-0.43); behavioral, z=0.03 (95% CI -0.02-0.08); cognitive, z=0.10 (95% CI 0.06-0.14); and ‘other,’ z=0.08 (95% CI 0.03-0.13). Aggregate, study-level demographic and clinical factors were not predictive of the relationship between R/S and mental health. There was little indication of publication or reporting biases. Conclusions The relationship between R/S and mental health is generally a positive one. The strength of that relationship is modest and varies as a function of R/S dimensions and mental health domains assessed. Identification of optimal R/S measures and more sophisticated methodological approaches are needed to advance research. PMID:26258536
Giannì, Costanza; Prosperini, Luca; Jonsdottir, Johanna; Cattaneo, Davide
To determine whether there are demographic, clinical, and instrumental variables useful to detect fall status of patients with multiple sclerosis. PubMed and the Cochrane Library. Eligible studies were identified by two independent investigators. Only studies having a clear distinction between fallers and non-fallers were included and meta-analysed. Odds ratios (ORs) and standard mean differences (SMDs) were calculated and pooled using fixed effect models. Among 115 screened articles, 15 fulfilled criteria for meta-analyses, with a total of 2425 patients included. Proportion of fallers may vary from 30% to 63% in a time frame from 1 to 12 months. No significant publication bias was found, even though 12/15 studies relied on retrospective reports of falls, thus introducing recall biases. Risk factors for falls varied across studies, owing to heterogeneity of populations included and clinical instruments used. The meta-analytic approach found that, compared with non-fallers, fallers had longer disease duration (SMD = 0.14, p = 0.02), progressive course of disease (OR = 2.02, p < 0.0001), assistive device for walking (OR = 3.16, p < 0.0001), greater overall disability level (SMD = 0.74, p < 0.0001), slower walking speed (SMD = 0.45, p = 0.0005), and worse performances in balance tests (Berg Balance Scale: SMD = -0.48, p = 0.002; Timed up-and-go test, SMD = 0.31, p = 0.04), and force-platform measures (postural sway) with eyes opened (SMD = 0.71, p = 0.006) and closed (SMD = 0.83, p = 0.01), respectively. Elucidations regarding risk factors for accidental falls in patients with multiple sclerosis (PwMs) are provided here, with worse disability score, progressive course, use of walking aid, and poorer performances in static and dynamic balance tests strongly associated with fall status. © The Author(s) 2014.
Salsman, John M; Pustejovsky, James E; Jim, Heather S L; Munoz, Alexis R; Merluzzi, Thomas V; George, Login; Park, Crystal L; Danhauer, Suzanne C; Sherman, Allen C; Snyder, Mallory A; Fitchett, George
Religion and spirituality (R/S) are patient-centered factors and often are resources for managing the emotional sequelae of the cancer experience. Studies investigating the correlation between R/S (eg, beliefs, experiences, coping) and mental health (eg, depression, anxiety, well being) in cancer have used very heterogeneous measures and have produced correspondingly inconsistent results. A meaningful synthesis of these findings has been lacking; thus, the objective of this review was to conduct a meta-analysis of the research on R/S and mental health. Four electronic databases were systematically reviewed, and 2073 abstracts met initial selection criteria. Reviewer pairs applied standardized coding schemes to extract indices of the correlation between R/S and mental health. In total, 617 effect sizes from 148 eligible studies were synthesized using meta-analytic generalized estimating equations, and subgroup analyses were performed to examine moderators of effects. The estimated mean correlation (Fisher z) was 0.19 (95% confidence interval [CI], 0.16-0.23), which varied as a function of R/S dimensions: affective R/S (z = 0.38; 95% CI, 0.33-0.43), behavioral R/S (z = 0.03; 95% CI, -0.02-0.08), cognitive R/S (z = 0.10; 95% CI, 0.06-0.14), and 'other' R/S (z = 0.08; 95% CI, 0.03-0.13). Aggregate, study-level demographic and clinical factors were not predictive of the relation between R/S and mental health. There was little indication of publication or reporting biases. The correlation between R/S and mental health generally was positive. The strength of that correlation was modest and varied as a function of the R/S dimensions and mental health domains assessed. The identification of optimal R/S measures and more sophisticated methodological approaches are needed to advance research. © 2015 American Cancer Society.
Daniel D. Goering
Full Text Available The distinctiveness between work engagement and burnout has long been an issue of debate. To address this issue, we use a recently developed technique by Yu et al. (2016 to specify and test a meta-analytic structural equation model (MASEM which accounts for the non-independence between engagement and burnout as well as the simultaneous effects of all relationships in our model, based on job demands-resources (JD-R theory. We also estimate the degree of variability of these relationships across subpopulations. We report the findings as a distribution of effect size estimates—each estimate in the distribution representing the true effect size for a potential subpopulation—around the mean average estimate for each relationship in the model. Based on the findings, we conclude that overall burnout and engagement display empirically distinct relationships within the JD-R model (i.e., they are not antipodal, particularly in terms of antecedents. Perhaps most interestingly, rather than a polar opposite pattern of relationships, challenge demands have a similarly positive relationship to both burnout (ß = 0.35, SD = 0.10 and engagement (ß = 0.35, SD = 0.08, suggesting that challenge demands simultaneously lead—in equal force—to both engagement and burnout. In addition, the distributions of effect sizes are nearly identical for both relationships, indicating that this holds true for nearly all subpopulations. As expected, hindrance demands have a positive relationship with burnout (ß = 0.31, SD = 0.10 and have a relatively weak, negative relationship on average to engagement (ß = −0.07, SD = 0.07; work resources have a negative relationship with burnout (ß = −0.15, SD = 0.06 and are positively related to engagement, but in absolute terms they are a stronger predictor of engagement (ß = 0.33, SD = 0.05. In terms of outcomes, burnout and engagement predict a variety of behavioral and attitudinal outcomes
Sundaram, Neisha; Schaetti, Christian; Merten, Sonja; Schindler, Christian; Ali, Said M; Nyambedha, Erick O; Lapika, Bruno; Chaignat, Claire-Lise; Hutubessy, Raymond; Weiss, Mitchell G
Controlling cholera remains a significant challenge in Sub-Saharan Africa. In areas where access to safe water and sanitation are limited, oral cholera vaccine (OCV) can save lives. Establishment of a global stockpile for OCV reflects increasing priority for use of cholera vaccines in endemic settings. Community acceptance of vaccines, however, is critical and sociocultural features of acceptance require attention for effective implementation. This study identifies and compares sociocultural determinants of anticipated OCV acceptance across populations in Southeastern Democratic Republic of Congo, Western Kenya and Zanzibar. Cross-sectional studies were conducted using similar but locally-adapted semistructured interviews among 1095 respondents in three African settings. Logistic regression models identified sociocultural determinants of OCV acceptance from these studies in endemic areas of Southeastern Democratic Republic of Congo (SE-DRC), Western Kenya (W-Kenya) and Zanzibar. Meta-analytic techniques highlighted common and distinctive determinants in the three settings. Anticipated OCV acceptance was high in all settings. More than 93% of community respondents overall indicated interest in a no-cost vaccine. Higher anticipated acceptance was observed in areas with less access to public health facilities. In all settings awareness of cholera prevention methods (safe food consumption and garbage disposal) and relating ingestion to cholera causation were associated with greater acceptance. Higher age, larger households, lack of education, social vulnerability and knowledge of oral rehydration solution for self-treatment were negatively associated with anticipated OCV acceptance. Setting-specific determinants of acceptance included reporting a reliable income (W-Kenya and Zanzibar, not SE-DRC). In SE-DRC, intention to purchase an OCV appeared unrelated to ability to pay. Rural residents were less likely than urban counterparts to accept an OCV in W-Kenya, but more
Often, human health risk assessments have relied on qualitative approaches for hazard identification to integrate evidence across multiple studies to conclude whether particular hazards exist. However, quantitative approaches for evidence integration, including the application o...
Full Text Available Abstract Background Many pathogens use a type III secretion system to translocate virulence proteins (called effectors in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized. Results In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility. Conclusions We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation
Todd, E Michelle; Watts, Logan L; Mulhearn, Tyler J; Torrence, Brett S; Turner, Megan R; Connelly, Shane; Mumford, Michael D
Despite the growing body of literature on training in the responsible conduct of research, few studies have examined the effectiveness of delivery formats used in ethics courses (i.e., face-to-face, online, hybrid). The present effort sought to address this gap in the literature through a meta-analytic review of 66 empirical studies, representing 106 ethics courses and 10,069 participants. The frequency and effectiveness of 67 instructional and process-based content areas were also assessed for each delivery format. Process-based contents were best delivered face-to-face, whereas contents delivered online were most effective when restricted to compliance-based instructional contents. Overall, hybrid courses were found to be most effective, suggesting that ethics courses are best delivered using a blend of formats and content areas. Implications and recommendations for future development of ethics education courses in the sciences are discussed.
Wang, Lin; Liu, Silvia; Ding, Ying; Yuan, Shin-Sheng; Ho, Yen-Yi; Tseng, George C
Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results. In this article, we proposed two meta-analytic approaches for LA analysis (MetaLA and MetaMLA) to combine multiple transcriptomic studies. To compensate demanding computing, we also proposed a two-step fast screening algorithm for more efficient genome-wide screening: bootstrap filtering and sign filtering. We applied the methods to five Saccharomyces cerevisiae datasets related to environmental changes. The fast screening algorithm reduced 98% of running time. When compared with single study analysis, MetaLA and MetaMLA provided stronger detection signal and more consistent and stable results. The top triplets are highly enriched in fundamental biological processes related to environmental changes. Our method can help biologists understand underlying regulatory mechanisms under different environmental exposure or disease states. A MetaLA R package, data and code for this article are available at http://tsenglab.biostat.pitt.edu/software.htm. email@example.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org
Chein, Jason M; Schneider, Walter
Functional magnetic resonance imaging and a meta-analysis of prior neuroimaging studies were used to characterize cortical changes resulting from extensive practice and to evaluate a dual-processing account of the neural mechanisms underlying human learning. Three core predictions of the dual processing theory are evaluated: 1) that practice elicits generalized reductions in regional activity by reducing the load on the cognitive control mechanisms that scaffold early learning; 2) that these control mechanisms are domain-general; and 3) that no separate processing pathway emerges as skill develops. To evaluate these predictions, a meta-analysis of prior neuroimaging studies and a within-subjects fMRI experiment contrasting unpracticed to practiced performance in a paired-associate task were conducted. The principal effect of practice was found to be a reduction in the extent and magnitude of activity in a cortical network spanning bilateral dorsal prefrontal, left ventral prefrontal, medial frontal (anterior cingulate), left insular, bilateral parietal, and occipito-temporal (fusiform) areas. These activity reductions are shown to occur in common regions across prior neuroimaging studies and for both verbal and nonverbal paired-associate learning in the present fMRI experiment. The implicated network of brain regions is interpreted as a domain-general system engaged specifically to support novice, but not practiced, performance.
Mingoia, John; Hutchinson, Amanda D; Wilson, Carlene; Gleaves, David H
Previous research has indicated that exposure to traditional media (i.e., television, film, and print) predicts the likelihood of internalization of a thin ideal; however, the relationship between exposure to internet-based social media on internalization of this ideal remains less understood. Social media differ from traditional forms of media by allowing users to create and upload their own content that is then subject to feedback from other users. This meta-analysis examined the association linking the use of social networking sites (SNSs) and the internalization of a thin ideal in females. Systematic searches were performed in the databases: PsychINFO, PubMed, Web of Science, Communication and Mass Media Complete, and ProQuest Dissertations and Theses Global. Six studies were included in the meta-analysis that yielded 10 independent effect sizes and a total of 1,829 female participants ranging in age from 10 to 46 years. We found a positive association between extent of use of SNSs and extent of internalization of a thin ideal with a small to moderate effect size ( r = 0.18). The positive effect indicated that more use of SNSs was associated with significantly higher internalization of a thin ideal. A comparison was also made between study outcomes measuring broad use of SNSs and outcomes measuring SNS use solely as a function of specific appearance-related features (e.g., posting or viewing photographs). The use of appearance-related features had a stronger relationship with the internalization of a thin ideal than broad use of SNSs. The finding suggests that the ability to interact with appearance-related features online and be an active participant in media creation is associated with body image disturbance. Future research should aim to explore the way SNS users interact with the media posted online and the relationship linking the use of specific appearance features and body image disturbance.
Full Text Available Previous research has indicated that exposure to traditional media (i.e., television, film, and print predicts the likelihood of internalization of a thin ideal; however, the relationship between exposure to internet-based social media on internalization of this ideal remains less understood. Social media differ from traditional forms of media by allowing users to create and upload their own content that is then subject to feedback from other users. This meta-analysis examined the association linking the use of social networking sites (SNSs and the internalization of a thin ideal in females. Systematic searches were performed in the databases: PsychINFO, PubMed, Web of Science, Communication and Mass Media Complete, and ProQuest Dissertations and Theses Global. Six studies were included in the meta-analysis that yielded 10 independent effect sizes and a total of 1,829 female participants ranging in age from 10 to 46 years. We found a positive association between extent of use of SNSs and extent of internalization of a thin ideal with a small to moderate effect size (r = 0.18. The positive effect indicated that more use of SNSs was associated with significantly higher internalization of a thin ideal. A comparison was also made between study outcomes measuring broad use of SNSs and outcomes measuring SNS use solely as a function of specific appearance-related features (e.g., posting or viewing photographs. The use of appearance-related features had a stronger relationship with the internalization of a thin ideal than broad use of SNSs. The finding suggests that the ability to interact with appearance-related features online and be an active participant in media creation is associated with body image disturbance. Future research should aim to explore the way SNS users interact with the media posted online and the relationship linking the use of specific appearance features and body image disturbance.
Conclusion: Future research is encouraged to grow and enrich the understanding of achievement goals within Elliot's complete Hierarchical Model of Approach and Avoidance Motivation to include both antecedents and outcomes simultaneously to improve upon the understanding of achievement motivation in sport, exercise, and physical activity settings.
Vast increases in the external costs of transport in the late twentieth century have caused national and international governmental bodies to worry about the sustainability of their transport systems. In this thesis we use meta-analysis as a research method to study various topics in transport economics that are relevant for sustainable transport policymaking. Meta-analysis is a research methodology that is based on the quantitative summarisation of a body of previously documented empirical evidence. In several fields of economic, meta-analysis has become a well-accepted research tool. Despite the appeal of the meta-analytical approach, there are methodological difficulties that need to be acknowledged. We study a specific methodological problem which is common in meta-analysis in economics, viz., within-study dependence caused by multiple sampling techniques. By means of Monte Carlo analysis we investigate the effect of such dependence on the performance of various multivariate estimators. In the applied part of the thesis we use and develop meta-analytical techniques to study the empirical variation in indicators of the price sensitivity of demand for aviation transport, the price sensitivity of demand for gasoline, the efficiency of urban public transport and the valuation of the external costs of noise from rail transport. We focus on the estimation of mean values for these indicators and on the identification of the impact of conditioning factors.
Tarrahi, Farid; Eisend, Martin
Previous research has suggested that judgment calls (i.e., methodological choices made in the process of conducting a meta-analysis) have a strong influence on meta-analytic findings and question their robustness. However, prior research applies case study comparison or reanalysis of a few meta-analyses with a focus on a few selected judgment calls. These studies neglect the fact that different judgment calls are related to each other and simultaneously influence the outcomes of a meta-analysis, and that meta-analytic findings can vary due to non-judgment call differences between meta-analyses (e.g., variations of effects over time). The current study analyzes the influence of 13 judgment calls in 176 meta-analyses in marketing research by applying a multivariate, multilevel meta-meta-analysis. The analysis considers simultaneous influences from different judgment calls on meta-analytic effect sizes and controls for alternative explanations based on non-judgment call differences between meta-analyses. The findings suggest that judgment calls have only a minor influence on meta-analytic findings, whereas non-judgment call differences between meta-analyses are more likely to explain differences in meta-analytic findings. The findings support the robustness of meta-analytic results and conclusions.
Audet, Patrick [Ottawa-Carleton Institute of Biology, Department of Biology, University of Ottawa, 30 Marie-Curie St., Ottawa, Ont. K1N 6N5 (Canada)]. E-mail: email@example.com; Charest, Christiane [Ottawa-Carleton Institute of Biology, Department of Biology, University of Ottawa, 30 Marie-Curie St., Ottawa, Ont. K1N 6N5 (Canada)]. E-mail: firstname.lastname@example.org
We conducted a literature survey and correlated heavy metal (HM) uptake and plant growth factors from published data to estimate the effectiveness of phytoextraction. The indicators of the actual plant HM uptake showed positive correlations with soil-HM concentrations, while the relative plant HM uptake showed negative correlations. Plant growth was negatively correlated with both the plant and soil-HM concentrations. These significant relationships were found for the majority of HM tested (e.g. Zn, Cd, Pb, Cu, Cr, and Fe) with a few exceptions (e.g. Ni, Co, and Mn). After fitting the correlation coefficients, the highest proportion of variance among the studies was mainly due to the experimental parameters or the plant species. When the metabolic costs of HM uptake are taken into account, the phytoextraction appears to be less effective beyond critical HM concentrations. Despite these constraints, it is emphasized that HM phytoextraction can play an important role in bioremediation. - This meta-analytical approach has revealed a compromise between growth and HM uptake when plants are subjected to toxic soil-HM levels.
Audet, Patrick; Charest, Christiane
We conducted a literature survey and correlated heavy metal (HM) uptake and plant growth factors from published data to estimate the effectiveness of phytoextraction. The indicators of the actual plant HM uptake showed positive correlations with soil-HM concentrations, while the relative plant HM uptake showed negative correlations. Plant growth was negatively correlated with both the plant and soil-HM concentrations. These significant relationships were found for the majority of HM tested (e.g. Zn, Cd, Pb, Cu, Cr, and Fe) with a few exceptions (e.g. Ni, Co, and Mn). After fitting the correlation coefficients, the highest proportion of variance among the studies was mainly due to the experimental parameters or the plant species. When the metabolic costs of HM uptake are taken into account, the phytoextraction appears to be less effective beyond critical HM concentrations. Despite these constraints, it is emphasized that HM phytoextraction can play an important role in bioremediation. - This meta-analytical approach has revealed a compromise between growth and HM uptake when plants are subjected to toxic soil-HM levels
Chan, Darius K-S.; Lam, Chun Bun; Chow, Suk Yee; Cheung, Shu Fai
This study was designed to examine the job-related, psychological, and physical outcomes of sexual harassment in the workplace. Using a meta-analytic approach, we analyzed findings from 49 primary studies, with a total sample size of 89,382, to obtain estimates of the population mean effect size of the association between sexual harassment and…
Savelsbergh, E.R.; Prins, G.T.; Rietbergen, C.; Fechner, S.; Vaessen, B.E.; Draijer, J.M.; Bakker, A.
Many teaching approaches have been tried to improve student attitudes and achievement in science and mathematics education. Achievement effects have been synthesized, but a systematic overview of attitude effects is missing. This study provides a meta analytic review based on 56 publications
Follmer, D. Jake
This article presents a meta-analytic review of the relation between executive function and reading comprehension. Results (N = 6,673) supported a moderate positive association between executive function and reading comprehension (r = 0.36). Moderator analyses suggested that correlations between executive function and reading comprehension did not…
Shadish, William R.; Lecy, Jesse D.
This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such…
Shadish, William R; Lecy, Jesse D
This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such contemporaneous developments as multilevel modeling, medical statistics, qualitative methods, program evaluation, and single-case design. Using a constrained snowball sample of citations, we highlight key articles that are either most highly cited or most central to the systematic review network. Then, the article examines why meta-analysis came to be in the 1970s in the social sciences through the work of Gene Glass, Robert Rosenthal, and Frank Schmidt, each of whom developed similar theories of meta-analysis at about the same time. The article ends by explaining how Simonton's chance configuration theory and Campbell's evolutionary epistemology can illuminate why meta-analysis occurred with these scholars when it did and not in medical sciences. Copyright © 2015 John Wiley & Sons, Ltd.
This book explains how to employ MASEM, the combination of meta-analysis (MA) and structural equation modelling (SEM). It shows how by using MASEM, a single model can be tested to explain the relationships between a set of variables in several studies. This book gives an introduction to MASEM, with a focus on the state of the art approach: the two stage approach of Cheung and Cheung & Chan. Both, the fixed and the random approach to MASEM are illustrated with two applications to real data. All steps that have to be taken to perform the analyses are discussed extensively. All data and syntax files are available online, so that readers can imitate all analyses. By using SEM for meta-analysis, this book shows how to benefit from all available information from all available studies, even if few or none of the studies report about all relationships that feature in the full model of interest.
Mallikarjun, Sreekanth; Sieburth, Rebecca McNeill
Aspartame (APM) is the most commonly used artificial sweetener and flavor enhancer in the world. There is a rise in concern that APM is carcinogenic due to a variation in the findings of the previous APM carcinogenic bioassays. This article conducts a meta-analytic review of all previous APM carcinogenic bioassays on rodents that were conducted before 31 December 2012. The search yielded 10 original APM carcinogenic bioassays on rodents. The aggregate effect sizes suggest that APM consumption has no significant carcinogenic effect in rodents.
Eerde, van W.
This meta-analysis contains the correlations of 121 studies examining the relation between procrastination and personality variables, motives, affect, and performance. The largest negative effect sizes were found in relation to conscientiousness and self-efficacy, and the largest positive relation
.... This study utilized data from 166 samples (N = 5,757) to test the general hypothesis that differences in testing methods could account for the cross-situational variation in validity. Only runs >2 km...
Sened, Haran; Lavidor, Michal; Lazarus, Gal; Bar-Kalifa, Eran; Rafaeli, Eshkol; Ickes, William
Empathic accuracy (EA; Ickes & Hodges, 2013) is the extent to which people accurately perceive their peers' thoughts, feelings, and other inner mental states. EA has particularly interested researchers in the context of romantic couples. Reviews of the literature suggest a possible link between romantic partners' EA and their relationship satisfaction (Ickes & Simpson, 2001; Sillars & Scott, 1983). To assess the magnitude of this association and examine possible moderators, we performed a meta-analytic review of 21 studies (total N = 2,739 participants) that examined the association between EA and satisfaction. We limited our review to studies measuring EA using the dyadic interaction paradigm (Ickes, Stinson, Bissonnette, & Garcia, 1990). We found a small but significant association between the two (r = .134, p .1). The association was also stronger in relationships of moderate length, suggesting that EA may be more meaningful when relationships are consolidating but before they become stable. Gender and procedural variations on the dyadic interaction paradigm did not moderate the association, and there was no difference depending on whether the association was between EA and perceivers' or targets' satisfaction (i.e., actor or partner effects). We discuss the implications of these findings and offer recommendations for future EA studies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Holt-Lunstad, Julianne; Smith, Timothy B; Layton, J Bradley
The quality and quantity of individuals' social relationships has been linked not only to mental health but also to both morbidity and mortality. This meta-analytic review was conducted to determine the extent to which social relationships influence risk for mortality, which aspects of social relationships are most highly predictive, and which factors may moderate the risk. Data were extracted on several participant characteristics, including cause of mortality, initial health status, and pre-existing health conditions, as well as on study characteristics, including length of follow-up and type of assessment of social relationships. Across 148 studies (308,849 participants), the random effects weighted average effect size was OR = 1.50 (95% CI 1.42 to 1.59), indicating a 50% increased likelihood of survival for participants with stronger social relationships. This finding remained consistent across age, sex, initial health status, cause of death, and follow-up period. Significant differences were found across the type of social measurement evaluated (psocial relationships on risk for mortality is comparable with well-established risk factors for mortality. Please see later in the article for the Editors' Summary.
Gao, S.; Assink, M.; Cipriani, A.; Lin, K.
Rejection sensitivity is a personality disposition characterized by oversensitivity to social rejection. Using a three-level meta-analytic model, 75 studies were reviewed that examined associations between rejection sensitivity and five mental health outcomes: depression, anxiety, loneliness,
Viñas-Guasch, Nestor; Wu, Yan Jing
The putamen is a subcortical structure that forms part of the dorsal striatum of basal ganglia, and has traditionally been associated with reinforcement learning and motor control, including speech articulation. However, recent studies have shown involvement of the left putamen in other language functions such as bilingual language processing (Abutalebi et al. 2012) and production, with some authors arguing for functional segregation of anterior and posterior putamen (Oberhuber et al. 2013). A further step in exploring the role of putamen in language would involve identifying the network of coactivations of not only the left, but also the right putamen, given the involvement of right hemisphere in high order language functions (Vigneau et al. 2011). Here, a meta-analytic connectivity modeling technique was used to determine the patterns of coactivation of anterior and bilateral putamen in the language domain. Based on previous evidence, we hypothesized that left putamen coactivations would include brain regions directly associated with language processing, whereas right putamen coactivations would encompass regions involved in broader semantic processes, such as memory and visual imagery. The results showed that left anterior putamen coactivated with clusters predominantly in left hemisphere, encompassing regions directly associated with language processing, a left posterior putamen network spanning both hemispheres, and cerebellum. In right hemisphere, coactivations were in both hemispheres, in regions associated with visual and orthographic processing. These results confirm the differential involvement of right and left putamen in different language components, thus highlighting the need for further research into the role of putamen in language.
Cemalcilar, Zeynep; Secinti, Ekin; Sumer, Nebi
Work values act as guiding principles for individuals' work-related behavior. Economic self-sufficiency is an important predictor for psychological well-being in adulthood. Longitudinal research has demonstrated work values to be an important predictor of economic behavior, and consequently of self-sufficiency. Socialization theories designate parents an important role in the socialization of their children to cultural values. Yet, extant literature is limited in demonstrating the role families play on how youth develop agentic pathways and seek self-sufficiency in transition to adulthood. This study presents a meta-analytic review investigating the intergenerational transmission of work values, which is frequently assessed in terms of parent-child value similarities. Thirty studies from 11 countries (N = 19,987; Median child age = 18.15) were included in the analyses. The results revealed a significant effect of parents on their children's work values. Both mothers' and fathers' work values, and their parenting behavior were significantly associated with their children's work values. Yet, similarity of father-child work values decreased as child age increased. Our findings suggest a moderate effect, suggesting the influence of general socio-cultural context, such as generational differences and peer influences, in addition to those of parents on youth's value acquisition. Our systematic review also revealed that, despite its theoretical and practical importance, social science literature is scarce in comprehensive and comparative empirical studies that investigate parent-child work value similarity. We discuss the implications of our findings for labor market and policy makers.
Nei, Darin; Snyder, Lori Anderson; Litwiller, Brett J
Because the health care field is expected to be the fastest growing job field until 2020, an urgent need to focus on nurse retention exists. The aim of this study was to examine the relationships between predictors of turnover (i.e., personal characteristics, role states, job characteristics, group/leader relations, organizational/environmental perceptions, attitudinal reactions) and turnover cognitions and intentions, as well as actual turnover among nurses, in an effort to determine the strongest predictors of voluntary turnover. Meta-analysis was used to determine best estimates of the effect of predictors on turnover based on 106 primary studies of employed nurses. Meta-analyzed correlations were subjected to path analysis to establish the structural relationships among the study variables. Supportive and communicative leadership, network centrality, and organizational commitment are the strongest predictors of voluntary turnover based on meta-analytic correlations. Additional variables that relate to nurse turnover intentions include job strain, role tension, work-family conflict, job control, job complexity, rewards/recognition, and team cohesion. The findings suggest that some factors, such as salary, are relatively less important in prediction of turnover. Administrators concerned about nurse turnover may more effectively direct resources toward altering certain job characteristics and work conditions in the effort to reduce voluntary turnover among nurses.
Angie, Amanda D; Connelly, Shane; Waples, Ethan P; Kligyte, Vykinta
During the past three decades, researchers interested in emotions and cognition have attempted to understand the relationship that affect and emotions have with cognitive outcomes such as judgement and decision-making. Recent research has revealed the importance of examining more discrete emotions, showing that same-valence emotions (e.g., anger and fear) differentially impact judgement and decision-making outcomes. Narrative reviews of the literature (Lerner & Tiedens, 2006 ; Pham, 2007 ) have identified some under-researched topics, but provide a limited synthesis of findings. The purpose of this study was to review the research examining the influence of discrete emotions on judgement and decision-making outcomes and provide an assessment of the observed effects using a meta-analytic approach. Results, overall, show that discrete emotions have moderate to large effects on judgement and decision-making outcomes. However, moderator analyses revealed differential effects for study-design characteristics and emotion-manipulation characteristics by emotion type. Implications are discussed.
Sherman, Allen C; Merluzzi, Thomas V; Pustejovsky, James E; Park, Crystal L; George, Login; Fitchett, George; Jim, Heather SL; Munoz, Alexis R; Danhauer, Suzanne C; Snyder, Mallory A; Salsman, John M
Background Religion and spirituality (R/S) play an important role in the daily lives of many cancer patients. There has been great interest in determining whether R/S factors are related to clinically-relevant health outcomes. This meta-analytic review examined associations between dimensions of R/S and social health (e.g., social roles and relationships). Methods A systematic search of PubMed, PsycInfo, Cochrane Library, and CINAHL databases was conducted, and data were extracted by four pairs of investigators. Bivariate associations between specific R/S dimensions and social health outcomes were examined in a meta-analysis using a generalized estimating equation (GEE) approach. Results A total of 78 independent samples encompassing 14,277 patients were included in the meta-analysis. Social health was significantly associated with overall R/S (Fisher z effect size = .20, Pcancer. Further research is needed to examine the temporal nature of these associations and the mechanisms that underlie them. PMID:26258730
Two of the key tasks facing the language-learning infant lie at the level of phonology: establishing which sounds are contrastive in the native inventory, and determining what their possible syllabic positions and permissible combinations (phonotactics) are. In 2002-2003, two theoretical proposals, one bearing on how infants can learn sounds (Maye, Werker, & Gerken, 2002) and the other on phonotactics (Chambers, Onishi, & Fisher, 2003), were put forward on the pages of Cognition, each supported by two laboratory experiments, wherein a group of infants was briefly exposed to a set of pseudo-words, and plausible phonological generalizations were tested subsequently. These two papers have received considerable attention from the general scientific community, and inspired a flurry of follow-up work. In the context of questions regarding the replicability of psychological science, the present work uses a meta-analytic approach to appraise extant empirical evidence for infant phonological learning in the laboratory. It is found that neither seminal finding (on learning sounds and learning phonotactics) holds up when close methodological replications are integrated, although less close methodological replications do provide some evidence in favor of the sound learning strand of work. Implications for authors and readers of this literature are drawn out. It would be desirable that additional mechanisms for phonological learning be explored, and that future infant laboratory work employ paradigms that rely on constrained and unambiguous links between experimental exposure and measured infant behavior. Copyright © 2017. Published by Elsevier B.V.
Demarzo, Marcelo M P; Montero-Marin, Jesús; Cuijpers, Pim; Zabaleta-del-Olmo, Edurne; Mahtani, Kamal R; Vellinga, Akke; Vicens, Caterina; López-del-Hoyo, Yolanda; García-Campayo, Javier
Positive effects have been reported after mindfulness-based interventions (MBIs) in diverse clinical and nonclinical populations. Primary care is a key health care setting for addressing common chronic conditions, and an effective MBI designed for this setting could benefit countless people worldwide. Meta-analyses of MBIs have become popular, but little is known about their efficacy in primary care. Our aim was to investigate the application and efficacy of MBIs that address primary care patients. We performed a meta-analytic review of randomized controlled trials addressing the effect of MBIs in adult patients recruited from primary care settings. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and Cochrane guidelines were followed. Effect sizes were calculated with the Hedges g in random effects models. The meta-analyses were based on 6 trials having a total of 553 patients. The overall effect size of MBI compared with a control condition for improving general health was moderate (g = 0.48; P = .002), with moderate heterogeneity (I(2) = 59; P .05). Although the number of randomized controlled trials applying MBIs in primary care is still limited, our results suggest that these interventions are promising for the mental health and quality of life of primary care patients. We discuss innovative approaches for implementing MBIs, such as complex intervention and stepped care. © 2015 Annals of Family Medicine, Inc.
Farahmand, Farahnaz K.; Duffy, Sophia N.; Tailor, Megha A.; Dubois, David L.; Lyon, Aaron L.; Grant, Kathryn E.; Zarlinski, Jennifer C.; Masini, Olivia; Zander, Keith J.; Nathanson, Alison M.
A meta-analytic review of 33 studies and 41 independent samples was conducted of the effectiveness of community-based mental health and behavioral programs for low-income urban youth. Findings indicated positive effects, with an overall mean effect of 0.25 at post-test. While this is comparable to previous meta-analytic intervention research with…
Anderson, Craig A.; Shibuya, Akiko; Ihori, Nobuko; Swing, Edward L.; Bushman, Brad J.; Sakamoto, Akira; Rothstein, Hannah R.; Saleem, Muniba
Meta-analytic procedures were used to test the effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, empathy/desensitization, and prosocial behavior. Unique features of this meta-analytic review include (a) more restrictive methodological quality inclusion criteria than in past…
Anderson, C.A.; Shibuya, A.; Ihori, N.; Swing, E.L.; Bushman, B.J.; Sakamoto, A.; Rothstein, H.R.; Saleem, M.; Barlett, C.P.
