WorldWideScience

Sample records for pubertal timing predicts

  1. Off-Time Pubertal Timing Predicts Physiological Reactivity to Postpuberty Interpersonal Stress

    Science.gov (United States)

    Smith, Anne Emilie; Powers, Sally I.

    2009-01-01

    We investigated associations between retrospectively assessed timing of pubertal development, interpersonal interactions, and hypothalamic-pituitary-adrenal axis reactivity to an interpersonal stress task in 110 young adult women. Participants provided salivary cortisol samples at points prior and subsequent to a video-taped conflict discussion…

  2. The value of shoe size for prediction of the timing of the pubertal growth spurt.

    NARCIS (Netherlands)

    Busscher, I.; Kingma, I.; Wapstra, F.H.; Bulstra, S.K.; Verkerke, G.J.; Veldhuizen, A.G.

    2011-01-01

    Background: Knowing the timing of the pubertal growth spurt of the spine, represented by sitting height, is essential for the prognosis and therapy of adolescent idiopathic scoliosis. There are several indicators that reflect growth or remaining growth of the patient. For example, distal body parts

  3. Pubertal timing and adolescent sexual behavior in girls.

    Science.gov (United States)

    Moore, Sarah R; Harden, K Paige; Mendle, Jane

    2014-06-01

    Girls who experience earlier pubertal timing relative to peers also exhibit earlier timing of sexual intercourse and more unstable sexual relationships. Although pubertal development initiates feelings of physical desire, the transition into romantic and sexual relationships involves complex biological and social processes contributing both to physical maturation and to individual interpretations of pubertal experiences. Using a sample of female sibling pairs (n = 923 pairs) from the National Longitudinal Study of Adolescent Health, the present study investigated associations among menarche and perceived pubertal timing, age of first sexual intercourse (AFI), and adolescent dating and sexual behavior using a behavioral genetic approach. Genetic factors influencing age at menarche and perceived pubertal timing predicted AFI through shared genetic pathways, whereas genetic factors related only to perceived pubertal timing predicted engagement in dating, romantic sex, and nonromantic sex in the previous 18 months. These results suggest that a girl's interpretation of her pubertal timing beyond objective timing is important to consider for the timing and the contexts of romantic and reproductive behavior. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  4. Pubertal Timing and Mexican-Origin Girls’ Internalizing and Externalizing Symptoms: The Influence of Harsh Parenting

    Science.gov (United States)

    Deardorff, J.; Cham, H.; Gonzales, NA.; White, R.M.B.; Tein, J.-Y.; Wong, J.; Roosa, M.W.

    2012-01-01

    Early-maturing girls are at risk for internalizing and externalizing problems. Scarce research has examined pubertal timing and mental health among Mexican Americans, or examined the influence of parenting behaviors on these relations. This study addressed these gaps. This was a prospective examination of 362 Mexican-origin girls and their mothers using three waves of data. Measures included girls’ self-report of pubertal development and girls’ and mothers’ report of maternal harsh parenting and daughters’ mental health. Using structural equation modeling, we examined whether pubertal timing in 5th grade predicted girls’ internalizing and externalizing outcomes in 10th grade. We also examined the mediating and moderating effects of harsh parenting on the relations between pubertal timing and internalizing and externalizing behaviors, as well as the influence of mothers’ and daughters’ nativity on these relations. Results differed depending on reporter and maternal nativity. Using daughters’ report, Mexican American mothers’ harsh parenting acted as a moderator. At high levels of harsh parenting, early pubertal timing predicted higher externalizing scores, while at low levels of harsh parenting, early timing predicted lower externalizing scores. For Mexican immigrant mothers, harsh parenting mediated the effects of pubertal timing on girls’ internalizing and externalizing problems. There were no significant pubertal effects for mothers’ report. Findings suggest that maternal harsh parenting plays a key role in the relations between early pubertal timing and behavioral and emotional outcomes among Mexican-origin girls. PMID:23231686

  5. Gene-Environment Interplay in the Association between Pubertal Timing and Delinquency in Adolescent Girls

    Science.gov (United States)

    Harden, K. Paige; Mendle, Jane

    2014-01-01

    Early pubertal timing places girls at elevated risk for a breadth of negative outcomes, including involvement in delinquent behavior. While previous developmental research has emphasized the unique social challenges faced by early maturing girls, this relation is complicated by genetic influences for both delinquent behavior and pubertal timing, which are seldom controlled for in existing research. The current study uses genetically informed data on 924 female-female twin and sibling pairs drawn from the National Longitudinal Study of Adolescent Health to (1) disentangle biological versus environmental mechanisms for the effects of early pubertal timing and (2) test for gene-environment interactions. Results indicate that early pubertal timing influences girls’ delinquency through a complex interplay between biological risk and environmental experiences. Genes related to earlier age at menarche and higher perceived development significantly predict increased involvement in both non-violent and violent delinquency. Moreover, after accounting for this genetic association between pubertal timing and delinquency, the impact of non-shared environmental influences on delinquency are significantly moderated by pubertal timing, such that the non-shared environment is most important among early maturing girls. This interaction effect is particularly evident for non-violent delinquency. Overall, results suggest early maturing girls are vulnerable to an interaction between genetic and environmental risks for delinquent behavior. PMID:21668078

  6. Pubertal development timing in urban Chinese boys.

    Science.gov (United States)

    Ma, H-M; Chen, S-K; Chen, R-M; Zhu, C; Xiong, F; Li, T; Wang, W; Liu, G-L; Luo, X-P; Liu, L; Du, M-L

    2011-10-01

    We describe current pubertal development in healthy urban Chinese boys. A cross-sectional study of the pubertal development of 18,807 urban Chinese boys aged from 3.50 to 18.49years was conducted between 2003 and 2005. Testicular volume was evaluated with a Prader orchidometer. Pubic hair development was assessed according to the Tanner method. Data on spermarche were collected using the status quo method. Probit analysis was used to calculate the median age and 95% CI at different stages of testicular development, pubic hair development and spermarche. By age 9, 12.99% of the boys had a testicular volume of 4mL or greater. The median age of onset of puberty defined as the age at attainment of testicular volume of 4mL or greater was 10.55 (95% CI 10.27-10.79) years. The median age for onset of pubic hair development (PH(2) ) and spermarche was 12.78 (95%CI 12.67-12.89) years and 14.05 (95%CI 13.80-14.32) years, respectively. Pubertal onset in urban Chinese boys is earlier than currently used clinical norms but their pubic hair development occurs relatively late in comparison with the reported data from numerous other countries. There is also evidence of a secular trend towards an earlier age of spermarche since 1979 in Chinese urban boys. © 2011 The Authors. International Journal of Andrology © 2011 European Academy of Andrology.

  7. The influence of pubertal timing and stressful life events on depression and delinquency among Chinese adolescents.

    Science.gov (United States)

    Chen, Jie; Yu, Jing; Wu, Yun; Zhang, Jianxin

    2015-06-01

    This study aimed to investigate the influences of pubertal timing and stressful life events on Chinese adolescents' depression and delinquency. Sex differences in these influences were also examined. A large sample with 4,228 participants aged 12-15 years (53% girls) was recruited in Beijing, China. Participants' pubertal development, stressful life events, depressive symptoms, and delinquency were measured using self-reported questionnaires. Both early maturing girls and boys displayed more delinquency than their same-sex on-time and late maturing peers. Early maturing girls displayed more depressive symptoms than on-time and late maturing girls, but boys in the three maturation groups showed similar levels of depressive symptoms. The interactive effects between early pubertal timing and stressful life events were significant in predicting depression and delinquency, particularly for girls. Early pubertal maturation is an important risk factor for Chinese adolescents' depression and delinquency. Stressful life events intensified the detrimental effects of early pubertal maturation on adolescents' depression and delinquency, particularly for girls. © 2015 The Institute of Psychology, Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

  8. Understanding the Link Between Pubertal Timing in Girls and the Development of Depressive Symptoms: The Role of Sexual Harassment.

    Science.gov (United States)

    Skoog, Therése; Bayram Özdemir, Sevgi; Stattin, Håkan

    2016-02-01

    The link between sexual maturation, or pubertal timing, in girls and adolescent depressive symptoms is well-documented, but the underlying processes remain unclear. We examined whether sexual harassment, which has previously been linked to both pubertal timing and depressive symptoms, mediates this link, using a two-wave longitudinal study including 454 girls in 7th (M age  = 13.42, SD = .53) and 8th grade (M age  = 14.42, SD = .55). Pubertal timing was linked to depressive symptoms in both age groups, and predicted an increase in depressive symptoms among the 7th graders. Sexual harassment significantly mediated the link between pubertal timing and depressive symptoms among the 7th, but not the 8th grade girls. Together, our findings suggest that one way to prevent depressive symptoms among early-maturing girls could be to address sexual harassment in preventive intervention in early adolescence.

  9. Association Between Urinary Phthalates and Pubertal Timing in Chinese Adolescents

    Directory of Open Access Journals (Sweden)

    Huijing Shi

    2015-09-01

    Full Text Available Background: Phthalates are synthetic chemicals and ubiquitous environmental contaminants, with hormonal activity that may alter the course of pubertal development in children. Objectives: To determine whether exposure to phthalate metabolites is associated with timing of pubertal development in a cross-sectional study of a school-based clustered sample of 503 children from a suburban district in Shanghai, China, who were 7–14 years of age at enrollment (2010 October to November. Methods: We analyzed six phthalate metabolites in urine samples by isotope-dilution liquid chromatography tandem mass spectrometry. The associations of exposures to phthalates with pubertal timing of testes, breast, and pubic hair development (represented as Tanner stages were evaluated using an ordered logistic regression model adjusted for chronological age, body fat proportion (BF%, and parental education. Results: In boys, urinary mono-n-butyl phthalate (MBP levels were negatively associated with testicular volume, and mono (2-ethyl-5-hydroxyhexyl phthalate (MEHHP and mono (2-ethyl-5-oxohexyl phthalate (MEOHP levels were negatively associated with pubic hair stages. The odds of being in an advanced stage were decreased by 43%–51%. In girls, mono (2-ethylhexyl phthalate (MEHP, MEHHP, and MEOHP levels, as well as the sum of these levels, were positively associated with breast stages, and the association was much stronger in girls with high BF%; the odds of being in an advanced stage were increase by 29% to 50%. Conclusions: Phthalate metabolites investigated in this study show significant associations with pubertal timing both in boys and in girls, especially among girls with high BF%.

  10. Role of amygdala kisspeptin in pubertal timing in female rats.

    Directory of Open Access Journals (Sweden)

    Daniel A Adekunbi

    Full Text Available To investigate the mechanism by which maternal obesity disrupts reproductive function in offspring, we examined Kiss1 expression in the hypothalamic arcuate (ARC and anteroventral periventricular (AVPV nuclei, and posterodorsal medial amygdala (MePD of pre-pubertal and young adult offspring. Sprague-Dawley rats were fed either a standard or energy-dense diet for six weeks prior to mating and throughout pregnancy and lactation. Male and female offspring were weaned onto normal diet on postnatal day (pnd 21. Brains were collected on pnd 30 or 100 for qRT-PCR to determine Kiss1 mRNA levels. Maternal obesity increased Kiss1 mRNA expression in the MePD of pre-pubertal male and female offspring, whereas Kiss1 expression was not affected in the ARC or AVPV at this age. Maternal obesity reduced Kiss1 expression in all three brain regions of 3 month old female offspring, but only in MePD of males. The role of MePD kisspeptin on puberty, estrous cyclicity and preovulatory LH surges was assessed directly in a separate group of post-weanling and young adult female rats exposed to a normal diet throughout their life course. Bilateral intra-MePD cannulae connected to osmotic mini-pumps for delivery of kisspeptin receptor antagonist (Peptide 234 for 14 days were chronically implanted on pnd 21 or 100. Antagonism of MePD kisspeptin delayed puberty onset, disrupted estrous cyclicity and reduced the incidence of LH surges. These data show that the MePD plays a key role in pubertal timing and ovulation and that maternal obesity may act via amygdala kisspeptin signaling to influence reproductive function in the offspring.

  11. Pubertal timing and health-related behaviours in adolescence - socio- economic outcomes in a follow-up study from Finland

    Directory of Open Access Journals (Sweden)

    Leena K Koivusilta

    2006-03-01

    Full Text Available

    Background. Pubertal timing is connected with health-related lifestyle in adulthood. We studied whether early or late pubertal timing is predictive of socio-economic outcomes in early adulthood and whether the associations are mediated by health behaviours.

    Methods. Survey data (1981, 1983, 1985, 1987 from samples of 14-year-old Finns (N=4246, response rate 85% were linked with respondents’ attained educational level, socio-economic and labour market position in 2001 (ages 28-34. Ages of menarche and first ejaculation indicated pubertal timing.

    Results. As compared to adolescents with average age pubertal timing, boys and girls maturing at an early age more often participated in health-compromising behaviours, while those maturing at a later age participated less frequently. Pubertal timing was not associated with attained educational level or socioeconomic position in girls and not with labour market position at the time of follow-up in either sex. In boys, independently of health behaviours, early or late onset of puberty predicted low educational level, while late onset predicted low socio-economic position.

    Conclusion. Timing of puberty has a stronger connection with socio-economic outcomes in boys than in girls. Deviance from the normative pace of physical development, especially late maturation, is among boys slightly depicted in the hierarchy of socio-economic positions of the society. As pubertal timing is connected with health-related behaviours – especially with smoking – the pacing of developmental transitions should be considered in planning programmes preventing unhealthy behavioural patterns often linked with negative attitudes towards schooling.

  12. Ethnicity, Perceived Pubertal Timing, Externalizing Behaviors, and Depressive Symptoms among Black Adolescent Girls

    Science.gov (United States)

    Carter, Rona; Caldwell, Cleopatra Howard; Matusko, Niki; Antonucci, Toni; Jackson, James S.

    2011-01-01

    An accumulation of research evidence suggests that early pubertal timing plays a significant role in girls' behavioral and emotional problems. If early pubertal timing is a problematic event, then early developing Black girls should manifest evidence of this crisis because they tend to be the earliest to develop compared to other girls from…

  13. The Role of Peer Stress and Pubertal Timing on Symptoms of Psychopathology during Early Adolescence

    Science.gov (United States)

    Sontag, Lisa M.; Graber, Julia A.; Clemans, Katherine H.

    2011-01-01

    Stress is known to amplify the link between pubertal timing and psychopathology. However, few studies have examined the role of peer stress as a context for this link. The present study examined the interaction between perceived pubertal timing and peer stress on symptoms of psychopathology in early adolescence. The sample consisted of 264…

  14. Recent changes in pubertal timing in healthy Danish boys

    DEFF Research Database (Denmark)

    Sørensen, K; Aksglæde, Lise; Petersen, Jørgen Holm

    2010-01-01

    In the 1990s, the American population-based study NHANES III renewed the focus on possible secular trends in male puberty. However, no conclusions could be made on pubertal onset due to the lack of compatible data.......In the 1990s, the American population-based study NHANES III renewed the focus on possible secular trends in male puberty. However, no conclusions could be made on pubertal onset due to the lack of compatible data....

  15. Pubertal Timing and Youth Internalizing Psychopathology: The Role of Relational Aggression.

    Science.gov (United States)

    Pomerantz, Hayley; Parent, Justin; Forehand, Rex; Breslend, Nicole Lafko; Winer, Jeffrey P

    2017-02-01

    The current study examined relational aggression as a potential mechanism that explains the association between off-time pubertal development and internalizing problems in youth. Youth gender was also examined as a moderator for the association between these variables. It was hypothesized that early pubertal maturation would be associated with higher levels of relationally aggressive behavior which, in turn, would be associated with elevated levels of internalizing problems. Parents of 372 children between the ages of 8 and 17 were recruited through Amazon's Mechanical Turk. Parents responded to demographic information about themselves, as well as information about their child's pubertal timing, relationally aggressive behavior, and anxiety and depressive symptoms. Findings indicated that early pubertal timing was associated with higher levels of anxiety directly, and higher levels of both anxiety and depressive symptoms indirectly through higher levels of relational aggression. In all but one of the pathways examined, gender was not found to moderate the associations between the study variables of interest. This study is the first to examine relational aggression as a mechanism by which early pubertal timing leads to internalizing problems. The findings suggest that relational aggression could be a target for intervention among early developing youth who are at risk for internalizing problems.

  16. Pubertal timing and early sexual intercourse in the offspring of teenage mothers.

    Science.gov (United States)

    De Genna, Natacha M; Larkby, Cynthia; Cornelius, Marie D

    2011-10-01

    Early puberty is associated with stressful family environments, early sexual intercourse, and teenage pregnancy. We examined pubertal timing and sexual debut among the 14-year-old offspring of teenage mothers. Mothers (71% Black, 29% White) were recruited as pregnant teenagers (12-18 years old). Data were collected during pregnancy and when offspring were 6, 10 and 14 years old (n = 318). Adolescents (50% male) compared the timing of their pubertal maturation to same-sex peers. There was a significant 3-way interaction effect of race, sex, and pubertal timing on sexual debut (n = 305). This effect remained significant in a model controlling for maternal age at first intercourse, substance use, exposure to trauma, authoritative parenting, and peer sexual activity (n = 255). Early maturation was associated with early sex in daughters, and may be one pathway for the inter-generational transfer of risk for teenage pregnancy among daughters of teenage mothers.

  17. Pubertal timing and substance use: associations between and within families across late adolescence.

    Science.gov (United States)

    Dick, D M; Rose, R J; Viken, R J; Kaprio, J

    2000-03-01

    In the present study, between-family analyses of data from adolescent twin girls offer new evidence that early menarche is associated with earlier initiation and greater frequency of smoking and drinking. The role of personality factors and peer relationships in that association was investigated, and little support was found for their involvement. Novel within-family analyses replicating associations of substance use with pubertal timing in contrasts of twin sisters selected for extreme discordance for age at menarche are reported. Within-family replications demonstrated that the association of pubertal timing with substance use cannot be explained solely by between-family confounds. Within-family analyses demonstrated contextual modulation of the influence of pubertal timing: Its impact on drinking frequency is apparent only among girls in urban settings. Sibling comparisons illustrate a promising analytic tool for studying diverse developmental outcomes.

  18. Pubertal Onset in Boys and Girls Is Influenced by Pubertal Timing of Both Parents

    DEFF Research Database (Denmark)

    Wohlfahrt-Veje, Christine; Mouritsen, Annette; Hagen, Casper P

    2016-01-01

    children in a longitudinal cohort study. Information on parental timing of puberty (earlier, comparable to, or later compared to peers) and menarche age was retrieved from questionnaires. PARTICIPANTS: A total of 672 girls and 846 boys. MAIN OUTCOME MEASURES: Age at onset of pubic hair (PH2+), breasts (B2...

  19. Pubertal Timing and Early Sexual Intercourse in the Offspring of Teenage Mothers

    Science.gov (United States)

    De Genna, Natacha M.; Larkby, Cynthia; Cornelius, Marie D.

    2011-01-01

    Early puberty is associated with stressful family environments, early sexual intercourse, and teenage pregnancy. We examined pubertal timing and sexual debut among the 14-year-old offspring of teenage mothers. Mothers (71% Black, 29% White) were recruited as pregnant teenagers (12-18 years old). Data were collected during pregnancy and when…

  20. Early Adolescent Boys' Exposure to Internet Pornography: Relationships to Pubertal Timing, Sensation Seeking, and Academic Performance

    Science.gov (United States)

    Beyens, Ine; Vandenbosch, Laura; Eggermont, Steven

    2015-01-01

    Research has demonstrated that adolescents regularly use Internet pornography. This two-wave panel study aimed to test an integrative model in early adolescent boys (M[subscript age] = 14.10; N = 325) that (a) explains their exposure to Internet pornography by looking at relationships with pubertal timing and sensation seeking, and (b) explores…

  1. Early adolescent boys’ exposure to Internet pornography: relationships to pubertal timing, sensation seeking, and academic performance

    NARCIS (Netherlands)

    Beyens, I.; Vandenbosch, L.; Eggermont, S.

    2015-01-01

    Research has demonstrated that adolescents regularly use Internet pornography. This two-wave panel study aimed to test an integrative model in early adolescent boys (Mage = 14.10; N = 325) that (a) explains their exposure to Internet pornography by looking at relationships with pubertal timing and

  2. Subjective Age in Early Adolescence: Relationships with Chronological Age, Pubertal Timing, Desired Age, and Problem Behaviors

    Science.gov (United States)

    Hubley, Anita M.; Arim, Rubab G.

    2012-01-01

    Subjective age generally refers to the age that one feels. In a cross-sectional questionnaire study of 245 adolescents ages 10-14 years, we examined (a) whether, and when, a cross-over in subjective age occurs, (b) differences in subjective age among pubertal timing groups, (c) correlations between subjective age and each of desired age and five…

  3. Pubertal Timing and Mexican-Origin Girls' Internalizing and Externalizing Symptoms: The Influence of Harsh Parenting

    Science.gov (United States)

    Deardorff, Julianna; Cham, Heining; Gonzales, Nancy A.; White, Rebecca M. B.; Tein, Jenn-Yun; Wong, Jessie J.; Roosa, Mark W.

    2013-01-01

    Early-maturing girls are at risk for internalizing and externalizing problems. Research concerning pubertal timing and mental health among Mexican Americans or the influence of parenting behaviors on these relations has been scarce. This study addressed these gaps. This was a prospective examination of 362 Mexican-origin girls and their mothers in…

  4. Coming of age in Roman Britain: Osteological evidence for pubertal timing.

    Science.gov (United States)

    Arthur, Nichola A; Gowland, Rebecca L; Redfern, Rebecca C

    2016-04-01

    Puberty is a key transitional phase of the human life course, with important biological and social connotations. Novel methods for the identification of the pubertal growth spurt and menarche in skeletal remains have recently been proposed (Shapland and Lewis, 2013, 2014). In this study we applied the methods to two Romano-British cemetery samples (1st-early 5th centuries AD) in order to investigate the timing of puberty during this period and further assess the veracity of the methods. Shapland and Lewis' methods (2013, 2014) were applied to 38 adolescents (aged 8-20 years) from the British cemetery sites of Roman London (1st-early 5th centuries AD) and Queenford Farm, Oxfordshire (4th-early 5th centuries AD). Overall, the Romano-British males and females experienced the onset of puberty at similar ages to modern European adolescents, but subsequently experienced a longer period of pubertal development. Menarche occurred between the ages of 15 and 17 years for these Romano-British females, around 2 to 4 years later than for present-day European females. The observed Romano-British pattern of pubertal timing has various possible explanations, including exposure to environmental stressors in early urban environments. The pattern of pubertal timing is largely congruent with social age transitions alluded to in ancient texts and funerary evidence for this period. While there are limitations to the application of these techniques to archaeological samples, they were successfully applied in this study, and may have important implications for understandings of past life courses, as well as providing a long-term perspective on pubertal timing and biocultural interactions. © 2016 Wiley Periodicals, Inc.

  5. Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity

    NARCIS (Netherlands)

    Cousminer, Diana L.; Berry, Diane J.; Timpson, Nicholas J.; Ang, Wei; Thiering, Elisabeth; Byrne, Enda M.; Taal, H. Rob; Huikari, Ville; Bradfield, Jonathan P.; Kerkhof, Marjan; Groen-Blokhuis, Maria M.; Kreiner-Moller, Eskil; Marinelli, Marcella; Holst, Claus; Leinonen, Jaakko T.; Perry, John R. B.; Surakka, Ida; Pietilainen, Olli; Kettunen, Johannes; Anttila, Verneri; Kaakinen, Marika; Sovio, Ulla; Pouta, Anneli; Das, Shikta; Lagou, Vasiliki; Power, Chris; Prokopenko, Inga; Evans, David M.; Kemp, John P.; St Pourcain, Beate; Ring, Susan; Palotie, Aarno; Kajantie, Eero; Osmond, Clive; Lehtimaki, Terho; Viikari, Jorma S.; Kahonen, Mika; Warrington, Nicole M.; Lye, Stephen J.; Palmer, Lyle J.; Tiesler, Carla M. T.; Flexeder, Claudia; Montgomery, Grant W.; Medland, Sarah E.; Hofman, Albert; Hakonarson, Hakon; Guxens, Monica; Bartels, Meike; Salomaa, Veikko; Koppelman, Gerard H.

    2013-01-01

    The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and

  6. A Twin Study of Objective and Subjective Pubertal Timing and Peer Influence on Risk-Taking.

    Science.gov (United States)

    Kretsch, Natalie; Mendle, Jane; Harden, K Paige

    2016-03-01

    The current study used a behavioral genetic design to test whether three measures of pubertal timing moderated peer influence on risk-taking in a sample of 248 female adolescent twin pairs ( M age =16.0, SD =1.5) from the National Longitudinal Study of Adolescent Health. Peer influence was operationalized as the quasi-causal association between girls' self-reported risk-taking and the risk-taking reported by their friends. Girls with earlier ages at menarche and who perceived themselves as more developed than peers were more susceptible to peer influence on risk-taking. However, age-standardized ratings of body changes did not moderate peer influence. This study highlights distinctions between multiple measures of pubertal timing, using an innovative synthesis of genetically informative data and peer nomination data.

  7. The Interaction Between Pubertal Timing and Peer Popularity for Boys and Girls: An Integration of Biological and Interpersonal Perspectives on Adolescent Depression.

    Science.gov (United States)

    Teunissen, Hanneke A; Adelman, Caroline B; Prinstein, Mitchell J; Spijkerman, Renske; Poelen, Evelien A P; Engels, Rutger C M E; Scholte, Ron H J

    2011-04-01

    The transition to adolescence marks a time of sharply increased vulnerability to the development of depression, particularly among girls. Past research has examined isolated risk factors from individual theoretical models (e.g., biological, interpersonal, and cognitive) of depression, but few have examined integrative models. This study investigated the conjoint effects of early pubertal timing and popularity in the longitudinal prediction of depressive symptoms. A total of 319 girls and 294 boys (ages 11-14) provided information on their pubertal status, depressive symptoms, and the social status (i.e., popularity) of their peers. Adolescents completed a second measure of depressive symptoms 11 months after the initial time point. Findings supported an integrated biological-interpersonal model in explaining the development of depressive symptoms during adolescence. Early pubertal development was associated with increase in depressive symptoms only when accompanied by low levels of popularity. High levels of popularity buffered the association between early pubertal development and later depressive symptoms. Unexpectedly, these results were significant both for girls and boys. Results are discussed in terms of dynamic systems theories.

  8. Effects of harsh parenting and positive parenting practices on youth aggressive behavior: The moderating role of early pubertal timing.

    Science.gov (United States)

    Chen, Frances R; Raine, Adrian

    2018-01-01

    Prior research indicates that early pubertal timing is associated with aggressive behavior, particularly in the context of adversity as postulated in the contextual amplification hypothesis. However, few studies have examined harsh parenting as the context for the effect of early pubertal timing. Even fewer studies have tested the interactive effect of early pubertal timing and positive parenting on aggressive behavior. In this study, we tested the proposition that early pubertal timing, contrary to the general conception of it as a vulnerability, indexed susceptibility, and thus early maturing individuals were affected more by their environment in a "for better and for worse" manner. The sample consisted of 411 community-recruited youth aged 11-12 years (51% boys, 80% African Americans). Participants reported Tanner Stages of pubertal development, aggressive behavior and harsh parenting practice of their parents. Puberty scores were standardized with groups of the same age, sex, and ethnicity, and those that scored the top one-third were defined as early maturing individuals. Parents reported youth's aggressive behavior and their parenting practices towards the youth, including harsh parenting and positive parenting. Early pubertal timing significantly moderated the relationship between harsh/positive parenting and aggressive behavior. Specifically, harsh parenting was positively associated with aggressive behavior to a larger degree among early maturing individuals than among on-time/late-maturing individuals. Positive parenting was inversely associated with aggressive behavior but only among early maturing individuals. This study is the first to document support for early pubertal timing as susceptibility to the environmental influences in relation to aggressive behavior. Theoretical and intervention implications are discussed. © 2017 Wiley Periodicals, Inc.

  9. Sex 'n' drugs 'n' rock 'n' roll: the meaning and social consequences of pubertal timing.

    Science.gov (United States)

    Waylen, Andrea; Wolke, Dieter

    2004-11-01

    This is a brief review of the normal changes in adolescent behaviour and the interplay between biology and social factors that occur at and around puberty, in an attempt to explain when this transition may become problematic The onset of puberty is a biological marker for an individual's transition from a non-reproductive to a reproductive state. Adolescence is a normal developmental transition associated with clearly visible physical changes, reorganization and pruning of neuronal circuits in the brain and the occurrence of new behaviours and interests. It is a time when new life tasks (orientation towards peers of the other sex, romantic and sexual involvement and mastering an educational career) need to be mastered. Parent-child conflict increases and becomes more intense as the adolescent struggles for more independence while still requiring support. These normal changes can become problematic if biological and social expectations diverge e.g. entering puberty very early or very late. While early pubertal onset in boys is likely to have beneficial effects, in girls precocious pubertal timing may have a negative impact on body-image, affect (or emotional well-being) and sex-role expectations. Other individual biological predispositions and genetic endowment may interact with social factors (e.g. peers, parenting style, neighbourhood) making adolescence either an adaptive or a challenging transition. There is a lack of sufficiently large longitudinal studies that have been able to study this interaction between genetics, biology and social environment on adolescent development. The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort provides a unique opportunity to investigate the impact of pubertal timing on social behaviour. Planned assessments and concepts are outlined.

  10. Prediction of basal metabolic rate in obese children and adolescents considering pubertal stages and anthropometric characteristics or body composition.

    Science.gov (United States)

    Lazzer, S; Patrizi, A; De Col, A; Saezza, A; Sartorio, A

    2014-06-01

    To develop and crossvalidate new equations for predicting basal metabolic rate (BMR) in obese children and adolescents in relation to pubertal stages, anthropometric characteristics or body composition. A total of 1696 obese Caucasian children and adolescents (mean body mass index z-score: 3.5±0.8) participated in this study. BMR was determined by indirect calorimetry and fat-free mass (FFM) and fat mass (FM) by bioelectrical impedance analysis. Equations were derived by stepwise multiple regression analysis using a calibration cohort of 848 subjects, and the equations were crossvalidated with a Bland and Altman method in the remaining 848 subjects. Two new specific equations based on gender (1: males; 0: females), pubertal stages (from 1 to 5, assessed according Marshall & Tanner methods) and body weight (BW, kg), stature (m) or body composition (kg) were generated as follows: (1) BMR=(BW × 0.044)+(stature × 2.836)-(pubertal stage × 0.148)+(gender × 0.781)-0.551 (adjusted coefficient of determination (R(2)adj)= 0.69 and root mean squared error (RMSE)=0.954 MJ); (2) BMR=(FFM × 0.082)+(FM × 0.037)-(pubertal stage × 0.125)+(gender × 0.706)+2.528 (R(2)adj= 0.70 and RMSE=0.943 MJ). In the crossvalidation group, mean-predicted BMR was not significantly different from the mean-measured BMR (MBMR) for all children and adolescents, as well as for boys and girls (differenceBMR was predicted accurately (90-110% of MBMR) in 67% of subjects. The new prediction equations considering the pubertal stages allow an accurate and more appropriate (vs equations using chronological age) estimation of BMR in obese children and adolescents.

  11. Role Of Serum Lectin In Derangement Of PUBERTAL Timing In Thalassaemic Patients

    International Nuclear Information System (INIS)

    MOAWAD, A.T.; NASSAR, E.M.; EL-NASHAR, N.A.

    2010-01-01

    patients with delayed puberty. Serum leptin levels showed a negative correlation with ferritin levels and positive correlations with each of serum FSH, LH and testosterone in males and estradiol in females in both patients groups. It could be concluded that adipose tissue dysfunction, due to iron overload, could be considered as one of the endocrinopathies affecting thalassaemic patients. The consequent low leptin levels might be a cofactor in the derangement of pubertal timing observed in thalassaemic patients at puberty which necessitates newer protocols of treatment, correct blood transfusion and chelation therapy.

  12. Development and Lability in the Parent-Child Relationship During Adolescence: Associations With Pubertal Timing and Tempo

    Science.gov (United States)

    Marceau, Kristine; Ram, Nilam; Susman, Elizabeth

    2014-01-01

    Adolescents' and parents' reactions to pubertal development are hypothesized to contribute to changes in family dynamics. Using 7-year longitudinal data from the NICHD-SECCYD (488 boys, 475 girls) we examined relations between pubertal development (timing, tempo) and trajectories (developmental change and year-to-year lability) of parent-child conflict and closeness from age 8.5 to 15.5 years. Changes were mostly characterized by year-to-year fluctuations – lability. Parent-child conflict increased and closeness decreased some with age. Pubertal timing and tempo were more consistently associated with lability in parent-child relationships than with long-term trends, although faster tempo was associated with steeper decreases in parent-child closeness. Findings provide a platform for examining how puberty contributes to both long-term and transient changes in adolescents' relationships and adjustment. PMID:26321856

  13. The Moderating Effects of Pubertal Timing on the Longitudinal Associations between Parent-Child Relationship Quality and Adolescent Substance Use

    Science.gov (United States)

    Shelton, Katherine H.; Van Den Bree, Marianne B. M.

    2010-01-01

    This prospective, longitudinal study investigated the moderating role of pubertal timing on reciprocal links between adolescent appraisals of parent-child relationship quality and girls' (N = 1,335) and boys' (N = 1,203) cigarette and alcohol use across a 12-month period. Reciprocal effects were found between parent-child relations and on-time…

  14. Recent changes in pubertal timing in healthy Danish boys: associations with body mass index

    DEFF Research Database (Denmark)

    Sørensen, Kaspar; Aksglaede, Lise; Petersen, Jørgen Holm

    2010-01-01

    In the 1990s, the American population-based study NHANES III renewed the focus on possible secular trends in male puberty. However, no conclusions could be made on pubertal onset due to the lack of compatible data.......In the 1990s, the American population-based study NHANES III renewed the focus on possible secular trends in male puberty. However, no conclusions could be made on pubertal onset due to the lack of compatible data....

  15. Exposure to peer delinquency as a mediator between self-report pubertal timing and delinquency: A longitudinal study of mediation

    Science.gov (United States)

    Negriff, Sonya; Ji, Juye; Trickett, Penelope K.

    2013-01-01

    This study examined exposure to peer delinquency as a mediator between pubertal timing and self-reported delinquency longitudinally and whether this mediational model was moderated by either gender or maltreatment experience. Data were obtained from Time 1, 2, and 3 of a longitudinal study of maltreatment and development. At Time 1 the sample comprised 454 children aged 9–13 years. Analyses via structural equation modeling supported full mediation. Gender did not moderate this mediational relationship, but maltreatment experience did. The results show that early maturing males and females are both at risk for being exposed to peers that may draw them into delinquent behavior. Additionally, the mechanism linking early pubertal timing to delinquency differs depending on maltreatment experience. PMID:21262055

  16. Pubertal development, personality, and substance use: a 10-year longitudinal study from childhood to adolescence.

    Science.gov (United States)

    Castellanos-Ryan, Natalie; Parent, Sophie; Vitaro, Frank; Tremblay, Richard E; Séguin, Jean R

    2013-08-01

    Most research linking early pubertal development to substance use has focused on the effects of pubertal timing (age at which a certain stage of pubertal development is reached or pubertal status at a particular age--related to the maturation disparity hypothesis), but little research has focused on pubertal tempo (rate of growth through pubertal stages--related to the maturation compression hypothesis). However, both timing and tempo have not only been identified as important components of pubertal development, with different predictors, but have also been shown to be independently associated with other adolescent psychopathologies. Using latent growth-curve modeling, this study examined how pubertal status at age 12 and pubertal tempo (between 11 and 13 years) related to substance use from 15 to 16 years in boys from low socioeconomic backgrounds (N = 871). Results showed that both pubertal status at age 12 and tempo were significant predictors of increased levels of substance use and problems in mid to late adolescence. In an attempt to identify mechanisms that may explain the association between pubertal development and substance use it was found that sensation seeking partially mediated the association between pubertal status at age 12 and substance use behaviors. Impulse control was found to moderate the association sensation seeking had with marijuana use frequency, with high sensation-seeking scores predicting higher marijuana use frequency only at low levels of impulse control. These findings highlight the importance of considering multiple sources of individual variability in the pubertal development of boys and provide support for both the maturational disparity and compression hypotheses. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  17. Forty years trends in timing of pubertal growth spurt in 157,000 Danish school children

    DEFF Research Database (Denmark)

    Aksglæde, Lise; Olsen, Lina Wøhlk; Sørensen, Thorkild I.A.

    2008-01-01

    to 1969 who attended primary school in the Copenhagen Municipality. 135,223 girls and 21,612 boys fulfilled the criteria for determining age at OGS and age at PHV. These physiological events were used as markers of pubertal development in our computerized method in order to evaluate any secular trends...... in pubertal maturation during the study period (year of birth 1930 to 1969). In this period, age at OGS declined statistically significantly by 0.2 and 0.4 years in girls and boys, respectively, whereas age at PHV declined statistically significantly by 0.5 and 0.3 years in girls and boys, respectively...

  18. Peer and Individual Risk Factors in Adolescence Explaining the Relationship between Girls' Pubertal Timing and Teenage Childbearing

    Science.gov (United States)

    Hendrick, C. Emily; Cance, Jessica Duncan; Maslowsky, Julie

    2016-01-01

    Girls with early pubertal timing are at elevated risk for teenage childbearing; however, the modifiable mechanisms driving this relationship are not well understood. The objective of the current study was to determine whether substance use, perceived peer substance use, and older first sexual partners mediate the relationships among girls' pubertal timing, sexual debut, and teenage childbearing. Data are from Waves 1 – 15 of the female cohort of the National Longitudinal Surveys of Youth 1997 (NLSY97), a nationwide, ongoing cohort study of U.S. men and women born between 1980 and 1984. The analytic sample (N=2,066) was 12-14 years old in 1997 and ethnically diverse (51% white, 27% black, 22% Latina). Using structural equation modeling, we found substance use in early adolescence and perceived peer substance use each partially mediated the relationships among girls' pubertal timing, sexual debut, and teenage childbearing. Our findings suggest early substance use behavior as one modifiable mechanism to be targeted by interventions aimed at preventing teenage childbearing among early developing girls. PMID:26769576

  19. Longitudinal impacts of pubertal timing and weight status on adolescent Internet use: Analysis from a cohort study of Taiwanese youths.

    Directory of Open Access Journals (Sweden)

    Meng-Che Tsai

    Full Text Available To investigate the longitudinal impacts of pubertal timing and weight status on Internet use in adolescents.Three waves of data on a longitudinal cohort of 7th grade students (N = 2430 were retrieved from the Taiwan Youth Project. Univariate and multivariate regression models were applied using crude and adjusted odds ratios (OR with 95% confidence intervals (CI to examine the concomitant impacts of pubertal timing and weight status on adolescent Internet use.The dataset identified 210 (8.7% students using the Internet for more than 20 hours/week, and 81 (3.3% were viewing pornographic material online. Early maturing and thin-weight adolescents were at 35% and 46% increased risks of spending long hours on Internet use, respectively. While early puberty was associated with online pornography viewing among males (adjusted OR 1.84, 95% CI 1.04-3.28, early puberty was contrarily a protective factor against online gaming in females (adjusted OR 0.59, 95% CI 0.36-0.96.Early puberty was found to be positively related to adolescent Internet use. Appropriate health education and guidance regarding Internet use should be provided to those with different developing needs.

  20. Longitudinal impacts of pubertal timing and weight status on adolescent Internet use: Analysis from a cohort study of Taiwanese youths.

    Science.gov (United States)

    Tsai, Meng-Che; Strong, Carol; Chen, Wan-Ting; Lee, Chih-Ting; Lin, Chung-Ying

    2018-01-01

    To investigate the longitudinal impacts of pubertal timing and weight status on Internet use in adolescents. Three waves of data on a longitudinal cohort of 7th grade students (N = 2430) were retrieved from the Taiwan Youth Project. Univariate and multivariate regression models were applied using crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) to examine the concomitant impacts of pubertal timing and weight status on adolescent Internet use. The dataset identified 210 (8.7%) students using the Internet for more than 20 hours/week, and 81 (3.3%) were viewing pornographic material online. Early maturing and thin-weight adolescents were at 35% and 46% increased risks of spending long hours on Internet use, respectively. While early puberty was associated with online pornography viewing among males (adjusted OR 1.84, 95% CI 1.04-3.28), early puberty was contrarily a protective factor against online gaming in females (adjusted OR 0.59, 95% CI 0.36-0.96). Early puberty was found to be positively related to adolescent Internet use. Appropriate health education and guidance regarding Internet use should be provided to those with different developing needs.

  1. Peer substance use as a mediator between early pubertal timing and adolescent substance use: longitudinal associations and moderating effect of maltreatment.

    Science.gov (United States)

    Negriff, Sonya; Trickett, Penelope K

    2012-11-01

    Early pubertal timing has received considerable empirical support as a risk for adolescent substance use. However, few studies have examined the mediators linking these variables. Therefore, the aims of this study were (1) to examine peer substance use as a mediator between pubertal timing and adolescent substance use longitudinally and (2) to test gender and maltreatment experience as moderators of the mediational model. Data were obtained from time 1, 2, and 3 of a longitudinal study of maltreatment and development. At time 1 the sample was comprised of 303 maltreated and 151 comparison children aged 9-13 years (213 females and 241 males). Longitudinal mediation was tested using structural equation modeling and moderating effects were tested using multiple group analysis. Peer substance use mediated the relationship between early pubertal timing and later adolescent substance use for the total sample. Moderation analyses indicated this significant indirect effect did not differ for males and females. However, it did differ for maltreated versus comparison adolescents with the mediational effect only remaining significant for the comparison group. This is one of the first studies to examine peer substance use as a mediator of pubertal timing and adolescent substance use using a longitudinal design. Early maturing males are at equal risk to early maturing females for interacting with peers that may draw them into substance use. Additionally, the findings indicate that while peers are mediators for comparison adolescents a different mechanism may link early puberty to substance use for maltreated adolescents. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  2. Patterns and correlates of pubertal development in Canadian youth: effects of family context.

    Science.gov (United States)

    Arim, Rubab G; Shapka, Jennifer D; Dahinten, V Susan; Willms, J Douglas

    2007-01-01

    Current health literature suggests that there has been a decline in the age of pubertal onset, and that pubertal development is influenced by social context. Unfortunately, contemporary Canadian-specific data have not been available. This study examined the odds of having entered puberty at various ages during adolescence, before and after controlling for the effects of family socio-economic status and family structure. Longitudinal data for this study were drawn from the first four cycles of the National Longitudinal Survey of Children and Youth. The final sample consisted of 7977 adolescents ranging in age from 10 to 17. Pubertal status of the participants was identified based on pubic hair, facial hair growth, and voice change, for boys; and pubic hair, breast development, and menstruation, for girls. Trajectories of pubertal development were analyzed with HLM growth curve modelling techniques. The results indicated that, compared to boys, the odds of having entered puberty at age 13 were 6.45 times higher for girls and that girls go through puberty more quickly. Low family socio-economic status and living with a stepfather were found to predict early onset of pubertal development. Contextual factors are related to pubertal development. Additional research is needed to develop a more solid understanding of how psychosocial factors interact to predict gendered patterns of pubertal development.

