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Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time., This work has been partially supported by project DPI2014-57746-C03-2-R and FEDER funds.

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756–68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individual’s AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.

Background Influenza illness burden is substantial, particularly among young children, older adults, and those with underlying conditions. Initiatives are underway to develop better global estimates for influenza-associated hospitalizations and deaths. Knowledge gaps remain regarding the role of influenza viruses in severe respiratory disease and hospitalizations among adults, particularly in lower-income settings. Methods and findings We aggregated published data from a systematic review and unpublished data from surveillance platforms to generate global meta-analytic estimates for the proportion of acute respiratory hospitalizations associated with influenza viruses among adults. We searched 9 online databases (Medline, Embase, CINAHL, Cochrane Library, Scopus, Global Health, LILACS, WHOLIS, and CNKI; 1 January 1996–31 December 2016) to identify observational studies of influenza-associated hospitalizations in adults, and assessed eligible papers for bias using a simplified Newcastle–Ottawa scale for observational data. We applied meta-analytic proportions to global estimates of lower respiratory infections (LRIs) and hospitalizations from the Global Burden of Disease study in adults ≥20 years and by age groups (20–64 years and ≥65 years) to obtain the number of influenza-associated LRI episodes and hospitalizations for 2016. Data from 63 sources showed that influenza was associated with 14.1% (95% CI 12.1%–16.5%) of acute respiratory hospitalizations among all adults, with no significant differences by age group. The 63 data sources represent published observational studies (n = 28) and unpublished surveillance data (n = 35), from all World Health Organization regions (Africa, n = 8; Americas, n = 11; Eastern Mediterranean, n = 7; Europe, n = 8; Southeast Asia, n = 11; Western Pacific, n = 18). Data quality for published data sources was predominantly moderate or high (75%, n = 56/75). We estimate 32,126,000 (95% CI 20,484,000–46,129,000) influenza-associated LRI episodes and 5,678,000 (95% CI 3,205,000–9,432,000) LRI hospitalizations occur each year among adults. While adults, In this meta-analysis, Kathryn E. Lafond and colleagues estimate the global hospitalisation burden from influenza infections in adults., Author summary Why was this study done? Every year, millions of people become infected with influenza viruses, which infect the airways and cause symptoms that include a fever, tiredness and weakness, general aches and pains, sore throat, and cough. Most infected individuals recover quickly, but some develop more severe illness such as pneumonia, and may require hospitalization. Influenza vaccination can prevent infections and severe disease, but decisions on how to prioritize risk groups, particularly in limited-resource settings, require a robust evidence base, including global, regional, and local data. What did the researchers do and find? We conducted a systematic review to identify published articles on influenza-associated respiratory disease in hospitalized adults. We then aggregated the data from these articles with data collected by hospital-based influenza surveillance, to calculate a pooled estimate of the proportion of adults hospitalized with respiratory disease who are positive for influenza. Through this approach, we estimated that influenza was associated with 14% of acute respiratory hospitalizations among adults worldwide. This equates to over 5 million influenza-associated hospitalizations per year, with the highest rates among those 65 years and older. What do these findings mean? Estimating the disease burden of influenza allows researchers and policymakers to better understand its global impact across different target groups and allows comparisons with other causes of morbidity and mortality. As global populations age, the importance of vaccinating adults, particularly older adults, is likely to increase.

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs. ispartof: PLOS ONE vol:15 issue:12 ispartof: location:United States status: published

Social axioms or general social beliefs represent people’s cognitive map of their social world acquired through social experiences. Empirical research has related the central constructs in the study of psychology and social axioms, establishing a broad nomological network in various cultural settings. This paper studies the validity of the Social Axioms Survey II (SASII) short form, Spanish version, on the individual level in Melilla as North Africa´s borderland. Participants were 410 high school students from 14 to 18 years of age. The reliability analysis, the discriminant validity analysis, and the confirmatory factor analysis through the structural model equation, showed similar results to previous studies in other contexts and allowing the use of the survey in Melilla. In addition it is presented a fitted model that improves the psychometric results showing significant differences with the initial model. The confirmatory multi-group analysis of the fitted model shows measurement invariance across educational centers, allowing new research possibilities in the cultural context of Melilla.