Meta-analytic procedures were used to test the effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, empathy/desensitization, and prosocial behavior. Unique features of this meta-analytic review include (a) more restrictive
Peterson, Larry L
This best-selling and classic book teaches you the key principles of computer networks with examples drawn from the real world of network and protocol design. Using the Internet as the primary example, the authors explain various protocols and networking technologies. Their systems-oriented approach encourages you to think about how individual network components fit into a larger, complex system of interactions. Whatever your perspective, whether it be that of an application developer, network administrator, or a designer of network equipment or protocols, you will come away with a "big pictur
Mihov, Konstantin M.; Denzler, Markus; Forster, Jens
In the last two decades research on the neurophysiological processes of creativity has found contradicting results. Whereas most research suggests right hemisphere dominance in creative thinking, left-hemisphere dominance has also been reported. The present research is a meta-analytic review of the literature to establish how creative thinking…
Brown, Steven D.; Lent, Robert W.; Telander, Kyle; Tramayne, Selena
We performed a meta-analytic path analysis of an abbreviated version of social cognitive career theory's (SCCT) model of work performance (Lent, Brown, & Hackett, 1994). The model we tested included the central cognitive predictors of performance (ability, self-efficacy, performance goals), with the exception of outcome expectations. Results…
Cole, M.S.; Walter, F.; Bedeian, A.G.; O'Boyle, E.H.
Drawing on 50 unique samples (from 37 studies), the authors used meta-analytical techniques to assess the extent to which job burnout and employee engagement are independent and useful constructs. The authors found that (a) dimension-level correlations between burnout and engagement are high, (b)
Porath-Waller, Amy J.; Beasley, Erin; Beirness, Douglas J.
This investigation used meta-analytic techniques to evaluate the effectiveness of school-based prevention programming in reducing cannabis use among youth aged 12 to 19. It summarized the results from 15 studies published in peer-reviewed journals since 1999 and identified features that influenced program effectiveness. The results from the set of…
Gozuyesil, Eda; Dikici, Ayhan
This study's aim is to measure the effect sizes of the quantitative studies that examined the effectiveness of brain-based learning on students' academic achievement and to examine with the meta-analytical method if there is a significant difference in effect in terms of the factors of education level, subject matter, sampling size, and the…
Diener, Marc J.; Geenen, Rinie; Koelen, Jurrijn A.; Aarts, Floor; Gerdes, Victor E. A.; Brandjes, Dees P. M.; Hinnen, Chris
Theoretical considerations and empirical results suggest that attachment quality is relevant to obesity. This study used meta-analytic methods to systematically examine the empirical, peer-reviewed evidence regarding the relationship between attachment quality and body mass index (BMI) in separate
de Haan, A.M.; Boon, A.E.; de Jong, J.T.V.M.; Hoeve, M.; Vermeiren, R.R.J.M.
A large proportion (28% up to 75%) of the treatments in youth mental health care results in premature termination (dropout). It is important to gain knowledge of the determinants of dropout because it can have very severe consequences. The aim of our meta-analytic review was to provide an overview
Cheung, Mike W.-L.; Cheung, Shu Fai
Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…
Mihov, K.M.; Denzler, M.; Förster, J.
In the last two decades research on the neurophysiological processes of creativity has found contradicting results. Whereas most research suggests right hemisphere dominance in creative thinking, left-hemisphere dominance has also been reported. The present research is a meta-analytic review of the
Prof. Dr. Jo J.M.A Hermanns; Prof. Dr. Ruben R.G. Fukkink; dr. Christa C.C. Nieuwboer
Background. A number of parenting programs, aimed at improving parenting competencies,have recently been adapted or designed with the use of online technologies. Although webbased services have been claimed to hold promise for parent support, a meta-analytic review of online parenting interventions
Nieuwboer, C.C.; Fukkink, R.G.; Hermanns, J.M.A.
Background: A number of parenting programs, aimed at improving parenting competencies, have recently been adapted or designed with the use of online technologies. Although web-based services have been claimed to hold promise for parent support, a meta-analytic review of online parenting
Perotti, Jose M.
This viewgraph presentation provides information on hardware and software configurations for a network architecture for sensors. The hardware configuration uses a central station and remote stations. The software configuration uses the 'lost station' software algorithm. The presentation profiles a couple current examples of this network architecture in use.
Hartnell, Chad A; Ou, Amy Yi; Kinicki, Angelo
We apply Quinn and Rohrbaugh's (1983) competing values framework (CVF) as an organizing taxonomy to meta-analytically test hypotheses about the relationship between 3 culture types and 3 major indices of organizational effectiveness (employee attitudes, operational performance [i.e., innovation and product and service quality], and financial performance). The paper also tests theoretical suppositions undergirding the CVF by investigating the framework's nomological validity and proposed internal structure (i.e., interrelationships among culture types). Results based on data from 84 empirical studies with 94 independent samples indicate that clan, adhocracy, and market cultures are differentially and positively associated with the effectiveness criteria, though not always as hypothesized. The findings provide mixed support for the CVF's nomological validity and fail to support aspects of the CVF's proposed internal structure. We propose an alternative theoretical approach to the CVF and delineate directions for future research.
Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George
Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K -means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups.
Montoya, R Matthew; Kershaw, Christine; Prosser, Julie L
We present a meta-analysis that investigated the relation between self-reported interpersonal attraction and enacted behavior. Our synthesis focused on (a) identifying the behaviors related to attraction; (b) evaluating the efficacy of models of the relation between attraction and behavior; (c) testing the impact of several moderators, including evaluative threat salience, cognitive appraisal salience, and the sex composition of the social interaction; and (d) investigating the degree of agreement between the meta-analytic findings and an ethnographic analysis. Using a multilevel modeling approach, an analysis of 309 effect sizes (N = 5,422) revealed a significant association (z = .20) between self-reported attraction and enacted behavior. Key findings include: (a) that the specific behaviors associated with attraction (e.g., eye contact, smiling, laughter, mimicry) are those behaviors research has linked to the development of trust/rapport; (b) direct behaviors (e.g., physical proximity, talking to), compared with indirect behaviors (e.g., eye contact, smiling, mimicry), were more strongly related to self-reported attraction; and (c) evaluative threat salience (e.g., fear of rejection) reduced the magnitude of the relation between direct behavior and affective attraction. Moreover, an ethnographic analysis revealed consistency between the behaviors identified by the meta-analysis and those behaviors identified by ethnographers as predictive of attraction. We discuss the implications of our findings for models of the relation between attraction and behavior, for the behavioral expressions of emotions, and for how attraction is measured and conceptualized. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Melby-Lervåg, Monica; Lyster, Solveig-Alma Halaas; Hulme, Charles
The authors report a systematic meta-analytic review of the relationships among 3 of the most widely studied measures of children's phonological skills (phonemic awareness, rime awareness, and verbal short-term memory) and children's word reading skills. The review included both extreme group studies and correlational studies with unselected samples (235 studies were included, and 995 effect sizes were calculated). Results from extreme group comparisons indicated that children with dyslexia show a large deficit on phonemic awareness in relation to typically developing children of the same age (pooled effect size estimate: -1.37) and children matched on reading level (pooled effect size estimate: -0.57). There were significantly smaller group deficits on both rime awareness and verbal short-term memory (pooled effect size estimates: rime skills in relation to age-matched controls, -0.93, and reading-level controls, -0.37; verbal short-term memory skills in relation to age-matched controls, -0.71, and reading-level controls, -0.09). Analyses of studies of unselected samples showed that phonemic awareness was the strongest correlate of individual differences in word reading ability and that this effect remained reliable after controlling for variations in both verbal short-term memory and rime awareness. These findings support the pivotal role of phonemic awareness as a predictor of individual differences in reading development. We discuss whether such a relationship is a causal one and the implications of research in this area for current approaches to the teaching of reading and interventions for children with reading difficulties.
Glenn, Catherine R; Kleiman, Evan M; Cha, Christine B; Deming, Charlene A; Franklin, Joseph C; Nock, Matthew K
The field is in need of novel and transdiagnostic risk factors for suicide. The National Institute of Mental Health's Research Domain Criteria (RDoC) provides a framework that may help advance research on suicidal behavior. We conducted a meta-analytic review of existing prospective risk and protective factors for suicidal thoughts and behaviors (ideation, attempts, and deaths) that fall within one of the five RDoC domains or relate to a prominent suicide theory. Predictors were selected from a database of 4,082 prospective risk and protective factors for suicide outcomes. A total of 460 predictors met inclusion criteria for this meta-analytic review and most examined risk (vs. protective) factors for suicidal thoughts and behaviors. The overall effect of risk factors was statistically significant, but relatively small, in predicting suicide ideation (weighted mean odds ratio: wOR = 1.72; 95% CI: 1.59-1.87), suicide attempt (wOR = 1.66 [1.57-1.76), and suicide death (wOR = 1.41 [1.24-1.60]). Across all suicide outcomes, most risk factors related to the Negative Valence Systems domain, although effect sizes were of similar magnitude across RDoC domains. This study demonstrated that the RDoC framework provides a novel and promising approach to suicide research; however, relatively few studies of suicidal behavior fit within this framework. Future studies must go beyond the "usual suspects" of suicide risk factors (e.g., mental disorders, sociodemographics) to understand the processes that combine to lead to this deadly outcome. © 2017 Wiley Periodicals, Inc.
Full Text Available Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH (Seeley et al., 2009 by identifying structural and functional covariance in this hippocampal network. Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049. Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays. Keywords: Anatomic likelihood estimation, ALE, BrainMap, Functional
Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta... motivation : A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology , 85, 678-707. Cooper, H. (2003). Editorial...error management training: A meta-analysis. Journal of Applied Psychology , 93, 59-69. Klein, H. J., Noe, R. A., & Wang, C. W. (2006). Motivation to
Moatt, Joshua P; Nakagawa, Shinichi; Lagisz, Malgorzata; Walling, Craig A
Dietary restriction (DR), a reduction in the amount of food or particular nutrients eaten, is the most consistent environmental manipulation to extend lifespan and protect against age related diseases. Current evolutionary theory explains this effect as a shift in the resolution of the trade-off between lifespan and reproduction. However, recent studies have questioned the role of reproduction in mediating the effect of DR on longevity and no study has quantitatively investigated the effect of DR on reproduction across species. Here we report a comprehensive comparative meta-analysis of the effect of DR on reproduction. In general, DR reduced reproduction across taxa, but several factors moderated this effect. The effect of DR on reproduction was greater in well-studied model species (yeast, nematode worms, fruit flies and rodents) than non-model species. This mirrors recent results for longevity and, for reproduction, seems to result from a faster rate of decline with decreasing resources in model species. Our results also suggested that not all reproductive traits are affected equally by DR. High and moderate cost reproductive traits suffered a significant reduction with DR, but low cost traits, such as ejaculate production, did not. Although the effect of DR on reproduction was stronger in females than males, this sex difference reduced to near zero when accounting for other co-factors such as the costliness of the reproductive trait. Thus, sex differences in the effect of DR on longevity may be due to a failure to expose males to as complete a range of the costs of reproduction as females. We suggest that to better understand the generality of the effect of DR, future studies should attempt to address the cause of the apparent model species bias and ensure that individuals are exposed to as many of the costs of reproduction as possible. Furthermore, our meta-analytic approach reveals a general shortage of DR studies that record reproduction, particularly in
AIMS: This meta-analysis sought to identify which subgroups of clients with severe mental illness (SMI) benefited from evidence-based supported employment. METHODS: We used meta-analysis to pool the samples from 4 randomized controlled trials comparing the Individual Placement and Support (IPS) model of supported employment to well-regarded vocational approaches using stepwise models and brokered services. Meta-analysis was used to determine the magnitude of effects for IPS\\/control group differences within specific client subgroups (defined by 2 work history, 7 sociodemographic, and 8 clinical variables) on 3 competitive employment outcomes (obtaining a job, total weeks worked, and job tenure). RESULTS: The findings strongly favored IPS, with large effect sizes across all outcomes: 0.96 for job acquisition, 0.79 for total weeks worked, and 0.74 for job tenure. Overall, 90 (77%) of the 117 effect sizes calculated for the 39 subgroups exceeded 0.70, and all 117 favored IPS. CONCLUSIONS: IPS produces better competitive employment outcomes for persons with SMI than alternative vocational programs regardless of background demographic, clinical, and employment characteristics.
Grijalva, Emily; Newman, Daniel A; Tay, Louis; Donnellan, M Brent; Harms, P D; Robins, Richard W; Yan, Taiyi
Despite the widely held belief that men are more narcissistic than women, there has been no systematic review to establish the magnitude, variability across measures and settings, and stability over time of this gender difference. Drawing on the biosocial approach to social role theory, a meta-analysis performed for Study 1 found that men tended to be more narcissistic than women (d = .26; k = 355 studies; N = 470,846). This gender difference remained stable in U.S. college student cohorts over time (from 1990 to 2013) and across different age groups. Study 1 also investigated gender differences in three facets of the Narcissistic Personality Inventory (NPI) to reveal that the narcissism gender difference is driven by the Exploitative/Entitlement facet (d = .29; k = 44 studies; N = 44,108) and Leadership/Authority facet (d = .20; k = 40 studies; N = 44,739); whereas the gender difference in Grandiose/Exhibitionism (d = .04; k = 39 studies; N = 42,460) was much smaller. We further investigated a less-studied form of narcissism called vulnerable narcissism-which is marked by low self-esteem, neuroticism, and introversion-to find that (in contrast to the more commonly studied form of narcissism found in the DSM and the NPI) men and women did not differ on vulnerable narcissism (d = -.04; k = 42 studies; N = 46,735). Study 2 used item response theory to rule out the possibility that measurement bias accounts for observed gender differences in the three facets of the NPI (N = 19,001). Results revealed that observed gender differences were not explained by measurement bias and thus can be interpreted as true sex differences. Discussion focuses on the implications for the biosocial construction model of gender differences, for the etiology of narcissism, for clinical applications, and for the role of narcissism in helping to explain gender differences in leadership and aggressive behavior. Readers are warned against overapplying small effect sizes to perpetuate gender
Full Text Available Adrián Montesano,1 María Angeles López-González,2 Luis Angel Saúl,2 Guillem Feixas1 1Department of Personality, Assessment and Psychological Treatments, University of Barcelona, Barcelona, 2Department of Personality, Assessment and Psychological Treatments, Faculty of Psychology, National Distance Education University, Madrid, Spain Abstract: Recent research has highlighted the role of implicative dilemmas in a variety of clinical conditions. These dilemmas are a type of cognitive conflict, in which different aspects of the self are countered in such a way that a desired change in a personal dimension (eg, symptom improvement may be hindered by the need of personal coherence in another dimension. The aim of this study was to summarize, using a meta-analytical approach, the evidence relating to the presence and the level of this conflict, as well as its relationship with well-being, in various clinical samples. A systematic review using multiple electronic databases found that out of 37 articles assessed for eligibility, nine fulfilled the inclusion criteria for meta-analysis. Random effects model was applied when computing mean effect sizes and testing for heterogeneity level. Statistically significant associations were observed between the clinical status and the presence of dilemmas, as well as level of conflict across several clinical conditions. Likewise, the level of conflict was associated with symptom severity. Results highlighted the clinical relevance and the transdiagnostic nature of implicative dilemmas. Keywords: implicative dilemmas, cognitive conflicts, intrapersonal conflicts, meta-analysis
Ferguson, Christopher J
Social scientists continue to debate the impact of spanking and corporal punishment (CP) on negative child outcomes including externalizing and internalizing behavior problems and cognitive performance. Previous meta-analytic reviews have mixed long- and short-term studies and relied on bivariate r, which may inflate effect sizes. The current meta-analysis focused on longitudinal studies, and compared effects using bivariate r and better controlled partial r coefficients controlling for time-1 outcome variables. Consistent with previous findings, results based on bivariate r found small but non-trivial long-term relationships between spanking/CP use and negative outcomes. Spanking and CP correlated .14 and .18 respectively with externalizing problems, .12 and .21 with internalizing problems and -.09 and -.18 with cognitive performance. However, when better controlled partial r coefficients (pr) were examined, results were statistically significant but trivial (at or below pr = .10) for externalizing (.07 for spanking, .08 for CP) and internalizing behaviors (.10 for spanking, insufficient studies for CP) and near the threshold of trivial for cognitive performance (-.11 for CP, insufficient studies for spanking). It is concluded that the impact of spanking and CP on the negative outcomes evaluated here (externalizing, internalizing behaviors and low cognitive performance) are minimal. It is advised that psychologists take a more nuanced approach in discussing the effects of spanking/CP with the general public, consistent with the size as well as the significance of their longitudinal associations with adverse outcomes.
Нина Васильевна Опанасенко
Full Text Available The article is devoted to issues of network approach application in political communication studies. The author considers communication in online and offline areas and gives the definition of rhizome, its characteristics, identifies links between rhizome and network approach. The author also analyses conditions and possibilities of the network approach in modern political communication. Both positive and negative features of the network approach are emphasized.
Michael M. Mackay
Full Text Available This article offers a correlation matrix of meta-analytic estimates between various employee job attitudes (i.e., Employee engagement, job satisfaction, job involvement, and organizational commitment and indicators of employee effectiveness (i.e., Focal performance, contextual performance, turnover intention, and absenteeism. The meta-analytic correlations in the matrix are based on over 1100 individual studies representing over 340,000 employees. Data was collected worldwide via employee self-report surveys. Structural path analyses based on the matrix, and the interpretation of the data, can be found in “Investigating the incremental validity of employee engagement in the prediction of employee effectiveness: a meta-analytic path analysis” (Mackay et al., 2016 . Keywords: Meta-analysis, Job attitudes, Job performance, Employee, Engagement, Employee effectiveness
This handbook aims to highlight fundamental, methodological and computational aspects of networks of queues to provide insights and to unify results that can be applied in a more general manner. The handbook is organized into five parts: Part 1 considers exact analytical results such as of product form type. Topics include characterization of product forms by physical balance concepts and simple traffic flow equations, classes of service and queue disciplines that allow a product form, a unified description of product forms for discrete time queueing networks, insights for insensitivity, and aggregation and decomposition results that allow subnetworks to be aggregated into single nodes to reduce computational burden. Part 2 looks at monotonicity and comparison results such as for computational simplification by either of two approaches: stochastic monotonicity and ordering results based on the ordering of the proces generators, and comparison results and explicit error bounds based on an underlying Markov r...
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Jakobsen, Janus Christian; Wetterslev, Jorn; Winkel, Per
BACKGROUND: Thresholds for statistical significance when assessing meta-analysis results are being insufficiently demonstrated by traditional 95% confidence intervals and P-values. Assessment of intervention effects in systematic reviews with meta-analysis deserves greater rigour. METHODS......: Methodologies for assessing statistical and clinical significance of intervention effects in systematic reviews were considered. Balancing simplicity and comprehensiveness, an operational procedure was developed, based mainly on The Cochrane Collaboration methodology and the Grading of Recommendations...... Assessment, Development, and Evaluation (GRADE) guidelines. RESULTS: We propose an eight-step procedure for better validation of meta-analytic results in systematic reviews (1) Obtain the 95% confidence intervals and the P-values from both fixed-effect and random-effects meta-analyses and report the most...
Nohe, Christoph; Hertel, Guido
Based on social exchange theory, we examined and contrasted attitudinal mediators (affective organizational commitment, job satisfaction) and relational mediators (trust in leader, leader-member exchange; LMX) of the positive relationship between transformational leadership and organizational citizenship behavior (OCB). Hypotheses were tested using meta-analytic path models with correlations from published meta-analyses (761 samples with 227,419 individuals overall). When testing single-mediator models, results supported our expectations that each of the mediators explained the relationship between transformational leadership and OCB. When testing a multi-mediator model, LMX was the strongest mediator. When testing a model with a latent attitudinal mechanism and a latent relational mechanism, the relational mechanism was the stronger mediator of the relationship between transformational leadership and OCB. Our findings help to better understand the underlying mechanisms of the relationship between transformational leadership and OCB.
Sedikides, Constantine; Gaertner, Lowell; Vevea, Jack L
C. Sedikides, L. Gaertner, and Y. Toguchi (2003) reported findings favoring the universality of self-enhancement. S. J. Heine (2005) challenged the authors' research on evidential and logical grounds. In response, the authors carried out 2 meta-analytic investigations. The results backed the C. Sedikides et al. (2003) theory and findings. Both Westerners and Easterners self-enhanced tactically. Westerners self-enhanced on attributes relevant to the cultural ideal of individualism, whereas Easterners self-enhanced on attributes relevant to the cultural ideal of collectivism (in both cases, because of the personal importance of the ideal). Self-enhancement motivation is universal, although its manifestations are strategically sensitive to cultural context. The authors respond to other aspects of Heine's critique by discussing why researchers should empirically validate the comparison dimension (individualistic vs. collectivistic) and defending why the better-than-average effect is a valid measure of self-enhancement.
Ng, Thomas W H; Feldman, Daniel C
This study examines the criterion-related and incremental validity of ethical leadership (EL) with meta-analytic data. Across 101 samples published over the last 15 years (N = 29,620), we observed that EL demonstrated acceptable criterion-related validity with variables that tap followers' job attitudes, job performance, and evaluations of their leaders. Further, followers' trust in the leader mediated the relationships of EL with job attitudes and performance. In terms of incremental validity, we found that EL significantly, albeit weakly in some cases, predicted task performance, citizenship behavior, and counterproductive work behavior-even after controlling for the effects of such variables as transformational leadership, use of contingent rewards, management by exception, interactional fairness, and destructive leadership. The article concludes with a discussion of ways to strengthen the incremental validity of EL. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Colquitt, J A; Conlon, D E; Wesson, M J; Porter, C O; Ng, K Y
The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.
Sala, Giovanni; Tatlidil, K Semir; Gobet, Fernand
As a result of considerable potential scientific and societal implications, the possibility of enhancing cognitive ability by training has been one of the most influential topics of cognitive psychology in the last two decades. However, substantial research into the psychology of expertise and a recent series of meta-analytic reviews have suggested that various types of cognitive training (e.g., working memory training) benefit performance only in the trained tasks. The lack of skill generalization from one domain to different ones-that is, far transfer-has been documented in various fields of research such as working memory training, music, brain training, and chess. Video game training is another activity that has been claimed by many researchers to foster a broad range of cognitive abilities such as visual processing, attention, spatial ability, and cognitive control. We tested these claims with three random-effects meta-analytic models. The first meta-analysis (k = 310) examined the correlation between video game skill and cognitive ability. The second meta-analysis (k = 315) dealt with the differences between video game players and nonplayers in cognitive ability. The third meta-analysis (k = 359) investigated the effects of video game training on participants' cognitive ability. Small or null overall effect sizes were found in all three models. These outcomes show that overall cognitive ability and video game skill are only weakly related. Importantly, we found no evidence of a causal relationship between playing video games and enhanced cognitive ability. Video game training thus represents no exception to the general difficulty of obtaining far transfer. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Naess, Kari-Anne B.; Lyster, Solveig-Alma Halaas; Hulme, Charles; Melby-Lervag, Monica
This study presents a meta-analytic review of language and verbal short-term memory skills in children with Down syndrome. The study examines the profile of strengths and weaknesses in children with Down syndrome compared to typically developing children matched for nonverbal mental age. The findings show that children with Down syndrome have…
Kalaian, Sema A.; Kasim, Rafa M.
This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who…
Hartnell, Chad A.; Ou, Amy Yi; Kinicki, Angelo
We apply Quinn and Rohrbaugh's (1983) competing values framework (CVF) as an organizing taxonomy to meta-analytically test hypotheses about the relationship between 3 culture types and 3 major indices of organizational effectiveness (employee attitudes, operational performance [i.e., innovation and product and service quality], and financial…
Özdas, Faysal; Batdi, Veli
This thematic-based meta-analytic study aims to examine the effect of creativity on the academic success and learning retention scores of students. In the context of this aim, 18 out of 225 studies regarding creativity that were carried out between 2001 and 2011 have been obtained from certain national and international databases. The studies…
McDougall, Dennis; Ornelles, Cecily; Mersberg, Kawika; Amona, Kekama
In this meta-analytic review, we critically evaluate procedures and outcomes from nine intervention studies in which students used tactile-cued self-monitoring in educational settings. Findings suggest that most tactile-cued self-monitoring interventions have moderate to strong effects, have emerged only recently, and have not yet achieved the…
Yonker, Julie E.; Schnabelrauch, Chelsea A.; DeHaan, Laura G.
The present study used meta-analytic techniques to examine the association between spirituality and religiosity (S/R) and psychological outcomes in adolescents and emerging adults. The outcome measures of risk behavior, depression, well-being, self-esteem, and personality were examined with respect to the influence of S/R across 75 independent…
Steel, Piers; Taras, Vasyl; Uggerslev, Krista; Bosco, Frank
Do cultural values enhance financial and subjective well-being (SWB)? Taking a multidisciplinary approach, we meta-analytically reviewed the field, found it thinly covered, and focused on individualism. In counter, we collected a broad array of individual-level data, specifically an Internet sample of 8,438 adult respondents. Individual SWB was most strongly associated with cultural values that foster relationships and social capital, which typically accounted for more unique variance in life satisfaction than an individual's salary. At a national level, we used mean-based meta-analysis to construct a comprehensive cultural and SWB database. Results show some reversals from the individual level, particularly masculinity's facet of achievement orientation. In all, the happy nation has low power distance and low uncertainty avoidance, but is high in femininity and individualism, and these effects are interrelated but still partially independent from political and economic institutions. In short, culture matters for individual and national well-being.
Diana M.E. Torta
Full Text Available Anatomical, morphological and histological data have consistently shown that the cingulate cortex can be divided into four main regions. However, less is known about parcellations of the cingulate cortex when involved in active tasks. Here, we aimed at comparing how the pattern of clusterization of the cingulate cortex changes across different levels of task complexity. We parcellated the cingulate cortex using the results of a meta-analytic study and of three experimental studies. The experimental studies, which included two active tasks and a resting state protocol, were used to control the results obtained with the meta-analytic parcellation. We explored the meta-analytic parcellation by applying a meta-analytic clustering (MaC to papers retrieved from the BrainMap database. The MaC is a meta-analytic connectivity driven parcellation technique recently developed by our group which allowed us to parcellate the cingulate cortex on the basis of its pattern of co-activations during active tasks. The MaC results indicated that the cingulate cortex can be parcellated into three clusters. These clusters covered different percentages of the cingulate parenchyma and had a different density of foci, with the first cluster being more densely connected. The control experiments showed different clusterization results, suggesting that the co-activations of the cingulate cortex are highly dependent on the task that is tested. Our results highlight the importance of the cingulate cortex as a hub, which modifies its pattern of co-activations depending on the task requests and on the level of task complexity. The neurobiological meaning of these results is discussed.
Tolan, Patrick H.; Henry, David B.; Schoeny, Michael S.; Lovegrove, Peter; Nichols, Emily
Objectives To conduct a meta-analytic review of selective and indicated mentoring interventions for effects for youth at risk on delinquency and key associated outcomes (aggression, drug use, academic functioning). We also undertook the first systematic evaluation of intervention implementation features and organization and tested for effects of theorized key processes of mentor program effects. Methods Campbell Collaboration review inclusion criteria and procedures were used to search and evaluate the literature. Criteria included a sample defined as at-risk for delinquency due to individual behavior such as aggression or conduct problems or environmental characteristics such as residence in high-crime community. Studies were required to be random assignment or strong quasi-experimental design. Of 163 identified studies published 1970 - 2011, 46 met criteria for inclusion. Results Mean effects sizes were significant and positive for each outcome category (ranging form d =.11 for Academic Achievement to d = .29 for Aggression). Heterogeneity in effect sizes was noted for all four outcomes. Stronger effects resulted when mentor motivation was professional development but not by other implementation features. Significant improvements in effects were found when advocacy and emotional support mentoring processes were emphasized. Conclusions This popular approach has significant impact on delinquency and associated outcomes for youth at-risk for delinquency. While evidencing some features may relate to effects, the body of literature is remarkably lacking in details about specific program features and procedures. This persistent state of limited reporting seriously impedes understanding about how mentoring is beneficial and ability to maximize its utility. PMID:25386111
Lewis, Jenny; Ricard, Lykke Margot
Leaders’ ego-networks within an organization are pivotal as focal points that point to other organizational factors such as innovation capacity and leadership effectiveness. The aim of the paper is to provide a framework for exploring leaders’ ego-networks within the boundary of an organization. We...... a survey of senior administrators and politicians from Copenhagen municipality, we examine strategic information networks. Whole network analysis is used first to identify important individuals on the basis of centrality measures. The ego-networks of these individuals are then analysed to examine...
Reports an error in "Racial Bias in Mock Juror Decision-Making: A Meta-Analytic Review of Defendant Treatment" by Tara L. Mitchell, Ryann M. Haw, Jeffrey E. Pfeifer and Christian A. Meissner ( Law and Human Behavior , 2005[Dec], Vol 29, 621-637). In the article, all of the numbers in Appendix A were correct, but the signs were reversed for z' in a number of studies, which are listed. Also, in Appendix B, some values were incorrect, some signs were reversed, and some values were missing. The corrected appendix is included. (The following abstract of the original article appeared in record 2006-00971-001.) Common wisdom seems to suggest that racial bias, defined as disparate treatment of minority defendants, exists in jury decision-making, with Black defendants being treated more harshly by jurors than White defendants. The empirical research, however, is inconsistent--some studies show racial bias while others do not. Two previous meta-analyses have found conflicting results regarding the existence of racial bias in juror decision-making (Mazzella & Feingold, 1994, Journal of Applied Social Psychology, 24, 1315-1344; Sweeney & Haney, 1992, Behavioral Sciences and the Law, 10, 179-195). This research takes a meta-analytic approach to further investigate the inconsistencies within the empirical literature on racial bias in juror decision-making by defining racial bias as disparate treatment of racial out-groups (rather than focusing upon the minority group alone). Our results suggest that a small, yet significant, effect of racial bias in decision-making is present across studies, but that the effect becomes more pronounced when certain moderators are considered. The state of the research will be discussed in light of these findings. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Husslage, B.G.M.; Lindelauf, R.; Hamers, H.J.M.
Abstract: Lindelauf et al. (2009a) introduced a quantitative approach to investigate optimal structures of covert networks. This approach used an objective function which is based on the secrecy versus information trade-off these organizations face. Sageman (2008) hypothesized that covert networks
Wang, Ping; Liu, Han-Hui; Zhu, Xing-Ting; Meng, Tian; Li, Hui-Jie; Zuo, Xi-Nian
Action video game (AVG) has attracted increasing attention from both the public and from researchers. More and more studies found video game training improved a variety of cognitive functions. However, it remains controversial whether healthy adults can benefit from AVG training, and whether young and older adults benefit similarly from AVG training. In the present study, we aimed to quantitatively assess the AVG training effect on the cognitive ability of adults and to compare the training effects on young and older adults by conducting a meta-analysis on previous findings. We systematically searched video game training studies published between January 1986 and July 2015. Twenty studies were included in the present meta-analysis, for a total of 313 participants included in the training group and 323 participants in the control group. The results demonstrate that healthy adults achieve moderate benefit from AVG training in overall cognitive ability and moderate to small benefit in specific cognitive domains. In contrast, young adults gain more benefits from AVG training than older adults in both overall cognition and specific cognitive domains. Age, education, and some methodological factors, such as the session duration, session number, total training duration, and control group type, modulated the training effects. These meta-analytic findings provide evidence that AVG training may serve as an efficient way to improve the cognitive performance of healthy adults. We also discussed several directions for future AVG training studies.
Strauss, Clara; Hale, Lucy; Stobie, Blake
Accommodation of obsessive compulsive disorder (OCD) symptoms by family members is common. This paper presents a systematic meta-analytic review on family accommodation and OCD symptom severity. Fourteen studies investigating the relationship between family accommodation and OCD symptoms were selected. The medium effect size of the relationship between family accommodation and OCD symptom severity was significant (r = .35; 95% CI: .23 to .47), based on a Hunter-Schmidt random effects model with a total of 849 participants. Although there was some evidence of publication bias, Rosenthal's fail-safe N suggested that 596 studies with zero effect would be needed to reduce the mean effect size to non-significant. Findings are discussed in the context of the limitations of the studies, and in particular the reliance on cross-sectional designs which impede causal conclusions. Future research to evaluate a family accommodation intervention in a randomized controlled design and using mediation analysis to explore change mechanisms is called for. Copyright © 2015 Elsevier Ltd. All rights reserved.