  3. Secular Trends on Birth Parameters, Growth, and Pubertal Timing in Girls with Turner Syndrome

    Directory of Open Access Journals (Sweden)

    Joachim Woelfle

    2018-02-01

    Full Text Available BackgroundWhether children with chromosomal disorders of growth and puberty are affected by secular trends (STs as observed in the general population remains unanswered, but this question has relevance for expectations of spontaneous development and treatment responses.ObjectivesThe aim of the study was to evaluate STs in birth parameters, growth, and pubertal development in girls with Turner syndrome (TS.Study designRetrospective analysis of KIGS data (Pfizer International Growth Database. We included all TS patients who entered KIGS between 1987 and 2012 and were born from 1975 to 2004, who were prepubertal and growth treatment naïve at first entry (total number: 7,219. Pretreatment height and ages at the start of treatment were compared across 5-year birth year groups, with subgroup analyses stratified by induced or spontaneous puberty start.ResultsWe observed significant STs across the birth year groups for birth weight [+0.18 SD score (SDS, p < 0.001], pretreatment height at mean age 8 years (+0.73 SDS, p < 0.001, height at the start of growth hormone (GH therapy (+0.38 SDS, p < 0.001 and start of puberty (+0.42 SDS, p < 0.001. Spontaneous puberty onset increased from 15 to 30% (p < 0.001. Mean age at the start of GH treatment decreased from 10.8 to 7.4 years (−3.4 years; p < 0.001, and substantial declines were seen in ages at onset of spontaneous and induced puberty (−2.0 years; p < 0.001 and menarche (−2.1 years; p < 0.001.ConclusionEnvironmental changes leading to increased height and earlier and also more common, spontaneous puberty are applicable in TS as in normal girls. In addition, greater awareness for TS may underlie trends to earlier start of GH therapy and induction of puberty at a more physiological age.

  4. Is Time Predictability Quantifiable?

    DEFF Research Database (Denmark)

    Schoeberl, Martin

    2012-01-01

    Computer architects and researchers in the realtime domain start to investigate processors and architectures optimized for real-time systems. Optimized for real-time systems means time predictable, i.e., architectures where it is possible to statically derive a tight bound of the worst......-case execution time. To compare different approaches we would like to quantify time predictability. That means we need to measure time predictability. In this paper we discuss the different approaches for these measurements and conclude that time predictability is practically not quantifiable. We can only...... compare the worst-case execution time bounds of different architectures....

  5. Elite athletes and pubertal delay.

    Science.gov (United States)

    Kapczuk, Karina

    2017-10-01

    Intensive physical training and participation in competitive sports during childhood and early adolescence may affect athletes' pubertal development. On the other hand, pubertal timing, early or late, may impact on an athlete selection for a particular sport. Genetic predisposition, training load, nutritional status and psychological stress determine athletes' pubertal timing. Athletes that practice esthetic sports, especially gymnasts, are predisposed to a delay in pubertal development. The growing evidence indicates that energy deficiency, not a systemic training per se, plays a crucial role in the pathogenesis of functional hypothalamic hypogonadism in female athletes. Metabolic and psychologic stress activate hypothalamic-pituitary-adrenal axis and suppress hypothalamic-pituitary-ovarian axis. Female athletes who do not begin secondary sexual development by the age of 14 or menstruation by the age of 16 warrant a comprehensive evaluation and a targeted treatment. Somatic growth and sexual maturation of elite female athletes are largely sport-specific since each sport favors a particular somatotype and requires a specific training. Chronic negative energy balance resulting from a systemic physical training and inadequate energy intake may delay pubertal development in elite athletes. Youth athletes, especially those engaged in competitive sports that emphasize prepubertal or lean appearance, are at risk of developing relative energy deficiency in sport associated with disordered eating or eating disorders. Management strategies should address the complex conditions underlying functional hypothalamic hypogonadism.

  6. Pubertal breast development in primary school girls in Sokoto, North ...

    African Journals Online (AJOL)

    Background. There is wide variation in normal pubertal timing among various populations. Objectives. To determine the mean age of pubertal stages of breast development and menarche, and the influence of nutrition and ethnicity on pubertal onset in primary school girls in Sokoto, North-Western Nigeria. Methods.

  7. Longitudinal Effects of Self-Report Pubertal Timing and Menarcheal Age on Adolescent Psychological and Behavioral Outcomes in Female Youths from Northern Taiwan.

    Science.gov (United States)

    Lee, Chih-Ting; Tsai, Meng-Che; Lin, Chung-Ying; Strong, Carol

    2017-08-01

    Early puberty is linked to adverse developmental outcomes in adolescents in Western societies. However, little is known about this relationship in an East Asian context. In addition, whether the impact of subjective pubertal timing (PT) and menarcheal age (MA) on adolescent psychosocial development persists into early adulthood remains unclear and is worthy of investigation. A subset of data was retrieved from the Taiwan Youth Project, which recruited and followed a longitudinal cohort of 7 th - and 9 th -grade female Taiwanese students from 2000 to 2007. Subjective PT was defined using the Pubertal Developmental Scale (PDS), which mainly measures pubertal changes. MA was recalled by participants themselves. Various psychological and behavioral factors were recorded and measured until the age of 20, including the use of alcohol and cigarettes, psychological well-being, sexual activity, and socially problematic behaviors. A χ 2 test for linear-by-linear association and one-way analysis of variance followed by multivariate regression models were used to dissect the differential effects of PT and MA in the association with the outcome variables. In total, 1545 female participants with an average age of 14.5 (±1.1) years were deemed valid for analysis. Among them, 257 (16.6%) participants perceived themselves as having early PT, defined as more than 1 standard deviation above the mean PDS score, and 82 (5.3%) had early MA (occurring before the 4 th grade). In univariate analysis, participants with early PT had higher rates of smoking and sexual activity, and MA was not related to their psychobehavioral outcomes. After multivariate adjustment, only late PT was significantly correlated with lower amounts of cigarette smoking and sexual activity before the age of 20. Conceptual and actual pubertal developments may be differentially associated with psychobehavioral outcomes among young Taiwanese girls. Clinical attention should be given to adolescent self-perception of

  8. Depressive symptoms among Hong Kong adolescents: relation to atypical sexual feelings and behaviors, gender dissatisfaction, pubertal timing, and family and peer relationships.

    Science.gov (United States)

    Lam, T H; Stewart, Sunita M; Leung, Gabriel M; Lee, Peter W H; Wong, Joy P S; Ho, L M; Youth Sexuality Task Force

    2004-10-01

    A representative community sample of Hong Kong boys (n = 1,024) and girls (n = 1,403), age 14-18 years, provided information regarding same-sex attraction, gender dissatisfaction, pubertal timing, early experience with sexual intercourse, and depressive symptoms. They also rated the quality of their family and peer relationships and self-perceived attractiveness. Depressive symptoms were higher in youths reporting same-sex attraction, gender dissatisfaction, early pubertal maturation, and early sexual intercourse. Family relationships were less satisfactory for those who reported same-sex attraction, gender dissatisfaction, and early sexual intercourse, and peer relationships were also worse for those who reported gender dissatisfaction. In multivariate analyses, same-sex attraction, early sexual intercourse, and early pubertal maturation were unique and direct contributors to depressive symptoms; however, gender dissatisfaction's association with depressive symptoms was largely accounted for by shared correlations with negative family and peer relationships. The multivariate model explained 11% of the variance of depressive symptoms. These findings offer a preliminary documentation of the prevalence and correlates of atypical sexual self-assessments and behavior among adolescents in Hong Kong. Such information is important if theories of sexual identity and risk factors for depressive symptoms are to have cross-cultural utility. Copyright 2004 Springer Science + Business Media, Inc.

  9. The Effects of Pubertal Timing on Body Image, School Behavior, and Deviance.

    Science.gov (United States)

    Duncan, Paula Duke; And Others

    1985-01-01

    Data from the National Health Examination Survey, a national probability sample of children and youth aged 12-17, was used to investigate the relationships between maturational timing and body image, school behavior, and deviance. (Author/LMO)

  10. Pubertal development in Danish children

    DEFF Research Database (Denmark)

    Juul, A; Teilmann, G; Scheike, Thomas Harder

    2006-01-01

    .0012). In Danish boys we found that age at genital stage 2 (G2) was 11.83 years. Both sexes were significantly taller compared with data from 1964, but timing of pubertal maturation seemed unaltered. Finally, puberty occurred much later in Denmark compared with recent data from USA. We could not detect any...

  11. Time-predictable architectures

    CERN Document Server

    Rochange, Christine; Uhrig , Sascha

    2014-01-01

    Building computers that can be used to design embedded real-time systems is the subject of this title. Real-time embedded software requires increasingly higher performances. The authors therefore consider processors that implement advanced mechanisms such as pipelining, out-of-order execution, branch prediction, cache memories, multi-threading, multicorearchitectures, etc. The authors of this book investigate the timepredictability of such schemes.

  12. Comparison of lumbar force between pubertal and post-pubertal adolescents: interference of physical growth, body fat and lifestyle.

    Directory of Open Access Journals (Sweden)

    Mikael Seabra Moraes

    2018-01-01

    Full Text Available Abstract Aim: To compare performance in the lumbar force test in pubertal and post-pubertal adolescents by controlling the interference of physical growth, body fat, screen time and physical activity. Methods: A cross-sectional study with 933 adolescents (492 girls aged 14-19 from the city of São José, Brazil. Lumbar strength was assessed using the isometric lumbar extension test proposed by the Canadian Society of Exercise Physiology. Sexual maturation was classified according to Tanner’s criteria. Physical growth variables (age, body weight, stature, BMI, body fat (triceps and subscapular skinfolds, sedentary behavior based on screen time and overall physical activity were controlled in the Analysis of Covariance (ANCOVA, with a significance level of 5%. Results: Post-pubertal boys presented higher lumbar force compared to pubertal ones only when interference of BMI, body fat, screen time and physical activity was controlled. Pubertal girls presented higher lumbar force compared to post-pubertal ones, both when controlling the analysis for the studied variables and when not controlled by them. Conclusion: BMI, body fat, screen time and physical activity interfere in the difference in lumbar strength of boys, in which post-pubertal boys presented better performance in lumbar force compared to pubertal ones. Regardless of interference or not of these variables, pubertal girls presented better performance in lumbar force when compared to post-pubertal ones.

  13. Predicting chaotic time series

    International Nuclear Information System (INIS)

    Farmer, J.D.; Sidorowich, J.J.

    1987-01-01

    We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

  14. Nutrition and pubertal development

    OpenAIRE

    Soliman, Ashraf; De Sanctis, Vincenzo; Elalaily, Rania

    2014-01-01

    Nutrition is one of the most important factors affecting pubertal development. Puberty entails a progressive nonlinear process starting from prepubescent to full sexual maturity through the interaction and cooperation of biological, physical, and psychological changes. Consuming an adequate and balanced healthy diet during all phases of growth (infancy, childhood and puberty) appears necessary both for proper growth and normal pubertal development. Girls begin puberty at an earlier age compar...

  15. Pubertal Development and Prepubertal Height and Weight Jointly Predict Young Adult Height and Body Mass Index in a Prospective Study in South Africa.

    Science.gov (United States)

    Stein, Aryeh D; Lundeen, Elizabeth A; Martorell, Reynaldo; Suchdev, Parminder S; Mehta, Neil K; Richter, Linda M; Norris, Shane A

    2016-07-01

    Height and adiposity track over childhood, but few studies, to our knowledge, have longitudinally examined the mediating relation of the timing and progression of puberty. We assessed interrelations between prepubertal height and body mass index, the progression through puberty, and young adult height and adiposity. We analyzed data from the Birth to Twenty Plus study (females, n = 823; males, n = 765). Serial measures of anthropometry and pubertal development were obtained between ages 9 and 16 y. We used latent class growth analysis to categorize pubertal development with respect to pubic hair (females and males), breasts (females), and genitalia (males) development. Adult height and weight were obtained at ages 18 to 20 y. Among females, higher latent class (earlier initiation and faster progression through puberty) was associated with an increased risk of obesity [pubic hair class 3 compared with class 1: RR, 3.41 (95% CI: 1.57, 7.44)] and inconsistent associations with height. Among males, higher latent class was associated with increased adult height [pubic hair development class 3 compared with class 1: 2.43 cm (95% CI: 0.88, 4.00)] and increased risk of overweight/obesity [pubic hair development class 3 compared with class 1: OR, 3.44 (95% CI: 1.44, 8.20)]. In females, the association with adult height became inverse after adjusting for prepubertal height [pubic hair development class 3 compared with class 1: females, -1.31 cm (95% CI: -2.32, -0.31)]; in males, the association with height was attenuated with this adjustment [-0.56 cm (95% CI: -1.63, 0.52)]. Associations with adiposity were attenuated after adjusting for prepubertal adiposity. Progression through puberty modifies the relation between prepubertal and adult anthropometry. Screening for early or rapid progression of puberty might identify children at an increased risk of becoming overweight or obese adults.

  16. Pubertal Onset in Apparently Healthy Indian Boys and Impact of Obesity.

    Science.gov (United States)

    Surana, Vineet; Dabas, Aashima; Khadgawat, Rajesh; Marwaha, Raman Kumar; Sreenivas, V; Ganie, M Ashraf; Gupta, Nandita; Mehan, Neena

    2017-01-01

    Primary - to determine the age of pubertal onset in Indian boys. Secondary - (a) to assess the impact of obesity on pubertal timing, (b) to assess the relationship between gonadotropins and puberty. Cross-sectional. General community-seven schools across New Delhi. Random sample of 1306 school boys, aged 6-17 years. Anthropometric measurement for weight and height and pubertal staging was performed for all subjects. Body mass index (BMI) was calculated to define overweight/obesity. Serum luteinizing hormone (LH), follicle stimulating hormone, and serum testosterone were measured in every sixth subject. Age at pubertal onset-testicular volume ≥4 mL (gonadarche) and pubic hair Stage II. Median age of attaining gonadarche and pubarche was 10.41 years (95% confidence interval [CI]: 10.2-10.6 years) and 13.60 (95% CI: 13.3-14.0 years), respectively. No significant difference in the age of attainment of gonadarche was observed in boys with normal or raised BMI, though pubarche occurred 8 months earlier in the latter group. Serum gonadotropins and testosterone increased with increasing stages of puberty but were unaffected by BMI. Serum LH level of 1.02 mIU/mL and testosterone level of >0.14 ng/mL showed the best prediction for pubertal onset. The study establishes a secular trend of the age of onset of puberty in Indian boys. Pubarche occurred earlier in overweight/obese boys. The cutoff levels of serum LH and testosterone for prediction of pubertal onset have been established.

  17. Nutrition and pubertal development

    Directory of Open Access Journals (Sweden)

    Ashraf Soliman

    2014-01-01

    Full Text Available Nutrition is one of the most important factors affecting pubertal development. Puberty entails a progressive nonlinear process starting from prepubescent to full sexual maturity through the interaction and cooperation of biological, physical, and psychological changes. Consuming an adequate and balanced healthy diet during all phases of growth (infancy, childhood and puberty appears necessary both for proper growth and normal pubertal development. Girls begin puberty at an earlier age compared to past decades. Excessive eating of many processed, high-fat foods, may be the cause of this phenomenon. Overweight or obese children are more likely to enter puberty early. Some evidence suggests that obesity can accelerate the onset of puberty in girls and may delay the onset of puberty in boys. Moreover, the progression of puberty is affected by nutrition. On the other hand, puberty triggers a growth spurt, which increases nutritional needs including macro and micronutrients. Increased caloric, protein, iron, calcium, zinc and folate needs have to be provided during this critical period of rapid growth. Severe primary or secondary malnutrition also can delay the onset and progression of puberty. The higher incidence of anorexia nervosa and bulimia in adolescents imposes a nutritional risk on pubertal development. Moreover, many environmental endocrine disruptors (EDs have been identified that can significantly impair the normal course of puberty. This mini-review sums up some important findings in this important complex that link nutrition and pubertal development.

  18. Early pubertal onset and its relationship with sexual risk taking, substance use and anti-social behaviour: a preliminary cross-sectional study

    Directory of Open Access Journals (Sweden)

    Bellis Mark A

    2009-12-01

    Full Text Available Abstract Background In many countries age at pubertal onset has declined substantially. Relatively little attention has been paid to how this decline may affect adolescent behaviours such as substance use, violence and unprotected sex and consequently impact on public health. Methods In the UK, two opportunistic samples (aged 16-45 years, paper-based (n = 976 and online (n = 1117, examined factors associated with earlier pubertal onset and whether earlier age of onset predicted sexual risk-taking, substance use and anti-social behaviours during early adolescence. Results Overall, 45.6% of females reported menarche ≤ 12 years and 53.3% of males were categorised as having pubertal onset ≤ 11 years. For both sexes earlier pubertal onset was associated with poorer parental socio-economic status. Other pre-pubertal predictors of early onset were being overweight, more childhood illnesses (females and younger age at time of survey (males. For both sexes earlier puberty predicted having drunk alcohol, been drunk, smoked and used drugs Conclusion Results provide sufficient evidence for changes in age of pubertal onset to be further explored as a potential influence on trends in adolescent risk behaviours. Further insight into the relationship between early puberty and both obesity and socio-economic status may help inform early interventions to tackle the development of risk behaviours and health inequalities during early adolescence.

  19. Peer Exclusion During the Pubertal Transition: The Role of Social Competence.

    Science.gov (United States)

    Carter, Rona; Halawah, Amira; Trinh, Sarah L

    2018-01-01

    For some youth, early puberty is accompanied by peer exclusion. Yet early developers may experience less peer exclusion if they have social competence, which would bolster their ability to develop and maintain positive relationships with their peers. Accordingly, the present study tests whether pubertal timing and tempo predicts decrements in children's social competence and whether decrements in social competence account for associations between puberty (timing and tempo) and peer exclusion over time. Longitudinal data were drawn from 1364 families (48% female; 76% White; M = 9.32 years, SD = .48, at Wave 3) who participated in Waves 3-5 (i.e., grades 4-6) of Phase III of the NICHD-SECCYD. The results from latent growth curve models indicated that earlier pubertal timing and more rapid pubertal tempo among girls were associated with high initial levels of peer exclusion. Moreover, mediation analyses revealed that early developers' susceptibility to peer exclusion was associated with their initial level of social competence. In boys, pubertal timing and tempo were not directly associated with peer exclusion; instead, indirect effects of pubertal timing on peer exclusion (intercept, slope) occurred through initial levels of social competence. On average, early developers' who had low levels of social competence also had high initial levels of peer exclusion but experienced decrements in peer exclusion over time. The association between the intercepts for puberty and peer exclusion and the slopes for social competence and peer exclusion were stronger for boys than girls. Overall, our findings suggest that early developers' susceptibility to and experiences of peer exclusion are associated with their development of social competence.

  20. Time-predictable Stack Caching

    DEFF Research Database (Denmark)

    Abbaspourseyedi, Sahar

    completely. Thus, in systems with hard deadlines the worst-case execution time (WCET) of the real-time software running on them needs to be bounded. Modern architectures use features such as pipelining and caches for improving the average performance. These features, however, make the WCET analysis more...... addresses, provides an opportunity to predict and tighten the WCET of accesses to data in caches. In this thesis, we introduce the time-predictable stack cache design and implementation within a time-predictable processor. We introduce several optimizations to our design for tightening the WCET while...... keeping the timepredictability of the design intact. Moreover, we provide a solution for reducing the cost of context switching in a system using the stack cache. In design of these caches, we use custom hardware and compiler support for delivering time-predictable stack data accesses. Furthermore...

  1. Contributions of Function-Altering Variants in Genes Implicated in Pubertal Timing and Body Mass for Self-Limited Delayed Puberty.

    Science.gov (United States)

    Howard, Sasha R; Guasti, Leonardo; Poliandri, Ariel; David, Alessia; Cabrera, Claudia P; Barnes, Michael R; Wehkalampi, Karoliina; O'Rahilly, Stephen; Aiken, Catherine E; Coll, Anthony P; Ma, Marcella; Rimmington, Debra; Yeo, Giles S H; Dunkel, Leo

    2018-02-01

    Self-limited delayed puberty (DP) is often associated with a delay in physical maturation, but although highly heritable the causal genetic factors remain elusive. Genome-wide association studies of the timing of puberty have identified multiple loci for age at menarche in females and voice break in males, particularly in pathways controlling energy balance. We sought to assess the contribution of rare variants in such genes to the phenotype of familial DP. We performed whole-exome sequencing in 67 pedigrees (125 individuals with DP and 35 unaffected controls) from our unique cohort of familial self-limited DP. Using a whole-exome sequencing filtering pipeline one candidate gene [fat mass and obesity-associated gene (FTO)] was identified. In silico, in vitro, and mouse model studies were performed to investigate the pathogenicity of FTO variants and timing of puberty in FTO+/- mice. We identified potentially pathogenic, rare variants in genes in linkage disequilibrium with genome-wide association studies of age at menarche loci in 283 genes. Of these, five genes were implicated in the control of body mass. After filtering for segregation with trait, one candidate, FTO, was retained. Two FTO variants, found in 14 affected individuals from three families, were also associated with leanness in these patients with DP. One variant (p.Leu44Val) demonstrated altered demethylation activity of the mutant protein in vitro. Fto+/- mice displayed a significantly delayed timing of pubertal onset (P puberty in the general population may contribute to the pathogenesis of self-limited DP. Copyright © 2017 Endocrine Society

  2. Pubertal development in ICSI children

    NARCIS (Netherlands)

    Belva, F.; Roelants, M.; Painter, R.; Bonduelle, M.; Devroey, P.; de Schepper, J.

    2012-01-01

    To date, information on the pubertal development of adolescents born after ICSI is scarce, since the very first cohort is only now reaching young adulthood. In this study, pubertal development at the age of 14 was characterized in a longitudinally followed cohort of ICSI-conceived teenagers and

  3. Time-Predictable Virtual Memory

    DEFF Research Database (Denmark)

    Puffitsch, Wolfgang; Schoeberl, Martin

    2016-01-01

    Virtual memory is an important feature of modern computer architectures. For hard real-time systems, memory protection is a particularly interesting feature of virtual memory. However, current memory management units are not designed for time-predictability and therefore cannot be used...... in such systems. This paper investigates the requirements on virtual memory from the perspective of hard real-time systems and presents the design of a time-predictable memory management unit. Our evaluation shows that the proposed design can be implemented efficiently. The design allows address translation...... and address range checking in constant time of two clock cycles on a cache miss. This constant time is in strong contrast to the possible cost of a miss in a translation look-aside buffer in traditional virtual memory organizations. Compared to a platform without a memory management unit, these two additional...

  4. Time-Predictable Computer Architecture

    Directory of Open Access Journals (Sweden)

    Schoeberl Martin

    2009-01-01

    Full Text Available Today's general-purpose processors are optimized for maximum throughput. Real-time systems need a processor with both a reasonable and a known worst-case execution time (WCET. Features such as pipelines with instruction dependencies, caches, branch prediction, and out-of-order execution complicate WCET analysis and lead to very conservative estimates. In this paper, we evaluate the issues of current architectures with respect to WCET analysis. Then, we propose solutions for a time-predictable computer architecture. The proposed architecture is evaluated with implementation of some features in a Java processor. The resulting processor is a good target for WCET analysis and still performs well in the average case.

  5. Pubertal changes in emotional information processing: pupillary, behavioral, and subjective evidence during emotional word identification.

    Science.gov (United States)

    Silk, Jennifer S; Siegle, Greg J; Whalen, Diana J; Ostapenko, Laura J; Ladouceur, Cecile D; Dahl, Ronald E

    2009-01-01

    This study investigated pupillary and behavioral responses to an emotional word valence identification paradigm among 32 pre-/early pubertal and 34 mid-/late pubertal typically developing children and adolescents. Participants were asked to identify the valence of positive, negative, and neutral words while pupil dilation was assessed using an eyetracker. Mid-/late pubertal children showed greater peak pupillary reactivity to words presented during the emotional word identification task than pre-/early pubertal children, regardless of word valence. Mid-/late pubertal children also showed smaller sustained pupil dilation than pre-/early pubertal children after the word was no longer on screen. These findings were replicated controlling for participants' age. In addition, mid-/late pubertal children had faster reaction times to all words, and rated themselves as more emotional during their laboratory visit compared to pre-/early pubertal children. Greater recall of emotional words following the task was associated with mid-/late pubertal status, and greater recall of emotional words was also associated with higher peak pupil dilation. These results provide physiological, behavioral, and subjective evidence consistent with a model of puberty-specific changes in neurobehavioral systems underpinning emotional reactivity.

  6. Pubertal development in Danish children

    DEFF Research Database (Denmark)

    Juul, A; Teilmann, G; Scheike, Thomas Harder

    2006-01-01

    differences between USA and Denmark, as well as to look for possible secular trends in pubertal development. Healthy Caucasian children from public schools in Denmark participated in the study which was carried out in 1991-1993. A total number of 826 boys and 1,100 girls (aged 6.0-19.9 years) were included......, and pubertal stages were assessed by clinical examination according to methods of Tanner. In boys testicular volume was determined using an orchidometer. We found that age at breast development 2 (B2) was 10.88 years, and mean menarcheal age was 13.42 years. Girls with body mass index (BMI) above the median...... genetic polymorphisms, nutrition, physical activity or endocrine disrupting chemicals must therefore also be considered. Therefore, we believe it is crucial to monitor the pubertal development closely in Denmark in the coming decades....

  7. The duration of pubertal growth peak among three skeletal classes

    Directory of Open Access Journals (Sweden)

    Waqar Jeelani

    Full Text Available ABSTRACT Introduction: Pubertal growth peak is closely associated with a rapid increase in mandibular length and offers a wide range of therapeutic modifiability. Objective: The aim of the present study was to determine and compare the mean ages of onset and duration of pubertal growth peak among three skeletal classes. Methods: A retrospective cross-sectional study was conducted using lateral cephalograms of 230 subjects with growth potential (110 males, 120 females. Subjects were categorized into three classes (Class I = 81, Class II = 82, Class III = 67, according to the sagittal relationship established between the maxilla and the mandible. The cervical vertebral maturation stage was recorded by means of Baccetti's method. The mean ages at CS3 and CS4 and the CS3-CS4 age interval were compared between boys and girls and among three skeletal classes. Results: Pubertal growth peak occurred on average four months earlier in girls than boys (p = 0.050. The average duration of pubertal growth peak was 11 months in Class I, seven months in Class II and 17 months in Class III subjects. Interclass differences were highly significant (Cohen's d > 0.08. However, no significant difference was found in the timing of pubertal growth peak onset among three skeletal classes (p = 0.126 in boys, p = 0.262 in girls. Conclusions: Girls enter pubertal growth peak on average four months earlier than boys. Moreover, the duration of pubertal growth peak is on average four months shorter in Class II and six months longer in Class III subjects as compared to Class I subjects.

  8. Maternal pre-pregnancy body mass index and pubertal development among sons

    DEFF Research Database (Denmark)

    Hounsgaard, M L; Håkonsen, L B; Vested, A

    2014-01-01

    Maternal overweight and obesity in pregnancy has been associated with earlier age of menarche in daughters as well as reduced semen quality in sons. We aimed at investigating pubertal development in sons born by mothers with a high body mass index (BMI). The study included 2522 sons of mothers...... indicators of pubertal development, results also indicated earlier pubertal development among sons of obese mothers. After excluding sons of underweight mothers in a subanalysis, we observed an inverse trend between maternal pre-pregnancy BMI and age at regular shaving, acne and first nocturnal emission....... In conclusion, maternal pre-pregnant obesity may be related to earlier timing of pubertal milestones among sons. More research, preferably based on prospectively collected information about pubertal development, is needed to draw firm conclusions....

  9. Exciting fear in adolescence: Does pubertal development alter threat processing?

    Directory of Open Access Journals (Sweden)

    Jeffrey M. Spielberg

    2014-04-01

    Full Text Available Adolescent development encompasses an ostensible paradox in threat processing. Risk taking increases dramatically after the onset of puberty, contributing to a 200% increase in mortality. Yet, pubertal maturation is associated with increased reactivity in threat-avoidance systems. In the first part of this paper we propose a heuristic model of adolescent affective development that may help to reconcile aspects of this paradox, which focuses on hypothesized pubertal increases in the capacity to experience (some fear-evoking experiences as an exciting thrill. In the second part of this paper, we test key features of this model by examining brain activation to threat cues in a longitudinal study that disentangled pubertal and age effects. Pubertal increases in testosterone predicted increased activation to threat cues, not only in regions associated with threat avoidance (i.e., amygdala, but also regions associated with reward pursuit (i.e., nucleus accumbens. These findings are consistent with our hypothesis that puberty is associated with a maturational shift toward more complex processing of threat cues—which may contribute to adolescent tendencies to explore and enjoy some types of risky experiences.

  10. Exciting fear in adolescence: does pubertal development alter threat processing?

    Science.gov (United States)

    Spielberg, Jeffrey M; Olino, Thomas M; Forbes, Erika E; Dahl, Ronald E

    2014-04-01

    Adolescent development encompasses an ostensible paradox in threat processing. Risk taking increases dramatically after the onset of puberty, contributing to a 200% increase in mortality. Yet, pubertal maturation is associated with increased reactivity in threat-avoidance systems. In the first part of this paper we propose a heuristic model of adolescent affective development that may help to reconcile aspects of this paradox, which focuses on hypothesized pubertal increases in the capacity to experience (some) fear-evoking experiences as an exciting thrill. In the second part of this paper, we test key features of this model by examining brain activation to threat cues in a longitudinal study that disentangled pubertal and age effects. Pubertal increases in testosterone predicted increased activation to threat cues, not only in regions associated with threat avoidance (i.e., amygdala), but also regions associated with reward pursuit (i.e., nucleus accumbens). These findings are consistent with our hypothesis that puberty is associated with a maturational shift toward more complex processing of threat cues--which may contribute to adolescent tendencies to explore and enjoy some types of risky experiences. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. The physiology and timing of male puberty

    DEFF Research Database (Denmark)

    Tinggaard, Jeanette; Mieritz, Mikkel Grunnet; Sørensen, Kaspar

    2012-01-01

    To describe available markers of male puberty, discuss associations between adiposity and pubertal timing and to review recent evidence of a possible secular trend in male pubertal timing.......To describe available markers of male puberty, discuss associations between adiposity and pubertal timing and to review recent evidence of a possible secular trend in male pubertal timing....

  12. Transitions in body and behavior: a meta-analytic study on the relationship between pubertal development and adolescent sexual behavior.

    Science.gov (United States)

    Baams, Laura; Dubas, Judith Semon; Overbeek, Geertjan; van Aken, Marcel A G

    2015-06-01

    The present meta-analysis studies the relations of pubertal timing and status with sexual behavior and sexual risk behavior among youth aged 10.5-22.4 years. We included biological sex, age, and ethnicity as potential moderators. Four databases were searched for studies (published between 1980 and 2012) on the relation between pubertal timing or status and sexual behavior. The outcomes were (1) sexual intercourse; (2) combined sexual behavior; and (3) risky sexual behavior. Earlier pubertal timing or more advanced pubertal status was related to earlier and more sexual behavior, and earlier pubertal timing was related to more risky sexual behavior. Further, the links between (1) pubertal status and combined sexual behavior and (2) pubertal timing and sexual intercourse status, combined sexual behavior, and risky sexual behavior were stronger for girls than boys. Most links between pubertal status, timing, and sexual behavior and sexual risk behavior were stronger for younger adolescents. Moderation by ethnicity did not yield consistent results. There was significant variation in results among studies that was not fully explained by differences in biological sex, age, and ethnicity. Future research is needed to identify moderators that explain the variation in effects and to design sexual health interventions for young adolescents. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  13. Brief communication: a proposed osteological method for the estimation of pubertal stage in human skeletal remains.

    Science.gov (United States)

    Shapland, Fiona; Lewis, Mary E

    2013-06-01

    Puberty forms an important threshold between childhood and adulthood, but this subject has received little attention in bioarchaeology. The new application of clinical methods to assess pubertal stage in adolescent skeletal remains is explored, concentrating on the development of the mandibular canine, hamate, hand phalanges, iliac crest and distal radius. Initial results from the medieval cemetery of St. Peter's Church, Barton-upon-Humber, England suggest that application of these methods may provide insights into aspects of adolescent development. This analysis indicates that adolescents from this medieval site were entering the pubertal growth spurt at a similar age to their modern counterparts, but that the later stages of pubertal maturation were being significantly delayed, perhaps due to environmental stress. Continued testing and refinement of these methods on living adolescents is still necessary to improve our understanding of their significance and accuracy in predicting pubertal stages. Copyright © 2013 Wiley Periodicals, Inc.

  14. Long-time predictions in nonlinear dynamics

    Science.gov (United States)

    Szebehely, V.

    1980-01-01

    It is known that nonintegrable dynamical systems do not allow precise predictions concerning their behavior for arbitrary long times. The available series solutions are not uniformly convergent according to Poincare's theorem and numerical integrations lose their meaningfulness after the elapse of arbitrary long times. Two approaches are the use of existing global integrals and statistical methods. This paper presents a generalized method along the first approach. As examples long-time predictions in the classical gravitational satellite and planetary problems are treated.

  15. Experienced travel time prediction for congested freeways

    OpenAIRE

    Yildirimoglu, Mehmet; Geroliminis, Nikolaos

    2013-01-01

    Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time ass...

  16. Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood

    DEFF Research Database (Denmark)

    Almstrup, Kristian; Johansen, Marie Lindhardt; Busch, Alexander S.

    2016-01-01

    Puberty marks numerous physiological processes which are initiated by central activation of the hypothalamic–pituitary–gonadal axis, followed by development of secondary sexual characteristics. To a large extent, pubertal timing is heritable, but current knowledge of genetic polymorphismsonly...... explains few months in the large inter-individual variation in the timing of puberty. We have analysed longitudinal genome-wide changes in DNA methylation in peripheral blood samples (n = 102) obtained from 51 healthy children before and after pubertal onset. We show that changes in single methylation...... sites are tightly associated with physiological pubertal transition and altered reproductive hormone levels. These methylation sites cluster in and around genes enriched for biological functions related to pubertal development. Importantly, we identified that methylation of the genomic region containing...

  17. Trunk-to-Peripheral Fat Ratio Predicts Subsequent Blood Pressure Levels in Pubertal Children With Relatively Low Body Fat - Three-Year Follow-up Study.

    Science.gov (United States)

    Kouda, Katsuyasu; Ohara, Kumiko; Fujita, Yuki; Nakamura, Harunobu; Iki, Masayuki

    2016-07-25

    Only a few studies have examined the relationship between fat distribution measured by dual-energy X-ray absorptiometry (DXA) and blood pressure (BP), and no cohort study has targeted a pediatric population. The source population comprised all students registered as fifth graders in the 2 elementary schools in Hamamatsu, Japan. Of these, 258 children participated in both baseline (at age 11) and follow-up (at age 14) surveys. Body fat distribution was assessed using trunk-to-appendicular fat ratio (TAR) and trunk-to-leg fat ratio (TLR) measured by DXA. Relationships between BP levels and fat distribution (TAR or TLR) were examined after stratification by tertiles of whole-body fat.Systolic BP at follow-up was significantly (Pfat. Moreover, adjusted means of systolic and diastolic BPs in girls showed a significant increase from the lowest to highest tertile of TAR within the lowest tertile of whole-body fat. Body fat distribution in childhood could predict subsequent BP levels in adolescence. Children with a relatively low body fat that is more centrally distributed tended to show relatively high BP later on. (Circ J 2016; 80: 1838-1845).

  18. Can We Predict Patient Wait Time?

    Science.gov (United States)

    Pianykh, Oleg S; Rosenthal, Daniel I

    2015-10-01

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

  19. Urinary phthalate excretion in 555 healthy Danish boys with and without pubertal gynaecomastia

    DEFF Research Database (Denmark)

    Mieritz, Mikkel G; Frederiksen, Hanne; Sørensen, Kaspar

    2012-01-01

    Pubertal gynaecomastia is a clinical sign of an oestrogen-androgen imbalance, which occurs in 40-60% of adolescent Caucasian boys. In most cases no underlying endocrinopathy can be identified. A recent study reports higher plasma phthalate levels in Turkish boys with pubertal gynaecomastia....... Therefore, we asked whether there was an association between concurrent measures of urinary phthalate metabolites and pubertal timing as well as the presence of gynaecomastia in otherwise healthy boys. We studied a total of 555 healthy boys (age 6.07-19.83 years) as part of the COPENHAGEN Puberty Study....... Anthropometry and pubertal stages (PH1-6 and G1-5) were evaluated, and the presence of gynaecomastia was assessed. Non-fasting blood samples were analysed for serum testosterone and morning urine samples were analysed for the total content of 12 phthalate metabolites (MEP, MnBP, MiBP, MBzP, MEHP, MEHHP, MEOHP...

  20. Uncertainties in container failure time predictions

    International Nuclear Information System (INIS)

    Williford, R.E.

    1990-01-01

    Stochastic variations in the local chemical environment of a geologic waste repository can cause corresponding variations in container corrosion rates and failure times, and thus in radionuclide release rates. This paper addresses how well the future variations in repository chemistries must be known in order to predict container failure times that are bounded by a finite time period within the repository lifetime. Preliminary results indicate that a 5000 year scatter in predicted container failure times requires that repository chemistries be known to within ±10% over the repository lifetime. These are small uncertainties compared to current estimates. 9 refs., 3 figs

  1. Time series prediction: statistical and neural techniques

    Science.gov (United States)

    Zahirniak, Daniel R.; DeSimio, Martin P.

    1996-03-01

    In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

  2. Prenatal and pubertal testosterone affect brain lateralization

    NARCIS (Netherlands)

    Beking, T; Geuze, R H; van Faassen, M; Kema, I P; Kreukels, B P C; Groothuis, T G G

    After decades of research, the influence of prenatal testosterone on brain lateralization is still elusive, whereas the influence of pubertal testosterone on functional brain lateralization has not been investigated, although there is increasing evidence that testosterone affects the brain in

  3. Candidate gene expression in Bos indicus ovarian tissues: pre-pubertal and post-pubertal heifers in diestrus

    Directory of Open Access Journals (Sweden)

    Mayara Morena Del Cambre Amaral Weller

    2016-10-01

    Full Text Available Growth factors such as bone morphogenetic proteins 6, 7, 15 and two isoforms of transforming growth factor-beta (BMP6, BMP7, BMP15, TGFB1 and TGFB2 and insulin-like growth factor system act as local regulators of ovarian follicular development. To elucidate if these factors as well as others candidate genes such as estrogen receptor 1 (ESR1, growth differentiation factor 9 (GDF9, follicle stimulating hormone receptor (FSHR, luteinizing hormone receptor (LHR, bone morphogenetic protein receptor, type 2 (BMPR2, type 1 insulin-like growth factor receptor (IGFR1, and key steroidogenic enzymes cytochrome P450 aromatase and 3-β-hydroxysteroid dehydrogenase (CYP19A1 and HSD3B1 could modulate or influence diestrus on the onset of puberty in Brahman heifers, their ovarian mRNA expression was measured before and after puberty (luteal phase. Six post-pubertal (POST heifers were euthanized on the luteal phase of their second cycle, confirmed by corpus luteum observation, and six pre-pubertal (PRE heifers were euthanized in the same day. Quantitative real-time PCR analysis showed that the expression of FSHR, BMP7, CYP19A1, IGF1 and IGFR1 mRNA was greater in PRE heifers, when contrasted to POST heifers. The expression of LHR and HSD3B1 was lower in PRE heifers. Differential expression of ovarian genes could be associated with changes in follicular dynamics and different cell populations that have emerged as consequence of puberty and the luteal phase. The emerging hypothesis is that BMP7 and IGF1 are co-expressed and may modulate the expression of FSHR, LHR and IGFR1 and CYP19A1. BMP7 could influence the down-regulation of LHR and up-regulation of FSHR and CYP19A1, which mediates the follicular dynamics in heifer ovaries. Up-regulation of IGF1 expression pre-puberty, compared to post-puberty diestrus, correlates with increased levels FSHR and CYP19A1. Thus, BMP7 and IGF1 may play synergic roles and were predicted to interact, from the expression data (P = 0

  4. Predicting the Times of Retweeting in Microblogs

    Directory of Open Access Journals (Sweden)

    Li Kuang

    2014-01-01

    Full Text Available Recently, microblog services accelerate the information propagation among peoples, leaving the traditional media like newspaper, TV, forum, blogs, and web portals far behind. Various messages are spread quickly and widely by retweeting in microblogs. In this paper, we take Sina microblog as an example, aiming to predict the possible number of retweets of an original tweet in one month according to the time series distribution of its top n retweets. In order to address the problem, we propose the concept of a tweet’s lifecycle, which is mainly decided by three factors, namely, the response time, the importance of content, and the interval time distribution, and then the given time series distribution curve of its top n retweets is fitted by a two-phase function, so as to predict the number of its retweets in one month. The phases in the function are divided by the lifecycle of the original tweet and different functions are used in the two phases. Experiment results show that our solution can address the problem of predicting the times of retweeting in microblogs with a satisfying precision.

  5. A Time-predictable Stack Cache

    DEFF Research Database (Denmark)

    Abbaspour, Sahar; Brandner, Florian; Schoeberl, Martin

    2013-01-01

    Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to le...... of a cache for stack allocated data. Our port of the LLVM C++ compiler supports the management of the stack cache. The combination of stack cache instructions and the hardware implementation of the stack cache is a further step towards timepredictable architectures.......Real-time systems need time-predictable architectures to support static worst-case execution time (WCET) analysis. One architectural feature, the data cache, is hard to analyze when different data areas (e.g., heap allocated and stack allocated data) share the same cache. This sharing leads to less...... precise results of the cache analysis part of the WCET analysis. Splitting the data cache for different data areas enables composable data cache analysis. The WCET analysis tool can analyze the accesses to these different data areas independently. In this paper we present the design and implementation...

  6. Maths performance as a function of sex, laterality, and age of pubertal onset.

    Science.gov (United States)

    Sappington, John; Topolski, Richard

    2005-07-01

    Sex differences in math/spatial performance demand explanations. Within the biological view, the complexity and number of variables make the explanation difficult at best. Laterality and age of pubertal onset have been investigated prominently in this context but rarely considered as interactions in the same study. Some 468 college subjects with SAT MATH (SAT M) scores were divided into 12 groups defined by sex, laterality, and age (early, middle, and late) of pubertal onset. Significant main effects for sex and age of onset emerged, as did an interaction between lateral preference and pubertal onset. Generally males outperformed females. The combination of maleness, sinistrality, and early maturation was associated with high SAT M scores. Sinistrality and late maturation among females predicted very poor math performance.

  7. The effect of tamoxifen on pubertal bone development in adolescents with pubertal gynecomastia.

    Science.gov (United States)

    Akgül, Sinem; Derman, Orhan; Kanbur, Nuray

    2016-01-01

    During puberty, estrogen has a biphasic effect on epiphyses; at low levels, it leads to an increase in height and bone mass, whereas at high levels, it leads to closure of the epiphysis. Tamoxifen is a selective estrogen receptor modulator that has been used in the treatment of pubertal gynecomastia. Although it has not been approved for this indication, studies have shown it to be both successful and safe. In males, the peak of pubertal bone development occurs during Tanner stage 3-4, which is also when pubertal gynecomastia reaches its highest prevalence. Thus tamoxifen treatment could potentially effect pubertal bone development. The aim of this study was to assess the effects of tamoxifen on bone mineral density (BMD) and skeletal maturation when used for pubertal gynecomastia. We evaluated 20 boys with pubertal gynecomastia receiving tamoxifen for at least 4 months. BMD was measured with dual-energy X-ray absorptiometry. Z-score and absolute BMD (g/cm(2)) was determined at baseline and 2 months after completing tamoxifen treatment. Bone age and height was evaluated before treatment and again one year later. Using absolute BMD (g/cm(2)), the mean difference from baseline was significant between the two groups both at spine (p=0.002) and femur (p=0.001), but not with the Z-score. This result was attributed to the expected increase during puberty according to sex and age. No significant effect on skeletal maturation was found (p=1.112). We conclude that when pubertal bone development is concerned, tamoxifen is safe for the treatment of pubertal gynecomastia as neither bone mineralization nor growth potential was affected.

