What predicts performance during clinical psychology training?

PubMed Central

Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

2014-01-01

Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Results Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Conclusions Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. Practitioner points While referee and selection interview ratings did not predict performance during training, they may be useful in screening out unsuitable candidates at the application stage High school final academic performance was the best predictor of good performance during clinical psychology training The findings are derived from seven cohorts of one training course, the UK's largest; they cannot be assumed to generalize to all training courses PMID:24206117

Metal radomes for reduced RCS performance

NASA Astrophysics Data System (ADS)

Wahid, M.; Morris, S. B.

A frequency selective surface (FSS) comprising a square grid and a hexagonal array of disks is proposed as a means of reducing the Radar Cross Section (RCS) of a radar bay over a wide (2 GHz to 14.6 GHz) frequency bandwidth. Results are presented in terms of transmission loss for an 'A'-type sandwich radome consisting of two FSS layers for normal and non-normal incidence. A single FSS layer on a GRP flat panel is also considered. Good agreement is found between the predicted and measured results. The proposed FSS shows good performance and is relatively insensitive to angle of incidence between 3.8 GHz and 10.1 GHz. Predicted Insertion Phase Delay (IPD) and cross-polar performances are also given. Parametric studies have indicated the versatility of the proposed structure.

A proteinuria cut-off level of 0.7 g/day after 12 months of treatment best predicts long-term renal outcome in lupus nephritis: data from the MAINTAIN Nephritis Trial

PubMed Central

Tamirou, Farah; Lauwerys, Bernard R; Dall'Era, Maria; Mackay, Meggan; Rovin, Brad; Cervera, Ricard; Houssiau, Frédéric A

2015-01-01

Background Although an early decrease in proteinuria has been correlated with good long-term renal outcome in lupus nephritis (LN), studies aimed at defining a cut-off proteinuria value are missing, except a recent analysis performed on patients randomised in the Euro-Lupus Nephritis Trial, demonstrating that a target value of 0.8 g/day at month 12 optimised sensitivity and specificity for the prediction of good renal outcome. The objective of the current work is to validate this target in another LN study, namely the MAINTAIN Nephritis Trial (MNT). Methods Long-term (at least 7 years) renal function data were available for 90 patients randomised in the MNT. Receiver operating characteristic curves were built to test the performance of proteinuria measured within the 1st year as short-term predictor of long-term renal outcome. We calculated the positive and negative predictive values (PPV, NPV). Results After 12 months of treatment, achievement of a proteinuria <0.7 g/day best predicted good renal outcome, with a sensitivity and a specificity of 71% and 75%, respectively. The PPV was high (94%) but the NPV low (29%). Addition of the requirement of urine red blood cells ≤5/hpf as response criteria at month 12 reduced sensitivity from 71% to 41%. Conclusions In this cohort of mainly Caucasian patients suffering from a first episode of LN in most cases, achievement of a proteinuria <0.7 g/day at month 12 best predicts good outcome at 7 years and inclusion of haematuria in the set of criteria at month 12 undermines the sensitivity of early proteinuria decrease for the prediction of good outcome. The robustness of these conclusions stems from the very similar results obtained in two distinct LN cohorts. Trial registration number: NCT00204022. PMID:26629352

Low-Density Parity-Check (LDPC) Codes Constructed from Protographs

NASA Astrophysics Data System (ADS)

Thorpe, J.

2003-08-01

We introduce a new class of low-density parity-check (LDPC) codes constructed from a template called a protograph. The protograph serves as a blueprint for constructing LDPC codes of arbitrary size whose performance can be predicted by analyzing the protograph. We apply standard density evolution techniques to predict the performance of large protograph codes. Finally, we use a randomized search algorithm to find good protographs.

Are corticosterone levels a good indicator of food availability and reproductive performance in a kittiwake colony?

USGS Publications Warehouse

Lanctot, Richard B.; Hatch, Shyla A.; Gill, Verena A.; Eens, Marcel

2003-01-01

We evaluated the use of corticosterone to gauge forage availability and predict reproductive performance in black-legged kittiwakes (Rissa tridactyla) breeding in Alaska during 1999 and 2000. We modeled the relationship between baseline levels of corticosterone and a suite of individual and temporal characteristics of the sampled birds. We also provided supplemental food to a sample of pairs and compared their corticosterone levels with that of pairs that were not fed. Corticosterone levels were a good predictor of forage availability in some situations, although inconsistencies between corticosterone levels and reproductive performance of fed and unfed kittiwakes suggested that this was not always the case. In general, higher corticosterone levels were found in birds that lacked breeding experience and in birds sampled shortly after arriving from their wintering grounds. All parameters investigated, however, explained only a small proportion of the variance in corticosterone levels. We also investigated whether corticosterone, supplemental feeding, year of the study, breeding experience, body weight, and sex of a bird were able to predict laying, hatching, and fledging success in kittiwakes. Here, breeding experience, year of the study, and body weight were the best predictors of a bird’s performance. Corticosterone level and supplemental feeding were good predictors of kittiwake reproductive performance in some cases. For example, corticosterone levels of birds sampled during the arrival stage reliably predicted laying success, but were less reliable at predicting hatching and fledging success. Counts of active nests with eggs or chicks may be more reliable estimates of the actual productivity of the colony. Supplemental feeding had strong effects on kittiwake productivity when natural forage was poor, but had little effect when natural forage was plentiful.

Gender, TIMI risk score and in-hospital mortality in STEMI patients undergoing primary PCI: results from the Belgian STEMI registry.

PubMed

Gevaert, Sofie A; De Bacquer, Dirk; Evrard, Patrick; Convens, Carl; Dubois, Philippe; Boland, Jean; Renard, Marc; Beauloye, Christophe; Coussement, Patrick; De Raedt, Herbert; de Meester, Antoine; Vandecasteele, Els; Vranckx, Pascal; Sinnaeve, Peter R; Claeys, Marc J

2014-01-22

The relationship between the predictive performance of the TIMI risk score for STEMI and gender has not been evaluated in the setting of primary PCI (pPCI). Here, we compared in-hospital mortality and predictive performance of the TIMI risk score between Belgian women and men undergoing pPCI. In-hospital mortality was analysed in 8,073 (1,920 [23.8%] female and 6,153 [76.2%] male patients) consecutive pPCI-treated STEMI patients, included in the prospective, observational Belgian STEMI registry (January 2007 to February 2011). A multivariable logistic regression model, including TIMI risk score variables and gender, evaluated differences in in-hospital mortality between men and women. The predictive performance of the TIMI risk score according to gender was evaluated in terms of discrimination and calibration. Mortality rates for TIMI scores in women and men were compared. Female patients were older, had more comorbidities and longer ischaemic times. Crude in-hospital mortality was 10.1% in women vs. 4.9% in men (OR 2.2; 95% CI: 1.82-2.66, p<0.001). When adjusting for TIMI risk score variables, mortality remained higher in women (OR 1.47, 95% CI: 1.15-1.87, p=0.002). The TIMI risk score provided a good predictive discrimination and calibration in women as well as in men (c-statistic=0.84 [95% CI: 0.809-0.866], goodness-of-fit p=0.53 and c-statistic=0.89 [95% CI: 0.873-0.907], goodness-of-fit p=0.13, respectively), but mortality prediction for TIMI scores was better in men (p=0.02 for TIMI score x gender interaction). In the Belgian STEMI registry, pPCI-treated women had a higher in-hospital mortality rate even after correcting for TIMI risk score variables. The TIMI risk score was effective in predicting in-hospital mortality but performed slightly better in men. The database was registered with clinicaltrials.gov (NCT00727623).

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

Experimental and predicted cavitation performance of an 80.6 deg helical inducer in high temperature water

NASA Technical Reports Server (NTRS)

Kovich, G.

1972-01-01

The cavitating performance of a stainless steel 80.6 degree flat-plate helical inducer was investigated in water over a range of liquid temperatures and flow coefficients. A semi-empirical prediction method was used to compare predicted values of required net positive suction head in water with experimental values obtained in water. Good agreement was obtained between predicted and experimental data in water. The required net positive suction head in water decreased with increasing temperature and increased with flow coefficient, similar to that observed for a like inducer in liquid hydrogen.

Training or non-surgical factors-what determines a good surgical performance? A randomised controlled trial.

PubMed

Lindlohr, Cornelia; Lefering, R; Saad, S; Heiss, M M; Pape-Köhler, C

2017-06-01

Acquiring laparoscopic skills is a necessity for every young surgeon. Whether it is a talent or a non-surgical skill that determines the surgical performance of an endoscopic operation has been discussed for years. In other disciplines aptitude testing has become the norm. Airlines, for example, have implemented assessments to test the natural aptitude of future pilots to predict their performance later on. In the medical field, especially surgery, there are no similar comparable tests implemented or even available. This study investigates the influence of potential factors that may predict the successful performance of a complex laparoscopic operation, such as the surgeon's age, gender or learning method. This study focussed 70 surgical trainees. It was designed as a secondary analysis of data derived from a 2 × 2 factorial randomised controlled trial of practical training and/or multimedia training (four groups) in an experimental exercise. Both before and then after the training sessions, the participating trainees performed a laparoscopic cholecystectomy in a pelvitrainer. Surgical performance was then evaluated using a modified objective structured assessment of technical skills (OSATS). Participants were classified as 'Skilled' (high score in the pre-test), 'Good Learner' (increase from pre- to post-test) or 'Others' based on the OSATS results. Based on the results of the recorded performance, the training methods as well as non-surgical skills were eventually evaluated in a univariate and in a multivariate analysis. In the pre-training performance 11 candidates were categorised as 'Skilled' (15.7%), 35 participants as 'Good Learners' (50.0%) and 24 participants were classified as 'Others'. The univariate analysis showed that the age, a residency in visceral surgery, and participation in a multimedia training were significantly associated with this grouping. Multivariate analyses revealed that residency in visceral surgery was the most predictive factor for the 'Skilled' participants (p = 0.059), and multimedia training was most predictive for the 'Good Learner' (p = 0.006). Participants in the group of 'Others' who were neither 'Skilled' nor improved in the training phase were younger (p = 0.011) and did not receive multimedia (p < 0.001) or practical (p = 0.025) training. The type of learning method has been shown to be the most effective factor to improve laparoscopic skills, with multimedia training proving to be more effective than practical training.

When bad stress goes good: increased threat reactivity predicts improved category learning performance.

PubMed

Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

2011-02-01

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Preschool Inhibitory Control Predicts ADHD Group Status and Inhibitory Weakness in School.

PubMed

Jacobson, Lisa A; Schneider, Heather; Mahone, E Mark

2017-12-26

Discriminative utility of performance measures of inhibitory control was examined in preschool children with and without ADHD to determine whether performance measures added to diagnostic prediction and to prediction of informant-rated day-to-day executive function. Children ages 4-5 years (N = 105, 61% boys; 54 ADHD, medication-naïve) were assessed using performance measures (Auditory Continuous Performance Test for Preschoolers-Commission errors, Conflicting Motor Response Test, NEPSY Statue) and caregiver (parent, teacher) ratings of inhibition (Behavior Rating Inventory of Executive Function-Preschool version). Performance measures and parent and teacher reports of inhibitory control significantly and uniquely predicted ADHD group status; however, performance measures did not add to prediction of group status beyond parent reports. Performance measures did significantly predict classroom inhibitory control (teacher ratings), over and above parent reports of inhibitory control. Performance measures of inhibitory control may be adequate predictors of ADHD status and good predictors of young children's classroom inhibitory control, demonstrating utility as components of clinical assessments. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Predicting story goodness performance from cognitive measures following traumatic brain injury.

PubMed

Lê, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-05-01

This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Lê, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. One hundred sixty-seven individuals with TBI participated in the study. Story retellings were analyzed using the SGI protocol. Three cognitive measures--Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) Sorting Test, Wechsler Memory Scale--Third Edition (WMS-III; Wechsler, 1997) Working Memory Primary Index (WMI), and WMS-III Immediate Memory Primary Index (IMI)--were entered into a multiple linear regression model for each discourse measure. Two sets of regression analyses were performed, the first with the Sorting Test as the first predictor and the second with it as the last. The first set of regression analyses identified the Sorting Test and IMI as the only significant predictors of performance on measures of the SGI. The second set identified all measures as significant predictors when evaluating each step of the regression function. The cognitive variables predicted performance on the SGI measures, although there were differences in the amount of explained variance. The results (a) suggest that storytelling ability draws on a number of underlying skills and (b) underscore the importance of using discrete cognitive tasks rather than broad cognitive indices to investigate the cognitive substrates of discourse.

Interfacing VPSC with finite element codes. Demonstration of irradiation growth simulation in a cladding tube

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

Patra, Anirban; Tome, Carlos

This Milestone report shows good progress in interfacing VPSC with the FE codes ABAQUS and MOOSE, to perform component-level simulations of irradiation-induced deformation in Zirconium alloys. In this preliminary application, we have performed an irradiation growth simulation in the quarter geometry of a cladding tube. We have benchmarked VPSC-ABAQUS and VPSC-MOOSE predictions with VPSC-SA predictions to verify the accuracy of the VPSCFE interface. Predictions from the FE simulations are in general agreement with VPSC-SA simulations and also with experimental trends.

A NEW LOG EVALUATION METHOD TO APPRAISE MESAVERDE RE-COMPLETION OPPORTUNITIES

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

Albert Greer

2003-09-11

Artificial intelligence tools, fuzzy logic and neural networks were used to evaluate the potential of the behind pipe Mesaverde formation in BMG's Mancos formation wells. A fractal geostatistical mapping algorithm was also used to predict Mesaverde production. Additionally, a conventional geological study was conducted. To date one Mesaverde completion has been performed. The Janet No.3 Mesaverde completion was non-economic. Both the AI method and the geostatistical methods predicted the failure of the Janet No.3. The Gavilan No.1 in the Mesaverde was completed during the course of the study and was an extremely good well. This well was not included inmore » the statistical dataset. The AI method predicted very good production while the fractal map predicted a poor producer.« less

Performance of Several Density Functional Theory Methods on Describing Hydrogen-Bond Interactions.

PubMed

Rao, Li; Ke, Hongwei; Fu, Gang; Xu, Xin; Yan, Yijing

2009-01-13

We have investigated eleven density functionals, including LDA, PBE, mPWPW91, TPSS, B3LYP, X3LYP, PBE0, O3LYP, B97-1, MPW1K, and TPSSh, for their performances on describing hydrogen bond (HB) interactions. The emphasis has been laid not only on their abilities to calculate the intermolecular hydrogen bonding energies but also on their performances in predicting the relative energies of intermolecular H-bonded complexes and the conformer stabilities due to intramolecular hydrogen bondings. As compared to the best theoretical values, we found that although PBE and PBE0 gave the best estimation of HB strengths, they might fail to predict the correct order of relative HB energies, which might lead to a wrong prediction of the global minimum for different conformers. TPSS and TPSSh did not always improve over PBE and PBE0. B3LYP was found to underestimate the intermolecular HB strengths but was among the best performers in calculating the relative HB energies. We showed here that X3LYP and B97-1 were able to give good values for both absolute HB strengths and relative HB energies, making these functionals good candidates for HB description.

Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

PubMed

Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

2017-05-01

physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

Neurophysiological prediction of neurological good and poor outcome in post-anoxic coma.

PubMed

Grippo, A; Carrai, R; Scarpino, M; Spalletti, M; Lanzo, G; Cossu, C; Peris, A; Valente, S; Amantini, A

2017-06-01

Investigation of the utility of association between electroencephalogram (EEG) and somatosensory-evoked potentials (SEPs) for the prediction of neurological outcome in comatose patients resuscitated after cardiac arrest (CA) treated with therapeutic hypothermia, according to different recording times after CA. Glasgow Coma Scale, EEG and SEPs performed at 12, 24 and 48-72 h after CA were assessed in 200 patients. Outcome was evaluated by Cerebral Performance Category 6 months after CA. Within 12 h after CA, grade 1 EEG predicted good outcome and bilaterally absent (BA) SEPs predicted poor outcome. Because grade 1 EEG and BA-SEPs were never found in the same patient, the recording of both EEG and SEPs allows us to correctly prognosticate a greater number of patients with respect to the use of a single test within 12 h after CA. At 48-72 h after CA, both grade 2 EEG and BA-SEPs predicted poor outcome with FPR=0.0%. When these neurophysiological patterns are both present in the same patient, they confirm and strengthen their prognostic value, but because they also occurred independently in eight patients, poor outcome is predictable in a greater number of patients. The combination of EEG/SEP findings allows prediction of good and poor outcome (within 12 h after CA) and of poor outcome (after 48-72 h). Recording of EEG and SEPs in the same patients allows always an increase in the number of cases correctly classified, and an increase of the reliability of prognostication in a single patient due to concordance of patterns. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Does Nutrition Knowledge and Practice of Athletes Translate to Enhanced Athletic Performance? Cross-Sectional Study Amongst Nigerian Undergraduate Athletes

PubMed Central

Folasire, Oluyemisi F.; Akomolafe, Abiola A.; Sanusi, Rasaki A.