Guttormsen, Linn Stokke; Kefalianos, Elaina; Næss, Kari-Anne B
This article presents a meta-analytic review of differences in communication attitudes between children who stutter (CWS) and children who do not stutter (CWNS). To be included in this review, the studies had to include a group of CWS and CWNS between the ages of 3-18 years and a measurement of communication attitudes. The journal articles were identified by using the key words stutter*, speech disfluenc*, fluency disorder*, and stammer* cross-referenced to awareness*, reaction*, attitude*, KiddyCAT, CAT, A-19 Scale, PASS and OASES. A total of 18 studies met the inclusion criteria for this meta-analysis. The results showed that CWS exhibit more negative communication attitudes than CWNS from the preschool years. The differences between the groups increased with age, but were not influenced by gender. The results indicate that negative communication attitudes can be an effect of stuttering. Key issues requiring further investigation are whether communication attitudes differ as a function of age at stuttering onset and whether communication attitudes influence the development of stuttering. After reading this article, the reader will be able to: (a) summarise empirical findings with regard to the relationship between communication attitudes and childhood stuttering; (b) describe the different instruments used to measure communication attitudes; (c) discuss the relationship between communication attitudes, age and gender. Copyright © 2015 Elsevier Inc. All rights reserved.
Full Text Available For more than four decades, researchers have examined theoretically and empirically the relationship between internationalization and firm performance. While existing studies have provided important contributions, the stream of research still lacks consistency due to ambiguous findings on the internationalization-firm performance relationship. Moreover, previous research has often been limited to developed countries. The present study focuses on the emerging Chindia countries and determines the direction and the strength of the internationalization-firm performance relationship. Additionally, we have identified moderators of the relationship. Drawing on 21 studies, based on 9026 firms, we utilize a meta-analytic review to assess our hypotheses. Our results show that there is a significant and positive internationalization-firm performance relationship in Chindia countries. The effect of internationalization in India and China does not significantly differ. Moreover, we find that the effect of internationalization is significantly stronger in the United States as compared to the Chindia countries. The time period of data collection did not play an important role as a moderator. The present study contributes to the International Business literature by examining how and to which extent internationalization influences firm performance and offers implications for theory and practice as well as recommendations for future research.
Lee, Eun-Suk; Park, Tae-Youn; Koo, Bonjin
Organizational identification has been argued to have a unique value in explaining individual attitudes and behaviors in organizations, as it involves the essential definition of entities (i.e., individual and organizational identities). This review seeks meta-analytic evidence of the argument by examining how this identity-relevant construct functions in the nexus of attitudinal/behavioral constructs. The findings show that, first, organizational identification is significantly associated with key attitudes (job involvement, job satisfaction, and affective organizational commitment) and behaviors (in-role performance and extra-role performance) in organizations. Second, in the classic psychological model of attitude-behavior relations (Fishbein & Ajzen, 1975), organizational identification is positioned as a basis from which general sets of those attitudes and behaviors are engendered; organizational identification has a direct effect on general behavior above and beyond the effect of general attitude. Third, the effects of organizational identification are moderated by national culture, a higher-level social context wherein the organization is embedded, such that the effects are stronger in a collectivistic culture than in an individualistic culture. Theoretical and practical implications of the findings and future research directions are discussed. (c) 2015 APA, all rights reserved).
Greer, Lindred L; de Jong, Bart A; Schouten, Maartje E; Dannals, Jennifer E
Hierarchy has the potential to both benefit and harm team effectiveness. In this article, we meta-analytically investigate different explanations for why and when hierarchy helps or hurts team effectiveness, drawing on results from 54 prior studies (N = 13,914 teams). Our findings show that, on net, hierarchy negatively impacts team effectiveness (performance: ρ = -.08; viability: ρ = -.11), and that this effect is mediated by increased conflict-enabling states. Additionally, we show that the negative relationship between hierarchy and team performance is exacerbated by aspects of the team structure (i.e., membership instability, skill differentiation) and the hierarchy itself (i.e., mutability), which make hierarchical teams prone to conflict. The predictions regarding the positive effect of hierarchy on team performance as mediated by coordination-enabling processes, and the moderating roles of several aspects of team tasks (i.e., interdependence, complexity) and the hierarchy (i.e., form) were not supported, with the exception that task ambiguity enhanced the positive effects of hierarchy. Given that our findings largely support dysfunctional views on hierarchy, future research is needed to understand when and why hierarchy may be more likely to live up to its purported functional benefits. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Steffens, Niklas K; Haslam, S Alexander; Schuh, Sebastian C; Jetten, Jolanda; van Dick, Rolf
We provide a meta-analytical review examining two decades of work on the relationship between individuals' social identifications and health in organizations (102 effect sizes, k = 58, N = 19,799). Results reveal a mean-weighted positive association between organizational identification and health ( r = .21, T = .14). Analysis identified a positive relationship for both workgroup ( r = .21) and organizational identification ( r = .21), and in studies using longitudinal/experimental ( r = .13) and cross-sectional designs ( r = .22). The relationship is stronger (a) for indicators of the presence of well-being ( r = .27) than absence of stress ( r = .18), (b) for psychological ( r = .23) than physical health ( r = .16), (c) to the extent that identification is shared among group members, and (d) as the proportion of female participants in a sample decreases. Overall, results indicate that social identifications in organizations are positively associated with health but that there is also substantial variation in effect size strength. We discuss implications for theory and practice and outline a roadmap for future research.
Kvaale, Erlend P; Haslam, Nick; Gottdiener, William H
Reducing stigma is crucial for facilitating recovery from psychological problems. Viewing these problems biomedically may reduce the tendency to blame affected persons, but critics have cautioned that it could also increase other facets of stigma. We report on the first meta-analytic review of the effects of biogenetic explanations on stigma. A comprehensive search yielded 28 eligible experimental studies. Four separate meta-analyses (Ns=1207-3469) assessed the effects of biogenetic explanations on blame, perceived dangerousness, social distance, and prognostic pessimism. We found that biogenetic explanations reduce blame (Hedges g=-0.324) but induce pessimism (Hedges g=0.263). We also found that biogenetic explanations increase endorsement of the stereotype that people with psychological problems are dangerous (Hedges g=0.198), although this result could reflect publication bias. Finally, we found that biogenetic explanations do not typically affect social distance. Promoting biogenetic explanations to alleviate blame may induce pessimism and set the stage for self-fulfilling prophecies that could hamper recovery from psychological problems. Copyright © 2013 Elsevier Ltd. All rights reserved.
Moore, Todd M; Stuart, Gregory L; Meehan, Jeffrey C; Rhatigan, Deborah L; Hellmuth, Julianne C; Keen, Stefanie M
The present investigation employed meta-analytic procedures to quantitatively evaluate the empirical evidence on the relationship between drug abuse and aggression between intimate partners. Data from 96 studies yielding 547 effect sizes indicated that increases in drug use and drug-related problems were significantly associated with increases in aggression between intimate partners (d= .27). Cocaine emerged as the illicit substance with the strongest relationship to psychological, physical, and sexual aggression (ds= .39 to .62). Marijuana was also identified as having a significant association with partner aggression. Results showed comparable effect sizes for men and women, regardless of the sex of the drug user and/or perpetrator of partner aggression, with female reports of aggression having yielded larger effect sizes than male reports. Moderator analyses revealed that relative to other groups, married or cohabiting couples and Black participants evidenced significantly stronger effect sizes. The findings are discussed in relation to possible mechanisms linking drugs to partner aggression, and implications for future research are discussed in terms of focusing on conducting studies that assess the interaction of context and temporal sequencing of drugs and partner aggression.
Holt-Lunstad, Julianne; Smith, Timothy B; Baker, Mark; Harris, Tyler; Stephenson, David
Actual and perceived social isolation are both associated with increased risk for early mortality. In this meta-analytic review, our objective is to establish the overall and relative magnitude of social isolation and loneliness and to examine possible moderators. We conducted a literature search of studies (January 1980 to February 2014) using MEDLINE, CINAHL, PsycINFO, Social Work Abstracts, and Google Scholar. The included studies provided quantitative data on mortality as affected by loneliness, social isolation, or living alone. Across studies in which several possible confounds were statistically controlled for, the weighted average effect sizes were as follows: social isolation odds ratio (OR) = 1.29, loneliness OR = 1.26, and living alone OR = 1.32, corresponding to an average of 29%, 26%, and 32% increased likelihood of mortality, respectively. We found no differences between measures of objective and subjective social isolation. Results remain consistent across gender, length of follow-up, and world region, but initial health status has an influence on the findings. Results also differ across participant age, with social deficits being more predictive of death in samples with an average age younger than 65 years. Overall, the influence of both objective and subjective social isolation on risk for mortality is comparable with well-established risk factors for mortality. © The Author(s) 2015.
Cañadas-De la Fuente, Guillermo A; Gómez-Urquiza, Jose L; Ortega-Campos, Elena M; Cañadas, Gustavo R; Albendín-García, Luis; De la Fuente-Solana, Emilia I
To determine the prevalence of high levels of emotional exhaustion and depersonalization and low personal accomplishment in nursing professionals in oncology services. A meta-analytical study was performed. The search was carried out in March 2017 in Pubmed, CINAHL, Scopus, Scielo, Proquest, CUIDEN, and LILACS databases. Studies using Maslach Burnout Inventory for the assessment of burnout were included. The total sample of oncology nurses was n = 9959. The total number of included studies was n = 17, with n = 21 samples for the meta-analysis of emotional exhaustion and n = 18 for depersonalization and low personal accomplishment. The prevalence of emotional exhaustion and of depersonalization was 30% (95% CI = 26%-33%) and 15% (95% CI = 9%-23%), respectively, and that of low personal performance was 35% (95% CI = 27%-43%). The are many oncology nurses with emotional exhaustion and low levels of personal accomplishment. The presence and the risk of burnout among these staff members are considerable. Copyright © 2018 John Wiley & Sons, Ltd.
Santi, D; De Vincentis, S; Magnani, E; Spaggiari, G
Considering the widespread use of assisted reproductive techniques (ART), DNA methylation of specific genes involved in spermatogenesis achieves increasingly clinical relevance, representing a possible explanation of increased incidence of syndromes related to genomic imprinting in medically assisted pregnancies. Several trials suggested a relationship between male sub-fertility and sperm DNA methylation, although its weight on seminal parameters alteration is still a matter of debate. To evaluate whether aberrant sperm DNA methylation of imprinted genes is associated with impaired sperm parameters. Meta-analysis of controlled clinical trials evaluating imprinted genes sperm DNA methylation comparing men with idiopathic infertility to fertile controls. Twenty-four studies were included, allowing a meta-analytic evaluation for H19, MEST, SNRPN, and LINE-1. When a high heterogeneity of the results was demonstrated, the random effect model was used. H19 methylation levels resulted significantly lower in 879 infertile compared with 562 fertile men (7.53%, 95% CI: 5.14-9.93%, p male infertility is associated with altered sperm methylation at H19, MEST, and SNRPN. Although its role in infertility remains unclear, sperm DNA methylation could be associated with the epigenetic risk in ART. In this setting, before proposing this analysis in clinical practice, an accurate identification of the most representative genes and a cost-effectiveness evaluation should be assessed in ad hoc prospective studies. © 2017 American Society of Andrology and European Academy of Andrology.
Simpson, David M; Leonhardt, Nathan D; Hawkins, Alan J
Despite recent policy initiatives and substantial federal funding of individually oriented relationship education programs for youth, there have been no meta-analytic reviews of this growing field. This meta-analytic study draws on 17 control-group studies and 13 one-group/pre-post studies to evaluate the effectiveness of relationship education programs on adolescents' and emerging adults' relationship knowledge, attitudes, and skills. Overall, control-group studies produced a medium effect (d = .36); one-group/pre-post studies also produced a medium effect (d = .47). However, the lack of studies with long-term follow-ups of relationship behaviors in the young adult years is a serious weakness in the field, limiting what we can say about the value of these programs for helping youth achieve their aspirations for healthy romantic relationships and stable marriages.
Colquitt, J A; LePine, J A; Noe, R A
This article meta-analytically summarizes the literature on training motivation, its antecedents, and its relationships with training outcomes such as declarative knowledge, skill acquisition, and transfer. Significant predictors of training motivation and outcomes included individual characteristics (e.g., locus of control, conscientiousness, anxiety, age, cognitive ability, self-efficacy, valence, job involvement) and situational characteristics (e.g., climate). Moreover, training motivation explained incremental variance in training outcomes beyond the effects of cognitive ability. Meta-analytic path analyses further showed that the effects of personality, climate, and age on training outcomes were only partially mediated by self-efficacy, valence, and job involvement. These findings are discussed in terms of their practical significance and their implications for an integrative theory of training motivation.
Woodin, Erica M
This meta-analysis of 64 studies (5,071 couples) used a metacoding system to categorize observed couple conflict behaviors into categories differing in terms of valence (positive to negative) and intensity (high to low) and resulting in five behavioral categories: hostility, distress, withdrawal, problem solving, and intimacy. Aggregate effect sizes indicated that women were somewhat more likely to display hostility, distress, and intimacy during conflict, whereas men were somewhat more likely to display withdrawal and problem solving. Gender differences were of a small magnitude. For both men and women, hostility was robustly associated with lower relationship satisfaction (medium effect), distress and withdrawal were somewhat associated (small effect), and intimacy and problem solving were both closely associated with relationship satisfaction (medium effect). Effect sizes were moderated in several cases by study characteristics including year of publication, developmental period of the sample, recruitment design, duration of observed conflict, method used to induce conflict, and type of coding system used. Findings from this meta-analysis suggest that high-intensity conflict behaviors of both a positive and negative nature are important correlates of relationship satisfaction and underscore the relatively small gender differences in many conflict behaviors. 2011 APA, all rights reserved
Maldini, Carla; Druce, Katie; Basu, Neil; LaValley, Michael P; Mahr, Alfred
Surveys of Behçet's disease (BD) have shown substantial geographic variations in prevalence, but some of these differences may result from methodological inconsistencies. This meta-analysis explored the effect of geographic location and study methodology on the prevalence of BD. We systematically searched the literature in electronic databases and by handsearching to identify population-based prevalence surveys of BD. Studies were eligible if they provided an original population-based prevalence estimate for BD with the number of prevalent cases identified in the study area. Pooled prevalence proportions across all studies were computed by using random effects models based on a Poisson normal distribution. Pre-defined subgroup analyses and meta-regression were used to investigate the effect of covariates on the prevalence proportions. We included 45 reports published from 1974 to 2015 and covering worldwide areas. The pooled estimates of prevalence proportions (expressed as cases/100 000 inhabitants) were 10.3 (95% CI 6.1, 17.7) for all studies and 119.8 (59.8, 239.9) for Turkey, 31.8 (12.9, 78.4) for the Middle East, 4.5 (2.2, 9.4) for Asia and 3.3 (2.1, 5.2) for Europe. Subgroup analyses showed a strikingly greater prevalence for studies with a sample survey design than a census design [82.5 (95% CI 47.3, 143.9) vs 3.6 (2.6, 5.1)]. Metaregression identified study design as an independent covariate significantly affecting BD prevalence proportions. Differences in BD prevalence proportions likely reflect a combination of true geographic variation and methodological artefacts. In particular, use of a sample or census study design may strongly affect the estimated prevalence. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: email@example.com
Kim, Boram; Jee, Sooin; Lee, Joungwha; An, Sunghee; Lee, Sang Min
This study is a meta-analysis of 19 relevant studies, with 95,434 participants, investigating the relationships between various types of social support and 3 dimensions of student burnout. The overall results indicate that social support is negatively correlated with student burnout. Specifically, school or teacher supports have the strongest negative relationship to student burnout. Social supports from parents and from peers also have a significant negative relationship with student burnout. Among the 3 dimensions of student burnout, inefficacy was more strongly related to social support than emotional exhaustion or cynicism. The results of a moderation analysis suggest that the type of schools (secondary school and postsecondary school) affected the relationships between the overall social support and student burnout. We discuss the implications to ameliorate student burnout. Copyright © 2017 John Wiley & Sons, Ltd.
Esteban, Raquel; Barrutia, Oihana; Artetxe, Unai; Fernández-Marín, Beatriz; Hernández, Antonio; García-Plazaola, José Ignacio
Photosynthetic pigment composition has been a major study target in plant ecophysiology during the last three decades. Although more than 2000 papers have been published, a comprehensive evaluation of the responses of photosynthetic pigment composition to environmental conditions is not yet available. After an extensive survey, we compiled data from 525 papers including 809 species (subkingdom Viridiplantae) in which pigment composition was described. A meta-analysis was then conducted to assess the ranges of photosynthetic pigment content. Calculated frequency distributions of pigments were compared with those expected from the theoretical pigment composition. Responses to environmental factors were also analysed. The results revealed that lutein and xanthophyll cycle pigments (VAZ) were highly responsive to the environment, emphasizing the high phenotypic plasticity of VAZ, whereas neoxanthin was very stable. The present meta-analysis supports the existence of relatively narrow limits for pigment ratios and also supports the presence of a pool of free 'unbound' VAZ. Results from this study provide highly reliable ranges of photosynthetic pigment contents as a framework for future research on plant pigments. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.
Wang, X T; Rao, Li-Lin; Zheng, Hongming
We hypothesize that framing effects (risk-averse in the positive frame and risk-seeking in the negative frame) are likely to occur when ambiguous social contexts result in ambiguous or ambivalent risk preferences, leading the decision-maker to search for more subtle cues, such as verbal framing. In a functional magnetic resonance imaging (fMRI) study, we examined framing effects in both unambiguous homogeneous group and more ambiguous heterogeneous group contexts. We began by conducting a meta-analysis and identified three regions of interest: the right inferior frontal gyrus, the left anterior cingulate (ACC)/ventromedial prefrontal cortex (vmPFC), and the left amygdala. Our own fMRI data were collected while the participants made choices between a sure option and a gamble framed in terms of the number of lives to either save or die. The framing effect was evident in a heterogeneous context with a mixture of kin and strangers, but disappeared in a homogeneous group of either all kin-members or all strangers. The fMRI results revealed a greater activation in the right middle/inferior frontal gyrus under the negative than the positive framing, and less ACC/vmPFC deactivation under positive framing in the heterogamous/ambiguous context. The activation of the amygdala was correlated with greater risk-seeking preference in homogeneous kinship contexts.
Sterkowicz-Przybycien, Katarzyna; Gualdi-Russo, Emanuela
Studies on the anthropometric characteristics of athletes have a long history, but there are no published reviews on the somatotype of artistic gymnasts. Practitioners and professional coaches can gain guidance from improved understanding of the ideal body constitution and the impact of high-intensity training since preadolescence on body build. The present review is designed to provide this information. Academic Search Complete, SPORTDiscus, Medline, Google Scholar, and ResearchGate were searched in January 2017. All studies on the body composition of male artistic gymnasts were included. We identified 19 studies assessing somatotype in male gymnasts. We found high heterogeneity of somatotype components between younger gymnasts (≤ 18 years) and older gymnasts (> 18 years) (1.6±0.3 -5.4±0.8 - 3.0±0.6 vs. 1.8±0.4 -5.9±0.6 -2.2±0.4). Four different somatotypes resulted from the literature analysis, and ectomorphic mesomorph was the prevalent somatotype in both younger and older athletes. The main result showed a significant difference in ectomorphy (lower in older gymnasts than in younger gymnasts). Proper interpretation of the literature data may inform future research and enable professional coaches to longitudinally monitor gymnasts' somatotype components during growth and in talent identification. More research is needed to better understand the most suitable somatotype variations related to the different apparatuses used in artistic gymnastics.
Sarah R. Haile
Full Text Available Abstract Background Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them. Methods Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined. Results We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small. Conclusions We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties
Rojo, Joan; Latorre, Jose I.; Del Debbio, Luigi; Forte, Stefano; Piccione, Andrea
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits
Wigman, Johanna T. W.; de Vos, Stijn; Wichers, Marieke; van Os, Jim; Bartels-Velthuis, Agna A.
Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be
Chaplin, Tara M.; Aldao, Amelia
Emotion expression is an important feature of healthy child development that has been found to show gender differences. However, there has been no empirical review of the literature on gender and facial, vocal, and behavioral expressions of different types of emotions in children. The present study constitutes a comprehensive meta-analytic review of gender differences, and moderators of differences, in emotion expression from infancy through adolescence. We analyzed 555 effect sizes from 166 studies with a total of 21,709 participants. Significant, but very small, gender differences were found overall, with girls showing more positive emotions (g = −.08) and internalizing emotions (e.g., sadness, anxiety, sympathy; g = −.10) than boys, and boys showing more externalizing emotions (e.g., anger; g = .09) than girls. Notably, gender differences were moderated by age, interpersonal context, and task valence, underscoring the importance of contextual factors in gender differences. Gender differences in positive emotions were more pronounced with increasing age, with girls showing more positive emotions than boys in middle childhood (g = −.20) and adolescence (g = −.28). Boys showed more externalizing emotions than girls at toddler/preschool age (g = .17) and middle childhood (g = .13) and fewer externalizing emotions than girls in adolescence (g = −.27). Gender differences were less pronounced with parents and were more pronounced with unfamiliar adults (for positive emotions) and with peers/when alone (for externalizing emotions). Our findings of gender differences in emotion expression in specific contexts have important implications for gender differences in children’s healthy and maladaptive development. PMID:23231534
Chaplin, Tara M; Aldao, Amelia
Emotion expression is an important feature of healthy child development that has been found to show gender differences. However, there has been no empirical review of the literature on gender and facial, vocal, and behavioral expressions of different types of emotions in children. The present study constitutes a comprehensive meta-analytic review of gender differences and moderators of differences in emotion expression from infancy through adolescence. We analyzed 555 effect sizes from 166 studies with a total of 21,709 participants. Significant but very small gender differences were found overall, with girls showing more positive emotions (g = -.08) and internalizing emotions (e.g., sadness, anxiety, sympathy; g = -.10) than boys, and boys showing more externalizing emotions (e.g., anger; g = .09) than girls. Notably, gender differences were moderated by age, interpersonal context, and task valence, underscoring the importance of contextual factors in gender differences. Gender differences in positive emotions were more pronounced with increasing age, with girls showing more positive emotions than boys in middle childhood (g = -.20) and adolescence (g = -.28). Boys showed more externalizing emotions than girls at toddler/preschool age (g = .17) and middle childhood (g = .13) and fewer externalizing emotions than girls in adolescence (g = -.27). Gender differences were less pronounced with parents and were more pronounced with unfamiliar adults (for positive emotions) and with peers/when alone (for externalizing emotions). Our findings of gender differences in emotion expression in specific contexts have important implications for gender differences in children's healthy and maladaptive development. 2013 APA, all rights reserved
Naragon-Gainey, Kristin; McMahon, Tierney P; Chacko, Thomas P
Emotion regulation has been examined extensively with regard to important outcomes, including psychological and physical health. However, the literature includes many different emotion regulation strategies but little examination of how they relate to one another, making it difficult to interpret and synthesize findings. The goal of this meta-analysis was to examine the underlying structure of common emotion regulation strategies (i.e., acceptance, behavioral avoidance, distraction, experiential avoidance, expressive suppression, mindfulness, problem solving, reappraisal, rumination, worry), and to evaluate this structure in light of theoretical models of emotion regulation. We also examined how distress tolerance-an important emotion regulation ability -relates to strategy use. We conducted meta-analyses estimating the correlations between emotion regulation strategies (based on 331 samples and 670 effect sizes), as well as between distress tolerance and strategies. The resulting meta-analytic correlation matrix was submitted to confirmatory and exploratory factor analyses. None of the confirmatory models, based on prior theory, was an acceptable fit to the data. Exploratory factor analysis suggested that 3 underlying factors best characterized these data. Two factors-labeled Disengagement and Aversive Cognitive Perseveration-emerged as strongly correlated but distinct factors, with the latter consisting of putatively maladaptive strategies. The third factor, Adaptive Engagement, was a less unified factor and weakly related to the other 2 factors. Distress tolerance was most closely associated with low levels of repetitive negative thought and experiential avoidance, and high levels of acceptance and mindfulness. We discuss the theoretical implications of these findings and applications to emotion regulation assessment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Pan, Steven C; Rickard, Timothy C
Attempting recall of information from memory, as occurs when taking a practice test, is one of the most potent training techniques known to learning science. However, does testing yield learning that transfers to different contexts? In the present article, we report the findings of the first comprehensive meta-analytic review into that question. Our review encompassed 192 transfer effect sizes extracted from 122 experiments and 67 published and unpublished articles (N = 10,382) that together comprise more than 40 years of research. A random-effects model revealed that testing can yield transferrable learning as measured relative to a nontesting reexposure control condition (d = 0.40, 95% CI [0.31, 0.50]). That transfer of learning is greatest across test formats, to application and inference questions, to problems involving medical diagnoses, and to mediator and related word cues; it is weakest to rearranged stimulus-response items, to untested materials seen during initial study, and to problems involving worked examples. Moderator analyses further indicated that response congruency and elaborated retrieval practice, as well as initial test performance, strongly influence the likelihood of positive transfer. In two assessments for publication bias using PET-PEESE and various selection methods, the moderator effect sizes were minimally affected. However, the intercept predictions were substantially reduced, often indicating no positive transfer when none of the aforementioned moderators are present. Overall, our results motivate a three-factor framework for transfer of test-enhanced learning and have practical implications for the effective use of practice testing in educational and other training contexts. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Poos, Jackie M; Jiskoot, Lize C; Papma, Janne M; van Swieten, John C; van den Berg, Esther
A meta-analysis of the extent, nature and pattern of memory performance in behavioral variant frontotemporal dementia (bvFTD). Multiple observational studies have challenged the relative sparing of memory in bvFTD as stated in the current diagnostic criteria. We performed a meta-analytic review covering the period 1967 to February 2017 of case-control studies on episodic memory in bvFTD versus control participants (16 studies, 383 patients, 603 control participants), and patients with bvFTD versus those with Alzheimer's disease (AD) (20 studies, 452 bvFTD, 874 AD). Differences between both verbal and non-verbal working memory, episodic memory learning and recall, and recognition memory were examined. Data were extracted from the papers and combined into a common metric measure of effect, Hedges' d. Patients with bvFTD show large deficits in memory performance compared to controls (Hedges' d -1.10; 95% confidence interval [CI] [-1.23, -0.95]), but perform significantly better than patients with AD (Hedges' d 0.85; 95% CI [0.69, 1.03]). Learning and recall tests differentiate best between patients with bvFTD and AD (p<.01). There is 37-62% overlap in test scores between the two groups. This study points to memory disorders in patients with bvFTD, with performance at an intermediate level between controls and patients with AD. This indicates that, instead of being an exclusion criterion for bvFTD diagnosis, memory deficits should be regarded as a potential integral part of the clinical spectrum. (JINS, 2018, 24, 1-13).
Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Canada, M J
The networking approach to providing needed services to pregnant and parenting teenagers has numerous merits. An historical overview of the formation of the Brooklyn Teen Pregnancy Network highlights service agency need for information and resource sharing, and improved client referral systems as key factors in the genesis of the Network. The borough-wide approach and its spread as an agency model throughout New York City's other boroughs and several other northeastern cities is also attributed to its positive client impact, including: improved family communication and cooperation; early prenatal care with its concomitant improved pregnancy outcomes; financial support for teens; continued teen education; and parenting skills development. Resource information is provided regarding networks operating in the Greater New York metropolitan area. A planned Eastern Regional network initiative is under development.
Unique among computer networking texts, the Seventh Edition of the popular Computer Networking: A Top Down Approach builds on the author’s long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating readers by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for readers interested in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The Seventh Edition has been updated to reflect the most important and exciting recent advances in networking.
Cong, Jin; Liu, Haitao
The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).
de Castro, Ana Paula A.; da Silva, José D. S.
This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.
Bühlmayer, Lucia; Birrer, Daniel; Röthlin, Philipp; Faude, Oliver; Donath, Lars
Mindfulness as a present-oriented form of mental training affects cognitive processes and is increasingly considered meaningful for sport psychological training approaches. However, few intervention studies have examined the effects of mindfulness practice on physiological and psychological performance surrogates or on performance outcomes in sports. The aim of the present meta-analytical review was to examine the effects of mindfulness practice or mindfulness-based interventions on physiological and psychological performance surrogates and on performance outcomes in sports in athletes over 15 years of age. A structured literature search was conducted in six electronic databases (CINAHL, EMBASE, ISI Web of Knowledge, PsycINFO, MEDLINE and SPORTDiscus). The following search terms were used with Boolean conjunction: (mindful* OR meditat* OR yoga) AND (sport* OR train* OR exercis* OR intervent* OR perform* OR capacity OR skill*) AND (health* OR adult* OR athlete*). Randomized and non-randomized controlled studies that compared mindfulness practice techniques as an intervention with an inactive control or a control that followed another psychological training program in healthy sportive participants were screened for eligibility. Eligibility and study quality [Physiotherapy Evidence Database (PEDro)] scales were independently assessed by two researchers. A third independent researcher was consulted to achieve final consensus in case of disagreement between both researchers. Standardized mean differences (SMDs) were calculated as weighted Hedges' g and served as the main outcomes in comparing mindfulness practice versus control. Statistical analyses were conducted using a random-effects inverse-variance model. Nine trials of fair study quality (mean PEDro score 5.4, standard deviation 1.1) with 290 healthy sportive participants (athletics, cyclists, dart throwers, hammer throwers, hockey players, hurdlers, judo fighters, rugby players, middle-distance runners, long
Steel, Piers; Taras, Vasyl; Uggerslev, Krista; Bosco, Frank
Do cultural values enhance financial and subjective well-being (SWB)? Taking a multidisciplinary approach, we meta-analytically reviewed the field, found it thinly covered, and focused on individualism. In counter, we collected a broad array of individual-level data, specifically an Internet sample of 8,438 adult respondents. Individual SWB was most strongly associated with cultural values that foster relationships and social capital, which typically accounted for more unique variance in life satisfaction than an individual’s salary. At a national level, we used mean-based meta-analysis to construct a comprehensive cultural and SWB database. Results show some reversals from the individual level, particularly masculinity’s facet of achievement orientation. In all, the happy nation has low power distance and low uncertainty avoidance, but is high in femininity and individualism, and these effects are interrelated but still partially independent from political and economic institutions. In short, culture matters for individual and national well-being. PMID:28770649
Calkins, Monica E; Iacono, William G; Ones, Deniz S
Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit, saccade and fixation deficits in first-degree biological relatives, we conducted a set of meta-analytic investigations. Among 18 measures of EMD, memory-guided saccade accuracy and error rate, global smooth pursuit dysfunction, intrusive saccades during fixation, antisaccade error rate and smooth pursuit closed-loop gain emerged as best differentiating relatives from controls (standardized mean differences ranged from .46 to .66), with no significant differences among these measures. Anticipatory saccades, but no other smooth pursuit component measures were also increased in relatives. Visually-guided reflexive saccades were largely normal. Moderator analyses examining design characteristics revealed few variables affecting the magnitude of the meta-analytically observed effects. Moderate effect sizes of relatives v. controls in selective aspects of EMD supports their endophenotype potential. Future work should focus on facilitating endophenotype utility through attention to heterogeneity of EMD performance, relationships among forms of EMD, and application in molecular genetics studies.
Blumenthal, J A
Courts and legislatures have begun to develop the "reasonable woman standard" (RWS) as a criterion for deciding sexual harassment trials. This standard rests on assumptions of a "wide divergence" between the perceptions of men and women when viewing social-sexual behavior that may be considered harassing. Narrative reviews of the literature on such perceptions have suggested that these assumptions are only minimally supported. To test these assumptions quantitatively, a meta-analytic review was conducted that assessed the size, stability, and moderators of gender differences in perceptions of sexual harassment. The effect of the actor's status relative to the target also was evaluated meta-analytically, as one alternative to the importance of gender effects. Results supported the claims of narrative reviews for a relatively small gender effect, and draw attention to the status effect. In discussing legal implications of the present findings, earlier claims are echoed suggesting caution in establishing the reasonable woman standard, and one alternative to the RWS, the "reasonable victim standard," is discussed.
In this paper, a network based formulation of a permutation flow shop problem is presented. Two nuances of flow shop problems with different levels of complexity are solved using different approaches to the linear programming formulation. Key flow shop parameters inclosing makespan of the flow shop problems were ...
Newman, F.D.; Raff, U.; Stroud, D.
An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined
Sattari, Pegah; Markopoulou, Athina; Fragouli, Christina
network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities and we show that it improves several aspects of tomography, including the identifiability of links, the tradeoff between estimation accuracy and bandwidth efficiency......, and the complexity of probe path selection. We discuss the cases of inferring the loss rates of links in a tree topology or in a general topology. In the latter case, the benefits of our approach are even more pronounced compared to standard techniques but we also face novel challenges, such as dealing with cycles...
Mikulecky, D. C.; Huf, E. G.; Thomas, S. R.