Background The psychological health of female sex workers (FSWs) has emerged as a major public health concern in many low- and middle-income countries (LMICs). Key risk factors include poverty, low education, violence, alcohol and drug use, human immunodeficiency virus (HIV), and stigma and discrimination. This systematic review and meta-analysis aimed to quantify the prevalence of mental health problems among FSWs in LMICs, and to examine associations with common risk factors. Method and findings The review protocol was registered with PROSPERO, number CRD42016049179. We searched 6 electronic databases for peer-reviewed, quantitative studies from inception to 26 April 2020. Study quality was assessed with the Centre for Evidence-Based Management (CEBM) Critical Appraisal Tool. Pooled prevalence estimates were calculated for depression, anxiety, post-traumatic stress disorder (PTSD), and suicidal behaviour. Meta-analyses examined associations between these disorders and violence, alcohol/drug use, condom use, and HIV/sexually transmitted infection (STI). A total of 1,046 studies were identified, and 68 papers reporting on 56 unique studies were eligible for inclusion. These were geographically diverse (26 countries), representing all LMIC regions, and included 24,940 participants. All studies were cross-sectional and used a range of measurement tools; none reported a mental health intervention. Of the 56 studies, 14 scored as strong quality, 34 scored as moderate, and 8 scored as weak. The average age of participants was 28.9 years (age range: 11–64 years), with just under half (46%) having up to primary education or less. The pooled prevalence rates for mental disorders among FSWs in LMICs were as follows: depression 41.8% (95% CI 35.8%–48.0%), anxiety 21.0% (95% CI: 4.8%–58.4%), PTSD 19.7% (95% CI 3.2%–64.6%), psychological distress 40.8% (95% CI 20.7%–64.4%), recent suicide ideation 22.8% (95% CI 13.2%–36.5%), and recent suicide attempt 6.3% (95% CI 3.4%–11.4%). Meta-analyses found significant associations between violence experience and depression, violence experience and recent suicidal behaviour, alcohol use and recent suicidal behaviour, illicit drug use and depression, depression and inconsistent condom use with clients, and depression and HIV infection. Key study limitations include a paucity of longitudinal studies (necessary to assess causality), non-random sampling of participants by many studies, and the use of different measurement tools and cut-off scores to measure mental health problems and other common risk factors. Conclusions In this study, we found that mental health problems are highly prevalent among FSWs in LMICs and are strongly associated with common risk factors. Study findings support the concept of overlapping vulnerabilities and highlight the urgent need for interventions designed to improve the mental health and well-being of FSWs., Tara Beattie and co-workers assess the evidence on mental ill-health in female sex workers in low- and middle-income countries., Author summary Why was this study done? Understanding the prevalence of mental health problems, and key risk factors associated with poor mental health, is essential for building evidence-based prevention. To our knowledge, there has been no previous quantitative synthesis of the literature examining the prevalence of mental health problems among female sex workers (FSWs) in low- and middle-income countries (LMICs). This evidence is critical to inform evidence-based policymaking and intervention design and is timely given the growing interest in mental health globally. What did the researchers do and find? We undertook a quantitative systematic review to estimate the prevalence of mental health problems and to estimate the magnitude of associations between mental health problems and sex workers’ experience of violence, harmful alcohol and drug use, condom use, and human immunodeficiency virus (HIV) and sexually transmitted infections (STIs). Meta-analyses across 31 studies with 18,524 FSWs in 17 LMICs suggest that mental health problems—including depression, anxiety, post-traumatic stress disorder (PTSD), and suicidal thoughts and attempts—are highly prevalent among FSWs. Sex workers with a mental health problem were more likely to have experienced violence, to report harmful alcohol or drug use, to report inconsistent condom use with clients (but not regular partners), and to be HIV positive compared with sex workers who did not have a mental health problem (18 studies with 14,115 FSWs). What do these findings mean? The quantitative evidence clearly demonstrates that mental health problems are an important burden of disease experienced by many FSWs in LMICs, with levels substantially greater than those experienced by women in the general population. As no study described a mental health intervention, evidence-based mental health interventions for FSWs are urgently required to address the current treatment gap. The prevention and treatment of key risk factors such as violence and harmful alcohol and drug use would also help improve the mental health of FSWs. Longitudinal research is needed in order to unpack pathways of causation. Future studies should use validated tools and consistent cut-offs and timeframes to enable more rigorous comparisons between studies.