2015-01-01

Introduction and Objectives: Nutrition knowledge of an athlete, as well as practice, is expected to influence athlete’s performance. The study assessed the nutrition knowledge and practice as well as athletes’ performance and identified the factors predicting the athletes’ performance. Methodology: A cross-sectional survey, involved 110 purposively selected undergraduate athletes (47 females, 63 males) of University of Ibadan, Nigeria, between July 2013 and December 2013. A semi-structured, self-administered questionnaire assessed the nutrition knowledge and practice. 24-hr diet recall and food frequency questionnaire were done. Anthropometric measurements were taken; body composition was determined by bioelectrical impedance analysis method. Handgrip strength (HGS), as an indirect measure of athlete performance, was assessed with the hand dynamometer. Chi-square and t-test analysis were used for the bivariate analysis. Pearson correlation and simple linear regression were used to determine relationships and predict athletic performance. The level of statistical significance was p<0.05. Results: More than half (58.2%) had good nutrition knowledge (NK), and 62.7% had good nutrition practices (NP). Majority (75.4%) had normal handgrip strength (HGS). More than 70.0% frequently do not consume cereals, roots and tubers, fruits and vegetables, legumes/nuts. About 30.0-40.0% frequently do not consume eggs/milk, meat/fish. Having good NK was significantly associated with good NP (χ2 = 15.520, p=0.000), but not with athlete’s performance (HGS). There is no significant correlation between NK, NP, and HGS. There is a significant positive correlation between HGS and lean muscle mass (LMM) (r=.670, p=0.000), weight (r=.492, p=0.000), height (r=.521, p=0.000) and energy intake (r=.386, p=0.000). There is a significant negative correlation between HGS and percentage body fat (r=-.400, p=0.000). Athletes’ performance was significantly predicted by the resting metabolic rate (β= .454 C.I=0.011 to 0.045, p=0.003), Lean muscle mass (β =.297 C.I=.059 to 0.562, p=0.024) and the weight (β =.228, C.I=1.852 to .489, p=0.047). Conclusion: Having good nutrition knowledge or practice did not directly determine athletic performance. However, there is the need for nutrition education interventions, to improve athlete’s performance by promoting adequate energy intake, lean muscle mass and appropriate weight gain in athletes. PMID:26156896

Exploring the motivation jungle: predicting performance on a novel task by investigating constructs from different motivation perspectives in tandem.

PubMed

Van Nuland, Hanneke J C; Dusseldorp, Elise; Martens, Rob L; Boekaerts, Monique

2010-08-01

Different theoretical viewpoints on motivation make it hard to decide which model has the best potential to provide valid predictions on classroom performance. This study was designed to explore motivation constructs derived from different motivation perspectives that predict performance on a novel task best. Motivation constructs from self-determination theory, self-regulation theory, and achievement goal theory were investigated in tandem. Performance was measured by systematicity (i.e. how systematically students worked on a problem-solving task) and test score (i.e. score on a multiple-choice test). Hierarchical regression analyses on data from 259 secondary school students showed a quadratic relation between a performance avoidance orientation and both performance outcomes, indicating that extreme high and low performance avoidance resulted in the lowest performance. Furthermore, two three-way interaction effects were found. Intrinsic motivation seemed to play a key role in test score and systematicity performance, provided that effort regulation and metacognitive skills were both high. Results indicate that intrinsic motivation in itself is not enough to attain a good performance. Instead, a moderate score on performance avoidance, together with the ability to remain motivated and effectively regulate and control task behavior, is needed to attain a good performance. High time management skills also contributed to higher test score and systematicity performance and a low performance approach orientation contributed to higher systematicity performance. We concluded that self-regulatory skills should be trained in order to have intrinsically motivated students perform well on novel tasks in the classroom.

Predicting the names of the best teams after the knock-out phase of a cricket series.

PubMed

Lemmer, Hermanus Hofmeyr

2014-01-01

Cricket players' performances can best be judged after a large number of matches had been played. For test or one-day international (ODI) players, career data are normally used to calculate performance measures. These are normally good indicators of future performances, although various factors influence the performance of a player in a specific match. It is often necessary to judge players' performances based on a small number of scores, e.g. to identify the best players after a short series of matches. The challenge then is to use the best available criteria in order to assess performances as accurately and fairly as possible. In the present study the results of the knock-out phase of an International Cricket Council (ICC) World Cup ODI Series are used to predict the names of the best teams by means of a suitably formulated logistic regression model. Despite using very sparse data, the methods used are reasonably successful. It is also shown that if the same technique is applied to career ratings, very good results are obtained.

When Did You Last Predict a Good Idea?

ERIC Educational Resources Information Center

Penaluna, Kathryn; Penaluna, Andrew; Jones, Colin; Matlay, Harry

2014-01-01

It has been noted elsewhere that an idea is acknowledged to be creative if it is novel, or surprising and adaptive. So how does that fit with education's desire to measure student performance against fixed, consistent and predicted learning outcomes? This study explores practical measures and theoretical constructs that address the dearth of…

On the use and the performance of software reliability growth models

NASA Technical Reports Server (NTRS)

Keiller, Peter A.; Miller, Douglas R.

1991-01-01

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models, and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria. Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six of the former models and eight of the latter are evaluated, based on their performance on twenty data sets. Many open questions remain regarding the use and the performance of software reliability growth models.

Relationship between dean's letter rankings and later evaluations by residency program directors.

PubMed

Lurie, Stephen J; Lambert, David R; Grady-Weliky, Tana A

2007-01-01

It is not known how well dean's letter rankings predict later performance in residency. To assess the accuracy of dean's letter rankings to predict clinical performance in internship. Participants were medical students who graduated from the University of Rochester School of Medicine and Dentistry in the classes of 2003 and 2004. In their Dean's Letter, each student was ranked as either "Outstanding" (upper quartile), "Excellent" (second quartile), "Very good" (lower 2 quartiles), or "Good" (lowest few percentile). We compared these dean's letter rankings against results of questionnaires sent to program directors 9 months after graduation. Response rate to the questionnaire was 58.9% (109 of 185 eligible graduates). There were no differences in response rate across the four dean's letter ranking categories. Program directors rated students in the top two categories of dean's letter rankings significantly higher than those in the very good group. Students in all three groups were rated significantly higher than those in the good group, F (3, 105) = 13.37, p < .001. Students in the very good group were most variable in their ratings by program directors, with many receiving similarly high ratings as students in the upper 2 groups. There were no differences by gender or specialty. Dean's letter rankings are a significant predictor of later performance in internship among graduates of our medical school. Students in the bottom half of the class are most likely either to underperform or overperform in internship.

Measured and predicted rotor performance for the SERI advanced wind turbine blades

NASA Astrophysics Data System (ADS)

Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

1992-02-01

Measured and predicted rotor performance for the Solar Energy Research Institute (SERI) advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction.

Inter-comparison of time series models of lake levels predicted by several modeling strategies

NASA Astrophysics Data System (ADS)

Khatibi, R.; Ghorbani, M. A.; Naghipour, L.; Jothiprakash, V.; Fathima, T. A.; Fazelifard, M. H.

2014-04-01

Five modeling strategies are employed to analyze water level time series of six lakes with different physical characteristics such as shape, size, altitude and range of variations. The models comprise chaos theory, Auto-Regressive Integrated Moving Average (ARIMA) - treated for seasonality and hence SARIMA, Artificial Neural Networks (ANN), Gene Expression Programming (GEP) and Multiple Linear Regression (MLR). Each is formulated on a different premise with different underlying assumptions. Chaos theory is elaborated in a greater detail as it is customary to identify the existence of chaotic signals by a number of techniques (e.g. average mutual information and false nearest neighbors) and future values are predicted using the Nonlinear Local Prediction (NLP) technique. This paper takes a critical view of past inter-comparison studies seeking a superior performance, against which it is reported that (i) the performances of all five modeling strategies vary from good to poor, hampering the recommendation of a clear-cut predictive model; (ii) the performances of the datasets of two cases are consistently better with all five modeling strategies; (iii) in other cases, their performances are poor but the results can still be fit-for-purpose; (iv) the simultaneous good performances of NLP and SARIMA pull their underlying assumptions to different ends, which cannot be reconciled. A number of arguments are presented including the culture of pluralism, according to which the various modeling strategies facilitate an insight into the data from different vantages.

[Predictive model based multimetric index of macroinvertebrates for river health assessment].

PubMed

Chen, Kai; Yu, Hai Yan; Zhang, Ji Wei; Wang, Bei Xin; Chen, Qiu Wen

2017-06-18

Improving the stability of integrity of biotic index (IBI; i.e., multi-metric indices, MMI) across temporal and spatial scales is one of the most important issues in water ecosystem integrity bioassessment and water environment management. Using datasets of field-based macroinvertebrate and physicochemical variables and GIS-based natural predictors (e.g., geomorphology and climate) and land use variables collected at 227 river sites from 2004 to 2011 across the Zhejiang Province, China, we used random forests (RF) to adjust the effects of natural variations at temporal and spatial scales on macroinvertebrate metrics. We then developed natural variations adjusted (predictive) and unadjusted (null) MMIs and compared performance between them. The core me-trics selected for predictive and null MMIs were different from each other, and natural variations within core metrics in predictive MMI explained by RF models ranged between 11.4% and 61.2%. The predictive MMI was more precise and accurate, but less responsive and sensitive than null MMI. The multivariate nearest-neighbor test determined that 9 test sites and 1 most degraded site were flagged outside of the environmental space of the reference site network. We found that combination of predictive MMI developed by using predictive model and the nearest-neighbor test performed best and decreased risks of inferring type I (designating a water body as being in poor biological condition, when it was actually in good condition) and type II (designating a water body as being in good biological condition, when it was actually in poor condition) errors. Our results provided an effective method to improve the stability and performance of integrity of biotic index.

Quantifying effectiveness of failure prediction and response in HPC systems : methodology and example.

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

Mayo, Jackson R.; Chen, Frank Xiaoxiao; Pebay, Philippe Pierre

2010-06-01

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe context-relevant methodologies for determining themore » accuracy and cost-benefit of predictors. While many research studies have quantified the expected impact of growing system size, and the associated shortened mean time to failure (MTTF), on application performance in large-scale high-performance computing (HPC) platforms, there has been little if any work to quantify the possible gains from predicting system resource failures with significant but imperfect accuracy. This possibly stems from HPC system complexity and the fact that, to date, no one has established any good predictors of failure in these systems. Our work in the OVIS project aims to discover these predictors via a variety of data collection techniques and statistical analysis methods that yield probabilistic predictions. The question then is, 'How good or useful are these predictions?' We investigate methods for answering this question in a general setting, and illustrate them using a specific failure predictor discovered on a production system at Sandia.« less

The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Cruz, David; Hernández, Sandra; Genis, Alma; Carrillo-Guerrero, Mariana Y; Avilés Reyes, Rubén; Guàrdia-Olmos, Joan

2010-12-01

Major depressive disorder (MDD) is treated with antidepressants, but only between 50% and 70% of the patients respond to the initial treatment. Several authors suggested different factors that could predict antidepressant response, including clinical, psychophysiological, neuropsychological, neuroimaging, and genetic variables. However, these different predictors present poor prognostic sensitivity and specificity by themselves. The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms in the prediction of the response to fluoxetine after 4weeks of treatment in a sample of patient with MDD. 64 patients with MDD were genotyped according to the above-mentioned polymorphisms, and were clinically and neuropsychologically assessed before a 4-week fluoxetine treatment. Fluoxetine response was assessed by using the Hamilton Depression Rating Scale. We carried out a binary logistic regression model for the potential predictive variables. Out of the clinical variables studied, only the number of anxiety disorders comorbid with MDD have predicted a poor response to the treatment. A combination of a good performance in variables of attention and low performance in planning could predict a good response to fluoxetine in patients with MDD. None of the genetic variables studied had predictive value in our model. The possible placebo effect has not been controlled. Our study is focused on response prediction but not in remission prediction. Our work suggests that the combination of the number of comorbid anxiety disorders, an attentional variable, and two planning variables makes it possible to correctly classify 82% of the depressed patients who responded to the treatment with fluoxetine, and 74% of the patients who did not respond to that treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

Phenotypic plasticity and specialization in clonal versus non-clonal plants: A data synthesis

NASA Astrophysics Data System (ADS)

Fazlioglu, Fatih; Bonser, Stephen P.

2016-11-01

Reproductive strategies can be associated with ecological specialization and generalization. Clonal plants produce lineages adapted to the maternal habitat that can lead to specialization. However, clonal plants frequently display high phenotypic plasticity (e.g. clonal foraging for resources), factors linked to ecological generalization. Alternately, sexual reproduction can be associated with generalization via increasing genetic variation or specialization through rapid adaptive evolution. Moreover, specializing to high or low quality habitats can determine how phenotypic plasticity is expressed in plants. The specialization hypothesis predicts that specialization to good environments results in high performance trait plasticity and specialization to bad environments results in low performance trait plasticity. The interplay between reproductive strategies, phenotypic plasticity, and ecological specialization is important for understanding how plants adapt to variable environments. However, we currently have a poor understanding of these relationships. In this study, we addressed following questions: 1) Is there a relationship between phenotypic plasticity, specialization, and reproductive strategies in plants? 2) Do good habitat specialists express greater performance trait plasticity than bad habitat specialists? We searched the literature for studies examining plasticity for performance traits and functional traits in clonal and non-clonal plant species from different habitat types. We found that non-clonal (obligate sexual) plants expressed greater performance trait plasticity and functional trait plasticity than clonal plants. That is, non-clonal plants exhibited a specialist strategy where they perform well only in a limited range of habitats. Clonal plants expressed less performance loss across habitats and a more generalist strategy. In addition, specialization to good habitats did not result in greater performance trait plasticity. This result was contrary to the predictions of the specialization hypothesis. Overall, reproductive strategies are associated with ecological specialization or generalization through phenotypic plasticity. While specialization is common in plant populations, the evolution of specialization does not control the nature of phenotypic plasticity as predicted under the specialization hypothesis.

Design of Supersonic Transport Flap Systems for Thrust Recovery at Subsonic Speeds

NASA Technical Reports Server (NTRS)

Mann, Michael J.; Carlson, Harry W.; Domack, Christopher S.

1999-01-01

A study of the subsonic aerodynamics of hinged flap systems for supersonic cruise commercial aircraft has been conducted using linear attached-flow theory that has been modified to include an estimate of attainable leading edge thrust and an approximate representation of vortex forces. Comparisons of theoretical predictions with experimental results show that the theory gives a reasonably good and generally conservative estimate of the performance of an efficient flap system and provides a good estimate of the leading and trailing-edge deflection angles necessary for optimum performance. A substantial reduction in the area of the inboard region of the leading edge flap has only a minor effect on the performance and the optimum deflection angles. Changes in the size of the outboard leading-edge flap show that performance is greatest when this flap has a chord equal to approximately 30 percent of the wing chord. A study was also made of the performance of various combinations of individual leading and trailing-edge flaps, and the results show that aerodynamic efficiencies as high as 85 percent of full suction are predicted.

A Soil Temperature Model for Closed Canopied Forest Stands

Treesearch

James M. Vose; Wayne T. Swank

1991-01-01

A microcomputer-based soil temperature model was developed to predict temperature at the litter-soil interface and soil temperatures at three depths (0.10 m, 0.20 m, and 1.25 m) under closed forest canopies. Comparisons of predicted and measured soil temperatures indicated good model performance under most conditions. When generalized parameters describing soil...

Matching Pupils and Teachers to Maximize Expected Outcomes.

ERIC Educational Resources Information Center

Ward, Joe H., Jr.; And Others

To achieve a good teacher-pupil match, it is necessary (1) to predict the learning outcomes that will result when each student is instructed by each teacher, (2) to use the predicted performance to compute an Optimality Index for each teacher-pupil combination to indicate the quality of each combination toward maximizing learning for all students,…

Predicting the 10-year risk of hip and major osteoporotic fracture in rheumatoid arthritis and in the general population: an independent validation and update of UK FRAX without bone mineral density

PubMed Central

Klop, Corinne; de Vries, Frank; Bijlsma, Johannes W J; Leufkens, Hubert G M; Welsing, Paco M J

2016-01-01

Objectives FRAX incorporates rheumatoid arthritis (RA) as a dichotomous predictor for predicting the 10-year risk of hip and major osteoporotic fracture (MOF). However, fracture risk may deviate with disease severity, duration or treatment. Aims were to validate, and if needed to update, UK FRAX for patients with RA and to compare predictive performance with the general population (GP). Methods Cohort study within UK Clinical Practice Research Datalink (CPRD) (RA: n=11 582, GP: n=38 755), also linked to hospital admissions for hip fracture (CPRD-Hospital Episode Statistics, HES) (RA: n=7221, GP: n=24 227). Predictive performance of UK FRAX without bone mineral density was assessed by discrimination and calibration. Updating methods included recalibration and extension. Differences in predictive performance were assessed by the C-statistic and Net Reclassification Improvement (NRI) using the UK National Osteoporosis Guideline Group intervention thresholds. Results UK FRAX significantly overestimated fracture risk in patients with RA, both for MOF (mean predicted vs observed 10-year risk: 13.3% vs 8.4%) and hip fracture (CPRD: 5.5% vs 3.1%, CPRD-HES: 5.5% vs 4.1%). Calibration was good for hip fracture in the GP (CPRD-HES: 2.7% vs 2.4%). Discrimination was good for hip fracture (RA: 0.78, GP: 0.83) and moderate for MOF (RA: 0.69, GP: 0.71). Extension of the recalibrated UK FRAX using CPRD-HES with duration of RA disease, glucocorticoids (>7.5 mg/day) and secondary osteoporosis did not improve the NRI (0.01, 95% CI −0.04 to 0.05) or C-statistic (0.78). Conclusions UK FRAX overestimated fracture risk in RA, but performed well for hip fracture in the GP after linkage to hospitalisations. Extension of the recalibrated UK FRAX did not improve predictive performance. PMID:26984006

Forecasting electricity usage using univariate time series models

NASA Astrophysics Data System (ADS)

Hock-Eam, Lim; Chee-Yin, Yip

2014-12-01

Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.

PubMed

Lustgarten, Jonathan Lyle; Balasubramanian, Jeya Balaji; Visweswaran, Shyam; Gopalakrishnan, Vanathi

2017-03-01

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Predicting Performance in Higher Education Using Proximal Predictors.

PubMed

Niessen, A Susan M; Meijer, Rob R; Tendeiro, Jorge N

2016-01-01

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance.

Calibration power of the Braden scale in predicting pressure ulcer development.