We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc. PMID:262387
Crawford, Eean R; Lepine, Jeffery A; Rich, Bruce Louis
We refine and extend the job demands-resources model with theory regarding appraisal of stressors to account for inconsistencies in relationships between demands and engagement, and we test the revised theory using meta-analytic structural modeling. Results indicate support for the refined and updated theory. First, demands and burnout were positively associated, whereas resources and burnout were negatively associated. Second, whereas relationships among resources and engagement were consistently positive, relationships among demands and engagement were highly dependent on the nature of the demand. Demands that employees tend to appraise as hindrances were negatively associated with engagement, and demands that employees tend to appraise as challenges were positively associated with engagement. Implications for future research are discussed. Copyright 2010 APA, all rights reserved
Safari, Reza; Van der Linden, Marietta L; Mercer, Tom H
Although exercise training has been advocated as a nonpharmacological treatment for multiple sclerosis (MS) related fatigue, no consensus exists regarding its effectiveness. To address this, we collated meta-analytic reviews that explored the effectiveness of exercise training for the treatment of MS-related fatigue. We searched five online databases for relevant reviews, published since 2005, and identified 172 records. Five reviews were retained for systematic extraction of information and evidence quality analysis. Although our review synthesis indicated that exercise training interventions have a moderate effect on fatigue reduction in people with MS, no clear insight was obtained regarding the relative effectiveness of specific types or modes of exercise intervention. Moreover, Grading of Recommendation Assessment, Development and Evaluation revealed that the overall quality of evidence emanating from these five reviews was 'very low'.
Menting, Ankie T A; Orobio de Castro, Bram; Matthys, Walter
The present meta-analytic review examined effectiveness of the Incredible Years parent training (IYPT) regarding disruptive and prosocial child behavior, and aimed to explain variability in intervention outcomes. Fifty studies, in which an intervention group receiving the IYPT was compared to a comparison group immediately after intervention, were included in the analyses. Results showed that the IYPT is an effective intervention. Positive effects for distinct outcomes and distinct informants were found, including a mean effect size of d=.27 concerning disruptive child behavior across informants. For parental report, treatment studies were associated with larger effects (d=.50) than indicated (d=.20) and selective (d=.13) prevention studies. Furthermore, initial severity of child behavior revealed to be the strongest predictor of intervention effects, with larger effects for studies including more severe cases. Findings indicate that the IYPT is successful in improving child behavior in a diverse range of families, and that the parent program may be considered well-established. © 2013.
Jasinski, Lindsey J; Berry, David T R; Shandera, Anni L; Clark, Jessica A
Twenty-four studies utilizing the Wechsler Adult Intelligence Scale (WAIS) Digit Span subtest--either the Reliable Digit Span (RDS) or Age-Corrected Scaled Score (DS-ACSS) variant--for malingering detection were meta-analytically reviewed to evaluate their effectiveness in detecting malingered neurocognitive dysfunction. RDS and DS-ACSS effectively discriminated between honest responders and dissimulators, with average weighted effect sizes of 1.34 and 1.08, respectively. No significant differences were found between RDS and DS-ACSS. Similarly, no differences were found between the Digit Span subtest from the WAIS or Wechsler Memory Scale (WMS). Strong specificity and moderate sensitivity were observed, and optimal cutting scores are recommended.
Fontoura Costa, Luciano da
An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges
Shimokawa, Kenichi; Lambert, Michael J.; Smart, David W.
Objective: Outcome research has documented worsening among a minority of the patient population (5% to 10%). In this study, we conducted a meta-analytic and mega-analytic review of a psychotherapy quality assurance system intended to enhance outcomes in patients at risk of treatment failure. Method: Original data from six major studies conducted…
Gaastra, Geraldina F; Groen, Yvonne; Tucha, Lara; Tucha, Oliver
Children with attention-deficit/hyperactivity disorder (ADHD) often exhibit problem behavior in class, which teachers often struggle to manage due to a lack of knowledge and skills to use classroom management strategies. The aim of this meta-analytic review was to determine the effectiveness of
Kangas, Maria; Bovbjerg, Dana H.; Montgomery, Guy H.
Reports an error in "Cancer-related fatigue: A systematic and meta-analytic review of non-pharmacological therapies for cancer patients" by Maria Kangas, Dana H. Bovbjerg and Guy H. Montgomery (Psychological Bulletin, 2008[Sep], Vol 134, 700-741). The URL to the Supplemental Materials for the article is listed incorrectly in two places in the…
Hampton, Justin; Strand, Paul S.
The present study utilized meta-analytic procedures to estimate the diagnostic validity of instruments used to screen young children, ages 1.5-5 years, for autism. Five scales met inclusion criteria, and data from 18 studies contributed the meta-analysis. Results revealed that 4 of 5 scales met criteria for "good" validity, including two…
Auconi, P; Caldarelli, G; Scala, A; Ierardo, G; Polimeni, A
Network analysis, a recent advancement in complexity science, enables understanding of the properties of complex biological processes characterized by the interaction, adaptive regulation, and coordination of a large number of participating components. We applied network analysis to orthodontics to detect and visualize the most interconnected clinical, radiographic, and functional data pertaining to the orofacial system. The sample consisted of 104 individuals from 7 to 13 years of age in the mixed dentition phase without previous orthodontic intervention. The subjects were divided according to skeletal class; their clinical, radiographic, and functional features were represented as vertices (nodes) and links (edges) connecting them. Class II subjects exhibited few highly connected orthodontic features (hubs), while Class III patients showed a more compact network structure characterized by strong co-occurrence of normal and abnormal clinical, functional, and radiological features. Restricting our analysis to the highest correlations, we identified critical peculiarities of Class II and Class III malocclusions. The topology of the dentofacial system obtained by network analysis could allow orthodontists to visually evaluate and anticipate the co-occurrence of auxological anomalies during individual craniofacial growth and possibly localize reactive sites for a therapeutic approach to malocclusion. © 2011 John Wiley & Sons A/S.
Ardila, Alfredo; Bernal, Byron; Rosselli, Monica
Understanding the functions of different brain areas has represented a major endeavor of neurosciences. Historically, brain functions have been associated with specific cortical brain areas; however, modern neuroimaging developments suggest cognitive functions are associated to networks rather than to areas. The purpose of this paper was to analyze the connectivity of Brodmann area (BA) 37 (posterior, inferior, and temporal/fusiform gyrus) in relation to (1) language and (2) visual processing. Two meta-analyses were initially conducted (first level analysis). The first one was intended to assess the language network in which BA37 is involved. The second one was intended to assess the visual perception network. A third meta-analysis (second level analysis) was then performed to assess contrasts and convergence between the two cognitive domains (language and visual perception). The DataBase of Brainmap was used. Our results support the role of BA37 in language but by means of a distinct network from the network that supports its second most important function: visual perception. It was concluded that left BA37 is a common node of two distinct networks-visual recognition (perception) and semantic language functions.
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated solu...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well......This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...
Hiller, Rachel M; Meiser-Stedman, Richard; Fearon, Pasco; Lobo, Sarah; McKinnon, Anna; Fraser, Abigail; Halligan, Sarah L
Understanding the natural course of child and adolescent posttraumatic stress disorder (PTSD) has significant implications for the identification of, and intervention for, at-risk youth. We used a meta-analytic approach to examine longitudinal changes in youth PTSD prevalence and symptoms over the first 12 months posttrauma. We conducted a systematic review to identify longitudinal studies of PTSD in young people (5-18 years old), excluding treatment trials. The search yielded 27 peer-reviewed studies and one unpublished dataset for analysis of pooled prevalence estimates, relative prevalence reduction and standardised mean symptom change. Key moderators were also explored, including age, proportion of boys in the sample, initial prevalence of PTSD and PTSD measurement type. Analyses demonstrated moderate declines in PTSD prevalence and symptom severity over the first 3-6 months posttrauma. From 1 to 6 months posttrauma, the prevalence of PTSD reduced by approximately 50%. Symptoms also showed moderate decline, particularly across the first 3 months posttrauma. There was little evidence of further change in prevalence or symptom severity after 6 months, suggesting that it is unlikely a child would lose a PTSD diagnosis without intervention beyond this point. The current findings provide key information about the likelihood of posttrauma recovery in the absence of intervention and have important implications for our understanding of child and adolescent PTSD. Results are discussed with reference to the timing of PTSD screening and the potential role of early interventions. Findings particularly highlight the importance of future research to develop our understanding of what factors prevent the action of normal recovery from the 'acute' posttrauma period. © 2016 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.
Full Text Available Background. Understanding the functions of different brain areas has represented a major endeavor of neurosciences. Historically, brain functions have been associated with specific cortical brain areas; however, modern neuroimaging developments suggest cognitive functions are associated to networks rather than to areas. Objectives. The purpose of this paper was to analyze the connectivity of Brodmann area (BA 37 (posterior, inferior, and temporal/fusiform gyrus in relation to (1 language and (2 visual processing. Methods. Two meta-analyses were initially conducted (first level analysis. The first one was intended to assess the language network in which BA37 is involved. The second one was intended to assess the visual perception network. A third meta-analysis (second level analysis was then performed to assess contrasts and convergence between the two cognitive domains (language and visual perception. The DataBase of Brainmap was used. Results. Our results support the role of BA37 in language but by means of a distinct network from the network that supports its second most important function: visual perception. Conclusion. It was concluded that left BA37 is a common node of two distinct networks—visual recognition (perception and semantic language functions.
Full Text Available The economic world in which tourism companies act today is in a continuous changing process. The most important factor of these changes is the globalization of their environment, both in economic, social, natural and cultural aspects. The tourism companies can benefit from the opportunities brought by globalization, but also could be menaced by the new context. How could react the companies to these changes in order to create and maintain long term competitive advantage for their business? In the present paper we make a literature review of the new tourism companies´ business approach: the networks - a result and/or a reason for exploiting the opportunities or, on the contrary, for keeping their actual position on the market. It’s a qualitative approach and the research methods used are analyses, synthesis, abstraction, which are considered the most appropriate to achieve the objective of the paper.
Wagh, Sanjeev; Prasad, Ramjee
The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach.......The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor...
Ardila, Alfredo; Bernal, Byron; Rosselli, Monica
Background. Understanding the functions of different brain areas has represented a major endeavor of neurosciences. Historically, brain functions have been associated with specific cortical brain areas; however, modern neuroimaging developments suggest cognitive functions are associated to networks rather than to areas. Objectives. The purpose of this paper was to analyze the connectivity of Brodmann area (BA) 37 (posterior, inferior, and temporal/fusiform gyrus) in relation to (1) language a...
Japkowicz, Nathalie; Smith, Reuben
.... We use the autoassociator to build prototype software to cluster network alerts generated by a Snort intrusion detection system, and discuss how the results are significant, and how they can be applied to other types of network events.
Glöckner, Andreas; Heinen, Thomas; Johnson, Joseph G; Raab, Markus
This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants' initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts' decision making in sports and provide perspectives for further theoretical developments. Copyright © 2011 Elsevier B.V. All rights reserved.
Zaliapin, I; Foufoula-Georgiou, E; Ghil, M
This study is motivated by problems related to environmental transport on river networks. We establish statistical properties of a flow along a directed branching network and suggest its compact parameterization. The downstream network transport is treated as a particular case of nearest-neighbor hierarchical aggregation with respect to the metric induced by the branching structure of the river network. We describe the static geometric structure of a drainage network by a tree, referred to as...
Wang, Lamei; Mesman, Judi
In the last 30 years, China has undergone one of the largest rural-to-urban migrations in human history, with many children left behind because of parental migration. We present a meta-analytic review of empirical studies on Chinese children's rural-to-urban migration and on rural children left behind because of parental migration. We examine how these events relate to children's emotional, social, and academic developmental outcomes. We include publications in English and in Chinese to uncover and quantify a part of the research literature that has been inaccessible to most Western scholars in the field of child and family studies. Overall, both migrant children and children left behind by migrant parents in China show significantly less favorable functioning across domains than other Chinese children. It appears that, similar to processes found in other parts of the world, the experience of economic and acculturation stress as well as disrupted parent-child relations constitute a risk for nonoptimal child functioning in the Chinese context. Further, we found evidence for publication bias against studies showing less favorable development for migrant children and children left behind. We discuss the results in terms of challenges to Chinese society and to future empirical research on Chinese family life. © The Author(s) 2015.
Butts, Marcus M; Casper, Wendy J; Yang, Tae Seok
This meta-analysis examines relationships between work-family support policies, which are policies that provide support for dependent care responsibilities, and employee outcomes by developing a conceptual model detailing the psychological mechanisms through which policy availability and use relate to work attitudes. Bivariate results indicated that availability and use of work-family support policies had modest positive relationships with job satisfaction, affective commitment, and intentions to stay. Further, tests of differences in effect sizes showed that policy availability was more strongly related to job satisfaction, affective commitment, and intentions to stay than was policy use. Subsequent meta-analytic structural equation modeling results indicated that policy availability and use had modest effects on work attitudes, which were partially mediated by family-supportive organization perceptions and work-to-family conflict, respectively. Additionally, number of policies and sample characteristics (percent women, percent married-cohabiting, percent with dependents) moderated the effects of policy availability and use on outcomes. Implications of these findings and directions for future research on work-family support policies are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.
Ventura, Joseph; Wood, Rachel C; Jimenez, Amy M; Hellemann, Gerhard S
In schizophrenia patients, one of the most commonly studied deficits of social cognition is emotion processing (EP), which has documented links to facial recognition (FR). But, how are deficits in facial recognition linked to emotion processing deficits? Can neurocognitive and symptom correlates of FR and EP help differentiate the unique contribution of FR to the domain of social cognition? A meta-analysis of 102 studies (combined n=4826) in schizophrenia patients was conducted to determine the magnitude and pattern of relationships between facial recognition, emotion processing, neurocognition, and type of symptom. Meta-analytic results indicated that facial recognition and emotion processing are strongly interrelated (r=.51). In addition, the relationship between FR and EP through voice prosody (r=.58) is as strong as the relationship between FR and EP based on facial stimuli (r=.53). Further, the relationship between emotion recognition, neurocognition, and symptoms is independent of the emotion processing modality - facial stimuli and voice prosody. The association between FR and EP that occurs through voice prosody suggests that FR is a fundamental cognitive process. The observed links between FR and EP might be due to bottom-up associations between neurocognition and EP, and not simply because most emotion recognition tasks use visual facial stimuli. In addition, links with symptoms, especially negative symptoms and disorganization, suggest possible symptom mechanisms that contribute to FR and EP deficits. © 2013 Elsevier B.V. All rights reserved.
Fedewa, Alicia L; Ahn, Soyeon; Reese, Robert J; Suarez, Marietta M; Macquoid, Ahjane; Davis, Matthew C; Prout, H Thompson
The present study is a quantitative synthesis of the available literature to investigate the efficacy of psychotherapy for children's mental health outcomes. In particular, this study focuses on potential moderating variables-study design, treatment, client, and therapist characteristics-that may influence therapeutic outcomes for youth but have not been thoroughly accounted for in prior meta-analytic studies. An electronic search of relevant databases resulted in 190 unpublished and published studies that met criteria for inclusion in the analysis. Effect sizes differed by study design. Pre-post-test designs resulted in absolute magnitudes of treatment effects ranging from |-0.02| to |-0.76| while treatment versus control group comparison designs resulted in absolute magnitudes of treatment effects ranging from |-0.14| to |-2.39|. Changes in youth outcomes larger than 20% were found, irrespective of study design, for outcomes focused on psychosomatization (29% reduction), school attendance (25% increase), and stress (48% reduction). The magnitude of changes after psychotherapy ranged from 6% (externalizing problems) to 48% (stress). Several moderator variables significantly influenced psychotherapy treatment effect sizes, including frequency and length of treatment as well as treatment format. However, results did not support the superiority of a single type of intervention for most outcomes. Implications for therapy with school-aged youth and future research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.
Yang, Liu-Qin; Caughlin, David E; Gazica, Michele W; Truxillo, Donald M; Spector, Paul E
This meta-analytic study summarizes relations between workplace mistreatment climate-MC (specific to incivility, aggression, and bullying) and potential outcomes. We define MC as individual or shared perceptions of organizational policies, procedures, and practices that deter interpersonal mistreatment. We located 35 studies reporting results with individual perceptions of MC (psychological MC) that yielded 36 independent samples comprising 91,950 employees. Through our meta-analyses, we found significant mean correlations between psychological MC and employee and organizational outcomes including mistreatment reduction effort (motivation and performance), mistreatment exposure, strains, and job attitudes. Moderator analyses revealed that the psychological MC-outcome relations were generally stronger for perceived civility climate than for perceived aggression-inhibition climate, and content contamination of existing climate scales accentuated the magnitude of the relations between psychological MC and some outcomes (mistreatment exposure and employee strains). Further, the magnitudes of the psychological MC-outcome relations were generally comparable across studies using dominant (i.e., most commonly used) and other climate scales, but for some focal relations, magnitudes varied with respect to cross-sectional versus prospective designs. The 4 studies that assessed MC at the unit-level had results largely consistent with those at the employee level.
Cobley, Stephen; Baker, Joseph; Wattie, Nick; McKenna, Jim
Annual age-grouping is a common organizational strategy in sport. However, such a strategy appears to promote relative age effects (RAEs). RAEs refer both to the immediate participation and long-term attainment constraints in sport, occurring as a result of chronological age and associated physical (e.g. height) differences as well as selection practices in annual age-grouped cohorts. This article represents the first meta-analytical review of RAEs, aimed to collectively determine (i) the overall prevalence and strength of RAEs across and within sports, and (ii) identify moderator variables. A total of 38 studies, spanning 1984-2007, containing 253 independent samples across 14 sports and 16 countries were re-examined and included in a single analysis using odds ratios and random effects procedures for combining study estimates. Overall results identified consistent prevalence of RAEs, but with small effect sizes. Effect size increased linearly with relative age differences. Follow-up analyses identified age category, skill level and sport context as moderators of RAE magnitude. Sports context involving adolescent (aged 15-18 years) males, at the representative (i.e. regional and national) level in highly popular sports appear most at risk to RAE inequalities. Researchers need to understand the mechanisms by which RAEs magnify and subside, as well as confirm whether RAEs exist in female and more culturally diverse contexts. To reduce and eliminate this social inequality from influencing athletes' experiences, especially within developmental periods, direct policy, organizational and practitioner intervention is required.
Winer, E. Samuel; Salem, Taban
Cognitive theories of depression and anxiety have traditionally emphasized the role of attentional biases in the processing of negative information. The dot-probe task has been widely used to study this phenomenon. Recent findings suggest that biased processing of positive information might also be an important aspect of developing psychopathological symptoms. However, despite some evidence suggesting persons with symptoms of depression and anxiety may avoid positive information, many dot-probe studies have produced null findings. The present review used conventional and novel meta-analytic methods to evaluate dot-probe attentional biases away from positive information and, for comparison, toward negative information, in depressed and anxious individuals. Results indicated that avoidance of positive information is a real effect exhibiting substantial evidential value among persons experiencing psychopathology, with individuals evidencing primary symptoms of depression clearly demonstrating this effect. Different theoretical explanations for these findings are evaluated, including those positing threat-processing structures, even-handedness, self-regulation, and reward devaluation, with the novel theory of reward devaluation emphasized and expanded. These novel findings and theory suggest that avoidance of prospective reward helps to explain the cause and sustainability of depressed states. Suggestions for future research and methodological advances are discussed. PMID:26619211
Casillas, Katherine L; Fauchier, Angèle; Derkash, Bridget T; Garrido, Edward F
In recent years there has been an increase in the popularity of home visitation programs as a means of addressing risk factors for child maltreatment. The evidence supporting the effectiveness of these programs from several meta-analyses, however, is mixed. One potential explanation for this inconsistency explored in the current study involves the manner in which these programs were implemented. In the current study we reviewed 156 studies associated with 9 different home visitation program models targeted to caregivers of children between the ages of 0 and 5. Meta-analytic techniques were used to determine the impact of 18 implementation factors (e.g., staff selection, training, supervision, fidelity monitoring, etc.) and four study characteristics (publication type, target population, study design, comparison group) in predicting program outcomes. Results from analyses revealed that several implementation factors, including training, supervision, and fidelity monitoring, had a significant effect on program outcomes, particularly child maltreatment outcomes. Study characteristics, including the program's target population and the comparison group employed, also had a significant effect on program outcomes. Implications of the study's results for those interested in implementing home visitation programs are discussed. A careful consideration and monitoring of program implementation is advised as a means of achieving optimal study results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Scalco, Andrea; Noventa, Stefano; Sartori, Riccardo; Ceschi, Andrea
During the last decade, the purchase of organic food within a sustainable consumption context has gained momentum. Consequently, the amount of research in the field has increased, leading in some cases to discrepancies regarding both methods and results. The present review examines those works that applied the theory of planned behavior (TPB; Ajzen, 1991) as a theoretical framework in order to understand and predict consumers' motivation to buy organic food. A meta-analysis has been conducted to assess the strength of the relationships between attitude, subjective norms, perceived behavioral control, and intention, as well as between intention and behavior. Results confirm the major role played by individual attitude in shaping buying intention, followed by subjective norms and perceived behavioral control. Intention-behavior shows a large effect size, few studies however explicitly reported such an association. Furthermore, starting from a pooled correlation matrix, a meta-analytic structural equation model has been applied to jointly evaluate the strength of the relationships among the factors of the original model. Results suggest the robustness of the TPB model. In addition, mediation analysis indicates a potential direct effect from subjective norms to individual attitude in the present context. Finally, some issues regarding methodological aspects of the application of the TPB within the context of organic food are discussed for further research developments. Copyright © 2017 Elsevier Ltd. All rights reserved.
Patros, Connor H G; Alderson, R Matt; Kasper, Lisa J; Tarle, Stephanie J; Lea, Sarah E; Hudec, Kristen L
Impulsive behavior is a core DSM-5 diagnostic feature of attention-deficit/hyperactivity disorder (ADHD) that is associated with several pejorative outcomes. Impulsivity is multidimensional, consisting of two sub-constructs: rapid-response impulsivity and reward-delay impulsivity (i.e., choice-impulsivity). While previous research has extensively examined the presence and implications of rapid-response impulsivity in children with ADHD, reviews of choice-impulsive behavior have been both sparse and relatively circumscribed. This review used meta-analytic methods to comprehensively examine between-group differences in choice-impulsivity among children and adolescents with and without ADHD. Twenty-eight tasks (from 26 studies), consisting of 4320 total children (ADHD=2360, TD=1,960), provided sufficient information to compute an overall between-group effect size for choice-impulsivity performance. Results revealed a medium-magnitude between-group effect size (g=.47), suggesting that children and adolescents with ADHD exhibited moderately increased impulsive decision-making compared to TD children and adolescents. Further, relative to the TD group, children and adolescents with ADHD exhibited similar patterns of impulsive decision-making across delay discounting and delay of gratification tasks. However, the use of single-informant diagnostic procedures relative to multiple informants yielded larger between-group effects, and a similar pattern was observed across samples that excluded females relative to samples that included females. Copyright © 2015 Elsevier Ltd. All rights reserved.
Sagi-Schwartz, Abraham; van IJzendoorn, Marinus H; Bakermans-Kranenburg, Marian J
In a series of meta-analyses with the second generation of Holocaust survivors, no evidence for secondary traumatization was found (Van IJzendoorn, Bakermans-Kranenburg, & Sagi-Schwartz, 2003). With regard to third generation traumatization, various reports suggest the presence of intergenerational transmission of trauma. Some scholars argue that intergenerational transmission of trauma might skip a generation. Therefore, we focus in this study on the transmission of trauma to the third generation offspring (the grandchildren) of the first generation's traumatic Holocaust experiences (referred to as "tertiary traumatization"), and we present a narrative review of the pertinent studies. Meta-analytic results of 13 non-clinical samples involving 1012 participants showed no evidence for tertiary traumatization in Holocaust survivor families. Our previous meta-analytic study on secondary traumatization and the present one on third generation's psychological consequences of the Holocaust indicate a remarkable resilience of profoundly traumatized survivors in their (grand-)parental roles.
Hagger, Martin; Chan, Dervin K. C.; Protogerou, Cleo; Chatzisarantis, Nikos L. D.
Objective Synthesizing research on social cognitive theories applied to health behavior is an important step in the development of an evidence base of psychological factors as targets for effective behavioral interventions. However, few meta-analyses of research on social cognitive theories in health contexts have conducted simultaneous tests of theoretically-stipulated pattern effects using path analysis. We argue that conducting path analyses of meta-analytic effects among constructs fr...
Goh, Swee C.; Elliott, Catherine; Quon, Tony K.
Purpose: The purpose of this paper is to present a meta-analysis of a subset of published empirical research papers that measure learning capability and link it to organizational performance. It also seeks to examine both financial and non-financial performance. Design/methodology/approach: In a search of published research on learning capability…
Engelmann, Katharina; Neuhaus, Birgit J.; Fischer, Frank
Scientific reasoning skills are not just for researchers, they are also increasingly relevant for making informed decisions in our everyday lives. How can these skills be facilitated? The current state of research on supporting scientific reasoning includes intervention studies but lacks an integrated analysis of the approaches to foster…
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation
Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand
Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.
Effectiveness of cognitive behavioral therapy in the treatment of fibromyalgia syndrome: a meta-analytic literature review Effectiveness of cognitive behavioral therapy in the treatment of fibromyalgia syndrome: a meta-analytic literature review
Full Text Available Fibromyalgia (FM is a chronic disorder caused by a dysfunction of central nervous system sensitization. This syndrome is characterized by widespread pain and diffuse tenderness, but often also presents fatigue, sleep disturbances, and a whole range of symptoms such as morning stiffness, decreased physical function and dyscognition. FM is usually treated with pharmacological and non-pharmacological treatments. The non-pharmacological interventions include cognitive behavioral therapy (CBT, physiotherapy, acupuncture and patient education programs. In order to evaluate the efficacy of CBT and compare it with other non-pharmacological treatments, we performed a review of the meta-analytic literature. We evaluated the methodological quality of publications found by following the recommendations of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Data showed that CBT does not provide better results than other non-pharmacological treatments on outcomes of pain, fatigue, sleep disturbance and quality of life, at either a short or long-term evaluation. On the contrary, CBT seems to be more effective on symptoms of depression for a short period, whereas it considerably improves the pain self-management and reduces the number of visits to the doctor. The data currently available indicate that cost-effectiveness studies could help us to understand whether the reduction in the number of visits to the doctor could balance the cost of CBT to the health public system.Fibromyalgia (FM is a chronic disorder caused by a dysfunction of central nervous system sensitization. This syndrome is characterized by widespread pain and diffuse tenderness, but often also presents fatigue, sleep disturbances, and a whole range of symptoms such as morning stiffness, decreased physical function and dyscognition. FM is usually treated with pharmacological and non-pharmacological treatments. The nonpharmacological interventions include
Boeckle, Markus; Schrimpf, Marlene; Liegl, Gregor; Pieh, Christoph
Somatoform disorders (SD) are common medical disorders with prevalence rates between 3.5% and 18.4%, depending on country and medical setting. SD as outlined in the ICD-10 exhibits various biological, social, and psychological pathogenic factors. Little is known about the neural correlates of SD. The aims of this meta-analysis are to identify neuronal areas that are involved in SD and consistently differ between patients and healthy controls. We conducted a systematic literature research on neuroimaging studies of SD. Ten out of 686 studies fulfilled the inclusion criteria and were analyzed using activation likelihood estimation. Five neuronal areas differ between patients with SD and healthy controls namely the premotor and supplementary motor cortexes, the middle frontal gyrus, the anterior cingulate cortex, the insula, and the posterior cingulate cortex. These areas seem to have a particular importance for the occurrence of SD. Out of the ten studies two did not contribute to any of the clusters. Our results seem to largely overlap with the circuit network model of somatosensory amplification for SD. It is conceivable that functional disorders, independent of the clinical impression, show similar neurobiological processes. While overlaps do occur it is necessary to understand single functional somatic syndromes and their aetiology for future research, terminology, and treatment guidelines.
Full Text Available Somatoform disorders (SD are common medical disorders with prevalence rates between 3.5% and 18.4%, depending on country and medical setting. SD as outlined in the ICD-10 exhibits various biological, social, and psychological pathogenic factors. Little is known about the neural correlates of SD. The aims of this meta-analysis are to identify neuronal areas that are involved in SD and consistently differ between patients and healthy controls. We conducted a systematic literature research on neuroimaging studies of SD. Ten out of 686 studies fulfilled the inclusion criteria and were analyzed using activation likelihood estimation. Five neuronal areas differ between patients with SD and healthy controls namely the premotor and supplementary motor cortexes, the middle frontal gyrus, the anterior cingulate cortex, the insula, and the posterior cingulate cortex. These areas seem to have a particular importance for the occurrence of SD. Out of the ten studies two did not contribute to any of the clusters. Our results seem to largely overlap with the circuit network model of somatosensory amplification for SD. It is conceivable that functional disorders, independent of the clinical impression, show similar neurobiological processes. While overlaps do occur it is necessary to understand single functional somatic syndromes and their aetiology for future research, terminology, and treatment guidelines.
van der Schaft, Abraham; Rao, S.; Jayawardhana, B.
A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a
Peterson, Larry L
Computer Networks, 4E is the only introductory computer networking book written by authors who have had first-hand experience with many of the protocols discussed in the book, who have actually designed some of them as well, and who are still actively designing the computer networks today. This newly revised edition continues to provide an enduring, practical understanding of networks and their building blocks through rich, example-based instruction. The authors' focus is on the why of network design, not just the specifications comprising today's systems but how key technologies and p
Yoneoka, Daisuke; Henmi, Masayuki
Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.
Hayhurst, Kelly J.; Shier, Douglas R.
The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.
Colquitt, Jason A; Scott, Brent A; Rodell, Jessica B; Long, David M; Zapata, Cindy P; Conlon, Donald E; Wesson, Michael J
Although a flurry of meta-analyses summarized the justice literature at the turn of the millennium, interest in the topic has surged in the decade since. In particular, the past decade has witnessed the rise of social exchange theory as the dominant lens for examining reactions to justice, and the emergence of affect as a complementary lens for understanding such reactions. The purpose of this meta-analytic review was to test direct, mediating, and moderating hypotheses that were inspired by those 2 perspectives, to gauge their adequacy as theoretical guides for justice research. Drawing on a review of 493 independent samples, our findings revealed a number of insights that were not included in prior meta-analyses. With respect to social exchange theory, our results revealed that the significant relationships between justice and both task performance and citizenship behavior were mediated by indicators of social exchange quality (trust, organizational commitment, perceived organizational support, and leader-member exchange), though such mediation was not apparent for counterproductive behavior. The strength of those relationships did not vary according to whether the focus of the justice matched the target of the performance behavior, contrary to popular assumptions in the literature, or according to whether justice was referenced to a specific event or a more general entity. With respect to affect, our results showed that justice-performance relationships were mediated by positive and negative affect, with the relevant affect dimension varying across justice and performance variables. Our discussion of these findings focuses on the merit in integrating the social exchange and affect lenses in future research.
Joseph, Dana L; Jin, Jing; Newman, Daniel A; O'Boyle, Ernest H
Recent empirical reviews have claimed a surprisingly strong relationship between job performance and self-reported emotional intelligence (also commonly called trait EI or mixed EI), suggesting self-reported/mixed EI is one of the best known predictors of job performance (e.g., ρ = .47; Joseph & Newman, 2010b). Results further suggest mixed EI can robustly predict job performance beyond cognitive ability and Big Five personality traits (Joseph & Newman, 2010b; O'Boyle, Humphrey, Pollack, Hawver, & Story, 2011). These criterion-related validity results are problematic, given the paucity of evidence and the questionable construct validity of mixed EI measures themselves. In the current research, we update and reevaluate existing evidence for mixed EI, in light of prior work regarding the content of mixed EI measures. Results of the current meta-analysis demonstrate that (a) the content of mixed EI measures strongly overlaps with a set of well-known psychological constructs (i.e., ability EI, self-efficacy, and self-rated performance, in addition to Conscientiousness, Emotional Stability, Extraversion, and general mental ability; multiple R = .79), (b) an updated estimate of the meta-analytic correlation between mixed EI and supervisor-rated job performance is ρ = .29, and (c) the mixed EI-job performance relationship becomes nil (β = -.02) after controlling for the set of covariates listed above. Findings help to establish the construct validity of mixed EI measures and further support an intuitive theoretical explanation for the uncommonly high association between mixed EI and job performance--mixed EI instruments assess a combination of ability EI and self-perceptions, in addition to personality and cognitive ability. PsycINFO Database Record (c) 2015 APA, all rights reserved.