PubMed: 32271853 In this paper, we generalize and compare Gompertz and Logistic dynamic equations in order to describe the growth patterns of bacteria and tumor. First of all, we introduce two types of Gompertz equations, where the first type 4-paramater and 3-parameter Gompertz curves do not include the logarithm of the number of individuals, and then we derive 4- parameter and 3-parameter Logistic equations. We notice that Logistic curves are better in modeling bacteria whereas the growth pattern of tumor is described better by Gompertz curves. Increasing the number of parameters of Logistic curves give favorable results for bacteria while decreasing the number of parameters of Gompertz curves for tumor improves the curve fitting. Moreover, our results overshadow some of the existing results in the literature. © 2020 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world’s population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time., Author summary Dengue and influenza-like illness (ILI) are leading causes of viral infection in the world and hence it is important to develop accurate methods for forecasting their incidence. We use Autoregressive Likelihood Ratio method, which is a computationally efficient implementation of the variable selection method, in order to obtain a sparse (non-lasso) representation of time series, Google Trends and electronic health records (for ILI) data. This method is used to forecast dengue incidence in five countries/states and ILI incidence in USA. We show that this method outperforms existing time series methods in forecasting these diseases. The method is general and can also be used to forecast other diseases.

We generalize the notion of lambda-superstrings, presented in a previous paper, to the notion of weighted lambda-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted lambda-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic lambda-superstrings that at the same time present similar conformations to the Nef virus proteins. This research was supported in part by the Basque Government, grants IT753-13 and IT974-16 and by the UPV/EHU and Basque Center of Applied Mathematics, grant US18/21. This research was also in part by the Slovenian Research Agency (I0-0035, research program P1-0285, and research projects N1-0032, J1-7051, and J1-9110). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user. This work was partially supported by the ERDF/Spanish Ministry of Science, Innovation and Universities - National Research Agency/PhysComp project, TIN2017-85409-P and by the Department of Education, Universities and Research of the Basque Government (ADIAN research group, grant IT980-16).

Needles are commonly used in the clinic for percutaneous procedures. The outcome of such procedures heavily depends on accurate placement of the needle. There are two main challenges to achieve high accuracy: First, aligning the needle with the targeted lesion, and second, compensating for the deflection of the needle in the tissue. In order to address these challenges, scientists have developed several robotic setups for needle steering. However, the subject is still under research and reliable implementations which can be used in clinical practice are not yet available. In this paper, we have taken some steps in order to bring needle steering closer to practice. A new hybrid control algorithm is developed, which enables us to control a flexible needle by combing base-manipulation and beveled-tip steering methods. A pre-operative path planner is developed which considers the clinical requirements. The proposed method is tested in the lung of a fresh-frozen human cadaver. The work-flow of the experiments are similar to the current clinical practice. Three experimental cases are used to evaluate the proposed steering algorithm. Experimental Case I shows that using the proposed steering algorithm controllability of the needle is increased. In Case II and Case III, the needle is steered in a gelatin phantom and a human cadaver, respectively. The targeting accuracy of 1.35 +/- 0.49mm in gelatin phantom and 1.97 +/- 0.89mm in cadave is achieved. A feasibility study is performed, in which a fine needle aspiration (FNA) needle is steered in the lungs of a human cadaver under computed tomography guidance. The targeting error for the feasibility study is 2.89 +/- 0.22mm. The results suggest that such a robotic system can be beneficial for clinical use and the patient receives less x-ray radiation.

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets -or clusters- and then generating in an automatic way a new song which is somehow "inspired" in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is. This work was supported by IT900-16 Research Team from the Basque Government.

The model of population growth is revised in this paper. A new model is proposed based on the concept of fractional differentiation that uses the generalized Mittag-Leffler function as kernel of differentiation. The new model includes the choice of sexuality. The existence of unique solution is investigated and numerical solution is provided.