PubMed

Chen, Hong-Lin; Cao, Ying-Juan; Wang, Jing; Huai, Bao-Sha

2016-11-02

Calibration is the degree of correspondence between the estimated probability produced by a model and the actual observed probability. The aim of this study was to investigate the calibration power of the Braden scale in predicting pressure ulcer development (PU). A retrospective analysis was performed among consecutive patients in 2013. The patients were separated into training a group and a validation group. The predicted incidence was calculated using a logistic regression model in the training group and the Hosmer-Lemeshow test was used for assessing the goodness of fit. In the validation cohort, the observed and the predicted incidence were compared by the Chi-square (χ 2 ) goodness of fit test for calibration power. We included 2585 patients in the study, of these 78 patients (3.0%) developed a PU. Between the training and validation groups the patient characteristics were non-significant (p>0.05). In the training group, the logistic regression model for predicting pressure ulcer was Logit(P) = -0.433*Braden score+2.616. The Hosmer-Lemeshow test showed no goodness fit (χ 2 =13.472; p=0.019). In the validation group, the predicted pressure ulcer incidence also did not fit well with the observed incidence (χ 2 =42.154, p=0.000 by Braden scores; and χ 2 =17.223, p=0.001 by Braden scale risk classification). The Braden scale has low calibration power in predicting PU formation.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

A comprehensive mechanistic model for upward two-phase flow in wellbores

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

Sylvester, N.D.; Sarica, C.; Shoham, O.

1994-05-01

A comprehensive model is formulated to predict the flow behavior for upward two-phase flow. This model is composed of a model for flow-pattern prediction and a set of independent mechanistic models for predicting such flow characteristics as holdup and pressure drop in bubble, slug, and annular flow. The comprehensive model is evaluated by using a well data bank made up of 1,712 well cases covering a wide variety of field data. Model performance is also compared with six commonly used empirical correlations and the Hasan-Kabir mechanistic model. Overall model performance is in good agreement with the data. In comparison withmore » other methods, the comprehensive model performed the best.« less

Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal).

PubMed

Collins, G S; Altman, D G

2012-07-10

Early identification of colorectal cancer is an unresolved challenge and the predictive value of single symptoms is limited. We evaluated the performance of QCancer (Colorectal) prediction model for predicting the absolute risk of colorectal cancer in an independent UK cohort of patients from general practice records. A total of 2.1 million patients registered with a general practice surgery between 01 January 2000 and 30 June 2008, aged 30-84 years (3.7 million person-years) with 3712 colorectal cancer cases were included in the analysis. Colorectal cancer was defined as incident diagnosis of colorectal cancer during the 2 years after study entry. The results from this independent and external validation of QCancer (Colorectal) prediction model demonstrated good performance data on a large cohort of general practice patients. QCancer (Colorectal) had very good discrimination with an area under the ROC curve of 0.92 (women) and 0.91 (men), and explained 68% (women) and 66% (men) of the variation. QCancer (Colorectal) was well calibrated across all tenths of risk and over all age ranges with predicted risks closely matching observed risks. QCancer (Colorectal) appears to be a useful tool for identifying undetected cases of undiagnosed colorectal cancer in primary care in the United Kingdom.

Monte Carlo Simulation of Plumes Spectral Emission

DTIC Science & Technology

2005-06-07

ERIM experimental data for hot cell radiance has been performed. It has been shown that NASA standard infrared optical model [3] provides good...Influence of different optical models on predicted numerical data on hot cell radiance for ERIM experimental conditions has been studied. 7...prediction (solid line) of the Hot cell radiance. NASA Standard Infrared Radiation model ; averaged rotational line structure (JLBL=0); spectral

Stationary measure in the multiverse

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

Linde, Andrei; Vanchurin, Vitaly; Winitzki, Sergei, E-mail: alinde@stanford.edu, E-mail: vitaly@cosmos2.phy.tufts.edu, E-mail: winitzki@physik.uni-muenchen.de

2009-01-15

We study the recently proposed ''stationary measure'' in the context of the string landscape scenario. We show that it suffers neither from the ''Boltzmann brain'' problem nor from the ''youngness'' paradox that makes some other measures predict a high CMB temperature at present. We also demonstrate a good performance of this measure in predicting the results of local experiments, such as proton decay.

Fine-motor skills testing and prediction of endovascular performance.

PubMed

Bech, Bo; Lönn, Lars; Schroeder, Torben V; Ringsted, Charlotte

2013-12-01

Performing endovascular procedures requires good control of fine-motor digital movements and hand-eye coordination. Objective assessment of such skills is difficult. Trainees acquire control of catheter/wire movements at various paces. However, little is known to what extent talent plays for novice candidates at entry to practice. To study the association between performance in a novel aptitude test of fine-motor skills and performance in simulated procedures. The test was based on manual course-tracking using a proprietary hand-operated roller-bar device coupled to a personal computer with monitor view rotation. A total of 40 test repetitions were conducted separately with each hand. Test scores were correlated with simulator performance. Group A (n = 14), clinicians with various levels of endovascular experience, performed a simulated procedure of contralateral iliac artery stenting. Group B (n = 19), medical students, performed 10 repetitions of crossing a challenging aortic bifurcation in a simulator. The test score differed markedly between the individuals in both groups, in particular with the non-dominant hand. Group A: the test score with the non-dominant hand correlated significantly with simulator performance assessed with the global rating scale SAVE (R = -0.69, P = 0.007). There was no association observed from performances with the dominant hand. Group B: there was no significant association between the test score and endovascular skills acquisition neither with the dominant nor with the non-dominant hand. Clinicians with increasing levels of endovascular technical experience had developed good fine-motor control of the non-dominant hand, in particular, that was associated with good procedural performance in the simulator. The aptitude test did not predict endovascular skills acquisition among medical students, thus, cannot be suggested for selection of novice candidates. Procedural experience and practice probably supplant the influence of innate abilities (talent) over time.

The predictive validity of a situational judgement test, a clinical problem solving test and the core medical training selection methods for performance in specialty training .

PubMed

Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David

2017-02-01

The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.

Performance of Simplified Acute Physiology Score 3 In Predicting Hospital Mortality In Emergency Intensive Care Unit.

PubMed

Ma, Qing-Bian; Fu, Yuan-Wei; Feng, Lu; Zhai, Qiang-Rong; Liang, Yang; Wu, Meng; Zheng, Ya-An

2017-07-05

Since the 1980s, severity of illness scoring systems has gained increasing popularity in Intensive Care Units (ICUs). Physicians used them for predicting mortality and assessing illness severity in clinical trials. The objective of this study was to assess the performance of Simplified Acute Physiology Score 3 (SAPS 3) and its customized equation for Australasia (Australasia SAPS 3, SAPS 3 [AUS]) in predicting clinical prognosis and hospital mortality in emergency ICU (EICU). A retrospective analysis of the EICU including 463 patients was conducted between January 2013 and December 2015 in the EICU of Peking University Third Hospital. The worst physiological data of enrolled patients were collected within 24 h after admission to calculate SAPS 3 score and predicted mortality by regression equation. Discrimination between survivals and deaths was assessed by the area under the receiver operator characteristic curve (AUC). Calibration was evaluated by Hosmer-Lemeshow goodness-of-fit test through calculating the ratio of observed-to-expected numbers of deaths which is known as the standardized mortality ratio (SMR). A total of 463 patients were enrolled in the study, and the observed hospital mortality was 26.1% (121/463). The patients enrolled were divided into survivors and nonsurvivors. Age, SAPS 3 score, Acute Physiology and Chronic Health Evaluation Score II (APACHE II), and predicted mortality were significantly higher in nonsurvivors than survivors (P < 0.05 or P < 0.01). The AUC (95% confidence intervals [CI s]) for SAPS 3 score was 0.836 (0.796-0.876). The maximum of Youden's index, cutoff, sensitivity, and specificity of SAPS 3 score were 0.526%, 70.5 points, 66.9%, and 85.7%, respectively. The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 demonstrated a Chi-square test score of 10.25, P = 0.33, SMR (95% CI) = 0.63 (0.52-0.76). The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 (AUS) demonstrated a Chi-square test score of 9.55, P = 0.38, SMR (95% CI) = 0.68 (0.57-0.81). Univariate and multivariate analyses were conducted for biochemical variables that were probably correlated to prognosis. Eventually, blood urea nitrogen (BUN), albumin,lactate and free triiodothyronine (FT3) were selected as independent risk factors for predicting prognosis. The SAPS 3 score system exhibited satisfactory performance even superior to APACHE II in discrimination. In predicting hospital mortality, SAPS 3 did not exhibit good calibration and overestimated hospital mortality, which demonstrated that SAPS 3 needs improvement in the future.

What Makes for a Good Teacher and Who Can Tell? Working Paper 30

ERIC Educational Resources Information Center

Harris, Douglas N.; Sass, Tim R.

2009-01-01

Mounting pressure in the policy arena to improve teacher productivity either by improving signals that predict teacher performance or through creating incentive contracts based on performance--has spurred two related questions: Are there important determinants of teacher productivity that are not captured by teacher credentials but that can be…

Predictive performance and inter-laboratory reproducibility in assessing eye irritation potential of water- and oil-soluble mixtures using the Short Time Exposure test method.

PubMed

Abo, Takayuki; Hilberer, Allison; Behle-Wagner, Christine; Watanabe, Mika; Cameron, David; Kirst, Annette; Nukada, Yuko; Yuki, Takuo; Araki, Daisuke; Sakaguchi, Hitoshi; Itagaki, Hiroshi

2018-04-01

The Short Time Exposure (STE) test method is an alternative method for assessing eye irritation potential using Statens Seruminstitut Rabbit Cornea cells and has been adopted as test guideline 491 by the Organisation for Economic Co-operation and Development. Its good predictive performance in identifying the Globally Harmonized System (GHS) No Category (NC) or Irritant Category has been demonstrated in evaluations of water-soluble substances, oil-soluble substances, and water-soluble mixtures. However, the predictive performance for oil-soluble mixtures was not evaluated. Twenty-four oil-soluble mixtures were evaluated using the STE test method. The GHS NC or Irritant Category of 22 oil-soluble mixtures were consistent with that of a Reconstructed human Cornea-like Epithelium (RhCE) test method. Inter-laboratory reproducibility was then confirmed using 20 water- and oil-soluble mixtures blind-coded. The concordance in GHS NC or Irritant Category among four laboratories was 90%-100%. In conclusion, the concordance in comparison with the results of RhCE test method using 24 oil-soluble mixtures and inter-laboratory reproducibility using 20 water- and oil-soluble mixtures blind-coded were good, indicating that the STE test method is a suitable alternative for predicting the eye irritation potential of both substances and mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B

PubMed Central

Cheng, Jieyao; Hou, Jinlin; Ding, Huiguo; Chen, Guofeng; Xie, Qing; Wang, Yuming; Zeng, Minde; Ou, Xiaojuan; Ma, Hong; Jia, Jidong

2015-01-01

Background and Aims Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients. Methods The diagnostic performance of ten noninvasive models (HALF index, FibroScan, S index, Zeng model, Youyi model, Hui model, APAG, APRI, FIB-4 and FibroTest) was assessed against the liver histology by ROC curve analysis in CHB patients. The reproducibility of the ten models were evaluated by recalculating the diagnostic values at the given cut-off values defined by the original studies. Results Six models (HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest) had AUROCs higher than 0.70 in predicting any fibrosis stage and 2 of them had best diagnostic performance with AUROCs to predict F≥2, F≥3 and F4 being 0.83, 0.89 and 0.89 for HALF index, 0.82, 0.87 and 0.87 for FibroScan, respectively. Four models (HALF index, FibroScan, Zeng model and Youyi model) showed good diagnostic values at given cut-offs. Conclusions HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest show a good diagnostic performance and all of them, except S index and FibroTest, have good reproducibility for evaluating liver fibrosis in CHB patients. Registration Number ChiCTR-DCS-07000039. PMID:26709706

Validation of the Sepsis Severity Score Compared with Updated Severity Scores in Predicting Hospital Mortality in Sepsis Patients.

PubMed

Khwannimit, Bodin; Bhurayanontachai, Rungsun; Vattanavanit, Veerapong

2017-06-01

Recently, the Sepsis Severity Score (SSS) was constructed to predict mortality in sepsis patients. The aim of this study was to compare performance of the SSS with the Acute Physiology and Chronic Health Evaluation (APACHE) II-IV, Simplified Acute Physiology Score (SAPS) II, and SAPS 3 scores in predicting hospital outcome in sepsis patients. A retroprospective analysis was conducted in the medical intensive care unit of a tertiary university hospital. A total of 913 patients were enrolled; 476 of these patients (52.1%) had septic shock. The median SSS was 80 (range 20-137). The SSS presented good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.892. However, the AUC of the SSS did not differ significantly from that of APACHE II (P = 0.07), SAPS II (P = 0.06), and SAPS 3 (P = 0.11). The APACHE IV score showed the best discrimination with an AUC of 0.948 and the overall performance by a Brier score of 0.096. The AUC of the APACHE IV score was statistically greater than the SSS, APACHE II, SAPS II, and SAPS 3 (P <0.0001 for all) and APACHE III (P = 0.0002). The calibration of all scores was poor with the Hosmer-Lemeshow goodness-of-fit H test <0.05. The SSS provided as good discrimination as the APACHE II, SAPS II, and SAPS 3 scores. However, the APACHE IV score had the best discrimination and overall performance in our sepsis patients. The SSS needs to be adapted and modified with new parameters to improve its performance.

Does Parsonnet scoring model predict mortality following adult cardiac surgery in India?

PubMed

Srilata, Moningi; Padhy, Narmada; Padmaja, Durga; Gopinath, Ramachandran

2015-01-01

To validate the Parsonnet scoring model to predict mortality following adult cardiac surgery in Indian scenario. A total of 889 consecutive patients undergoing adult cardiac surgery between January 2010 and April 2011 were included in the study. The Parsonnet score was determined for each patient and its predictive ability for in-hospital mortality was evaluated. The validation of Parsonnet score was performed for the total data and separately for the sub-groups coronary artery bypass grafting (CABG), valve surgery and combined procedures (CABG with valve surgery). The model calibration was performed using Hosmer-Lemeshow goodness of fit test and receiver operating characteristics (ROC) analysis for discrimination. Independent predictors of mortality were assessed from the variables used in the Parsonnet score by multivariate regression analysis. The overall mortality was 6.3% (56 patients), 7.1% (34 patients) for CABG, 4.3% (16 patients) for valve surgery and 16.2% (6 patients) for combined procedures. The Hosmer-Lemeshow statistic was <0.05 for the total data and also within the sub-groups suggesting that the predicted outcome using Parsonnet score did not match the observed outcome. The area under the ROC curve for the total data was 0.699 (95% confidence interval 0.62-0.77) and when tested separately, it was 0.73 (0.64-0.81) for CABG, 0.79 (0.63-0.92) for valve surgery (good discriminatory ability) and only 0.55 (0.26-0.83) for combined procedures. The independent predictors of mortality determined for the total data were low ejection fraction (odds ratio [OR] - 1.7), preoperative intra-aortic balloon pump (OR - 10.7), combined procedures (OR - 5.1), dialysis dependency (OR - 23.4), and re-operation (OR - 9.4). The Parsonnet score yielded a good predictive value for valve surgeries, moderate predictive value for the total data and for CABG and poor predictive value for combined procedures.

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Risk factors for treatment failure after allogeneic transplantation of patients with CLL: a report from the European Society for Blood and Marrow Transplantation.

PubMed

Schetelig, J; de Wreede, L C; van Gelder, M; Andersen, N S; Moreno, C; Vitek, A; Karas, M; Michallet, M; Machaczka, M; Gramatzki, M; Beelen, D; Finke, J; Delgado, J; Volin, L; Passweg, J; Dreger, P; Henseler, A; van Biezen, A; Bornhäuser, M; Schönland, S O; Kröger, N

2017-04-01

For young patients with high-risk CLL, BTK-/PI3K-inhibitors or allogeneic stem cell transplantation (alloHCT) are considered. Patients with a low risk of non-relapse mortality (NRM) but a high risk of failure of targeted therapy may benefit most from alloHCT. We performed Cox regression analyses to identify risk factors for 2-year NRM and 5-year event-free survival (using EFS as a surrogate for long-term disease control) in a large, updated EBMT registry cohort (n= 694). For the whole cohort, 2-year NRM was 28% and 5-year EFS 37%. Higher age, lower performance status, unrelated donor type and unfavorable sex-mismatch had a significant adverse impact on 2-year NRM. Two-year NRM was calculated for good- and poor-risk reference patients. Predicted 2-year-NRM was 11 and 12% for male and female good-risk patients compared with 42 and 33% for male and female poor-risk patients. For 5-year EFS, age, performance status, prior autologous HCT, remission status and sex-mismatch had a significant impact, whereas del(17p) did not. The model-based prediction of 5-year EFS was 55% and 64%, respectively, for male and female good-risk patients. Good-risk transplant candidates with high-risk CLL and limited prognosis either on or after failure of targeted therapy should still be considered for alloHCT.

Payment for Environmental Services: Hypotheses and Evidence

PubMed Central

Alston, Lee J.; Andersson, Krister; Smith, Steven M.

2014-01-01

The use of payment for environmental services (PES) is not a new type of contract, but PES programs have become more in vogue because of the potential for sequestering carbon by paying to prevent deforestation and degradation of forestlands. We provide a framework utilizing transaction costs to hypothesize which services are more likely to be provided effectively. We then interpret the literature on PES programs to see the extent to which transaction costs vary as predicted across the type of service and to assess the performance of PES programs. As predicted, we find that transaction costs are the least for club goods like water and greatest for pure public goods like carbon reduction. Actual performance is difficult to measure and varies across the examples. More work and experimentation are needed to gain a better outlook on what elements support effective delivery of environmental services. PMID:25143798

Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13-16 years.

PubMed

Burns, Ryan D; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Shultz, Barry B; Saint-Maurice, Pedro F; Welk, Gregory J; Mahar, Matthew T

2016-01-01

A popular algorithm to predict VO2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years. The 1MRW was administered on an outside track and a laboratory VO2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO2Peak = 7.34 × (1MRW speed in m s(-1)) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg(-1) · min(-1), with measured VO2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM's Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO2Peak in adolescents aged 13-16 years.