Gronau, Quentin Frederik; Duizer, Monique; Bakker, Marjan; Wagenmakers, Eric-Jan
Publication bias and questionable research practices have long been known to corrupt the published record. One method to assess the extent of this corruption is to examine the meta-analytic collection of significant p values, the so-called p -curve (Simonsohn, Nelson, & Simmons, 2014a). Inspired by statistical research on false-discovery rates, we propose a Bayesian mixture model analysis of the p -curve. Our mixture model assumes that significant p values arise either from the null-hypothesis H ₀ (when their distribution is uniform) or from the alternative hypothesis H1 (when their distribution is accounted for by a simple parametric model). The mixture model estimates the proportion of significant results that originate from H ₀, but it also estimates the probability that each specific p value originates from H ₀. We apply our model to 2 examples. The first concerns the set of 587 significant p values for all t tests published in the 2007 volumes of Psychonomic Bulletin & Review and the Journal of Experimental Psychology: Learning, Memory, and Cognition; the mixture model reveals that p values higher than about .005 are more likely to stem from H ₀ than from H ₁. The second example concerns 159 significant p values from studies on social priming and 130 from yoked control studies. The results from the yoked controls confirm the findings from the first example, whereas the results from the social priming studies are difficult to interpret because they are sensitive to the prior specification. To maximize accessibility, we provide a web application that allows researchers to apply the mixture model to any set of significant p values. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Sanne P. A. Rasing
Full Text Available Depression and anxiety disorders are among the most common mental disorders during adolescence. During this life phase, the incidence of these clinical disorders rises dramatically, and even more adolescents suffer from symptoms of depression or anxiety that are just below the clinical threshold. Both clinical and subclinical levels of depression or anxiety symptoms are related to decreased functioning in various areas, such as social and academic functioning. Prevention of depression and anxiety in adolescents is therefore imperative. We conducted a meta-analytic review of the effects of school-based and community-based prevention programs that are based on cognitive behavioral therapy with the primary goal preventing depression, anxiety, or both in high risk adolescents. Articles were obtained by searching databases and hand searching reference lists of relevant articles and reviews. The selection process yielded 32 articles in the meta-analyses. One article reported on two studies and three articles reported on both depression and anxiety. This resulted in a total of 36 studies, 23 on depression and 13 on anxiety. For depression prevention aimed at high risk adolescents, meta-analysis showed a small effect of prevention programs directly after the intervention, but no effect at 3–6 months and at 12 months follow-up. For anxiety prevention aimed at high risk adolescents, no short-term effect was found, nor at 12 months follow-up. Three to six months after the preventive intervention, symptoms of anxiety were significantly decreased. Although effects on depression and anxiety symptoms were small and temporary, current findings cautiously suggest that depression and anxiety prevention programs based on CBT might have small effects on mental health of adolescents. However, it also indicates that there is still much to be gained for prevention programs. Current findings and possibilities for future research are discussed in order to further
Porta, Sergio; Crucitti, Paolo; Latora, Vito
The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the authors addresses a study of six cases of urban street networks characterised by diff...
The use of contextually aware, pervasive, distributed computing, and sensor networks to bridge the gap between the physical and online worlds is the basis of mobile social networking. This book shows how applications can be built to provide mobile social networking, the research issues that need to be solved to enable this vision, and how mobile social networking can be used to provide computational intelligence that will improve daily life. With contributions from the fields of sociology, computer science, human-computer interaction and design, this book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.
Gilkerson, Linda; Hofherr, Jennifer; Heffron, Mary Claire; Sims, Jennifer Murphy; Jalowiec, Barbara; Bromberg, Stacey R.; Paul, Jennifer J.
Erikson Institute Fussy Baby Network[R] (FBN) developed an approach to engaging parents around their urgent concerns about their baby's crying, sleeping, or feeding in a way which builds their longer-term capacities as parents. This approach, called the FAN, is now in place in new Fussy Baby Network programs around the country and is being infused…
D. E. Dobrinskaya
Full Text Available The network is an efficient way of social structure analysis for contemporary sociologists. It gives broad opportunities for detailed and fruitful research of different patterns of ties and social relations by quantitative analytical methods and visualization of network models. The network metaphor is used as the most representative tool for description of a new type of society. This new type is characterized by flexibility, decentralization and individualization. Network organizational form became the dominant form in modern societies. The network is also used as a mode of inquiry. Actually three theoretical network approaches in the Internet research case are the most relevant: social network analysis, “network society” theory and actor-network theory. Every theoretical approach has got its own notion of network. Their special methodological and theoretical features contribute to the Internet studies in different ways. The article represents a brief overview of these network approaches. This overview demonstrates the absence of a unified semantic space of the notion of “network” category. This fact, in turn, points out the need for detailed analysis of these approaches to reveal their theoretical and empirical possibilities in application to the Internet studies.
Crossley, N A; Fox, P T; Bullmore, E T
Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.
Software Defined Networks discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. The book gives you the state of the art knowledge needed for successful deployment of an SDN, including: How to explain to the non-technical business decision makers in your organization the potential benefits, as well as the risks, in shifting parts of a network to the SDN modelHow to make intelligent decisions about when to integrate SDN technologies in a networkHow to decide if your organization should be developing its own SDN applications or
Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J
This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.
Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao
Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.
Wolf Fredric M
Full Text Available Abstract Background Meta-analysis can be used to pool rate measures across studies, but challenges arise when follow-up duration varies. Our objective was to compare different statistical approaches for pooling count data of varying follow-up times in terms of estimates of effect, precision, and clinical interpretability. Methods We examined data from a published Cochrane Review of asthma self-management education in children. We selected two rate measures with the largest number of contributing studies: school absences and emergency room (ER visits. We estimated fixed- and random-effects standardized weighted mean differences (SMD, stratified incidence rate differences (IRD, and stratified incidence rate ratios (IRR. We also fit Poisson regression models, which allowed for further adjustment for clustering by study. Results For both outcomes, all methods gave qualitatively similar estimates of effect in favor of the intervention. For school absences, SMD showed modest results in favor of the intervention (SMD -0.14, 95% CI -0.23 to -0.04. IRD implied that the intervention reduced school absences by 1.8 days per year (IRD -0.15 days/child-month, 95% CI -0.19 to -0.11, while IRR suggested a 14% reduction in absences (IRR 0.86, 95% CI 0.83 to 0.90. For ER visits, SMD showed a modest benefit in favor of the intervention (SMD -0.27, 95% CI: -0.45 to -0.09. IRD implied that the intervention reduced ER visits by 1 visit every 2 years (IRD -0.04 visits/child-month, 95% CI: -0.05 to -0.03, while IRR suggested a 34% reduction in ER visits (IRR 0.66, 95% CI 0.59 to 0.74. In Poisson models, adjustment for clustering lowered the precision of the estimates relative to stratified IRR results. For ER visits but not school absences, failure to incorporate study indicators resulted in a different estimate of effect (unadjusted IRR 0.77, 95% CI 0.59 to 0.99. Conclusions Choice of method among the ones presented had little effect on inference but affected the
Guevara, James P; Berlin, Jesse A; Wolf, Fredric M
Meta-analysis can be used to pool rate measures across studies, but challenges arise when follow-up duration varies. Our objective was to compare different statistical approaches for pooling count data of varying follow-up times in terms of estimates of effect, precision, and clinical interpretability. We examined data from a published Cochrane Review of asthma self-management education in children. We selected two rate measures with the largest number of contributing studies: school absences and emergency room (ER) visits. We estimated fixed- and random-effects standardized weighted mean differences (SMD), stratified incidence rate differences (IRD), and stratified incidence rate ratios (IRR). We also fit Poisson regression models, which allowed for further adjustment for clustering by study. For both outcomes, all methods gave qualitatively similar estimates of effect in favor of the intervention. For school absences, SMD showed modest results in favor of the intervention (SMD -0.14, 95% CI -0.23 to -0.04). IRD implied that the intervention reduced school absences by 1.8 days per year (IRD -0.15 days/child-month, 95% CI -0.19 to -0.11), while IRR suggested a 14% reduction in absences (IRR 0.86, 95% CI 0.83 to 0.90). For ER visits, SMD showed a modest benefit in favor of the intervention (SMD -0.27, 95% CI: -0.45 to -0.09). IRD implied that the intervention reduced ER visits by 1 visit every 2 years (IRD -0.04 visits/child-month, 95% CI: -0.05 to -0.03), while IRR suggested a 34% reduction in ER visits (IRR 0.66, 95% CI 0.59 to 0.74). In Poisson models, adjustment for clustering lowered the precision of the estimates relative to stratified IRR results. For ER visits but not school absences, failure to incorporate study indicators resulted in a different estimate of effect (unadjusted IRR 0.77, 95% CI 0.59 to 0.99). Choice of method among the ones presented had little effect on inference but affected the clinical interpretability of the findings. Incidence rate
Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.
van der Schaft, A. J.; Rao, S.; Jayawardhana, B.
A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.
Motter, Adilson E.; Gray, Kimberly A.
A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...
Full Text Available Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS, among many others. Recent technologies like Software-Defined Networks (SDN have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.
Fu, King-Wa; Yip, Paul S F
Evidence suggests that there is an increase in the suicide rate following incidents of celebrity suicide in different countries, but there are no data on the overall suicide risk across countries. The duration of increased suicide rates is usually assumed to be on a monthly basis, but the weekly increase remains uncertain. This study aims at estimating the risk for suicide after the suicide deaths of entertainment celebrities in Asia during the first 4 weeks after the celebrity suicides and on a weekly basis. An ecological, retrospective time-series analysis and a meta-analysis of the suicide deaths in 3 Asian regions: Hong Kong (from 2001 to 2003), Taiwan, and South Korea (both from 2003 to 2005). The combined risks for suicide were found to be 1.43 (95% CI = 1.23 to 1.66), 1.29 (95% CI = 1.12 to 1.50), and 1.25 (95% CI = 1.08 to 1.45) in the first, second, and third week, respectively, after suicides of entertainment celebrities, while adjusting for secular trends, seasonality, economic situation, and temporal autocorrelation. The same-gender and same-method specific increases suggest that as people identify more with the celebrity, their risk for suicide rises. A medium-term rise in suicides up to 24 weeks after the incidents of celebrity suicide is also evident. This study is the first to estimate risk for suicides following celebrity suicides across 3 Asian regions. The results provide important information for public health policy makers in assessing the elevated risk associated with excessive media coverage of celebrity suicide and developing timely evidence-based interventions. Copyright 2009 Physicians Postgraduate Press, Inc.
Jackson, Dan; Kirkbride, James; Croudace, Tim; Morgan, Craig; Boydell, Jane; Errazuriz, Antonia; Murray, Robin M; Jones, Peter B
A recent systematic review and meta-analysis of the incidence and prevalence of schizophrenia and other psychoses in England investigated the variation in the rates of psychotic disorders. However, some of the questions of interest, and the data collected to answer these, could not be adequately addressed using established meta-analysis techniques. We developed a novel statistical method, which makes combined use of fractional polynomials and meta-regression. This was used to quantify the evidence of gender differences and a secondary peak onset in women, where the outcome of interest is the incidence of schizophrenia. Statistically significant and epidemiologically important effects were obtained using our methods. Our analysis is based on data from four studies that provide 50 incidence rates, stratified by age and gender. We describe several variations of our method, in particular those that might be used where more data is available, and provide guidance for assessing the model fit. Copyright © 2013 John Wiley & Sons, Ltd.
Network traffic decision algorithms have been in place since the creation of the Internet. These algorithms are successful in redirecting...example, the fifth line indicates a location of 29° 42’ 48”N, 47° 31’ 06”E and a time-on target of 1200 Zulu on the 24th of January. A typical ATO is
E-H. Klijn (Erik-Hans); J.F.M. Koppenjan (Joop)
markdownabstract__Abstract__ In this article we address the elaboratlon of the central concepts of a theory of networks and of network management. We suggest that the network approach builds on several theoretical traditions After this we clarify the theoretical concepts and axioms of the policy
the approach and methods used in this analysis to organize, analyze, and explore the geospatial, statistical , and social network data...requirements for the degree of MASTER OF SCIENCE IN INFORMATION STRATEGY AND POLITICAL WARFARE from the NAVAL POSTGRADUATE SCHOOL December...research utilizes both descriptive statistics and regression analysis of social network data to explore the changes within the AQIM network 2012
Hoff, Peter D; Raftery, Adrian E; Handcock, Mark S
.... In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified...
Cotacallapa, M; Hase, M O
A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network. (paper)
Cotacallapa, M.; Hase, M. O.
A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network.
Trabelsi, Zouheir; Al Braiki, Arwa; Mathew, Sujith Samuel
The attacks on computers and business networks are growing daily, and the need for security professionals who understand how malfeasants perform attacks and compromise networks is a growing requirement to counter the threat. Network security education generally lacks appropriate textbooks with detailed, hands-on exercises that include both offensive and defensive techniques. Using step-by-step processes to build and generate attacks using offensive techniques, Network Attacks and Defenses: A Hands-on Approach enables students to implement appropriate network security solutions within a laborat
Glassmeier, Franziska; Feingold, Graham
Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth’s climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis’s Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.
Zafar, M.F.; Naheed, F.; Ahmad, Z.; Anwar, M.M.
Security is an essential element of information technology (IT) infrastructure and applications. Concerns about security of networks and information systems have been growing along with the rapid increase in the number of network users and the value of their transactions. The hasty security threats have driven the development of security products known as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) to detect and protect the network, server and desktop infrastructure ahead of the threat. Authentication and signing techniques are used to prevent integrity threats. Users, devices, and applications should always be authenticated and authorized before they are allowed to access networking resources. Though a lot of information is available on the internet about IDS and IPS but it all is spread on so many sites and one has to spend a considerable part of his precious time to search it. In this regard a thorough survey has been conducted to facilitate and assist the researchers. The issues and defend challenges in fighting with cyber attacks have been discussed. A comparison of the categories of network security technologies has been presented. In this paper an effort has been made to gather the scattered information and present it at one place. This survey will provide best available up-to-date advancement in the area. A brief description of open source IPS has also been presented. (author)
Bada Algom, O; Fabry, C; Leroy, P L; Hornick, J-L
Kouri (Bos taurus) is a breed aboriginal from Lake Chad and threatened with extinction. This study aimed to compare milk fatty acid profiles measured on Kouri cows and on high-yielding dairy cattle in Europe and elsewhere as reported by meta-analytical data (22 experimentations). Milk samples were collected from 14 Kouri dairy cows in dry season (March to June) and fatty acids (FA) were determined by gas chromatography. Overall, 32 FA have been identified. Kouri showed lower values (P pastures by Kouri cows.
María Jesús Barroso-Méndez
Full Text Available Partnerships between businesses and non-governmental organizations (NGOs have become widely adopted mechanisms for collaboration in addressing complex social issues, the aim being to take advantage of the two types of organizational rationale to generate mutual value. Many such alliances have proved to be unsuccessful, however. To assist managers improve the likelihood of success of their collaborative relationships, the authors propose a success model of business-NGO partnering processes based on Relationship Marketing Theory. They also analyse the theoretical bases of the model's hypotheses through a meta-analytical study of the existing literature.
Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.
This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.
Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT
Rocco S, Claudio M.; Ramirez-Marquez, Jose Emmanuel
This paper introduces an evolutionary optimization approach that can be readily applied to solve deterministic network interdiction problems. The network interdiction problem solved considers the minimization of the maximum flow that can be transmitted between a source node and a sink node for a fixed network design when there is a limited amount of resources available to interdict network links. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link. For this problem, the solution approach developed is based on three steps that use: (1) Monte Carlo simulation, to generate potential network interdiction strategies, (2) Ford-Fulkerson algorithm for maximum s-t flow, to analyze strategies' maximum source-sink flow and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate the approach. In terms of computational effort, the results illustrate that solutions are obtained from a significantly restricted solution search space. Finally, the authors discuss the need for a reliability perspective to network interdiction, so that solutions developed address more realistic scenarios of such problem
van Dijk, Johannes A.G.M.
Social and media networks, the Internet in particular, increasingly link interpersonal, organizational and mass communication. It is argued that this gives a cause for an interdisciplinary and multilevel approach of the network society. This will have to link traditional micro- and meso-level
Travieso, Gonzalo; Ruggiero, Carlos Antônio; Bruno, Odemir Martinez; Costa, Luciano da Fontoura
Cloud computing has become an important means to speed up computing. One problem influencing heavily the performance of such systems is the choice of nodes as servers responsible for executing the clients’ tasks. In this article we report how complex networks can be used to model such a problem. More specifically, we investigate the performance of the processing respectively to cloud systems underlaid by Erdős–Rényi (ER) and Barabási-Albert (BA) topology containing two servers. Cloud networks involving two communities not necessarily of the same size are also considered in our analysis. The performance of each configuration is quantified in terms of the cost of communication between the client and the nearest server, and the balance of the distribution of tasks between the two servers. Regarding the latter, the ER topology provides better performance than the BA for smaller average degrees and opposite behaviour for larger average degrees. With respect to cost, smaller values are found in the BA topology irrespective of the average degree. In addition, we also verified that it is easier to find good servers in ER than in BA networks. Surprisingly, balance and cost are not too much affected by the presence of communities. However, for a well-defined community network, we found that it is important to assign each server to a different community so as to achieve better performance. (paper: interdisciplinary statistical mechanics )
F. I. Karpelevich
Full Text Available R.L. Dobrushin (1929-1995 made substantial contributions to Queueing Network Theory (QNT. A review of results from QNT which arose from his ideas or were connected to him in other ways is given. We also comment on various related open problems.
2003). More and more scale-free networks have been discovered. How can such diverse systems have the same architecture and properties? Part of the...Rabei Ousmane Sayed Ahmed (a.k.a. the Egyptian ) convinced the group to pursuit jihad at home, where they had the material resources to act (Atran, 2010
Dekker, Kim; Blanken, Tessa F; Van Someren, Eus J W
Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N= 2089)
Dekker, Kim; Blanken, Tessa F; Van Someren, Eus J W
Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N =
Gilles, R.P.; Lazarova, E.A.; Ruys, P.H.M.
We consider a network economy in which economic agents are connected within a structure of value-generating relationships. Agents are assumed to be able to participate in three types of economic activities: autarkic self-provision; binary matching interactions; and multi-person cooperative
Zerenner, Tanja, E-mail: firstname.lastname@example.org [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.
Zerenner, Tanja; Friederichs, Petra; Hense, Andreas; Lehnertz, Klaus
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately
Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)
In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.
Anusic, Ivana; Schimmack, Ulrich
The stability of individual differences is a fundamental issue in personality psychology. Although accumulating evidence suggests that many psychological attributes are both stable and change over time, existing research rarely takes advantage of theoretical models that capture both stability and change. In this article, we present the Meta-Analytic Stability and Change model (MASC), a novel meta-analytic model for synthesizing data from longitudinal studies. MASC is based on trait-state models that can separate influences of stable and changing factors from unreliable variance (Kenny & Zautra, 1995). We used MASC to evaluate the extent to which personality traits, life satisfaction, affect, and self-esteem are influenced by these different factors. The results showed that the majority of reliable variance in personality traits is attributable to stable influences (83%). Changing factors had a greater influence on reliable variance in life satisfaction, self-esteem, and affect than in personality (42%-56% vs. 17%). In addition, changing influences on well-being were more stable than changing influences on personality traits, suggesting that different changing factors contribute to personality and well-being. Measures of affect were less reliable than measures of the other 3 constructs, reflecting influences of transient factors, such as mood on affective judgments. After accounting for differences in reliability, stability of affect did not differ from other well-being variables. Consistent with previous research, we found that stability of individual differences increases with age. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Bigler, Erin D; Farrer, Thomas J; Pertab, Jon L; James, Kelly; Petrie, Jo Ann; Hedges, Dawson W
In 2009 Pertab, James, and Bigler published a critique of two prior meta-analyses by Binder, Rohling, and Larrabee (1997) and Frencham, Fox, and Maybery (2005) that showed small effect size difference at least 3 months post-injury in individuals who had sustained a mild traumatic brain injury (mTBI). The Binder et al. and Frencham et al. meta-analyses have been widely cited as showing no lasting effect of mTBI. In their critique Pertab et al. (2009) point out many limitations of these two prior meta-analyses, demonstrating that depending on how inclusion/exclusion criteria were defined different meta-analytic findings occur, some supporting the persistence of neuropsychological impairments beyond 3 months. Rohling et al. (2011) have now critiqued Pertab et al. (2009). Herein we respond to the Rolling et al. (2011) critique reaffirming the original findings of Pertab et al. (2009), providing additional details concerning the flaws in prior meta-analytic mTBI studies and the effects on neuropsychological performance.
Haynie, Dana L; Doogan, Nathan J; Soller, Brian
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth ( N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties.
Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241
Corblin, Fabien; Fanchon, Eric; Trilling, Laurent
A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language. In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria. The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.
Shultz, Thomas R.
This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…
Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas
, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...
Brambini-Pedersen, Jan Vang; Brambini, Annalisa
Hospitals across the globe are prone to numerous wicked problems. Wicked problems are difficult to solve and continue to negatively influence hospital systems. The proponents of the networked governance approach suggest that a new governance mode embracing a collaborative innovation approach to s...
D. E. Dobrinskaya
Full Text Available Internet studies are carried out by various scientific disciplines and in different research perspectives. Sociological studies of the Internet deal with a new technology, a revolutionary means of mass communication and a social space. There is a set of research difficulties associated with the Internet. Firstly, the high speed and wide spread of Internet technologies’ development. Secondly, the collection and filtration of materials concerning with Internet studies. Lastly, the development of new conceptual categories, which are able to reflect the impact of the Internet development in contemporary world. In that regard the question of the “network” category use is essential. Network is the base of Internet functioning, on the one hand. On the other hand, network is the ground for almost all social interactions in modern society. So such society is called network society. Three theoretical network approaches in the Internet research case are the most relevant: network society theory, social network analysis and actor-network theory. Each of these theoretical approaches contributes to the study of the Internet. They shape various images of interactions between human beings in their entity and dynamics. All these approaches also provide information about the nature of these interactions.
SUN Wei-Gang; CAO Jian-Ting; WANG Ru-Bin
In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our Sndings might provide valuable insights on the determination of brain death.%@@ In clinical practice, brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis axe derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.
Jay Krishna Thakur
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.
attain quite substantial savings. 11. Optimal algorithms for energy harvesting in wireless networks. We use a Markov- decision-process (MDP) based...approach to obtain optimal policies for transmissions . The key advantage of our approach is that it holistically considers information and energy in a...Coding technique to minimize delays and the number of transmissions in Wireless Systems. As we approach an era of ubiquitous computing with information
Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte
This paper presents a novel intelligent observability approach for active distribution systems. Observability assessment of the measured power system network, which is a preliminary task in state estimation, is handled via an algebraic method that uses the triangular factors of singular, symmetric...... gain matrix accompanied by a minimum meter placement technique. In available literature, large numbers of pseudo measurements are used to cover the scarcity of sufficient real measurements in distribution systems; the values of these virtual meters are calculated value based on the available real...... measurements, network topology, and network parameters. However, since there are large margin of errors exist in the calculation phase, estimated states may be significantly differed from the actual values though network is classified as observable. Hence, an approach based on numerical observability analysis...
Full Text Available This paper evaluates meta-heuristic and deterministic approaches for distribution network voltage control. As part of this evaluation, a novel meta-heuristic algorithm, Cuckoo Search, is applied for distribution network voltage control and compared with a deterministic voltage control algorithm, the oriented discrete coordinate decent method (ODCDM. ODCDM has been adopted in a state-of-the-art industrial product and applied in real distribution networks. These two algorithms have been evaluated under a set of test cases, which were generated to represent the voltage control problems in current and future distribution networks. Sampled test results have been presented, and findings have been discussed regarding the adoption of different optimization algorithms for current and future distribution networks.
Grygorenko Tetyana M.
Methodical approaches to selecting strategic areas of managing the future location of franchise retail network outlets are presented. The main stages in the assessment of strategic areas of managing the future location of franchise retail network outlets have been determined and the evaluation criteria have been suggested. Since such selection requires consideration of a variety of indicators and directions of the assessment, the author proposes a scale of evaluation, which ...
Rocco S, Claudio M.; Moreno, Jose Ali
Two cellular automata (CA) models that evaluate the s-t connectedness and shortest path in a network are presented. CA based algorithms enhance the performance of classical algorithms, since they allow a more reliable and straightforward parallel implementation resulting in a dynamic network evaluation, where changes in the connectivity and/or link costs can readily be incorporated avoiding recalculation from scratch. The paper also demonstrates how these algorithms can be applied for network reliability evaluation (based on Monte-Carlo approach) and for finding s-t path with maximal reliability
The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.
Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F
The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of
Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon
The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.
Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E
A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.
Spielberg, Jeffrey M; Heller, Wendy; Miller, Gregory A
Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal-pursuit processes (e.g., motivation) has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity) vital to goal-pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.
Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia
A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.
The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.
Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S
Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness. (c) 2015 APA, all rights reserved.
Choi, Daejeong; Oh, In-Sue; Colbert, Amy E
We examined the relationships between the Five-Factor Model (FFM) of personality traits and three forms of organizational commitment (affective, normative, and continuance commitment) and their variability across individualistic and collectivistic cultures. Meta-analytic results based on 55 independent samples from 50 studies (N = 18,262) revealed that (a) all FFM traits had positive relationships with affective commitment; (b) all FFM traits had positive relationships with normative commitment; and (c) Emotional Stability, Extraversion, and Openness to Experience had negative relationships with continuance commitment. In particular, Agreeableness was found to be the trait most strongly related to both affective and normative commitment. The results also showed that Agreeableness had stronger relationships with affective and normative commitment in collectivistic cultures than in individualistic cultures. We provide theoretical and practical implications of these findings for personality, job attitudes, and employee selection and retention. (c) 2015 APA, all rights reserved).
Lent, Robert W; Sheu, Hung-Bin; Miller, Matthew J; Cusick, Megan E; Penn, Lee T; Truong, Nancy N
We tested the interest and choice portion of social-cognitive career theory (SCCT; Lent, Brown, & Hackett, 1994) in the context of science, technology, engineering, and mathematics (STEM) domains. Data from 143 studies (including 196 independent samples) conducted over a 30-year period (1983 through 2013) were subjected to meta-analytic path analyses. The interest/choice model was found to fit the data well over all samples as well as within samples composed primarily of women and men and racial/ethnic minority and majority persons. The model also accounted for large portions of the variance in interests and choice goals within each path analysis. Despite the general predictive utility of SCCT across gender and racial/ethnic groups, we did find that several parameter estimates differed by group. We present both the group similarities and differences and consider their implications for future research, intervention, and theory refinement. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Dudley, Nicole M; Orvis, Karin A; Lebiecki, Justin E; Cortina, José M
Researchers of broad and narrow traits have debated whether narrow traits are important to consider in the prediction of job performance. Because personality-performance relationship meta-analyses have focused almost exclusively on the Big Five, the predictive power of narrow traits has not been adequately examined. In this study, the authors address this question by meta-analytically examining the degree to which the narrow traits of conscientiousness predict above and beyond global conscientiousness. Results suggest that narrow traits do incrementally predict performance above and beyond global conscientiousness, yet the degree to which they contribute depends on the particular performance criterion and occupation in question. Overall, the results of this study suggest that there are benefits to considering the narrow traits of conscientiousness in the prediction of performance. (c) 2006 APA, all rights reserved.
Greitemeyer, Tobias; Mügge, Dirk O
Whether video game play affects social behavior is a topic of debate. Many argue that aggression and helping are affected by video game play, whereas this stance is disputed by others. The present research provides a meta-analytical test of the idea that depending on their content, video games do affect social outcomes. Data from 98 independent studies with 36,965 participants revealed that for both violent video games and prosocial video games, there was a significant association with social outcomes. Whereas violent video games increase aggression and aggression-related variables and decrease prosocial outcomes, prosocial video games have the opposite effects. These effects were reliable across experimental, correlational, and longitudinal studies, indicating that video game exposure causally affects social outcomes and that there are both short- and long-term effects.
Senior, Alistair M; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J
Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.
Full Text Available Due to the increasing complexity and heterogeneity of networks and services, many efforts have been made to develop intelligent techniques for management. Network intelligent management is a key technology for operating large heterogeneous data transmission networks. This paper presents a proposal for an architecture that integrates management object specifications and the knowledge of expert systems. We present a new approach named Integrated Expert Management, for learning objects based on expert management rules and describe the design and implementation of an integrated intelligent management platform based on OSI and Internet management models. The main contributions of our approach is the integration of both expert system and managed models, so we can make use of them to construct more flexible intelligent management network. The prototype SONAP (Software for Network Assistant and Performance is accuracy-aware since it can control and manage a network. We have tested our system on real data to the fault diagnostic in a telecommunication system of a power utility. The results validate the model and show a significant improvement with respect to the number of rules and the error rate in others systems.
Hagger, Martin S; Chan, Derwin K C; Protogerou, Cleo; Chatzisarantis, Nikos L D
Synthesizing research on social cognitive theories applied to health behavior is an important step in the development of an evidence base of psychological factors as targets for effective behavioral interventions. However, few meta-analyses of research on social cognitive theories in health contexts have conducted simultaneous tests of theoretically-stipulated pattern effects using path analysis. We argue that conducting path analyses of meta-analytic effects among constructs from social cognitive theories is important to test nomological validity, account for mediation effects, and evaluate unique effects of theory constructs independent of past behavior. We illustrate our points by conducting new analyses of two meta-analyses of a popular theory applied to health behaviors, the theory of planned behavior. We conducted meta-analytic path analyses of the theory in two behavioral contexts (alcohol and dietary behaviors) using data from the primary studies included in the original meta-analyses augmented to include intercorrelations among constructs and relations with past behavior missing from the original analysis. Findings supported the nomological validity of the theory and its hypotheses for both behaviors, confirmed important model processes through mediation analysis, demonstrated the attenuating effect of past behavior on theory relations, and provided estimates of the unique effects of theory constructs independent of past behavior. Our analysis illustrates the importance of conducting a simultaneous test of theory-stipulated effects in meta-analyses of social cognitive theories applied to health behavior. We recommend researchers adopt this analytic procedure when synthesizing evidence across primary tests of social cognitive theories in health. Copyright © 2016 Elsevier Inc. All rights reserved.
Smith, Robert W; van Sluijs, Bob; Fleck, Christian
Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.
Sun Wei-Gang; Cao Jian-Ting; Wang Ru-Bin
In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death. (cross-disciplinary physics and related areas of science and technology)
We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...
The science of graphs and networks has become by now a well-established tool for modelling and analyzing a variety of systems with a large number of interacting components. Starting from the physical sciences, applications have spread rapidly to the natural and social sciences, as well as to economics, and are now further extended, in this volume, to the concept of innovations, viewed broadly. In an abstract, systems-theoretical approach, innovation can be understood as a critical event which destabilizes the current state of the system, and results in a new process of self-organization leading to a new stable state. The contributions to this anthology address different aspects of the relationship between innovation and networks. The various chapters incorporate approaches in evolutionary economics, agent-based modeling, social network analysis and econophysics and explore the epistemic tension between insights into economics and society-related processes, and the insights into new forms of complex dynamics.
Bolzoni, D.; Etalle, S.; Di Pietro, R.; Mancini, L.V.
Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at
Bolzoni, D.; Etalle, Sandro
Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at
Chen, S.; Lukkien, J.J.; Bosman, R.P.; Verhoeven, R.
This paper presents a generic service interfacing approach which enables the interoperability of networked devices and the reusability of services. Services are specified through a set of interfaces which are language and deployment platform independent. External service orchestration is applied to
Rivas, T.; Taboada, J.; Ordonez, C.; Matias, J. M.
We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques - classification trees, support vector machines and Bayesian networks - were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.
Golden, B.; Wang, Q.; Sun, X.; Jia, J.
In the orienteering problem, we are given a transportation network in which a start point and an end point are specified. Other points have associated scores. Given a fixed amount of time, the goal is to determine a path from start to end through a subset of locations in order to maximize the total path score. This problem has received a considerable amount of attention in the last ten years. The TSP is a variant of the orienteering problem. This paper applies a modified, continuous Hopfield neural network to attack this NP-hard optimization problem. In it, we design an effective energy function and learning algorithm. Unlike some applications of neural networks to optimization problems, this approach is shown to perform quite well.
Kustiawan, I.; Purnama, W.
Seamless mobility and service continuity anywhere at any time are an important issue in the wireless Internet. This research proposes a scheme to make handoff decisions effectively in heterogeneous wireless networks using a fuzzy system. Our design lies in an inference engine which takes RSS (received signal strength), data rate, network latency, and user preference as strategic determinants. The logic of our engine is realized on a UE (user equipment) side in faster reaction to network dynamics while roaming across different radio access technologies. The fuzzy system handles four metrics jointly to deduce a moderate decision about when to initiate handoff. The performance of our design is evaluated by simulating move-out mobility scenarios. Simulation results show that our scheme outperforms other approaches in terms of reducing unnecessary handoff.
Grygorenko Tetyana M.
Full Text Available Methodical approaches to selecting strategic areas of managing the future location of franchise retail network outlets are presented. The main stages in the assessment of strategic areas of managing the future location of franchise retail network outlets have been determined and the evaluation criteria have been suggested. Since such selection requires consideration of a variety of indicators and directions of the assessment, the author proposes a scale of evaluation, which allows generalizing and organizing the research data and calculations of the previous stages of the analysis. The most important criteria and sequence of the selection of the potential franchisees for the franchise retail network have been identified, the technique for their evaluation has been proposed. The use of the suggested methodological approaches will allow the franchiser making sound decisions on the selection of potential target markets, minimizing expenditures of time and efforts on the selection of franchisees and hence optimizing the process of development of the franchise retail network, which will contribute to the formation of its structure.