  8. The Few, the Changing, the Different: Pubertal Onset, Perceived School Climate and Body Image in Ethnically Diverse Sixth Grade Girls

    OpenAIRE

    Morales, Jessica

    2012-01-01

    The present study examined the impact of pubertal onset, race/ethnicity, and school racial/ethnic composition on girls' body image and perceived school climate (school safety, school liking, and loneliness in school) during the middle school transition. The sample (N = 1,626) included 6th grade Black, Mexican American, White, and Asian girls from 20 diverse middle schools. Hierarchical analyses supported both the early-timing and stressful change hypothesis. That is, experiencing pubertal ons...

  9. Pubertal status, interaction with significant others, and self-esteem of adolescent girls.

    Science.gov (United States)

    Lacković-Grgin, K; Dekovíc, M; Opacić, G

    1994-01-01

    The aim of this study was to examine the relationship between pubertal status, the quality of interactions with significant others, and the self-esteem of adolescent girls. The model which was tested, hypothesized that pubertal status affects self-esteem through girls' interactions with their parents and friends. Pubertal status was operationalized as the number of months between occurrence of the first menstrual periods and time of the investigation. The measure of self-esteem was the shortened form of the Coopersmith Self-Esteem Inventory. Analyses revealed that girls who begun menstruating six months before the investigation obtained higher scores on the measure of self-esteem than did girls who had been menstruating 13 months or more. The best predictor of self-esteem, however, was the quality of interaction with their mothers. The results support the theoretical view that stresses the importance of interaction with significant others for the development of self-esteem.

  10. Implementation and testing of the travel time prediction system (TIPS)

    OpenAIRE

    PANT, Prahlad D; UNIVERSITY OF CINCINNATI

    2001-01-01

    RAPPORT DE RECHERCHE FINAL The Travel Time Prediction System (TIPS) is a portable automated system for predicting and displaying travel time for motorists in advance of and through freeway construction work zones,on a real-time basis

  11. Real Time Seismic Prediction while Drilling

    Science.gov (United States)

    Schilling, F. R.; Bohlen, T.; Edelmann, T.; Kassel, A.; Heim, A.; Gehring, M.; Lüth, S.; Giese, R.; Jaksch, K.; Rechlin, A.; Kopf, M.; Stahlmann, J.; Gattermann, J.; Bruns, B.

    2009-12-01

    Efficient and safe drilling is a prerequisite to enhance the mobility of people and goods, to improve the traffic as well as utility infrastructure of growing megacities, and to ensure the growing energy demand while building geothermal and in hydroelectric power plants. Construction within the underground is often building within the unknown. An enhanced risk potential for people and the underground building may arise if drilling enters fracture zones, karsts, brittle rocks, mixed solid and soft rocks, caves, or anthropogenic obstacles. Knowing about the material behavior ahead of the drilling allows reducing the risk during drilling and construction operation. In drilling operations direct observations from boreholes can be complemented with geophysical investigations. In this presentation we focus on “real time” seismic prediction while drilling which is seen as a prerequisite while using geophysical methods in modern drilling operations. In solid rocks P- and S-wave velocity, refraction and reflection as well as seismic wave attenuation can be used for the interpretation of structures ahead of the drilling. An Integrated Seismic Imaging System (ISIS) for exploration ahead of a construction is used, where a pneumatic hammer or a magnetostrictive vibration source generate repetitive signals behind the tunneling machine. Tube waves are generated which travel along the tunnel to the working face. There the tube waves are converted to mainly S- but also P-Waves which interact with the formation ahead of the heading face. The reflected or refracted waves travel back to the working front are converted back to tube waves and recorded using three-component geophones which are fit into the tips of anchor rods. In near real time, the ISIS software allows for an integrated 3D imaging and interpretation of the observed data, geological and geotechnical parameters. Fracture zones, heterogeneities, and variations in the rock properties can be revealed during the drilling

  12. Real-time travel time prediction framework for departure time and route advice

    NARCIS (Netherlands)

    Calvert, S.C.; Snelder, M.; Bakri, T.; Heijligers, B.; Knoop, V.L.

    2015-01-01

    Heavily used urban networks remain a challenge for travel time prediction because traffic flow is rarely homogeneous and is also subject to a wide variety of disturbances. Various models, some of which use traffic flow theory and some of which are data driven, have been developed to predict traffic

  13. Validity of self-assessment of pubertal maturation

    DEFF Research Database (Denmark)

    Rasmussen, Anna; Wohlfahrt-Veje, Christine; Tefre de Renzy-Martin, Katrine

    2015-01-01

    BACKGROUND AND OBJECTIVES: Studies of adolescents often use self-assessment of pubertal maturation, the reliability of which has shown conflicting results. We aimed to examine the reliability of child and parent assessments of healthy boys and girls. METHODS: A total of 898 children (418 girls, 480...... overestimated older than their peers who made correct assessments. Girls and their parents tended to underestimate, whereas boys overestimated their pubertal stage. CONCLUSIONS: Pubertal assessment by the child or the parents is not a reliable measure of exact pubertal staging and should be augmented...

  14. Nonparametric conditional predictive regions for time series

    NARCIS (Netherlands)

    de Gooijer, J.G.; Zerom Godefay, D.

    2000-01-01

    Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the

  15. Repeatability and accuracy of reproductive tract scoring to determine pubertal status in beef heifers.

    Science.gov (United States)

    Rosenkrans, Kelly S; Hardin, David K

    2003-03-01

    The objective of this study was to compare the repeatability and accuracy of palpation per rectum to transrectal ultrasonography and serum progesterone concentrations for determining pubertal status in beef heifers. One hundred and seventy-four rectal examinations were performed on 29 predominantly Angus heifers by two veterinarians (A and B) and assigned individual reproductive tract scores (RTS) during monthly examinations over a 3-month period. Heifers were examined in the morning by both veterinarians, randomized, and re-examined in the afternoon. The size and location of ovarian structures of each heifer were determined by ultrasonography. Heifers with follicles >10mm in diameter or corpora lutea were classified as pubertal. Serum progesterone concentrations at the time of the examination and 10 days later were determined by radioimmunoassay and used to classify heifers as prepubertal (or=1 ng/ml). Kappa, which describes degree of agreement beyond chance, was used to determine repeatability of the RTS system. Multicategory Kappa for agreement was 0.64 within veterinarian, 0.46 between veterinarian, and 0.35 between palpation per rectum and transrectal ultrasonography. Sensitivity and specificity of palpation per rectum for diagnosis of pubertal status compared to serum progesterone levels were higher (82 and 69%, respectively) than sensitivity and specificity of ultrasonography (79 and 59%, respectively). This study validates the RTS system as a repeatable and accurate screening test to evaluate pubertal status in groups of heifers prior to the onset of the breeding season.

  16. Prediction and Geometry of Chaotic Time Series

    National Research Council Canada - National Science Library

    Leonardi, Mary

    1997-01-01

    This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

  17. Time series prediction of apple scab using meteorological ...

    African Journals Online (AJOL)

    A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

  18. Prediction of Commuter’s Daily Time Allocation

    Directory of Open Access Journals (Sweden)

    Fang Zong

    2013-10-01

    Full Text Available This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

  19. Pubertal development in The Netherlands 1965-1997

    NARCIS (Netherlands)

    D. Mul (Dick); A.M. Fredriks; S. van Buuren (Stef); W. Oostdijk (Wilma); S.P. Verloove-Vanhorick; J.M. Wit (Jan)

    2001-01-01

    textabstractWe investigated pubertal development of 4019 boys and 3562 girls >8 y of age participating in a cross-sectional survey in The Netherlands and compared the results with those of two previous surveys. Reference curves for all pubertal stages were constructed. The 50th

  20. Reproductive ability of pubertal male and female rats

    Directory of Open Access Journals (Sweden)

    T. Zemunik

    2003-07-01

    Full Text Available Ten Fisher rats 50 to 55 days of age made up the pubertal group, and ten rats 90 to 95 days of age served as the controls. The testicular and epididymal weights and volumes of the pubertal males were lower than those of the controls (P0.05. At the beginning of gestation, the pubertal dams weighed less than the controls (P<0.001 but following uterectomy the body weights were equal. Pubertal dams delivered fewer pups than the controls (8.1 ± 2.5 vs 10.4 ± 1.3, P<0.05. There was no difference in the body weights of their offspring or in the weights of their placentas. The results suggest that, in contrast to their female counterparts, pubertal male rats are not fully mature and have not reached complete reproductive capacity at 50-55 days of age.

  1. On the Scalability of Time-predictable Chip-Multiprocessing

    DEFF Research Database (Denmark)

    Puffitsch, Wolfgang; Schoeberl, Martin

    2012-01-01

    Real-time systems need a time-predictable execution platform to be able to determine the worst-case execution time statically. In order to be time-predictable, several advanced processor features, such as out-of-order execution and other forms of speculation, have to be avoided. However, just using...... simple processors is not an option for embedded systems with high demands on computing power. In order to provide high performance and predictability we argue to use multiprocessor systems with a time-predictable memory interface. In this paper we present the scalability of a Java chip......-multiprocessor system that is designed to be time-predictable. Adding time-predictable caches is mandatory to achieve scalability with a shared memory multi-processor system. As Java bytecode retains information about the nature of memory accesses, it is possible to implement a memory hierarchy that takes...

  2. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  3. Time and prediction in quantum cosmology

    International Nuclear Information System (INIS)

    Hartle, J.B.

    1989-01-01

    In this paper a generalized quantum mechanics for cosmological spacetimes is suggested in which no variable plays the special role of the time of familiar quantum mechanics. In this generalization the central role of time in familiar quantum mechanics arises, not at a fundamental aspect of the formalism, but rather as an approximation appropriate to those initial conditions of the universe which lead to classical spacetime when it is large

  4. Course and forecast of the hypothalamic pubertal syndrome

    International Nuclear Information System (INIS)

    Kayusheva, I.V.

    1987-01-01

    A total of 223 patients with the hypothalamic pubertal syndrome (HPS) were followed up for 1 to 22 years. The course of HPS was regressive, stable , recurrent or progressive and dependent on the initial depth and spread of hypothalamic lesion, repeated unfavourable hypothalamic exposures, and timely and regular treatment. HPS outcomes were followed up in 190 cases. The recovery was complete in 21.05%, obesity alone persisted in 10.53%, vegetovascular dystonia was persistent in 7.36%, and polycystic ovaries in 5.79%. Neuroendocrine hypothalamic syndrome was the most common (50.53%) HPS outcome. Hormone levels in blood were investigated using radioimmunoassay in patients with neuroendocrine form of HPS

  5. Predictive modeling without notion of time

    NARCIS (Netherlands)

    Hoogendoorn, Mark; Funk, Burkhardt

    2018-01-01

    Supervised learning approaches that do not explicitly take the time component into account are briefly discussed in this chapter. The approaches explained include feedforward neural networks, support vector machines, k-nearest neighbor, decision trees, naïve bayes and ensembles. Guidelines are

  6. Time-series prediction and applications a machine intelligence approach

    CERN Document Server

    Konar, Amit

    2017-01-01

    This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

  7. Predicting response times for the Spotify backend

    OpenAIRE

    Yanggratoke, Rerngvit; Kreitz, Gunnar; Goldmann, Mikael; Stadler, Rolf

    2012-01-01

    We model and evaluate the performance of a distributed key-value storage system that is part of the Spotify backend. Spotify is an on-demand music streaming service, offering low-latency access to a library of over 16 million tracks and serving over 10 million users currently. We first present a simplified model of the Spotify storage architecture, in order to make its analysis feasible. We then introduce an analytical model for the distribution of the response time, a key metric in the Spoti...

  8. Chaotic time series prediction: From one to another

    International Nuclear Information System (INIS)

    Zhao Pengfei; Xing Lei; Yu Jun

    2009-01-01

    In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.

  9. ISOL yield predictions from holdup-time measurements

    International Nuclear Information System (INIS)

    Spejewski, Eugene H.; Carter, H Kennon; Mervin, Brenden T.; Prettyman, Emily S.; Kronenberg, Andreas; Stracener, Daniel W

    2008-01-01

    A formalism based on a simple model is derived to predict ISOL yields for all isotopes of a given element based on a holdup-time measurement of a single isotope of that element. Model predictions, based on parameters obtained from holdup-time measurements, are compared to independently-measured experimental values

  10. PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

    Science.gov (United States)

    Spreadsheet and short-cut methods have been developed for predicting evaporation rates and evaporation times for spills (and constrained baths) of chemical mixtures. Steady-state and time-varying predictions of evaporation rates can be made for six-component mixtures, includ...

  11. Assessing the Predictability of Scheduled-Vehicle Travel Times

    DEFF Research Database (Denmark)

    Tiesyte, Dalia; Jensen, Christian Søndergaard

    2009-01-01

    One of the most desired and challenging services in collective transport systems is the real-time prediction of the near-future travel times of scheduled vehicles, especially public buses, thus improving the experience of the transportation users, who may be able to better schedule their travel......, and also enabling system operators to perform real-time monitoring. While travel-time prediction has been researched extensively during the past decade, the accuracies of existing techniques fall short of what is desired, and proposed mathematical prediction models are often not transferable to other...... systems because the properties of the travel-time-related data of vehicles are highly context-dependent, making the models difficult to fit. We propose a framework for evaluating various predictability types of the data independently of the model, and we also compare predictability analysis results...

  12. Pubertal development and prostate cancer risk

    DEFF Research Database (Denmark)

    Bonilla, Carolina; Lewis, Sarah J; Martin, Richard M

    2016-01-01

    , 0.91-1.00) and prostate cancer-specific mortality (hazard ratio amongst cases, per tertile: 0.94; 95 % CI, 0.90-0.98), but not with disease grade. CONCLUSIONS: Older age at sexual maturation is causally linked to a reduced risk of later prostate cancer, especially aggressive disease.......BACKGROUND: Epidemiological studies have observed a positive association between an earlier age at sexual development and prostate cancer, but markers of sexual maturation in boys are imprecise and observational estimates are likely to suffer from a degree of uncontrolled confounding. To obtain...... to a difference of one Tanner stage between pubertal boys of the same age) was associated with a 77 % (95 % CI, 43-91 %) reduced odds of high Gleason prostate cancer. In PRACTICAL, the puberty genetic score was associated with prostate cancer stage (OR of advanced vs. localized cancer, per tertile: 0.95; 95 % CI...

  13. Adolescents' increasing stress response to social evaluation: pubertal effects on cortisol and alpha-amylase during public speaking.

    Science.gov (United States)

    van den Bos, Esther; de Rooij, Mark; Miers, Anne C; Bokhorst, Caroline L; Westenberg, P Michiel

    2014-01-01

    Stress responses to social evaluation are thought to increase during adolescence, which may be due to pubertal maturation. However, empirical evidence is scarce. This study is the first to investigate the relation between pubertal development and biological responses to a social-evaluative stressor longitudinally. Participants performed the Leiden Public Speaking Task twice, with a 2-year interval (N = 217; age at Time 1: 8-17 years). The results support an increase in sensitivity to social evaluation during adolescence. The overall cortisol and alpha-amylase responses increased-both between and within participants-and were more strongly related to self-reported pubertal development than to age. The cortisol response shifted from speech delivery toward anticipation. The alpha-amylase response increased in both phases. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

  14. Facing changes and changing faces in adolescence: a new model for investigating adolescent-specific interactions between pubertal, brain and behavioral development.

    Science.gov (United States)

    Scherf, K Suzanne; Behrmann, Marlene; Dahl, Ronald E

    2012-04-01

    Adolescence is a time of dramatic physical, cognitive, emotional, and social changes as well as a time for the development of many social-emotional problems. These characteristics raise compelling questions about accompanying neural changes that are unique to this period of development. Here, we propose that studying adolescent-specific changes in face processing and its underlying neural circuitry provides an ideal model for addressing these questions. We also use this model to formulate new hypotheses. Specifically, pubertal hormones are likely to increase motivation to master new peer-oriented developmental tasks, which will in turn, instigate the emergence of new social/affective components of face processing. We also predict that pubertal hormones have a fundamental impact on the re-organization of neural circuitry supporting face processing and propose, in particular, that, the functional connectivity, or temporal synchrony, between regions of the face-processing network will change with the emergence of these new components of face processing in adolescence. Finally, we show how this approach will help reveal why adolescence may be a period of vulnerability in brain development and suggest how it could lead to prevention and intervention strategies that facilitate more adaptive functional interactions between regions within the broader social information processing network. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Facing changes and changing faces in adolescence: A new model for investigating adolescent-specific interactions between pubertal, brain and behavioral development

    Science.gov (United States)

    Scherf, K. Suzanne; Behrmann, Marlene; Dahl, Ronald E.

    2015-01-01

    Adolescence is a time of dramatic physical, cognitive, emotional, and social changes as well as a time for the development of many social-emotional problems. These characteristics raise compelling questions about accompanying neural changes that are unique to this period of development. Here, we propose that studying adolescent-specific changes in face processing and its underlying neural circuitry provides an ideal model for addressing these questions. We also use this model to formulate new hypotheses. Specifically, pubertal hormones are likely to increase motivation to master new peer-oriented developmental tasks, which will in turn, instigate the emergence of new social/affective components of face processing. We also predict that pubertal hormones have a fundamental impact on the reorganization of neural circuitry supporting face processing and propose, in particular, that, the functional connectivity, or temporal synchrony, between regions of the face-processing network will change with the emergence of these new components of face processing in adolescence. Finally, we show how this approach will help reveal why adolescence may be a period of vulnerability in brain development and suggest how it could lead to prevention and intervention strategies that facilitate more adaptive functional interactions between regions within the broader social information processing network. PMID:22483070

  16. Individual differences in boys' and girls' timing and tempo of puberty: modeling development with nonlinear growth models.

    Science.gov (United States)

    Marceau, Kristine; Ram, Nilam; Houts, Renate M; Grimm, Kevin J; Susman, Elizabeth J

    2011-09-01

    Pubertal development is a nonlinear process progressing from prepubescent beginnings through biological, physical, and psychological changes to full sexual maturity. To tether theoretical concepts of puberty with sophisticated longitudinal, analytical models capable of articulating pubertal development more accurately, we used nonlinear mixed-effects models to describe both the timing and tempo of pubertal development in the sample of 364 White boys and 373 White girls measured across 6 years as part of the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development. Individual differences in timing and tempo were extracted with models of logistic growth. Differential relations emerged for how boys' and girls' timing and tempo of development were related to physical characteristics (body mass index, height, and weight) and psychological outcomes (internalizing problems, externalizing problems, and risky sexual behavior). Timing and tempo are associated in boys but not girls. Pubertal timing and tempo are particularly important for predicting psychological outcomes in girls but only sparsely related to boys' psychological outcomes. Results highlight the importance of considering the nonlinear nature of puberty and expand the repertoire of possibilities for examining important aspects of how and when pubertal processes contribute to development.

  17. Exciting fear in adolescence: Does pubertal development alter threat processing?

    OpenAIRE

    Spielberg, JM; Olino, TM; Forbes, EE; Dahl, RE

    2014-01-01

    Adolescent development encompasses an ostensible paradox in threat processing. Risk taking increases dramatically after the onset of puberty, contributing to a 200% increase in mortality. Yet, pubertal maturation is associated with increased reactivity in threat-avoidance systems. In the first part of this paper we propose a heuristic model of adolescent affective development that may help to reconcile aspects of this paradox, which focuses on hypothesized pubertal increases in the capacity t...

  18. Safety-critical Java on a time-predictable processor

    DEFF Research Database (Denmark)

    Korsholm, Stephan E.; Schoeberl, Martin; Puffitsch, Wolfgang

    2015-01-01

    For real-time systems the whole execution stack needs to be time-predictable and analyzable for the worst-case execution time (WCET). This paper presents a time-predictable platform for safety-critical Java. The platform consists of (1) the Patmos processor, which is a time-predictable processor......; (2) a C compiler for Patmos with support for WCET analysis; (3) the HVM, which is a Java-to-C compiler; (4) the HVM-SCJ implementation which supports SCJ Level 0, 1, and 2 (for both single and multicore platforms); and (5) a WCET analysis tool. We show that real-time Java programs translated to C...... and compiled to a Patmos binary can be analyzed by the AbsInt aiT WCET analysis tool. To the best of our knowledge the presented system is the second WCET analyzable real-time Java system; and the first one on top of a RISC processor....

  19. Fertility of the Small East African goat following pre-pubertal infection with Trypanosoma congolense

    International Nuclear Information System (INIS)

    O'Hara, H.B.; Gombe, S.

    1991-01-01

    Pre-pubertal male and female Small East African goats were infected with Trypanosoma congolense at 4-5 months of age. Changes in body weight and haemogram were monitored weekly. Progesterone and testosterone measurements were made three times weekly until the goats either reached puberty or 18 months of age. Onset of puberty was determined from observation of oestrus behaviour, mating or increase in libidio; this was confirmed by elevation in plasma progesterone or testosterone levels. Trypanosomiasis affected pre-pubertal goats by reducing body weight gain and delaying onset of puberty. Histological examination of the gonads showed pronounced pathological changes. These effects were reversed by treatment with isometamidium chloride (Samorin, May and Baker). It was concluded that early treatment of infected goats before serious gonadal damage could occur allowed full restoration of reproductive function. (author). 6 refs, 4 figs, 1 tab

  20. Data-aware remaining time prediction of business process instances

    NARCIS (Netherlands)

    Polato, M.; Sperduti, A.; Burattin, A.; Leoni, de M.

    2014-01-01

    Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process

  1. Three Millennia of Seemingly Time-Predictable Earthquakes, Tell Ateret

    Science.gov (United States)

    Agnon, Amotz; Marco, Shmuel; Ellenblum, Ronnie

    2014-05-01

    Among various idealized recurrence models of large earthquakes, the "time-predictable" model has a straightforward mechanical interpretation, consistent with simple friction laws. On a time-predictable fault, the time interval between an earthquake and its predecessor is proportional to the slip during the predecessor. The alternative "slip-predictable" model states that the slip during earthquake rupture is proportional to the preceding time interval. Verifying these models requires extended records of high precision data for both timing and amount of slip. The precision of paleoearthquake data can rarely confirm or rule out predictability, and recent papers argue for either time- or slip-predictable behavior. The Ateret site, on the trace of the Dead Sea fault at the Jordan Gorge segment, offers unique precision for determining space-time patterns. Five consecutive slip events, each associated with deformed and offset sets of walls, are correlated with historical earthquakes. Two correlations are based on detailed archaeological, historical, and numismatic evidence. The other three are tentative. The offsets of three of the events are determined with high precision; the other two are not as certain. Accepting all five correlations, the fault exhibits a striking time-predictable behavior, with a long term slip rate of 3 mm/yr. However, the 30 October 1759 ~0.5 m rupture predicts a subsequent rupture along the Jordan Gorge toward the end of the last century. We speculate that earthquakres on secondary faults (the 25 November 1759 on the Rachaya branch and the 1 January 1837 on the Roum branch, both M≥7) have disrupted the 3 kyr time-predictable pattern.

  2. Weight Suppression Predicts Time to Remission from Bulimia Nervosa

    Science.gov (United States)

    Lowe, Michael R.; Berner, Laura A.; Swanson, Sonja A.; Clark, Vicki L.; Eddy, Kamryn T.; Franko, Debra L.; Shaw, Jena A.; Ross, Stephanie; Herzog, David B.

    2011-01-01

    Objective: To investigate whether, at study entry, (a) weight suppression (WS), the difference between highest past adult weight and current weight, prospectively predicts time to first full remission from bulimia nervosa (BN) over a follow-up period of 8 years, and (b) weight change over time mediates the relationship between WS and time to first…

  3. Evolutionary neural network modeling for software cumulative failure time prediction

    International Nuclear Information System (INIS)

    Tian Liang; Noore, Afzel

    2005-01-01

    An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches

  4. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  5. A Time-predictable Memory Network-on-Chip

    DEFF Research Database (Denmark)

    Schoeberl, Martin; Chong, David VH; Puffitsch, Wolfgang

    2014-01-01

    To derive safe bounds on worst-case execution times (WCETs), all components of a computer system need to be time-predictable: the processor pipeline, the caches, the memory controller, and memory arbitration on a multicore processor. This paper presents a solution for time-predictable memory...... arbitration and access for chip-multiprocessors. The memory network-on-chip is organized as a tree with time-division multiplexing (TDM) of accesses to the shared memory. The TDM based arbitration completely decouples processor cores and allows WCET analysis of the memory accesses on individual cores without...

  6. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

    Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

  7. Grammar-based feature generation for time-series prediction

    CERN Document Server

    De Silva, Anthony Mihirana

    2015-01-01

    This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

  8. Time evolution of predictability of epidemics on networks

    Science.gov (United States)

    Holme, Petter; Takaguchi, Taro

    2015-04-01

    Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information—i.e., knowing the state of each individual with respect to the disease—the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

  9. Pubertal development among girls with classical congenital adrenal hyperplasia initiated on treatment at different ages

    Directory of Open Access Journals (Sweden)

    Bindu Kulshreshtha

    2012-01-01

    Full Text Available Introduction: Children with congenital adrenal hyperplasia (CAH provide us an opportunity to study the clinical effects of androgen excess in humans. We studied the sequence of pubertal development in girls with congenital adrenal hyperplasia initiated on treatment at different ages, to assess the effects of androgen exposure on the Hypothalamic-Pituitary-Ovarian (HPO axis. Materials and Methods: Girls more than 18 years of age, with CAH, on follow-up at this hospital were the subjects for this study. Details of history, physical findings, laboratory evaluation, and medication were noted from their case records and verified from the patients and their / parents, in addition to assessment of their present health status. Result: We studied 24 patients of classical CAH (SW-2, SV-22, average age - 24.5 ± 6.6 years. All had varying degrees of genital ambiguity (Prader stage 3 (n = 13, Prader stage 2 (n = 10, Prader stage 1 (n = 1. Among them were13 girls, who were started on steroids after eight years of age. Girls who received treatment from infancy and early childhood had normal pubertal development (mean age at menarche 11.4 ± 1.7 years. Hirsutism was not a problem among them. Untreated children had progressive clitoral enlargement throughout childhood, developed pubic hair at around three to six years of age, and facial hair between nine and eleven years. Plasma testosterone ranged from 3 to 6 ng / ml prior to treatment. Six of the 13 untreated CAH girls had subtle breast development starting at ages 11 - 16 years and three had spontaneous infrequent vaginal bleeding starting at ages 11 - 17. Steroid supplementation initiated pubertal changes in older girls in two-to-six months′ time. Conclusion: There was a delay in HPO axis maturation (as evidenced by delayed pubertal development in the absence of treatment in girls with CAH. This could be corrected with steroid supplementation.

  10. Clinical time series prediction: Toward a hierarchical dynamical system framework.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2015-09-01

    Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Clinical time series prediction: towards a hierarchical dynamical system framework

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  12. The Prediction of Teacher Turnover Employing Time Series Analysis.

    Science.gov (United States)

    Costa, Crist H.

    The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

  13. Predicting travel time variability for cost-benefit analysis

    NARCIS (Netherlands)

    Peer, S.; Koopmans, C.; Verhoef, E.T.

    2010-01-01

    Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways.

  14. Predictive timing disturbance is a precise marker of schizophrenia

    Directory of Open Access Journals (Sweden)

    Valentina Ciullo

    2018-06-01

    Our findings shed new light on the debate over the specificity of timing distortions in SZ, providing evidence that predictive timing is a precise marker of SZ, more sensitive than duration estimation, serving as a valid heuristic for studying the pathophysiology of the disorder.

  15. Real-time Tsunami Inundation Prediction Using High Performance Computers

    Science.gov (United States)

    Oishi, Y.; Imamura, F.; Sugawara, D.

    2014-12-01

    Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the

  16. State-space prediction model for chaotic time series

    Science.gov (United States)

    Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

    1998-08-01

    A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

  17. Chaotic time series. Part II. System Identification and Prediction

    Directory of Open Access Journals (Sweden)

    Bjørn Lillekjendlie

    1994-10-01

    Full Text Available This paper is the second in a series of two, and describes the current state of the art in modeling and prediction of chaotic time series. Sample data from deterministic non-linear systems may look stochastic when analysed with linear methods. However, the deterministic structure may be uncovered and non-linear models constructed that allow improved prediction. We give the background for such methods from a geometrical point of view, and briefly describe the following types of methods: global polynomials, local polynomials, multilayer perceptrons and semi-local methods including radial basis functions. Some illustrative examples from known chaotic systems are presented, emphasising the increase in prediction error with time. We compare some of the algorithms with respect to prediction accuracy and storage requirements, and list applications of these methods to real data from widely different areas.

  18. Effect of Rainfall on Travel Time and Accuracy of Travel Time prediction with rainfall

    OpenAIRE

    CHUNG, E; EL-FAOUZI, NE; KUWAHARA, M

    2007-01-01

    Travel time is an important parameter to report to travelers. From the user's perspective, accurate predictions and an estimate of their precision are more beneficial than the current travel time since conditions may change significantly before a traveler completes the journey. Past researches have developed travel time prediction models without considering accidents and rain. Normally accident and Rain may cause to increase travel time. Therefore, it may be interesting to consider Rain and a...

  19. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2016-01-01

    (ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

  20. Acute ischaemic stroke prediction from physiological time series patterns

    Directory of Open Access Journals (Sweden)

    Qing Zhang,

    2013-05-01

    Full Text Available BackgroundStroke is one of the major diseases with human mortality. Recent clinical research has indicated that early changes in common physiological variables represent a potential therapeutic target, thus the manipulation of these variables may eventually yield an effective way to optimise stroke recovery.AimsWe examined correlations between physiological parameters of patients during the first 48 hours after a stroke, and their stroke outcomes after 3 months. We wanted to discover physiological determinants that could be used to improve health outcomes by supporting the medical decisions that need to be made early on a patient’s stroke experience.Method We applied regression-based machine learning techniques to build a prediction algorithm that can forecast 3-month outcomes from initial physiological time series data during the first 48 hours after stroke. In our method, not only did we use statistical characteristics as traditional prediction features, but also we adopted trend patterns of time series data as new key features.ResultsWe tested our prediction method on a real physiological data set of stroke patients. The experiment results revealed an average high precision rate: 90%. We also tested prediction methods only considering statistical characteristics of physiological data, and concluded an average precision rate: 71%.ConclusionWe demonstrated that using trend pattern features in prediction methods improved the accuracy of stroke outcome prediction. Therefore, trend patterns of physiological time series data have an important role in the early treatment of patients with acute ischaemic stroke.

  1. School performance in pubertal adolescents with dysmenorrhea

    Directory of Open Access Journals (Sweden)

    Syamsir Alam

    2011-08-01

    Full Text Available Background Dysmenorrhea is a common gynecological symptom reported in adolescent girls. Prevalence of the condition has been reported to be 45 - 75%. Absenteeism from work and school as a result of dysmenorrhea is common (13 - 51% of women have been absent at least once, and 5 - 14% are often absent due to the severity of symptoms. Objective To compare school performance in pubertal adolescent girls with and without dysmenorrhea. Methods This cross-sectional study was conducted in June 2010 in adolescent females aged 12 - 18 years from the Musthafawiyah School, Mandailing Natal district, North Sumatera. Adolescent females with and without dysmenorrhea were recruited for this study. All participants completed questionnaires including age of menarche, length of menstrual cycle, length of bleeding, number of sanitary napkins used daily and school absences. School reports from two consecutive semesters in one year were used to evaluate subjects’ academic performance. An academic score of higher than 7.5 was considered good performance while scores of less than 7.5 were considered poor. We used the chi-square test to analyze differences in school performance between girls with and without dysmenorrhea. Results One hundred and sixteen participants were divided into 2 groups, those with and without dysmenorrhea, of 58 subjects each. We found no significant difference in school performance between the two groups, P=0.176 (95% CI -0.009 to -0.048 and P=0.08 (95%CI -0.052 to 0.024. Conclusion There was no significant difference in school performance of girls with and without dysmenorrhea.

  2. Multivariate performance reliability prediction in real-time

    International Nuclear Information System (INIS)

    Lu, S.; Lu, H.; Kolarik, W.J.

    2001-01-01

    This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

  3. State-based Communication on Time-predictable Multicore Processors

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo; Schoeberl, Martin; Sparsø, Jens

    2016-01-01

    Some real-time systems use a form of task-to-task communication called state-based or sample-based communication that does not impose any flow control among the communicating tasks. The concept is similar to a shared variable, where a reader may read the same value multiple times or may not read...... a given value at all. This paper explores time-predictable implementations of state-based communication in network-on-chip based multicore platforms through five algorithms. With the presented analysis of the implemented algorithms, the communicating tasks of one core can be scheduled independently...... of tasks on other cores. Assuming a specific time-predictable multicore processor, we evaluate how the read and write primitives of the five algorithms contribute to the worst-case execution time of the communicating tasks. Each of the five algorithms has specific capabilities that make them suitable...

  4. Time and activity sequence prediction of business process instances

    DEFF Research Database (Denmark)

    Polato, Mirko; Sperduti, Alessandro; Burattin, Andrea

    2018-01-01

    The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict...... future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed...

  5. Pubertal induction in hypogonadism: Current approaches including use of gonadotrophins.

    Science.gov (United States)

    Zacharin, Margaret

    2015-06-01

    Primary disorders of the gonad or those secondary to abnormalities of the hypothalamic pituitary axis result in hypogonadism. The range of health problems of childhood and adolescence that affect this axis has increased, as most children now survive chronic illness, but many have persisting deficits in gonadal function as a result of their underlying condition or its treatment. An integrated approach to hormone replacement is needed to optimize adult hormonal and bone health, and to offer opportunities for fertility induction and preservation that were not considered possible in the past. Timing of presentation ranges from birth, with disorders of sexual development, through adolescent pubertal failure, to adult fertility problems. This review addresses diagnosis and management of hypogonadism and focuses on new management strategies to address current concerns with fertility preservation. These include Turner syndrome, and fertility presevation prior to childhood cancer treatment. New strategies for male hormone replacement therapy that may impinge upon future fertility are emphasized. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Time dependent patient no-show predictive modelling development.

    Science.gov (United States)

    Huang, Yu-Li; Hanauer, David A

    2016-05-09

    Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

  7. Multiple-Factor Based Sparse Urban Travel Time Prediction

    Directory of Open Access Journals (Sweden)

    Xinyan Zhu

    2018-02-01

    Full Text Available The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

  8. Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

    Energy Technology Data Exchange (ETDEWEB)

    Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit

    2016-05-02

    This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

  9. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  10. Time-To-Complete Prediction for Data Transfers

    CERN Document Server

    Toler, Wesley

    2016-01-01

    Currently, there is no prediction provided to users for the amount of time a particular data transfer from one site in the Worldwide LHC Computing Grid to another will take to complete. To develop a time-to-complete prediction, network performance data and per-file information is gathered from two separate databases and fused, and the resulting cleaned data is fitted using random forest regression. Results are shown for two separate links: the link from CERN Data Centre to Brookhaven National Laboratory’s ATLAS data center, and the link from CERN Data Centre to SARA-MATRIX in Amsterdam. A total RMS error of 25.93 minutes between predicted and test data is found for the CERN-PROD -> BNL-ATLAS link, while the CERN-PROD -> SARA-MATRIX link yields a total RMS error of 3.00 minutes.

  11. Study on real-time elevator brake failure predictive system

    Science.gov (United States)

    Guo, Jun; Fan, Jinwei

    2013-10-01

    This paper presented a real-time failure predictive system of the elevator brake. Through inspecting the running state of the coil by a high precision long range laser triangulation non-contact measurement sensor, the displacement curve of the coil is gathered without interfering the original system. By analyzing the displacement data using the diagnostic algorithm, the hidden danger of the brake system can be discovered in time and thus avoid the according accident.

  12. Time series prediction with simple recurrent neural networks ...

    African Journals Online (AJOL)

    A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used. In this study, we evaluated the performance of these neural networks on three established bench mark time series prediction problems. Results from the experiments showed that Jordan neural network performed significantly ...

  13. The pattern of cognitive symptoms predicts time to dementia onset.

    NARCIS (Netherlands)

    Sacuiu, S.; Gustafson, D.; Johansson, B.; Thorvaldsson, V.; Berg, S.; Sjogren, J.M.C.; Guo, X.; Ostling, S.; Skoog, I.

    2009-01-01

    BACKGROUND: Few studies have examined whether cognitive symptom patterns differ by age and length of time before dementia onset. Our objective was to investigate whether different patterns of cognitive symptoms at ages 70, 75, and 79 years predict short-term (< or =5 years) and long-term (>5 years)

  14. Conditional mode regression: Application to functional time series prediction

    OpenAIRE

    Dabo-Niang, Sophie; Laksaci, Ali

    2008-01-01

    We consider $\\alpha$-mixing observations and deal with the estimation of the conditional mode of a scalar response variable $Y$ given a random variable $X$ taking values in a semi-metric space. We provide a convergence rate in $L^p$ norm of the estimator. A useful and typical application to functional times series prediction is given.

  15. Support for the Logical Execution Time Model on a Time-predictable Multicore Processor

    DEFF Research Database (Denmark)

    Kluge, Florian; Schoeberl, Martin; Ungerer, Theo

    2016-01-01

    The logical execution time (LET) model increases the compositionality of real-time task sets. Removal or addition of tasks does not influence the communication behavior of other tasks. In this work, we extend a multicore operating system running on a time-predictable multicore processor to support...... the LET model. For communication between tasks we use message passing on a time-predictable network-on-chip to avoid the bottleneck of shared memory. We report our experiences and present results on the costs in terms of memory and execution time....

  16. Real-time prediction of the occurrence of GLE events

    Science.gov (United States)

    Núñez, Marlon; Reyes-Santiago, Pedro J.; Malandraki, Olga E.

    2017-07-01

    A tool for predicting the occurrence of Ground Level Enhancement (GLE) events using the UMASEP scheme is presented. This real-time tool, called HESPERIA UMASEP-500, is based on the detection of the magnetic connection, along which protons arrive in the near-Earth environment, by estimating the lag correlation between the time derivatives of 1 min soft X-ray flux (SXR) and 1 min near-Earth proton fluxes observed by the GOES satellites. Unlike current GLE warning systems, this tool can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min with respect to the first NM station's alert and 15 min to the GLE Alert Plus's warning. We have tested the model by replacing the GOES proton data with SOHO/EPHIN proton data, and the results are similar in terms of POD, FAR, and AWT for the same period. The paper also presents a comparison with a GLE warning system.

  17. Real-time eSports Match Result Prediction

    OpenAIRE

    Yang, Yifan; Qin, Tian; Lei, Yu-Heng

    2016-01-01

    In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contai...

  18. Real coded genetic algorithm for fuzzy time series prediction

    Science.gov (United States)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  19. Biological and socio-cultural factors during the school years predicting women’s lifetime educational attainment

    Science.gov (United States)

    Hendrick, C. Emily; Cohen, Alison K.; Deardorff, Julianna

    2015-01-01

    BACKGROUND Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In the current study, we examine the roles of socio-cultural factors in youth and an understudied biological life event, pubertal timing, in predicting women’s lifetime educational attainment. METHODS Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level socio-cultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother’s education, mother’s age at first birth) and early menarche, a marker of early pubertal development, on women’s educational attainment after age 24. RESULTS Pubertal timing and all socio-cultural factors in youth, other than year of birth, predicted women’s lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth and pubertal timing were no longer significant. CONCLUSION Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. PMID:26830508

  20. Generation and prediction of time series by a neural network

    International Nuclear Information System (INIS)

    Eisenstein, E.; Kanter, I.; Kessler, D.A.; Kinzel, W.

    1995-01-01

    Generation and prediction of time series are analyzed for the case of a bit generator: a perceptron where in each time step the input units are shifted one bit to the right with the state of the leftmost input unit set equal to the output unit in the previous time step. The long-time dynamical behavior of the bit generator consists of cycles whose typical period scales polynomially with the size of the network and whose spatial structure is periodic with a typical finite wavelength. The generalization error on a cycle is zero for a finite training set, and global dynamical behaviors can also be learned in a finite time. Hence, a projection of a rule can be learned in a finite time

  1. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    Science.gov (United States)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  2. Traffic Incident Clearance Time and Arrival Time Prediction Based on Hazard Models

    Directory of Open Access Journals (Sweden)

    Yang beibei Ji

    2014-01-01

    Full Text Available Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

  3. Time Series Outlier Detection Based on Sliding Window Prediction

    Directory of Open Access Journals (Sweden)

    Yufeng Yu

    2014-01-01

    Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

  4. Optimal model-free prediction from multivariate time series

    Science.gov (United States)

    Runge, Jakob; Donner, Reik V.; Kurths, Jürgen

    2015-05-01

    Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.

  5. Predicting physical time series using dynamic ridge polynomial neural networks.

    Directory of Open Access Journals (Sweden)

    Dhiya Al-Jumeily

    Full Text Available Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.

  6. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  7. Techniques for building timing-predictable embedded systems

    CERN Document Server

    Guan, Nan

    2016-01-01

    This book describes state-of-the-art techniques for designing real-time computer systems. The author shows how to estimate precisely the effect of cache architecture on the execution time of a program, how to dispatch workload on multicore processors to optimize resources, while meeting deadline constraints, and how to use closed-form mathematical approaches to characterize highly variable workloads and their interaction in a networked environment. Readers will learn how to deal with unpredictable timing behaviors of computer systems on different levels of system granularity and abstraction. Introduces promising techniques for dealing with challenges associated with deploying real-time systems on multicore platforms; Provides a complete picture of building timing-predictable computer systems, at the program level, component level and system level; Leverages different levels of abstraction to deal with the complexity of the analysis.

  8. Time-Predictable Communication on a Time-Division Multiplexing Network-on-Chip Multicore

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo

    This thesis presents time-predictable inter-core communication on a multicore platform with a time-division multiplexing (TDM) network-on-chip (NoC) for hard real-time systems. The thesis is structured as a collection of papers that contribute within the areas of: reconfigurable TDM NoCs, static...... TDM scheduling, and time-predictable inter-core communication. More specifically, the work presented in this thesis investigates the interaction between hardware and software involved in time-predictable inter-core communication on the multicore platform. The thesis presents: a new generation...... of the Argo NoC network interface (NI) that supports instantaneous reconfiguration, a TDM traffic scheduler that generates virtual circuit (VC) configurations for the Argo NoC, and software functions for two types of intercore communication. The new generation of the Argo NoC adds the capability...

  9. Serum inhibin B in healthy pubertal and adolescent boys

    DEFF Research Database (Denmark)

    Andersson, A M; Juul, A; Petersen, J H

    1997-01-01

    Inhibin B levels were measured in serum from 400 healthy Danish prepubertal, pubertal, and adolescent males, aged 6-20 yr, in a cross-sectional study using a recently developed immunoassay that is specific for inhibin B, the physiologically important inhibin form in men. In addition, serum levels...

  10. Melatonin and LH secretion patterns in pubertal boys

    International Nuclear Information System (INIS)

    Fevre, M.; Boyar, R.M.; Rollag, M.D.

    1979-01-01

    Plasma melatonin and LH were measured at 20 minute intervals for 24 hours in four normal pubertal boys. All four subjects showed a significant augmentation of LH and melatonin during nocturnal sleep. There was also a significant correlation between the LH and melatonin levels (p [fr

  11. Earthquake prediction in Japan and natural time analysis of seismicity

    Science.gov (United States)

    Uyeda, S.; Varotsos, P.