Predictors of adolescent compliance with oral hygiene instructions during two-arch multibracket fixed orthodontic treatment.

PubMed

Al-Jewair, Thikriat S; Suri, Sunjay; Tompson, Bryan D

2011-05-01

To determine compliance with oral hygiene instructions (OHI) of adolescents receiving two-arch multibracket fixed appliances and identify its predictive factors. Forty-one patients in a longitudinal study were provided standardized OHI and assessed at baseline: before bonding (T0mo), approximately 30 days after bonding (T1mo), and approximately 150 days (T5mo) after bonding straight-wire appliances simultaneously in the maxillary and mandibular arches. Oral hygiene (OH) performance was measured using plaque and gingival indices. Compliance predictors were identified from questionnaires administered to patients and their parents and from patients' charts. OH performance worsened from T0mo to T1mo but then improved from T1mo to T5mo. At T5mo, 73% of the sample had good OH. Univariate analyses found perceived severity of malocclusion, school performance, and parental marital status to be significant predictors of good OH performance at T5mo. Multiple logistic regressions identified having married parents and good academic performance in school as significant predictors. In the sample studied, after initially worsening, compliance with OHI improved at 5 months after bonding. Adolescents with married parents and those reporting good academic performance in school were found more likely to have complied with OHI provided at baseline and to perform better OH.

Extended resource allocation index for link prediction of complex network

NASA Astrophysics Data System (ADS)

Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

2017-08-01

Recently, a number of similarity-based methods have been proposed to predict the missing links in complex network. Among these indices, the resource allocation index performs very well with lower time complexity. However, it ignores potential resources transferred by local paths between two endpoints. Motivated by the resource exchange taking places between endpoints, an extended resource allocation index is proposed. Empirical study on twelve real networks and three synthetic dynamic networks has shown that the index we proposed can achieve a good performance, compared with eight mainstream baselines.

Novel and conventional serum biomarkers predicting acute kidney injury in adult cardiac surgery--a prospective cohort study.

PubMed

Haase-Fielitz, Anja; Bellomo, Rinaldo; Devarajan, Prasad; Story, David; Matalanis, George; Dragun, Duska; Haase, Michael

2009-02-01

To compare the value of novel with conventional serum biomarkers in the prediction of acute kidney injury (AKI) in adult cardiac surgical patients according to preoperative renal function. Single-center, prospective observational study. Tertiary hospital. One hundred adult cardiac surgical patients. We measured concentrations of plasma neutrophil gelatinase-associated lipocalin (NGAL), and serum cystatin C, and creatinine and urea at baseline, on arrival in the intensive care unit (ICU) and at 24 hours postoperatively. We assessed such biomarkers in relation to the development of AKI (>50% increase in creatinine from baseline) and to a composite end point (need for renal replacement therapy and in-hospital mortality). We defined an area under the receiver operating characteristic curve of 0.60-0.69 as poor, 0.70-0.79 as fair, 0.80-0.89 as good, and 0.90-1.00 as excellent in terms of predictive value. On arrival in ICU, plasma NGAL and serum cystatin C were of good predictive value, but creatinine and urea were of poor predictive value. After exclusion of patients with preoperative renal impairment (estimated glomerular filtration rate <60 mL/min), the predictive performance for AKI of all renal biomarkers on arrival in ICU remained unchanged except for cystatin C, which was of fair value in such patients. At 24 hours postoperatively, all renal biomarkers were of good predictive value. On arrival in ICU, novel biomarkers were superior to conventional biomarkers (p < 0.05). Plasma NGAL (p = 0.015) and serum cystatin C (p = 0.007) were independent predictors of AKI and of excellent value in the prediction of the composite end point. Early postoperative measurement of plasma NGAL was of good value in identifying patients who developed AKI after adult cardiac surgery. Plasma NGAL and serum cystatin C were superior to conventional biomarkers in the prediction of AKI and were also of prognostic value in this setting.

Interactions of age and cognitive functions in predicting decision making under risky conditions over the life span.

PubMed

Brand, Matthias; Schiebener, Johannes

2013-01-01

Little is known about how normal healthy aging affects decision-making competence. In this study 538 participants (age 18-80 years) performed the Game of Dice Task (GDT). Subsamples also performed the Iowa Gambling Task as well as tasks measuring logical thinking and executive functions. In a moderated regression analysis, the significant interaction between age and executive components indicates that older participants with good executive functioning perform well on the GDT, while older participants with reduced executive functions make more risky choices. The same pattern emerges for the interaction of age and logical thinking. Results demonstrate that age and cognitive functions act in concert in predicting the decision-making performance.

Nutrition screening tools: does one size fit all? A systematic review of screening tools for the hospital setting.

PubMed

van Bokhorst-de van der Schueren, Marian A E; Guaitoli, Patrícia Realino; Jansma, Elise P; de Vet, Henrica C W

2014-02-01

Numerous nutrition screening tools for the hospital setting have been developed. The aim of this systematic review is to study construct or criterion validity and predictive validity of nutrition screening tools for the general hospital setting. A systematic review of English, French, German, Spanish, Portuguese and Dutch articles identified via MEDLINE, Cinahl and EMBASE (from inception to the 2nd of February 2012). Additional studies were identified by checking reference lists of identified manuscripts. Search terms included key words for malnutrition, screening or assessment instruments, and terms for hospital setting and adults. Data were extracted independently by 2 authors. Only studies expressing the (construct, criterion or predictive) validity of a tool were included. 83 studies (32 screening tools) were identified: 42 studies on construct or criterion validity versus a reference method and 51 studies on predictive validity on outcome (i.e. length of stay, mortality or complications). None of the tools performed consistently well to establish the patients' nutritional status. For the elderly, MNA performed fair to good, for the adults MUST performed fair to good. SGA, NRS-2002 and MUST performed well in predicting outcome in approximately half of the studies reviewed in adults, but not in older patients. Not one single screening or assessment tool is capable of adequate nutrition screening as well as predicting poor nutrition related outcome. Development of new tools seems redundant and will most probably not lead to new insights. New studies comparing different tools within one patient population are required. Copyright © 2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Three-dimensional viscous rotor flow calculations using a viscous-inviscid interaction approach

NASA Technical Reports Server (NTRS)

Chen, Ching S.; Bridgeman, John O.

1990-01-01

A three-dimensional viscous-inviscid interaction analysis was developed to predict the performance of rotors in hover and in forward flight at subsonic and transonic tip speeds. The analysis solves the full-potential and boundary-layer equations by finite-difference numerical procedures. Calculations were made for several different model rotor configurations. The results were compared with predictions from a two-dimensional integral method and with experimental data. The comparisons show good agreement between predictions and test data.

New test of the dynamic theory of neutron diffraction by a moving grating

NASA Astrophysics Data System (ADS)

Zakharov, Maxim; Frank, Alexander; Kulin, German; Goryunov, Semyon

2018-04-01

Recently, multiwave dynamical theory of neutron diffraction by a moving grating was developed. The theory predicts that at a certain height of the grating profile a significant suppression of the zero-order diffraction may occur. The experiment to confirm predictions of this theory was performed. The resulting diffracted UCNs spectra were measured using time-of-flight Fourier diffractometer. The experimental data were compared with the results of numerical simulation and were found in a good agreement with theoretical predictions.

Prediction of wastewater treatment plants performance based on artificial fish school neural network

NASA Astrophysics Data System (ADS)

Zhang, Ruicheng; Li, Chong

2011-10-01

A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

External validation of the ability of the DRAGON score to predict outcome after thrombolysis treatment.

PubMed

Ovesen, C; Christensen, A; Nielsen, J K; Christensen, H

2013-11-01

Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant plasminogen activator between 2009 and 2011 were included. Upon admission all patients underwent physical and neurological examination using the National Institutes of Health Stroke Scale along with non-contrast CT scans and CT angiography. Patients were followed up through the Outpatient Clinic and their modified Rankin Scale (mRS) was assessed after 3 months. Three hundred and three patients were included in the analysis. The DRAGON scale proved to have a good discriminative ability for predicting highly unfavourable outcome (mRS 5-6) (area under the curve-receiver operating characteristic [AUC-ROC]: 0.89; 95% confidence interval [CI] 0.81-0.96; p<0.001) and good outcome (mRS 0-2) (AUC-ROC: 0.79; 95% CI 0.73-0.85; p<0.001). When only patients with M1 occlusions were selected the DRAGON scale provided good discriminative capability (AUC-ROC: 0.89; 95% CI 0.78-1.0; p=0.003) for highly unfavourable outcome. We confirmed the validity of the DRAGON scale in predicting outcome after thrombolysis treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Alternate high capacity heat pipe

NASA Technical Reports Server (NTRS)

Voss, F. E.

1986-01-01

The performance predictions for a fifty foot heat pipe (4 foot evaporator - 46 foot condensor) are discussed. These performance predictions are supported by experimental data for a four foot heat pipe. Both heat pipes have evaporators with axial groove wick structures and condensers with powder metal external artery wick structures. The predicted performance of a rectangular axial groove/external artery heat pipe operating in space is given. Heat transport versus groove width is plotted for 100, 200 and 300 grooves in the evaporator. The curves show that maximum power is achieved for groove widths from 0.040 to 0.053 as the number of grooves varies from 300 to 100. The corresponding range of maximum power is 3150 to 2400 watts. The relationships between groove width and heat pipe evaporate diameter for 100, 200 and 300 grooves in the evaporator are given. A four foot heat pipe having a three foot condenser and one foot evaporator was built and tested. The evaporator wick structure used axial grooves with rectangular cross sections, and the condenser wick structure used powder metal with an external artery configuration. Fabrication drawings are enclosed. The predicted and measured performance for this heat pipe is shown. The agreement between predicted and measured performance is good and therefore substantiates the predicted performance for a fifty foot heat pipe.

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

NASA Technical Reports Server (NTRS)

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.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Performance Prediction of Constrained Waveform Design for Adaptive Radar

DTIC Science & Technology

2016-11-01

Kullback - Leibler divergence. χ2 Goodness - of - Fit Test We compute the estimated CDF for both models with 10000 MC trials. For Model 1 we observed a p-value of ...was clearly similar in its physical attributes, but the measures used , ( Kullback - Leibler , Chi-Square Test and the trace of the covariance) showed...models goodness - of - fit we look at three measures (1) χ2- Test (2) Trace of the inverse

Performance of Four Frailty Classifications in Older Patients With Cancer: Prospective Elderly Cancer Patients Cohort Study.

PubMed

Ferrat, Emilie; Paillaud, Elena; Caillet, Philippe; Laurent, Marie; Tournigand, Christophe; Lagrange, Jean-Léon; Droz, Jean-Pierre; Balducci, Lodovico; Audureau, Etienne; Canouï-Poitrine, Florence; Bastuji-Garin, Sylvie

2017-03-01

Purpose Frailty classifications of older patients with cancer have been developed to assist physicians in selecting cancer treatments and geriatric interventions. They have not been compared, and their performance in predicting outcomes has not been assessed. Our objectives were to assess agreement among four classifications and to compare their predictive performance in a large cohort of in- and outpatients with various cancers. Patients and Methods We prospectively included 1,021 patients age 70 years or older who had solid or hematologic malignancies and underwent a geriatric assessment in one of two French teaching hospitals between 2007 and 2012. Among them, 763 were assessed using four classifications: Balducci, International Society of Geriatric Oncology (SIOG) 1, SIOG2, and a latent class typology. Agreement was assessed using the κ statistic. Outcomes were 1-year mortality and 6-month unscheduled admissions. Results All four classifications had good discrimination for 1-year mortality (C-index ≥ 0.70); discrimination was best with SIOG1. For 6-month unscheduled admissions, discrimination was good with all four classifications (C-index ≥ 0.70). For classification into three (fit, vulnerable, or frail) or two categories (fit v vulnerable or frail and fit or vulnerable v frail), agreement among the four classifications ranged from very poor (κ ≤ 0.20) to good (0.60 < κ ≤ 0.80). Agreement was best between SIOG1 and the latent class typology and between SIOG1 and Balducci. Conclusion These four frailty classifications have good prognostic performance among older in- and outpatients with various cancers. They may prove useful in decision making about cancer treatments and geriatric interventions and/or in stratifying older patients with cancer in clinical trials.

Predicting the 10-year risk of hip and major osteoporotic fracture in rheumatoid arthritis and in the general population: an independent validation and update of UK FRAX without bone mineral density.

PubMed

Klop, Corinne; de Vries, Frank; Bijlsma, Johannes W J; Leufkens, Hubert G M; Welsing, Paco M J

2016-12-01

FRAX incorporates rheumatoid arthritis (RA) as a dichotomous predictor for predicting the 10-year risk of hip and major osteoporotic fracture (MOF). However, fracture risk may deviate with disease severity, duration or treatment. Aims were to validate, and if needed to update, UK FRAX for patients with RA and to compare predictive performance with the general population (GP). Cohort study within UK Clinical Practice Research Datalink (CPRD) (RA: n=11 582, GP: n=38 755), also linked to hospital admissions for hip fracture (CPRD-Hospital Episode Statistics, HES) (RA: n=7221, GP: n=24 227). Predictive performance of UK FRAX without bone mineral density was assessed by discrimination and calibration. Updating methods included recalibration and extension. Differences in predictive performance were assessed by the C-statistic and Net Reclassification Improvement (NRI) using the UK National Osteoporosis Guideline Group intervention thresholds. UK FRAX significantly overestimated fracture risk in patients with RA, both for MOF (mean predicted vs observed 10-year risk: 13.3% vs 8.4%) and hip fracture (CPRD: 5.5% vs 3.1%, CPRD-HES: 5.5% vs 4.1%). Calibration was good for hip fracture in the GP (CPRD-HES: 2.7% vs 2.4%). Discrimination was good for hip fracture (RA: 0.78, GP: 0.83) and moderate for MOF (RA: 0.69, GP: 0.71). Extension of the recalibrated UK FRAX using CPRD-HES with duration of RA disease, glucocorticoids (>7.5 mg/day) and secondary osteoporosis did not improve the NRI (0.01, 95% CI -0.04 to 0.05) or C-statistic (0.78). UK FRAX overestimated fracture risk in RA, but performed well for hip fracture in the GP after linkage to hospitalisations. Extension of the recalibrated UK FRAX did not improve predictive performance. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Prospective, observational study comparing automated and visual point-of-care urinalysis in general practice

PubMed Central

van Delft, Sanne; Goedhart, Annelijn; Spigt, Mark; van Pinxteren, Bart; de Wit, Niek; Hopstaken, Rogier

2016-01-01

Objective Point-of-care testing (POCT) urinalysis might reduce errors in (subjective) reading, registration and communication of test results, and might also improve diagnostic outcome and optimise patient management. Evidence is lacking. In the present study, we have studied the analytical performance of automated urinalysis and visual urinalysis compared with a reference standard in routine general practice. Setting The study was performed in six general practitioner (GP) group practices in the Netherlands. Automated urinalysis was compared with visual urinalysis in these practices. Reference testing was performed in a primary care laboratory (Saltro, Utrecht, The Netherlands). Primary and secondary outcome measures Analytical performance of automated and visual urinalysis compared with the reference laboratory method was the primary outcome measure, analysed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and Cohen's κ coefficient for agreement. Secondary outcome measure was the user-friendliness of the POCT analyser. Results Automated urinalysis by experienced and routinely trained practice assistants in general practice performs as good as visual urinalysis for nitrite, leucocytes and erythrocytes. Agreement for nitrite is high for automated and visual urinalysis. κ's are 0.824 and 0.803 (ranked as very good and good, respectively). Agreement with the central laboratory reference standard for automated and visual urinalysis for leucocytes is rather poor (0.256 for POCT and 0.197 for visual, respectively, ranked as fair and poor). κ's for erythrocytes are higher: 0.517 (automated) and 0.416 (visual), both ranked as moderate. The Urisys 1100 analyser was easy to use and considered to be not prone to flaws. Conclusions Automated urinalysis performed as good as traditional visual urinalysis on reading of nitrite, leucocytes and erythrocytes in routine general practice. Implementation of automated urinalysis in general practice is justified as automation is expected to reduce human errors in patient identification and transcribing of results. PMID:27503860

Prospective, observational study comparing automated and visual point-of-care urinalysis in general practice.

PubMed

van Delft, Sanne; Goedhart, Annelijn; Spigt, Mark; van Pinxteren, Bart; de Wit, Niek; Hopstaken, Rogier

2016-08-08

Point-of-care testing (POCT) urinalysis might reduce errors in (subjective) reading, registration and communication of test results, and might also improve diagnostic outcome and optimise patient management. Evidence is lacking. In the present study, we have studied the analytical performance of automated urinalysis and visual urinalysis compared with a reference standard in routine general practice. The study was performed in six general practitioner (GP) group practices in the Netherlands. Automated urinalysis was compared with visual urinalysis in these practices. Reference testing was performed in a primary care laboratory (Saltro, Utrecht, The Netherlands). Analytical performance of automated and visual urinalysis compared with the reference laboratory method was the primary outcome measure, analysed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and Cohen's κ coefficient for agreement. Secondary outcome measure was the user-friendliness of the POCT analyser. Automated urinalysis by experienced and routinely trained practice assistants in general practice performs as good as visual urinalysis for nitrite, leucocytes and erythrocytes. Agreement for nitrite is high for automated and visual urinalysis. κ's are 0.824 and 0.803 (ranked as very good and good, respectively). Agreement with the central laboratory reference standard for automated and visual urinalysis for leucocytes is rather poor (0.256 for POCT and 0.197 for visual, respectively, ranked as fair and poor). κ's for erythrocytes are higher: 0.517 (automated) and 0.416 (visual), both ranked as moderate. The Urisys 1100 analyser was easy to use and considered to be not prone to flaws. Automated urinalysis performed as good as traditional visual urinalysis on reading of nitrite, leucocytes and erythrocytes in routine general practice. Implementation of automated urinalysis in general practice is justified as automation is expected to reduce human errors in patient identification and transcribing of results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Personality traits measured at baseline can predict academic performance in upper secondary school three years late.