Preterm delivery affects about one tenth of human births and is associated with an increased perinatal morbimortality as well as with remarkable costs. Even if there are a number of predictors and markers of preterm delivery, none of them has a high accuracy. In order to find quantitative indicators of the immediacy of labor, 142 cardiotocographies (CTG) recorded from women consulting because of suspected threatened premature delivery with gestational ages comprehended between 24 and 35 weeks were collected and analyzed. These 142 samples were divided into two groups: the delayed labor group (n = 75), formed by the women who delivered more than seven days after the tocography was performed, and the anticipated labor group (n = 67), which corresponded to the women whose labor took place during the seven days following the recording. As a means of finding significant differences between the two groups, some key informational properties were analyzed by applying nonlinear techniques on the tocography recordings. Both the regularity and the persistence levels of the delayed labor group, which were measured by Approximate Entropy (ApEn) and Generalized Hurst Exponent (GHE) respectively, were found to be significantly different from the anticipated labor group. As delivery approached, the values of ApEn tended to increase while the values of GHE tended to decrease, suggesting that these two methods are sensitive to labor immediacy. On this paper, for the first time, we have been able to estimate childbirth immediacy by applying nonlinear methods on tocographies. We propose the use of the techniques herein described as new quantitative diagnosis tools for premature birth that significantly improve the current protocols for preterm labor prediction worldwide. Work by the first author was supported by the Basque Government grant PRE-2015-1-194,and work by the first and second authors by the Basque Government grant IT974-16. This work was also supported in part by FERRING laboratories Madrid, Spain (RM), https://www.ferring.com/en/home/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Maternal smoking during pregnancy is linked to reduced birth weight but the gestation at onset of this relationship is not certain. We present a systematic review of the literature describing associations between maternal smoking during pregnancy and ultrasound measurements of fetal size, together with an accompanying meta-analysis. Studies were selected from electronic databases (OVID, EMBASE and Google Scholar) that examined associations between maternal smoking or smoke exposure and antenatal fetal ultrasound measurements. Outcome measures were first, second or third trimester fetal measurements. There were 284 abstracts identified, 16 papers were included in the review and the meta-analysis included data from eight populations. Maternal smoking was associated with reduced second trimester head size (mean reduction 0.09 standard deviation (SD) [95% CI 0.01, 0.16]) and femur length (0.06 [0.01, 0.10]) and reduced third trimester head size (0.18 SD [0.13, 0.23]), femur length (0.27 SD [0.21, 0.32]) and estimated fetal weight (0.18 SD [0.11, 0.24]). Higher maternal cigarette consumption was associated with a lower z score for head size in the second (mean difference 0.09 SD [0, 0.19]) and third (0.15 SD [0.03, 0.26]) trimesters compared to lower consumption. Fetal measurements were not reduced for those whose mothers quit before or after becoming pregnant compared to mothers who had never smoked. Maternal smoking during pregnancy is associated with reduced fetal measurements after the first trimester, particularly reduced head size and femur length. These effects may be attenuated if mothers quit or reduce cigarette consumption during pregnancy. The study was supported by the European Cooperation in Science and Technology, who provided funds for publication. KMG is supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and by the European Union's Seventh Framework Programme (FP7/2007-2013), projects Early Nutrition and ODIN under grant agreement numbers 289346 and 613977.

This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results. This work has been partly funded by the University of Vic - Central University of Catalonia under the grant R0947 to JSC and grants from ANPCyT - PICT 2012 -1519 and CONICET - PIP 114-201101-00021, Argentina, to CFC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work has been partly funded by the University of Vic - Central University of Catalonia under the grant R0947 and grants from ANPCyT - PICT 2012-1519 and CONICET - PIP 114-201101-00021, Argentina. The authors would like to acknowledge Prof. Christian Jutten (GIPSA Lab, Grenoble, France) for his discussions and guidance across this research work. The authors thank reviewers for providing deep and useful comments on the first version of the manuscript.

The world is still recovering from the financial crisis peaking in September 2008. The triggering event was the bankruptcy of Lehman Brothers. To detect such turmoils, one can investigate the time-dependent behaviour of correlations between assets or indices. These cross-correlations have been connected to the systemic risks within markets by several studies in the aftermath of this crisis. We study 37 different US indices which cover almost all aspects of the US economy and show that monitoring an average investor's behaviour can be used to quantify times of increased risk. In this paper the overall investing strategy is approximated by the ground-states of the mean-variance model along the efficient frontier bound to real world constraints. Changes in the behaviour of the average investor is utlilized as a early warning sign.

WOS:000338512200037 PubMed ID: 24972050 This paper presents responses of an edge circular cantilever beam under the effect of an impact force. The beam is excited by a transverse triangular force impulse modulated by a harmonic motion. The Kelvin-Voigt model for the material of the beam is used. The cracked beam is modelled as an assembly of two sub-beams connected through a massless elastic rotational spring. The considered problem is investigated within the Bernoulli-Euler beam theory by using energy based finite element method. The system of equations of motion is derived by using Lagrange's equations. The obtained system of linear differential equations is reduced to a linear algebraic equation system and solved in the time domain by using Newmark average acceleration method. In the study, the effects of the location of crack, the depth of the crack, on the characteristics of the reflected waves are investigated in detail. Also, the positions of the cracks are calculated by using reflected waves.

In many fields of research, so-called ‘multiblock’ data are collected, i.e., data containing multivariate observations that are nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then corresponds to a ‘data block’. For such data, it may be interesting to investigate the extent to which the correlation structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by means of component analysis, one may want to explore which components are common across all data blocks and which components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific components. Model estimation and model selection procedures are described and simulation results validate their performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value. ispartof: PLoS One vol:8 issue:5 pages:1-14 ispartof: location:United States status: published