    2011-12-01

    M9 super-giant earthquake with huge tsunami devastated East Japan on 11 March, causing more than 20,000 casualties and serious damage of Fukushima nuclear plant. This earthquake was predicted neither short-term nor long-term. Seismologists were shocked because it was not even considered possible to happen at the East Japan subduction zone. However, it was not the only un-predicted earthquake. In fact, throughout several decades of the National Earthquake Prediction Project, not even a single earthquake was predicted. In reality, practically no effective research has been conducted for the most important short-term prediction. This happened because the Japanese National Project was devoted for construction of elaborate seismic networks, which was not the best way for short-term prediction. After the Kobe disaster, in order to parry the mounting criticism on their no success history, they defiantly changed their policy to "stop aiming at short-term prediction because it is impossible and concentrate resources on fundamental research", that meant to obtain "more funding for no prediction research". The public were and are not informed about this change. Obviously earthquake prediction would be possible only when reliable precursory phenomena are caught and we have insisted this would be done most likely through non-seismic means such as geochemical/hydrological and electromagnetic monitoring. Admittedly, the lack of convincing precursors for the M9 super-giant earthquake has adverse effect for us, although its epicenter was far out off shore of the range of operating monitoring systems. In this presentation, we show a new possibility of finding remarkable precursory signals, ironically, from ordinary seismological catalogs. In the frame of the new time domain termed natural time, an order parameter of seismicity, κ1, has been introduced. This is the variance of natural time kai weighted by normalised energy release at χ. In the case that Seismic Electric Signals

  12. Time-dependent fatigue--phenomenology and life prediction

    International Nuclear Information System (INIS)

    Coffin, L.F.

    1979-01-01

    The time-dependent fatigue behavior of materials used or considered for use in present and advanced systems for power generation is outlined. A picture is first presented to show how basic mechanisms and phenomenological information relate to the performance of the component under consideration through the so-called local strain approach. By this means life prediction criteria and design rules can be formulated utilizing laboratory test information which is directly translated to predicting the performance of a component. The body of phenomenological information relative to time-dependent fatigue is reviewed. Included are effects of strain range, strain rate and frequency, environment and wave shape, all of which are shown to be important in developing both an understanding and design base for time dependent fatigue. Using this information, some of the current methods being considered for the life prediction of components are reviewed. These include the current ASME code case, frequency-modified fatigue equations, strain range partitioning, the damage function method, frequency separation and damage rate equations. From this review, it is hoped that a better perspective on future directions for basic material science at high temperature can be achieved

  13. Chaos Time Series Prediction Based on Membrane Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Meng Li

    2015-01-01

    Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.

  14. A time-spectral approach to numerical weather prediction

    Science.gov (United States)

    Scheffel, Jan; Lindvall, Kristoffer; Yik, Hiu Fai

    2018-05-01

    Finite difference methods are traditionally used for modelling the time domain in numerical weather prediction (NWP). Time-spectral solution is an attractive alternative for reasons of accuracy and efficiency and because time step limitations associated with causal CFL-like criteria, typical for explicit finite difference methods, are avoided. In this work, the Lorenz 1984 chaotic equations are solved using the time-spectral algorithm GWRM (Generalized Weighted Residual Method). Comparisons of accuracy and efficiency are carried out for both explicit and implicit time-stepping algorithms. It is found that the efficiency of the GWRM compares well with these methods, in particular at high accuracy. For perturbative scenarios, the GWRM was found to be as much as four times faster than the finite difference methods. A primary reason is that the GWRM time intervals typically are two orders of magnitude larger than those of the finite difference methods. The GWRM has the additional advantage to produce analytical solutions in the form of Chebyshev series expansions. The results are encouraging for pursuing further studies, including spatial dependence, of the relevance of time-spectral methods for NWP modelling.

  15. Linear and nonlinear dynamic systems in financial time series prediction

    Directory of Open Access Journals (Sweden)

    Salim Lahmiri

    2012-10-01

    Full Text Available Autoregressive moving average (ARMA process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE and mean absolute deviation (MAD statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.

  16. Time scaling internal state predictive control of a solar plant

    Energy Technology Data Exchange (ETDEWEB)

    Silva, R.N. [DEE-FCT/UNL, Caparica (Portugal); Rato, L.M. [INESC-ID/University, Evora (Portugal); Lemos, J.M. [INESC-ID/IST, Lisboa (Portugal)

    2003-12-01

    The control of a distributed collector solar field is addressed in this work, exploiting the plant's transport characteristic. The plant is modeled by a hyperbolic type partial differential equation (PDE) where the transport speed is the manipulated flow, i.e. the controller output. The model has an external distributed source, which is the solar radiation captured along the collector, approximated to depend only of time. From the solution of the PDE, a linear discrete state space model is obtained by using time-scaling and the redefinition of the control input. This method allows overcoming the dependency of the time constants with the operating point. A model-based predictive adaptive controller is derived with the internal temperature distribution estimated with a state observer. Experimental results at the solar power plant are presented, illustrating the advantages of the approach under consideration. (author)

  17. NASA AVOSS Fast-Time Wake Prediction Models: User's Guide

    Science.gov (United States)

    Ahmad, Nash'at N.; VanValkenburg, Randal L.; Pruis, Matthew

    2014-01-01

    The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.

  18. Dynamic thermal signature prediction for real-time scene generation

    Science.gov (United States)

    Christie, Chad L.; Gouthas, Efthimios (Themie); Williams, Owen M.; Swierkowski, Leszek

    2013-05-01

    At DSTO, a real-time scene generation framework, VIRSuite, has been developed in recent years, within which trials data are predominantly used for modelling the radiometric properties of the simulated objects. Since in many cases the data are insufficient, a physics-based simulator capable of predicting the infrared signatures of objects and their backgrounds has been developed as a new VIRSuite module. It includes transient heat conduction within the materials, and boundary conditions that take into account the heat fluxes due to solar radiation, wind convection and radiative transfer. In this paper, an overview is presented, covering both the steady-state and transient performance.

  19. The influence of chronic conditions and the environment on pubertal development. An example from medieval England.

    Science.gov (United States)

    Lewis, M E; Shapland, F; Watts, R

    2016-03-01

    Adolescence is a unique period in human development encompassing sexual maturation (puberty) and the physical and psychological transition into adulthood. It is a crucial time for healthy development and any adverse environmental conditions, poor nutrition, or chronic infection can alter the timing of these physical changes; delaying menarche in girls or the age of peak height velocity in boys. This study explores the impact of chronic illness on the tempo of puberty in 607 adolescent skeletons from medieval England (AD 900-1550). A total of 135 (22.2%) adolescents showed some delay in their pubertal development, and this lag increased with age. Of those with a chronic condition, 40.0% (n=24/60) showed delay compared to only 20.3% (n=111/547) of the non-pathology group. This difference was statistically significant. A binary logistic regression model demonstrated a significant association between increasing delay in pubertal stage attainment with age in the pathology group. This is the first time that chronic conditions have been directly associated with a delay in maturation in the osteological record, using a new method to assess stages of puberty in skeletal remains. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting

    Directory of Open Access Journals (Sweden)

    Norbert A. Agana

    2018-02-01

    Full Text Available Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD and a deep belief network (DBN. The proposed method first decomposes the data into several intrinsic mode functions (IMFs using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP and support vector regression (SVR. The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.

  1. Memory controllers for real-time embedded systems predictable and composable real-time systems

    CERN Document Server

    Akesson, Benny

    2012-01-01

      Verification of real-time requirements in systems-on-chip becomes more complex as more applications are integrated. Predictable and composable systems can manage the increasing complexity using formal verification and simulation.  This book explains the concepts of predictability and composability and shows how to apply them to the design and analysis of a memory controller, which is a key component in any real-time system. This book is generally intended for readers interested in Systems-on-Chips with real-time applications.   It is especially well-suited for readers looking to use SDRAM memories in systems with hard or firm real-time requirements. There is a strong focus on real-time concepts, such as predictability and composability, as well as a brief discussion about memory controller architectures for high-performance computing. Readers will learn step-by-step how to go from an unpredictable SDRAM memory, offering highly variable bandwidth and latency, to a predictable and composable shared memory...

  2. An unanswered question in pediatric urology: the post pubertal persistence of prepubertal congenital penile curvature correction by tunical plication.

    Science.gov (United States)

    Ozkuvanci, Ünsal; Ziylan, Orhan; Dönmez, M Irfan; Yucel, Omer Baris; Oktar, Tayfun; Ander, Haluk; Nane, Ismet

    2017-01-01

    The aim of this study is to analyze post pubertal results of pre pubertal tunica albuginea plication with non-absorbable sutures in the correction of CPC. The files of patients who underwent tunica albuginea plication without incision (dorsal/lateral) were retrospectively reviewed. Patients younger than 13 years of age at the time of operation and older than 14 years of age in November 2015 were included. Patients with a penile curvature of less than 30 degrees & more than 45 degrees and penile/urethral anomalies were excluded. All of the patients underwent surgery followed by circumcision. The mean age of patients at the time of the operation was 9.7 years (range, 6-13 years). The mean degree of ventral penile curvature measured during the operation was 39 degrees while it was 41 degrees in the lateral curvatures. All of the patients were curvature-free at the end of the operation. At the time of the follow-up examination, the mean age was 16.7 years (range, 14-25 years). Six patients had a straight (0-10 degrees) penis during erection and seven patients had recurrent penile curvatures ranging from 30 to 50 degrees. Pre pubertal tunica albuginea plication of congenital penile curvature (30-45 degrees) with non-absorbable sutures performed without incision is a minimal invasive method especially when performed during circumcision. However, recurrence might be observed in half of the patients after puberty. Copyright® by the International Brazilian Journal of Urology.

  3. An unanswered question in pediatric urology: the post pubertal persistence of prepubertal congenital penile curvature correction by tunical plication

    Directory of Open Access Journals (Sweden)

    Ünsal Ozkuvanci

    Full Text Available ABSTRACT Objective: The aim of this study is to analyze post pubertal results of pre pubertal tunica albuginea plication with non-absorbable sutures in the correction of CPC. Materials and Methods: The files of patients who underwent tunica albuginea plication without incision (dorsal/lateral were retrospectively reviewed. Patients younger than 13 years of age at the time of operation and older than 14 years of age in November 2015 were included. Patients with a penile curvature of less than 30 degrees & more than 45 degrees and penile/urethral anomalies were excluded. All of the patients underwent surgery followed by circumcision. Results: The mean age of patients at the time of the operation was 9.7 years (range, 6-13 years. The mean degree of ventral penile curvature measured during the operation was 39 degrees while it was 41 degrees in the lateral curvatures. All of the patients were curvature-free at the end of the operation. At the time of the follow-up examination, the mean age was 16.7 years (range, 14-25 years. Six patients had a straight (0-10 degrees penis during erection and seven patients had recurrent penile curvatures ranging from 30 to 50 degrees. Conclusion: Pre pubertal tunica albuginea plication of congenital penile curvature (30-45 degrees with non-absorbable sutures performed without incision is a minimal invasive method especially when performed during circumcision. However, recurrence might be observed in half of the patients after puberty.

  4. Predictability of Landslide Timing From Quasi-Periodic Precursory Earthquakes

    Science.gov (United States)

    Bell, Andrew F.

    2018-02-01

    Accelerating rates of geophysical signals are observed before a range of material failure phenomena. They provide insights into the physical processes controlling failure and the basis for failure forecasts. However, examples of accelerating seismicity before landslides are rare, and their behavior and forecasting potential are largely unknown. Here I use a Bayesian methodology to apply a novel gamma point process model to investigate a sequence of quasiperiodic repeating earthquakes preceding a large landslide at Nuugaatsiaq in Greenland in June 2017. The evolution in earthquake rate is best explained by an inverse power law increase with time toward failure, as predicted by material failure theory. However, the commonly accepted power law exponent value of 1.0 is inconsistent with the data. Instead, the mean posterior value of 0.71 indicates a particularly rapid acceleration toward failure and suggests that only relatively short warning times may be possible for similar landslides in future.

  5. Time series prediction by feedforward neural networks - is it difficult?

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang

    2003-01-01

    The difficulties that a neural network faces when trying to learn from a quasi-periodic time series are studied analytically using a teacher-student scenario where the random input is divided into two macroscopic regions with different variances, 1 and 1/γ 2 (γ >> 1). The generalization error is found to decrease as ε g ∝ exp(-α/γ 2 ), where α is the number of examples per input dimension. In contradiction to this very slow vanishing generalization error, the next output prediction is found to be almost free of mistakes. This picture is consistent with learning quasi-periodic time series produced by feedforward neural networks, which is dominated by enhanced components of the Fourier spectrum of the input. Simulation results are in good agreement with the analytical results

  6. Female Pubertal Timing and Problem Behaviour: The Role of Culture

    Science.gov (United States)

    Skoog, Therese; Stattin, Hakan; Ruiselova, Zdena; Ozdemir, Metin

    2013-01-01

    We tested the peer-socialization/contextual-amplification explanation for the link between early female puberty and problem behaviour. We propose that in cultures with high tolerance for adolescent heterosexual involvement, early puberty should be linked with problem behaviour--not in other cultures. We compared girls in two cultures (Slovakia and…

  7. Relationships between anthropometric features, body composition, and anaerobic alactic power in elite post-pubertal and mature male taekwondo athletes

    Directory of Open Access Journals (Sweden)

    Boraczyński Michał

    2017-12-01

    Full Text Available Purpose. The paper describes the relationships between anthropometric features, body composition, and anaerobic alactic power (AAP in elite post-pubertal and mature male taekwondo athletes. Methods. The sample of 41 taekwondo athletes was divided into two groups: post-pubertal (P-P, n = 19, Mage = 15.6 ± 1.1 years and mature (M, n = 22, Mage = 20.7 ± 2.8 years. Anthropometric features (WB-150, ZPU Tryb-Wag, Poland, body composition (BC-418 MA, Tanita, Japan, maturational status (Pubertal Maturational Observational Scale, and AAP (10-s version of the Wingate Anaerobic Test were assessed. Results. Post-hoc testing revealed significant between-group differences (3.2-20.4%, p < 0.01 in all anthropometric and body composition measures, with effect sizes (ES between −0.79 and −1.25 (p < 0.001, except for fat content and percentage of skeletal muscle mass (SMM (p ≥ 0.05. In group M, the maximal power output (Pmax was greater (ES = −1.15, p < 0.001 and the time of its attainment shorter (ES = 0.59, p < 0.001 than in group P-P. Correlation analyses indicated notably strong associations between body mass (BM and Pmax in group P-P (r = 0.950 [95% CI, 0.85-0.98], p < 0.001 and M (r = 0.926 [95% CI, 0.81-0.97], p < 0.001, and similar-sized strong correlations between fat-free mass (FFM and Pmax in group P-P (r = 0.955 [95% CI, 0.86-0.99], p < 0.001 and M (r = 0.924 [95% CI, 0.82-0.96], p < 0.001. Additionally, a strong correlation was found between body height and Pmax in groups P-P and M (r = 0.805 [95% CI, 0.54-0.92], p < 0.001 and r = 0.819 [95% CI, 0.58-0.93], p < 0.001, respectively. Linear regression analyses demonstrated that FFM, BM, and absolute SMM best explained the variance in Pmax in both groups (r, 0.939-0.951; r2, 0.882-0.909. Conclusions. The strong correlations observed in both groups between BM, FFM, SMM, and Pmax demonstrate the significant effects of body size and composition on AAP. By determining the current levels of these

  8. Visual prediction: psychophysics and neurophysiology of compensation for time delays.

    Science.gov (United States)

    Nijhawan, Romi

    2008-04-01

    A necessary consequence of the nature of neural transmission systems is that as change in the physical state of a time-varying event takes place, delays produce error between the instantaneous registered state and the external state. Another source of delay is the transmission of internal motor commands to muscles and the inertia of the musculoskeletal system. How does the central nervous system compensate for these pervasive delays? Although it has been argued that delay compensation occurs late in the motor planning stages, even the earliest visual processes, such as phototransduction, contribute significantly to delays. I argue that compensation is not an exclusive property of the motor system, but rather, is a pervasive feature of the central nervous system (CNS) organization. Although the motor planning system may contain a highly flexible compensation mechanism, accounting not just for delays but also variability in delays (e.g., those resulting from variations in luminance contrast, internal body temperature, muscle fatigue, etc.), visual mechanisms also contribute to compensation. Previous suggestions of this notion of "visual prediction" led to a lively debate producing re-examination of previous arguments, new analyses, and review of the experiments presented here. Understanding visual prediction will inform our theories of sensory processes and visual perception, and will impact our notion of visual awareness.

  9. Short Sleep Times Predict Obesity in Internal Medicine Clinic Patients

    Science.gov (United States)

    Buscemi, Dolores; Kumar, Ashwani; Nugent, Rebecca; Nugent, Kenneth

    2007-01-01

    Study Objectives: Epidemiological studies have demonstrated an association between short sleep times and obesity as defined by body mass index (BMI). We wanted to determine whether this association occurs in patients with chronic medical diagnoses since the number of confounding factors is likely higher in patients than the general population. Methods: Two hundred patients attending internal medicine clinics completed a survey regarding sleep habits, lifestyle characteristics, and medical diagnoses. An independent surveyor collected the information on the questionnaires and reviewed the medical records. Height and weight were measured by clinic personnel. Data were analyzed with multivariate logistic regression. Results: Subjects with short sleep times (< 7 hours) had an increased likelihood of obesity as defined by a BMI ≥ 30 kg/m2 when compared to the reference group of (8, 9] hours (odds ratio 2.93; 95% confidence interval, 1.06–8.09). There was a U-shaped relationship between obesity and sleep time in women but not in men. Young age (18 to 49 years), not smoking, drinking alcohol, hypertension, diabetes, and sleep apnea were also associated with obesity in the overall model. Conclusions: This study demonstrates an association between short sleep times and obesity in undifferentiated patients attending an internal medicine clinic using models adjusting for age, lifestyle characteristics, and some medical diagnoses. The U-shaped relationship in women suggests that sleep patterns may have gender specific associations. These observations provide the background for therapeutic trials in weight loss in patients with established medical problems. Citation: Buscemi D; Kumar A; Nugent R; Nugent K. Short sleep times predict obesity in internal medicine clinic patients. J Clin Sleep Med 2007;3(7):681–688. PMID:18198800

  10. Prediction of Classroom Reverberation Time using Neural Network

    Science.gov (United States)

    Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

    2018-04-01

    In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

  11. Dopamine reward prediction errors reflect hidden state inference across time

    Science.gov (United States)

    Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

    2017-01-01

    Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

  12. Effects of programmed physical activity on body composition in post-pubertal schoolchildren

    Directory of Open Access Journals (Sweden)

    Edson dos Santos Farias

    2015-04-01

    Full Text Available OBJECTIVE: To assess body composition modifications in post-pubertal schoolchildren after practice of a physical activity program during one school year. METHODS: The sample consisted of 386 students aged between 15 and 17 years and divided into two groups: the study group (SG comprised 195 students and the control group (CG, 191. The SG was submitted to a physical activity program and the CG attended conventional physical education classes. Body composition was assessed using body mass index (BMI, percentage of body fat (%BF, fat mass (FM, and lean mass (LM. RESULTS: A positive effect of the physical activity program on body composition in the SG (p < 0.001 was observed, as well as on the interaction time x group in all the variables analyzed in both genders. A reduction in %BF (mean of differences = -5.58% and waist circumference (-2.33 cm, as well as an increase in LM (+2.05 kg were observed in the SG for both genders, whereas the opposite was observed in the CG. CONCLUSION: The practice of programmed physical activity promotes significant reduction of body fat in post-pubertal schoolchildren.

  13. Comparison of clinical and microbiological features of vulvovaginitis in prepubertal and pubertal girls.

    Science.gov (United States)

    Yilmaz, Ayse E; Celik, Nurullah; Soylu, Gul; Donmez, Ahsen; Yuksel, Cigdem

    2012-07-01

    Vulvovaginitisis the most common gynecological problem of childhood. The aim of the study was to determine and compare clinical and microbiological features of vulvovaginitis in prepubertal and adolescent girls. In this retrospective study, the records of patients who were diagnosed with vulvovaginitis between January 2005 and December 2010 in the pediatric outpatient clinic at Fatih University Hospital were retrieved. Information regarding age, symptoms, history of antibiotic use within 1 month prior to presentation, findings on urinalysis, serum antistreptolysin-O levels, and results of urine/vaginal cultures was collected. The records of 112 patients were evaluated, 72 of which were prepubertal (64.2%) and 40 were pubertal (35.7%) at the time of diagnosis. Thirty-eight prepubertal patients (52.7%) had a positive result on vaginal culture, the most commonly encountered microorganism being group A beta-hemolytic streptococcus (15.2%). Culture positivity rate in the pubertal group was 47.5% (19 patients), with Candida albicans being the most frequently isolated microorganism (27.5%). The etiopathogenesis and culture results differ between prepubertal and adolescent girls with vulvovaginitis, which should be taken into consideration in the treatment approach of this disorder. Copyright © 2012. Published by Elsevier B.V.

  14. Peri-pubertal gonadotropin-releasing hormone agonist treatment affects sex biased gene expression of amygdala in sheep.

    Science.gov (United States)

    Nuruddin, Syed; Krogenæs, Anette; Brynildsrud, Ola Brønstad; Verhaegen, Steven; Evans, Neil P; Robinson, Jane E; Haraldsen, Ira Ronit Hebold; Ropstad, Erik

    2013-12-01

    The nature of hormonal involvement in pubertal brain development has attracted wide interest. Structural changes within the brain that occur during pubertal development appear mainly in regions closely linked with emotion, motivation and cognitive functions. Using a sheep model, we have previously shown that peri-pubertal pharmacological blockade of gonadotropin releasing hormone (GnRH) receptors, results in exaggerated sex-differences in cognitive executive function and emotional control, as well as sex and hemisphere specific patterns of expression of hippocampal genes associated with synaptic plasticity and endocrine signaling. In this study, we explored effects of this treatment regime on the gene expression profile of the ovine amygdala. The study was conducted with 30 same-sex twin lambs (14 female and 16 male), half of which were treated with the GnRH agonist (GnRHa) goserelin acetate every 4th week, beginning before puberty, until approximately 50 weeks of age. Gene expression profiles of the left and right amygdala were measured using 8×15 K Agilent ovine microarrays. Differential expression of selected genes was confirmed by qRT-PCR (Quantitative real time PCR). Networking analyses and Gene Ontology (GO) Term analyses were performed with Ingenuity Pathway Analysis (IPA), version 7.5 and DAVID (Database for Annotation, Visualization and integrated Discovery) version 6.7 software packages, respectively. GnRHa treatment was associated with significant sex- and hemisphere-specific differential patterns of gene expression. GnRHa treatment was associated with differential expression of 432 (|logFC|>0.3, adj. p value expressed as a result of GnRHa treatment in the male animals. The results indicated that GnRH may, directly and/or indirectly, be involved in the regulation of sex- and hemisphere-specific differential expression of genes in the amygdala. This finding should be considered when long-term peri-pubertal GnRHa treatment is used in children. Copyright

  15. Predicting armed conflict: Time to adjust our expectations?

    Science.gov (United States)

    Cederman, Lars-Erik; Weidmann, Nils B

    2017-02-03

    This Essay provides an introduction to the general challenges of predicting political violence, particularly compared with predicting other types of events (such as earthquakes). What is possible? What is less realistic? We aim to debunk myths about predicting violence, as well as to illustrate the substantial progress in this field. Copyright © 2017, American Association for the Advancement of Science.

  16. A prediction method based on wavelet transform and multiple models fusion for chaotic time series

    International Nuclear Information System (INIS)

    Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha

    2017-01-01

    In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

  17. [Pubertal growth of 1,453 healthy children according to age at pubertal growth spurt onset. The Barcelona longitudinal growth study].

    Science.gov (United States)

    Carrascosa, Antonio; Yeste, Diego; Moreno-Galdó, Antonio; Gussinyé, Miquel; Ferrández, Ángel; Clemente, María; Fernández-Cancio, Mónica

    2018-02-20

    Pubertal growth pattern differs according to age at pubertal growth spurt onset which occurs over a five years period (girls: 8-13 years, boys: 10-15 years). The need for more than one pubertal reference pattern has been proposed. We aimed to obtain five 1-year-age-interval pubertal patterns. Longitudinal (6 years of age-adult height) growth study of 1,453 healthy children to evaluate height-for-age, growth velocity-for-age and weight-for-age values. According to age at pubertal growth spurt onset girls were considered: very-early matures (8-9 years, n=119), early matures (9-10 years, n=157), intermediate matures (10-11 years, n=238), late matures (11-12 years, n=127) and very-late matures (12-13 years, n=102), and boys: very-early matures (10-11 years, n=110), early matures (11-12 years, n=139), intermediate matures (12-13 years, n=225), late matures (13-14 years, n=133) and very-late matures (14-15 years, n=103). Age at menarche and growth up to adult height were recorded. In both sexes, statistically-significant (P<.0001) and clinically-pertinent differences in pubertal growth pattern (mean height-for-age, mean growth velocity-for-age and mean pubertal height gain, values) were found among the five pubertal maturity groups and between each group and the whole population, despite similar adult height values. The same occurred for age at menarche and growth from menarche to adult height (P<.05). In both sexes, pubertal growth spurt onset is a critical milestone determining pubertal growth and sexual development. The contribution of our data to better clinical evaluation of growth according to the pubertal maturity tempo of each child will obviate the mistakes made when only one pubertal growth reference is used. Copyright © 2018. Publicado por Elsevier España, S.L.U.

  18. Giant pubertal prolactinoma: Complete resolution following short ...

    African Journals Online (AJOL)

    2016-04-06

    Apr 6, 2016 ... disappeared in a short time with cabergoline treatment. Key words: ... Hyperprolactinemia may cause impotence and hypogonadism in adult men, and rarely ... safe treatment method for male patients with giant prolactinoma.

  19. Osteoporosis-Related Mortality: Time-Trends and Predictive Factors

    Directory of Open Access Journals (Sweden)

    Nelly Ziadé

    2014-07-01

    Full Text Available Osteoporosis is one of the leading causes of handicap worldwide and a major contributor to the global burden of diseases. In particular, osteoporosis is associated with excess mortality. We reviewed the impact of osteoporosis on mortality in a population by defining three categories: mortality following hip fractures, mortality following other sites of fractures, and mortality associated with low bone mineral density (BMD. Hip fractures, as well as other fractures at major sites are all associated with excess mortality, except at the forearm site. This excess mortality is higher during the first 3-6 months after the fracture and then declines over time, but remains higher than the mortality of the normal population up to 22 years after the fracture. Low BMD is also associated with high mortality, with hazard ratios of around 1.3 for every decrease in 1 standard deviation of bone density at 5 years, independently of fractures, reflecting a more fragile population. Finally predictors of mortality were identified and categorised in demographic known factors (age and male gender and in factors reflecting a poor general health status such as the number of comorbidities, low mental status, or level of social dependence. Our results indicate that the management of a patient with osteoporosis should include a multivariate approach that could be based on predictive models in the future.

  20. Induction of a hypothyroid state during juvenile development delays pubertal reactivation of the neuroendocrine system governing luteinising hormone secretion in the male rhesus monkey (Macaca mulatta).

    Science.gov (United States)

    Mann, D R; Bhat, G K; Stah, C D; Pohl, C R; Plant, T M

    2006-09-01

    The present study aimed to determine the influence of thyroid status on the timing of the pubertal resurgence in gonadotrophin-releasing hormone pulse generator activity [tracked by circulating luteinising hormone (LH) levels] in male rhesus monkeys. Six juvenile monkeys were orchidectomised and then treated with the antithyroid drug, methimazole, from 15-19 months until 36 months of age, at which time thyroxine (T(4)) replacement was initiated. Four additional agonadal monkeys served as controls. Blood samples were drawn weekly for hormonal assessments. Body weight, crown-rump length and bone age were monitored at regular intervals. By 8 weeks of methimazole treatment, plasma T(4) had fallen sharply, and the decline was associated with a plasma thyroid-stimulating hormone increase. In controls, plasma LH levels remained undetectable until the pubertal rise occurred at 29.3 +/- 0.2 months of age. This developmental event occurred in only half of the methimazole-treated animals before 36 months of age when T(4) replacement was initiated. The hypothyroid state was associated with a profound arrest of growth and bone maturation, but increased body mass indices and plasma leptin levels. T(4) replacement in methimazole-treated monkeys was associated with the pubertal rise in LH in the remaining three animals and accelerated somatic development in all six animals. Although pubertal resurgence in LH secretion occurred at a later chronological age in methimazole-treated animals compared to controls, bone age, crown-rump length and body weight at that time did not differ between groups. There were no long-term differences in plasma prolactin between groups. We conclude that juvenile hypothyroidism in male primates causes a marked delay in the pubertal resurgence of LH secretion, probably occasioned at the hypothalamic level. Whether this effect is meditated by an action of thyroid hormone directly on the hypothalamus or indirectly as a result of the concomitant deficit in

  1. The mammary gland is a sensitive pubertal target in CD-1 and C57Bl/6 mice following perinatal perfluorooctanoic acid (PFOA) exposure.

    Science.gov (United States)

    Tucker, Deirdre K; Macon, Madisa B; Strynar, Mark J; Dagnino, Sonia; Andersen, Erik; Fenton, Suzanne E

    2015-07-01

    Perfluorooctanoic acid (PFOA) is a developmental toxicant in mice, with varied strain outcomes depending on dose and period of exposure. The impact of PFOA on female mouse pubertal development at low doses (≤1mg/kg) has yet to be determined. Therefore, female offspring from CD-1 and C57Bl/6 dams exposed to PFOA, creating serum concentrations similar to humans, were examined for pubertal onset, including mammary gland development. Pups demonstrated a shorter PFOA elimination half-life than that reported for adult mice. Prenatal exposure to PFOA caused significant mammary developmental delays in female offspring in both strains. Delays started during puberty and persisted into young adulthood; severity was dose-dependent. Also an evaluation of female serum hormone levels and pubertal timing onset revealed no effects of PFOA compared to controls in either strain. These data suggest that the mammary gland is more sensitive to early low level PFOA exposures compared to other pubertal endpoints, regardless of strain. Published by Elsevier Inc.

  2. Resolution, Scales and Predictability: Is High Resolution Detrimental To Predictability At Extended Forecast Times?

    Science.gov (United States)

    Mesinger, F.

    The traditional views hold that high-resolution limited area models (LAMs) down- scale large-scale lateral boundary information, and that predictability of small scales is short. Inspection of various rms fits/errors has contributed to these views. It would follow that the skill of LAMs should visibly deteriorate compared to that of their driver models at more extended forecast times. The limited area Eta Model at NCEP has an additional handicap of being driven by LBCs of the previous Avn global model run, at 0000 and 1200 UTC estimated to amount to about an 8 h loss in accuracy. This should make its relative skill compared to that of the Avn deteriorate even faster. These views are challenged by various Eta results including rms fits to raobs out to 84 h. It is argued that it is the largest scales that contribute the most to the skill of the Eta relative to that of the Avn.

  3. Prenatal androgen excess programs metabolic derangements in pubertal female rats.

    Science.gov (United States)

    Yan, Xiaonan; Dai, Xiaonan; Wang, Jing; Zhao, Nannan; Cui, Yugui; Liu, Jiayin

    2013-04-01

    Owing to the heterogeneity in the clinical symptoms of polycystic ovary syndrome (PCOS), the early pathophysiological mechanisms of PCOS remain unclear. Clinical, experimental, and genetic evidence supports an interaction between genetic susceptibility and the influence of maternal environment in the pathogenesis of PCOS. To determine whether prenatal androgen exposure induced PCOS-related metabolic derangements during pubertal development, we administrated 5α-dihydrotestosterone (DHT) in pregnant rats and observed their female offspring from postnatal 4 to 8 weeks. The prenatally androgenized (PNA) rats exhibited more numerous total follicles, cystic follicles, and atretic follicles than the controls. Fasting glucose, insulin, leptin levels, and homeostatic model assessment for insulin resistance were elevated in the PNA rats at the age of 5-8 weeks. Following intraperitoneal glucose tolerance tests, glucose and insulin levels did not differ between two groups; however, the PNA rats showed significantly higher 30- and 60-min glucose levels than the controls after insulin stimulation during 5-8 weeks. In addition, prenatal DHT treatment significantly decreased insulin-stimulated phosphorylation of AKT in the skeletal muscles of 6-week-old PNA rats. The abundance of IR substrate 1 (IRS1) and IRS2 was decreased in the skeletal muscles and liver after stimulation with insulin in the PNA group, whereas phosphorylation of insulin-signaling proteins was unaltered in the adipose tissue. These findings validate the contribution of prenatal androgen excess to metabolic derangements in pubertal female rats, and the impaired insulin signaling through IRS and AKT may result in the peripheral insulin resistance during pubertal development.

  4. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  5. Blood spotting on underpants: Case report of urethral prolapse in a pre-pubertal Chinese girl

    Directory of Open Access Journals (Sweden)

    Hei Yi Wong

    2015-05-01

    Full Text Available Urethral prolapse is a rare urological condition with non-specific clinical manifestations which is mostly seen in pre-pubertal black girls and postmenopausal woman. The exact etiology still remains unknown. We herein present a case report of urethral mucosa prolapse in a 5 year-old Chinese pre-pubertal girl.

  6. Gingival crevicular fluid alkaline phosphatase activity in relation to pubertal growth spurt and dental maturation: A multiple regression study

    Directory of Open Access Journals (Sweden)

    Perinetti, G.

    2016-04-01

    Full Text Available Introduction: The identification of the onset of the pubertal growth spurt has major clinical implications when dealing with orthodontic treatment in growing subjects. Aim: Through multivariate methods, this study evaluated possible relationships between the gingival crevicular fluid (GCF alkaline phosphatase (ALP activity and pubertal growth spurt and dentition phase. Materials and methods: One hundred healthy growing subjects (62 females, 38 males; mean age, 11.5±2.4 years were enrolled into this doubleblind, prospective, cross-sectional-design study. Phases of skeletal maturation (pre - pubertal, pubertal, post - pubertal was assessed using the cervical vertebral maturation method. Samples of GCF for the ALP activity determination were collected at the mesial and distal sites of the mandibular central incisors. The phases of the dentition were recorded as intermediate mixed, late mixed, or permanent. A multinomial multiple logistic regression model was used to assess relationships of the enzymatic activity to growth phases and dentition phases. Results: The GCF ALP activity was greater in the pubertal growth phase as compared to the pre - pubertal and post - pubertal growth phases. Significant adjusted odds ratios for the GCF ALP activity for the pre - pubertal and post - pubertal subjects, in relation to the pubertal group, were 0.76 and 0.84, respectively. No significant correlations were seen for the dentition phase. Conclusions: The GCF ALP activity is a valid candidate as a non - invasive biomarker for the identification of the pubertal growth spurt irrespective of the dentition phase.

  7. Granular Vulvovaginitis Syndrome in Nelore pubertal and post pubertal replacement heifers under tropical conditions: role of Mycoplasma spp., Ureaplasma diversum and BHV-1.

    Science.gov (United States)

    Gambarini, M L; Kunz, T L; Oliveira Filho, B D; Porto, R N G; Oliveira, C M G; Brito, W M E D; Viu, M A O

    2009-10-01

    In order to determine the role of Mycoplasma spp, Ureaplasma diversum and BHV-1 as causal agents of Granular Vulvovaginitis Syndrome in Nelore heifers raised under tropical conditions and based on the hypothesis that stressful conditions during puberty or breeding season would be a determinant factor for the infection, 340 heifers not vaccinated against BHV-1 were divided in Post-pubertal, in the beginning of the first breeding season, and Pubertal heifers. The vaginal lesion score (VLS) Grade 1 to 4 was giving according to lesion area and severity. Vaginal mucus was used to isolate Mycoplasma spp., Ureaplasma diversum and BHV-1. The predominant VLS was 2. No sample was positive for BHV-1; 48% were positive for Mycoplasma spp., Ureaplasma diversum, or both, with predominance of Ureaplasma diversum. Serum neutralization for BHV-1 showed more positive animals in pubertal group (23%); 3 of the paired sera demonstrated seroconversion. These data indicated that post-pubertal and pubertal Nelore heifers raised under extensive conditions are more susceptible to Mycoplasma spp. and Ureaplasma diversum. The hypothesis that the stress of pubertal period could lead to an acute vaginal infection by HBV-1 was not proofed.

  8. The organizational effects of pubertal testosterone on sexual proficiency in adult male Syrian hamsters.

    Science.gov (United States)

    De Lorme, Kayla C; Sisk, Cheryl L

    2016-10-15

    Social proficiency requires making appropriate behavioral adaptations as a result of social experience. For example, male rodents become sexually proficient with experience as demonstrated by a reduction in ectopic (misdirected) mounts, mount-to-intromission ratio, and latency to ejaculation. We previously found that over a series of timed tests with a receptive female, male hamsters deprived of testosterone specifically during puberty (NoT@P) have overall lower levels of sexual behavior and continue to display high levels of ectopic mounts, compared with males that experienced endogenous testosterone during puberty (T@P). These results suggested that pubertal testosterone programs sexual proficiency in adulthood, but because NoT@P males engaged in less sexual behavior than T@P males in these tests, the amount of sexual experience may have been insufficient to improve sexual proficiency. To more rigorously test the hypothesis that pubertal testosterone is necessary for social proficiency in adulthood, the present study compared the behavior of NoT@P and T@P males in a series of 4 trials with a 48-h interval between each trial. Sexual experience was equated by limiting each trial to 5 intromissions. Sexually-naïve males were either gonadectomized prepubertally (NoT@P) or in adulthood (T@P) and received subcutaneous testosterone capsules four weeks later. Two weeks after testosterone replacement, these groups and a group of adult gonad-intact controls began sexual behavior testing. We found that NoT@P males had more ectopic mounts/min across all four tests compared to gonad-intact and T@P males. Moreover, both gonad-intact and T@P males, but not NoT@P males, showed an increase in the number of mounts and intromissions/min between trials 1 and 3. Unexpectedly, both gonad-intact and T@P, but not NoT@P, males showed a decrease in sexual behaviors during trial 4. Thus, T@P males display multiple behavioral adaptations to sexual experience that are not observed in No

  9. Motor Synchronization in Patients With Schizophrenia: Preserved Time Representation With Abnormalities in Predictive Timing

    Directory of Open Access Journals (Sweden)

    Hélène Wilquin

    2018-05-01

    Full Text Available Objective: Basic temporal dysfunctions have been described in patients with schizophrenia, which may impact their ability to connect and synchronize with the outer world. The present study was conducted with the aim to distinguish between interval timing and synchronization difficulties and more generally the spatial-temporal organization disturbances for voluntary actions. A new sensorimotor synchronization task was developed to test these abilities.Method: Twenty-four chronic schizophrenia patients matched with 27 controls performed a spatial-tapping task in which finger taps were to be produced in synchrony with a regular metronome to six visual targets presented around a virtual circle on a tactile screen. Isochronous (time intervals of 500 ms and non-isochronous auditory sequences (alternated time intervals of 300/600 ms were presented. The capacity to produce time intervals accurately versus the ability to synchronize own actions (tap with external events (tone were measured.Results: Patients with schizophrenia were able to produce the tapping patterns of both isochronous and non-isochronous auditory sequences as accurately as controls producing inter-response intervals close to the expected interval of 500 and 900 ms, respectively. However, the synchronization performances revealed significantly more positive asynchrony means (but similar variances in the patient group than in the control group for both types of auditory sequences.Conclusion: The patterns of results suggest that patients with schizophrenia are able to perceive and produce both simple and complex sequences of time intervals but are impaired in the ability to synchronize their actions with external events. These findings suggest a specific deficit in predictive timing, which may be at the core of early symptoms previously described in schizophrenia.

  10. Prediction of antibiotic resistance: time for a new preclinical paradigm?

    DEFF Research Database (Denmark)

    Sommer, Morten Otto Alexander; Munck, Christian; Toft-Kehler, Rasmus Vendler

    2017-01-01

    Predicting the future is difficult, especially for evolutionary processes that are influenced by numerous unknown factors. Still, this is what is required of drug developers when they assess the risk of resistance arising against a new antibiotic candidate during preclinical development. In this ......Predicting the future is difficult, especially for evolutionary processes that are influenced by numerous unknown factors. Still, this is what is required of drug developers when they assess the risk of resistance arising against a new antibiotic candidate during preclinical development....... In this Opinion article, we argue that the traditional procedures that are used for the prediction of antibiotic resistance today could be markedly improved by including a broader analysis of bacterial fitness, infection dynamics, horizontal gene transfer and other factors. This will lead to more informed...

  11. [Diabetes and predictive medicine--parallax of the present time].

    Science.gov (United States)

    Rybka, J

    2010-04-01

    Predictive genetics uses genetic testing to estimate the risk in asymptomatic persons. Since in the case of multifactorial diseases predictive genetic analysis deals with findings which allow wider interpretation, it has a higher predictive value in expressly qualified diseases (monogenous) with high penetration compared to multifactorial (polygenous) diseases with high participation of environmental factors. In most "civilisation" (multifactorial) diseases including diabetes, heredity and environmental factors do not play two separate, independent roles. Instead, their interactions play a principal role. The new classification of diabetes is based on the implementation of not only ethiopathogenetic, but also genetic research. Diabetes mellitus type 1 (DM1T) is a polygenous multifactorial disease with the genetic component carrying about one half of the risk, the non-genetic one the other half. The study of the autoimmune nature of DM1T in connection with genetic analysis is going to bring about new insights in DM1T prediction. The author presents new pieces of knowledge on molecular genetics concerning certain specific types of diabetes. Issues relating to heredity in diabetes mellitus type 2 (DM2T) are even more complex. The disease has a polygenous nature, and the phenotype of a patient with DM2T, in addition to environmental factors, involves at least three, perhaps even tens of different genetic variations. At present, results at the genom-wide level appear to be most promising. The current concept of prediabetes is a realistic foundation for our prediction and prevention of DM2T. A multifactorial, multimarker approach based on our understanding of new pathophysiological factors of DM2T, tries to outline a "map" of prediabetes physiology, and if these tests are combined with sophisticated methods of genetic forecasting of DM2T, this may represent a significant step in our methodology of diabetes prediction. So far however, predictive genetics is limited by the

  12. PREDICT: A next generation platform for near real-time prediction of cholera

    Science.gov (United States)

    Jutla, A.; Aziz, S.; Akanda, A. S.; Alam, M.; Ahsan, G. U.; Huq, A.; Colwell, R. R.

    2017-12-01

    Data on disease prevalence and infectious pathogens is sparingly collected/available in region(s) where climatic variability and extreme natural events intersect with population vulnerability (such as lack of access to water and sanitation infrastructure). Therefore, traditional time series modeling approach of calibration and validation of a model is inadequate. Hence, prediction of diarrheal infections (such as cholera, Shigella etc) remain a challenge even though disease causing pathogens are strongly associated with modalities of regional climate and weather system. Here we present an algorithm that integrates satellite derived data on several hydroclimatic and ecological processes into a framework that can determine high resolution cholera risk on global scales. Cholera outbreaks can be classified in three forms- epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode endemic (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Using data from multiple satellites (AVHRR, TRMM, GPM, MODIS, VIIRS, GRACE), we will show examples from Haiti, Yemen, Nepal and several other regions where our algorithm has been successful in capturing risk of outbreak of infection in human population. A spatial model validation algorithm will also be presented that has capabilities to self-calibrate as new hydroclimatic and disease data become available.

  13. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

    OpenAIRE

    David Ebert

    2006-01-01

    One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

  14. Real-Time Optimization for Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca

    2012-01-01

    In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...

  15. Local dimension and finite time prediction in coupled map lattices

    Indian Academy of Sciences (India)

    In most of the cases these systems often exhibit highly complex type of ... tical applications bred vectors are different in two aspects. Firstly, for bred .... prediction 〈y| x〉 using the conditional distribution obtained from the joint distri- bution p(y, x) ...

  16. Statistical timing for parametric yield prediction of digital integrated circuits

    NARCIS (Netherlands)

    Jess, J.A.G.; Kalafala, K.; Naidu, S.R.; Otten, R.H.J.M.; Visweswariah, C.