PubMed

Rosander, Pia; Bäckström, Martin

2014-12-01

The aim of the present study was to explore the ability of personality to predict academic performance in a longitudinal study of a Swedish upper secondary school sample. Academic performance was assessed throughout a three-year period via final grades from the compulsory school and upper secondary school. The Big Five personality factors (Costa & McCrae, ) - particularly Conscientiousness and Neuroticism - were found to predict overall academic performance, after controlling for general intelligence. Results suggest that Conscientiousness, as measured at the age of 16, can explain change in academic performance at the age of 19. The effect of Neuroticism on Conscientiousness indicates that, as regarding getting good grades, it is better to be a bit neurotic than to be stable. The study extends previous work by assessing the relationship between the Big Five and academic performance over a three-year period. The results offer educators avenues for improving educational achievement. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Predictors of long-term renal outcome in lupus nephritis trials: lessons learned from the Euro-Lupus Nephritis cohort.

PubMed

Dall'Era, Maria; Cisternas, Miriam G; Smilek, Dawn E; Straub, Laura; Houssiau, Frédéric A; Cervera, Ricard; Rovin, Brad H; Mackay, Meggan

2015-05-01

There is a need to determine which response measures in lupus nephritis trials are most predictive of good long-term renal function. We used data from the Euro-Lupus Nephritis Trial to evaluate the performance of proteinuria, serum creatinine (Cr), and urinary red blood cells (RBCs) as predictors of good long-term renal outcome. Patients from the Euro-Lupus Nephritis Trial with proteinuria, serum Cr, and urinary RBC measurements at 3, 6, or 12 months and with a minimum of 7 years of followup were included (n = 76). We assessed the ability of these clinical biomarkers at 3, 6, and 12 months after randomization to predict good long-term renal outcome (defined as a serum Cr value ≤1.0 mg/dl) at 7 years. Receiver operating characteristic curves were generated to assess parameter performance at these time points and to select the best cutoff for individual parameters. Sensitivity and specificity were calculated for the parameters alone and in combination. A proteinuria value of <0.8 gm/day at 12 months after randomization was the single best predictor of good long-term renal function (sensitivity 81% and specificity 78%). The addition of serum Cr to proteinuria as a composite predictor did not improve the performance of the outcome measure; addition of urinary RBCs as a predictor significantly decreased the sensitivity to 47%. This study demonstrates that the level of proteinuria at 12 months is the individual best predictor of long-term renal outcome in patients with lupus nephritis. Inclusion of urinary RBCs as part of a composite outcome measure actually undermined the predictive value of the trial data. We therefore suggest that urinary RBCs should not be included as a component of clinical trial response criteria in lupus nephritis. © 2015, American College of Rheumatology.

Fatigue Life Methodology for Bonded Composite Skin/Stringer Configurations

NASA Technical Reports Server (NTRS)

Krueger, Ronald; Paris, Isabelle L.; OBrien, T. Kevin; Minguet, Pierre J.

2001-01-01

A methodology is presented for determining the fatigue life of composite structures based on fatigue characterization data and geometric nonlinear finite element (FE) analyses. To demonstrate the approach, predicted results were compared to fatigue tests performed on specimens which represented a tapered composite flange bonded onto a composite skin. In a first step, tension tests were performed to evaluate the debonding mechanisms between the flange and the skin. In a second step, a 2D FE model was developed to analyze the tests. To predict matrix cracking onset, the relationship between the tension load and the maximum principal stresses transverse to the fiber direction was determined through FE analysis. Transverse tension fatigue life data were used to -enerate an onset fatigue life P-N curve for matrix cracking. The resulting prediction was in good agreement with data from the fatigue tests. In a third step, a fracture mechanics approach based on FE analysis was used to determine the relationship between the tension load and the critical energy release rate. Mixed mode energy release rate fatigue life data were used to create a fatigue life onset G-N curve for delamination. The resulting prediction was in good agreement with data from the fatigue tests. Further, the prediction curve for cumulative life to failure was generated from the previous onset fatigue life curves. The results showed that the methodology offers a significant potential to Predict cumulative fatigue life of composite structures.

Species and temperature predictions in a semi-industrial MILD furnace using a non-adiabatic conditional source-term estimation formulation

NASA Astrophysics Data System (ADS)

Labahn, Jeffrey William; Devaud, Cecile

2017-05-01

A Reynolds-Averaged Navier-Stokes (RANS) simulation of the semi-industrial International Flame Research Foundation (IFRF) furnace is performed using a non-adiabatic Conditional Source-term Estimation (CSE) formulation. This represents the first time that a CSE formulation, which accounts for the effect of radiation on the conditional reaction rates, has been applied to a large scale semi-industrial furnace. The objective of the current study is to assess the capabilities of CSE to accurately reproduce the velocity field, temperature, species concentration and nitrogen oxides (NOx) emission for the IFRF furnace. The flow field is solved using the standard k-ε turbulence model and detailed chemistry is included. NOx emissions are calculated using two different methods. Predicted velocity profiles are in good agreement with the experimental data. The predicted peak temperature occurs closer to the centreline, as compared to the experimental observations, suggesting that the mixing between the fuel jet and vitiated air jet may be overestimated. Good agreement between the species concentrations, including NOx, and the experimental data is observed near the burner exit. Farther downstream, the centreline oxygen concentration is found to be underpredicted. Predicted NOx concentrations are in good agreement with experimental data when calculated using the method of Peters and Weber. The current study indicates that RANS-CSE can accurately predict the main characteristics seen in a semi-industrial IFRF furnace.

Construction of a model for predicting creatinine clearance in Japanese patients treated with Cisplatin therapy.

PubMed

Yajima, Airi; Uesawa, Yoshihiro; Ogawa, Chiaki; Yatabe, Megumi; Kondo, Naoki; Saito, Shinichiro; Suzuki, Yoshihiko; Atsuda, Kouichiro; Kagaya, Hajime

2015-05-01

There exist various useful predictive models, such as the Cockcroft-Gault model, for estimating creatinine clearance (CLcr). However, the prediction of renal function is difficult in patients with cancer treated with cisplatin. Therefore, we attempted to construct a new model for predicting CLcr in such patients. Japanese patients with head and neck cancer who had received cisplatin-based chemotherapy were used as subjects. A multiple regression equation was constructed as a model for predicting CLcr values based on background and laboratory data. A model for predicting CLcr, which included body surface area, serum creatinine and albumin, was constructed. The model exhibited good performance prior to cisplatin therapy. In addition, it performed better than previously reported models after cisplatin therapy. The predictive model constructed in the present study displayed excellent potential and was useful for estimating the renal function of patients treated with cisplatin therapy. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Development of Pelton turbine using numerical simulation

NASA Astrophysics Data System (ADS)

Patel, K.; Patel, B.; Yadav, M.; Foggia, T.

2010-08-01

This paper describes recent research and development activities in the field of Pelton turbine design. Flow inside Pelton turbine is most complex due to multiphase (mixture of air and water) and free surface in nature. Numerical calculation is useful to understand flow physics as well as effect of geometry on flow. The optimized design is obtained using in-house special optimization loop. Either single phase or two phase unsteady numerical calculation could be performed. Numerical results are used to visualize the flow pattern in the water passage and to predict performance of Pelton turbine at full load as well as at part load. Model tests are conducted to determine performance of turbine and it shows good agreement with numerically predicted performance.

Purposes and methods of scoring earthquake forecasts

NASA Astrophysics Data System (ADS)

Zhuang, J.

2010-12-01

There are two kinds of purposes in the studies on earthquake prediction or forecasts: one is to give a systematic estimation of earthquake risks in some particular region and period in order to give advice to governments and enterprises for the use of reducing disasters, the other one is to search for reliable precursors that can be used to improve earthquake prediction or forecasts. For the first case, a complete score is necessary, while for the latter case, a partial score, which can be used to evaluate whether the forecasts or predictions have some advantages than a well know model, is necessary. This study reviews different scoring methods for evaluating the performance of earthquake prediction and forecasts. Especially, the gambling scoring method, which is developed recently, shows its capacity in finding good points in an earthquake prediction algorithm or model that are not in a reference model, even if its overall performance is no better than the reference model.

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

PubMed

Schwartz, H Andrew; Sap, Maarten; Kern, Margaret L; Eichstaedt, Johannes C; Kapelner, Adam; Agrawal, Megha; Blanco, Eduardo; Dziurzynski, Lukasz; Park, Gregory; Stillwell, David; Kosinski, Michal; Seligman, Martin E P; Ungar, Lyle H

2016-01-01

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

PubMed

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Wave Fluid Film Bearing Tests for an Aviation Gearbox

NASA Technical Reports Server (NTRS)

Dimofte, Florin; Proctor, Margaret P.; Fleming, David P.; Keith, Theo G., Jr.

2000-01-01

An oil-lubricated wave journal-thrust bearing assembly was successfully tested at conditions found in general aviation engine gearboxes. The bearing performed well at both steady state conditions and in start-stop tests. It ran stably under all loading conditions, including zero load, at all speeds up to 16 000 rpm. The bearing carried 25 percent more load than required for the gearbox application, supporting 8900 N (94 bars average pressure), and showed very good thermal stability. 450 start-stop cycles were also performed, including 350 cycles without oil supply during starting and stopping. Test results and numerical predictions were in good agreement.

Impact of the revised International Prognostic Scoring System, cytogenetics and monosomal karyotype on outcome after allogeneic stem cell transplantation for myelodysplastic syndromes and secondary acute myeloid leukemia evolving from myelodysplastic syndromes: a retrospective multicenter study of the European Society of Blood and Marrow Transplantation

PubMed Central

Koenecke, Christian; Göhring, Gudrun; de Wreede, Liesbeth C.; van Biezen, Anja; Scheid, Christof; Volin, Liisa; Maertens, Johan; Finke, Jürgen; Schaap, Nicolaas; Robin, Marie; Passweg, Jakob; Cornelissen, Jan; Beelen, Dietrich; Heuser, Michael; de Witte, Theo; Kröger, Nicolaus

2015-01-01

The aim of this study was to determine the impact of the revised 5-group International Prognostic Scoring System cytogenetic classification on outcome after allogeneic stem cell transplantation in patients with myelodysplastic syndromes or secondary acute myeloid leukemia who were reported to the European Society for Blood and Marrow Transplantation database. A total of 903 patients had sufficient cytogenetic information available at stem cell transplantation to be classified according to the 5-group classification. Poor and very poor risk according to this classification was an independent predictor of shorter relapse-free survival (hazard ratio 1.40 and 2.14), overall survival (hazard ratio 1.38 and 2.14), and significantly higher cumulative incidence of relapse (hazard ratio 1.64 and 2.76), compared to patients with very good, good or intermediate risk. When comparing the predictive performance of a series of Cox models both for relapse-free survival and for overall survival, a model with simplified 5-group cytogenetics (merging very good, good and intermediate cytogenetics) performed best. Furthermore, monosomal karyotype is an additional negative predictor for outcome within patients of the poor, but not the very poor risk group of the 5-group classification. The revised International Prognostic Scoring System cytogenetic classification allows patients with myelodysplastic syndromes to be separated into three groups with clearly different outcomes after stem cell transplantation. Poor and very poor risk cytogenetics were strong predictors of poor patient outcome. The new cytogenetic classification added value to prediction of patient outcome compared to prediction models using only traditional risk factors or the 3-group International Prognostic Scoring System cytogenetic classification. PMID:25552702

Analysis of two-equation turbulence models for recirculating flows

NASA Technical Reports Server (NTRS)

Thangam, S.

1991-01-01

The two-equation kappa-epsilon model is used to analyze turbulent separated flow past a backward-facing step. It is shown that if the model constraints are modified to be consistent with the accepted energy decay rate for isotropic turbulence, the dominant features of the flow field, namely the size of the separation bubble and the streamwise component of the mean velocity, can be accurately predicted. In addition, except in the vicinity of the step, very good predictions for the turbulent shear stress, the wall pressure, and the wall shear stress are obtained. The model is also shown to provide good predictions for the turbulence intensity in the region downstream of the reattachment point. Estimated long time growth rates for the turbulent kinetic energy and dissipation rate of homogeneous shear flow are utilized to develop an optimal set of constants for the two equation kappa-epsilon model. The physical implications of the model performance are also discussed.

Looking good or doing good? Motivations for organisational citizenship behaviour in Turkish versus South Korean collectivists.

PubMed

Alabak, Merve; Peker, Müjde; Booth, Robert W

2016-06-01

The purpose of this article is to explore potential motivations to perform organisational citizenship behaviour (OCB) in collectivistic Turkish and South Korean societies. Although collectivism has been proposed as a predictor of OCB, previous research has not fully explored the possibility that collectivistic individuals' OCB may result from their self-oriented motives (i.e. social desirability concerns) or their future-oriented motives (i.e. long-term orientation concerns). We predicted that OCB stems from social desirability concerns among Turkish collectivists, meaning it is used for maintaining a positive image within the organisation. However, for South Korean collectivists, we predicted that OCB stems from their long-term orientation concerns, meaning it is used to make the organisation better. The results were in line with our predictions, and the findings are discussed in terms of their implications for firms in collectivistic societies. © 2015 International Union of Psychological Science.

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

PubMed

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Efficient differentially private learning improves drug sensitivity prediction.

PubMed

Honkela, Antti; Das, Mrinal; Nieminen, Arttu; Dikmen, Onur; Kaski, Samuel

2018-02-06

Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. We show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements in the accuracy of private drug sensitivity prediction with a new robust private regression method. Our method matches the predictive accuracy of the state-of-the-art non-private lasso regression using only 4x more samples under relatively strong differential privacy guarantees. Good performance with limited data is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. The proposed differentially private regression method combines theoretical appeal and asymptotic efficiency with good prediction accuracy even with moderate-sized data. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields. This article was reviewed by Zoltan Gaspari and David Kreil.

Clinical outcomes and predictive factors related to good outcomes in plasma exchange in severe attack of NMOSD and long extensive transverse myelitis: Case series and review of the literature.

PubMed

Aungsumart, Saharat; Apiwattanakul, Metha

2017-04-01

To investigate the predictive factors associated with good outcomes of plasma exchange in severe attacks through neuromyelitis optica spectrum disorder (NMOSD) and long extensive transverse myelitis (LETM). In addition, to review the literature of predictive factors associated with the good outcomes of plasma exchange in central nervous system inflammatory demyelinating diseases (CNS IDDs). Retrospective study in 27 episodes of severe acute attacks myelitis and optic neuritis in 24 patients, including 20 patients with NMOSD seropositive, 1 patient with NMOSD seronegative and 3 patients with LETM. Plasma exchange was performed, reflecting poor responses to high-dose intravenous methylprednisolone (IVMP) therapy. The outcomes of the present study were the functional outcome improvements at 6 months after plasma exchange. The predictive factors of good outcomes after plasma exchange were determined in this cohort, and additional factors reported in the literature were reviewed. Plasma exchange was performed in 16 spinal cord attacks and 11 attacks of optic neuritis. Twenty patients were female (83%). The median age of the patients at the time of plasma exchange was 41 years old. The median disease duration was 0.6 years. The AQP4-IgG status was positive in 20 patients (83%). Plasma exchange following IVMP therapy led to a significant improvement in 81% of the cases after 6 months of follow up. A baseline Expanded Disability Status Scale (EDSS) score ≤6 before the attack was associated with significant improvement at 6 months (p=0.02, OR 58.33, 95%CI 1.92-1770). In addition, we reviewed the evidence for factors associated with good outcomes of plasma exchange in CNS IDDs, classified according to pre-plasma exchange, post-plasma exchange, and radiological features. Plasma exchange following IVMP therapy is effective as a treatment for patients experiencing a severe attack of NMOSD or LETM. The factors associated with good outcomes after plasma exchange in CNS IDDs are reviewed in the literature. We classified 3 different aspects, including pre-plasma exchange factors, based on minimal disability at baseline, preserved reflexes, early initiation, and short disease duration; post plasma exchange factors, including early improvement or lower disability at last follow up; and radiographic factors, for which the presence of active gadolinium lesions and the absence of spinal cord atrophy seem to be good outcomes for plasmapheresis. Copyright © 2017. Published by Elsevier B.V.

Modeling ready biodegradability of fragrance materials.

PubMed

Ceriani, Lidia; Papa, Ester; Kovarich, Simona; Boethling, Robert; Gramatica, Paola

2015-06-01

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials. © 2015 SETAC.

STGSTK- PREDICTING MULTISTAGE AXIAL-FLOW COMPRESSOR PERFORMANCE BY A MEANLINE STAGE-STACKING METHOD

NASA Technical Reports Server (NTRS)

Steinke, R. J.

1994-01-01

The STGSTK computer program was developed for predicting the off-design performance of multistage axial-flow compressors. The axial-flow compressor is widely used in aircraft engines. In addition to its inherent advantage of high mass flow per frontal area, it can exhibit very good aerodynamic performance. However, good aerodynamic performance over an acceptable range of operating conditions is not easily attained. STGSTK provides an analytical tool for the development of new compressor designs. The simplicity of a one-dimensional compressible flow model enables the stage-stacking method used in STGSTK to have excellent convergence properties and short computer run times. Also, the simplicity of the model makes STGSTK a manageable code that eases the incorporation, or modification, of empirical correlations directly linked to test data. Thus, the user can adapt the code to meet varying design needs. STGSTK uses a meanline stage-stacking method to predict off-design performance. Stage and cumulative compressor performance is calculated from representative meanline velocity diagrams located at rotor inlet and outlet meanline radii. STGSTK includes options for the following: 1) non-dimensional stage characteristics may be input directly or calculated from stage design performance input, 2) stage characteristics may be modified for off-design speed and blade reset, and 3) rotor design deviation angle may be modified for off-design flow, speed, and blade setting angle. Many of the code's options use correlations that are normally obtained from experimental data. The STGSTK user may modify these correlations as needed. This program is written in FORTRAN IV for batch execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 85K of 8 bit bytes. STGSTK was developed in 1982.