Spielberg, Jeffrey M; Miller, Gregory A; Warren, Stacie L; Engels, Anna S; Crocker, Laura D; Banich, Marie T; Sutton, Bradley P; Heller, Wendy
Research indicates that dorsolateral prefrontal cortex (DLPFC) is important for pursuing goals, and areas of DLPFC are differentially involved in approach and avoidance motivation. Given the complexity of the processes involved in goal pursuit, DLPFC is likely part of a network that includes orbitofrontal cortex (OFC), cingulate, amygdala, and basal ganglia. This hypothesis was tested with regard to one component of goal pursuit, the maintenance of goals in the face of distraction. Examination of connectivity with motivation-related areas of DLPFC supported the network hypothesis. Differential patterns of connectivity suggest a distinct role for DLPFC areas, with one involved in selecting approach goals, one in selecting avoidance goals, and one in selecting goal pursuit strategies. Finally, differences in trait motivation moderated connectivity between DLPFC and OFC, suggesting that this connectivity is important for instantiating motivation. Copyright © 2012 Society for Psychophysiological Research.
Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.
Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.
Mikulecky, D C; Huf, E G; Thomas, S R
We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc.
Adamic, Lada A; Suresh, K; Shi Xiaolin
Information on any given topic is often scattered across the Web. Previously this scatter has been characterized through the inequality of distribution of facts (i.e. pieces of information) across webpages. Such an approach conceals how specific facts (e.g. rare facts) occur in specific types of pages (e.g. fact-rich pages). To reveal such regularities, we construct bipartite networks, consisting of two types of vertices: the facts contained in webpages and the webpages themselves. Such a representation enables the application of a series of network analysis techniques, revealing structural features such as connectivity, robustness and clustering. Not only does network analysis yield new insights into information scatter, but we also illustrate the benefit of applying new and existing analysis techniques directly to a bipartite network as opposed to its one-mode projection. We discuss the implications of each network feature to the users' ability to find comprehensive information online. Finally, we compare the bipartite graph structure of webpages and facts with the hyperlink structure between the webpages
Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir
Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research.
Manser, K.; Hillebrand, B.; Klein Woolthuis, R.J.A.; Ziggers, G.W.; Driessen, P.H.; Bloemer, J.M.M.; Klein Woolthuis, R.
Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves
Manser, Kristina; Hillebrand, Bas; Klein Woolthuis, R.J.A.; Ziggers, Gerrit Willem; Driessen, Paul H.; Bloemer, Josée
Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves
Cacace, Simone; Camilli, Fabio; De Maio, Raul; Tosin, Andrea
We consider a class of optimal control problems for measure-valued nonlinear transport equations describing traffic flow problems on networks. The objective isto minimise/maximise macroscopic quantities, such as traffic volume or average speed,controlling few agents, for example smart traffic lights and automated cars. The measuretheoretic approach allows to study in a same setting local and nonlocal drivers interactionsand to consider the control variables as additional measures interacting ...
Chiaruttini, C.; Roberto, V.
After discussing the current status of the automation in signal interpretation from seismic networks, a new approach, based on artificial-intelligence tecniques, is proposed. The knowledge of the human expert analyst is examined, with emphasis on its objects, strategies and reasoning techniques. It is argued that knowledge-based systems (or expert systems) provide the most appropriate tools for designing an automatic system, modelled on the expert behaviour
44], , where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07
network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions Stochastic Boolean networks (SBNs are proposed as an efficient approach to modelling gene regulatory networks (GRNs. The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files.
A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.
Fang, H.; Zhu, J.
In this study, we develop a new approach to calculate groundwater flowrate and hydraulic head distribution in two-dimensional discrete fracture network (DFN) where both laminar and turbulent flows co-exist in individual fractures. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures where flow is laminar, while the Forchheimer's law is used to quantify turbulent flow behaviors. Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and non-linear equations is solved iteratively to determine flowrates in all fractures and hydraulic heads at all intersections. We examine potential errors in both flowrate and hydraulic head from the approach of uniform flow assumption. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flowrate when turbulent flow may exist while applying the Forchheimer's law indiscriminately underestimate the flowrate when laminar flows exist in the network. The contrast of apertures of large and small fractures in the DFN has significant impact on the potential errors of using only the cubic law or the Forchheimer's law. Both the cubic law and Forchheimer's law simulate similar hydraulic head distributions as the main difference between these two approaches lies in predicting different flowrates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or the Forchheimer's law if network configuration remains similar. Relative density of fractures does not significantly affect the relative performance of the cubic law and Forchheimer's law.
Jeffrey Martin Spielberg
Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak
Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.
Full Text Available This work proposes a unique approach for improving voltage stability limit using a Probabilistic Neural Network (PNN classifier that gives corrective controls available in the system in the scenario of contingencies. The sensitivity of system is analyzed to identify weak buses with ENVCI evaluation approaching zero. The input to the classifier, termed as voltage stability enhancing neural network (VSENN classifier, for training are line flows and bus voltages near the notch point of the P–V curve and the output of the VSENN is a control variable. For various contingencies the control action that improves the voltage profile as well as stability index is identified and trained accordingly. The trained VSENN is finally tested for its robustness to improve load margin and ENVCI as well, apart from trained set of operating condition of the system along with contingencies. The proposed approach is verified in IEEE 39-bus test system.
Lewis, Jenny M
The last decade has witnessed a significant move towards new modes of governing that are based on coordination and collaboration. In particular, local level partnerships have been widely introduced around the world. There are few comprehensive approaches for researching the effects of these partnerships. The aim of this paper is to outline a network approach that combines structure and agency based explanations to research partnerships in health. Network research based on two Primary Care Partnerships (PCPs) in Victoria is used to demonstrate the utility of this approach. The paper examines multiple types of ties between people (structure), and the use and value of relationships to partners (agency), using interviews with the people involved in two PCPs--one in metropolitan Melbourne and one in a rural area. Network maps of ties based on work, strategic information and policy advice, show that there are many strong connections in both PCPs. Not surprisingly, PCP staff are central and highly connected. Of more interest are the ties that are dependent on these dedicated partnership staff, as they reveal which actors become weakly linked or disconnected without them. Network measures indicate that work ties are the most dispersed and strategic information ties are the most concentrated around fewer people. Divisions of general practice are weakly linked, while local government officials and Department of Human Services (DHS) regional staff appear to play important bridging roles. Finally, the relationships between partners have changed and improved, and most of those interviewed value their new or improved links with partners. Improving service coordination and health promotion planning requires engaging people and building strong relationships. Mapping ties is a useful means for assessing the strengths and weaknesses of partnerships, and network analysis indicates concentration and dispersion, the importance of particular individuals, and the points at which they
Full Text Available Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN. However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.
Correlations between variations of stock prices reveal information about relationships between companies. Different methods of analysis have been applied to such data in order to uncover the taxonomy of the market. We use Mantegna's miminum spanning tree (MST) method for daily data in a dynamic way: By introducing a moving window we study the temporal changes in the structure of the network defined by this ``asset tree.'' The MST is scale free with a significantly changing exponent of the degree distribution for crash periods, which demonstrates the restructuring of the network due to the enhancement of correlations. This approach is compared to that based on what we call ``asset graphs:'' We start from an empty graph with no edges where the vertices correspond to stocks and then, one by one, we insert edges between the vertices according to the rank of their correlation strength. We study the properties of the creatred (weighted) networks, such as topologically different growth types, number and size of clusters and clustering coefficient. Furthermore, we define new tools like subgraph intensity and coherence to describe the role of the weights. We also investigate the time shifted cross correlation functions for high frequency data and find a characteristic time delay in many cases representing that some stocks lead the price changes while others follow them. These data can be used to construct a directed network of influence.
Bostian, Moriah; Färe, Rolf; Grosskopf, Shawna; Lundgren, Tommy
This study examines the role of investment in environmental production practices for both environmental performance and energy efficiency over time. We employ a network DEA approach that links successive production technologies through intertemporal investment decisions with a period by period estimation. This allows us to estimate energy efficiency and environmental performance separately, as well as productivity change and its associated decompositions into efficiency change and technology change. Incorporating a network model also allows us to account for both short-term environmental management practices and long-term environmental investments in each of our productivity measures. We apply this framework to a panel of detailed plant-level production data for Swedish manufacturing firms covering the years 2002–2008. - Highlights: • We use a network DEA model to account for intertemporal environmental investment decisionsin measures of firm productivity. • We apply our network technology model to a panel of firms in Sweden's pulp and paperindustry for the years 2002 - 2008. • We model environmental investments and expenditures separately from other productionoriented inputs. • We find evidence of positive relationships between energy efficiency, environmental performance, and firm productivity.
Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro
Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.
Full Text Available Achieving sustainability in sports events requires effective management, political leadership, and ensuring that all stakeholders adhere to a sustainable philosophy. In order to stage a mega-event, tremendous infrastructure and construction are required with significant consumption of private and public resources. Multiple stakeholder groups are recognized as key entities responsible for an efficient trigger of a mega-event. The aim of this study is to conduct a systematic review of Korean sport literature with regard to CSR practices (ES-linked of different stakeholder groups and examine through a meta-analytic methodology their impact on the “images” of these groups. The CMA program was utilized as the main analysis tool to calculate the effect sizes from the selected empirical studies. The results indicated that CSR performance of governmental organizations had the highest effect size level on their own image (brand identity as perceived by visitors and participants. Among the stakeholder groups, effect size levels of their CSR performances were followed by those of corporate sponsors and professional teams. It was found that stakeholder groups are pressured to maintain a balance between financial performance, consumer well-being, and brand identity to bring in external investment.
Bogusch, Leah M; O'Brien, William H
Mindfulness-based interventions (MBIs) have improved psychological outcomes for multiple chronic health conditions, including diabetes. A meta-analytic review of the literature was conducted on all located studies (n = 14) investigating MBIs that targeted diabetes-related distress (DRD) and diabetes-related outcomes among people with Type 1 and Type 2 diabetes. PsychInfo, PubMed, Medline, and Web of Science were searched for MBIs that were designed to improve DRD and other secondary outcomes, including quality of life and measures of metabolic control. A meta-analysis of these outcomes uncovered small-to-moderate effect sizes for intervention studies measuring pretreatment to posttreatment changes in DRD and metabolic control among treatment group participants. However, the pretreatment to follow-up comparisons for DRD and metabolic control were small and unreliable. For control groups, all pre-treatment to post-treatment and pre-treatment to follow-up comparisons were unreliable for all outcomes. A moderate effect size for treatment-control comparisons was found for intervention studies measuring quality of life outcomes at posttreatment, but not at follow-up comparisons. All other effect sizes for treatment-control comparisons were unreliable. Limitations and implications for MBIs among individuals with diabetes are discussed.
Ferguson, Christopher John
Video game violence has become a highly politicized issue for scientists and the general public. There is continuing concern that playing violent video games may increase the risk of aggression in players. Less often discussed is the possibility that playing violent video games may promote certain positive developments, particularly related to visuospatial cognition. The objective of the current article was to conduct a meta-analytic review of studies that examine the impact of violent video games on both aggressive behavior and visuospatial cognition in order to understand the full impact of such games. A detailed literature search was used to identify peer-reviewed articles addressing violent video game effects. Effect sizes r (a common measure of effect size based on the correlational coefficient) were calculated for all included studies. Effect sizes were adjusted for observed publication bias. Results indicated that publication bias was a problem for studies of both aggressive behavior and visuospatial cognition. Once corrected for publication bias, studies of video game violence provided no support for the hypothesis that violent video game playing is associated with higher aggression. However playing violent video games remained related to higher visuospatial cognition (r (x) = 0.36). Results from the current analysis did not support the conclusion that violent video game playing leads to aggressive behavior. However, violent video game playing was associated with higher visuospatial cognition. It may be advisable to reframe the violent video game debate in reference to potential costs and benefits of this medium.
Leppanen, Jenni; Sedgewick, Felicity; Treasure, Janet; Tchanturia, Kate
This meta-analytic review examines the theory of mind profiles in both patients with anorexia nervosa (AN) and autistic individuals. The studies examining theory of mind were divided into the following categories: emotional theory of mind, understanding simple social situations, understanding complex social interactions, and implicit social attribution. All included studies investigated differences between healthy control (HCs) individuals and people with AN or autistic people. Differences in theory of mind profile between people with AN and autistic people were explored by conducting moderator analyses. People with AN and autistic people showed a similar theory of mind profile, but autistic individuals showed greater difficulties, particularly in emotional theory of mind. Although both people with AN and autistic people have significant difficulties in all aspects of theory of mind relative to the HCs, some differences in the underlying profile may be present. However, due to relative paucity of theory of mind research among people with AN, further research is still needed before firm conclusion can be drawn. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.
McMorris, Terry; Hale, Beverley J; Corbett, Jo; Robertson, Kevin; Hodgson, Christopher I
The primary purpose of this study was to examine, using meta-analytical measures, whether research into the performance of whole-body, psychomotor tasks following moderate and heavy exercise demonstrates an inverted-U effect. A secondary purpose was to compare the effects of acute exercise on tasks requiring static maintenance of posture versus dynamic, ballistic skills. Moderate intensity exercise was determined as being between 40% and 79% maximum power output (ẆMAX) or equivalent, while ≥80% ẆMAX was considered to be heavy. There was a significant difference (Zdiff=4.29, p=0.001, R(2)=0.42) between the mean effect size for moderate intensity exercise (g=0.15) and that for heavy exercise size (g=-0.86). These data suggest a catastrophe effect during heavy exercise. Mean effect size for static tasks (g=-1.24) was significantly different (Zdiff=3.24, p=0.001, R(2)=0.90) to those for dynamic/ballistic tasks (g=-0.30). The result for the static versus dynamic tasks moderating variables point to perception being more of an issue than peripheral fatigue for maintenance of static posture. The difference between this result and those found in meta-analyses examining the effects of acute exercise on cognition shows that, when perception and action are combined, the complexity of the interaction induces different effects to when cognition is detached from motor performance. Copyright © 2015 Elsevier Inc. All rights reserved.
Nahrgang, Jennifer D; Morgeson, Frederick P; Hofmann, David A
In this article, we develop and meta-analytically test the relationship between job demands and resources and burnout, engagement, and safety outcomes in the workplace. In a meta-analysis of 203 independent samples (N = 186,440), we found support for a health impairment process and for a motivational process as mechanisms through which job demands and resources relate to safety outcomes. In particular, we found that job demands such as risks and hazards and complexity impair employees' health and positively relate to burnout. Likewise, we found support for job resources such as knowledge, autonomy, and a supportive environment motivating employees and positively relating to engagement. Job demands were found to hinder an employee with a negative relationship to engagement, whereas job resources were found to negatively relate to burnout. Finally, we found that burnout was negatively related to working safely but that engagement motivated employees and was positively related to working safely. Across industries, risks and hazards was the most consistent job demand and a supportive environment was the most consistent job resource in terms of explaining variance in burnout, engagement, and safety outcomes. The type of job demand that explained the most variance differed by industry, whereas a supportive environment remained consistent in explaining the most variance in all industries.
Humphrey, Stephen E; Nahrgang, Jennifer D; Morgeson, Frederick P
The authors developed and meta-analytically examined hypotheses designed to test and extend work design theory by integrating motivational, social, and work context characteristics. Results from a summary of 259 studies and 219,625 participants showed that 14 work characteristics explained, on average, 43% of the variance in the 19 worker attitudes and behaviors examined. For example, motivational characteristics explained 25% of the variance in subjective performance, 2% in turnover perceptions, 34% in job satisfaction, 24% in organizational commitment, and 26% in role perception outcomes. Beyond motivational characteristics, social characteristics explained incremental variances of 9% of the variance in subjective performance, 24% in turnover intentions, 17% in job satisfaction, 40% in organizational commitment, and 18% in role perception outcomes. Finally, beyond both motivational and social characteristics, work context characteristics explained incremental variances of 4% in job satisfaction and 16% in stress. The results of this study suggest numerous opportunities for the continued development of work design theory and practice. (c) 2007 APA.
Frolov, I.; Vaguine, A.; Silin, A.
This paper describes a new approach in development of data flow control and investigation system for computer networks. This approach was developed and applied in the Moscow Radiotechnical Institute for control and investigations of Institute computer network. It allowed us to solve our network current problems successfully. Description of our approach is represented below along with the most interesting results of our work. (author)
Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele
Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.
Noël, Pierre-André; Allard, Antoine; Hébert-Dufresne, Laurent; Marceau, Vincent; Dubé, Louis J
Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible and susceptible-infectious-removed dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.
Ferreri, Luca; Ivaldi, Marco; Daolio, Fabio; Giacobini, Mario; Rainoldi, Alberto; Tomassini, Marco
In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.
Niu, Jianjun; Deng, Zhidong
Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs. PMID:22408491
Full Text Available Energy constraints restrict the lifetime of wireless sensor networks (WSNs with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes’ energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs.
Shashurin, A.; Fang, X.; Zemlyanov, D.; Keidar, M.
Graphene platelet networks (GPNs) comprised of randomly oriented graphene flakes two to three atomic layers thick are synthesized using a novel plasma-based approach. The approach uses a substrate capable of withstanding synthesis temperatures around 800 °C, but is fully independent of the substrate material. The synthesis occurs directly on the substrate surface without the necessity of any additional steps. GPNs were synthesized on various substrate materials including silicon (Si), thermally oxidized Si (SiO2), molybdenum (Mo), nickel (Ni) and copper (Cu), nickel-chromium (NiCr) alloy and alumina ceramics (Al2O3). The mismatch between the atomic structures of sp2 honeycomb carbon networks and the substrate material is fully eliminated shortly after the synthesis initiation, namely when about 100 nm thick deposits are formed on the substrate. GPN structures synthesized on a substrate at a temperature of about 800 °C are significantly more porous in comparison to the much denser packed amorphous carbon deposits synthesized at lower temperatures. The method proposed here can potentially revolutionize the area of electrochemical energy storage by offering a single-step direct approach for the manufacture of graphene-based electrodes for non-Faradaic supercapacitors. Mass production can be achieved using this method if a roll-to-roll system is utilized.
Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.
A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5
Chiprianov, Vanea; Kermarrec, Yvon; Alff, Patrick D.
Present day Telecommunications market imposes a short concept-to-market time for service providers. To reduce it, we propose a computer-aided, model-driven, service-specific tool, with support for collaborative work and for checking properties on models. We started by defining a prototype of the Meta-model (MM) of the service domain. Using this prototype, we defined a simple graphical modeling language specific for service designers. We are currently enlarging the MM of the domain using model transformations from Network Abstractions Layers (NALs). In the future, we will investigate approaches to ensure the support for collaborative work and for checking properties on models.
Azadeh, A.; Maghsoudi, A.; Sohrabkhani, S.
This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.
Boccia, Maddalena; Piccardi, Laura; Palermo, Liana; Nori, Raffaella; Palmiero, Massimiliano
Many studies have assessed the neural underpinnings of creativity, failing to find a clear anatomical localization. We aimed to provide evidence for a multi-componential neural system for creativity. We applied a general activation likelihood estimation (ALE) meta-analysis to 45 fMRI studies. Three individual ALE analyses were performed to assess creativity in different cognitive domains (Musical, Verbal, and Visuo-spatial). The general ALE revealed that creativity relies on clusters of activations in the bilateral occipital, parietal, frontal, and temporal lobes. The individual ALE revealed different maximal activation in different domains. Musical creativity yields activations in the bilateral medial frontal gyrus, in the left cingulate gyrus, middle frontal gyrus, and inferior parietal lobule and in the right postcentral and fusiform gyri. Verbal creativity yields activations mainly located in the left hemisphere, in the prefrontal cortex, middle and superior temporal gyri, inferior parietal lobule, postcentral and supramarginal gyri, middle occipital gyrus, and insula. The right inferior frontal gyrus and the lingual gyrus were also activated. Visuo-spatial creativity activates the right middle and inferior frontal gyri, the bilateral thalamus and the left precentral gyrus. This evidence suggests that creativity relies on multi-componential neural networks and that different creativity domains depend on different brain regions. PMID:26322002
Full Text Available Understanding the functions of different brain areas has represented a major endeavor of contemporary neurosciences. The purpose of this paper was to pinpoint the connectivity of Brodmann area 20 (BA20 (inferior temporal gyrus, fusiform gyrus in language tasks. A meta-analysis was conducted to assess the language network in which BA20 is involved. The DataBase of Brainmap was used; 11 papers corresponding to 12 experimental conditions with a total of 207 subjects were included in this analysis. Our results demonstrated seven clusters of activation including other temporal lobe areas (BA3, BA21, the insula, and the prefrontal cortex; minor clusters in the cingulate gyrus and the occipital lobe were observed; however, the volumes of all the activation clusters were small. Our results suggest that regardless of BA20 having certain participation in language processes it cannot be considered as a core language processing area (Wernicke’s area; nonetheless, it could be regarded as kind of language processing marginal area, participating in “extended Wernicke’s area” or simply “Wernicke’s system.” It is suggested that “core Wernicke’s area” roughly corresponds to BA21, BA22, BA41, and BA42, while a “language associations area” roughly corresponds to BA20, BA37, BA38, BA39, and BA40 (“extended Wernicke’s area” or “Wernicke’s system”.
Zhang, Xiaoge; Mahadevan, Sankaran; Deng, Xinyang
In practical applications of reliability assessment of a system in-service, information about the condition of a system and its components is often available in text form, e.g., inspection reports. Estimation of the system reliability from such text-based records becomes a challenging problem. In this paper, we propose a four-step framework to deal with this problem. In the first step, we construct an evidential network with the consideration of available knowledge and data. Secondly, we train a Naive Bayes text classification algorithm based on the past records. By using the trained Naive Bayes algorithm to classify the new records, we build interval basic probability assignments (BPA) for each new record available in text form. Thirdly, we combine the interval BPAs of multiple new records using an evidence combination approach based on evidence theory. Finally, we propagate the interval BPA through the evidential network constructed earlier to obtain the system reliability. Two numerical examples are used to demonstrate the efficiency of the proposed method. We illustrate the effectiveness of the proposed method by comparing with Monte Carlo Simulation (MCS) results. - Highlights: • We model reliability analysis with linguistic data using evidential network. • Two examples are used to demonstrate the efficiency of the proposed method. • We compare the results with Monte Carlo Simulation (MCS).
Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.
In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.
Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing
Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".
Andersen, Molte Emil Strange; Zinner, Nikolaj Thomas
Assembling large-scale quantum networks is a key goal of modern physics research with applications in quantum information and computation. Quantum wires and waveguides in which massive particles propagate in tailored confinement is one promising platform for realizing a quantum network. In the literature, such networks are often treated as quantum graphs, that is, the wave functions are taken to live on graphs of one-dimensional edges meeting in vertices. Hitherto, it has been unclear what boundary conditions on the vertices produce the physical states one finds in nature. This paper treats a quantum network from a physical approach, explicitly finds the physical eigenstates and compares them to the quantum-graph description. The basic building block of a quantum network is an X-shaped potential well made by crossing two quantum wires, and we consider a massive particle in such an X well. The system is analyzed using a variational method based on an expansion into modes with fast convergence and it provides a very clear intuition for the physics of the problem. The particle is found to have a ground state that is exponentially localized to the center of the X well, and the other symmetric solutions are formed so to be orthogonal to the ground state. This is in contrast to the predictions of the conventionally used so-called Kirchoff boundary conditions in quantum graph theory that predict a different sequence of symmetric solutions that cannot be physically realized. Numerical methods have previously been the only source of information on the ground-state wave function and our results provide a different perspective with strong analytical insights. The ground-state wave function has a spatial profile that looks very similar to the shape of a solitonic solution to a nonlinear Schrödinger equation, enabling an analytical prediction of the wave number. When combining multiple X wells into a network or grid, each site supports a solitonlike localized state. These
Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.
The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474
Larremore, Daniel B; Clauset, Aaron; Buckee, Caroline O
The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.
Daniel B Larremore
Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.
Full Text Available Most experimental studies of decision-making have specifically examined situations in which a single less-predictable correct answer exists (externally guided decision-making under uncertainty. Along with such externally guided decision-making, there are instances of decision making in which no correct answer based on external circumstances is available for the subject (internally guided decision-making. Such decisions are usually made in the context of moral decision making as well as in preference judgment, where the answer depends on the subject’s own, i.e. internal, preferences rather than on external, i.e. circumstantial, criteria. The neuronal and psychological mechanisms that allow guidance of decisions based on more internally oriented criteria in the absence of external ones remain unclear. This study was undertaken to compare decision making of these two kinds empirically and theoretically. First, we reviewed studies of decision making to clarify experimental–operational differences between externally guided and internally guided decision-making. Second, using MKDA, a whole-brain-based quantitative meta-analysis of neuroimaging studies was performed. Our meta-analysis revealed that the neural network used predominantly for internally guided decision-making differs from that for externally guided decision-making under uncertainty. This result suggests that studying only externally guided decision-making under uncertainty is insufficient to account for decision-making processes in the brain. Finally, based on the review and results of the meta-analysis, we discuss the differences and relations between decision making of these two types in terms of their operational, neuronal, and theoretical characteristics.
Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.
Full Text Available Working memory training has been increasingly popular in the last year. Previous studies has shown that children with intellectual disabilities have low working memory capacity and therefore have a great potential for improvement by this type of intervention. The aim of this study was to investigate the effect of working memory and cognitive training for children with intellectual disabilities. The effects reported in previous studies have varied and therefore meta-analysis of articles in the major databases was conducted. Inclusion criteria included to have a pretest-posttest design with a training group and a control group and to have measures of working memory or short-term memory. Ten studies with 28 comparisons were included. The results reveal a significant overall pretest-posttest small effect size for of working memory training for children with intellectual disabilities compared to controls. A mixed working memory approach, considering both verbal and visuo-spatial components and working mainly on strategies, was the only significantly effective training type with a medium effect size. The most commonly reported training type with 60 percent of the included comparisons, visuo-spatial working memory training, had a non-significant effect size that was close to zero. We conclude that even if there is an overall effect of working memory training, a mixed working memory approach appears to cause this effect. Given the few studies included and the different characteristics of the included studies, interpretations should be done with caution. However, different types of interventions appear to have different effects. Even if the results were promising, more studies are needed to better understand how to design an effective working memory intervention for this group and to understand if, and how, these short-term effects remains over time and transfer to everyday activities.
Latkin, Carl A; Davey-Rothwell, Melissa A; Knowlton, Amy R; Alexander, Kamila A; Williams, Chyvette T; Boodram, Basmattee
This article reviews the current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates, treatment access, and outcomes. Social network analysis is a valuable tool to link social structural factors to individual behaviors. Social networks provide an avenue for low-cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social networks can be utilized as a viable approach to recruitment for HIV testing and counseling, HIV prevention interventions, optimizing HIV medical care, and medication adherence. Social network interventions may be face-to-face or through social media. Key issues in designing social network interventions are contamination due to social diffusion, network stability, density, and the choice and training of network members. There are also ethical issues involved in the development and implementation of social network interventions. Social network analyses can also be used to understand HIV transmission dynamics.
Hildebrand, Martin; Wibbelink, Carlijn J M; Verschuere, Bruno
Self-report measures provide an important source of information in correctional/forensic settings, yet at the same time the validity of that information is often questioned because self-reports are thought to be highly vulnerable to self-presentation biases. Primary studies in offender samples have provided mixed results with regard to the impact of socially desirable responding on self-reports. The main aim of the current study was therefore to investigate-via a meta-analytic review of published studies-the association between the two dimensions of socially desirable responding, impression management and self-deceptive enhancement, and self-report measures with content of dynamic risk factors using the Balanced Inventory of Desirable Responding (BIDR) in offender samples. These self-report measures were significantly and negatively related with self-deception (r = -0.120, p impression management (r = -0.158, p impression management effect with the trim and fill method indicating that the relation is probably even smaller (r = -0.07). The magnitude of the effect sizes was small. Moderation analyses suggested that type of dynamic risk factor (e.g., antisocial cognition versus antisocial personality), incentives, and publication year affected the relationship between impression management and self-report measures with content of dynamic risk factors, whereas sample size, setting (e.g., incarcerated, community), and publication year influenced the relation between self-deception and these self-report measures. The results indicate that the use of self-report measures to assess dynamic risk factors in correctional/forensic settings is not inevitably compromised by socially desirable responding, yet caution is warranted for some risk factors (antisocial personality traits), particularly when incentives are at play. Copyright © 2018 Elsevier Ltd. All rights reserved.
Sugden, Nicole A; Marquis, Alexandra R
Infants show facility for discriminating between individual faces within hours of birth. Over the first year of life, infants' face discrimination shows continued improvement with familiar face types, such as own-race faces, but not with unfamiliar face types, like other-race faces. The goal of this meta-analytic review is to provide an effect size for infants' face discrimination ability overall, with own-race faces, and with other-race faces within the first year of life, how this differs with age, and how it is influenced by task methodology. Inclusion criteria were (a) infant participants aged 0 to 12 months, (b) completing a human own- or other-race face discrimination task, (c) with discrimination being determined by infant looking. Our analysis included 30 works (165 samples, 1,926 participants participated in 2,623 tasks). The effect size for infants' face discrimination was small, 6.53% greater than chance (i.e., equal looking to the novel and familiar). There was a significant difference in discrimination by race, overall (own-race, 8.18%; other-race, 3.18%) and between ages (own-race: 0- to 4.5-month-olds, 7.32%; 5- to 7.5-month-olds, 9.17%; and 8- to 12-month-olds, 7.68%; other-race: 0- to 4.5-month-olds, 6.12%; 5- to 7.5-month-olds, 3.70%; and 8- to 12-month-olds, 2.79%). Multilevel linear (mixed-effects) models were used to predict face discrimination; infants' capacity to discriminate faces is sensitive to face characteristics including race, gender, and emotion as well as the methods used, including task timing, coding method, and visual angle. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Schneider, Martha; Voracek, Martin; Tran, Ulrich S
Humor and mental health are interconnected as is evidenced by a large number of studies. However, associations are only small and inconsistent as the operationalization of humor poses a methodological challenge. The Humor Styles Questionnaire (HSQ) differentiates four humor styles that might be beneficial or harmful to mental health. The aim of the present study was to meta-analytically aggregate studies using the HSQ to assess the associations of different humor styles with four areas of mental health (self-esteem, life satisfaction, optimism, depression). An extensive electronic database literature search identified 37 studies that reported correlations between the HSQ scales and the four areas of mental health in 45 independent samples (total N = 12,734). In total, 16 meta-analyses were conducted. Moderating effects of participant age, sex, and geographic region were examined via subgroup analyses and meta-regression. Humor styles differed in terms of their associations with mental health. Health-promoting humor styles were overall positively correlated with mental health (small-to-medium effect sizes). Self-defeating humor was overall negatively correlated with mental health. Aggressive humor was overall unrelated with mental health. Moderator analyses suggested geographic differences (Eastern vs. Western samples) and sex differences for some of these associations. Fostering specific humor styles may be beneficial for mental health. In addition, observing the habitual use of humor styles might help therapists to develop a better understanding of their clients. Differences in the utilization and the correlates of humor styles in Eastern and Western societies, and sex differences, need to be addressed in future research. © 2018 Scandinavian Psychological Associations and John Wiley & Sons Ltd.
Geraldina F Gaastra
Full Text Available Children with attention-deficit/hyperactivity disorder (ADHD often exhibit problem behavior in class, which teachers often struggle to manage due to a lack of knowledge and skills to use classroom management strategies. The aim of this meta-analytic review was to determine the effectiveness of several types of classroom interventions (antecedent-based, consequence-based, self-regulation, combined that can be applied by teachers in order to decrease off-task and disruptive classroom behavior in children with symptoms of ADHD. A second aim was to identify potential moderators (classroom setting, type of measure, students' age, gender, intelligence, and medication use. Finally, it was qualitatively explored whether the identified classroom interventions also directly or indirectly affected behavioral and academic outcomes of classmates. Separate meta-analyses were performed on standardized mean differences (SMDs for 24 within-subjects design (WSD and 76 single-subject design (SSD studies. Results showed that classroom interventions reduce off-task and disruptive classroom behavior in children with symptoms of ADHD (WSDs: MSMD = 0.92; SSDs: MSMD = 3.08, with largest effects for consequence-based (WSDs: MSMD = 1.82 and self-regulation interventions (SSDs: MSMD = 3.61. Larger effects were obtained in general education classrooms than in other classroom settings. No reliable conclusions could be formulated about moderating effects of type of measure and students' age, gender, intelligence, and medication use, mainly because of power problems. Finally, classroom interventions appeared to also benefit classmates' behavioral and academic outcomes.
Johnson, C. E.; Aikin, K. E.
While seismic networks have expanded over the past few decades, and social needs for accurate and timely information has increased dramatically, approaches to the operational needs of both global and regional seismic observatories have been slow to adopt new technologies. This presentation presents the xQuake system that provides a fresh approach to seismic network analytics based on complexity theory and an adaptive architecture of streaming connected microservices as diverse data (picks, beams, and other data) flow into a final, curated catalog of events. The foundation for xQuake is the xGraph (executable graph) framework that is essentially a self-organizing graph database. An xGraph instance provides both the analytics as well as the data storage capabilities at the same time. Much of the analytics, such as synthetic annealing in the detection process and an evolutionary programing approach for event evolution, draws from the recent GLASS 3.0 seismic associator developed by and for the USGS National Earthquake Information Center (NEIC). In some respects xQuake is reminiscent of the Earthworm system, in that it comprises processes interacting through store and forward rings; not surprising as the first author was the lead architect of the original Earthworm project when it was known as "Rings and Things". While Earthworm components can easily be integrated into the xGraph processing framework, the architecture and analytics are more current (e.g. using a Kafka Broker for store and forward rings). The xQuake system is being released under an unrestricted open source license to encourage and enable sthe eismic community support in further development of its capabilities.