    2006-01-01

    Uncertainty in circuit performance due to manufacturing and environmental variations is increasing with each new generation of technology. It is therefore important to predict the performance of a chip as a probabilistic quantity. This paper proposes three novel path-based algorithms for statistical

  17. Predicting the Timing and Location of the next Hawaiian Volcano

    Science.gov (United States)

    Russo, Joseph; Mattox, Stephen; Kildau, Nicole

    2010-01-01

    The wealth of geologic data on Hawaiian volcanoes makes them ideal for study by middle school students. In this paper the authors use existing data on the age and location of Hawaiian volcanoes to predict the location of the next Hawaiian volcano and when it will begin to grow on the floor of the Pacific Ocean. An inquiry-based lesson is also…

  18. Local dimension and finite time prediction in coupled map lattices

    Indian Academy of Sciences (India)

    Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour.

  19. Climate Prediction Center (CPC) Global Temperature Time Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

  20. Climate Prediction Center (CPC) Global Precipitation Time Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

  1. Online Learning Solutions for Freeway Travel Time Prediction

    NARCIS (Netherlands)

    Van Lint, J.W.C.

    2008-01-01

    Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time

  2. EFFECT OF GnRH AND PHOSPHORUS IN DELAYED PUBERTAL SURTI BUFFALO HEIFERS

    Directory of Open Access Journals (Sweden)

    H.B. Dhamsaniya

    2016-06-01

    Full Text Available The study was conducted on eighteen delayed pubertal Surti buffalo heifers, divided into three equal groups (6 in each to evaluate the efficacy of GnRH alone and in combination of phosphorus. The buffalo heifers in Group-I and Group-II were treated with Buserelin acetate (5 ml, IM. Buffalo heifers in Group-II also received additional injection of Toldimphos sodium (10 ml, IM at 3 day interval for 4 times, while buffalo heifers in Group-III served as control. The percentage of induced estrus was highest (83.33% in each treated groups as compared to control group (50%. The mean estrus induction intervals were significantly (P<0.05 shorter in Group-I (20.20 ± 2.18 days and Group-II (18.80 ± 2.32 days as compared to control group (30.24 ± 0.81 days. The conception rate at induced estrus was highest in Group-II (50% followed by Group-I (33.33%. The plasma progesterone levels being significantly lowest on the day of estrus (less than 0.5 ng/ml as compared to pre-treatment days in all groups. The mean total protein and triglycerides levels were differed significantly between the groups on the day of estrus and being significantly higher in Group-II as compared to Group-I and III on that day. A significantly higher level of cholesterol in both treatment groups as compared to the control group during different intervals and also being higher on the day of estrus as compared to pre-treatment days. The mean plasma glucose levels were differed nonsignificantly between and within the treatment and control groups. It is concluded that estrus can be successfully induced in delayed pubertal heifers with the use of GnRH alone and in combination with phosphorus.

  3. HVM-TP: A Time Predictable, Portable Java Virtual Machine for Hard Real-Time Embedded Systems

    DEFF Research Database (Denmark)

    Luckow, Kasper Søe; Thomsen, Bent; Korsholm, Stephan Erbs

    2014-01-01

    We present HVMTIME; a portable and time predictable JVM implementation with applications in resource-constrained hard real-time embedded systems. In addition, it implements the Safety Critical Java (SCJ) Level 1 specification. Time predictability is achieved by a combination of time predictable...... algorithms, exploiting the programming model of the SCJ specification, and harnessing static knowledge of the hosted SCJ system. This paper presents HVMTIME in terms of its design and capabilities, and demonstrates how a complete timing model of the JVM represented as a Network of Timed Automata can...... be obtained using the tool TetaSARTSJVM. Further, using the timing model, we derive Worst Case Execution Times (WCETs) and Best Case Execution Times (BCETs) of the Java Bytecodes....

  4. Timeliness and Predictability in Real-Time Database Systems

    National Research Council Canada - National Science Library

    Son, Sang H

    1998-01-01

    The confluence of computers, communications, and databases is quickly creating a globally distributed database where many applications require real time access to both temporally accurate and multimedia data...

  5. Evaluation of a real-time travel time prediction system in a freeway construction work zone : final report, March 2001.

    Science.gov (United States)

    2001-03-01

    A real-time travel time prediction system (TIPS) was evaluated in a construction work zone. TIPS includes changeable message signs (CMSs) displaying the travel time and distance to the end of the work zone to motorists. The travel times displayed by ...

  6. Evaluation of a real-time travel time prediction system in a freeway construction work zone : executive summary.

    Science.gov (United States)

    2001-03-01

    A real-time travel time prediction system (TIPS) was evaluated in a construction work : zone. TIPS includes changeable message signs (CMSs) displaying the travel time and : distance to the end of the work zone to motorists. The travel times displayed...

  7. Smoking status predicts cancer patients' quality of life over time

    Directory of Open Access Journals (Sweden)

    Ursula Martinez

    2018-03-01

    These results extend previous findings showing that QOL improves in cancer patients who quit smoking. Specifically, patients who quit smoking experience a greater reduction in depression and pain levels at all time points, and the reduction increases over time. In the case of fatigue, the results suggest that patients experience the greatest improvement with longer (≥ 4 months abstinence.

  8. Serum inhibin B in healthy pubertal and adolescent boys

    DEFF Research Database (Denmark)

    Andersson, A M; Juul, A; Petersen, J H

    1997-01-01

    correlated strongly with age, and when the effect of age was taken into account, only the partial correlation between inhibin B and LH/testosterone remained statistically significant. At stage II of puberty, the positive partial correlation between inhibin B and LH/testosterone was still present. At stage......Inhibin B levels were measured in serum from 400 healthy Danish prepubertal, pubertal, and adolescent males, aged 6-20 yr, in a cross-sectional study using a recently developed immunoassay that is specific for inhibin B, the physiologically important inhibin form in men. In addition, serum levels...... of FSH, LH, testosterone, and estradiol levels were measured. Serum levels of inhibin B, FSH, LH, testosterone, and estradiol all increased significantly between stages I and II of puberty. From stage II of puberty the inhibin B level was relatively constant, whereas the FSH level continued to increase...

  9. Hormonal, anthropometric and lipid factors associated with idiopathic pubertal gynecomastia.

    Science.gov (United States)

    Al Alwan, Ibrahim; Al Azkawi, Hanan; Badri, Motasim; Tamim, Hani; Al Dubayee, Mohammed; Tamimi, Waleed

    2013-01-01

    To determine factors associated with pubertal gynecomastia. A cross-sectional study among healthy male school children and adolescents in Riyadh, Saudi Arabia. Subjects were selected from diverse socioeconomic backgrounds. Tanner stage, height, weight, blood hormonal levels (leutilizing hormone [LH], follicle-stimulating hormone [FSH], total testosterone, and estradiol), and anthropometric and lipid parameters (body mass index [BMI], triglycerides, high-density lipoprotein [HDL], and low-density lipoprotein [LDL]), were collected and compared in children with and without gynecomastia. The study included 542 children and adolescents. Median (interquartile range) age in the whole group was 11(8-13) years. The prevalence of gynecomastia was 185/542 (34%), with a peak at age 14. The 2 groups compared had nonsignificant difference in cholesterol (P=.331), LH (P=.215) and FSH (P=.571) levels. Those with gynecomastia were significantly older, had lower gonad stage, had higher anthropometric (height, weight, and BMI), and lipid (triglycerides, HDL, and LDL) values. In multivariate regression analysis, factors significantly associated with gynecomastia were BMI (odds ratio [OR]=1.05; 95%CI 1.00-1.10; P=.013), HDL (OR=0.42; 95%CI 0.19-0.92; P=.03), and gonad (Stage II OR=2.23; 95%CI 1.27-3.92; P=.005, Stage III OR=6.40; 95%CI 2.70-15.0; P gynecomastia tends to increase in mid-puberty. In our setting, BMI, HDL, and gonad stage were the major factors associated with the development of pubertal gynecomastia.

  10. Prediction of residence time distributions in food processing machinery

    DEFF Research Database (Denmark)

    Karlson, Torben; Friis, Alan; Szabo, Peter

    1996-01-01

    The velocity field in a co-rotating disc scraped surface heat exchanger (CDHE) is calculated using a finite element method. The residence time distribution for the CDHE is then obtained by tracing particles introduced in the inlet.......The velocity field in a co-rotating disc scraped surface heat exchanger (CDHE) is calculated using a finite element method. The residence time distribution for the CDHE is then obtained by tracing particles introduced in the inlet....

  11. Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

    Directory of Open Access Journals (Sweden)

    Jun Bi

    2018-04-01

    Full Text Available Battery electric vehicles (BEVs reduce energy consumption and air pollution as compared with conventional vehicles. However, the limited driving range and potential long charging time of BEVs create new problems. Accurate charging time prediction of BEVs helps drivers determine travel plans and alleviate their range anxiety during trips. This study proposed a combined model for charging time prediction based on regression and time-series methods according to the actual data from BEVs operating in Beijing, China. After data analysis, a regression model was established by considering the charged amount for charging time prediction. Furthermore, a time-series method was adopted to calibrate the regression model, which significantly improved the fitting accuracy of the model. The parameters of the model were determined by using the actual data. Verification results confirmed the accuracy of the model and showed that the model errors were small. The proposed model can accurately depict the charging time characteristics of BEVs in Beijing.

  12. Just-In-Time predictive control for a two-wheeled robot

    OpenAIRE

    Nakpong, Nuttapun; Yamamoto, Shigeru

    2012-01-01

    In this paper, we introduce the use of Just-In-Time predictive control to enhance the stability of a two-wheeled robot. Just-In-Time predictive control uses a database which includes a huge amounts of input-output data of the two-wheeled robot and predicts its future movements based on a Just-In-Time algorithm. © 2012 IEEE.

  13. Timing of food intake predicts weight loss effectiveness.

    Science.gov (United States)

    Garaulet, M; Gómez-Abellán, P; Alburquerque-Béjar, J J; Lee, Y-C; Ordovás, J M; Scheer, F A J L

    2013-04-01

    There is emerging literature demonstrating a relationship between the timing of feeding and weight regulation in animals. However, whether the timing of food intake influences the success of a weight-loss diet in humans is unknown. To evaluate the role of food timing in weight-loss effectiveness in a sample of 420 individuals who followed a 20-week weight-loss treatment. Participants (49.5% female subjects; age (mean ± s.d.): 42 ± 11 years; BMI: 31.4 ± 5.4 kg m(-2)) were grouped in early eaters and late eaters, according to the timing of the main meal (lunch in this Mediterranean population). 51% of the subjects were early eaters and 49% were late eaters (lunch time before and after 1500 hours, respectively), energy intake and expenditure, appetite hormones, CLOCK genotype, sleep duration and chronotype were studied. Late lunch eaters lost less weight and displayed a slower weight-loss rate during the 20 weeks of treatment than early eaters (P=0.002). Surprisingly, energy intake, dietary composition, estimated energy expenditure, appetite hormones and sleep duration was similar between both groups. Nevertheless, late eaters were more evening types, had less energetic breakfasts and skipped breakfast more frequently that early eaters (all; Pmeal (P=0.015) with a higher frequency of minor allele (C) carriers among the late eaters (P=0.041). Neither sleep duration, nor CLOCK SNPs or morning/evening chronotype was independently associated with weight loss (all; P>0.05). Eating late may influence the success of weight-loss therapy. Novel therapeutic strategies should incorporate not only the caloric intake and macronutrient distribution - as is classically done - but also the timing of food.

  14. Predicting timing performance of advanced mechatronics control systems

    NARCIS (Netherlands)

    Voeten, J.P.M.; Hendriks, T.; Theelen, B.D.; Schuddemat, J.; Tabingh Suermondt, W.; Gemei, J.; Kotterink, C.; Huet, van J.; Eichler, G.; Kuepper, A.; Schau, V.; Fouchal, H.; Unger, H.

    2011-01-01

    Embedded control is a key product technology differentiator for many high-tech industries, including ASML. The strong increase in complexity of embedded control systems, combined with the occurrence of late changes in control requirements, results in many timing performance problems showing up only

  15. Predicting seed dormancy loss and germination timing for Bromus tectorum in a semi-arid environment using hydrothermal time models

    Science.gov (United States)

    Susan E. Meyer; Phil S. Allen

    2009-01-01

    A principal goal of seed germination modelling for wild species is to predict germination timing under fluctuating field conditions. We coupled our previously developed hydrothermal time, thermal and hydrothermal afterripening time, and hydration-dehydration models for dormancy loss and germination with field seed zone temperature and water potential measurements from...

  16. Insulin resistance in obese pre-pubertal children: Relation to body ...

    African Journals Online (AJOL)

    Secondary outcome is to determine the frequency of the metabolic syndrome components. Subjects and methods: Twenty-three pre-pubertal obese children were ... oral glucose tolerance testing (OGTT) and DXA scan for body composition.

  17. Sex variations in youth anxiety symptoms: effects of pubertal development and gender role orientation.

    Science.gov (United States)

    Carter, Rona; Silverman, Wendy K; Jaccard, James

    2011-01-01

    This study evaluated whether pubertal development and gender role orientation (i.e., masculinity and femininity) can partially explain sex variations in youth anxiety symptoms among clinic-referred anxious youth (N = 175; ages 9-13 years; 74% Hispanic; 48% female). Using youth and parent ratings of youth anxiety symptoms, structural equation modeling results indicated that youth who reported being more advanced in their pubertal development reported high levels of femininity and anxiety symptoms. Youth who reported high levels of masculinity had low levels of anxiety symptoms as reported by both youths and parents. The estimated effects of pubertal development, femininity, and masculinity on youth and parent ratings of youth anxiety symptoms were not significantly moderated by biological sex. Pubertal development and gender role orientation appear to be important in explaining levels of youth anxiety symptoms among clinic-referred anxious youth.

  18. A pre-pubertal girl with giant juvenile fibroadenoma: A rare case report

    Directory of Open Access Journals (Sweden)

    Kumar Gaurav

    2015-01-01

    Conclusion: Through this case we want to emphasize that these giant benign neoplasms should be suspected in any pre-pubertal girl with breast lump and should always be treated with breast conserving surgery.

  19. Pubertal testosterone influences threat-related amygdala-orbitofrontal cortex coupling.

    Science.gov (United States)

    Spielberg, Jeffrey M; Forbes, Erika E; Ladouceur, Cecile D; Worthman, Carol M; Olino, Thomas M; Ryan, Neal D; Dahl, Ronald E

    2015-03-01

    Growing evidence indicates that normative pubertal maturation is associated with increased threat reactivity, and this developmental shift has been implicated in the increased rates of adolescent affective disorders. However, the neural mechanisms involved in this pubertal increase in threat reactivity remain unknown. Research in adults indicates that testosterone transiently decreases amygdala-orbitofrontal cortex (OFC) coupling. Consequently, we hypothesized that increased pubertal testosterone disrupts amygdala-OFC coupling, which may contribute to developmental increases in threat reactivity in some adolescents. Hypotheses were tested in a longitudinal study by examining the impact of testosterone on functional connectivity. Findings were consistent with hypotheses and advance our understanding of normative pubertal changes in neural systems instantiating affect/motivation. Finally, potential novel insights into the neurodevelopmental pathways that may contribute to adolescent vulnerability to behavioral and emotional problems are discussed. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. Putative effects of endocrine disrupters on pubertal development in the human

    DEFF Research Database (Denmark)

    Teilmann, Grete; Juul, Anders; Skakkebaek, Niels E

    2002-01-01

    developing countries to industrialized countries often develop precocious puberty. Not only precocious puberty, but also delayed puberty can, theoretically, be associated with exposure to endocrine disrupters. While it is very plausible that endocrine disrupters may disturb pubertal development...

  1. Lazy Spilling for a Time-Predictable Stack Cache: Implementation and Analysis

    DEFF Research Database (Denmark)

    Abbaspourseyedi, Sahar; Jordan, Alexander; Brandner, Florian

    2014-01-01

    The growing complexity of modern computer architectures increasingly complicates the prediction of the run-time behavior of software. For real-time systems, where a safe estimation of the program's worst-case execution time is needed, time-predictable computer architectures promise to resolve......, we show that lazy spilling can be analyzed with little extra effort, which benefits the worst-case spilling behavior that is relevant for a real-time system....

  2. Meditation-induced states predict attentional control over time.

    Science.gov (United States)

    Colzato, Lorenza S; Sellaro, Roberta; Samara, Iliana; Baas, Matthijs; Hommel, Bernhard

    2015-12-01

    Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Prediction of dynamic expected time to system failure

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Lee, Chong Chul [Korea Nuclear Fuel Co., Ltd., Taejon (Korea, Republic of)

    1997-12-31

    The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)

  4. Prediction of dynamic expected time to system failure

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Lee, Chong Chul [Korea Nuclear Fuel Co., Ltd., Taejon (Korea, Republic of)

    1998-12-31

    The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)

  5. Computational micromagnetics: prediction of time dependent and thermal properties

    International Nuclear Information System (INIS)

    Schrefl, T.; Scholz, W.; Suess, Dieter; Fidler, J.

    2001-01-01

    Finite element modeling treats magnetization processes on a length scale of several nanometers and thus gives a quantitative correlation between the microstructure and the magnetic properties of ferromagnetic materials. This work presents a novel finite element/boundary element micro-magnetics solver that combines a wavelet-based matrix compression technique for magnetostatic field calculations with a BDF/GMRES method for the time integration of the Gilbert equation of motion. The simulations show that metastable energy minima and nonuniform magnetic states within the grains are important factors in the reversal dynamics at finite temperature. The numerical solution of the Gilbert equation shows how reversed domains nucleate and expand. The switching time of submicron magnetic elements depends on the shape of the elements. Elements with slanted ends decrease the overall reversal time, as a transverse demagnetizing field suppresses oscillations of the magnetization. Thermal activated processes can be included adding a random thermal field to the effective magnetic field. Thermally assisted reversal was studied for CoCrPtTa thin-film media

  6. A hybrid scheme for real-time prediction of bus trajectories

    NARCIS (Netherlands)

    Fadaei, Masoud; Cats, O.; Bhaskar, Ashish

    2016-01-01

    The uncertainty associated with public transport services can be partially counteracted by developing real-time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model

  7. Duration of the pubertal peak in skeletal Class I and Class III subjects.

    Science.gov (United States)

    Kuc-Michalska, Małgorzata; Baccetti, Tiziano

    2010-01-01

    To estimate and compare the duration of the pubertal growth peak in Class I and Class III subjects. The data examined consisted of pretreatment lateral cephalometric records of 218 skeletal Class I or Class III subjects (93 female and 125 male subjects) of white ancestry. The duration of the pubertal peak was calculated from the average chronological age intervals between stages CS3 and CS4 of the cervical vertebral maturation in Class I vs Class III groups (t-test). In skeletal Class I subjects, the pubertal peak had a mean duration of 11 months, whereas in Class III subjects it lasted 16 months. The average difference (5 months) was statistically significant (P < .001). The growth interval corresponding to the pubertal growth spurt (CS3-CS4) was longer in Class III subjects than in subjects with normal skeletal relationships; the larger increases in mandibular length during the pubertal peak reported in the literature for Class III subjects may be related to the longer duration of the pubertal peak.

  8. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    International Nuclear Information System (INIS)

    Xu Ruirui; Chen Tianlun; Gao Chengfeng

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  9. Nonlinear Time Series Prediction Using Chaotic Neural Networks

    Science.gov (United States)

    Li, Ke-Ping; Chen, Tian-Lun

    2001-06-01

    A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020

  10. Predicting the Market Potential Using Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Halmet Bradosti

    2015-12-01

    Full Text Available The aim of this analysis is to forecast a mini-market sales volume for the period of twelve months starting August 2015 to August 2016. The study is based on the monthly sales in Iraqi Dinar for a private local mini-market for the month of April 2014 to July 2015. As revealed on the graph and of course if the stagnant economic condition continues, the trend of future sales is down-warding. Based on time series analysis, the business may continue to operate and generate small revenues until August 2016. However, due to low sales volume, low profit margin and operating expenses, the revenues may not be adequate enough to produce positive net income and the business may not be able to operate afterward. The principal question rose from this is the forecasting sales in the region will be difficult where the business cycle so dynamic and revolutionary due to systematic risks and unforeseeable future.

  11. Near Real Time MISR Wind Observations for Numerical Weather Prediction

    Science.gov (United States)

    Mueller, K. J.; Protack, S.; Rheingans, B. E.; Hansen, E. G.; Jovanovic, V. M.; Baker, N.; Liu, J.; Val, S.

    2014-12-01

    The Multi-angle Imaging SpectroRadiometer (MISR) project, in association with the NASA Langley Atmospheric Science Data Center (ASDC), has this year adapted its original production software to generate near-real time (NRT) cloud-motion winds as well as radiance imagery from all nine MISR cameras. These products are made publicly available at the ASDC with a latency of less than 3 hours. Launched aboard the sun-synchronous Terra platform in 1999, the MISR instrument continues to acquire near-global, 275 m resolution, multi-angle imagery. During a single 7 minute overpass of any given area, MISR retrieves the stereoscopic height and horizontal motion of clouds from the multi-angle data, yielding meso-scale near-instantaneous wind vectors. The ongoing 15-year record of MISR height-resolved winds at 17.6 km resolution has been validated against independent data sources. Low-level winds dominate the sampling, and agree to within ±3 ms-1 of collocated GOES and other observations. Low-level wind observations are of particular interest to weather forecasting, where there is a dearth of observations suitable for assimilation, in part due to reliability concerns associated with winds whose heights are assigned by the infrared brightness temperature technique. MISR cloud heights, on the other hand, are generated from stereophotogrammetric pattern matching of visible radiances. MISR winds also address data gaps in the latitude bands between geostationary satellite coverage and polar orbiting instruments that obtain winds from multiple overpasses (e.g. MODIS). Observational impact studies conducted by the Naval Research Laboratory (NRL) and by the German Weather Service (Deutscher Wetterdienst) have both demonstrated forecast improvements when assimilating MISR winds. An impact assessment using the GEOS-5 system is currently in progress. To benefit air quality forecasts, the MISR project is currently investigating the feasibility of generating near-real time aerosol products.

  12. Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Md. Rabiul Islam

    2012-01-01

    Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

  13. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  14. Adults' future time perspective predicts engagement in physical activity.

    Science.gov (United States)

    Stahl, Sarah T; Patrick, Julie Hicks

    2012-07-01

    Our aim was to examine how the relations among known predictors of physical activity, such as age, sex, and body mass index, interact with future time perspective (FTP) and perceived functional limitation to explain adults' engagement in physical activity. Self-report data from 226 adults (range 20-88 years) were collected to examine the hypothesis that a more expansive FTP is associated with engagement in physical activity. Results indicated a good fit of the data to the model χ(2) (4, N = 226) = 7.457, p = .14 and accounted for a moderate amount of variance in adults' physical activity (R(2) = 15.7). Specifically, results indicated that perceived functional limitation (β = -.140) and FTP (β = .162) were directly associated with physical activity. Age was indirectly associated with physical activity through its association with perceived functional limitation (β = -.264) and FTP (β = .541). Results indicate that FTP may play an important role in explaining engagement in health promoting behaviors across the life span. Researchers should consider additional constructs and perhaps adopt socioemotional selectivity theory when explaining adults' engagement in physical activity.

  15. External validation of prediction models for time to death in potential donors after circulatory death

    NARCIS (Netherlands)

    Kotsopoulos, A.M.M.; Böing-Messing, F.; Jansen, N.E.; Vos, P.; Abdo, W.F.

    2018-01-01

    Predicting time to death in controlled donation after circulatory death (cDCD) donors following withdrawal of life‐sustaining treatment (WLST) is important but poses a major challenge. The aim of this study is to determine factors predicting time to circulatory death within 60 minutes after WSLT and

  16. The IPERMOB System for Effective Real-Time Road Travel Time Measurement and Prediction

    OpenAIRE

    Martelli, Francesca; Renda, Maria Elena; Santi, Paolo

    2010-01-01

    Accurate, real-time measurement and estimation of road travel time is considered a central problem in the design of advanced Intelligent Transportation Systems. In particular, whether eective, real-time collection of travel time measurements in a urban area is possible is, to the best of our knowledge, still an open problem. In this paper, we introduce the IPERMOB system for efficient, real-time collection of travel time measurements in urban areas through vehicular networks. We demonstrate t...

  17. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  18. Analytical solutions for prediction of the ignition time of wood particles based on a time and space integral method

    NARCIS (Netherlands)

    Haseli, Y.; Oijen, van J.A.; Goey, de L.P.H.

    2012-01-01

    The main idea of this paper is to establish a simple approach for prediction of the ignition time of a wood particle assuming that the thermo-physical properties remain constant and ignition takes place at a characteristic ignition temperature. Using a time and space integral method, explicit

  19. Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

    Science.gov (United States)

    Georgakakos, K. P.

    2006-05-01

    The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

  20. Investigation of pre-pubertal sex differences in wheel running and social behavior in three mouse strains.

    Science.gov (United States)

    Gordon, Elizabeth A; Corbitt, Cynthia

    2015-08-01

    Sex differences in social behaviors exist in mammals during adulthood, and further evidence suggests that sex differences in behavior are present before sexual maturity. In order to model behavioral disorders in animals, it is important to assess baseline sex-related behavioral differences, especially when studying disorders for which sex-related behavioral effects are expected. We investigated the effect of sex on behavior in 3 strains of pre-pubertal mice (C57BL/6, CFW, and CF1) using a wheel-running assay. We found no significant sex differences in latency to run on the wheel or total duration of wheel running within each strain. During the social interaction test, there were no differences between sexes in latency or total duration of contact or following between a subject and novel mouse. We also evaluated behavioral patterns of wheel running and stereotypical behaviors, such as burrowing and grooming. Both sexes showed characteristic wheel running behavior, spending the majority of each trial interacting with the wheel when it was free and more time performing other activities ( e.g. , stereotypical behaviors, general locomotion) when it was jammed. These results provide evidence that, among various strains of pre-pubertal mice, baseline sex-related behavioral differences are not strong enough to influence the measured behaviors.

  1. Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

    Science.gov (United States)

    Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

    2018-03-01

    The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

  2. Prediction about chaotic times series of natural circulation flow under rolling motion

    International Nuclear Information System (INIS)

    Yuan Can; Cai Qi; Guo Li; Yan Feng

    2014-01-01

    The paper have proposed a chaotic time series prediction model, which combined phase space reconstruction with support vector machines. The model has been used to predict the coolant volume flow, in which a synchronous parameter optimization method was brought up based on particle swarm optimization algorithm, since the numerical value selection of related parameter was a key factor for the prediction precision. The average relative error of prediction values and actual observation values was l,5% and relative precision was 0.9879. The result indicated that the model could apply for the natural circulation coolant volume flow prediction under rolling motion condition with high accuracy and robustness. (authors)

  3. Attachment, parenting styles and bullying during pubertal years.

    Science.gov (United States)

    van der Watt, Ronél

    2014-01-01

    Research that focuses on combining attachment, parenting styles, bullying and the reciprocal nature thereof in the parent-adolescent and peer relationships is limited. The bio-psychosocial changes that adolescents experience open up broader social realities and are perceived differently by parents and adolescents. Attachment processes and parenting styles may elicit dissimilar perceptions. These processes are also associated with the multifaceted dynamics of bullying. The aim of the article is to advocate for research on the possible link between the implications of attachment, parenting styles and bullying. Exploring the association between attachment, parenting styles and bullying can deepen the understanding of the developmental challenges within the parent-adolescent relationship, add insight to the different perceptions of adolescents and parents, and complement intervention programmes accordingly. Firstly, this article outlines bio-psychosocial changes in the pubertal years as related to the social realities of the adolescent. Secondly, a discussion on the concepts 'attachment', 'parenting styles', 'bullying', and the potential link between these concepts will follow. Thirdly, an outline of the clinical implications of the apparent association between these concepts is given. The article concludes with recommendations that researchers can consider while exploring the relationship between attachment, parenting styles, and bullying and the delineation thereof in the parent-adolescent relationship.

  4. Cardiovascular risk factors in pre-pubertal schoolchildren in Angola.

    Science.gov (United States)

    Silva, Amílcar B; Capingana, Daniel P; Magalhães, Pedro; Gonçalves, Mauer A; Molina, Maria Del Carmen B; Rodrigues, Sërgio L; Baldo, Marcelo P; Mateus, Miguel S; Mill, Josë Geraldo

    The incidence of obesity is increasing worldwide, especially in countries with accelerated economic growth. We determined the prevalence of and associations between overweight/obesity and cardiovascular risk factors in pre-pubertal (seven- to 11-year-old) schoolchildren (both genders, n = 198) in Luanda, Angola. Biochemical (fasting blood) and clinical examinations were obtained in a single visit. Data are reported as prevalence (95% confidence intervals) and association (r, Pearson). Prevalence of overweight/obesity was 17.7% (12.4- 23.0%), high blood pressure (BP > 90% percentile) was 14.6% (9.7-19.5%), elevated glucose level was 16.7% (11.5-21.9%) and total cholesterol level > 170 mg/dl (4.4 mmol/l) was 69.2% (62.8-75.6%). Significant associations between body mass index (BMI) and systolic and diastolic BP (r = 0.46 and 0.40, respectively; p Angola and fat accumulation was directly associated with blood pressure increase but not with other cardiovascular risk factors.

  5. Effects on steroid hormones secretion resulting from the acute stimulation of sectioning the superior ovarian nerve to pre-pubertal rats

    Directory of Open Access Journals (Sweden)

    Morales-Ledesma Leticia

    2012-10-01

    Full Text Available Abstract In the adult rat, neural signals arriving to the ovary via the superior ovarian nerve (SON modulate progesterone (P4, testosterone (T and estradiol (E2 secretion. The aims of the present study were to analyze if the SON in the pre-pubertal rat also modulates ovarian hormone secretion and the release of follicle stimulating hormone (FSH and luteinizing (LH hormone. P4, T, E2, FSH and LH serum levels were measured 30 or 60 minutes after sectioning the SON of pre-pubertal female rats. Our results indicate that the effects on hormone levels resulting from unilaterally or bilaterally sectioning the SON depends on the analyzed hormone, and the time lapse between surgery and autopsy, and that the treatment yielded asymmetric results. The results also suggest that in the pre-pubertal rat the neural signals arriving to the ovaries via the SON regulate the enzymes participating in P4, T and E2 synthesis in a non-parallel way, indicating that the mechanisms regulating the synthesis of each hormone are not regulated by the same signals. Also, that the changes in the steroids hormones are not explained exclusively by the modifications in gonadotropins secretion. The observed differences in hormone levels between rats sacrificed 30 and 60 min after surgery reflect the onset of the compensatory systems regulating hormones secretion.

  6. Maternal swimming exercise during pregnancy attenuates anxiety/depressive-like behaviors and voluntary morphine consumption in the pubertal male and female rat offspring born from morphine dependent mothers.

    Science.gov (United States)

    Torabi, Masoumeh; Pooriamehr, Alireza; Bigdeli, Imanollah; Miladi-Gorji, Hossein

    2017-10-17

    This study was designed to examine whether maternal swimming exercise during pregnancy would attenuate prenatally morphine-induced anxiety, depression and voluntary consumption of morphine in the pubertal male and female rat offspring. Pregnant rats during the development of morphine dependence were allowed to swim (30-45min/d, 3days per a week) on gestational days 11-18. Then, the pubertal male and female rat offspring were tested for the elevated plus-maze (EPM), sucrose preference test (SPT) and voluntary morphine consumption using a two-bottle choice (TBC) paradigm. The results showed that male and female rat offspring born of the swimmer morphine-dependent mothers exhibited an increase in EPM open arm time and entries, higher levels of sucrose preference than their sedentary control mothers. Voluntary consumption of morphine was less in the male and female rat offspring born of the swimmer morphine-dependent mothers as compared with their sedentary control mothers during three periods of the intake of drug. Thus, swimming exercise in pregnant morphine dependent mothers decreased anxiety, depressive-like behavior and also the voluntary morphine consumption in the pubertal male and female offspring, which may prevent prenatally morphine-induced behavioral sensitization in offspring. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Trip time prediction in mass transit companies. A machine learning approach

    OpenAIRE

    João M. Moreira; Alípio Jorge; Jorge Freire de Sousa; Carlos Soares

    2005-01-01

    In this paper we discuss how trip time prediction can be useful foroperational optimization in mass transit companies and which machine learningtechniques can be used to improve results. Firstly, we analyze which departmentsneed trip time prediction and when. Secondly, we review related work and thirdlywe present the analysis of trip time over a particular path. We proceed by presentingexperimental results conducted on real data with the forecasting techniques wefound most adequate, and concl...

  8. Flow time prediction for a single-server order picking workstation using aggregate process times

    NARCIS (Netherlands)

    Andriansyah, R.; Etman, L.F.P.; Rooda, J.E.

    2010-01-01

    In this paper we propose a simulation modeling approach based on aggregate process times for the performance analysis of order picking workstations in automated warehouses. The aggregate process time distribution is calculated from tote arrival and departure times. We refer to the aggregate process

  9. Real-time numerical shake prediction and updating for earthquake early warning

    Science.gov (United States)

    Wang, Tianyun; Jin, Xing; Wei, Yongxiang; Huang, Yandan

    2017-12-01

    Ground motion prediction is important for earthquake early warning systems, because the region's peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and precisely with limited station wave records, we propose a real-time numerical shake prediction and updating method. Our method first predicts the ground motion based on the ground motion prediction equation after P waves detection of several stations, denoted as the initial prediction. In order to correct the prediction error of the initial prediction, an updating scheme based on real-time simulation of wave propagation is designed. Data assimilation technique is incorporated to predict the distribution of seismic wave energy precisely. Radiative transfer theory and Monte Carlo simulation are used for modeling wave propagation in 2-D space, and the peak ground motion is calculated as quickly as possible. Our method has potential to predict shakemap, making the potential disaster be predicted before the real disaster happens. 2008 M S8.0 Wenchuan earthquake is studied as an example to show the validity of the proposed method.

  10. Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach

    DEFF Research Database (Denmark)

    O'Sullivan, Aidan; Pereira, Francisco Camara; Zhao, Jinhua

    2016-01-01

    Arrival time predictions for the next available bus or train are a key component of modern traveler information systems (TISs). A great deal of research has been conducted within the intelligent transportation system community in developing an assortment of different algorithms that seek...... sources. In this paper, we tackle the issue of uncertainty in bus arrival time predictions using an alternative approach. Rather than endeavor to develop a superior method for prediction, we take existing predictions from a TIS and treat the algorithm generating them as a black box. The presence...... of heteroscedasticity in the predictions is demonstrated and then a metamodel approach is deployed, which augments existing predictive systems using quantile regression to place bounds on the associated error. As a case study, this approach is applied to data from a real-world TIS in Boston. This method allows bounds...

  11. Pubertal development in children diagnosed with diabetes mellitus type 1 before puberty.

    Science.gov (United States)

    Pereira, K C X; Pugliese, B S; Guimarães, M M; Gama, M P

    2015-02-01

    To investigate an association between pubertal development and timing of menarche with glycemic control, disease duration, and body mass index (BMI) in patients diagnosed with diabetes mellitus type 1 (DM1) before puberty. Retrospective study. The study was performed at the diabetes outpatient clinic of Instituto de Puericultura e Pediatria Martagão Gesteira--IPPMG of the Federal University of Rio de Janeiro--UFRJ. A total of 131 children, 61 girls and 70 boys, diagnosed with DM1 before puberty participated in the study. The study investigated how age at puberty onset relates to mean glycated hemoglobin (HbA1c) before puberty, BMI percentile, and disease duration; how puberty duration relates to mean HbA1c before and during puberty and to disease duration; and how timing of menarche relates to mean HbA1c before puberty, BMI percentile, and disease duration. Age at puberty onset was positively correlated with mean HbA1c before puberty (r = 0.204, R(2) = 0.042; P = .019) and disease duration (r = 0.451, R(2) = 0.203; P puberty later than those diagnosed more recently. Girls in higher BMI percentiles reached menarche sooner.

  12. Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

    Science.gov (United States)

    Limongi, Roberto; Silva, Angélica M

    2016-11-01

    The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

  13. Real-time prediction of respiratory motion based on local regression methods

    International Nuclear Information System (INIS)

    Ruan, D; Fessler, J A; Balter, J M

    2007-01-01

    Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

  14. "Personal best times in an olympic distance triathlon and a marathon predict an ironman race time for recreational female triathletes".

    Science.gov (United States)

    Rüst, Christoph Alexander; Knechtle, Beat; Wirth, Andrea; Knechtle, Patrizia; Ellenrieder, Birte; Rosemann, Thomas; Lepers, Romuald

    2012-06-30

    "The aim of this study was to investigate whether the characteristics of anthropometry, training or previous performance were related to an Ironman race time in recreational female Ironman triathletes. These characteristics were correlated to an Ironman race time for 53 recreational female triathletes in order to determine the predictor variables, and so be able to predict an Ironman race time for future novice triathletes. In the bi-variate analysis, no anthropometric characteristic was related to race time. The weekly cycling kilometers (r = -0.35) and hours (r = -0.32), as well as the personal best time in an Olympic distance triathlon (r = 0.49) and in a marathon (r = 0.74) were related to an Ironman race time (marathon (P = 0.0030) were the best predictors for the Ironman race time (n = 28, r² = 0.53). The race time in an Ironman triathlon might be partially predicted by the following equation (r² = 0.53, n = 28): Race time (min) = 186.3 + 1.595 × (personal best time in an Olympic distance triathlon, min) + 1.318 × (personal best time in a marathon, min) for recreational female Ironman triathletes."

  15. A framework for predicting three-dimensional prostate deformation in real time

    NARCIS (Netherlands)

    Jahya, Alex; Herink, Mark; Misra, Sarthak

    2013-01-01

    Background Surgical simulation systems can be used to estimate soft tissue deformation during pre- and intra-operative planning. Such systems require a model that can accurately predict the deformation in real time. In this study, we present a back-propagation neural network for predicting

  16. An improved grey model for the prediction of real-time GPS satellite clock bias

    Science.gov (United States)

    Zheng, Z. Y.; Chen, Y. Q.; Lu, X. S.

    2008-07-01

    In real-time GPS precise point positioning (PPP), real-time and reliable satellite clock bias (SCB) prediction is a key to implement real-time GPS PPP. It is difficult to hold the nuisance and inenarrable performance of space-borne GPS satellite atomic clock because of its high-frequency, sensitivity and impressionable, it accords with the property of grey model (GM) theory, i. e. we can look on the variable process of SCB as grey system. Firstly, based on limits of quadratic polynomial (QP) and traditional GM to predict SCB, a modified GM (1,1) is put forward to predict GPS SCB in this paper; and then, taking GPS SCB data for example, we analyzed clock bias prediction with different sample interval, the relationship between GM exponent and prediction accuracy, precision comparison of GM to QP, and concluded the general rule of different type SCB and GM exponent; finally, to test the reliability and validation of the modified GM what we put forward, taking IGS clock bias ephemeris product as reference, we analyzed the prediction precision with the modified GM, It is showed that the modified GM is reliable and validation to predict GPS SCB and can offer high precise SCB prediction for real-time GPS PPP.

  17. Improving performance of single-path code through a time-predictable memory hierarchy

    DEFF Research Database (Denmark)

    Cilku, Bekim; Puffitsch, Wolfgang; Prokesch, Daniel

    2017-01-01

    -predictable memory hierarchy with a prefetcher that exploits the predictability of execution traces in single-path code to speed up code execution. The new memory hierarchy reduces both the cache-miss penalty time and the cache-miss rate on the instruction cache. The benefit of the approach is demonstrated through...

  18. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2017-11-01

    Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

  19. Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

    Directory of Open Access Journals (Sweden)

    Faming Zhang

    2016-11-01

    Full Text Available The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

  20. Predicting Homework Time Management at the Secondary School Level: A Multilevel Analysis

    Science.gov (United States)

    Xu, Jianzhong

    2010-01-01

    The purpose of this study is to test empirical models of variables posited to predict homework time management at the secondary school level. Student- and class-level predictors of homework time management were analyzed in a survey of 1895 students from 111 classes. Most of the variance in homework time management occurred at the student level,…

  1. The Case For Prediction-based Best-effort Real-time Systems.

    Science.gov (United States)

    1999-01-01

    Real - time Systems Peter A. Dinda Loukas Kallivokas January...DISTRIBUTION STATEMENT A Approved for Public Release Distribution Unlimited DTIG QUALBR DISSECTED X The Case For Prediction-based Best-effort Real - time Systems Peter...Mellon University Pittsburgh, PA 15213 A version of this paper appeared in the Seventh Workshop on Parallel and Distributed Real - Time Systems

  2. Upper Bounds Prediction of the Execution Time of Programs Running on ARM Cortex-A Systems

    OpenAIRE

    Fedotova , Irina; Krause , Bernd; Siemens , Eduard

    2017-01-01

    Part 6: Embedded and Real Time Systems; International audience; This paper describes the application of statistical analysis of the timing behavior for a generic real-time task model. Using specific processor of ARM Cortex-A series and an empirical approach of time values retrieval, the algorithm to predict the upper bounds for the task of the time acquisition operation has been formulated. For the experimental verification of the algorithm, we have used the robust Measurement-Based Probabili...

  3. Real-Time Noise Prediction of V/STOL Aircraft in Maneuvering Flight, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This proposal outlines a plan for enhancing and integrating new breakthrough technologies to provide accurate real-time noise prediction of V/STOL aircraft in...

  4. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

    Science.gov (United States)

    Xu, Tao; Li, Xiang; Claramunt, Christophe

    2018-06-01

    Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

  5. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  6. Cure modeling in real-time prediction: How much does it help?

    Science.gov (United States)

    Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

    2017-08-01

    Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

    Science.gov (United States)

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

  8. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

    Science.gov (United States)

    Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

    2015-01-01

    Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

  9. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction.

    Science.gov (United States)

    Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

    2017-08-08

    Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

  10. New prediction of chaotic time series based on local Lyapunov exponent

    International Nuclear Information System (INIS)

    Zhang Yong

    2013-01-01

    A new method of predicting chaotic time series is presented based on a local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After reconstructing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula by using the definition of the local Lyapunov exponent. Numerical simulations are carried out to test its effectiveness and verify its higher precision over two older methods. The effects of the number of referential state vectors and added noise on forecasting accuracy are also studied numerically. (general)

  11. Serum AMH levels are lower in healthy boys who develop pubertal gynaecomastia

    DEFF Research Database (Denmark)

    Mieritz, Mikkel G.; Hagen, Casper P.; Almstrup, Kristian

    2015-01-01

    Background: Pubertal gynaecomastia is thought to be a clinical sign of an oestrogen-androgen imbalance, affecting up to 60% of boys. In most cases no underlying endocrinopathy can be identified. In boys, Anti-mullerian hormone (AMH) is produced by immature Sertoli cells and circulating level...... decreases as testosterone increases during pubertal maturation. In a previous cross sectional study we found significant lower levels of AMH in boys with pubertal gynaecomastia (Mieritz et al., Clin Endocrinol, 2013). Objective and hypotheses: To investigate serum AMH levels and genetic polymorphisms...... in boys with or without gynaecomastia. Method: 99 healthy Danish boys (aged 5.8-16.4 years) were followed in a prospective cohort over 8 years with semi-annual examinations (total examinations, n=951), including breast palpations and blood samples. Serum AMH concentrations were analysed by immunoassay...

  12. Elevated serum IGF-I, but unaltered sex steroid levels, in healthy boys with pubertal gynaecomastia

    DEFF Research Database (Denmark)

    Mieritz, Mikkel G; Sorensen, Kaspar; Aksglaede, Lise

    2014-01-01

    OBJECTIVE: Pubertal gynaecomastia is a very common condition. Although the underlying aetiology is poorly understood, it is generally accepted that excess of oestrogens and deficit of androgens are involved in the pathogenesis. Furthermore, adiposity as well as the GH/IGF-I axis may play a role....... In this study, we elucidate the association of adiposity and levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex hormone-binding globulin (SHBG), testosterone, oestrogen, IGF-I and IGFBP-3 with the presence of pubertal gynaecomastia in a large cohort of healthy boys. PATIENTS: A total...... of 501 healthy Danish school boys (aged 6·1-19·8 year) from the COPENHAGEN Puberty Study. MEASUREMENTS: Anthropometry and pubertal stages (PH1-6 and G1-5) were evaluated, and the presence of gynaecomastia was assessed. Body fat percentage was calculated by means of four skin folds and impedance...