Toward Biopredictive Dissolution for Enteric Coated Dosage Forms.

PubMed

Al-Gousous, J; Amidon, G L; Langguth, P

2016-06-06

The aim of this work was to develop a phosphate buffer based dissolution method for enteric-coated formulations with improved biopredictivity for fasted conditions. Two commercially available enteric-coated aspirin products were used as model formulations (Aspirin Protect 300 mg, and Walgreens Aspirin 325 mg). The disintegration performance of these products in a physiological 8 mM pH 6.5 bicarbonate buffer (representing the conditions in the proximal small intestine) was used as a standard to optimize the employed phosphate buffer molarity. To account for the fact that a pH and buffer molarity gradient exists along the small intestine, the introduction of such a gradient was proposed for products with prolonged lag times (when it leads to a release lower than 75% in the first hour post acid stage) in the proposed buffer. This would allow the method also to predict the performance of later-disintegrating products. Dissolution performance using the accordingly developed method was compared to that observed when using two well-established dissolution methods: the United States Pharmacopeia (USP) method and blank fasted state simulated intestinal fluid (FaSSIF). The resulting dissolution profiles were convoluted using GastroPlus software to obtain predicted pharmacokinetic profiles. A pharmacokinetic study on healthy human volunteers was performed to evaluate the predictions made by the different dissolution setups. The novel method provided the best prediction, by a relatively wide margin, for the difference between the lag times of the two tested formulations, indicating its being able to predict the post gastric emptying onset of drug release with reasonable accuracy. Both the new and the blank FaSSIF methods showed potential for establishing in vitro-in vivo correlation (IVIVC) concerning the prediction of Cmax and AUC0-24 (prediction errors not more than 20%). However, these predictions are strongly affected by the highly variable first pass metabolism necessitating the evaluation of an absorption rate metric that is more independent of the first-pass effect. The Cmax/AUC0-24 ratio was selected for this purpose. Regarding this metric's predictions, the new method provided very good prediction of the two products' performances relative to each other (only 1.05% prediction error in this regard), while its predictions for the individual products' values in absolute terms were borderline, narrowly missing the regulatory 20% prediction error limits (21.51% for Aspirin Protect and 22.58% for Walgreens Aspirin). The blank FaSSIF-based method provided good Cmax/AUC0-24 ratio prediction, in absolute terms, for Aspirin Protect (9.05% prediction error), but its prediction for Walgreens Aspirin (33.97% prediction error) was overwhelmingly poor. Thus it gave practically the same average but much higher maximum prediction errors compared to the new method, and it was strongly overdiscriminating as for predicting their performances relative to one another. The USP method, despite not being overdiscriminating, provided poor predictions of the individual products' Cmax/AUC0-24 ratios. This indicates that, overall, the new method is of improved biopredictivity compared to established methods.

Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

PubMed

Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette

2017-05-15

We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.

Shuttle active thermal control system development testing. Volume 3: Modular radiator system test data correlation with thermal model

NASA Technical Reports Server (NTRS)

Phillips, M. A.

1973-01-01

Results are presented of an analysis which compares the performance predictions of a thermal model of a multi-panel modular radiator system with thermal vacuum test data. Comparisons between measured and predicted individual panel outlet temperatures and pressure drops and system outlet temperatures have been made over the full range of heat loads, environments and plumbing arrangements expected for the shuttle radiators. Both two sided and one sided radiation have been included. The model predictions show excellent agreement with the test data for the maximum design conditions of high load and hot environment. Predictions under minimum design conditions of low load-cold environments indicate good agreement with the measured data, but evaluation of low load predictions should consider the possibility of parallel flow instabilities due to main system freezing. Performance predictions under intermediate conditions in which the majority of the flow is not in either the main or prime system are adequate although model improvements in this area may be desired. The primary modeling objective of providing an analytical technique for performance predictions of a multi-panel radiator system under the design conditions has been met.

Predicting discharge mortality after acute ischemic stroke using balanced data.

PubMed

Ho, King Chung; Speier, William; El-Saden, Suzie; Liebeskind, David S; Saver, Jeffery L; Bui, Alex A T; Arnold, Corey W

2014-01-01

Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.

Actual versus predicted performance of an active solar heating system - A comparison using FCHART 4.0

NASA Astrophysics Data System (ADS)

Wetzel, P. E.

1981-11-01

The performance of an active solar heating system added to a house in Denver, CO was compared with predictions made by the FCHART 4.0 computer program. The house featured 43.23 sq m of collectors with an ethylene-glycol/water heat transfer fluid, and a 3.23 cu m storage tank. The house hot water was preheated in the storage tank, and home space heat was furnished whenever the storage water was above 32 C. Actual meteorological and heating demand data were used for the comparison, rather than long-term averages. Although monthly predictions by the FCHART program were found to diverge from measured data, the annual demand and supply predictions provided a good fit, i.e. within 9%, and were within 1% of the measured solar energy contributed to storage.

CFD Predictions for Transonic Performance of the ERA Hybrid Wing-Body Configuration

NASA Technical Reports Server (NTRS)

Deere, Karen A.; Luckring, James M.; McMillin, S. Naomi; Flamm, Jeffrey D.; Roman, Dino

2016-01-01

A computational study was performed for a Hybrid Wing Body configuration that was focused at transonic cruise performance conditions. In the absence of experimental data, two fully independent computational fluid dynamics analyses were conducted to add confidence to the estimated transonic performance predictions. The primary analysis was performed by Boeing with the structured overset-mesh code OVERFLOW. The secondary analysis was performed by NASA Langley Research Center with the unstructured-mesh code USM3D. Both analyses were performed at full-scale flight conditions and included three configurations customary to drag buildup and interference analysis: a powered complete configuration, the configuration with the nacelle/pylon removed, and the powered nacelle in isolation. The results in this paper are focused primarily on transonic performance up to cruise and through drag rise. Comparisons between the CFD results were very good despite some minor geometric differences in the two analyses.

Effect of time step size and turbulence model on the open water hydrodynamic performance prediction of contra-rotating propellers

NASA Astrophysics Data System (ADS)

Wang, Zhan-zhi; Xiong, Ying

2013-04-01

A growing interest has been devoted to the contra-rotating propellers (CRPs) due to their high propulsive efficiency, torque balance, low fuel consumption, low cavitations, low noise performance and low hull vibration. Compared with the single-screw system, it is more difficult for the open water performance prediction because forward and aft propellers interact with each other and generate a more complicated flow field around the CRPs system. The current work focuses on the open water performance prediction of contra-rotating propellers by RANS and sliding mesh method considering the effect of computational time step size and turbulence model. The validation study has been performed on two sets of contra-rotating propellers developed by David W Taylor Naval Ship R & D center. Compared with the experimental data, it shows that RANS with sliding mesh method and SST k-ω turbulence model has a good precision in the open water performance prediction of contra-rotating propellers, and small time step size can improve the level of accuracy for CRPs with the same blade number of forward and aft propellers, while a relatively large time step size is a better choice for CRPs with different blade numbers.

The use and misuse of aircraft and missile RCS statistics

NASA Astrophysics Data System (ADS)

Bishop, Lee R.

1991-07-01

Both static and dynamic radar cross sections measurements are used for RCS predictions, but the static data are less complete than the dynamic. Integrated dynamics RCS data also have limitations for prediction radar detection performance. When raw static data are properly used, good first-order detection estimates are possible. The research to develop more-usable RCS statistics is reviewed, and windowing techniques for creating probability density functions from static RCS data are discussed.

Load Measurement in Structural Members Using Guided Acoustic Waves

NASA Astrophysics Data System (ADS)

Chen, Feng; Wilcox, Paul D.

2006-03-01

A non-destructive technique to measure load in structures such as rails and bridge cables by using guided acoustic waves is investigated both theoretically and experimentally. Robust finite element models for predicting the effect of load on guided wave propagation are developed and example results are presented for rods. Reasonably good agreement of experimental results with modelling prediction is obtained. The measurement technique has been developed to perform tests on larger specimens.

Assessment and prediction of drying shrinkage cracking in bonded mortar overlays

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

Beushausen, Hans, E-mail: hans.beushausen@uct.ac.za; Chilwesa, Masuzyo

2013-11-15

Restrained drying shrinkage cracking was investigated on composite beams consisting of substrate concrete and bonded mortar overlays, and compared to the performance of the same mortars when subjected to the ring test. Stress development and cracking in the composite specimens were analytically modeled and predicted based on the measurement of relevant time-dependent material properties such as drying shrinkage, elastic modulus, tensile relaxation and tensile strength. Overlay cracking in the composite beams could be very well predicted with the analytical model. The ring test provided a useful qualitative comparison of the cracking performance of the mortars. The duration of curing wasmore » found to only have a minor influence on crack development. This was ascribed to the fact that prolonged curing has a beneficial effect on tensile strength at the onset of stress development, but is in the same time not beneficial to the values of tensile relaxation and elastic modulus. -- Highlights: •Parameter study on material characteristics influencing overlay cracking. •Analytical model gives good quantitative indication of overlay cracking. •Ring test presents good qualitative indication of overlay cracking. •Curing duration has little effect on overlay cracking.« less

Super H-mode: theoretical prediction and initial observations of a new high performance regime for tokamak operation

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

Snyder, Philip B.; Solomon, Wayne M.; Burrell, Keith H.

2015-07-21

A new “Super H-mode” regime is predicted, which enables pedestal height and predicted fusion performance substantially higher than for H-mode operation. This new regime is predicted to exist by the EPED pedestal model, which calculates criticality constraints for peeling-ballooning and kinetic ballooning modes, and combines them to predict the pedestal height and width. EPED usually predicts a single (“H-mode”) pedestal solution for each set of input parameters, however, in strongly shaped plasmas above a critical density, multiple pedestal solutions are found, including the standard “Hmode” solution, and a “Super H-Mode” solution at substantially larger pedestal height and width. The Supermore » H-mode regime is predicted to be accessible by controlling the trajectory of the density, and to increase fusion performance for ITER, as well as for DEMO designs with strong shaping. A set of experiments on DIII-D has identified the predicted Super H-mode regime, and finds pedestal height and width, and their variation with density, in good agreement with theoretical predictions from the EPED model. Finally, the very high pedestal enables operation at high global beta and high confinement, including the highest normalized beta achieved on DIII-D with a quiescent edge.« less

Comparing spatial regression to random forests for large environmental data sets

EPA Science Inventory

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...

Walking Performance: Correlation between Energy Cost of Walking and Walking Participation. New Statistical Approach Concerning Outcome Measurement

PubMed Central

Franceschini, Marco; Rampello, Anais; Agosti, Maurizio; Massucci, Maurizio; Bovolenta, Federica; Sale, Patrizio

2013-01-01

Walking ability, though important for quality of life and participation in social and economic activities, can be adversely affected by neurological disorders, such as Spinal Cord Injury, Stroke, Multiple Sclerosis or Traumatic Brain Injury. The aim of this study is to evaluate if the energy cost of walking (CW), in a mixed group of chronic patients with neurological diseases almost 6 months after discharge from rehabilitation wards, can predict the walking performance and any walking restriction on community activities, as indicated by Walking Handicap Scale categories (WHS). One hundred and seven subjects were included in the study, 31 suffering from Stroke, 26 from Spinal Cord Injury and 50 from Multiple Sclerosis. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%. Our research reveal that, in our subjects, CW is the only predictor of the walking performance of in the community, to be compared with the score of WHS. We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it. In particular, these values could be used to predict the ability to walk in the community when discharged from the rehabilitation units, and to adjust the rehabilitative treatment to improve the performance. PMID:23468871

Investigation of chemical and electrochemical reactions mechanisms in a direct carbon fuel cell using olive wood charcoal as sustainable fuel

NASA Astrophysics Data System (ADS)

Elleuch, Amal; Halouani, Kamel; Li, Yongdan

2015-05-01

Direct carbon fuel cell (DCFC) is a high temperature fuel cell using solid carbon as fuel. The use of environmentally friendly carbon material constitutes a promising option for the DCFC future. In this context, this paper focuses on the use of biomass-derived charcoal renewable fuel. A practical investigation of Tunisian olive wood charcoal (OW-C) in planar DCFCs is conducted and good power density (105 mW cm-2) and higher current density (550 mA cm-2) are obtained at 700 °C. Analytical and predictive techniques are performed to explore the relationships between fuel properties and DCFC chemical and electrochemical mechanisms. High carbon content, carbon-oxygen groups and disordered structure, are the key parameters allowing the achieved good performance. Relatively complex chain reactions are predicted to explain the gas evolution within the anode. CO, H2 and CH4 participation in the anodic reaction is proved.

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

PubMed Central

Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

2016-01-01

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (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. PMID:27293423

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

PubMed

Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

2016-01-01

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (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.

Continuous Amplitude-Integrated Electroencephalographic Monitoring Is a Useful Prognostic Tool for Hypothermia-Treated Cardiac Arrest Patients.

PubMed

Oh, Sang Hoon; Park, Kyu Nam; Shon, Young-Min; Kim, Young-Min; Kim, Han Joon; Youn, Chun Song; Kim, Soo Hyun; Choi, Seung Pill; Kim, Seok Chan

2015-09-22

Modern treatments have improved the survival rate following cardiac arrest, but prognostication remains a challenge. We examined the prognostic value of continuous electroencephalography according to time by performing amplitude-integrated electroencephalography on patients with cardiac arrest receiving therapeutic hypothermia. We prospectively studied 130 comatose patients treated with hypothermia from September 2010 to April 2013. We evaluated the time to normal trace (TTNT) as a neurological outcome predictor and determined the prognostic value of burst suppression and status epilepticus, with a particular focus on their time of occurrence. Fifty-five patients exhibited a cerebral performance category score of 1 to 2. The area under the curve for TTNT was 0.97 (95% confidence interval, 0.92-0.99), and the sensitivity and specificity of TTNT<24 hours after resuscitation as a threshold for predicting good neurological outcome were 94.6% (95% confidence interval, 84.9%-98.9%) and 90.7% (95% confidence interval, 81.7%-96.2%), respectively. The threshold displaying 100% specificity for predicting poor neurological outcome was TTNT>36 hours. Burst suppression and status epilepticus predicted poor neurological outcome (positive predictive value of 98.3% and 96.4%, respectively). The combination of these factors predicted a negative outcome at a median of 6.2 hours after resuscitation (sensitivity and specificity of 92.0% and 96.4%, respectively). A TTNT<24 hours was associated with good neurological outcome. The lack of normal trace development within 36 hours, status epilepticus, and burst suppression were predictors of poor outcome. The combination of these negative predictors may improve their prognostic performance at an earlier stage. © 2015 The Authors.

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

NASA Astrophysics Data System (ADS)

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.

Pulsed CO2 characterization for lidar use

NASA Technical Reports Server (NTRS)

Jaenisch, Holger M.

1992-01-01

An account is given of a scaled functional testbed laser for space-qualified coherent-detection lidar applications which employs a CO2 laser. This laser has undergone modification and characterization for inherent performance capabilities as a model of coherent detection. While characterization results show good overall performance that is in agreement with theoretical predictions, frequency-stability and pulse-length limitations severely limit the laser's use in coherent detection.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

Optimal prediction of the number of unseen species

PubMed Central

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-01-01

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42−58], uses n samples to predict the number U of hitherto unseen species that would be observed if t⋅n new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45−63] constructed an intriguing estimator that predicts U for all t≤1. Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435−447] proposed a modification that empirically predicts U even for some t>1, but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to t∝log⁡n. We also show that this range is the best possible and that the estimator’s mean-square error is near optimal for any t. Our approach yields a provable guarantee for the Efron−Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product. PMID:27830649

Optimal prediction of the number of unseen species.

PubMed

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-11-22

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42-58], uses n samples to predict the number U of hitherto unseen species that would be observed if [Formula: see text] new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45-63] constructed an intriguing estimator that predicts U for all [Formula: see text] Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435-447] proposed a modification that empirically predicts U even for some [Formula: see text], but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to [Formula: see text] We also show that this range is the best possible and that the estimator's mean-square error is near optimal for any t Our approach yields a provable guarantee for the Efron-Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

PubMed Central

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Modern Prediction Methods for Turbomachine Performance

DTIC Science & Technology

1976-01-01

easier on the basis of C factor correlation. Generally, correlation has to be carefully established; statistical methods may be of good help when a , large...FLOW TURBOMACHINE BLADES Walker, G. J. Instn of Engrs,, Australia-3rd Australiasian Conference on Hydraulics 8 Fluid Mechanics-Proc, Nov 25-29 1968

Illusions of a Good Grade: Effort or Luck?

ERIC Educational Resources Information Center

Buckelew, Susan P.; Byrd, Nikki; Key, Colin W.; Thornton, Jessica; Merwin, Michelle M.

2013-01-01

This study assessed the relationships among the accuracy of grade predictions, actual grades, self-enhancement bias, and attributions about academic performance. As a group, students anticipated higher grades than were earned. Individual differences in self-enhancement bias were measured using the discrepancy between anticipated and attained…

Benchmarking test of empirical root water uptake models

NASA Astrophysics Data System (ADS)

dos Santos, Marcos Alex; de Jong van Lier, Quirijn; van Dam, Jos C.; Freire Bezerra, Andre Herman

2017-01-01

Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.

An unsupervised classification scheme for improving predictions of prokaryotic TIS.

PubMed

Tech, Maike; Meinicke, Peter

2006-03-09

Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes. We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool "TICO" (TIs COrrector) which is publicly available from our web site.

Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

PubMed

Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

2016-11-08

The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

Temporal prediction modulates the evaluative processing of "good" action feedback: An electrophysiological study.

PubMed

Kimura, Kenta; Kimura, Motohiro; Iwaki, Sunao

2016-10-01

The present study aimed to investigate whether or not the evaluative processing of action feedback can be modulated by temporal prediction. For this purpose, we examined the effects of the predictability of the timing of action feedback on an ERP effect that indexed the evaluative processing of action feedback, that is, an ERP effect that has been interpreted as a feedback-related negativity (FRN) elicited by "bad" action feedback or a reward positivity (RewP) elicited by "good" action feedback. In two types of experimental blocks, the participants performed a gambling task in which they chose one of two cards and received an action feedback that indicated monetary gain or loss. In fixed blocks, the time interval between the participant's choice and the onset of the action feedback was fixed at 0, 500, or 1,000 ms in separate blocks; thus, the timing of action feedback was predictable. In mixed blocks, the time interval was randomly chosen from the same three intervals with equal probability; thus, the timing was less predictable. The results showed that the FRN/RewP was smaller in mixed than fixed blocks for the 0-ms interval trial, whereas there was no difference between the two block types for the 500-ms and 1,000-ms interval trials. Interestingly, the smaller FRN/RewP was due to the modulation of gain ERPs rather than loss ERPs. These results suggest that temporal prediction can modulate the evaluative processing of action feedback, and particularly good feedback, such as that which indicates monetary gain. © 2016 Society for Psychophysiological Research.

Baseline strength can influence the ability of salivary free testosterone to predict squat and sprinting performance.

PubMed

Crewther, Blair T; Cook, Christian J; Gaviglio, Chris M; Kilduff, Liam P; Drawer, Scott

2012-01-01

The objective of this study was to determine if salivary free testosterone can predict an athlete's performance during back squats and sprints over time and the influence baseline strength on this relationship. Ten weight-trained male athletes were divided into 2 groups based on their 1 repetition maximum (1RM) squats, good squatters (1RM > 2.0 × body weight, n = 5) and average squatters (1RM < 1.9 × body weight, n = 5). The good squatters were stronger than the average squatters (p < 0.05). Each subject was assessed for squat 1RM and 10-m sprint times on 10 separate occasions over a 40-day period. A saliva sample was collected before testing and assayed for free testosterone and cortisol. The pooled testosterone correlations were strong and significant in the good squatters (r = 0.92 for squats, r = -0.87 for sprints, p < 0.01), but not significant for the average squatters (r = 0.35 for squats, r = -0.18 for sprints). Cortisol showed no significant correlations with 1RM squat and 10-m sprint performance, and no differences were identified between the 2 squatting groups. In summary, these results suggest that free testosterone is a strong individual predictor of squat and sprinting performance in individuals with relatively high strength levels but a poor predictor in less strong individuals. This information can assist coaches, trainers, and performance scientists working with stronger weight-trained athletes, for example, the preworkout measurement of free testosterone could indicate likely training outcomes or a readiness to train at a certain intensity level, especially if real-time measurements are made. Our results also highlight the need to separate group and individual hormonal data during the repeated testing of athletes with variable strength levels.

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

NASA Astrophysics Data System (ADS)

Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

2017-04-01

Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

QCD analysis of $W$- and $Z$-boson production at Tevatron

DOE PAGES

Camarda, S.; Belov, P.; Cooper-Sarkar, A. M.; ...

2015-09-28

Recent measurements of the W-boson charge asymmetry and of the Z-boson production cross sections, performed at the Tevatron collider in Run II by the D0 and CDF collaborations, are studied using the HERAFitter framework to assess their impact on the proton parton distribution functions (PDFs). Thus, the Tevatron measurements, together with deep-inelastic scattering data from HERA, are included in a QCD analysis performed at next-to-leading order, and compared to the predictions obtained using other PDF sets from different groups. Good agreement between measurements and theoretical predictions is observed. The Tevatron data provide significant constraints on the d-valence quark distribution.

QCD analysis of $W$- and $Z$-boson production at Tevatron

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

Camarda, S.; Belov, P.; Cooper-Sarkar, A. M.

Recent measurements of the W-boson charge asymmetry and of the Z-boson production cross sections, performed at the Tevatron collider in Run II by the D0 and CDF collaborations, are studied using the HERAFitter framework to assess their impact on the proton parton distribution functions (PDFs). Thus, the Tevatron measurements, together with deep-inelastic scattering data from HERA, are included in a QCD analysis performed at next-to-leading order, and compared to the predictions obtained using other PDF sets from different groups. Good agreement between measurements and theoretical predictions is observed. The Tevatron data provide significant constraints on the d-valence quark distribution.

I believe I'm good at orienting myself… But is that true?

PubMed

Nori, Raffaella; Piccardi, Laura

2015-08-01

The present study aimed to analyse beliefs that men and women have with respect to their sense of direction (SOD) and whether they correlate with spatial environmental task performance. Eighty-four students filled in the short version of the Familiarity and Spatial Cognitive Style Scale to evaluate beliefs on their SOD, knowledge of the city (TK), spatial ability (SA) and wayfinding (WA) and performed three spatial environmental tasks. Results showed that gender did not predict the performance on the spatial environmental tasks, whereas it can be predicted by participants' beliefs related to their SOD and TK. The findings point out the need to identify specific training aimed at improving women's metacognitive skills in order to delete or reduce gender differences in SA.

Atomic force microscopy characterization of Zerodur mirror substrates for the extreme ultraviolet telescopes aboard NASA's Solar Dynamics Observatory.

PubMed

Soufli, Regina; Baker, Sherry L; Windt, David L; Gullikson, Eric M; Robinson, Jeff C; Podgorski, William A; Golub, Leon

2007-06-01

The high-spatial frequency roughness of a mirror operating at extreme ultraviolet (EUV) wavelengths is crucial for the reflective performance and is subject to very stringent specifications. To understand and predict mirror performance, precision metrology is required for measuring the surface roughness. Zerodur mirror substrates made by two different polishing vendors for a suite of EUV telescopes for solar physics were characterized by atomic force microscopy (AFM). The AFM measurements revealed features in the topography of each substrate that are associated with specific polishing techniques. Theoretical predictions of the mirror performance based on the AFM-measured high-spatial-frequency roughness are in good agreement with EUV reflectance measurements of the mirrors after multilayer coating.

Predictive value of flat-panel CT for haemorrhagic transformations in patients with acute stroke treated with thrombectomy.

PubMed

Rouchaud, Aymeric; Pistocchi, Silvia; Blanc, Raphaël; Engrand, Nicolas; Bartolini, Bruno; Piotin, Michel

2014-03-01

Haemorrhagic transformations are pejorative for patients with acute ischaemic stroke (AIS). We estimated flat-panel CT performances to detect brain parenchymal hyperdense lesions immediately after mechanical thrombectomy directly on the angiography table in patients with AIS, and its ability to predict haemorrhagic transformation. We also evaluated an easy-reading protocol for post-procedure flat-panel CT evaluation by clinicians to enable them to determine the potential risk of haemorrhage. Two neuroradiologists retrospectively reviewed post-procedural flat-panel CT and 24 h follow-up imaging. We evaluated hyperdense lesions on flat-panel CT to predict the occurrence of haemorrhagic transformation within 24 h detected with conventional imaging. Of 63 patients, 60.3% presented post-procedural parenchymal hyperdensity and 54.0% had haemorrhagic transformation. Significantly more patients with hyperdense lesions on post-thrombectomy flat-panel CT presented haemorrhagic transformation (84.2% vs 8.0%; p<0.0001). No significant haemorrhagic transformations were detected for patients without parenchymal hyperdensity. Sensitivity and specificity of hyperdense lesions on flat-panel CT for the prediction of haemorrhagic transformation were 94.1% (80.3-99.3%) and 79.3% (60.3-92.0%), respectively. The positive and negative predictive values for the occurrence of haemorrhage were 84.2% (68.8-94.0%) and 92.0% (74.0-99.0%), respectively. For significant parenchymal haemorrhage type 2, sensitivity and negative predictive values were 100%. We observed good homogeneity between the different readers. Hyperdensity on post-procedural flat-panel CT was associated with a tendency for higher risk of death and lower risk of good clinical outcome. Flat-panel CT appears to be a good tool to detect brain parenchymal hyperdensities after mechanical thrombectomy in patients with AIS and to predict haemorrhagic transformation.

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Determination of performance of non-ideal aluminized explosives.

PubMed

Keshavarz, Mohammad Hossein; Mofrad, Reza Teimuri; Poor, Karim Esmail; Shokrollahi, Arash; Zali, Abbas; Yousefi, Mohammad Hassan

2006-09-01

Non-ideal explosives can have Chapman-Jouguet (C-J) detonation pressure significantly different from those expected from existing thermodynamic computer codes, which usually allows finding the parameters of ideal detonation of individual high explosives with good accuracy. A simple method is introduced by which detonation pressure of non-ideal aluminized explosives with general formula C(a)H(b)N(c)O(d)Al(e) can be predicted only from a, b, c, d and e at any loading density without using any assumed detonation products and experimental data. Calculated detonation pressures show good agreement with experimental values with respect to computed results obtained by complicated computer code. It is shown here how loading density and atomic composition can be integrated into an empirical formula for predicting detonation pressure of proposed aluminized explosives.

Rule extraction from minimal neural networks for credit card screening.

PubMed

Setiono, Rudy; Baesens, Bart; Mues, Christophe

2011-08-01

While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.

NAVO MSRC Navigator. Spring 2001

DTIC Science & Technology

2001-01-01

preparations for UGC 2001 are almost complete. This year’s conference promises to be a good one, afford- ing us the opportunity to extend some Gulf Coast...cur- rent market pricing, and other rea- sonable estimates would not signifi- cantly alter the predicted trends. The performance model1 estimates CPU

Predictive Heterosis in Multibreed Evaluations Using Quantitative and Molecular Approaches

USDA-ARS?s Scientific Manuscript database

Heterosis is the extra genetic boost in performance obtained by crossing two cattle breeds. It is an important tool for increasing the efficiency of beef production. It is also important to adjust data used to calculate genetic evaluations for differences in heterosis. Good estimates of heterosis...

Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine.

PubMed

Yan, Zhao-Da; Zhou, Chong-Guang; Su, Shi-Chuan; Liu, Zhen-Tao; Wang, Xi-Zhen

2003-01-01

In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect of the operating parameters on combustion rate was also studied by means of this model. The study showed that the predicted results were good agreement with the experimental data. It was proved that the developed combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.

Predicting geogenic arsenic contamination in shallow groundwater of south Louisiana, United States.

PubMed

Yang, Ningfang; Winkel, Lenny H E; Johannesson, Karen H

2014-05-20

Groundwater contaminated with arsenic (As) threatens the health of more than 140 million people worldwide. Previous studies indicate that geology and sedimentary depositional environments are important factors controlling groundwater As contamination. The Mississippi River delta has broadly similar geology and sedimentary depositional environments to the large deltas in South and Southeast Asia, which are severely affected by geogenic As contamination and therefore may also be vulnerable to groundwater As contamination. In this study, logistic regression is used to develop a probability model based on surface hydrology, soil properties, geology, and sedimentary depositional environments. The model is calibrated using 3286 aggregated and binary-coded groundwater As concentration measurements from Bangladesh and verified using 78 As measurements from south Louisiana. The model's predictions are in good agreement with the known spatial distribution of groundwater As contamination of Bangladesh, and the predictions also indicate high risk of As contamination in shallow groundwater from Holocene sediments of south Louisiana. Furthermore, the model correctly predicted 79% of the existing shallow groundwater As measurements in the study region, indicating good performance of the model in predicting groundwater As contamination in shallow aquifers of south Louisiana.

New developments in isotropic turbulent models for FENE-P fluids

NASA Astrophysics Data System (ADS)

Resende, P. R.; Cavadas, A. S.

2018-04-01

The evolution of viscoelastic turbulent models, in the last years, has been significant due to the direct numeric simulation (DNS) advances, which allowed us to capture in detail the evolution of the viscoelastic effects and the development of viscoelastic closures. New viscoelastic closures are proposed for viscoelastic fluids described by the finitely extensible nonlinear elastic-Peterlin constitutive model. One of the viscoelastic closure developed in the context of isotropic turbulent models, consists in a modification of the turbulent viscosity to include an elastic effect, capable of predicting, with good accuracy, the behaviour for different drag reductions. Another viscoelastic closure essential to predict drag reduction relates the viscoelastic term involving velocity and the tensor conformation fluctuations. The DNS data show the high impact of this term to predict correctly the drag reduction, and for this reason is proposed a simpler closure capable of predicting the viscoelastic behaviour with good performance. In addition, a new relation is developed to predict the drag reduction, quantity based on the trace of the tensor conformation at the wall, eliminating the need of the typically parameters of Weissenberg and Reynolds numbers, which depend on the friction velocity. This allows future developments for complex geometries.

Predictors for in-hospital mortality and need for clinical intervention in upper GI bleeding: a 5-year observational study.

PubMed

Balaban, D V; Strâmbu, V; Florea, B G; Cazan, A R; Brătucu, M; Jinga, M

2014-01-01

Upper GI bleeding (UGIB) is a potentially life threatening gastrointestinal emergency whose effective management depends on early risk stratification. We retrospectively studied 151 patients admitted to our unit with UGIB between 1st January 2007 and 31st December 2011 and in whom we calculated the clinical and complete Rockall, the Glasgow-Blatchford and modified Glasgow-Blatchford risk scores. We performed an analysis of the predictive value of these scores for in-hospital mortality and need for clinical intervention. Of the 151 patients enrolled, 68.87% were male, and the mean age was 59.48 years. One in three patients had a history of chronic liver disease and one in eight had a previous episode of UGIB. Clinically, 58.3% of the patients presented with melena, 18.5% with hematemesis and 23.1% with both hematemesis and melena. 22% of cases were variceal hemorrhages and the other non-variceal. 16 patients died during hospitalization. The prognostic accuracy of all four scores for in-hospital death and need for clinical intervention was good, the complete Rockall score having the best performance (AUROC 0.849 and 0.653 respectively). The Rockall and Blatchford scores were good predictors of mortality and need for clinical intervention in our study. The good predictive performance of these scores highlight the need for their use in day-to-day practice to select patients with likelihood of poor clinical outcome. Celsius.

Performance of the score systems Acute Physiology and Chronic Health Evaluation II and III at an interdisciplinary intensive care unit, after customization

PubMed Central

Markgraf, Rainer; Deutschinoff, Gerd; Pientka, Ludger; Scholten, Theo; Lorenz, Cristoph

2001-01-01

Background: Mortality predictions calculated using scoring scales are often not accurate in populations other than those in which the scales were developed because of differences in case-mix. The present study investigates the effect of first-level customization, using a logistic regression technique, on discrimination and calibration of the Acute Physiology and Chronic Health Evaluation (APACHE) II and III scales. Method: Probabilities of hospital death for patients were estimated by applying APACHE II and III and comparing these with observed outcomes. Using the split sample technique, a customized model to predict outcome was developed by logistic regression. The overall goodness-of-fit of the original and the customized models was assessed. Results: Of 3383 consecutive intensive care unit (ICU) admissions over 3 years, 2795 patients could be analyzed, and were split randomly into development and validation samples. The discriminative powers of APACHE II and III were unchanged by customization (areas under the receiver operating characteristic [ROC] curve 0.82 and 0.85, respectively). Hosmer-Lemeshow goodness-of-fit tests showed good calibration for APACHE II, but insufficient calibration for APACHE III. Customization improved calibration for both models, with a good fit for APACHE III as well. However, fit was different for various subgroups. Conclusions: The overall goodness-of-fit of APACHE III mortality prediction was improved significantly by customization, but uniformity of fit in different subgroups was not achieved. Therefore, application of the customized model provides no advantage, because differences in case-mix still limit comparisons of quality of care. PMID:11178223

Watching the clock

PubMed Central

Fetterman, J. Gregor; Killeen, Peter R.; Hall, Scott

2008-01-01

Four rats and four pigeons were monitored while performing retrospective timing tasks. All animals displayed collateral behaviors which could have mediated their temporal judgements. Statistical analysis made a good case for such mediation in the case of two pigeons performing on a spatially-differentiated response, but not for the two responding on a color-differentiated response. For the rats, all of which performed on a spatially-differentiated task, prediction of their temporal judgements was always better if based on collateral activity than if based on the passage of time. PMID:19701487

Sleep Quality Prediction From Wearable Data Using Deep Learning.

PubMed

Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-11-04

The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. ©Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.11.2016.

Sleep Quality Prediction From Wearable Data Using Deep Learning

PubMed Central

Sathyanarayana, Aarti; Joty, Shafiq; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-01-01

Background The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463). Conclusions Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. PMID:27815231

Stock price change rate prediction by utilizing social network activities.

PubMed

Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

Stock Price Change Rate Prediction by Utilizing Social Network Activities

PubMed Central

Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

Predicting Esophagitis After Chemoradiation Therapy for Non-Small Cell Lung Cancer: An Individual Patient Data Meta-Analysis

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

Palma, David A., E-mail: david.palma@uwo.ca; Senan, Suresh; Oberije, Cary

Purpose: Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE. Methods and Materials: After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade ≥2 and grade ≥3) weremore » assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model. Results: The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c > .60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade ≥2 and grade ≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60 <0.07%), intermediate (V60 0.07% to 16.99%), and high (V60 ≥17%). With use of the validation set, the predictive model performed inferiorly for the grade ≥2 endpoint (c = .58) but performed well for the grade ≥3 endpoint (c = .66). Conclusions: Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.« less

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. Copyright © 2018 Elsevier Inc. All rights reserved.

Dispersion of Self-Propelled Rods Undergoing Fluctuation-Driven Flips

NASA Astrophysics Data System (ADS)

Takagi, Daisuke; Braunschweig, Adam B.; Zhang, Jun; Shelley, Michael J.