Full Text Available Data mining is nontrivial extraction of implicit, previously unknown and potential useful information from the data. For a database with number of records and for a set of classes such that each record belongs to one of the given classes, the problem of classification is to decide the class to which the given record belongs. The classification problem is also to generate a model for each class from given data set. We are going to make use of supervised classification in which we have training dataset of record, and for each record the class to which it belongs is known. There are many approaches to supervised classification. Decision tree is attractive in data mining environment as they represent rules. Rules can readily expressed in natural languages and they can be even mapped o database access languages. Now a days classification based on decision trees is one of the important problems in data mining which has applications in many areas. Now a days database system have become highly distributed, and we are using many paradigms. we consider the problem of inducing decision trees in a large distributed network of highly distributed databases. The classification based on decision tree can be done on the existence of distributed databases in healthcare and in bioinformatics, human computer interaction and by the view that these databases are soon to contain large amounts of data, characterized by its high dimensionality. Current decision tree algorithms would require high communication bandwidth, memory, and they are less efficient and scalability reduces when executed on such large volume of data. So there are some approaches being developed to improve the scalability and even approaches to analyse the data distributed over a network.[keywords: Data mining, Decision tree, decision tree induction, distributed data, classification
Integer programming, network flow optimisation, passive optical network, ... This paper uses concepts from network flow optimisation to incorporate fibre duct shar ...  studied the survivable constrained ConFL problem and solved a number of.
Beatty, Garrett F; Cranley, Nicole M; Carnaby, Giselle; Janelle, Christopher M
Emotions motivate individuals to attain appetitive goals and avoid aversive consequences. Empirical investigations have detailed how broad approach and avoidance orientations are reflected in fundamental movement attributes such as the speed, accuracy, and variability of motor actions. Several theoretical perspectives propose explanations for how emotional states influence the speed with which goal directed movements are initiated. These perspectives include biological predisposition, muscle activation, distance regulation, cognitive evaluation, and evaluative response coding accounts. A comprehensive review of literature and meta-analysis were undertaken to quantify empirical support for these theoretical perspectives. The systematic review yielded 34 studies that contained 53 independent experiments producing 128 effect sizes used to evaluate the predictions of existing theories. The central tenets of the biological predisposition (Hedges' g = -0.356), distance regulation (g = -0.293; g = 0.243), and cognitive evaluation (g = -0.249; g = -0.405; g = -0.174) accounts were supported. Partial support was also identified for the evaluative response coding (g = -0.255) framework. Our findings provide quantitative evidence that substantiate existing theoretical perspectives, and provide potential direction for conceptual integration of these independent perspectives. Recommendations for future empirical work in this area are discussed. (c) 2016 APA, all rights reserved).
Keri B Dotson
Full Text Available Norms clarification has been identified as an effective component of college student drinking interventions, prompting research on norms clarification as a single-component intervention known as Personalized Normative Feedback (PNF. Previous reviews have examined PNF in combination with other components but not as a stand-alone intervention.To investigate the degree to which computer-delivered stand-alone personalized normative feedback interventions reduce alcohol consumption and alcohol-related harms among college students and to compare gender-neutral and gender-specific PNF.Electronic databases were searched systematically through November 2014. Reference lists were reviewed manually and forward and backward searches were conducted.Outcome studies that compared computer-delivered, stand-alone PNF intervention with an assessment only, attention-matched, or active treatment control and reported alcohol use and harms among college students.Between-group effect sizes were calculated as the standardized mean difference in change scores between treatment and control groups divided by pooled standard deviation. Within-group effect sizes were calculated as the raw mean difference between baseline and follow-up divided by pooled within-groups standard deviation.Eight studies (13 interventions with a total of 2,050 participants were included. Compared to control participants, students who received gender-neutral (dbetween = 0.291, 95% CI [0.159, 0.423] and gender-specific PNF (dbetween = 0.284, 95% CI [0.117, 0.451] reported greater reductions in drinking from baseline to follow-up. Students who received gender-neutral PNF reported 3.027 (95% CI [2.171, 3.882] fewer drinks per week at first follow-up and gender-specific PNF reported 3.089 (95% CI [0.992, 5.186] fewer drinks. Intervention effects were small for harms (dbetween = 0.157, 95% CI [0.037, 0.278].Computer-delivered PNF is an effective stand-alone approach for reducing college student
Dotson, Keri B; Dunn, Michael E; Bowers, Clint A
Norms clarification has been identified as an effective component of college student drinking interventions, prompting research on norms clarification as a single-component intervention known as Personalized Normative Feedback (PNF). Previous reviews have examined PNF in combination with other components but not as a stand-alone intervention. To investigate the degree to which computer-delivered stand-alone personalized normative feedback interventions reduce alcohol consumption and alcohol-related harms among college students and to compare gender-neutral and gender-specific PNF. Electronic databases were searched systematically through November 2014. Reference lists were reviewed manually and forward and backward searches were conducted. Outcome studies that compared computer-delivered, stand-alone PNF intervention with an assessment only, attention-matched, or active treatment control and reported alcohol use and harms among college students. Between-group effect sizes were calculated as the standardized mean difference in change scores between treatment and control groups divided by pooled standard deviation. Within-group effect sizes were calculated as the raw mean difference between baseline and follow-up divided by pooled within-groups standard deviation. Eight studies (13 interventions) with a total of 2,050 participants were included. Compared to control participants, students who received gender-neutral (dbetween = 0.291, 95% CI [0.159, 0.423]) and gender-specific PNF (dbetween = 0.284, 95% CI [0.117, 0.451]) reported greater reductions in drinking from baseline to follow-up. Students who received gender-neutral PNF reported 3.027 (95% CI [2.171, 3.882]) fewer drinks per week at first follow-up and gender-specific PNF reported 3.089 (95% CI [0.992, 5.186]) fewer drinks. Intervention effects were small for harms (dbetween = 0.157, 95% CI [0.037, 0.278]). Computer-delivered PNF is an effective stand-alone approach for reducing college student drinking and
Yue, Tai-Wen; Chiang, Suchen
In this paper, we propose a neural-network approach for visual authorization, which is an application of visual cryptography (VC). The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the user-share set. The shares in the user-share set are visually indistinguishable, i.e. they have the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can visually recognize the authority assigned to a particular user by viewing the information appearing in the superposed image of key-share and user-share. This approach is completely different from traditional VC approaches. The salient features include: (i) the access schemes are described using a set of graytone images, and (ii) the codebooks to fulfil them are not required; and (iii) the size of share images is the same as the size of target image.
Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan
Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which
Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...
Kang, Guoliang; Li, Jun; Tao, Dacheng
Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.
Lensa, W. von
The present situation of nuclear power in general and of the innovative nuclear reactor systems in particular requires more comprehensive, coordinated R and D efforts on a broad international level to respond to today's requirements with respect to public and economic acceptance as well as to globalization trends and global environmental problems. HTGR technology development has already reached a high degree of maturity that will be complemented by the operation of the two new test reactors in Japan and China, representing technological milestones for the demonstration of HTGR safety characteristics and Nuclear Process Heat generation capabilities. It is proposed by the IAEA 'International Working Group on Gas-Cooled Reactors' to establish a 'Global HTGR R and D Network' on basic HTGR technology for the stable, long-term advancement of the specific HTGR features and as a basis for the future market introduction of this innovative reactor system. The background and the motivation for this approach are illustrated, as well as first proposals on the main objectives, the structure and the further procedures for the implementation of such a multinational working sharing R and D network. Modern telecooperation methods are foreseen as an interactive tool for effective communication and collaboration on a global scale. (author)
This authored monograph covers a viability to approach to traffic management by advising to vehicles circulated on the network the velocity they should follow for satisfying global traffic conditions;. It presents an investigation of three structural innovations: The objective is to broadcast at each instant and at each position the advised celerity to vehicles, which could be read by auxiliary speedometers or used by cruise control devices. Namely, 1. Construct regulation feedback providing at each time and position advised velocities (celerities) for minimizing congestion or other requirements. 2. Taking into account traffic constraints of different type, the first one being to remain on the roads, to stop at junctions, etc. 3. Use information provided by the probe vehicles equipped with GPS to the traffic regulator; 4. Use other global traffic measures of vehicles provided by different types of sensors; These results are based on convex analysis, intertemporal optimization and viability theory as mathemati...
To better locate natural resources, treat pollution, and monitor underground networks associated with geothermal plants, nuclear waste repositories, and carbon dioxide sequestration sites, scientists need to be able to accurately characterize and image fluid seepage pathways below ground. With these images, scientists can gain knowledge of soil moisture content, the porosity of geologic formations, concentrations and locations of dissolved pollutants, and the locations of oil fields or buried liquid contaminants. Creating images of the unknown hydraulic environments underfoot is a difficult task that has typically relied on broad extrapolations from characteristics and tests of rock units penetrated by sparsely positioned boreholes. Such methods, however, cannot identify small-scale features and are very expensive to reproduce over a broad area. Further, the techniques through which information is extrapolated rely on clunky and mathematically complex statistical approaches requiring large amounts of computational power.
Qiao, D M; Shi, H B; Pang, H B
but has not yet been addressed. This paper presents and tests such an approach. The method is based on a neural network model, estimating the water uptake using different types of data that are easy to measure in the field. Sunflower grown in a sandy loam subjected to water stress and salinity was taken......Water uptake by plant roots is an important process in the hydrological cycle, not only for plant growth but also for the role it plays in shaping microbial community and bringing in physical and biochemical changes to soils. The ability of roots to extract water is determined by combined soil...... and plant characteristics, and how to model it has been of interest for many years. Most macroscopic models for water uptake operate at soil profile scale under the assumption that the uptake rate depends on root density and soil moisture. Whilst proved appropriate, these models need spatio-temporal root...
Pedroche, Francisco; Romance, Miguel; Criado, Regino
In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.
Pedroche, Francisco; Romance, Miguel; Criado, Regino
In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.
Karl, H.; Draexler, S.; Peuster, M.; Galis, A.; Bredel, M.; Ramos, A.; Martrat, J.; Siddiqui, M. S.; Van Rossem, S.; Tavernier, W.; Xilouris, G.
The Service Programming and Orchestration for Virtualised Software Networks (SONATA) project targets both the flexible programmability of software networks and the optimisation of their deployments by means of integrating Development and Operations in order to accelerate industry adoption of software networks and reduce time-to-market for networked services. SONATA supports network function chaining and orchestration, making service platforms modular and easier to customise to the needs of di...
Venkatesan, Sudarkodi; Vivek-Ananth, R. P.; Sreejith, R. P.; Mangalapandi, Pattulingam; Hassanali, Ali A.; Samal, Areejit
We have used molecular dynamics to simulate an amorphous glassy polymer with long chains to study the deformation mechanism of crazing and associated void statistics. The Van der Waals interactions and the entanglements between chains constituting the polymer play a crucial role in crazing. Thus, we have reconstructed two underlying weighted networks, namely, the Van der Waals network and the entanglement network from polymer configurations extracted from the molecular dynamics simulation. Subsequently, we have performed graph-theoretic analysis of the two reconstructed networks to reveal the role played by them in the crazing of polymers. Our analysis captured various stages of crazing through specific trends in the network measures for Van der Waals networks and entanglement networks. To further corroborate the effectiveness of network analysis in unraveling the underlying physics of crazing in polymers, we have contrasted the trends in network measures for Van der Waals networks and entanglement networks in the light of stress-strain behaviour and voids statistics during deformation. We find that the Van der Waals network plays a crucial role in craze initiation and growth. Although, the entanglement network was found to maintain its structure during craze initiation stage, it was found to progressively weaken and undergo dynamic changes during the hardening and failure stages of crazing phenomena. Our work demonstrates the utility of network theory in quantifying the underlying physics of polymer crazing and widens the scope of applications of network science to characterization of deformation mechanisms in diverse polymers.
Zweig, Katharina A
Network Analysis Literacy focuses on design principles for network analytics projects. The text enables readers to: pose a defined network analytic question; build a network to answer the question; choose or design the right network analytic methods for a particular purpose, and more.
Rosenow, Felix; van Alphen, Natascha; Becker, Albert; Chiocchetti, Andreas; Deichmann, Ralf; Deller, Thomas; Freiman, Thomas; Freitag, Christine M; Gehrig, Johannes; Hermsen, Anke M; Jedlicka, Peter; Kell, Christian; Klein, Karl Martin; Knake, Susanne; Kullmann, Dimitri M; Liebner, Stefan; Norwood, Braxton A; Omigie, Diana; Plate, Karlheinz; Reif, Andreas; Reif, Philipp S; Reiss, Yvonne; Roeper, Jochen; Ronellenfitsch, Michael W; Schorge, Stephanie; Schratt, Gerhard; Schwarzacher, Stephan W; Steinbach, Joachim P; Strzelczyk, Adam; Triesch, Jochen; Wagner, Marlies; Walker, Matthew C; von Wegner, Frederic; Bauer, Sebastian
Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. Part I includes the clinical phenotyping and diagnostic methods, EEG network-analysis, biomarkers, and personalized treatment approaches. In Part II, experimental and translational approaches will be discussed (Bauer et al., 2017) . Copyright © 2017 Elsevier Inc
Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.
Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.
This book presents the latest results on modeling and analysis of OBS networks. It classifies all the literature on the topic, and its scope extends to include discussion of high-speed communication networks with limited or no buffers.
Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other
Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
quality of service requirements and topologically induced constraints in the Bluetooth network, such as node and link capacity limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well as capacity limitations and flow requirements in the network. Simulation...... shows that the performance of Bluetooth network could be improved by applying the hybrid distributed iterative capacity allocation scheme....
Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.
Duysters, G.M.; Vanhaverbeke, W.P.M.
Interorganizational cooperation in some high-tech industries is no longer confined to two-company alliances, but entails industry-wide alliance networks. This article examines how industry analysis and network analysis can be combined to provide a thorough understanding of how network positions, and
Chen, Bowen; Zhao, Yongli; Zhang, Jie
In this paper, we develop a virtual link priority mapping (LPM) approach and a virtual node priority mapping (NPM) approach to improve the energy efficiency and to reduce the spectrum usage over the converged flexible bandwidth optical networks and data centers. For comparison, the lower bound of the virtual optical network mapping is used for the benchmark solutions. Simulation results show that the LPM approach achieves the better performance in terms of power consumption, energy efficiency, spectrum usage, and the number of regenerators compared to the NPM approach.
Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.
Dittmann, Lars; Christiansen, Henrik Lehrmann; Checko, Aleksandra
In future mobile networks aggregation at different levels is necessary but at the same time imposes challenges that mandate looking into new architectures. This paper presents the design consideration approach for a C-RAN based mobile aggregation network used in the EU HARP project....... With this architecture fronthaul aggregation is performed which might be an option for future generation of mobile networks....
This study examined the personal, household, and social structural attributes of alcoholimpaired : drivers in Maryland. The study used an egocentric approach of social network : analysis. This approach concentrated on specific actors (alcohol-impaire...
Johnson, C. E.
Modern seismic networks present a number of challenges, but perhaps most notably are those related to 1) extreme variation in station density, 2) temporal variation in station availability, and 3) the need to achieve detectability for much smaller events of strategic importance. The first of these has been reasonably addressed in the development of modern seismic associators, such as GLASS 3.0 by the USGS/NEIC, though some work still remains to be done in this area. However, the latter two challenges demand special attention. Station availability is impacted by weather, equipment failure or the adding or removing of stations, and while thresholds have been pushed to increasingly smaller magnitudes, new algorithms are needed to achieve even lower thresholds. Station availability can be addressed by a modern, adaptive architecture that maintains specified performance envelopes using adaptive analytics coupled with complexity theory. Finally, detection thresholds can be lowered using a novel approach that tightly couples waveform analytics with the event detection and association processes based on a principled repicking algorithm that uses particle realignment for enhanced phase discrimination.
Radiography is used by EDF for pipe inspection in nuclear power plants in order to detect defects. The radiographs obtained are then digitized in a well-defined protocol. The aim of EDF consists of developing a non destructive testing system for recognizing defects. In this note, we describe the recognition procedure of areas with defects. We first present the digitization protocol, specifies the poor quality of images under study and propose a procedure to enhance defects. We then examine the problem raised by the choice of good features for classification. After having proved that statistical or standard textural features such as homogeneity, entropy or contrast are not relevant, we develop a geometrical-statistical approach based on the cooperation between signal correlations study and regional extrema analysis. The principle consists of analysing and comparing for areas with defects and without any defect, the evolution of conditional probabilities matrices for increasing neighbourhood sizes, the shape of variograms and the location of regional minima. We demonstrate that anisotropy and surface of series of 'comet tails' associated with probability matrices, variograms slope and statistical indices, regional extrema location, are features able to discriminate areas with defects from areas without any. The classification is then realized by a neural network, which structure, properties and learning mechanisms are detailed. Finally we discuss the results. (author). 5 figs., 21 refs
Han, Lei; Dai, Jie; Zhang, Wei; Zhang, Changjiang; Feng, Hanlei
Nowcasting or very short-term forecasting convective storms is still a challenging problem due to the high nonlinearity and insufficient observation of convective weather. As the understanding of the physical mechanism of convective weather is also insufficient, the numerical weather model cannot predict convective storms well. Machine learning approaches provide a potential way to nowcast convective storms using various meteorological data. In this study, a deep belief network (DBN) is proposed to nowcast convective storms using the real-time re-analysis meteorological data. The nowcasting problem is formulated as a classification problem. The 3D meteorological variables are fed directly to the DBN with dimension of input layer 6*6*80. Three hidden layers are used in the DBN and the dimension of output layer is two. A box-moving method is presented to provide the input features containing the temporal and spatial information. The results show that the DNB can generate reasonable prediction results of the movement and growth of convective storms.
Ferguson, Laura B; Harris, R Adron; Mayfield, Roy Dayne
The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.
Budich, Reinhard; Nyberg, Per; Weigel, Tobias
Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.
Gong, Tao; Shuai, Lan; Wu, Yicheng
By analyzing complex networks constructed from authentic language data, Cong and Liu  advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system . Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.
Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.
István A Kovács
Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.
Latkin, Carl A.; Davey-Rothwell, Melissa A.; Knowlton, Amy R.; Alexander, Kamila A.; Williams, Chyvette T.; Boodram, Basmattee
This article reviews current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates and treatment access and outcomes. Social network analysis is a value tool to link social structural factors to individual behaviors. Social networks provide an avenue for low cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social ne...
Lorena Isabel Barona López
Full Text Available 5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.
Csermely, Peter; Rajnai, Gabor; Sulyok, Katalin
In 2006 a novel approach to talent support was promoted by several talent support programmes in Hungary. The new idea was a network approach. The nationwide network of so-called TalentPoints and its framework, the Hungarian Genius Program, gained substantial European Union funding in 2009, and today it is growing rapidly. A novel concept of talent…
Dr. S. Shanmugapriya; A. Kokila
A social networking site (SNS) or social media is an online platform that people use to build social networks or social relations with other people who share similar personal or career interests, activities, backgrounds or real-life connections. The advent of Social Networking sites and its resources have revolutionized the communication and social relation world. This paper aims to assess the user perception towards SNS like Facebook, Twitter and LinkedIn. In the study data was obtained thro...
Kreakie, B. J.; Hychka, K. C.; Belaire, J. A.; Minor, E.; Walker, H. A.
Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to over...
Arshad, Rabe; Elsawy, Hesham; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim
Cellular operators are continuously densifying their networks to cope with the ever-increasing capacity demand. Furthermore, an extreme densification phase for cellular networks is foreseen to fulfill the ambitious fifth generation (5G) performance requirements. Network densification improves spectrum utilization and network capacity by shrinking base stations' (BSs) footprints and reusing the same spectrum more frequently over the spatial domain. However, network densification also increases the handover (HO) rate, which may diminish the capacity gains for mobile users due to HO delays. In highly dense 5G cellular networks, HO delays may neutralize or even negate the gains offered by network densification. In this paper, we present an analytical paradigm, based on stochastic geometry, to quantify the effect of HO delay on the average user rate in cellular networks. To this end, we propose a flexible handover scheme to reduce HO delay in case of highly dense cellular networks. This scheme allows skipping the HO procedure with some BSs along users' trajectories. The performance evaluation and testing of this scheme for only single HO skipping shows considerable gains in many practical scenarios. © 2016 IEEE.
Fraiman, Daniel; Fraiman, Ricardo
The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
Cellular operators are continuously densifying their networks to cope with the ever-increasing capacity demand. Furthermore, an extreme densification phase for cellular networks is foreseen to fulfill the ambitious fifth generation (5G) performance requirements. Network densification improves spectrum utilization and network capacity by shrinking base stations\\' (BSs) footprints and reusing the same spectrum more frequently over the spatial domain. However, network densification also increases the handover (HO) rate, which may diminish the capacity gains for mobile users due to HO delays. In highly dense 5G cellular networks, HO delays may neutralize or even negate the gains offered by network densification. In this paper, we present an analytical paradigm, based on stochastic geometry, to quantify the effect of HO delay on the average user rate in cellular networks. To this end, we propose a flexible handover scheme to reduce HO delay in case of highly dense cellular networks. This scheme allows skipping the HO procedure with some BSs along users\\' trajectories. The performance evaluation and testing of this scheme for only single HO skipping shows considerable gains in many practical scenarios. © 2016 IEEE.
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Anderson, Brian D O
Geared toward upper-level undergraduates and graduate students, this book offers a comprehensive look at linear network analysis and synthesis. It explores state-space synthesis as well as analysis, employing modern systems theory to unite the classical concepts of network theory. The authors stress passive networks but include material on active networks. They avoid topology in dealing with analysis problems and discuss computational techniques. The concepts of controllability, observability, and degree are emphasized in reviewing the state-variable description of linear systems. Explorations
Li, Jun; Zhao, Patrick X
Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.
Drawing on contemporary evidence in the counselling and psychotherapy research field, this paper argues that there is growing support for a relationship-orientated approach to therapeutic practice. The paper reviews findings from a range of meta-analytical and individual studies which provide strong evidence for the centrality of relational…
Javidi, Mohammad M.; Aliahmadipour, Laya
An important and essential issue for wireless networks is routing protocol design that is a major technical challenge due to the function of the network. Game theory is a powerful mathematical tool that analyzes the strategic interactions among multiple decision makers and the results of researches show that applied game theory in routing protocol lead to improvement the network performance through reduce overhead and motivates selfish nodes to collaborate in the network. This paper presents a review and comparison for typical representatives of routing protocols designed that applied game theory approaches for various wireless networks such as ad hoc networks, mobile ad hoc networks and sensor networks that all of them lead to improve the network performance.
Murthy, K. M. S.
Providing personal communication systems supporting full mobility require intelligent networks for tracking mobile users and facilitating outgoing and incoming calls over different physical and network environments. In realizing the intelligent network functionalities, databases play a major role. Currently proposed network architectures envision using the SS7-based signaling network for linking these DB's and also for interconnecting DB's with switches. If the network has to support ubiquitous, seamless mobile services, then it has to support additionally mobile application parts, viz., mobile origination calls, mobile destination calls, mobile location updates and inter-switch handovers. These functions will generate significant amount of data and require them to be transferred between databases (HLR, VLR) and switches (MSC's) very efficiently. In the future, the users (fixed or mobile) may use and communicate with sophisticated CPE's (e.g. multimedia, multipoint and multisession calls) which may require complex signaling functions. This will generate volumness service handling data and require efficient transfer of these message between databases and switches. Consequently, the network providers would be able to add new services and capabilities to their networks incrementally, quickly and cost-effectively.
Zhu, Ling; Robinson, Scott E.; Torenvlied, René
The study of managerial networking has been growing in the field of public administration; a field that analyzes how managers in open system organizations interact with different external actors and organizations. Coincident with this interest in managerial networking is the use of self-reported
Ghassemi, F.; Fokkink, W.J.; Movaghar, A.
We introduced Computed Network Process Theory to reason about protocols for mobile ad hoc networks (MANETs). Here we explore the applicability of our framework in two regards: model checking and equational reasoning. The operational semantics of our framework is based on constrained labeled
Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.
The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…
ter Wal, L.J.
Local knowledge networks are often held responsible for the competitiveness and innovativeness of geographical clusters. However, the literature on spatial clustering tends to assume that firms in clusters have equal access to the knowledge that circulates in those networks and that this knowledge
Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.
In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a
Information and Communication Technology (ICT) is changing the way we live and has become an essential part of our life. With the advent of Internet of Things (IoT), and Wireless Sensor Networks (WSN) in particular, the number of devices that are networked is increasing exponentially over the years.
Pedersen, Martin Wæver; Burgess, Greg; Weng, Kevin C.
. We illustrate the method with real topographic data from a rugose coral reef where network performance is highly influenced by detection shadowing. Network performance is visualized by a coverage map indicating the probability of detection at any location in the study area. The reported unique...
Gerhardt, Guenther J.L. [Universidade Federal do Rio Grande do Sul-Hospital de Clinicas de Porto Alegre, Rua Ramiro Barcelos 2350/sala 2040/90035-003 Porto Alegre (Brazil); Departamento de Fisica e Quimica da Universidade de Caxias do Sul, Rua Francisco Getulio Vargas 1130, 95001-970 Caxias do Sul (Brazil); Lemke, Ney [Programa Interdisciplinar em Computacao Aplicada, Unisinos, Av. Unisinos, 950, 93022-000 Sao Leopoldo, RS (Brazil); Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, Campus Universitario, 59072 970 Natal, RN (Brazil)]. E-mail: email@example.com
In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.
The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection
Albert, Réka; Collins, James J; Glass, Leon
All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks
Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing
Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.
Full Text Available Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.
Çakır, Tunahan, E-mail: firstname.lastname@example.org [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Khatibipour, Mohammad Jafar [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Department of Chemical Engineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey)
The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.
Çakır, Tunahan; Khatibipour, Mohammad Jafar
The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.
Dušan Teodorović; Milica Šelmić; Ivana Vukićević
Hub facilities serve as switching and transshipment points in transportation and communication networks as well as in logistic systems. Hub networks have an influence on flows on the hub-to-hub links and ensure benefit from economies of scale in inter-hub transportation. The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs. This paper presents the model to determine the l...
Livneh, Ran; CERN. Geneva
The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to make precise decisions in a few nano-seconds. This poses a complicated inverse problem, arising from the inhomogeneous nature of the magnetic fields in ATLAS. This thesis presents a study of an application of Artificial Neural Networks to the muon triggering problem in the ATLAS end-cap. A comparison with realistic results from the ATLAS first level trigger simulation was in favour of the neural network, but this is mainly due to superior resolution available off-line. Other options for applying a neural network to this problem are discussed.
Tran, Duc A
Evidenced by the success of Facebook, Twitter, and LinkedIn, online social networks (OSNs) have become ubiquitous, offering novel ways for people to access information and communicate with each other. As the increasing popularity of social networking is undeniable, scalability is an important issue for any OSN that wants to serve a large number of users. Storing user data for the entire network on a single server can quickly lead to a bottleneck, and, consequently, more servers are needed to expand storage capacity and lower data request traffic per server. Adding more servers is just one step
Colace, Francesco; de Santo, Massimo; Ferrandino, Salvatore
The last decade has witnessed an intense spread of computer networks that has been further accelerated with the introduction of wireless networks. Simultaneously with, this growth has increased significantly the problems of network management. Especially in small companies, where there is no provision of personnel assigned to these tasks, the management of such networks is often complex and malfunctions can have significant impacts on their businesses. A possible solution is the adoption of Simple Network Management Protocol. Simple Network Management Protocol (SNMP) is a standard protocol used to exchange network management information. It is part of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol suite. SNMP provides a tool for network administrators to manage network performance, find and solve network problems, and plan for network growth. SNMP has a big disadvantage: its simple design means that the information it deals with is neither detailed nor well organized enough to deal with the expanding modern networking requirements. Over the past years much efforts has been given to improve the lack of Simple Network Management Protocol and new frameworks has been developed: A promising approach involves the use of Ontology. This is the starting point of this paper where a novel approach to the network management based on the use of the Slow Intelligence System methodologies and Ontology based techniques is proposed. Slow Intelligence Systems is a general-purpose systems characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptation, elimination and concentration. Therefore, the proposed approach aims to develop a system able to acquire, according to an SNMP standard, information from the various hosts that are in the managed networks and apply solutions in order to solve problems. To check the feasibility of this model first experimental results in a real scenario are showed.
Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J
Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
Meteorology and Oceanography Group, Space Applications Centre (ISRO), Ahmedabad 380 015, India. Microwave .... Total number of Qa observations in the sample-I dataset. techniques ... class of networks consists of multiple layers of com-.
Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defense, crisis management, traffic management, public
Burgos, José E
This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (C...
Antoci, Angelo; Sabatini, Fabio
There is growing evidence that face-to-face interaction is declining in many countries, exacerbating the phenomenon of social isolation. On the other hand, social interaction through online networking sites is steeply rising. To analyze these societal dynamics, we have built an evolutionary game model in which agents can choose between three strategies of social participation: 1) interaction via both online social networks and face-to-face encounters; 2) interaction by exclusive means of face...
Kosta , Eleni
International audience; One of the most remarkable cultural phenomena that blossomed in the Web 2.0 era are the social networking sites, such as Facebook, MySpace, Friendster, Bebo, Netlog or LinkedIn. The introduction of new communication channels facilitates interactive information sharing and collaboration between various actors over social networking sites. These actors, i.e. the providers and the users, do not always fit in the traditional communications models. In this paper we are goin...
Islam, Noman; Shaikh, Zubair A.
Mobile Adhoc Network (MANET) is a network of a number of mobile routers and associated hosts, organized in a random fashion via wireless links. During recent years MANET has gained enormous amount of attention and has been widely used for not only military purposes but for search-and-rescue operations, intelligent transportation system, data collection, virtual classrooms and ubiquitous computing. Service Discovery is one of the most important issues in MANET. It is defined as the process of ...
Lawrence, S; Giles, C L; Tsoi, A C; Back, A D
We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
Hussain, Md Asdaque; Alam, Md Nasre; Kwak, Kyung Sup
Wireless Body Area Networks (WBANs) designed for medical, sports, and entertainment applications, have drawn the attention of academia and industry alike. A WBAN is a special purpose network, designed to operate autonomously to connect various medical sensors and appliances, located inside and/or outside of a human body. This network enables physicians to remotely monitor vital signs of patients and provide real time feedback for medical diagnosis and consultations. The WBAN system can offer two significant advantages: patient mobility due to their use of portable monitoring devices and a location independent monitoring facility. With its appealing dimensions, it brings about a new set of challenges, which we do not normally consider in such small sensor networks. It requires a scalable network in terms of heterogeneous data traffic, low power consumption of sensor nodes, integration in and around the body networking and coexistence. This work presents a medium access control protocol for WBAN which tries to overcome the aforementioned challenges. We consider the use of multiple beam adaptive arrays (MBAA) at BAN Coordinator (BAN_C) node. When used as a BAN_C, an MBAA can successfully receive two or more overlapping packets at the same time. Each beam captures a different packet by automatically pointing its pattern toward one packet while annulling other contending packets. This paper describes how an MBAA can be integrated into a single hope star topology as a BAN_C. Simulation results show the performance of our proposed protocol.
Md. Asdaque Hussain
Full Text Available Wireless Body Area Networks (WBANs designed for medical, sports, and entertainment applications, have drawn the attention of academia and industry alike. A WBAN is a special purpose network, designed to operate autonomously to connect various medical sensors and appliances, located inside and/or outside of a human body. This network enables physicians to remotely monitor vital signs of patients and provide real time feedback for medical diagnosis and consultations. The WBAN system can offer two significant advantages: patient mobility due to their use of portable monitoring devices and a location independent monitoring facility. With its appealing dimensions, it brings about a new set of challenges, which we do not normally consider in such small sensor networks. It requires a scalable network in terms of heterogeneous data traffic, low power consumption of sensor nodes, integration in and around the body networking and coexistence. This work presents a medium access control protocol for WBAN which tries to overcome the aforementioned challenges. We consider the use of multiple beam adaptive arrays (MBAA at BAN Coordinator (BAN_C node. When used as a BAN_C, an MBAA can successfully receive two or more overlapping packets at the same time. Each beam captures a different packet by automatically pointing its pattern toward one packet while annulling other contending packets. This paper describes how an MBAA can be integrated into a single hope star topology as a BAN_C. Simulation results show the performance of our proposed protocol.
The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.