  13. Impact of Pubertal Development and Physical Activity on Heart Rate Variability in Overweight and Obese Children in Taiwan

    Science.gov (United States)

    Chen, Su-Ru; Chiu, Hung-Wen; Lee, Yann-Jinn; Sheen, Tzong-Chi; Jeng, Chii

    2012-01-01

    Child obesity is frequently associated with dysfunction of autonomic nervous system. Children in pubertal development were suggested to be vulnerable to autonomic nervous system problems such as decrease of heart rate variability from dysregulation of metabolic control. This study explored the influence of pubertal development on autonomic nervous…

  14. A Short-Term Longitudinal Study of Pubertal Change, Gender, and Psychological Well-Being of Mexican Early Adolescents.

    Science.gov (United States)

    Benjet, Corina; Hernandez-Guzman, Laura

    2002-01-01

    Studied the role of pubertal development on depression, externalizing behavior problems, self-esteem, and body-image of 951 Mexican early adolescents. Findings show that the acute experience of menarche adversely affected the psychological well-being of girls, specifically in terms of depressive symptomatology. Pubertal change in boys did not…

  15. Adolescents' Increasing Stress Response to Social Evaluation: Pubertal Effects on Cortisol and Alpha-Amylase during Public Speaking

    Science.gov (United States)

    van den Bos, Esther; de Rooij, Mark; Miers, Anne C.; Bokhorst, Caroline L.; Westenberg, P. Michiel

    2014-01-01

    Stress responses to social evaluation are thought to increase during adolescence, which may be due to pubertal maturation. However, empirical evidence is scarce. This study is the first to investigate the relation between pubertal development and biological responses to a social-evaluative stressor longitudinally. Participants performed the Leiden…

  16. Healthy work revisited: do changes in time strain predict well-being?

    Science.gov (United States)

    Moen, Phyllis; Kelly, Erin L; Lam, Jack

    2013-04-01

    Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the results only work environment (ROWE) in a white-collar organization. Cross-sectional (wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers.

  17. Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

    DEFF Research Database (Denmark)

    Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

    2017-01-01

    This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...

  18. Puberty: Maturation, Timing and Adjustment, and Sexual Identity Developmental Milestones among Lesbian, Gay, and Bisexual Youth

    Science.gov (United States)

    Grossman, Arnold H.; Foss, Alexander H.; D'Augelli, Anthony R.

    2014-01-01

    This study examined pubertal maturation, pubertal timing and outcomes, and the relationship of puberty and sexual identity developmental milestones among 507 lesbian, gay, and bisexual youth. The onset of menarche and spermarche occurred at the mean ages of 12.05 and 12.46, respectively. There was no statistically significant difference in…

  19. Predictive event modelling in multicenter clinical trials with waiting time to response.

    Science.gov (United States)

    Anisimov, Vladimir V

    2011-01-01

    A new analytic statistical technique for predictive event modeling in ongoing multicenter clinical trials with waiting time to response is developed. It allows for the predictive mean and predictive bounds for the number of events to be constructed over time, accounting for the newly recruited patients and patients already at risk in the trial, and for different recruitment scenarios. For modeling patient recruitment, an advanced Poisson-gamma model is used, which accounts for the variation in recruitment over time, the variation in recruitment rates between different centers and the opening or closing of some centers in the future. A few models for event appearance allowing for 'recurrence', 'death' and 'lost-to-follow-up' events and using finite Markov chains in continuous time are considered. To predict the number of future events over time for an ongoing trial at some interim time, the parameters of the recruitment and event models are estimated using current data and then the predictive recruitment rates in each center are adjusted using individual data and Bayesian re-estimation. For a typical scenario (continue to recruit during some time interval, then stop recruitment and wait until a particular number of events happens), the closed-form expressions for the predictive mean and predictive bounds of the number of events at any future time point are derived under the assumptions of Markovian behavior of the event progression. The technique is efficiently applied to modeling different scenarios for some ongoing oncology trials. Case studies are considered. Copyright © 2011 John Wiley & Sons, Ltd.

  20. Effects of Di-(2-ethylhexyl Phthalate on the Hypothalamus–Uterus in Pubertal Female Rats

    Directory of Open Access Journals (Sweden)

    Te Liu

    2016-11-01

    Full Text Available The pollution of endocrine disruptors and its impact on human reproductive system have attracted much attention. Di-(2-ethylhexyl phthalate (DEHP, an environmental endocrine disruptor, is widely used in food packages, containers, medical supplies and children’s toys. It can cause diseases such as infertility, sexual precocity and uterine bleeding and thus arouse concerns from the society and scholars. The effect of DEHP on pubertal female reproductive system is still not well-studied. This study was to investigate the effects of DEHP on the hypothalamus–uterus in pubertal female rats, reveal the reproductive toxicity of DEHP on pubertal female rats and its mechanism, and provide scientific evidence for the evaluation of toxicity and toxic mechanism of DEHP on reproductive system. Forty-eight pubertal female rats were randomly divided into four groups and respectively administered via oral gavage 0, 250, 500, or 1000 mg/kg/d DEHP in 0.1 mL corn oil/20 g body weight for up to four weeks. Compared with control rats, the DEHP-treated rats showed: (1 higher gonadotropin-releasing hormone (GnRH level in the hypothalamus; (2 higher protein levels of GnRH in the hypothalamus; and (3 higher mRNA and protein levels of GnRH receptor (GnRHR in the uterus. Our data reveal that DEHP exposure may lead to a disruption in pubertal female rats and an imbalance of hypothalamus–uterus. Meanwhile, DEHP may, through the GnRH in the hypothalamus and its receptor on the uterus, lead to diseases of the uterus. DEHP may impose a negative influence on the development and functioning of the reproductive system in pubertal female rats.

  1. Effects of Di-(2-ethylhexyl) Phthalate on the Hypothalamus–Uterus in Pubertal Female Rats

    Science.gov (United States)

    Liu, Te; Jia, Yiyang; Zhou, Liting; Wang, Qi; Sun, Di; Xu, Jin; Wu, Juan; Chen, Huaiji; Xu, Feng; Ye, Lin

    2016-01-01

    The pollution of endocrine disruptors and its impact on human reproductive system have attracted much attention. Di-(2-ethylhexyl) phthalate (DEHP), an environmental endocrine disruptor, is widely used in food packages, containers, medical supplies and children’s toys. It can cause diseases such as infertility, sexual precocity and uterine bleeding and thus arouse concerns from the society and scholars. The effect of DEHP on pubertal female reproductive system is still not well-studied. This study was to investigate the effects of DEHP on the hypothalamus–uterus in pubertal female rats, reveal the reproductive toxicity of DEHP on pubertal female rats and its mechanism, and provide scientific evidence for the evaluation of toxicity and toxic mechanism of DEHP on reproductive system. Forty-eight pubertal female rats were randomly divided into four groups and respectively administered via oral gavage 0, 250, 500, or 1000 mg/kg/d DEHP in 0.1 mL corn oil/20 g body weight for up to four weeks. Compared with control rats, the DEHP-treated rats showed: (1) higher gonadotropin-releasing hormone (GnRH) level in the hypothalamus; (2) higher protein levels of GnRH in the hypothalamus; and (3) higher mRNA and protein levels of GnRH receptor (GnRHR) in the uterus. Our data reveal that DEHP exposure may lead to a disruption in pubertal female rats and an imbalance of hypothalamus–uterus. Meanwhile, DEHP may, through the GnRH in the hypothalamus and its receptor on the uterus, lead to diseases of the uterus. DEHP may impose a negative influence on the development and functioning of the reproductive system in pubertal female rats. PMID:27845755

  2. Pubertal stage and the prevalence of violence and social/relational aggression.

    Science.gov (United States)

    Hemphill, Sheryl A; Kotevski, Aneta; Herrenkohl, Todd I; Toumbourou, John W; Carlin, John B; Catalano, Richard F; Patton, George C

    2010-08-01

    We examined associations between pubertal stage and violent adolescent behavior and social/relational aggression. The International Youth Development Study comprises statewide representative student samples in grades 5, 7, and 9 (N = 5769) in Washington State and Victoria, Australia, drawn as a 2-stage cluster sample in each state. We used a school-administered, self-report student survey to measure previous-year violent behavior (ie, attacking or beating up another person) and social/relational aggression (excluding peers from the group, threatening to spread lies or rumors), as well as risk and protective factors and pubertal development. Cross-sectional data were analyzed. Compared with early puberty, the odds of violent behavior were approximately threefold higher in midpuberty (odds ratio [OR]: 2.87 [95% confidence interval (CI): 1.81-4.55]) and late puberty (OR: 3.79 [95% CI: 2.25-6.39]) after adjustment for demographic factors. For social/relational aggression, there were weaker overall associations after adjustment, but these associations included an interaction between pubertal stage and age, and stronger associations with pubertal stage at younger age were shown (P = .003; midpuberty OR: 1.78 [95% CI: 1.20-2.63]; late puberty OR: 3.00 [95% CI: 1.95-4.63]). Associations between pubertal stage and violent behavior and social/relational aggression remained after the inclusion of social contextual mediators in the analyses. Pubertal stage was associated with higher rates of violent behavior and social/relational aggression, with the latter association seen only at younger ages. Puberty is an important phase at which to implement prevention programs to reduce adolescent violent and antisocial behaviors.

  3. Endothelial function in pre-pubertal children at risk of developing cardiomyopathy: a new frontier

    Directory of Open Access Journals (Sweden)

    Aline Cristina Tavares

    2012-01-01

    Full Text Available Although it is known that obesity, diabetes, and Kawasaki's disease play important roles in systemic inflammation and in the development of both endothelial dysfunction and cardiomyopathy, there is a lack of data regarding the endothelial function of pre-pubertal children suffering from cardiomyopathy. In this study, we performed a systematic review of the literature on pre-pubertal children at risk of developing cardiomyopathy to assess the endothelial function of pre-pubertal children at risk of developing cardiomyopathy. We searched the published literature indexed in PubMed, Bireme and SciELO using the keywords 'endothelial', 'children', 'pediatric' and 'infant' and then compiled a systematic review. The end points were age, the pubertal stage, sex differences, the method used for the endothelial evaluation and the endothelial values themselves. No studies on children with cardiomyopathy were found. Only 11 papers were selected for our complete analysis, where these included reports on the flow-mediated percentage dilatation, the values of which were 9.80±1.80, 5.90±1.29, 4.50±0.70, and 7.10±1.27 for healthy, obese, diabetic and pre-pubertal children with Kawasaki's disease, respectively. There was no significant difference in the dilatation, independent of the endothelium, either among the groups or between the genders for both of the measurements in children; similar results have been found in adolescents and adults. The endothelial function in cardiomyopathic children remains unclear because of the lack of data; nevertheless, the known dysfunctions in children with obesity, type 1 diabetes and Kawasaki's disease may influence the severity of the cardiovascular symptoms, the prognosis, and the mortality rate. The results of this study encourage future research into the consequences of endothelial dysfunction in pre-pubertal children.

  4. Running speed during training and percent body fat predict race time in recreational male marathoners

    OpenAIRE

    Knechtle, Beat; Barandun,; Knechtle,Patrizia; Klipstein,; Rüst,Christoph Alexander; Rosemann,Thomas; Lepers,Romuald

    2012-01-01

     Background: Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.Methods: Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.Results...

  5. Predict or classify: The deceptive role of time-locking in brain signal classification

    Science.gov (United States)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  6. The relations of age and pubertal development with cortisol and daily stress in youth at clinical risk for psychosis.

    Science.gov (United States)

    Moskow, Danielle M; Addington, Jean; Bearden, Carrie E; Cadenhead, Kristin S; Cornblatt, Barbara A; Heinssen, Robert; Mathalon, Daniel H; McGlashan, Thomas H; Perkins, Diana O; Seidman, Larry J; Tsuang, Ming T; Cannon, Tyrone D; Woods, Scott W; Walker, Elaine F

    2016-04-01

    Prodromal syndromes often begin in adolescence - a period of neurodevelopmental changes and heightened stress sensitivity. Research has shown elevated stress and cortisol in individuals at clinical high risk (CHR) for psychosis. This cross-sectional study examined relations of age and pubertal status with cortisol and self-reported stress in healthy controls (HCs) and CHR adolescents. It was hypothesized that the relations of age and pubertal stage with cortisol and stress would be more pronounced in CHR youth. Participants were 93 HCs and 348 CHR adolescents from the North American Prodrome Longitudinal Study (NAPLS). At baseline, measures of stress (Daily Stress Inventory - DSI), Tanner stage (TS), and salivary cortisol were obtained. ANCOVA revealed increased DSI scores with age for both groups, and higher DSI scores in CHR adolescents than HCs, with a more pronounced difference for females. Contrary to prediction, with age controlled, HCs showed greater TS-related DSI increases. Analysis of cortisol showed no significant interactions, but a main effect of age and a trend toward higher cortisol in the CHR group. Correlations of cortisol with TS were higher in HC than CHR group. Stress measures increased with age in HC and CHR adolescents, and DSI scores also increased with TS in HCs. The results do not support a more pronounced age or TS increase in stress measures in CHR adolescents, but instead suggest that stress indices tend to be elevated earlier in adolescence in the CHR group. Potential determinants of findings and future directions are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

    Science.gov (United States)

    Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

    2017-10-01

    To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

  8. A real-time prediction model for post-irradiation malignant cervical lymph nodes.

    Science.gov (United States)

    Lo, W-C; Cheng, P-W; Shueng, P-W; Hsieh, C-H; Chang, Y-L; Liao, L-J

    2018-04-01

    To establish a real-time predictive scoring model based on sonographic characteristics for identifying malignant cervical lymph nodes (LNs) in cancer patients after neck irradiation. One-hundred forty-four irradiation-treated patients underwent ultrasonography and ultrasound-guided fine-needle aspirations (USgFNAs), and the resultant data were used to construct a real-time and computerised predictive scoring model. This scoring system was further compared with our previously proposed prediction model. A predictive scoring model, 1.35 × (L axis) + 2.03 × (S axis) + 2.27 × (margin) + 1.48 × (echogenic hilum) + 3.7, was generated by stepwise multivariate logistic regression analysis. Neck LNs were considered to be malignant when the score was ≥ 7, corresponding to a sensitivity of 85.5%, specificity of 79.4%, positive predictive value (PPV) of 82.3%, negative predictive value (NPV) of 83.1%, and overall accuracy of 82.6%. When this new model and the original model were compared, the areas under the receiver operating characteristic curve (c-statistic) were 0.89 and 0.81, respectively (P real-time sonographic predictive scoring model was constructed to provide prompt and reliable guidance for USgFNA biopsies to manage cervical LNs after neck irradiation. © 2017 John Wiley & Sons Ltd.

  9. Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics.

    Science.gov (United States)

    Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas

    2017-02-15

    Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  10. Electro convulsive therapy in a pre-pubertal child with severe depression.

    Directory of Open Access Journals (Sweden)

    Russell P

    2002-10-01

    Full Text Available Electro Convulsive Therapy (ECT in pre-pubertal children is a controversial and underreported treatment. Even though the effectiveness and side effects of ECT in adolescents are comparable with those in adults, there is a pervasive reluctance to use ECT in children and adolescents. We report the case of a pre-pubertal child in an episode of severe depression with catatonic features, where a protracted course of ECT proved life-saving in spite of prolonged duration of seizures and delayed response to treatment. The case illustrates the safety and efficacy of ECT in children. Relevant literature is also reviewed along with the case report.

  11. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients.

    Science.gov (United States)

    Martinez, Bruno Prata; Gomes, Isabela Barboza; Oliveira, Carolina Santana de; Ramos, Isis Resende; Rocha, Mônica Diniz Marques; Forgiarini Júnior, Luiz Alberto; Camelier, Fernanda Warken Rosa; Camelier, Aquiles Assunção

    2015-05-01

    The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. This cross-sectional study analyzed 68 elderly patients (≥60 years of age) in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test). After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7%) of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%), pneumonia (19.1%) and abdominal disorders (10.2%). The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002). The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients.

  12. Prediction of Golden Time for Recovering the Safety Injection System in Severe LOCA Circumstances

    International Nuclear Information System (INIS)

    Yoo, Kwae Hwan; Kim, Dong Young; Choi, Geon Pil; Back, Ju Hyun; Na, Man Gyun

    2015-01-01

    In this study, the core uncovery and RV failure according to LOCA break sizes were analyzed by using the MAAP4 code when safety injection system (SIS) was not operating normally. We predicted the golden time of SIS recovery for accomplishing the reactor cold shutdown and preventing RV failure. MAAP4 code was used for severe accident analysis. The LOCA simulations were performed with break size in order to predict the golden time to recovery SIS. We predicted the golden time according to the SIS operation cases through the simulation of OPR1000. When LOCA occurred, the normal operation of SIS is very important in maintaining the integrity of NPPs. However if the SIS does not work or its actuation is delayed due to failure of the equipment, the DBA will lead to a severe accident. In this study, accident situations that SIS does not work normally were assumed and a number of MAAP4 code simulations were conducted. In addition, core uncovery time and RV failure time were predicted. If the recovery time of SIS for accident recovery is predicted, the core will not be exposed through appropriate action

  13. A maintenance time prediction method considering ergonomics through virtual reality simulation.

    Science.gov (United States)

    Zhou, Dong; Zhou, Xin-Xin; Guo, Zi-Yue; Lv, Chuan

    2016-01-01

    Maintenance time is a critical quantitative index in maintainability prediction. An efficient maintenance time measurement methodology plays an important role in early stage of the maintainability design. While traditional way to measure the maintenance time ignores the differences between line production and maintenance action. This paper proposes a corrective MOD method considering several important ergonomics factors to predict the maintenance time. With the help of the DELMIA analysis tools, the influence coefficient of several factors are discussed to correct the MOD value and the designers can measure maintenance time by calculating the sum of the corrective MOD time of each maintenance therbligs. Finally a case study is introduced, by maintaining the virtual prototype of APU motor starter in DELMIA, designer obtains the actual maintenance time by the proposed method, and the result verifies the effectiveness and accuracy of the proposed method.

  14. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chuan Zhu

    2014-01-01

    Full Text Available This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

  15. LPTA: location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks.

    Science.gov (United States)

    Zhu, Chuan; Wang, Yao; Han, Guangjie; Rodrigues, Joel J P C; Lloret, Jaime

    2014-01-01

    This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

  16. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

    Science.gov (United States)

    Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

    2017-01-01

    For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

  17. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Eric R. Edelman

    2017-06-01

    Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

  18. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

    Directory of Open Access Journals (Sweden)

    Ching-Hsue Cheng

    2018-01-01

    Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

  19. Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

    Science.gov (United States)

    Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

    2016-08-11

    Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

  20. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

    Science.gov (United States)

    2018-01-01

    The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

  1. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

    Science.gov (United States)

    Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

    2018-01-01

    The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

  2. Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

    Directory of Open Access Journals (Sweden)

    Haimin Yang

    2017-01-01

    Full Text Available Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam, for long short-term memory (LSTM to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

  3. Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

    Science.gov (United States)

    Yang, Haimin; Pan, Zhisong; Tao, Qing

    2017-01-01

    Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

  4. A time series based sequence prediction algorithm to detect activities of daily living in smart home.

    Science.gov (United States)

    Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F

    2015-01-01

    The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

  5. Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

    Science.gov (United States)

    Kennedy, Curtis E; Turley, James P

    2011-10-24

    Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

  6. Predictive analytics for truck arrival time estimation : a field study at a European distribution center

    NARCIS (Netherlands)

    van der Spoel, Sjoerd; Amrit, Chintan Amrit; van Hillegersberg, Jos

    2017-01-01

    Distribution centres (DCs) are the hubs connecting transport streams in the supply chain. The synchronisation of coming and going cargo at a DC requires reliable arrival times. To achieve this, a reliable method to predict arrival times is needed. A literature review was performed to find the

  7. A Real-time Breakdown Prediction Method for Urban Expressway On-ramp Bottlenecks

    Science.gov (United States)

    Ye, Yingjun; Qin, Guoyang; Sun, Jian; Liu, Qiyuan

    2018-01-01

    Breakdown occurrence on expressway is considered to relate with various factors. Therefore, to investigate the association between breakdowns and these factors, a Bayesian network (BN) model is adopted in this paper. Based on the breakdown events identified at 10 urban expressways on-ramp in Shanghai, China, 23 parameters before breakdowns are extracted, including dynamic environment conditions aggregated with 5-minutes and static geometry features. Different time periods data are used to predict breakdown. Results indicate that the models using 5-10 min data prior to breakdown performs the best prediction, with the prediction accuracies higher than 73%. Moreover, one unified model for all bottlenecks is also built and shows reasonably good prediction performance with the classification accuracy of breakdowns about 75%, at best. Additionally, to simplify the model parameter input, the random forests (RF) model is adopted to identify the key variables. Modeling with the selected 7 parameters, the refined BN model can predict breakdown with adequate accuracy.

  8. Physical activity reduces systemic blood pressure and improves early markers of atherosclerosis in pre-pubertal obese children.

    Science.gov (United States)

    Farpour-Lambert, Nathalie J; Aggoun, Yacine; Marchand, Laetitia M; Martin, Xavier E; Herrmann, François R; Beghetti, Maurice

    2009-12-15

    The aim of this study was to determine the effects of physical activity on systemic blood pressure (BP) and early markers of atherosclerosis in pre-pubertal obese children. Hypertension and endothelial dysfunction are premature complications of obesity. We performed a 3-month randomized controlled trial with a modified crossover design: 44 pre-pubertal obese children (age 8.9 + or - 1.5 years) were randomly assigned (1:1) to an exercise (n = 22) or a control group (n = 22). We recruited 22 lean children (age 8.5 + or - 1.5 years) for baseline comparison. The exercise group trained 60 min 3 times/week during 3 months, whereas control subjects remained relatively inactive. Then, both groups trained twice/week during 3 months. We assessed changes at 3 and 6 months in office and 24-h BP, arterial intima-media thickness (IMT) and stiffness, endothelial function (flow-mediated dilation), body mass index (BMI), body fat, cardiorespiratory fitness (maximal oxygen consumption [VO(2)max]), physical activity, and biological markers. Obese children had higher BP, arterial stiffness, body weight, BMI, abdominal fat, insulin resistance indexes, and C-reactive protein levels, and lower flow-mediated dilation, VO(2)max, physical activity, and high-density lipoprotein cholesterol levels than lean subjects. At 3 months, we observed significant changes in 24-h systolic BP (exercise -6.9 + or - 13.5 mm Hg vs. control 3.8 + or - 7.9 mm Hg, -0.8 + or - 1.5 standard deviation score [SDS] vs. 0.4 + or - 0.8 SDS), diastolic BP (-0.5 + or - 1.0 SDS vs. 0 + or - 1.4 SDS), hypertension rate (-12% vs. -1%), office BP, BMI z-score, abdominal fat, and VO(2)max. At 6 months, change differences in arterial stiffness and IMT were significant. A regular physical activity program reduces BP, arterial stiffness, and abdominal fat; increases cardiorespiratory fitness; and delays arterial wall remodeling in pre-pubertal obese children. (Effects of Aerobic Exercise Training on Arterial Function and

  9. Measurement and Prediction of Time-independent and Time-dependent Rheological Behavior of Waxy Crude Oil

    Directory of Open Access Journals (Sweden)

    Yavar Karimi

    2017-01-01

    Full Text Available Wax deposition phenomenon changes the rheological behavior of waxy crude oil completely. In the current work, the rheological time-dependent and time-independent behaviors of waxy crude oil samples are studied and flow curve and compliance function are measured for the oil samples with various wax contents at different temperatures. A decrease in temperature and an increase in wax content lead to an increase in the viscosity and yield stress but a significant drop in compliance function. A modified Burger model is developed to predict the behavior of the compliance function and a modified Casson model is used to predict the flow curve of the waxy crude oil samples within a vast range of wax contents and temperatures. The proposed Burger and Casson models match with experimental results with R2 of 99.7% and 97.33% respectively.

  10. Real-time stylistic prediction for whole-body human motions.

    Science.gov (United States)

    Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

    2012-01-01

    The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center.

    Science.gov (United States)

    Marchand-Maillet, Florence; Debes, Claire; Garnier, Fanny; Dufeu, Nicolas; Sciard, Didier; Beaussier, Marc

    2015-02-01

    Patients flow in outpatient surgical unit is a major issue with regards to resource utilization, overall case load and patient satisfaction. An electronic Radio Frequency Identification Device (RFID) was used to document the overall time spent by the patients between their admission and discharge from the unit. The objective of this study was to evaluate how a RFID-based data collection system could provide an accurate prediction of the actual time for the patient to be discharged from the ambulatory surgical unit after surgery. This is an observational prospective evaluation carried out in an academic ambulatory surgery center (ASC). Data on length of stay at each step of the patient care, from admission to discharge, were recorded by a RFID device and analyzed according to the type of surgical procedure, the surgeon and the anesthetic technique. Based on these initial data (n = 1520), patients were scheduled in a sequential manner according to the expected duration of the previous case. The primary endpoint was the difference between actual and predicted time of discharge from the unit. A total of 414 consecutive patients were prospectively evaluated. One hundred seventy four patients (42%) were discharged at the predicted time ± 30 min. Only 24% were discharged behind predicted schedule. Using an automatic record of patient's length of stay would allow an accurate prediction of the discharge time according to the type of surgery, the surgeon and the anesthetic procedure.

  12. Prediction of half-marathon race time in recreational female and male runners.

    Science.gov (United States)

    Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

    2014-01-01

    Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the

  13. Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

    International Nuclear Information System (INIS)

    Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

    2005-01-01

    The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

  14. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  15. Predictive display design for the vehicles with time delay in dynamic response

    Science.gov (United States)

    Efremov, A. V.; Tiaglik, M. S.; Irgaleev, I. H.; Efremov, E. V.

    2018-02-01

    The two ways for the improvement of flying qualities are considered: the predictive display (PD) and the predictive display integrated with the flight control system (FCS). The both ways allow to transforming the controlled element dynamics in the crossover frequency range, to improve the accuracy of tracking and to suppress the effect of time delay in the vehicle response too. The technique for optimization of the predictive law is applied to the landing task. The results of the mathematical modeling and experimental investigations carried out for this task are considered in the paper.

  16. The predictability of a lake phytoplankton community, over time-scales of hours to years

    DEFF Research Database (Denmark)

    Thomas, Mridul K.; Fontana, Simone; Reyes, Marta

    2018-01-01

    monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time-scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long......Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental...

  17. A multiple model approach to respiratory motion prediction for real-time IGRT

    International Nuclear Information System (INIS)

    Putra, Devi; Haas, Olivier C L; Burnham, Keith J; Mills, John A

    2008-01-01

    Respiration induces significant movement of tumours in the vicinity of thoracic and abdominal structures. Real-time image-guided radiotherapy (IGRT) aims to adapt radiation delivery to tumour motion during irradiation. One of the main problems for achieving this objective is the presence of time lag between the acquisition of tumour position and the radiation delivery. Such time lag causes significant beam positioning errors and affects the dose coverage. A method to solve this problem is to employ an algorithm that is able to predict future tumour positions from available tumour position measurements. This paper presents a multiple model approach to respiratory-induced tumour motion prediction using the interacting multiple model (IMM) filter. A combination of two models, constant velocity (CV) and constant acceleration (CA), is used to capture respiratory-induced tumour motion. A Kalman filter is designed for each of the local models and the IMM filter is applied to combine the predictions of these Kalman filters for obtaining the predicted tumour position. The IMM filter, likewise the Kalman filter, is a recursive algorithm that is suitable for real-time applications. In addition, this paper proposes a confidence interval (CI) criterion to evaluate the performance of tumour motion prediction algorithms for IGRT. The proposed CI criterion provides a relevant measure for the prediction performance in terms of clinical applications and can be used to specify the margin to accommodate prediction errors. The prediction performance of the IMM filter has been evaluated using 110 traces of 4-minute free-breathing motion collected from 24 lung-cancer patients. The simulation study was carried out for prediction time 0.1-0.6 s with sampling rates 3, 5 and 10 Hz. It was found that the prediction of the IMM filter was consistently better than the prediction of the Kalman filter with the CV or CA model. There was no significant difference of prediction errors for the

  18. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

    Science.gov (United States)

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi

    2014-05-01

    Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

  19. Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

    International Nuclear Information System (INIS)

    Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve

    2011-01-01

    Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

  20. Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

    Energy Technology Data Exchange (ETDEWEB)

    Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

    2011-11-15

    Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

  1. Time-varying predictability in crude-oil markets: the case of GCC countries

    International Nuclear Information System (INIS)

    El Hedi Arouri, Mohamed; Thanh Huong Dinh; Duc Khuong Nguyen

    2010-01-01

    This paper uses a time-varying parameter model with generalized autoregressive conditional heteroscedasticity effects to examine the dynamic behavior of crude-oil prices for the period February 7, 1997-January 8, 2010. Using data from four countries of the Gulf Cooperation Council, we find evidence of short-term predictability in oil-price changes over time, except for several short sub-periods. However, the hypothesis of convergence towards weak-form informational efficiency is rejected for all markets. In addition, we explore the possibility of structural breaks in the time-paths of the estimated predictability indices and detect only one breakpoint, for the oil markets in Qatar and the United Arab Emirates. Our empirical results therefore call for new empirical research to further gauge the predictability characteristics and the determinants of oil-price changes.

  2. Improved time series prediction with a new method for selection of model parameters

    International Nuclear Information System (INIS)

    Jade, A M; Jayaraman, V K; Kulkarni, B D

    2006-01-01

    A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

  3. Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

    KAUST Repository

    Wang, Bing; Zhang, Jun; Chen, Peng; Ji, Zhiwei; Deng, Shuping; Li, Chi

    2013-01-01

    Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.

  4. Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

    KAUST Repository

    Wang, Bing

    2013-05-09

    Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.

  5. GEKF, GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

    International Nuclear Information System (INIS)

    Wu Xuedong; Song Zhihuan

    2008-01-01

    On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering (EKF), the unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal (These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper). Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey–Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF. (general)

  6. Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

    Directory of Open Access Journals (Sweden)

    Knechtle P

    2011-08-01

    Full Text Available Christoph Alexander Rüst1, Beat Knechtle1,2, Patrizia Knechtle2, Thomas Rosemann1, Romuald Lepers31Institute of General Practice and Health Services Research, University of Zurich, Zurich, Switzerland; 2Gesundheitszentrum St Gallen, St Gallen, Switzerland; 3INSERM U887, University of Burgundy, Faculty of Sport Sciences, Dijon, FranceBackground: The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes.Methods: Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis.Results: A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001 and in an Olympic distance triathlon (P < 0.0001 were the best variables related to Ironman race time. Ironman race time (minutes might be partially predicted by the following equation: (r2 = 0.65, standard error of estimate = 56.8 = 152.1 + 1.332 × (personal best time in a marathon, minutes + 1.964 × (personal best time in an Olympic distance triathlon, minutes.Conclusion: These results suggest

  7. A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-03-01

    Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

  8. Task-related increases in fatigue predict recovery time after academic stress.

    Science.gov (United States)

    Blasche, Gerhard; Zilic, Jelena; Frischenschlager, Oskar

    2016-01-01

    The aim of this study was to investigate the time course of recovery after an academic exam as a model of high workload and its association with stress-related fatigue. Thirty-six medical students (17 females, 19 males) filled out diaries during an exam phase, starting 2 days prior to the exam, and a control phase 4 weeks after the exam for 14 days, respectively. Fatigue, distress, quality of sleep, and health complaints were assessed. Recovery time was determined for each individual and variable by comparing the 3-day average with the confidence interval of the control phase. Recovery time was predicted by Cox regression analyses. Recovery times of all variables except health complaints were predicted by stress-related fatigue. Half of the individuals had recovered after 6 days, and 80% of the individuals had recovered after 8 days. The time necessary for recovery from work demands is determined by fatigue as a measure of resource depletion.

  9. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    International Nuclear Information System (INIS)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

    2016-01-01

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  10. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

    2016-06-15

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  11. Dynamic Travel Time Prediction Models for Buses Using Only GPS Data

    Directory of Open Access Journals (Sweden)

    Wei Fan

    2015-01-01

    Full Text Available Providing real-time and accurate travel time information of transit vehicles can be very helpful as it assists passengers in planning their trips to minimize waiting times. The purpose of this research is to develop and compare dynamic travel time prediction models which can provide accurate prediction of bus travel time in order to give real-time information at a given downstream bus stop using only global positioning system (GPS data. Historical Average (HA, Kalman Filtering (KF and Artificial Neural Network (ANN models are considered and developed in this paper. A case has been studied by making use of the three models. Promising results are obtained from the case study, indicating that the models can be used to implement an Advanced Public Transport System. The implementation of this system could assist transit operators in improving the reliability of bus services, thus attracting more travelers to transit vehicles and helping relieve congestion. The performances of the three models were assessed and compared with each other under two criteria: overall prediction accuracy and robustness. It was shown that the ANN outperformed the other two models in both aspects. In conclusion, it is shown that bus travel time information can be reasonably provided using only arrival and departure time information at stops even in the absence of traffic-stream data.

  12. The Effect of Wallow on Growth Performance of Pre-Pubertal Pigs in ...

    African Journals Online (AJOL)

    A study was carried out to determine the influence of wallow on the growth performance of growing pigs. Sixteen (16) pre-pubertal pigs (8 males and 8 females) of large white breed, aged three months were randomly assigned to two treatments. There were eight animals per group designated as treatment A = with wallow ...

  13. Pubertal development and fertility in survivors of childhood acute myeloid leukemia treated with chemotherapy only

    DEFF Research Database (Denmark)

    Molgaard-Hansen, Lene; Skou, Anne-Sofie; Juul, Anders

    2013-01-01

    More than 60% of children with acute myeloid leukemia (AML) become long-term survivors. Most are cured using chemotherapy without hematopoietic stem cell transplantation (HSCT). We report on pubertal development and compare self-reported parenthood among AML survivors and their siblings....

  14. BMI percentile-for-age overestimates adiposity in early compared with late maturing pubertal children

    DEFF Research Database (Denmark)

    Sørensen, Kaspar; Juul, Anders

    2015-01-01

    and bioelectric impedance analyses (BIA) were used to estimate adiposity. Clinical pubertal markers (Tanner stages and testicular volume) were evaluated. LH, FSH, estradiol, testosterone, SHBG and IGF1 levels were determined by immunoassays. RESULTS: In all age groups, higher BMI (all 1 year age-groups, P ≤ 0...

  15. The Interplay between Gaze Following, Emotion Recognition, and Empathy across Adolescence; a Pubertal Dip in Performance?

    NARCIS (Netherlands)

    van Rooijen, R.; Junge, C.M.M.; Kemner, C.

    2018-01-01

    During puberty a dip in face recognition is often observed, possibly caused by heightened levels of gonadal hormones which in turn affects the re-organization of relevant cortical circuitry. In the current study we investigated whether a pubertal dip could be observed in three other abilities

  16. The Relationship among Pubertal Stage, Age, and Drinking in Adolescent Boys and Girls

    Science.gov (United States)

    Faden, Vivian B.; Ruffin, Beverly; Newes-Adeyi, Gabriella; Chen, Chiung

    2010-01-01

    This study used data from the Third National Household and Nutrition Examination Survey (NHANES) to examine the association between pubertal status (Tanner staging for boys and girls and menarche for girls) and alcohol use in a nationally representative sample of youths ages 12 to 17. Logistic regression was used to model the relationship. In…

  17. Insulin resistance in obese pre-pubertal children: Relation to body ...

    African Journals Online (AJOL)

    Heba Elsedfy

    2014-04-16

    Apr 16, 2014 ... of pre-pubertal obese children, and to investigate the relation- .... children P 10 years, HDL-Cholesterol <35 mg/dl) [18]. .... HDL: high density lipoprotein, TG: triglycerides, IFG: impaired fasting glucose, IGT: impaired glucose ...

  18. Pubertal maturation and sex steroids are related to alcohol use in adolescents

    NARCIS (Netherlands)

    Water, E. de; Braams, B.R.; Crone, E.A.; Peper, J.S.

    2013-01-01

    Adolescents often show risk-taking behavior, including experimentation with alcohol. Previous studies have shown that advanced pubertal maturation is related to increased alcohol use in adolescents, even when controlling for age. Little is known about the underlying mechanisms of this relation

  19. Sex Variations in Youth Anxiety Symptoms: Effects of Pubertal Development and Gender Role Orientation

    Science.gov (United States)

    Carter, Rona; Silverman, Wendy K.; Jaccard, James

    2011-01-01

    This study evaluated whether pubertal development and gender role orientation (i.e., masculinity and femininity) can partially explain sex variations in youth anxiety symptoms among clinic-referred anxious youth (N = 175; ages 9-13 years; 74% Hispanic; 48% female). Using youth and parent ratings of youth anxiety symptoms, structural equation…

  20. Salivary testosterone concentrations in pubertal ICSI boys compared with spontaneously conceived boys

    NARCIS (Netherlands)

    Belva, F.; Bonduelle, M.; Schiettecatte, J.; Tournaye, H.; Painter, R. C.; Devroey, P.; de Schepper, J.

    2011-01-01

    BACKGROUND: To date, no data exist about Leydig cell function of pubertal boys born after ICSI. To evaluate a potential risk of gonadal dysfunction in children born from fathers with compromised fertility, testicular function was assessed by the measurement of salivary testosterone. METHODS: Morning

  1. Tamoxifen therapy for the management of pubertal gynecomastia: a systematic review

    NARCIS (Netherlands)

    Lapid, Oren; van Wingerden, Jan J.; Perlemuter, Leon

    2013-01-01

    Objective: A systematic review to assess the efficacy of tamoxifen in the management of idiopathic pubertal gynecomastia. Data sources: Searches were conducted using the databases of Medline (search engine PubMed) and Web of Science (R). Study selection: Studies reporting the use of Tamoxifen for

  2. Tamoxifen treatment for pubertal gynecomastia in two siblings with partial androgen insensitivity syndrome.

    Science.gov (United States)

    Saito, Reiko; Yamamoto, Yukiyo; Goto, Motohide; Araki, Shunsuke; Kubo, Kazuyasu; Kawagoe, Rinko; Kawada, Yasusada; Kusuhara, Koichi; Igarashi, Maki; Fukami, Maki

    2014-01-01

    Although tamoxifen has been shown to be fairly safe and effective for idiopathic pubertal gynecomastia, it remains unknown whether it is also beneficial for gynecomastia associated with endocrine disorders. Here, we report the effect of tamoxifen on pubertal gynecomastia in 2 siblings with partial androgen insensitivity syndrome (PAIS). Cases 1 and 2 presented with persistent pubertal gynecomastia at 13 and 16 years of age, respectively. Physical examinations revealed breast of Tanner stage 3 and normal male-type external genitalia in both cases. Clinical features such as female-type pubic hair and borderline small testis indicated mildly impaired masculinization. Molecular analysis identified a previously reported p.Arg789Ser mutation in the androgen receptor gene (AR) in the 2 cases. Two months of oral administration of tamoxifen ameliorated gynecomastia to Tanner stage 2 with no adverse events. Additional treatment with testosterone enanthate showed negligible effects on body hair and penile length. Hormone values of the 2 cases during tamoxifen treatment remained similar to those in previously reported untreated patients with PAIS. The results indicate that tamoxifen was effective in treating pubertal gynecomastia in these 2 patients with PAIS and may be considered as a therapeutic option in this situation pending further studies.

  3. Arthrospira (Spirulina) platensis supplementation affects folliculogenesis, progesterone and ghrelin levels in fattening pre-pubertal gilts

    NARCIS (Netherlands)

    Abadjieva, Desislava; Nedeva, Radka; Marchev, Yordan; Jordanova, Gergana; Chervenkov, Mihail; Dineva, Julieta; Shimkus, Almantas; Shimkiene, Aldona; Teerds, Katja; Kistanova, Elena

    2018-01-01

    The aim of the present investigation was to study the effect of Arthrospira (Spirulina) platensis supplemented diet on follicular development and related endocrine parameters, such as estradiol and progesterone levels as well as ghrelin levels in pre-pubertal gilts. Twenty-one 60-day-old Danube

  4. IPRODIONE DELAYS MALE RAT PUBERTAL DEVELOPMENT, REDUCING SERUM TESTOSTERONE AND EX VIVO TESTOSTERONE PRODUCTION

    Science.gov (United States)

    Iprodione (IPRO) is a dichlorophenyl dicarboximide fungicide similar to the androgen receptor (AR) antagonist vinclozolin. The current studies were designed to determine if IPRO would delay male rat pubertal development like vinclozolin and to identify the mechanism(s) of action...

  5. EFFECTS OF DIBUTYL PHTHALATE IN MALE RABBITS FOLLOWING IN UTERO, ADOLESCENT OR POST-PUBERTAL EXPOSURE

    Science.gov (United States)

    Effects of dibutyl phthalate in male rabbits following in utero, adolescent, or post-pubertal exposureTy T. Higuchi1, Jennifer S. Palmer1, L. Earl Gray Jr2., and D. N. Rao Veeramachaneni11Animal Reproduction and Biotechnology Laboratory, Colorado State University, Fort

  6. Normal Pubertal Development in Daughters of Women With PCOS: A Controlled Study.

    Science.gov (United States)

    Legro, Richard S; Kunselman, Allen R; Stetter, Christy M; Gnatuk, Carol L; Estes, Stephanie J; Brindle, Eleanor; Vesper, Hubert W; Botelho, Julianne C; Lee, Peter A; Dodson, William C

    2017-01-01

    Daughters of women with polycystic ovary syndrome (PCOS) are thought to be at increased risk for developing stigmata of the syndrome, but the ontogeny during puberty is uncertain. We phenotyped daughters (n = 76) of mothers with PCOS and daughters (n = 80) from control mothers for reproductive and metabolic parameters characteristic of PCOS. We performed a matched case/control study at Penn State Hershey Medical Center that included non-Hispanic, white girls 4 to 17 years old. We obtained birth history, biometric, ovarian ultrasounds, whole-body dual-energy X-ray absorptiometry scan for body composition, 2-hour glucose challenged salivary insulin levels, and two timed urinary collections (12 hours overnight and 3 hours in the morning) for gonadotropins and sex steroids. We measured integrated urinary levels of adrenal (dehydroepiandrosterone sulfate) and ovarian [testosterone (TT)] steroids. Other endpoints included integrated salivary insulin levels and urinary luteinizing hormone levels. There were no differences in detection rates or mean levels for gonadotropins and sex steroids in timed urinary collections between PCOS daughters and control daughters, nor were there differences in integrated salivary insulin levels. Results showed that 69% of Tanner 4/5 PCOS daughters vs 31% of control daughters had hirsutism defined as a Ferriman-Gallwey score >8 (P = 0.04). There were no differences in body composition as determined by dual-energy X-ray absorptiometry between groups in the three major body contents (i.e., bone, lean body mass, and fat) or in ovarian volume between groups. Matched for pubertal stage, PCOS daughters have similar levels of urinary androgens and gonadotropins as well as glucose-challenged salivary insulin levels. Copyright © 2017 by the Endocrine Society

  7. Analytical solutions for prediction of the ignition time of wood particles based on a time and space integral method

    International Nuclear Information System (INIS)

    Haseli, Y.; Oijen, J.A. van; Goey, L.P.H. de

    2012-01-01

    Highlights: ► A simple model for prediction of the ignition time of a wood particle is presented. ► The formulation is given for both thermally thin and thermally thick particles. ► Transition from thermally thin to thick regime occurs at a critical particle size. ► The model is validated against a numerical model and various experimental data. - Abstract: The main idea of this paper is to establish a simple approach for prediction of the ignition time of a wood particle assuming that the thermo-physical properties remain constant and ignition takes place at a characteristic ignition temperature. Using a time and space integral method, explicit relationships are derived for computation of the ignition time of particles of three common shapes (slab, cylinder and sphere), which may be characterized as thermally thin or thermally thick. It is shown through a dimensionless analysis that the dimensionless ignition time can be described as a function of non-dimensional ignition temperature, reactor temperature or external incident heat flux, and parameter K which represents the ratio of conduction heat transfer to the external radiation heat transfer. The numerical results reveal that for the dimensionless ignition temperature between 1.25 and 2.25 and for values of K up to 8000 (corresponding to woody materials), the variation of the ignition time of a thermally thin particle with K and the dimensionless ignition temperature is linear, whereas the dependence of the ignition time of a thermally thick particle on the above two parameters obeys a quadratic function. Furthermore, it is shown that the transition from the regime of thermally thin to the regime of thermally thick occurs at K cr (corresponding to a critical size of particle) which is found to be independent of the particle shape. The model is validated by comparing the predicted and the measured ignition time of several wood particles obtained from different sources. Good agreement is achieved which

  8. Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2017-08-01

    Full Text Available Due to the sustainable and pollution-free characteristics, wind energy has been one of the fastest growing renewable energy sources. However, the intermittent and random fluctuation of wind speed presents many challenges for reliable wind power integration and normal operation of wind farm. Accurate wind speed prediction is the key to ensure the safe operation of power system and to develop wind energy resources. Therefore, this paper has presented a wavelet time series wind speed prediction model based on Lorenz disturbance. Therefore, in this paper, combined with the atmospheric dynamical system, a wavelet-time series improved wind speed prediction model based on Lorenz disturbance is proposed and the wind turbines of different climate types in Spain and China are used to simulate the disturbances of Lorenz equations with different initial values. The prediction results show that the improved model can effectively correct the preliminary prediction of wind speed, improving the prediction. In a word, the research work in this paper will be helpful to arrange the electric power dispatching plan and ensure the normal operation of the wind farm.

  9. Short Term Prediction of PM10 Concentrations Using Seasonal Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Hamid Hazrul Abdul

    2016-01-01

    Full Text Available Air pollution modelling is one of an important tool that usually used to make short term and long term prediction. Since air pollution gives a big impact especially to human health, prediction of air pollutants concentration is needed to help the local authorities to give an early warning to people who are in risk of acute and chronic health effects from air pollution. Finding the best time series model would allow prediction to be made accurately. This research was carried out to find the best time series model to predict the PM10 concentrations in Nilai, Negeri Sembilan, Malaysia. By considering two seasons which is wet season (north east monsoon and dry season (south west monsoon, seasonal autoregressive integrated moving average model were used to find the most suitable model to predict the PM10 concentrations in Nilai, Negeri Sembilan by using three error measures. Based on AIC statistics, results show that ARIMA (1, 1, 1 × (1, 0, 012 is the most suitable model to predict PM10 concentrations in Nilai, Negeri Sembilan.

  10. Time-series prediction of shellfish farm closure: A comparison of alternatives

    Directory of Open Access Journals (Sweden)

    Ashfaqur Rahman

    2014-08-01

    Full Text Available Shellfish farms are closed for harvest when microbial pollutants are present. Such pollutants are typically present in rainfall runoff from various land uses in catchments. Experts currently use a number of observable parameters (river flow, rainfall, salinity as proxies to determine when to close farms. We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall. Time-series event prediction consists of two steps: (i feature extraction, and (ii prediction. A number of data mining challenges exist for these scenarios: (i which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?, (ii The farm closure events occur infrequently and this leads to a class imbalance problem; the question is what is the best way to deal with this problem? In this paper we have analysed and compared different combinations of balancing methods (under-sampling and over-sampling, feature extraction methods (cluster profile, curve fitting, Fourier Transform, Piecewise Aggregate Approximation, and Wavelet Transform and learning algorithms (neural network, support vector machine, k-nearest neighbour, decision tree, and Bayesian Network to predict closure events accurately considering the above data mining challenges. We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall, given the above data mining challenges.

  11. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  12. Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes.

    Science.gov (United States)

    Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

    2011-01-01

    The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes. Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis. A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes). These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes.

  13. Brain Maturation, Cognition and Voice Pattern in a Gender Dysphoria Case under Pubertal Suppression.

    Science.gov (United States)

    Schneider, Maiko A; Spritzer, Poli M; Soll, Bianca Machado Borba; Fontanari, Anna M V; Carneiro, Marina; Tovar-Moll, Fernanda; Costa, Angelo B; da Silva, Dhiordan C; Schwarz, Karine; Anes, Maurício; Tramontina, Silza; Lobato, Maria I R

    2017-01-01

    Introduction: Gender dysphoria (GD) (DMS-5) is a condition marked by increasing psychological suffering that accompanies the incongruence between one's experienced or expressed gender and one's assigned gender. Manifestation of GD can be seen early on during childhood and adolescence. During this period, the development of undesirable sexual characteristics marks an acute suffering of being opposite to the sex of birth. Pubertal suppression with gonadotropin releasing hormone analogs (GnRHa) has been proposed for these individuals as a reversible treatment for postponing the pubertal development and attenuating psychological suffering. Recently, increased interest has been observed on the impact of this treatment on brain maturation, cognition and psychological performance. Objectives: The aim of this clinical report is to review the effects of puberty suppression on the brain white matter (WM) during adolescence. WM Fractional anisotropy, voice and cognitive functions were assessed before and during the treatment. MRI scans were acquired before, and after 22 and 28 months of hormonal suppression. Methods: We performed a longitudinal evaluation of a pubertal transgender girl undergoing hormonal treatment with GnRH analog. Three longitudinal magnetic resonance imaging (MRI) scans were performed for diffusion tensor imaging (DTI), regarding Fractional Anisotropy (FA) for regions of interest analysis. In parallel, voice samples for acoustic analysis as well as executive functioning with the Wechsler Intelligence Scale (WISC-IV) were performed. Results: During the follow-up, white matter fractional anisotropy did not increase, compared to normal male puberty effects on the brain. After 22 months of pubertal suppression, operational memory dropped 9 points and remained stable after 28 months of follow-up. The fundamental frequency of voice varied during the first year; however, it remained in the female range. Conclusion: Brain white matter fractional anisotropy

  14. Predicting The Exit Time Of Employees In An Organization Using Statistical Model

    Directory of Open Access Journals (Sweden)

    Ahmed Al Kuwaiti

    2015-08-01

    Full Text Available Employees are considered as an asset to any organization and each organization provide a better and flexible working environment to retain its best and resourceful workforce. As such continuous efforts are being taken to avoid or extend the exitwithdrawal of employees from the organization. Human resource managers are facing a challenge to predict the exit time of employees and there is no precise model existing at present in the literature. This study has been conducted to predict the probability of exit of an employee in an organization using appropriate statistical model. Accordingly authors designed a model using Additive Weibull distribution to predict the expected exit time of employee in an organization. In addition a Shock model approach is also executed to check how well the Additive Weibull distribution suits in an organization. The analytical results showed that when the inter-arrival time increases the expected time for the employees to exit also increases. This study concluded that Additive Weibull distribution can be considered as an alternative in the place of Shock model approach to predict the exit time of employee in an organization.

  15. Linear filters as a method of real-time prediction of geomagnetic activity

    International Nuclear Information System (INIS)

    McPherron, R.L.; Baker, D.N.; Bargatze, L.F.

    1985-01-01

    Important factors controlling geomagnetic activity include the solar wind velocity, the strength of the interplanetary magnetic field (IMF), and the field orientation. Because these quantities change so much in transit through the solar wind, real-time monitoring immediately upstream of the earth provides the best input for any technique of real-time prediction. One such technique is linear prediction filtering which utilizes past histories of the input and output of a linear system to create a time-invariant filter characterizing the system. Problems of nonlinearity or temporal changes of the system can be handled by appropriate choice of input parameters and piecewise approximation in various ranges of the input. We have created prediction filters for all the standard magnetic indices and tested their efficiency. The filters show that the initial response of the magnetosphere to a southward turning of the IMF peaks in 20 minutes and then again in 55 minutes. After a northward turning, auroral zone indices and the midlatitude ASYM index return to background within 2 hours, while Dst decays exponentially with a time constant of about 8 hours. This paper describes a simple, real-time system utilizing these filters which could predict a substantial fraction of the variation in magnetic activity indices 20 to 50 minutes in advance

  16. On determining the prediction limits of mathematical models for time series

    International Nuclear Information System (INIS)

    Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET

    2016-01-01

    Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

  17. A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [Universite catholique de Louvain

    2017-07-25

    This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.

  18. Temporal predictive mechanisms modulate motor reaction time during initiation and inhibition of speech and hand movement.

    Science.gov (United States)

    Johari, Karim; Behroozmand, Roozbeh

    2017-08-01

    Skilled movement is mediated by motor commands executed with extremely fine temporal precision. The question of how the brain incorporates temporal information to perform motor actions has remained unanswered. This study investigated the effect of stimulus temporal predictability on response timing of speech and hand movement. Subjects performed a randomized vowel vocalization or button press task in two counterbalanced blocks in response to temporally-predictable and unpredictable visual cues. Results indicated that speech and hand reaction time was decreased for predictable compared with unpredictable stimuli. This finding suggests that a temporal predictive code is established to capture temporal dynamics of sensory cues in order to produce faster movements in responses to predictable stimuli. In addition, results revealed a main effect of modality, indicating faster hand movement compared with speech. We suggest that this effect is accounted for by the inherent complexity of speech production compared with hand movement. Lastly, we found that movement inhibition was faster than initiation for both hand and speech, suggesting that movement initiation requires a longer processing time to coordinate activities across multiple regions in the brain. These findings provide new insights into the mechanisms of temporal information processing during initiation and inhibition of speech and hand movement. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

    Science.gov (United States)

    Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

    2017-01-01

    In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

  20. [A survey of pubertal development in children born with assisted reproductive technology].

    Science.gov (United States)

    Liu, Zi-Yuan; Wang, Xin-Li; Han, Tong-Yan; Cui, Yun-Pu; Wang, Xue-Mei; Tong, Xiao-Mei; Song, Yi; Wang, Hai-Jun; Li, Song

    2017-06-01

    To investigate the status of pubertal development in children born with assisted reproductive technology (ART). A retrospective analysis was performed on the pubertal development data of children born with ART in Peking University Third Hospital from 1994 to 2003 (ART group). The data in the cross-sectional study "Reports on the Physical Fitness and Health Research of Chinese School Students in 2010" were used as a control. The age at menarche and the age at spermarche were compared between the two groups. The status of pubertal development in the overweight and obese children in the ART group was evaluated to investigate the correlation between pubertal development and body mass index (BMI). A total of 200 children born with ART were enrolled in this study, and 72 of them (41 males and 31 females) completed the survey (response rate=36.0%). In the ART group, the mean age at spermarche and the mean age at menarche were 13.9 years (95%CI: 13.7-14.3 years) and 12.2 years (95%CI: 11.8-12.6 years), respectively. There were no significant differences in the age at spermarche and the age at menarche between the ART and control groups (P>0.05). In the ART group, there were no significant differences in the age at spermarche and the age at menarche between the overweight and obese children and the normal weight children (P>0.05). There were also no significant differences in overweight rate and obesity rate between the children in the ART group and the adolescents in Beijing (P>0.05). In the ART group, there was no significant correlation between the age at spermarche or menarche and BMI (P>0.05). No delayed or precocious puberty is observed in children born with ART. This is consistent with the normal control data. And there is no significant correlation between pubertal development and BMI in children born with ART.

  1. Ecological prediction with nonlinear multivariate time-frequency functional data models

    Science.gov (United States)

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  2. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

    Science.gov (United States)

    Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

    2005-09-01

    Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

  3. Running speed during training and percent body fat predict race time in recreational male marathoners.

    Science.gov (United States)

    Barandun, Ursula; Knechtle, Beat; Knechtle, Patrizia; Klipstein, Andreas; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

    2012-01-01

    Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners. Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times. After multivariate regression, running speed of the training units (β = -0.52, P marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r (2) = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) - 12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics. The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners.

  4. The Role of Present Time Perspective in Predicting Early Adolescent Violence.

    Science.gov (United States)

    Kruger, Daniel J; Carrothers, Jessica; Franzen, Susan P; Miller, Alison L; Reischl, Thomas M; Stoddard, Sarah A; Zimmerman, Marc A

    2018-06-01

    This study investigated the role of present and future time perspectives, and their relationships with subjective norms and beliefs regarding violence, in predicting violent behaviors among urban middle school students in the Midwestern United States. Although present time perspective covaried with subjective norms and beliefs, each made a unique prediction of self-reported violent behaviors. Future time perspective was not a significant predictor when accounting for these relationships. In addition, present orientation moderated the relationship between subjective norms and beliefs and rates of violent behaviors; those with higher present orientations exhibited stronger associations. We replicated this pattern of results in data from new participants in a subsequent wave of the study. Interventions that explicitly address issues related to time perspective may be effective in reducing early adolescent violence.

  5. Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

    Science.gov (United States)

    Lee, Hanbong

    2017-01-01

    In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

  6. Similarity-Based Prediction of Travel Times for Vehicles Traveling on Known Routes

    DEFF Research Database (Denmark)

    Tiesyte, Dalia; Jensen, Christian Søndergaard

    2008-01-01

    , historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory......The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular...... of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable...

  7. Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg-Marquardt learning algorithm

    International Nuclear Information System (INIS)

    Mirzaee, Hossein

    2009-01-01

    The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Glass time series. First the MLP network is trained with 1000 data, and then it is tested with next 500 data. After that the trained and tested network is applied for long-term prediction of next 120 data which come after test data. The prediction is such a way that, the first inputs to network for prediction are the four last data of test data, then the predicted value is shifted to the regression vector which is the input to the network, then after first four-step of prediction, the input regression vector to network is fully predicted values and in continue, each predicted data is shifted to input vector for subsequent prediction.

  8. Prediction of half-marathon race time in recreational female and male runners

    OpenAIRE

    Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

    2014-01-01

    Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were ...

  9. Prediction problem for target events based on the inter-event waiting time

    Science.gov (United States)

    Shapoval, A.

    2010-11-01

    In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082-1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749-755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.

  10. Predicting cycle time distributions for integrated processing workstations : an aggregate modeling approach

    NARCIS (Netherlands)

    Veeger, C.P.L.; Etman, L.F.P.; Lefeber, A.A.J.; Adan, I.J.B.F.; Herk, van J.; Rooda, J.E.

    2011-01-01

    To predict cycle time distributions of integrated processing workstations, detailed simulation models are almost exclusively used; these models require considerable development and maintenance effort. As an alternative, we propose an aggregate model that is a lumped-parameter representation of the

  11. What Time Is Sunrise? Revisiting the Refraction Component of Sunrise/set Prediction Models

    Science.gov (United States)

    Wilson, Teresa; Bartlett, Jennifer L.; Hilton, James Lindsay

    2017-01-01

    Algorithms that predict sunrise and sunset times currently have an error of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, even including difficulties determining when the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols could significantly improve the standard prediction. We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We then compare these predictions with data sets of observed rise/set times to create a better model. Sunrise/set times and meteorological data from multiple locations will be necessary for a thorough investigation of the problem. While there are a few data sets available, we will also begin collecting this data using smartphones as part of a citizen science project. The mobile application for this project will be available in the Google Play store. Data analysis will lead to more complete models that will provide more accurate rise/set times for the benefit of astronomers, navigators, and outdoorsmen everywhere.

  12. Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

    Science.gov (United States)

    Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

    2014-05-01

    Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

  13. Quality changes and freezing time prediction during freezing and thawing of ginger

    OpenAIRE

    Singha, Poonam; Muthukumarappan, Kasiviswanathan

    2015-01-01

    Abstract Effects of different freezing rates and four different thawing methods on chemical composition, microstructure, and color of ginger were investigated. Computer simulation for predicting the freezing time of cylindrical ginger for two different freezing methods (slow and fast) was done using ANSYS ? Multiphysics. Different freezing rates (slow and fast) and thawing methods significantly (P?

  14. Real-time distributed economic model predictive control for complete vehicle energy management

    NARCIS (Netherlands)

    Romijn, Constantijn; Donkers, Tijs; Kessels, John; Weiland, Siep

    2017-01-01

    In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving

  15. Prediction of retention times in comprehensive two-dimensional gas chromatography using thermodynamic models.

    Science.gov (United States)

    McGinitie, Teague M; Harynuk, James J

    2012-09-14

    A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC(p) for an analyte's interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both (1)t(r) and (2)t(r) for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for (1)t(r) and an average error of only 2.22% for (2)t(r). The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

  16. Dynamic state estimation and prediction for real-time control and operation

    NARCIS (Netherlands)

    Nguyen, P.H.; Venayagamoorthy, G.K.; Kling, W.L.; Ribeiro, P.F.

    2013-01-01

    Real-time control and operation are crucial to deal with increasing complexity of modern power systems. To effectively enable those functions, it is required a Dynamic State Estimation (DSE) function to provide accurate network state variables at the right moment and predict their trends ahead. This

  17. Towards cycle-accurate performance predictions for real-time embedded systems

    NARCIS (Netherlands)

    Triantafyllidis, K.; Bondarev, E.; With, de P.H.N.; Arabnia, H.R.; Deligiannidis, L.; Jandieri, G.

    2013-01-01

    In this paper we present a model-based performance analysis method for component-based real-time systems, featuring cycle-accurate predictions of latencies and enhanced system robustness. The method incorporates the following phases: (a) instruction-level profiling of SW components, (b) modeling the

  18. Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling

    Science.gov (United States)

    Chai, Kevin E. K.; Gibson, David

    2015-01-01

    Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist…

  19. Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

    Science.gov (United States)

    Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

    2013-05-01

    Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

  20. Predicting Time to Recovery Among Depressed Adolescents Treated in Two Psychosocial Group Interventions

    Science.gov (United States)

    Rohde, Paul; Seeley, John R.; Kaufman, Noah K.; Clarke, Gregory N.; Stice, Eric

    2006-01-01

    Aims were to identify the demographic, psychopathology, and psychosocial factors predicting time to major depressive disorder (MDD) recovery and moderators of treatment among 114 depressed adolescents recruited from a juvenile justice center and randomized to a cognitive behavioral treatment (CBT) condition or a life skills-tutoring control…

  1. Concept of frequency separation in life prediction for time-dependent fatigue

    International Nuclear Information System (INIS)

    Coffin, L.F.

    1976-01-01

    Two methods are described to improve the predictive capability of the frequency-modified fatigue equations for time-dependent fatigue. These built-on the earlier approach and are applicable to severely unbalanced hysteresis loops. Comparisons made with various wave-shape investigations show favorable results. A new form of hysteresis loop is introduced utilizing frequency separation concepts. 4 tables, 11 fig

  2. Factors that Predict Full-Time Community College Faculty Engagement in Online Instruction

    Science.gov (United States)

    Akroyd, Duane; Patton, Bess; Bracken, Susan

    2013-01-01

    This study is a secondary quantitative analysis of the 2004 National Study of Postsecondary Faculty (NSOPF) data. It examines the ability of human capital, intrinsic rewards, extrinsic rewards, and gender/race demographics to predict full-time community college faculty teaching on-line courses. Findings indicate that those faculty with higher…

  3. Compensation for the distortion in satellite laser range predictions due to varying pulse travel times

    Science.gov (United States)

    Paunonen, Matti

    1993-01-01

    A method for compensating for the effect of the varying travel time of a transmitted laser pulse to a satellite is described. The 'observed minus predicted' range differences then appear to be linear, which makes data screening or use in range gating more effective.

  4. High serum ACE activity predicts severe hypoglycaemia over time in patients with type 1 diabetes

    DEFF Research Database (Denmark)

    Færch, Louise; Pedersen-Bjergaard, Ulrik; Thorsteinsson, Birger

    2011-01-01

    High serum angiotensin-converting enzyme (ACE) activity is associated with increased risk of severe hypoglycaemia (SH) within 1 year in type 1 diabetes. We wanted to find out whether ACE activity is stable over time and predicts SH beyond 1 year, and if gender differences exist in the association...

  5. Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements

    Science.gov (United States)

    Deeg, H. J.

    2015-06-01

    Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

  6. Development of a real-time prediction model of driver behavior at intersections using kinematic time series data.

    Science.gov (United States)

    Tan, Yaoyuan V; Elliott, Michael R; Flannagan, Carol A C

    2017-09-01

    As connected autonomous vehicles (CAVs) enter the fleet, there will be a long period when these vehicles will have to interact with human drivers. One of the challenges for CAVs is that human drivers do not communicate their decisions well. Fortunately, the kinematic behavior of a human-driven vehicle may be a good predictor of driver intent within a short time frame. We analyzed the kinematic time series data (e.g., speed) for a set of drivers making left turns at intersections to predict whether the driver would stop before executing the turn. We used principal components analysis (PCA) to generate independent dimensions that explain the variation in vehicle speed before a turn. These dimensions remained relatively consistent throughout the maneuver, allowing us to compute independent scores on these dimensions for different time windows throughout the approach to the intersection. We then linked these PCA scores to whether a driver would stop before executing a left turn using the random intercept Bayesian additive regression trees. Five more road and observable vehicle characteristics were included to enhance prediction. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 at 94m away from the center of an intersection and steadily increased to 0.90 by 46m away from the center of an intersection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Deleterious effects of obesity on physical fitness in pre-pubertal children.

    Science.gov (United States)

    Ceschia, Arianna; Giacomini, Stefano; Santarossa, Simone; Rugo, Miriam; Salvadego, Desy; Da Ponte, Alessandro; Driussi, Caterina; Mihaleje, Martina; Poser, Stefano; Lazzer, Stefano

    2016-01-01

    The prevalence of obesity in children has increased dramatically during the past decades in Europe and understanding physical fitness and its components in children is critical to design and implement effective interventions. The objective of the present study was to analyse the association between physical fitness (aerobic, speed, agility, power, flexibility and balance) and body mass index (BMI) in pre-pubertal children. A total of 2411 healthy schoolchildren (7-11 years) participated in this study. Anthropometric characteristics and body composition were assessed by skinfold thickness. Physical fitness was measured by nine physical fitness tests: endurance running, 20 m running speed, agility, handgrip strength, standing long jump and squat jump, sit and reach, medicine ball forward throw and static balance. No relevant differences were observed between boys and girls regarding anthropometric characteristics, body composition and physical fitness. However, overweight and obese children showed significantly lower physical fitness levels in endurance running, speed and agility (mean: +18.8, +5.5 and +14.5% of time to complete tasks, respectively), lower limb power normalised to body mass (-23.3%) and balance tests (number of falls: +165.5%) than their normal weight counterparts. On the other hand, obesity did not affect handgrip, throwing and flexibility. In conclusion, increased BMI was associated with lower performance capabilities limiting proper motor skill development, which directly affects the ability of children to take on sports skills. Actions undertaken to promote children's wellness and fitness should be prioritised and introduced early in life with the aim of enhancing physical fitness as well as preventing overweight and obesity.

  8. Prediction of oral disintegration time of fast disintegrating tablets using texture analyzer and computational optimization.

    Science.gov (United States)

    Szakonyi, G; Zelkó, R

    2013-05-20

    One of the promising approaches to predict in vivo disintegration time of orally disintegrating tablets (ODT) is the use of texture analyzer instrument. Once the method is able to provide good in vitro in vivo correlation (IVIVC) in the case of different tablets, it might be able to predict the oral disintegration time of similar products. However, there are many tablet parameters that influence the in vivo and the in vitro disintegration time of ODT products. Therefore, the measured in vitro and in vivo disintegration times can occasionally differ, even if they coincide in most cases of the investigated products and the in vivo disintegration times may also change if the aimed patient group is suffering from a special illness. If the method is no longer able to provide good IVIVC, then the modification of a single instrumental parameter may not be successful and the in vitro method must be re-set in a complex manner in order to provide satisfactory results. In the present experiment, an optimization process was developed based on texture analysis measurements using five different tablets in order to predict their in vivo disintegration times, and the optimized texture analysis method was evaluated using independent tablets. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Stress hormones predict hyperbolic time-discount rates six months later in adults.

    Science.gov (United States)

    Takahashi, Taiki; Shinada, Mizuho; Inukai, Keigo; Tanida, Shigehito; Takahashi, Chisato; Mifune, Nobuhiro; Takagishi, Haruto; Horita, Yutaka; Hashimoto, Hirofumi; Yokota, Kunihiro; Kameda, Tatsuya; Yamagishi, Toshio

    2010-01-01

    Stress hormones have been associated with temporal discounting. Although time-discount rate is shown to be stable over a long term, no study to date examines whether individual differences in stress hormones could predict individuals' time-discount rates in the relatively distant future (e.g., six month later), which is of interest in neuroeconomics of stress-addiction association. We assessed 87 participants' salivary stress hormone (cortisol, cortisone, and alpha-amylase) levels and hyperbolic discounting of delayed rewards consisting of three magnitudes, at the time-interval of six months. For salivary steroid assays, we employed a liquid chromatography/ mass spectroscopy (LC/MS) method. The correlations between the stress hormone levels and time-discount rates were examined. We observed that salivary alpha-amylase (sAA) levels were negatively associated with time-discount rates in never-smokers. Notably, salivary levels of stress steroids (i.e., cortisol and cortisone) negatively and positively related to time-discount rates in men and women, respectively, in never-smokers. Ever-smokers' discount rates were not predicted from these stress hormone levels. Individual differences in stress hormone levels predict impulsivity in temporal discounting in the future. There are sex differences in the effect of stress steroids on temporal discounting; while there was no sex defference in the relationship between sAA and temporal discounting.

  10. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients

    Directory of Open Access Journals (Sweden)

    Bruno Prata Martinez

    2015-05-01

    Full Text Available OBJECTIVES: The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. METHODS: This cross-sectional study analyzed 68 elderly patients (≥60 years of age in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test. After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. RESULTS: In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7% of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%, pneumonia (19.1% and abdominal disorders (10.2%. The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002. CONCLUSION: The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients.

  11. Adolescents' technology and face-to-face time use predict objective sleep outcomes.

    Science.gov (United States)

    Tavernier, Royette; Heissel, Jennifer A; Sladek, Michael R; Grant, Kathryn E; Adam, Emma K

    2017-08-01

    The present study examined both within- and between-person associations between adolescents' time use (technology-based activities and face-to-face interactions with friends and family) and sleep behaviors. We also assessed whether age moderated associations between adolescents' time use with friends and family and sleep. Adolescents wore an actigraph monitor and completed brief evening surveys daily for 3 consecutive days. Adolescents (N=71; mean age=14.50 years old, SD=1.84; 43.7% female) were recruited from 3 public high schools in the Midwest. We assessed 8 technology-based activities (eg, texting, working on a computer), as well as time spent engaged in face-to-face interactions with friends and family, via questions on adolescents' evening surveys. Actigraph monitors assessed 3 sleep behaviors: sleep latency, sleep hours, and sleep efficiency. Hierarchical linear models indicated that texting and working on the computer were associated with shorter sleep, whereas time spent talking on the phone predicted longer sleep. Time spent with friends predicted shorter sleep latencies, while family time predicted longer sleep latencies. Age moderated the association between time spent with friends and sleep efficiency, as well as between family time and sleep efficiency. Specifically, longer time spent interacting with friends was associated with higher sleep efficiency but only among younger adolescents. Furthermore, longer family time was associated with higher sleep efficiency but only for older adolescents. Findings are discussed in terms of the importance of regulating adolescents' technology use and improving opportunities for face-to-face interactions with friends, particularly for younger adolescents. Copyright © 2017 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

  12. Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

    Directory of Open Access Journals (Sweden)

    Zhihua Wang

    2014-01-01

    Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.

  13. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Narayanan Manikandan

    2016-01-01

    Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  14. Multiplier method may be unreliable to predict the timing of temporary hemiepiphysiodesis for coronal angular deformity.

    Science.gov (United States)

    Wu, Zhenkai; Ding, Jing; Zhao, Dahang; Zhao, Li; Li, Hai; Liu, Jianlin

    2017-07-10

    The multiplier method was introduced by Paley to calculate the timing for temporary hemiepiphysiodesis. However, this method has not been verified in terms of clinical outcome measure. We aimed to (1) predict the rate of angular correction per year (ACPY) at the various corresponding ages by means of multiplier method and verify the reliability based on the data from the published studies and (2) screen out risk factors for deviation of prediction. A comprehensive search was performed in the following electronic databases: Cochrane, PubMed, and EMBASE™. A total of 22 studies met the inclusion criteria. If the actual value of ACPY from the collected date was located out of the range of the predicted value based on the multiplier method, it was considered as the deviation of prediction (DOP). The associations of patient characteristics with DOP were assessed with the use of univariate logistic regression. Only one article was evaluated as moderate evidence; the remaining articles were evaluated as poor quality. The rate of DOP was 31.82%. In the detailed individual data of included studies, the rate of DOP was 55.44%. The multiplier method is not reliable in predicting the timing for temporary hemiepiphysiodesis, even though it is prone to be more reliable for the younger patients with idiopathic genu coronal deformity.

  15. Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

    Science.gov (United States)

    Shahdoust, Maryam; Sadeghifar, Majid; Poorolajal, Jalal; Javanrooh, Niloofar; Amini, Payam

    2015-01-01

    Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.

  16. Real-Time Safety Monitoring and Prediction for the National Airspace System

    Science.gov (United States)

    Roychoudhury, Indranil

    2016-01-01

    As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have both an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecasts, predicted health of assets in the airspace, and so on. To this end, we have developed a Real-Time Safety Monitoring (RTSM) that first, estimates the state of the NAS using the dynamic models. Then, given the state estimate and a probability distribution of future inputs to the NAS, the framework predicts the evolution of the NAS, i.e., the future state, and analyzes these future states to predict the occurrence of unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and or parameters. We demonstrate our overall approach by predicting the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress.

  17. Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

    Directory of Open Access Journals (Sweden)

    Tomasz Dzida

    2017-09-01

    Full Text Available We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα, RNA polymerase (Pol II and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

  18. Controlling cyclic combustion timing variations using a symbol-statistics predictive approach in an HCCI engine

    International Nuclear Information System (INIS)

    Ghazimirsaied, Ahmad; Koch, Charles Robert

    2012-01-01

    Highlights: ► Misfire reduction in a combustion engine based on chaotic theory methods. ► Chaotic theory analysis of cyclic variation of a HCCI engine near misfire. ► Symbol sequence approach is used to predict ignition timing one cycle-ahead. ► Prediction is combined with feedback control to lower HCCI combustion variation. ► Feedback control extends the HCCI operating range into the misfire region. -- Abstract: Cyclic variation of a Homogeneous Charge Compression Ignition (HCCI) engine near misfire is analyzed using chaotic theory methods and feedback control is used to stabilize high cyclic variations. Variation of consecutive cycles of θ Pmax (the crank angle of maximum cylinder pressure over an engine cycle) for a Primary Reference Fuel engine is analyzed near misfire operation for five test points with similar conditions but different octane numbers. The return map of the time series of θ Pmax at each combustion cycle reveals the deterministic and random portions of the dynamics near misfire for this HCCI engine. A symbol-statistic approach is used to predict θ Pmax one cycle-ahead. Predicted θ Pmax has similar dynamical behavior to the experimental measurements. Based on this cycle ahead prediction, and using fuel octane as the input, feedback control is used to stabilize the instability of θ Pmax variations at this engine condition near misfire.

  19. Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

    Science.gov (United States)

    Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

    2009-11-03

    Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

  20. Working Memory and Auditory Imagery Predict Sensorimotor Synchronization with Expressively Timed Music.

    Science.gov (United States)

    Colley, Ian D; Keller, Peter E; Halpern, Andrea R

    2017-08-11

    Sensorimotor synchronization (SMS) is prevalent and readily studied in musical settings, as most people are able to perceive and synchronize with a beat (e.g. by finger tapping). We took an individual differences approach to understanding SMS to real music characterized by expressive timing (i.e. fluctuating beat regularity). Given the dynamic nature of SMS, we hypothesized that individual differences in working memory and auditory imagery-both fluid cognitive processes-would predict SMS at two levels: 1) mean absolute asynchrony (a measure of synchronization error), and 2) anticipatory timing (i.e. predicting, rather than reacting to beat intervals). In Experiment 1, participants completed two working memory tasks, four auditory imagery tasks, and an SMS-tapping task. Hierarchical regression models were used to predict SMS performance, with results showing dissociations among imagery types in relation to mean absolute asynchrony, and evidence of a role for working memory in anticipatory timing. In Experiment 2, a new sample of participants completed an expressive timing perception task to examine the role of imagery in perception without action. Results suggest that imagery vividness is important for perceiving and control is important for synchronizing with, irregular but ecologically valid musical time series. Working memory is implicated in synchronizing by anticipating events in the series.

  1. Space can substitute for time in predicting climate-change effects on biodiversity.

    Science.gov (United States)

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-04

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  2. Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

    Science.gov (United States)

    Wu, Cai; Li, Liang

    2018-05-15

    This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

  3. Data-adaptive Harmonic Decomposition and Real-time Prediction of Arctic Sea Ice Extent

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

    2017-04-01

    Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e. from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN, http://www.arcus.org/sipn) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models. Data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017], have been successfully applied to the nonlinear stochastic modeling, as well as retrospective and real-time forecasting of Multisensor Analyzed Sea Ice Extent (MASIE) dataset in key four Arctic regions. In particular, DAH-MSLM predictions outperformed most statistical models and physics-based models in real-time 2016 SIO submissions. The key success factors are associated with DAH ability to disentangle complex regional dynamics of MASIE by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental MSLMs stacked per frequency with fixed and small number of model coefficients to estimate.

  4. FREEZING AND THAWING TIME PREDICTION METHODS OF FOODS II: NUMARICAL METHODS

    Directory of Open Access Journals (Sweden)

    Yahya TÜLEK

    1999-03-01

    Full Text Available Freezing is one of the excellent methods for the preservation of foods. If freezing and thawing processes and frozen storage method are carried out correctly, the original characteristics of the foods can remain almost unchanged over an extended periods of time. It is very important to determine the freezing and thawing time period of the foods, as they strongly influence the both quality of food material and process productivity and the economy. For developing a simple and effectively usable mathematical model, less amount of process parameters and physical properties should be enrolled in calculations. But it is a difficult to have all of these in one prediction method. For this reason, various freezing and thawing time prediction methods were proposed in literature and research studies have been going on.

  5. Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network

    International Nuclear Information System (INIS)

    Ma Qianli; Zheng Qilun; Peng Hong; Qin Jiangwei; Zhong Tanwei

    2008-01-01

    This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series

  6. Geostatistical Spatio-Time model of crime in el Salvador: Structural and Predictive Analysis

    Directory of Open Access Journals (Sweden)

    Welman Rosa Alvarado

    2011-07-01

    Full Text Available Today, to study a geospatial and spatio-temporal phenomena requires searching statistical tools that enable the analysis of the dependency of space, time and interactions. The science that studies this kind of subjects is the Geoestatics which the goal is to predict spatial phenomenon. This science is considered the base for modeling phenomena that involves interactions between space and time. In the past 10 years, the Geostatistic had seen a great development in areas like the geology, soils, remote sensing, epidemiology, agriculture, ecology, economy, etc. In this research, the geostatistic had been apply to build a predictive map about crime in El Salvador; for that the variability of space and time together is studied to generate crime scenarios: crime hot spots are determined, crime vulnerable groups are identified, to improve political decisions and facilitate to decision makers about the insecurity in the country.

  7. The string prediction models as an invariants of time series in forex market

    OpenAIRE

    Richard Pincak; Marian Repasan

    2011-01-01

    In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is ...

  8. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

  9. Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling.

    Science.gov (United States)

    Kwasniok, Frank

    2013-11-01

    A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.

  10. New Results on Robust Model Predictive Control for Time-Delay Systems with Input Constraints

    Directory of Open Access Journals (Sweden)

    Qing Lu

    2014-01-01

    Full Text Available This paper investigates the problem of model predictive control for a class of nonlinear systems subject to state delays and input constraints. The time-varying delay is considered with both upper and lower bounds. A new model is proposed to approximate the delay. And the uncertainty is polytopic type. For the state-feedback MPC design objective, we formulate an optimization problem. Under model transformation, a new model predictive controller is designed such that the robust asymptotical stability of the closed-loop system can be guaranteed. Finally, the applicability of the presented results are demonstrated by a practical example.

  11. Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency.

    Science.gov (United States)

    Hisler, Garrett C; Phillips, Alison L; Krizan, Zlatan

    2017-06-01

    Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference's influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency. The current study examined whether individual differences in diurnal preference ("morningness-eveningness") predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals' typical time-of-exercise moderated the impact of diurnal preference on exercise frequency. One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise. Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference's relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one's diurnal preference. Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

  12. Predicting Ambulance Time of Arrival to the Emergency Department Using Global Positioning System and Google Maps

    Science.gov (United States)

    Fleischman, Ross J.; Lundquist, Mark; Jui, Jonathan; Newgard, Craig D.; Warden, Craig

    2014-01-01

    Objective To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application. Methods This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations. Results There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports. Conclusions An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of arrival to the emergency department with good accuracy. This model could be linked to global positioning system data and an automated Google Maps web

  13. Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

    DEFF Research Database (Denmark)

    Ozer, Mert; Keles, Ilkcan; Toroslu, Hakki

    2016-01-01

    In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal...... and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity...... the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets....

  14. Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design.

    Science.gov (United States)

    Spreco, Armin; Eriksson, Olle; Dahlström, Örjan; Cowling, Benjamin John; Timpka, Toomas

    2017-06-15

    Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning

  15. Pubertal stage and the prevalence of violence and social relational aggression

    Science.gov (United States)

    Hemphill, Sheryl A.; Kotevski, Aneta; Herrenkohl, Todd I.; Toumbourou, John W.; Carlin, John B.; Catalano, Richard F.; Patton, George C.

    2010-01-01

    Objective Violence and social relational aggression are global problems that become prominent in early adolescence. This study examines associations between pubertal stage and adolescent violent behavior and social relational aggression. Methods This paper draws on cross-sectional data from the International Youth Development Study (IYDS), which comprised two state-wide representative samples of students in grades 5, 7 and 9 (N = 5,769) in Washington State in the United States and Victoria, Australia, drawn as a 2-stage cluster sample in each state. The study used carefully matched methods to conduct a school-administered, self-report student survey measuring behavioral outcomes including past year violent behavior (measured as attacking or beating up another person) and social relational aggression (excluding peers from the group, threatening to spread lies or rumors), as well as a comprehensive range of risk and protective factors and pubertal development. Results Compared with early puberty, the odds of violent behavior were approximately three-fold higher in mid-puberty (odds ratio [OR]: 2.87; 95% confidence interval [CI]: 1.81,4.55) and late puberty (OR: 3.79; 95% CI: 2.25,6.39), after adjustment for age, gender, state, and state by gender interaction. For social relational aggression, there were weaker overall associations after adjustment but these included an interaction between pubertal stage and age, showing stronger associations with pubertal stage at younger age (p = .003; mid-puberty OR 1.78; 95% CI 1.20,2.63; late puberty OR 3.00; 95% CI 1.95,4.63. Associations between pubertal stage and violent behavior and social relational aggression remained (although the magnitude of effects was reduced), after the inclusion of social contextual mediators in the analyses. Conclusions Pubertal stage was associated with higher rates of violent behavior and social relational aggression, with the latter association seen only at younger ages. Puberty may be an

  16. Clinical Prediction Model for Time in Therapeutic Range While on Warfarin in Newly Diagnosed Atrial Fibrillation.

    Science.gov (United States)

    Williams, Brent A; Evans, Michael A; Honushefsky, Ashley M; Berger, Peter B

    2017-10-12

    Though warfarin has historically been the primary oral anticoagulant for stroke prevention in newly diagnosed atrial fibrillation (AF), several new direct oral anticoagulants may be preferred when anticoagulation control with warfarin is expected to be poor. This study developed a prediction model for time in therapeutic range (TTR) among newly diagnosed AF patients on newly initiated warfarin as a tool to assist decision making between warfarin and direct oral anticoagulants. This electronic medical record-based, retrospective study included newly diagnosed, nonvalvular AF patients with no recent warfarin exposure receiving primary care services through a large healthcare system in rural Pennsylvania. TTR was estimated as the percentage of time international normalized ratio measurements were between 2.0 and 3.0 during the first year following warfarin initiation. Candidate predictors of TTR were chosen from data elements collected during usual clinical care. A TTR prediction model was developed and temporally validated and its predictive performance was compared with the SAMe-TT 2 R 2 score (sex, age, medical history, treatment, tobacco, race) using R 2 and c-statistics. A total of 7877 newly diagnosed AF patients met study inclusion criteria. Median (interquartile range) TTR within the first year of starting warfarin was 51% (32, 67). Of 85 candidate predictors evaluated, 15 were included in the final validated model with an R 2 of 15.4%. The proposed model showed better predictive performance than the SAMe-TT 2 R 2 score ( R 2 =3.0%). The proposed prediction model may assist decision making on the proper mode of oral anticoagulant among newly diagnosed AF patients. However, predicting TTR on warfarin remains challenging. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

  17. Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

    Directory of Open Access Journals (Sweden)

    Jin Woo Moon

    2016-12-01

    Full Text Available The aim of this study was to develop an artificial neural network (ANN prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied to a target setpoint temperature (when a building was occupied. The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1 model development; (2 model optimization; and (3 performance evaluation. Two software programs—Matrix Laboratory (MATLAB and Transient Systems Simulation (TRNSYS—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature, presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9, hidden layers (3, moment (0.9, and learning rate (0.9. At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.

  18. Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

    Directory of Open Access Journals (Sweden)

    Guan Lian

    2018-01-01

    Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

  19. Big data prediction of durations for online collective actions based on peak's timing

    Science.gov (United States)

    Nie, Shizhao; Wang, Zheng; Pujia, Wangmo; Nie, Yuan; Lu, Peng

    2018-02-01

    Peak Model states that each collective action has a life circle, which contains four periods of "prepare", "outbreak", "peak", and "vanish"; and the peak determines the max energy and the whole process. The peak model's re-simulation indicates that there seems to be a stable ratio between the peak's timing (TP) and the total span (T) or duration of collective actions, which needs further validations through empirical data of collective actions. Therefore, the daily big data of online collective actions is applied to validate the model; and the key is to check the ratio between peak's timing and the total span. The big data is obtained from online data recording & mining of websites. It is verified by the empirical big data that there is a stable ratio between TP and T; furthermore, it seems to be normally distributed. This rule holds for both the general cases and the sub-types of collective actions. Given the distribution of the ratio, estimated probability density function can be obtained, and therefore the span can be predicted via the peak's timing. Under the scenario of big data, the instant span (how long the collective action lasts or when it ends) will be monitored and predicted in real-time. With denser data (Big Data), the estimation of the ratio's distribution gets more robust, and the prediction of collective actions' spans or durations will be more accurate.

  20. Real Time Radioactivity Monitoring and its Interface with predictive atmospheric transport modelling

    International Nuclear Information System (INIS)

    Raes, F.