Full Text Available This paper selects Changsha as a case study and constructs the models of the subway network and the urban spatial network by using planning data. In the network models, the districts of Changsha are regarded as nodes and the connections between each pair of districts are regarded as edges. The method is based on quantitative analysis of the node weights and the edge weights, which are defined in the complex network theory. And the structures of subway and urban space are visualized in the form of networks. Then, through analyzing the discrepancy coefficients of the corresponding nodes and edges, the paper carries out a comparison between the two networks to evaluate the coordination. The results indicate that only 21.4% of districts and 13.2% of district connections have a rational coordination. Finally, the strategies are put forward for optimization, which suggest adjusting subway transit density, regulating land-use intensity and planning new mass transits for the uncoordinated parts.
Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Brasjen, Noud; Uijlenhoet, Remko
The suitability of commercial microwave link networks for ground validation of GPM (Global Precipitation Measurement) data is evaluated here. Two state-of-the-art rainfall products are compared over the land surface of the Netherlands for a period of 7 months, i.e., rainfall maps from commercial cellular communication networks and Integrated Multi-satellite Retrievals for GPM (IMERG). Commercial microwave link networks are nowadays the core component in telecommunications worldwide. Rainfall rates can be retrieved from measurements of attenuation between transmitting and receiving antennas. If adequately set up, these networks enable rainfall monitoring tens of meters above the ground at high spatiotemporal resolutions (temporal sampling of seconds to tens of minutes, and spatial sampling of hundreds of meters to tens of kilometers). The GPM mission is the successor of TRMM (Tropical Rainfall Measurement Mission). For two years now, IMERG offers rainfall estimates across the globe (180°W - 180°E and 60°N - 60°S) at spatiotemporal resolutions of 0.1° x 0.1° every 30 min. These two data sets are compared against a Dutch gauge-adjusted radar data set, considered to be the ground truth given its accuracy, spatiotemporal resolution and availability. The suitability of microwave link networks in satellite rainfall evaluation is of special interest, given the independent character of this technique, its high spatiotemporal resolutions and availability. These are valuable assets for water management and modeling of floods, landslides, and weather extremes; especially in places where rain gauge networks are scarce or poorly maintained, or where weather radar networks are too expensive to acquire and/or maintain.
Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi
Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896
Full Text Available Sensor node in wireless sensor network is a micro-embedded system with limited memory, energy and communication capabilities. Some nodes will run out of energy and exit the network earlier than other nodes because of the uneven energy consumption. This will lead to partial or complete paralysis of the whole wireless sensor network. A balancing algorithm based on the assistance of approaching nodes is proposed. Via the set theory, notes are divided into neighbor nodes set and approaching nodes set. Approaching nodes will help weaker nodes forward part of massages to balance energy consumption. Simulation result has verified the rationality and feasibility of the balancing algorithm.
Riza Permana, Angga; Rintis Hadiani, Rr.; Syafi'i
Ponorogo Regency has 440 Irrigation Area with a total area of 17,950 Ha. Due to the limited budget and lack of maintenance cause decreased function on the irrigation. The aim of this study is to make an appropriate system to determine the indices weighted of the rank prioritization criteria for irrigation network maintenance using a fuzzy-based methodology. The criteria that are used such as the physical condition of irrigation networks, area of service, estimated maintenance cost, and efficiency of irrigation water distribution. 26 experts in the field of water resources in the Dinas Pekerjaan Umum were asked to fill out the questionnaire, and the result will be used as a benchmark to determine the rank of irrigation network maintenance priority. The results demonstrate that the physical condition of irrigation networks criterion (W1) = 0,279 has the greatest impact on the assessment process. The area of service (W2) = 0,270, efficiency of irrigation water distribution (W4) = 0,249, and estimated maintenance cost (W3) = 0,202 criteria rank next in effectiveness, respectively. The proposed methodology deals with uncertainty and vague data using triangular fuzzy numbers, and, moreover, it provides a comprehensive decision-making technique to assess maintenance priority on irrigation network.
Sharma, Kiran; Gopalakrishnan, Balagopal; Chakrabarti, Anindya S; Chakraborti, Anirban
We demonstrate the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and infer the relative importance of the sectors in the nominal network through measures of centrality and clustering algorithms. Eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with three metrics, viz., market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics are anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008-09) as well as periods of relative calmness (2012-13 and 2015-16).
Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.
Christian L Barrett
Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.
Jolley, Craig C.; Douglas, Trevor
Recent years have seen dramatic advances in computational models of chemical processes in the interstellar medium (ISM). Typically, these models have been used to calculate changes in chemical abundances with time; the calculated abundances can then be compared with chemical abundances derived from observations. In this study, the output from an astrochemical simulation has been used to generate directed graphs with weighted edges; these have been analyzed with the tools of network theory to uncover whole-network properties of reaction systems in dark molecular clouds. The results allow the development of a model in which global network properties can be rationalized in terms of the basic physical properties of the reaction system. The ISM network exhibits an exponential degree distribution, which is likely to be a generic feature of chemical networks involving a broad range of reaction rate constants. While species abundances span several orders of magnitude, the formation and destruction rates for most species are approximately balanced-departures from this rule indicate species (such as CO) that play a critical role in shaping the dynamics of the system. Future theoretical or observational studies focusing on individual molecular species will be able to situate them in terms of their role in the complete system or quantify the degree to which they deviate from the typical system behavior.
D’Andrea, Eleonora; Pagnotta, Stefano; Grifoni, Emanuela; Lorenzetti, Giulia; Legnaioli, Stefano; Palleschi, Vincenzo; Lazzerini, Beatrice
The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a ‘black box’ system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples. - Highlights: • A methodological approach to neural network analysis of LIBS spectra is proposed. • The architecture of the network and the number of inputs are optimized. • The method is tested on bronze samples already analyzed using a calibration-free LIBS approach. • The results are validated, compared and discussed
Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth
Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks
Yang, S; Wang, D
This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.
Youssef, Mina; Scoglio, Caterina
Many approaches have recently been proposed to model the spread of epidemics on networks. For instance, the Susceptible/Infected/Recovered (SIR) compartmental model has successfully been applied to different types of diseases that spread out among humans and animals. When this model is applied on a contact network, the centrality characteristics of the network plays an important role in the spreading process. However, current approaches only consider an aggregate representation of the network structure, which can result in inaccurate analysis. In this paper, we propose a new individual-based SIR approach, which considers the whole description of the network structure. The individual-based approach is built on a continuous time Markov chain, and it is capable of evaluating the state probability for every individual in the network. Through mathematical analysis, we rigorously confirm the existence of an epidemic threshold below which an epidemic does not propagate in the network. We also show that the epidemic threshold is inversely proportional to the maximum eigenvalue of the network. Additionally, we study the role of the whole spectrum of the network, and determine the relationship between the maximum number of infected individuals and the set of eigenvalues and eigenvectors. To validate our approach, we analytically study the deviation with respect to the continuous time Markov chain model, and we show that the new approach is accurate for a large range of infection strength. Furthermore, we compare the new approach with the well-known heterogeneous mean field approach in the literature. Ultimately, we support our theoretical results through extensive numerical evaluations and Monte Carlo simulations. Published by Elsevier Ltd.
Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia
Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.
A. Sarfaraz Ahmed
Full Text Available In mobile ad hoc networks, communication among mobile nodes occurs through wireless medium The design of ad hoc network protocol, generally based on a traditional “layered approach”, has been found ineffective to deal with receiving signal strength (RSS-related problems, affecting the physical layer, the network layer and transport layer. This paper proposes a design approach, deviating from the traditional network design, toward enhancing the cross-layer interaction among different layers, namely physical, MAC and network. The Cross-Layer design approach for Power control (CLPC would help to enhance the transmission power by averaging the RSS values and to find an effective route between the source and the destination. This cross-layer design approach was tested by simulation (NS2 simulator and its performance over AODV was found to be better.
Zwart, Sjoerd D; van de Poel, Ibo; van Mil, Harald; Brumsen, Michiel
In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room (GDR) session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some relevant ethical issues. We argue that a network approach is also useful for ethical analysis of issues in other fields of research and development. The abandoning of the overarching rationality assumption, which is central to network approaches, does not have to lead to ethical relativism.
Qin Buzhi; Lu Xinbiao
Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
Ирина Александровна Гавриленко
Full Text Available The approach to automated management of load flow in engineering networks considering functional reliability was proposed in the article. The improvement of the concept of operational and strategic management of load flow in engineering networks was considered. The verbal statement of the problem for thesis research is defined, namely, the problem of development of information technology for exact calculation of the functional reliability of the network, or the risk of short delivery of purpose-oriented product for consumers
Zwart, S.D.; Van de Poel, I.; Van Mil, H.; Brumsen, M.
In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room (GDR) session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some releva...
Christensen, Kim Hardam; Sørensen, Torben
This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...... penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least...
Duarte, Otto Carlos M B
The first chapter of this title concerns virtualization techniques that allow sharing computational resources basically, slicing a real computational environment into virtual computational environments that are isolated from one another.The Xen and OpenFlow virtualization platforms are then presented in Chapter 2 and a performance analysis of both is provided. This chapter also defines the primitives that the network virtualization infrastructure must provide for allowing the piloting plane to manage virtual network elements.Following this, interfaces for system management of the two platform
with a directed network where Aij is not always equivalent to Aji . When this occurs, the indegree and outdegree become nontrivial. In a scale free...piqmjq] ∀ q 6= i, j (13a) piq = Aiq + Aqi∑ j(Aij + Aji ) ∀ i 6= j (13b) mjq = Ajq + Aqj maxk(Ajk + Akj) ∀ j 6= k (13c) 29 piq is the ith, qth entry in...scenario and their associated utility. The average utility for the network is shown by the red line. The black line is representative of zero utility
Christensen, Kim Hardam; Sørensen, Torben
penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...
Etzion, E; Abramowicz, H; Benhammou, Ya; Horn, D; Levinson, L; Livneh, R
The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.
Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia
To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.
Murabito, E.; Smalbone, K.; Swinton, J.; Westerhoff, H.V.; Steuer, R.
Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual
Hofstra, B.; Corten, R.; van Tubergen, F.A.; Ellison, Nicole
Most research on segregation in social networks considers small circles of strong ties, and little is known about segregation among the much larger number of weaker ties. This article proposes a novel approach to the study of these more extended networks, through the use of data on personal ties in
Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel
We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset
In its efforts to improve geological support of the safety case, Ontario Power Generation's Deep Geologic Repository Technology Programme (DGRTP) has developed a procedure (Srivastava, 2002) for creating realistic 3-D fracture network models (FNMs) that honor information typically available at the time of preliminary site characterisation: By accommodating all of the these various pieces of 'hard' and 'soft' data, these FNMs provide a single, coherent and consistent model that can serve the needs of many preliminary site characterisation studies. The detailed, complex and realistic models of 3-D fracture geometry produced by this method can serve as the basis for developing rock property models to be used in flow and transport studies. They can also be used for exploring the suitability of a proposed site by providing quantitative assessments of the probability that a proposed repository with a specified geometry will be intersected by fractures. When integrated with state-of-the-art scientific visualisation, these models can also help in the planning of additional data gathering activities by identifying critical fractures that merit further detailed investigation. Finally, these FNMs can serve as one of the central elements of the presentation and explanation of the Descriptive Conceptual Geosphere Model (DCM) to other interested parties, including non-technical audiences. In addition to being ideally suited to preliminary site characterisation, the approach also readily incorporates field data that may become available during subsequent site investigations, including ground reconnaissance, borehole programmes and other subsurface studies. A single approach can therefore serve the needs of the site characterisation from its inception through several years of data collection and more detailed site-specific investigations, accommodating new data as they become available and updating the FNMs accordingly. The FNMs from this method are probabilistic in the sense that
Burgos, Jose E.
This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an "A-B-A" design…
Lokeswaran, S.; Eswaramoorthy, M.
This paper presents heat transfer analysis of solar parabolic dish cooker using Artificial Neural Network (ANN). The objective of this study to envisage thermal performance parameters such as receiver plate and pot water temperatures of the solar parabolic dish cooker by using the ANN for experimental data. An experiment is conducted under two cases (1) cooker with plain receiver and (2) cooker with porous receiver. The Back Propagation (BP) algorithm is used to train and test networks and ANN predictions are compared with experimental results. Different network configurations are studied by the aid of searching a relatively better network for prediction. The results showed a good regression analysis with the correlation coefficients in the range of 0.9968-0.9992 and mean relative errors (MREs) in the range of 1.2586-4.0346% for the test data set. Thus ANN model can successfully be used for the prediction of the thermal performance parameters of parabolic dish cooker with reasonable degree of accuracy. (authors)
Van Reisen, J.L.B.
Process plants have high energy consumption. Much energy can be saved by a proper design of the heat exchanger network, which contains the main heat transferring equipment of the plant. Existing plants can often be made more energy-efficient by a retrofit: the (physical) modification of the
criminality, in- surgency, regional conflict, and terrorism. Such environments can host destructive networks and various forms of ideological ...tion.23 Collective identities draw conceptual boundaries around the genus of individuals who are similarly affected by specific circumstances. However...organized across territorial boundaries and borders. The fact that the organization on the surface appears to be loose, there is an ideological connection
Iqbal, F.; Smets, R.; Kuipers, F.A.
The points-of-presence of optical networks are interconnected by photonic paths capable of carrying Terabits of data. However, signals along those photonic paths accumulate transmission impairments and thus can be unreadable at the receiver if the accumulated impairments are too high. Our
Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.
Jason L. Wright; Milos Manic
Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.
Martin, Julia W.; Hughes, Brian
This article highlights a middle ground for academic publishing between formal peer-reviewed journals and informal blogging that we call "Small "p" Publishing." Having implemented and tested a publishing network that illustrates this middle ground, we describe its unique contributions to scholars and learning communities. Three features that…
Arentze, T.A.; Berg, van den P.E.W.; Timmermans, H.J.P.
Social activities are responsible for a large proportion of travel demands of individuals. Modeling of the social network of a studied population offers a basis to predict social travel in a more comprehensive way than currently is possible. In this paper we develop a method to generate a whole
Afify, Laila H.; Elsawy, Hesham; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim
-in-Distribution approach that utilizes stochastic geometric tools to account for the network geometry in the performance characterization. Different from the other stochastic geometry models adopted in the literature, the developed analysis accounts for important
Full Text Available -1 An Efficient Approach for Node Localisation and Tracking in Wireless Sensor Networks Martin K. Mwila Submitted in partial fulfilment of the requirements for the degree Magister Technologiae: Electrical Engineering in the Department of Electrical Engineering...
Beck, Jessica; Kempener, Ruud; Cohen, Brett; Petrie, Jim
This paper explores a new approach to planning and optimization of energy networks, using a mix of global optimization and agent-based modeling tools. This approach takes account of techno-economic, environmental and social criteria, and engages explicitly with inherent network complexity in terms of the autonomous decision-making capability of individual agents within the network, who may choose not to act as economic rationalists. This is an important consideration from the standpoint of meeting sustainable development goals. The approach attempts to set targets for energy planning, by determining preferred network development pathways through multi-objective optimization. The viability of such plans is then explored through agent-based models. The combined approach is demonstrated for a case study of regional electricity generation in South Africa, with biomass as feedstock
Almoghathawi, Yasser; Barker, Kash; Rocco, Claudio M.; Nicholson, Charles D.
Analyzing network vulnerability is a key element of network planning in order to be prepared for any disruptive event that might impact the performance of the network. Hence, many importance measures have been proposed to identify the important components in a network with respect to vulnerability and rank them accordingly based on individual importance measure. However, in this paper, we propose a new approach to identify the most important network components based on multiple importance measures using a multi criteria decision making (MCDM) method, namely the technique for order performance by similarity to ideal solution (TOPSIS), able to take into account the preferences of decision-makers. We consider multiple edge-specific flow-based importance measures provided as the multiple criteria of a network where the alternatives are the edges. Accordingly, TOPSIS is used to rank the edges of the network based on their importance considering multiple different importance measures. The proposed approach is illustrated through different networks with different densities along with the effects of weighs. - Highlights: • We integrate several perspectives on network vulnerability to generate a component importance ranking. • We apply these measures to determine the importance of edges after disruptions. • Networks of varying size and density are explored.
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
Full Text Available Argentina is among the four largest producers of soybeans, sunflower, corn, and wheat, among other agricultural products. Institutional and policy changes during the 1990s fostered the development of Argentine agriculture and the introduction of innovative process and product technologies (no-till, agrochemicals, GMO, GPS and new investments in modern, large-scale sunflower and soybean processing plants. In addition to technological changes, a "quiet revolution" occurred in the way agricultural production was carried out and organized: from self-production or ownership agriculture to a contract-based agriculture. The objective of this paper is to explore and describe the emergence of networks in the Argentine crop production sector. The paper presents and describes four cases that currently represent about 50% of total grain and oilseed production in Argentina: "informal hybrid form", "agricultural trust fund", "investor-oriented corporate structure", and "network of networks". In all cases, hybrid forms involve a group of actors linked by common objectives, mainly to gain scale, share resources, and improve the profitability of the business. Informal contracts seem to be the most common way of organizing the agriculture process, but using short-term contracts and sequential interfirm collaboration. Networks of networks involve long-term relationships and social development, and reciprocal interfirm collaboration. Agricultural trust fund and investor-oriented corporate structures have combined interfirm collaboration and medium-term relationships. These organizational forms are highly flexible and show a great capacity to adapt to challenges; they are competitive because they enjoy aligned incentives, flexibility, and adaptability.
Mao, Yuxin; Wei, Guiyi
The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose. PMID:22163424
Full Text Available Abstract Collaboration among health care providers and across systems is proposed as a strategy to improve health care delivery the world over. Over the past two decades, health care providers have been encouraged to work in partnership and build interdisciplinary teams. More recently, the notion of networks has entered this discourse but the lack of consensus and understanding about what is meant by adopting a network approach in health services limits its use. Also crucial to this discussion is the work of distinguishing the nature and extent of the impact of social relationships – generally referred to as social capital. In this paper, we review the rationale for collaboration in health care systems; provide an overview and synthesis of key concepts; dispel some common misconceptions of networks; and apply the theory to an example of primary healthcare network reform in Alberta (Canada. Our central thesis is that a relational approach to systems change, one based on a synthesis of network theory and social capital can provide the fodation for a multi-focal approach to primary healthcare reform. Action strategies are recommended to move from an awareness of 'networks' to fully translating knowledge from existing theory to guide planning and practice innovations. Decision-makers are encouraged to consider a multi-focal approach that effectively incorporates a network and social capital approach in planning and evaluating primary healthcare reform.
Full Text Available The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.
Mao, Yuxin; Wei, Guiyi
The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.
A Belief-Space Approach to Integrated Intelligence- Research Area 10.3: Intelligent Networks The views, opinions and/or findings contained in this...Technology (MIT) Title: A Belief-Space Approach to Integrated Intelligence- Research Area 10.3: Intelligent Networks Report Term: 0-Other Email: tlp...students presented progress and received feedback from the research group . o wrote papers on their research and submitted them to leading conferences
Feedforward neural networks with error backpropagation are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed. (orig.)
Xiangyu He; Shanghong He
Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...
O’Keefe, Daniel J.; Wu, Daisy
Persuading people to undertake actions to prevent skin cancer is an important public health challenge. A number of studies have compared the effectiveness of gain-framed and loss-framed appeals in this domain, often expecting gain-framed appeals to be more persuasive. A meta-analytic review (k = 33, N = 4,168), however, finds no significant difference in the persuasiveness of gain- and loss-framed appeals for encouraging skin cancer prevention. This conclusion is unaffected by differences in the specific protective action advocated or by differences in the kind of outcomes invoked. But the results offer an intimation that men might be more susceptible to framing variations in this domain—with loss-framed appeals potentially having a persuasive advantage. PMID:22829794
Neshati, Mahmood; Hashemi, Seyyed Hadi; Beigy, Hamid
Expert finding problem in bibliographic networks has received increased interest in recent years. This problem concerns finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose two discriminative methods for realizing leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from a Microsoft academic search engine, show that the proposed model significantly improves the performance of expert finding in terms of all common information retrieval evaluation metrics.
Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang
Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.
Illinois 60607 email@example.com Rajmonda S. Caceres MIT Lincoln Laboratory 244 Wood St. Lexington, Massachussetts 02421 firstname.lastname@example.org ABSTRACT...pair of intervals, where the testing phase is online. 5 DATA SETS We use five data sets: Enron, MIT Reality Mining, Badge, Hypertext09, and Haggle...proximity network of 90 MIT students and faculty using data taken from cell phones from September 2004 to May 2005 (we only use data up until the end of
X. J. Li
Full Text Available The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentation rather than through a simple square window. Since PCNN segmentation agrees with the human visual system, the proposed method shows better spectral consistency. Our experiments, which have been carried out for both suburban and urban datasets, demonstrate that the proposed method outperforms other methods in multispectral pansharpening.
Mortensen, Kjeld Høyer; Schougaard, Kari Rye; Schultz, Ulrik Pagh
, even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....
Mortensen, Kjeld Høyer; Schougaard, Kari Sofie Fogh; Schultz, Ulrik Pagh
, even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....
Gracia-Lázaro, Carlos; Quijandría, Fernando; Hernández, Laura; Floría, Luis Mario; Moreno, Yamir
Starting from Axelrod's model of cultural dissemination, we introduce a rewiring probability, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter. For low values of tolerance, rewiring promotes the convergence to a frozen monocultural state. However, intermediate tolerance values prevent rewiring once the network is fragmented, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.
Xu, Xin; Cui, Qiang
This paper focuses on evaluating airline energy efficiency, which is firstly divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. The new four-stage network structure of airline energy efficiency is a modification of existing models. A new approach, integrated with Network Epsilon-based Measure and Network Slacks-based Measure, is applied to assess the overall energy efficiency and divisional efficiency of 19 international airlines from 2008 to 2014. The influencing factors of airline energy efficiency are analyzed through the regression analysis. The results indicate the followings: 1. The integrated model can identify the benchmarking airlines in the overall system and stages. 2. Most airlines' energy efficiencies keep steady during the period, except for some sharply fluctuations. The efficiency decreases mainly centralized in the year 2008–2011, affected by the financial crisis in the USA. 3. The average age of fleet is positively correlated with the overall energy efficiency, and each divisional efficiency has different significant influencing factors. - Highlights: • An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure is developed. • 19 airlines' energy efficiencies are evaluated. • Garuda Indonesia has the highest overall energy efficiency.
Wan, Thomas T H; Wang, Bill B L
This study examines the effects of integration on the performance ratings of the top 100 integrated healthcare networks (IHNs) in the United States. A strategic-contingency theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. To create a database for the panel study, the top 100 IHNs selected by the SMG Marketing Group in 1998 were followed up in 1999 and 2000. The data were merged with the Dorenfest data on information system integration. A growth curve model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' performance in 1998 and their subsequent rankings in the consecutive years were analyzed. IHNs' initial performance scores were positively influenced by network size, number of affiliated physicians and profit margin, and were negatively associated with average length of stay and technical efficiency. The continuing high performance, judged by maintaining higher performance scores, tended to be enhanced by the use of more managerial or executive decision-support systems. Future studies should include time-varying operational indicators to serve as predictors of network performance.
Blikra, Espen; The ATLAS collaboration
ATLAS is a high energy physics experiment in the Large Hadron Collider located at CERN. During the so called Long Shutdown 2 period scheduled for late 2019, ATLAS will undergo several modifications and upgrades on its data acquisition system in order to cope with the higher luminosity requirements. As part of these activities, a new read-out chain will be built for the New Small Wheel muon detector and the one of the Liquid Argon calorimeter will be upgraded. The subdetector specific electronic boards will be replaced with new commodity-server-based systems and instead of the custom serial-link-based communication, the new system will make use of a yet to be chosen commercial network technology. The new network will be used as a data acquisition network and at the same time it is intended to allow communication for the control, calibration and monitoring of the subdetectors. Therefore several types of traffic with different bandwidth requirements and different criticality will be competing for the same underl...
Stamenković Lidija J.
Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007
Andersen, Jacob; Bardram, Jakob Eyvind
Using body sensor networks (BSN) in critical clinical settings like emergency units in hospitals or in accidents requires that such a network can be deployed, configured, and started in a fast and easy way, while maintaining trust in the network. In this paper we present a novel approach called...... BLIG (Blinking Led Indicated Grouping) for easy deployment of BSNs on patients in critical situations, including mechanisms for uniquely identifying and grouping sensor nodes belonging to a patient in a secure and trusted way. This approach has been designed in close cooperation with users, and easy...
Full Text Available Cardiovascular diseases (CVDs are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.
Jiang, Zilong; Gao, Shu; Li, Mingjiang
Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.
Christiano Silva, Thiago; Raphael Amancio, Diego
Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.
Sean F. Everton, and Dan Cunningham. “Dark Network Resilience in a Hostile Environment: Optimizing Centralization and Density.” Criminology , Criminal...33 Sean F. Everton and Dan Cunningham, “Dark Network Resilience in a Hostile Environment: Optimizing Centralization and Density,” Criminology ...Centralization and Density” Criminology , Criminal Justice Law, & Society 16, no. 1 (2015): 1- 20. Gill, Paul, Jeongyoon Lee, Karl R. Rethemeyer, John
Kreakie, B. J.; Hychka, K. C.; Belaire, J. A.; Minor, E.; Walker, H. A.
Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago ( n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined.
Röhl, Annika; Bockmayr, Alexander
Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.
Grosso, Juan M.; Ocampo-Martinez, Carlos; Puig, Vicenç
This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.
Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng
Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.
Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah
Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.
Full Text Available Actor network theory as a qualitative approach to study complex social factors and process of socio-technical interaction provides new concepts and ideas to understand socio-technical nature of information systems. From the actor network theory viewpoint, agricultural climate information system is a network consisting of actors, actions and information related processes (production, transformation, storage, retrieval, integration, diffusion and utilization, control and management, and system mechanisms (interfaces and networks. Analysis of such systemsembody the identification of basic components and structure of the system (nodes –thedifferent sources of information production, extension, and users, and the understanding of how successfully the system works (interaction and links – in order to promote climate knowledge content and improve system performance to reach agricultural development. The present research attempted to introduce actor network theory as research framework based on network view of agricultural climate information system.
Akiki, Teddy J; Averill, Christopher L; Wrocklage, Kristen M; Scott, J Cobb; Averill, Lynnette A; Schweinsburg, Brian; Alexander-Bloch, Aaron; Martini, Brenda; Southwick, Steven M; Krystal, John H; Abdallah, Chadi G
Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing
Background: Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The application of the novel
Stephen M Pawson
Full Text Available Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.
Qiu, Lu; Gu, Changgui; Xiao, Qin; Yang, Huijie; Wu, Guolin
Extensive works have reported that a financial crisis can induce significant changes to topological structure of a stock network constructed with cross-correlations between stocks. But there are still some problems to be answered, such as what is the relationship between different crises in history and how to classify them? In the present work, we propose a new network-based solution to extract and display the relationships between the crises. The Dow Jones stock market is investigated as a typical example. The cross-correlation matrix between stocks is used to measure the state of stock market, called state matrix. All the states cluster into six sub-categories. A state network is constructed further to display the relationships between all the states, which contains a total of nine communities. It is found that three crises C , D and E (refer to the Lehman's bankruptcy in 2008, the Euro-zone and International Monetary Fund decide the first bailout for Greece in 2010, and the European sovereign debt crisis in 2011, respectively) belong to a specific sub-category and cluster in a single community. The mid-stage of C is closely linked with E, while the other stages with D. The other two crises A and B (refer to the financial crisis in Asia in 1997, and the burst of "dot-com bubble" in 2002, respectively) belong to another sub-category and gather in a corner of another single community. A and B are linked directly with C and D by two edges. By this way, we give a clear picture of the relationships between the crises.
Núñez-Serna, Rosa I.; Zamora, Juan M.
Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.
Ahamed, Syed V
This textbook offers an insightful study of the intelligent Internet-driven revolutionary and fundamental forces at work in society. Readers will have access to tools and techniques to mentor and monitor these forces rather than be driven by changes in Internet technology and flow of money. These submerged social and human forces form a powerful synergistic foursome web of (a) processor technology, (b) evolving wireless networks of the next generation, (c) the intelligent Internet, and (d) the motivation that drives individuals and corporations. In unison, the technological forces can tear
Casellas Lopez, Francesc
Català: En aquest projecte es realitza un estudi qualitatiu del grau de congestió i utilització de l'espectre radioelèctric en escenaris de Home Networking, a la vegada que es proposa una solució a aquesta problemàtica. Castellano: En este proyecto se realiza un estudio cualitativo del grado de congestión y utilización del espectro radioeléctrico en escenarios de Home Netowrking a la vez que se propone una solución a esta problemática English: This Project is researching the congestion ...
Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata
This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.
Biswas, M.A. Rafe; Robinson, Melvin D.; Fumo, Nelson
Some of the challenges to predict energy utilization has gained recognition in the residential sector due to the significant energy consumption in recent decades. However, the modeling of residential building energy consumption is still underdeveloped for optimal and robust solutions while this research area has become of greater relevance with significant advances in computation and simulation. Such advances include the advent of artificial intelligence research in statistical model development. Artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data. The development and validation of such models on one of the TxAIRE Research houses has been demonstrated in this paper. The TxAIRE houses have been designed to serve as realistic test facilities for demonstrating new technologies. The input variables used from the house data include number of days, outdoor temperature and solar radiation while the output variables are house and heat pump energy consumption. The models based on Levenberg-Marquardt and OWO-Newton algorithms had promising results of coefficients of determination within 0.87–0.91, which is comparable to prior literature. Further work will be explored to develop a robust model for residential building application. - Highlights: • A TxAIRE research house energy consumption data was collected in model development. • Neural network models developed using Levenberg–Marquardt or OWO-Newton algorithms. • Model results match well with data and statistically consistent with literature.
Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata
This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394
Full Text Available The paper deals with the development of simple electromagnetic models of small airplanes, which can contain composite materials in their construction. Electromagnetic waves can penetrate through the surface of the aircraft due to the specific electromagnetic properties of the composite materials, which can increase the intensity of fields inside the airplane and can negatively influence the functionality of the sensitive avionics. The airplane is simulated by two parallel dielectric layers (the left-hand side wall and the right-hand side wall of the airplane. The layers are put into a rectangular metallic waveguide terminated by the absorber in order to simulate the illumination of the airplane by the external wave (both of the harmonic nature and pulse one. Thanks to the simplicity of the model, the parametric analysis can be performed, and the results can be used in order to train an artificial neural network. The trained networks excel in further reduction of CPU-time demands of an airplane modeling.
Buice, Michael A.; Cowan, Jack D.
A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience
Ahmed, Z.; Nazir, B.; Zafar, M.F.; Anwar, M.M.; Azam, K.; Asar, A.U.
Network security is a discipline that focuses on securing networks from unauthorized access. Given the Escalating threats of malicious cyber attacks, modern enterprises employ multiple lines of defense. A comprehensive defense strategy against such attacks should include (I) an attack detection component that deter- mines the fact that a program is compromised, (2) an attack identification and prevention component that identifies attack packets so that one can block such packets in the future and prevents the attack from further propagation. Over the last decade, a significant amount of research has been vested in the systems that can detect cyber attacks either statically at compile time or dynamically at run time, However, not much effort is spent on automated attack packet identification or attack prevention. In this paper we present a unified solution to the problems mentioned above. We implemented this solution after the forward engineering of Open Source Security Information Management (OSSIM) system called Preventive Information Security management (PrISM) system that correlates input from different sensors so that the resulting product can automatically detect any cyber attack against it and prevents by identifying the actual attack packet(s). The PrISM was always able to detect the attacks, identify the attack packets and most often prevent by blocking the attacker's IP address to continue normal execution. There is no additional run-time performance overhead for attack prevention. (author)
Full Text Available This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS. We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.
Selfhout, Maarten; Burk, William; Branje, Susan; Denissen, Jaap; van Aken, Marcel; Meeus, Wim
The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Sociometric nominations and self-ratings on personality traits were gathered from 205 late adolescents (mean age=19 years) at 5 time points during the first year of university. SIENA, a novel multilevel statistical procedure for social network analysis, was used to examine effects of Big Five traits on friendship selection. Results indicated that friendship networks between just-acquainted individuals became increasingly more cohesive within the first 3 months and then stabilized. Whereas individuals high on Extraversion tended to select more friends than those low on this trait, individuals high on Agreeableness tended to be selected more as friends. In addition, individuals tended to select friends with similar levels of Agreeableness, Extraversion, and Openness.
This article describes a consulting approach aimed specifically at facilitating development of intra- and inter-organizational networks, a business phenomenon of growing importance. This approach is called Renga, after the classical Japanese style of composing linked verse, with which it is shown to
Dey, Ramendra Sundar; Hjuler, Hans Aage; Chi, Qijin
We report a facile and low-cost approach for the preparation of all-in-one supercapacitor electrodes using copper foam (CuF) integrated three-dimensional (3D) reduced graphene oxide (rGO) networks. The binderfree 3DrGO@CuF electrodes are capable of delivering high specific capacitance approaching...
The researchers took a commodity-based approach to evaluate the value of a list of selected commodities moved on the Texas freight network. This approach takes advantage of commodity-specific data sources and modeling processes. It provides a unique ...
Enns, Eva A; Brandeau, Margaret L
For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two "preventive" approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two "reactive" approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdös-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdös-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which