    1990-01-01

    After the Chernobyl accident, a programme was initiated at the Joint Research Centre of the Commission of the European Communities named 'Radioactivity Environmental Monitoring' (REM). The main aspects considered in REM are: data handling, atmospheric modelling and data quality control related to radioactivity in the environment. The first REM workshop was held in December 1987: 'Aerosol Measurements and Nuclear Accidents: A Reconsideration'. (CEC EUR 11755 EN). These are the proceedings of the second REM workshop, held in December 1989, dealing with real-time radioactivity monitoring and its interface with predictive atmospheric models. Atmospheric transport models, in applications extending over time scales of the order of a day or more become progressively less reliable to the extent that an interface with real-time radiological field data becomes highly desirable. Through international arrangements for early exchange of information in the event of a nuclear accident (European Community, IAEA) such data might become available on a quasi real-time basis. The question is how best to use such information to improve our predictive capabilities. During the workshop the present status of on-line monitoring networks for airborne radioactivity in the EC Member States has been reviewed. Possibilities were discussed to use data from such networks as soon as they become available, in order to update predictions made with long range transport models. This publication gives the full text of 13 presentations and a report of the Round Table Discussion held afterwards

  1. Prediction of drop time and impact velocity of rod cluster control assembly

    International Nuclear Information System (INIS)

    Choi, Kee Sung; Yim, Jeong Sik; Kim, Il Kon; Kim, Kyu Tae

    1992-01-01

    This paper deals with the drop modelling of rod cluster control assembly(RCCA) and the prediction of drop time and impact velocity of RCCA at scram event. On the scram, RCCA, dropping into the guide thimble of fuel assembly by the gravity, is subject to retarding forces such as hydraulic resistance, mechanical friction and buoyancy. Considering these retarding forces RCCA dynamic equation is derived and computerized it to solve the equation in conjunction with fluid equation which is coupled with the motion of the RCCA. Because the equation is nonlinear, coupled with fluid equations, the program is written in FORTRAN using numerical method in order to calculate the drop distance and velocity with time increment. To verify the program, its results are compared with those of other fuel vendors. Predicting identical tendency as other fuel vendors and the deviation is insignificant in values this program is expected to be used for predicting the drop time and impact velocity of RCCA when the parameters affecting the control rod drop time and impact velocity changes are occurred

  2. Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

    Science.gov (United States)

    Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

    2018-01-01

    When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley

  3. Sex steroids and brain structure in pubertal boys and girls: a mini-review of neuroimaging studies

    NARCIS (Netherlands)

    Peper, J.S.; Hulshoff Pol, H.E.; Crone, E.A.; van Honk, J.

    2011-01-01

    Puberty is an important period during development hallmarked by increases in sex steroid levels. Human neuroimaging studies have consistently reported that in typically developing pubertal children, cortical and subcortical gray matter is decreasing, whereas white matter increases well into

  4. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Xiaoping Yang

    2016-01-01

    Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

  5. Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Gergely Takács

    2014-01-01

    Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

  6. Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

    Directory of Open Access Journals (Sweden)

    J. Schmidt

    2008-04-01

    Full Text Available A project established at the National Institute of Water and Atmospheric Research (NIWA in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO Numerical Weather Prediction model (NWP are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

  7. What Time is Your Sunset? Accounting for Refraction in Sunrise/set Prediction Models

    Science.gov (United States)

    Wilson, Teresa; Bartlett, Jennifer Lynn; Chizek Frouard, Malynda; Hilton, James; Phlips, Alan; Edgar, Roman

    2018-01-01

    Algorithms that predict sunrise and sunset times currently have an uncertainty of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, including difficulties determining whether the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols could significantly improve the standard prediction.We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We, then, compared these predictions with data sets of observed rise/set times taken from Mount Wilson Observatory in California, University of Alberta in Edmonton, Alberta, and onboard the SS James Franco in the Atlantic. A thorough investigation of the problem requires a more substantial data set of observed rise/set times and corresponding meteorological data from around the world.We have developed a mobile application, Sunrise & Sunset Observer, so that anyone can capture this astronomical and meteorological data using their smartphone video recorder as part of a citizen science project. The Android app for this project is available in the Google Play store. Videos can also be submitted through the project website (riseset.phy.mtu.edu). Data analysis will lead to more complete models that will provide higher accuracy rise/set predictions to benefit astronomers, navigators, and outdoorsmen everywhere.

  8. Reservoir computer predictions for the Three Meter magnetic field time evolution

    Science.gov (United States)

    Perevalov, A.; Rojas, R.; Lathrop, D. P.; Shani, I.; Hunt, B. R.

    2017-12-01

    The source of the Earth's magnetic field is the turbulent flow of liquid metal in the outer core. Our experiment's goal is to create Earth-like dynamo, to explore the mechanisms and to understand the dynamics of the magnetic and velocity fields. Since it is a complicated system, predictions of the magnetic field is a challenging problem. We present results of mimicking the three Meter experiment by a reservoir computer deep learning algorithm. The experiment is a three-meter diameter outer sphere and a one-meter diameter inner sphere with the gap filled with liquid sodium. The spheres can rotate up to 4 and 14 Hz respectively, giving a Reynolds number near to 108. Two external electromagnets apply magnetic fields, while an array of 31 external and 2 internal Hall sensors measure the resulting induced fields. We use this magnetic probe data to train a reservoir computer to predict the 3M time evolution and mimic waves in the experiment. Surprisingly accurate predictions can be made for several magnetic dipole time scales. This shows that such a complicated MHD system's behavior can be predicted. We gratefully acknowledge support from NSF EAR-1417148.

  9. Real-time 3-D space numerical shake prediction for earthquake early warning

    Science.gov (United States)

    Wang, Tianyun; Jin, Xing; Huang, Yandan; Wei, Yongxiang

    2017-12-01

    In earthquake early warning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of source parameters. For computation efficiency, wave direction is assumed to propagate on the 2-D surface of the earth in these methods. In fact, since the seismic wave propagates in the 3-D sphere of the earth, the 2-D space modeling of wave direction results in inaccurate wave estimation. In this paper, we propose a 3-D space numerical shake prediction method, which simulates the wave propagation in 3-D space using radiative transfer theory, and incorporate data assimilation technique to estimate the distribution of wave energy. 2011 Tohoku earthquake is studied as an example to show the validity of the proposed model. 2-D space model and 3-D space model are compared in this article, and the prediction results show that numerical shake prediction based on 3-D space model can estimate the real-time ground motion precisely, and overprediction is alleviated when using 3-D space model.

  10. NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

    Science.gov (United States)

    Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

    2017-12-01

    NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

  11. Predicting pornography use over time: Does self-reported "addiction" matter?

    Science.gov (United States)

    Grubbs, Joshua B; Wilt, Joshua A; Exline, Julie J; Pargament, Kenneth I

    2018-07-01

    In recent years, several works have reported on perceived addiction to internet pornography, or the potential for some individuals to label their own use of pornography as compulsive or out of control. Such works have consistently found that perceived addiction is related to concerning outcomes such as psychological distress, relational distress, and other addictive behaviors. However, very little work has specifically examined whether or not perceived addiction is actually related to increased use of pornography, cross-sectionally or over time. The present work sought to address this deficit in the literature. Using two longitudinal samples (Sample 1, Baseline N = 3988; Sample 2, Baseline N = 1047), a variety of factors (e.g., male gender, lower religiousness, and lower self-control) were found to predict any use of pornography. Among those that acknowledged use (Sample 1, Baseline N = 1352; Sample 2, Baseline N = 793), perceived addiction to pornography consistently predicted greater average daily use of pornography. At subsequent longitudinal follow-ups (Sample 1, Baseline N = 265; Sample 2, One Month Later, N = 410, One Year Later, N = 360), only male gender and baseline average pornography use consistently predicted future use. These findings suggest that perceived addiction to pornography is associated with concurrent use of pornography, but does not appear to predict use over time, suggesting that perceived addiction may not always be an accurate indicator of behavior or addiction. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. How Predictable Is the Operative Time of Laparoscopic Surgery for Ovarian Endometrioma?

    Directory of Open Access Journals (Sweden)

    Pietro Gambadauro

    2015-01-01

    Full Text Available Endometriosis is a tricky albeit common disease whose management largely relies on laparoscopy. We have studied the operative times of laparoscopic endometrioma surgery in order to assess their predictability and possible predictors. One hundred forty-eight laparoscopies were included, with a median operative time of 70 minutes (mean 75.14; 95% CI: 70.03–80.24. Half of the cases had a duration within 15–20 minutes above or below the median (IQR: 55–93.75, but the whole dataset ranged from 20 to 180 minutes, and the standard deviation was relatively large (31.4. Surgical times were significantly related to technical (number and size of the cysts and nontechnical factors (age, parity, dysmenorrhea, and family history. At multiple logistic regression, after adjusting for number and size of the cysts, surgical times below the first quartile were associated with older age (>30 years old: aOR: 3.590; 95% CI: 1.417–9.091 and parity (≥1 delivery: aOR: 3.409; 95% CI: 1.343–8.651. Longer times, above the third quartile, were instead predicted by a familial anamnesis of endometriosis (aOR: 3.639; 95% CI: 1.246–10.627. Our findings indicate highly variable surgical times, which are predicted by unexpected nontechnical factors. This is consistent with the complexity of endometriosis and its treatment. Productivity and efficiency in endometriosis surgery should focus on the quality of healthcare outcomes rather than on the time spent in the operating theatres.

  13. Predicting Time Spent in Treatment in a Sample of Danish Survivors of Child Sexual Abuse.

    Science.gov (United States)

    Fletcher, Shelley; Elklit, Ask; Shevlin, Mark; Armour, Cherie

    2017-07-01

    The aim of this study was to identify significant predictors of length of time spent in treatment. In a convenience sample of 439 Danish survivors of child sexual abuse, predictors of time spent in treatment were examined. Assessments were conducted on a 6-month basis over a period of 18 months. A multinomial logistic regression analysis revealed that the experience of neglect in childhood and having experienced rape at any life stage were associated with less time in treatment. Higher educational attainment and being male were associated with staying in treatment for longer periods of time. These factors may be important for identifying those at risk of terminating treatment prematurely. It is hoped that a better understanding of the factors that predict time spent in treatment will help to improve treatment outcomes for individuals who are at risk of dropping out of treatment at an early stage.

  14. Space can substitute for time in predicting climate-change effects on biodiversity

    Science.gov (United States)

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  15. Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

    International Nuclear Information System (INIS)

    Ye Meiying; Wang Xiaodong

    2005-01-01

    A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of chaotic time series. The effectiveness of the method is demonstrated by applying it to the Henon map. This study also compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.

  16. Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models

    Science.gov (United States)

    Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.

    2011-01-01

    Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.

  17. Application of artificial neural network to predict the optimal start time for heating system in building

    International Nuclear Information System (INIS)

    Yang, In-Ho; Yeo, Myoung-Souk; Kim, Kwang-Woo

    2003-01-01

    The artificial neural network (ANN) approach is a generic technique for mapping non-linear relationships between inputs and outputs without knowing the details of these relationships. This paper presents an application of the ANN in a building control system. The objective of this study is to develop an optimized ANN model to determine the optimal start time for a heating system in a building. For this, programs for predicting the room air temperature and the learning of the ANN model based on back propagation learning were developed, and learning data for various building conditions were collected through program simulation for predicting the room air temperature using systems of experimental design. Then, the optimized ANN model was presented through learning of the ANN, and its performance to determine the optimal start time was evaluated

  18. Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

    Science.gov (United States)

    Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

    2015-07-01

    Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

  19. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

  20. Time-Series Prediction: Application to the Short-Term Electric Energy Demand

    OpenAIRE

    Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis

    2003-01-01

    This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and ...

  1. Thermodynamic-based retention time predictions of endogenous steroids in comprehensive two-dimensional gas chromatography.

    Science.gov (United States)

    Silva, Aline C A; Ebrahimi-Najafadabi, Heshmatollah; McGinitie, Teague M; Casilli, Alessandro; Pereira, Henrique M G; Aquino Neto, Francisco R; Harynuk, James J

    2015-05-01

    This work evaluates the application of a thermodynamic model to comprehensive two-dimensional gas chromatography (GC × GC) coupled with time-of-flight mass spectrometry for anabolic agent investigation. Doping control deals with hundreds of drugs that are prohibited in sports. Drug discovery in biological matrices is a challenging task that requires powerful tools when one is faced with the rapidly changing designer drug landscape. In this work, a thermodynamic model developed for the prediction of both primary and secondary retention times in GC × GC has been applied to trimethylsilylated hydroxyl (O-TMS)- and methoxime-trimethylsilylated carbonyl (MO-TMS)-derivatized endogenous steroids. This model was previously demonstrated on a pneumatically modulated GC × GC system, and is applied for the first time to a thermally modulated GC × GC system. Preliminary one-dimensional experiments allowed the calculation of thermodynamic parameters (ΔH, ΔS, and ΔC p ) which were successfully applied for the prediction of the analytes' interactions with the stationary phases of both the first-dimension column and the second-dimension column. The model was able to predict both first-dimension and second-dimension retention times with high accuracy compared with the GC × GC experimental measurements. Maximum differences of -8.22 s in the first dimension and 0.4 s in the second dimension were encountered for the O-TMS derivatives of 11β-hydroxyandrosterone and 11-ketoetiocholanolone, respectively. For the MO-TMS derivatives, the largest discrepancies were from testosterone (9.65 ) for the first-dimension retention times and 11-keto-etiocholanolone (0.4 s) for the second-dimension retention times.

  2. Prediction of leisure-time walking: an integration of social cognitive, perceived environmental, and personality factors

    Directory of Open Access Journals (Sweden)

    Blanchard Chris M

    2007-10-01

    Full Text Available Abstract Background Walking is the primary focus of population-based physical activity initiatives but a theoretical understanding of this behaviour is still elusive. The purpose of this study was to integrate personality, the perceived environment, and planning into a theory of planned behaviour (TPB framework to predict leisure-time walking. Methods Participants were a random sample (N = 358 of Canadian adults who completed measures of the TPB, planning, perceived neighbourhood environment, and personality at Time 1 and self-reported walking behaviour two months later. Results Analyses using structural equation modelling provided evidence that leisure-time walking is largely predicted by intention (standardized effect = .42 with an additional independent contribution from proximity to neighbourhood retail shops (standardized effect = .18. Intention, in turn, was predicted by attitudes toward walking and perceived behavioural control. Effects of perceived neighbourhood aesthetics and walking infrastructure on walking were mediated through attitudes and intention. Moderated regression analysis showed that the intention-walking relationship was moderated by conscientiousness and proximity to neighbourhood recreation facilities but not planning. Conclusion Overall, walking behaviour is theoretically complex but may best be addressed at a population level by facilitating strong intentions in a receptive environment even though individual differences may persist.

  3. Thermal time constant: optimising the skin temperature predictive modelling in lower limb prostheses using Gaussian processes.

    Science.gov (United States)

    Mathur, Neha; Glesk, Ivan; Buis, Arjan

    2016-06-01

    Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used which requires consistent positioning of sensors during donning and doffing. Predicting the residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. To predict the residual limb temperature, a machine learning algorithm - Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This Letter highlights the relevance of thermal time constant of prosthetic materials in Gaussian processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant, the model can be optimised and generalised for a given prosthetic setup, thereby making the predictions more reliable.

  4. Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.

    Science.gov (United States)

    Hake, Anna; Pfeifer, Nico

    2017-10-01

    Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient's viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.

  5. A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

    Science.gov (United States)

    Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

    2016-09-01

    This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

  6. Prospective versus predictive control in timing of hitting a falling ball.

    Science.gov (United States)

    Katsumata, Hiromu; Russell, Daniel M

    2012-02-01

    Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

  7. T-CREST: Time-predictable multi-core architecture for embedded systems

    DEFF Research Database (Denmark)

    Schoeberl, Martin; Abbaspourseyedi, Sahar; Jordan, Alexander

    2015-01-01

    -core architectures that are optimized for the WCET instead of the average-case execution time. The resulting time-predictable resources (processors, interconnect, memory arbiter, and memory controller) and tools (compiler, WCET analysis) are designed to ease WCET analysis and to optimize WCET performance. Compared...... domain shows that the WCET can be reduced for computation-intensive tasks when distributing the tasks on several cores and using the network-on-chip for communication. With three cores the WCET is improved by a factor of 1.8 and with 15 cores by a factor of 5.7.The T-CREST project is the result...

  8. Serum levels of INSL3, AMH, Inhibin B and Testosterone during pubertal transition in healthy boys

    DEFF Research Database (Denmark)

    Lindhardt Johansen, Marie; Anand-Ivell, Ravinder; Mouritsen, Annette

    2014-01-01

    to luteinizing hormone (LH), follicle-stimulating hormone (FSH), testosterone, inhibin B, and anti-Müllerian hormone (AMH) during puberty in healthy boys.MethodsTen boys were included from the longitudinal part of the COPENHAGEN Puberty Study. Pubertal evaluation, including testicular volume, was performed...... and blood samples drawn every 6 months for 5 years. Serum concentrations of testosterone were determined by a newly developed LC-MS/MS method, and serum concentrations of INSL3, AMH, inhibin B, FSH and LH, respectively, were determined by validated immunoassays.ResultsSerum INSL3 levels increased...... progressively with increasing age, pubertal onset and testicular volume. In six of ten boys, LH increased prior to the first observed increase in INSL3. In the remaining four boys, the increase in LH and INSL3 was observed at the same examination. The increases in serum concentrations of LH, testosterone...

  9. The association of pain and depression in preadolescent girls: moderation by race and pubertal stage.

    Science.gov (United States)

    Keenan, Kate; Hipwell, Alison E; Hinze, Amanda E; Babinski, Dara E

    2009-08-01

    To test whether an association between pain response and depression in females is present during preadolescence using a controlled pain stimulus and a clinically relevant assessment of depressive symptoms. In a sample of 232 girls, pain threshold and tolerance were assessed at age 10 years using the cold pressor task, and a diagnostic interview was used to assess depression symptoms at 10 and 11 years of age. Response to pain at age 10 was associated with depressive symptoms at ages 10 and 11; race and pubertal stage moderated the association. Pain response and depression were more strongly associated among girls who had reached advanced stages of pubertal development and among European American girls. The results add to the existing literature on the co-occurrence of depression and pain by demonstrating modest but consistent concurrent and prospective associations between response to pain and depression among girls during preadolescence.

  10. Giant fibroadenoma of the breast in a pre-pubertal girl: a case report

    Directory of Open Access Journals (Sweden)

    Sunder Goyal

    2014-02-01

    Full Text Available Juvenile fibroadenoma comprises about 4% of the total fibroadenomas. The incidence of giant juvenile fibroadenomas is merely 0.5% of all the fibroadenomas. Bilateral giant juvenile fibroadenomas are extremely rare. We are presenting a case of giant juvenile fibroadenomas in an 11-year-old pre-pubertal girl. The diagnosis was made on fine-needle aspiration cytology which was confirmed on histopathology. As these tumors are mostly benign, breast-conserving surgery is done so that patient can lead a normal life without psychological trauma.-----------------------------------Cite this article as: Goyal S, Garg G, Narang S. Giant fibroadenoma of the breast in a pre-pubertal girl: a case report. Int J Cancer Ther Oncol 2014; 2(1:020113.DOI: http://dx.doi.org/10.14319/ijcto.0201.13

  11. REMAINING LIFE TIME PREDICTION OF BEARINGS USING K-STAR ALGORITHM – A STATISTICAL APPROACH

    Directory of Open Access Journals (Sweden)

    R. SATISHKUMAR

    2017-01-01

    Full Text Available The role of bearings is significant in reducing the down time of all rotating machineries. The increasing trend of bearing failures in recent times has triggered the need and importance of deployment of condition monitoring. There are multiple factors associated to a bearing failure while it is in operation. Hence, a predictive strategy is required to evaluate the current state of the bearings in operation. In past, predictive models with regression techniques were widely used for bearing lifetime estimations. The Objective of this paper is to estimate the remaining useful life of bearings through a machine learning approach. The ultimate objective of this study is to strengthen the predictive maintenance. The present study was done using classification approach following the concepts of machine learning and a predictive model was built to calculate the residual lifetime of bearings in operation. Vibration signals were acquired on a continuous basis from an experiment wherein the bearings are made to run till it fails naturally. It should be noted that the experiment was carried out with new bearings at pre-defined load and speed conditions until the bearing fails on its own. In the present work, statistical features were deployed and feature selection process was carried out using J48 decision tree and selected features were used to develop the prognostic model. The K-Star classification algorithm, a supervised machine learning technique is made use of in building a predictive model to estimate the lifetime of bearings. The performance of classifier was cross validated with distinct data. The result shows that the K-Star classification model gives 98.56% classification accuracy with selected features.

  12. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    International Nuclear Information System (INIS)

    Rattá, G.A.; Vega, J.; Murari, A.

    2012-01-01

    Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  13. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    Energy Technology Data Exchange (ETDEWEB)

    Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)

    2012-09-15

    Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  14. Financial Distress Prediction Using Discrete-time Hazard Model and Rating Transition Matrix Approach

    Science.gov (United States)

    Tsai, Bi-Huei; Chang, Chih-Huei

    2009-08-01

    Previous studies used constant cut-off indicator to distinguish distressed firms from non-distressed ones in the one-stage prediction models. However, distressed cut-off indicator must shift according to economic prosperity, rather than remains fixed all the time. This study focuses on Taiwanese listed firms and develops financial distress prediction models based upon the two-stage method. First, this study employs the firm-specific financial ratio and market factors to measure the probability of financial distress based on the discrete-time hazard models. Second, this paper further focuses on macroeconomic factors and applies rating transition matrix approach to determine the distressed cut-off indicator. The prediction models are developed by using the training sample from 1987 to 2004, and their levels of accuracy are compared with the test sample from 2005 to 2007. As for the one-stage prediction model, the model in incorporation with macroeconomic factors does not perform better than that without macroeconomic factors. This suggests that the accuracy is not improved for one-stage models which pool the firm-specific and macroeconomic factors together. In regards to the two stage models, the negative credit cycle index implies the worse economic status during the test period, so the distressed cut-off point is adjusted to increase based on such negative credit cycle index. After the two-stage models employ such adjusted cut-off point to discriminate the distressed firms from non-distressed ones, their error of misclassification becomes lower than that of one-stage ones. The two-stage models presented in this paper have incremental usefulness in predicting financial distress.

  15. Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

    Science.gov (United States)

    Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

    2005-12-01

    Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

  16. Prediction of tablets disintegration times using near-infrared diffuse reflectance spectroscopy as a nondestructive method.

    Science.gov (United States)

    Donoso, M; Ghaly, Evone S

    2005-01-01

    The goals of this study are to user near-infrared reflectance (NIR) spectroscopy to measure the disintegration time of a series of tablets compacted at different compressional forces, calibrate NIR data vs. laboratory equipment data, develop a model equation, validate the model, and test the model's predictive ability. Seven theophylline tablet formulations of the same composition but with different disintegration time values (0.224, 1.141, 2.797, 5.492, 9.397, 16.8, and 30.092 min) were prepared along with five placebo tablet formulations with different disintegration times. Laboratory disintegration time was compared to near-infrared diffuse reflectance data. Linear regression, quadratic, cubic, and partial least square techniques were used to determine the relationship between disintegration time and near-infrared spectra. The results demonstrated that an increase in disintegration time produced an increase in near-infrared absorbance. Series of model equations, which depended on the mathematical technique used for regression, were developed from the calibration of disintegration time using laboratory equipment vs. the near-infrared diffuse reflectance for each formulation. The results of NIR disintegration time were similar to laboratory tests. The near-infrared diffuse reflectance spectroscopy method is an alternative nondestructive method for measurement of disintegration time of tablets.

  17. Robust model predictive control for constrained continuous-time nonlinear systems

    Science.gov (United States)

    Sun, Tairen; Pan, Yongping; Zhang, Jun; Yu, Haoyong

    2018-02-01

    In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.

  18. Ovarian function following pelvic irradiation in prepubertal and pubertal girls and young adult women

    International Nuclear Information System (INIS)

    Schuck, A.; Hamelmann, V.; Braemswig, J.H.

    2005-01-01

    Purpose: To analyze the effect of pelvic radiotherapy on ovarian function in prepubertal and pubertal girls and young adult women. Patients and methods: In a retrospective monoinstitutional analysis, patients 15 Gy to the ovaries developed hormone failure. In one case of a patient receiving an ovarian dose of 15 Gy, hormone failure was not found. In case of pelvic irradiation excluding at least one ovary, approximately half of the patients developed ovarian dysfunction, probably also due to the effects of polychemotherapy. (orig.)

  19. Putative effects of endocrine disrupters on pubertal development in the human

    DEFF Research Database (Denmark)

    Teilmann, Grete; Juul, Anders; Skakkebaek, Niels E

    2002-01-01

    -called endocrine disrupters. Precocious puberty has been described in several case reports of accidental exposure to oestrogenic compounds in cosmetic products, food and pharmaceuticals. Local epidemics of premature thelarche have also been suggested to be linked to endocrine disrupters. Children adopted from...... developing countries to industrialized countries often develop precocious puberty. Not only precocious puberty, but also delayed puberty can, theoretically, be associated with exposure to endocrine disrupters. While it is very plausible that endocrine disrupters may disturb pubertal development...

  20. A model for estimating pathogen variability in shellfish and predicting minimum depuration times.

    Science.gov (United States)

    McMenemy, Paul; Kleczkowski, Adam; Lees, David N; Lowther, James; Taylor, Nick

    2018-01-01

    Norovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer's protection and the shellfish industry's reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a 'worst case scenario' for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist

  1. Less-structured time in children's daily lives predicts self-directed executive functioning.

    Science.gov (United States)

    Barker, Jane E; Semenov, Andrei D; Michaelson, Laura; Provan, Lindsay S; Snyder, Hannah R; Munakata, Yuko

    2014-01-01

    Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

  2. Real-Time Prediction of Temperature Elevation During Robotic Bone Drilling Using the Torque Signal.

    Science.gov (United States)

    Feldmann, Arne; Gavaghan, Kate; Stebinger, Manuel; Williamson, Tom; Weber, Stefan; Zysset, Philippe

    2017-09-01

    Bone drilling is a surgical procedure commonly required in many surgical fields, particularly orthopedics, dentistry and head and neck surgeries. While the long-term effects of thermal bone necrosis are unknown, the thermal damage to nerves in spinal or otolaryngological surgeries might lead to partial paralysis. Previous models to predict the temperature elevation have been suggested, but were not validated or have the disadvantages of computation time and complexity which does not allow real time predictions. Within this study, an analytical temperature prediction model is proposed which uses the torque signal of the drilling process to model the heat production of the drill bit. A simple Green's disk source function is used to solve the three dimensional heat equation along the drilling axis. Additionally, an extensive experimental study was carried out to validate the model. A custom CNC-setup with a load cell and a thermal camera was used to measure the axial drilling torque and force as well as temperature elevations. Bones with different sets of bone volume fraction were drilled with two drill bits ([Formula: see text]1.8 mm and [Formula: see text]2.5 mm) and repeated eight times. The model was calibrated with 5 of 40 measurements and successfully validated with the rest of the data ([Formula: see text]C). It was also found that the temperature elevation can be predicted using only the torque signal of the drilling process. In the future, the model could be used to monitor and control the drilling process of surgeries close to vulnerable structures.

  3. Kernel density estimation-based real-time prediction for respiratory motion

    International Nuclear Information System (INIS)

    Ruan, Dan

    2010-01-01

    Effective delivery of adaptive radiotherapy requires locating the target with high precision in real time. System latency caused by data acquisition, streaming, processing and delivery control necessitates prediction. Prediction is particularly challenging for highly mobile targets such as thoracic and abdominal tumors undergoing respiration-induced motion. The complexity of the respiratory motion makes it difficult to build and justify explicit models. In this study, we honor the intrinsic uncertainties in respiratory motion and propose a statistical treatment of the prediction problem. Instead of asking for a deterministic covariate-response map and a unique estimate value for future target position, we aim to obtain a distribution of the future target position (response variable) conditioned on the observed historical sample values (covariate variable). The key idea is to estimate the joint probability distribution (pdf) of the covariate and response variables using an efficient kernel density estimation method. Then, the problem of identifying the distribution of the future target position reduces to identifying the section in the joint pdf based on the observed covariate. Subsequently, estimators are derived based on this estimated conditional distribution. This probabilistic perspective has some distinctive advantages over existing deterministic schemes: (1) it is compatible with potentially inconsistent training samples, i.e., when close covariate variables correspond to dramatically different response values; (2) it is not restricted by any prior structural assumption on the map between the covariate and the response; (3) the two-stage setup allows much freedom in choosing statistical estimates and provides a full nonparametric description of the uncertainty for the resulting estimate. We evaluated the prediction performance on ten patient RPM traces, using the root mean squared difference between the prediction and the observed value normalized by the

  4. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

  5. DNA methylation-based measures of biological age: meta-analysis predicting time to death

    Science.gov (United States)

    Chen, Brian H.; Marioni, Riccardo E.; Colicino, Elena; Peters, Marjolein J.; Ward-Caviness, Cavin K.; Tsai, Pei-Chien; Roetker, Nicholas S.; Just, Allan C.; Demerath, Ellen W.; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R.; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P.; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L.; Murabito, Joanne M.; Bandinelli, Stefania; Hernandez, Dena G.; Melzer, David; Nalls, Michael; Pilling, Luke C.; Price, Timothy R.; Singleton, Andrew B.; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M.; Shah, Sonia; Wray, Naomi R.; McRae, Allan F.; Franco, Oscar H.; Hofman, Albert; Uitterlinden, André G.; Absher, Devin; Assimes, Themistocles; Levine, Morgan E.; Lu, Ake T.; Tsao, Philip S.; Hou, Lifang; Manson, JoAnn E.; Carty, Cara L.; LaCroix, Andrea Z.; Reiner, Alexander P.; Spector, Tim D.; Feinberg, Andrew P.; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T.; Peters, Annette; Deary, Ian J.; Pankow, James S.; Ferrucci, Luigi; Horvath, Steve

    2016-01-01

    Estimates of biological age based on DNA methylation patterns, often referred to as “epigenetic age”, “DNAm age”, have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2×10−9), independent of chronological age, even after adjusting for additional risk factors (p<5.4×10−4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5×10−43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality. PMID:27690265

  6. Cooperative Multiagent System for Parking Availability Prediction Based on Time Varying Dynamic Markov Chains

    Directory of Open Access Journals (Sweden)

    Surafel Luleseged Tilahun

    2017-01-01

    Full Text Available Traffic congestion is one of the main issues in the study of transportation planning and management. It creates different problems including environmental pollution and health problem and incurs a cost which is increasing through years. One-third of this congestion is created by cars searching for parking places. Drivers may be aware that parking places are fully occupied but will drive around hoping that a parking place may become vacant. Opportunistic services, involving learning, predicting, and exploiting Internet of Things scenarios, are able to adapt to dynamic unforeseen situations and have the potential to ease parking search issues. Hence, in this paper, a cooperative dynamic prediction mechanism between multiple agents for parking space availability in the neighborhood, integrating foreseen and unforeseen events and adapting for long-term changes, is proposed. An agent in each parking place will use a dynamic and time varying Markov chain to predict the parking availability and these agents will communicate to produce the parking availability prediction in the whole neighborhood. Furthermore, a learning approach is proposed where the system can adapt to different changes in the parking demand including long-term changes. Simulation results, using synthesized data based on an actual parking lot data from a shopping mall in Geneva, show that the proposed model is promising based on the learning accuracy with service adaptation and performance in different cases.

  7. The role of mentor type and timing in predicting educational attainment.

    Science.gov (United States)

    Fruiht, Veronica M; Wray-Lake, Laura

    2013-09-01

    Having an adult mentor during adolescence has been found to predict academic success. Building on previous work, the present study examined interactions between the type of mentor (i.e., kin, teacher, friend, or community), the time that mentor became important (i.e., before, during, or after high school), and the ethnicity of the protégé in predicting educational attainment in young adulthood. Analyses used Waves III and IV of the National Longitudinal Study of Adolescent Health (N = 2,409). Participants' ages ranged from 18 to 27 (M = 21.75, SD = 1.79). The sample was 56.7 % female and nationally representative of ethnic diversity. Analyses showed that having a teacher-mentor was more predictive of educational attainment than having other types of mentors and that overall, having a mentor after high school predicts the most educational attainment. Kin- and community-mentors appeared to be more important to educational attainment during and before high school, respectively. Findings were consistent across ethnic groups. Overall, results highlight the value of teacher-mentors throughout childhood, adolescence, and early adulthood and our study further suggests that different types of mentors may be particularly useful at specific points in development.

  8. Determining if pretreatment PSA doubling time predicts PSA trajectories after radiation therapy for localized prostate cancer

    International Nuclear Information System (INIS)

    Soto, Daniel E.; Andridge, Rebecca R.; Pan, Charlie C.; Williams, Scott G.; Taylor, Jeremy M.G.; Sandler, Howard M.

    2009-01-01

    Introduction: To determine if pretreatment PSA doubling time (PSA-DT) can predict post-radiation therapy (RT) PSA trajectories for localized prostate cancer. Materials and methods: Three hundred and seventy-five prostate cancer patients treated with external beam RT without androgen deprivation therapy (ADT) were identified with an adequate number of PSA values. We utilized a linear mixed model (LMM) analysis to model longitudinal PSA data sets after definitive treatment. Post-treatment PSA trajectories were allowed to depend on the pre-RT PSA-DT, pre-RT PSA (iPSA), Gleason score (GS), and T-stage. Results: Pre-RT PSA-DT had a borderline impact on predicting the rate of PSA rise after nadir (p = 0.08). For a typical low risk patient (T1, GS ≤ 6, iPSA 10), the predicted PSA-DT post-nadir was 21% shorter for pre-RT PSA-DT 24 month (19 month vs. 24 month). Additional significant predictors of post-RT PSA rate of rise included GS (p < 0.0001), iPSA (p < 0.0001), and T-stage (p = 0.02). Conclusions: We observed a trend between rapidly rising pre-RT PSA and the post-RT post-nadir PSA rise. This effect appeared to be independent of iPSA, GS, or T-stage. The results presented suggest that pretreatment PSA-DT may help predict post-RT PSA trajectories

  9. Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.

    Science.gov (United States)

    Lin, Shih-Yun; Lai, Ying-Chih; Hsia, Chi-Chun; Su, Pei-Fang; Chang, Chih-Han

    2017-09-01

    This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HR net ). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HR net , respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.

  10. [Craniopharyngioma and Klinefelter syndrome during the pubertal transition: A diagnostic challenge].

    Science.gov (United States)

    Mocarbel, Yamile; Arébalo de Cross, Graciela; Lebrethon, Marie C; Thiry, Albert; Beckersd, Albert; Valdes-Socin, Hernan

    2017-04-01

    Craniopharyngioma is the most common pituitary tumor in childhood. It can compromise the pubertal development because of its evolution or treatment. Syndrome of Klinefelter is the most common cause of hipergonadotrophic hypogonadism in males. The concomitant presentation of both entities is extremely low (1/109) and the pathophysiological association is questionned. We present the case of a 18-year-old Belgian patient. He had a diagnosis of craniopharyngioma in childhood and he presented with panhypopituitarism after radiotherapy and surgical treatment. At the age of 14, he started pubertal induction with gonadotropin therapy without clinical response. Asociación de craneofaringioma y síndrome de Klinefelter en la transición puberal: un desafío diagnóstico Craniopharyngioma and Klinefelter syndrome during the pubertal transition: A diagnostic challenge A genetic evaluation confirmed a homogeneous 47, XXY karyotype. Failure of exogenous gonadotropin therapy revealed the hidden association of primary and secondary hypogonadism, demonstrating the importance of the followup and a multidisciplinary approach in these patients. Sociedad Argentina de Pediatría.

  11. The relationship between pubertal gynecomastia, prostate specific antigen, free androgen index, SHBG and sex steroids.

    Science.gov (United States)

    Kilic, Mustafa; Kanbur, Nuray; Derman, Orhan; Akgül, Sinem; Kutluk, Tezer

    2011-01-01

    To investigate the relationships between pubertal gynecomastia, prostate-specific antigen (PSA), free androgen index (FAI), sex hormone-binding globulin (SHBG) and sex steroids. A total of 61 male adolescents (10-17 years old; mean: 13.67 +/- 1.08) with gynecomastia were enrolled into the study group. A total of 65 healthy age-matched adolescents were included in the control group. Body mass index (BMI), Tanner staging, testis volume, stretched penis length (SPL) and bone age were evaluated. Serum follicle-stimulating hormone, luteinizing hormone (LH), estradiol (E2), testosterone, free testosterone, SHBG, PSA levels were determined and FAI was calculated. In the study group, free testosterone (p = 0.012) and FAI (p = 0.05) were significantly lower than the control group. In the control group, SHBG levels decreased (p 0.05). High FAI was found to decrease the risk of gynecomastia (odds ratio: 0.211, 95% confidence interval: 0.064-0.694, p = 0.01). PSA showed a positive correlation with FAI, free testosterone, Tanner staging, testosterone, E2 and LH levels. PSA is a good indicator of androgen activity during puberty. However, owing to FAI remaining as the single significant variable for pubertal gynecomastia, we suggest that it is still the best parameter to elucidate the etiopathogenesis of gynecomastia as well as other pubertal developmental abnormalities in male adolescents, and further longitudinal studies are needed to investigate the relationships between PSA and FAI in puberty.

  12. Sexual differentiation of human behavior: effects of prenatal and pubertal organizational hormones.

    Science.gov (United States)

    Berenbaum, Sheri A; Beltz, Adriene M

    2011-04-01

    A key question concerns the extent to which sexual differentiation of human behavior is influenced by sex hormones present during sensitive periods of development (organizational effects), as occurs in other mammalian species. The most important sensitive period has been considered to be prenatal, but there is increasing attention to puberty as another organizational period, with the possibility of decreasing sensitivity to sex hormones across the pubertal transition. In this paper, we review evidence that sex hormones present during the prenatal and pubertal periods produce permanent changes to behavior. There is good evidence that exposure to high levels of androgens during prenatal development results in masculinization of activity and occupational interests, sexual orientation, and some spatial abilities; prenatal androgens have a smaller effect on gender identity, and there is insufficient information about androgen effects on sex-linked behavior problems. There is little good evidence regarding long-lasting behavioral effects of pubertal hormones, but there is some suggestion that they influence gender identity and perhaps some sex-linked forms of psychopathology, and there are many opportunities to study this issue. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Mutational Analysis of TAC3 and TACR3 Genes in Patients with Idiopathic Central Pubertal Disorders

    Science.gov (United States)

    Tusset, Cintia; Noel, Sekoni D.; Trarbach, Ericka B.; Silveira, Letícia F. G.; Jorge, Alexander A. L.; Brito, Vinicius N.; Cukier, Priscila; Seminara, Stephanie B.; de Mendonça, Berenice B.; Kaiser, Ursula B.; Latronico, Ana Claudia

    2013-01-01

    Aim To investigate the presence of variants in the TAC3 and TACR3 genes, which encode NKB and its receptor (NK3R), respectively, in a large cohort of patients with idiopathic central pubertal disorders. Patients and Methods Two hundred and thirty seven patients were studied: 114 with central precocious puberty (CPP), 73 with normosmic isolated hypogonadotropic hypogonadism (IHH) and 50 with constitutional delay of growth and puberty (CDGP). The control group consisted of 150 Brazilian individuals with normal pubertal development. Genomic DNA was extracted from peripheral blood and the entire coding region of both TAC3 and TACR3 genes were amplified and automatically sequenced. Results We identified one variant (p.A63P) in NKB and four variants, p.G18D, p.L58L (c.172C>T), p.W275* and p.A449S in NK3R, which were absent in the control group. The p.A63P variant was identified in a girl with CPP, and p.A449S in a girl with CDGP. The known p.G18D, p.L58L and p.W275* variants were identified in three unrelated males with normosmic IHH. Conclusion Rare variants in the TAC3 and TACR3 genes were identified in patients with central pubertal disorders. Loss-of-function variants of TACR3 were associated with the normosmic IHH phenotype. PMID:23329188

  14. [Pubertal maturation, physical self-esteem and sexuality in a sample of French adolescents].

    Science.gov (United States)

    Potard, C; Courtois, R; Clarisse, R; Le Floc'h, N; Thomine, M; Réveillère, C

    2016-04-01

    The aim of this study was to explore the links between pubertal maturation, physical self-esteem and sexuality in adolescence, differentiating between boys and girls. The sample was comprised of 312 French secondary school children (seventh and ninth grades); 52.6 % (n=164) of whom were girls. Participants answered three self-evaluation questionnaires: the scale of sexuality (interests, emotions, relationships: IERS) in prime adolescence (12 to 15 years); (b) the self-administered rating scale for pubertal development and (c) the Physical Self-Description Questionnaire (PSDQ). Pubertal maturation was associated with higher scores on "Flirting with the aim of having sexual relations" and "Going out with someone", and a drop in overall and physical self-esteem, mainly in socially valued domains, namely "Body fat" for girls, and "Strength" and "Health" for boys. Overall physical self-esteem was associated with "Going out with someone" and "Flirting with the aim of having sexual relations" in boys. Physical changes at puberty induce two distinct trends in adolescents: sexual exploration and discovery (genitalized body), and self-depreciation (social body). Copyright © 2015 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

  15. Predicting long-term catchment nutrient export: the use of nonlinear time series models

    Science.gov (United States)

    Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

    2010-05-01

    After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the

  16. A simple method for HPLC retention time prediction: linear calibration using two reference substances.

    Science.gov (United States)

    Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

    2017-01-01

    Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

  17. Prediction of hourly PM2.5 using a space-time support vector regression model

    Science.gov (United States)

    Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

    2018-05-01

    Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

  18. Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

    International Nuclear Information System (INIS)

    Randazzo, Giuseppe Marco; Tonoli, David; Hambye, Stephanie; Guillarme, Davy; Jeanneret, Fabienne; Nurisso, Alessandra; Goracci, Laura; Boccard, Julien; Rudaz, Serge

    2016-01-01

    The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q"2) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification. - Highlights: • Difficulties regarding steroid identification are encountered when considering isotopomeric steroids. • Quantitative structure retention relationship (QSRR) models were developed from the linear solvent strength theory. • A dataset composed of 91 steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were used. • The list of candidates associated with identical monoisotopic masses was reduced, facilitating steroid identification.

  19. Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

    Energy Technology Data Exchange (ETDEWEB)

    Randazzo, Giuseppe Marco [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Tonoli, David [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel (Switzerland); Human Protein Sciences Department, University of Geneva, Geneva (Switzerland); Hambye, Stephanie; Guillarme, Davy [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Jeanneret, Fabienne [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel (Switzerland); Human Protein Sciences Department, University of Geneva, Geneva (Switzerland); Nurisso, Alessandra [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Goracci, Laura [Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia (Italy); Boccard, Julien [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Rudaz, Serge, E-mail: serge.rudaz@unige.ch [School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva (Switzerland); Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel (Switzerland)

    2016-04-15

    The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q{sup 2}) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification. - Highlights: • Difficulties regarding steroid identification are encountered when considering isotopomeric steroids. • Quantitative structure retention relationship (QSRR) models were developed from the linear solvent strength theory. • A dataset composed of 91 steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were used. • The list of candidates associated with identical monoisotopic masses was reduced, facilitating steroid identification.

  20. Brittle Creep Failure, Critical Behavior, and Time-to-Failure Prediction of Concrete under Uniaxial Compression

    Directory of Open Access Journals (Sweden)

    Yingchong Wang

    2015-01-01

    Full Text Available Understanding the time-dependent brittle deformation behavior of concrete as a main building material is fundamental for the lifetime prediction and engineering design. Herein, we present the experimental measures of brittle creep failure, critical behavior, and the dependence of time-to-failure, on the secondary creep rate of concrete under sustained uniaxial compression. A complete evolution process of creep failure is achieved. Three typical creep stages are observed, including the primary (decelerating, secondary (steady state creep regime, and tertiary creep (accelerating creep stages. The time-to-failure shows sample-specificity although all samples exhibit a similar creep process. All specimens exhibit a critical power-law behavior with an exponent of −0.51 ± 0.06, approximately equal to the theoretical value of −1/2. All samples have a long-term secondary stage characterized by a constant strain rate that dominates the lifetime of a sample. The average creep rate expressed by the total creep strain over the lifetime (tf-t0 for each specimen shows a power-law dependence on the secondary creep rate with an exponent of −1. This could provide a clue to the prediction of the time-to-failure of concrete, based on the monitoring of the creep behavior at the steady stage.