2013-01-01

Synthetic microswimmers may someday perform medical and technological tasks, but predicting their motion and dispersion is challenging. Here we show that chemically propelled rods tend to move on a surface along large circles but curiously show stochastic changes in the sign of the orbit curvature. By accounting for fluctuation-driven flipping of slightly curved rods, we obtain analytical predictions for the ensemble behavior in good agreement with our experiments. This shows that minor defects in swimmer shape can yield major long-term effects on macroscopic dispersion.

A pan-African medium-range ensemble flood forecast system

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

2015-04-01

The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. Also the forecasts showed on average a good reliability, and the CRPSS helped identifying regions to focus on for future improvements. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe and Mozambique) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a good prospective as an operational system, as it has demonstrated its significant potential to contribute to the reduction of flood-related losses in Africa by providing national and international aid organizations timely with medium-range flood forecast information. However, issues related to the practical implication will still need to be investigated.

Performance characteristics of broth-only cultures after revision total joint arthroplasty.

PubMed

Smith, Eric B; Cai, Jenny; Wynne, Rachael; Maltenfort, Mitchell; Good, Robert P

2014-11-01

Surgeons frequently obtain intraoperative cultures at the time of revision total joint arthroplasty. The use of broth or liquid medium before applying the sample to the agar medium may be associated with contamination and false-positive cultures; however, the degree to which this is the case is not known. We (1) calculated the performance characteristics of broth-only cultures (sensitivity, specificity, positive predictive value, and negative predictive value) and (2) characterized the organisms identified in broth to determine whether a specific organism showed increased proclivity for true-positive periprosthetic joint infection (PJI). A single-institution retrospective chart review was performed on 257 revision total joint arthroplasties from 2009 through 2010. One hundred ninety (74%) had cultures for review. All culture results, as well as treatment, if any, were documented and patients were followed for a minimum of 1 year for evidence of PJI. Cultures were measured as either positive from the broth only or broth negative. The true diagnosis of infection was determined by the Musculoskeletal Infection Society criteria during the preoperative workup or postoperatively at 1 year for purposes of calculating the performance characteristics of the broth-only culture. The sensitivity, specificity, positive predictive value, and negative predictive value were 19%, 88%, 13%, and 92%, respectively. The most common organism identified was coagulase-negative Staphylococcus (16 of 24 cases, 67%). Coagulase-negative Staphylococcus was present in all three true-positive cases; however, it was also found in 13 of the false-positive cases. The broth-only positive cultures showed poor sensitivity and positive predictive value but good specificity and negative predictive value. The good specificity indicates that it can help to rule in the presence of PJI; however, the poor sensitivity makes broth-only culture an unreliable screening test. We recommend that broth-only culture results be carefully scrutinized and decisions on the diagnosis and treatment of infection should be based specifically on the Musculoskeletal Infection Society criteria. Level IV, diagnostic study. See Instructions for Authors for a complete description of levels of evidence.

Predicting First Grade Reading Performance from Kindergarten Response to Tier 1 Instruction

PubMed Central

Al Otaiba, Stephanie; Folsom, Jessica S.; Schatschneider, Christopher; Wanzek, Jeanne; Greulich, Luana; Meadows, Jane; Li, Zhi; Connor, Carol M

2010-01-01

Many schools are beginning to implement multi-tier response to intervention (RTI) models for the prevention of reading difficulties and to assist in the identification of students with learning disabilities (LD). The present study was part of our larger ongoing longitudinal RTI investigation within the Florida Learning Disabilities Center grant. This study used a longitudinal correlational design, conducted in 7 ethnically and socio-economically diverse schools. We observed reading instruction in 20 classrooms, examined response rates to kindergarten Tier 1 instruction, and predicted students’ first grade reading performance based upon kindergarten growth and end of year reading performance (n = 203). Teachers followed an explicit core reading program and overall, classroom instruction was rated as effective. Results indicate that controlling for students’ end of kindergarten reading, their growth across kindergarten on a variety of language and literacy measures suppressed predictions of first grade performance. Specifically, the steeper the students’ trajectory to a satisfactory outcome, the less likely they were to demonstrate good performance in first grade. Implications for future research and RTI implementation are discussed. PMID:21857718

Psychological predictors of college students' cell phone use while driving.

PubMed

Schlehofer, Michèle M; Thompson, Suzanne C; Ting, Sarah; Ostermann, Sharon; Nierman, Angela; Skenderian, Jessica

2010-07-01

Despite the known risk, many people talk on a phone while driving. This study explored psychological predictors of cell phone use while driving. College students (final N=69) completed a survey and predicted their driving performance both with and without a simultaneous phone conversation. Their actual performance on a driving simulator was then assessed. Cell phone use reduced performance on the simulation task. Further, perceiving oneself as good at compensating for driving distractions, overestimating one's performance on the driving simulator, and high illusory control predicted more frequent cell phone use while driving in everyday life. Finally, those who talked more frequently on a phone while driving had poorer real-world driving records. These findings suggest illusory control and positive illusions partly explain driver's decisions of whether to use cell phones while driving. Copyright 2010 Elsevier Ltd. All rights reserved.

Recent Progress Towards Predicting Aircraft Ground Handling Performance

NASA Technical Reports Server (NTRS)

Yager, T. J.; White, E. J.

1981-01-01

The significant progress which has been achieved in development of aircraft ground handling simulation capability is reviewed and additional improvements in software modeling identified. The problem associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior is discussed and efforts to improve this complex model, and hence simulator fidelity, are described. Aircraft braking performance data obtained on several wet runway surfaces is compared to ground vehicle friction measurements and, by use of empirically derived methods, good agreement between actual and estimated aircraft braking friction from ground vehilce data is shown. The performance of a relatively new friction measuring device, the friction tester, showed great promise in providing data applicable to aircraft friction performance. Additional research efforts to improve methods of predicting tire friction performance are discussed including use of an instrumented tire test vehicle to expand the tire friction data bank and a study of surface texture measurement techniques.

Analytical performance, agreement and user-friendliness of six point-of-care testing urine analysers for urinary tract infection in general practice

PubMed Central

Schot, Marjolein J C; van Delft, Sanne; Kooijman-Buiting, Antoinette M J; de Wit, Niek J; Hopstaken, Rogier M

2015-01-01

Objective Various point-of-care testing (POCT) urine analysers are commercially available for routine urine analysis in general practice. The present study compares analytical performance, agreement and user-friendliness of six different POCT urine analysers for diagnosing urinary tract infection in general practice. Setting All testing procedures were performed at a diagnostic centre for primary care in the Netherlands. Urine samples were collected at four general practices. Primary and secondary outcome measures Analytical performance and agreement of the POCT analysers regarding nitrite, leucocytes and erythrocytes, with the laboratory reference standard, was the primary outcome measure, and analysed by calculating sensitivity, specificity, positive and negative predictive value, and Cohen's κ coefficient for agreement. Secondary outcome measures were the user-friendliness of the POCT analysers, in addition to other characteristics of the analysers. Results The following six POCT analysers were evaluated: Uryxxon Relax (Macherey Nagel), Urisys 1100 (Roche), Clinitek Status (Siemens), Aution 11 (Menarini), Aution Micro (Menarini) and Urilyzer (Analyticon). Analytical performance was good for all analysers. Compared with laboratory reference standards, overall agreement was good, but differed per parameter and per analyser. Concerning the nitrite test, the most important test for clinical practice, all but one showed perfect agreement with the laboratory standard. For leucocytes and erythrocytes specificity was high, but sensitivity was considerably lower. Agreement for leucocytes varied between good to very good, and for the erythrocyte test between fair and good. First-time users indicated that the analysers were easy to use. They expected higher productivity and accuracy when using these analysers in daily practice. Conclusions The overall performance and user-friendliness of all six commercially available POCT urine analysers was sufficient to justify routine use in suspected urinary tract infections in general practice. PMID:25986635

A new method to predict anatomical outcome after idiopathic macular hole surgery.

PubMed

Liu, Peipei; Sun, Yaoyao; Dong, Chongya; Song, Dan; Jiang, Yanrong; Liang, Jianhong; Yin, Hong; Li, Xiaoxin; Zhao, Mingwei

2016-04-01

To investigate whether a new macular hole closure index (MHCI) could predict anatomic outcome of macular hole surgery. A vitrectomy with internal limiting membrane peeling, air-fluid exchange, and gas tamponade were performed on all patients. The postoperative anatomic status of the macular hole was defined by spectral-domain OCT. MHCI was calculated as (M+N)/BASE based on the preoperative OCT status. M and N were the curve lengths of the detached photoreceptor arms, and BASE was the length of the retinal pigment epithelial layer (RPE layer) detaching from the photoreceptors. Postoperative anatomical outcomes were divided into three grades: A (bridge-like closure), B (good closure), and C (poor closure or no closure). Correlation analysis was performed between anatomical outcomes and MHCI. Receiver operating characteristic (ROC) curves were derived for MHCI, indicating good model discrimination. ROC curves were also assessed by the area under the curve, and cut-offs were calculated. Other predictive parameters reported previously, which included the MH minimum, the MH height, the macular hole index (MHI), the diameter hole index (DHI), and the tractional hole index (THI) had been compared as well. MHCI correlated significantly with postoperative anatomical outcomes (r = 0.543, p = 0.000), but other predictive parameters did not. The areas under the curves indicated that MHCI could be used as an effective predictor of anatomical outcome. Cut-off values of 0.7 and 1.0 were obtained for MHCI from ROC curve analysis. MHCI demonstrated a better predictive effect than other parameters, both in the correlation analysis and ROC analysis. MHCI could be an easily measured and accurate predictive index for postoperative anatomical outcomes.

Prediction of fracture load and stiffness of the proximal femur by CT-based specimen specific finite element analysis: cadaveric validation study.

PubMed

Miura, Michiaki; Nakamura, Junichi; Matsuura, Yusuke; Wako, Yasushi; Suzuki, Takane; Hagiwara, Shigeo; Orita, Sumihisa; Inage, Kazuhide; Kawarai, Yuya; Sugano, Masahiko; Nawata, Kento; Ohtori, Seiji

2017-12-16

Finite element analysis (FEA) of the proximal femur has been previously validated with large mesh size, but these were insufficient to simulate the model with small implants in recent studies. This study aimed to validate the proximal femoral computed tomography (CT)-based specimen-specific FEA model with smaller mesh size using fresh frozen cadavers. Twenty proximal femora from 10 cadavers (mean age, 87.1 years) were examined. CT was performed on all specimens with a calibration phantom. Nonlinear FEA prediction with stance configuration was performed using Mechanical Finder (mesh,1.5 mm tetrahedral elements; shell thickness, 0.2 mm; Poisson's coefficient, 0.3), in comparison with mechanical testing. Force was applied at a fixed vertical displacement rate, and the magnitude of the applied load and displacement were continuously recorded. The fracture load and stiffness were calculated from force-displacement curve, and the correlation between mechanical testing and FEA prediction was examined. A pilot study with one femur revealed that the equations proposed by Keller for vertebra were the most reproducible for calculating Young's modulus and the yield stress of elements of the proximal femur. There was a good linear correlation between fracture loads of mechanical testing and FEA prediction (R 2 = 0.6187) and between the stiffness of mechanical testing and FEA prediction (R 2 = 0.5499). There was a good linear correlation between fracture load and stiffness (R 2 = 0.6345) in mechanical testing and an excellent correlation between these (R 2 = 0.9240) in FEA prediction. CT-based specimen-specific FEA model of the proximal femur with small element size was validated using fresh frozen cadavers. The equations proposed by Keller for vertebra were found to be the most reproducible for the proximal femur in elderly people.

Use of sequential endorectal US to predict the tumor response of preoperative chemoradiotherapy in rectal cancer.

PubMed

Li, Ning; Dou, Lizhou; Zhang, Yueming; Jin, Jing; Wang, Guiqi; Xiao, Qin; Li, Yexiong; Wang, Xin; Ren, Hua; Fang, Hui; Wang, Weihu; Wang, Shulian; Liu, Yueping; Song, Yongwen

2017-03-01

Accurate prediction of the response to preoperative chemoradiotherapy (CRT) potentially assists in the individualized selection of treatment. Endorectal US (ERUS) is widely used for the pretreatment staging of rectal cancer, but its use for preoperatively predicting the effects of CRT is not well evaluated because of the inflammation, necrosis, and fibrosis induced by CRT. This study assessed the value of sequential ERUS in predicting the efficacy of preoperative CRT for locally advanced rectal cancer. Forty-one patients with clinical stage II/III rectal adenocarcinoma were enrolled prospectively. Radiotherapy was delivered to the pelvis with concurrent chemotherapy of capecitabine and oxaliplatin. Total mesorectal excision was performed 6 to 8 weeks later. EUS measurements of primary tumor maximum diameter were performed before (ERUS1), during (ERUS2), and 6 to 8 weeks after (ERUS3) CRT, and the ratios of these were calculated. Correlations between ERUS values, tumor regression grade (TRG), T down-staging rate, and pathologic complete response (pCR) rate were assessed, and survival was analyzed. There was no significant correlation between ERUS2/ERUS1 and TRG. The value of ERUS3/ERUS1 correlated with pCR rate and TRG but not T down-staging rate. An ERUS3 value of 6.3 mm and ERUS3/ERUS1 of 52% were used as the cut-off for predicting pCR, and patients were divided into good and poor prognosis groups. Although not statistically significant, 3-year recurrence and survival rates of the good prognosis group were better than those of the poor prognosis group. Sequential ERUS may predict therapeutic efficacy of preoperative CRT for locally advanced rectal cancer. (Clinical trial registration number: NCT01582750.). Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Humor Ability Reveals Intelligence, Predicts Mating Success, and Is Higher in Males

ERIC Educational Resources Information Center

Greengross, Gil; Miller, Geoffrey

2011-01-01

A good sense of humor is sexually attractive, perhaps because it reveals intelligence, creativity, and other "good genes" or "good parent" traits. If so, intelligence should predict humor production ability, which in turn should predict mating success. In this study, 400 university students (200 men and 200 women) completed…

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2016-09-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Diet Quality Scores and Prediction of All-Cause, Cardiovascular and Cancer Mortality in a Pan-European Cohort Study

PubMed Central

Lassale, Camille; Gunter, Marc J.; Romaguera, Dora; Peelen, Linda M.; Van der Schouw, Yvonne T.; Beulens, Joline W. J.; Freisling, Heinz; Muller, David C.; Ferrari, Pietro; Huybrechts, Inge; Fagherazzi, Guy; Boutron-Ruault, Marie-Christine; Affret, Aurélie; Overvad, Kim; Dahm, Christina C.; Olsen, Anja; Roswall, Nina; Tsilidis, Konstantinos K.; Katzke, Verena A.; Kühn, Tilman; Buijsse, Brian; Quirós, José-Ramón; Sánchez-Cantalejo, Emilio; Etxezarreta, Nerea; Huerta, José María; Barricarte, Aurelio; Bonet, Catalina; Khaw, Kay-Tee; Key, Timothy J.; Trichopoulou, Antonia; Bamia, Christina; Lagiou, Pagona; Palli, Domenico; Agnoli, Claudia; Tumino, Rosario; Fasanelli, Francesca; Panico, Salvatore; Bueno-de-Mesquita, H. Bas; Boer, Jolanda M. A.; Sonestedt, Emily; Nilsson, Lena Maria; Renström, Frida; Weiderpass, Elisabete; Skeie, Guri; Lund, Eiliv; Moons, Karel G. M.; Riboli, Elio; Tzoulaki, Ioanna

2016-01-01

Scores of overall diet quality have received increasing attention in relation to disease aetiology; however, their value in risk prediction has been little examined. The objective was to assess and compare the association and predictive performance of 10 diet quality scores on 10-year risk of all-cause, CVD and cancer mortality in 451,256 healthy participants to the European Prospective Investigation into Cancer and Nutrition, followed-up for a median of 12.8y. All dietary scores studied showed significant inverse associations with all outcomes. The range of HRs (95% CI) in the top vs. lowest quartile of dietary scores in a composite model including non-invasive factors (age, sex, smoking, body mass index, education, physical activity and study centre) was 0.75 (0.72–0.79) to 0.88 (0.84–0.92) for all-cause, 0.76 (0.69–0.83) to 0.84 (0.76–0.92) for CVD and 0.78 (0.73–0.83) to 0.91 (0.85–0.97) for cancer mortality. Models with dietary scores alone showed low discrimination, but composite models also including age, sex and other non-invasive factors showed good discrimination and calibration, which varied little between different diet scores examined. Mean C-statistic of full models was 0.73, 0.80 and 0.71 for all-cause, CVD and cancer mortality. Dietary scores have poor predictive performance for 10-year mortality risk when used in isolation but display good predictive ability in combination with other non-invasive common risk factors. PMID:27409582

An experimental investigation of hingeless helicopter rotor-body stability in hover

NASA Technical Reports Server (NTRS)

Bousman, W. G.

1978-01-01

Model tests of a 1.62 m diameter rotor were performed to investigate the aeromechanical stability of coupled rotor-body systems in hover. Experimental measurements were made of modal frequencies and damping over a wide range of rotor speeds. Good data were obtained for the frequencies of the rotor lead-lag regressing mode. The quality of the damping measurements of the body modes was poor due to nonlinear damping in the gimbal ball bearings. Simulated vacuum testing was performed using substitute blades of tantalum that reduced the effective lock number to 0.2% of the model scale value while keeping the blade inertia constant. The experimental data were compared with theoretical predictions, and the correlation was in general very good.

Effect of missing data on multitask prediction methods.

PubMed

de la Vega de León, Antonio; Chen, Beining; Gillet, Valerie J

2018-05-22

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises.

Evaluation of 3D-Jury on CASP7 models.

PubMed

Kaján, László; Rychlewski, Leszek

2007-08-21

3D-Jury, the structure prediction consensus method publicly available in the Meta Server http://meta.bioinfo.pl/, was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers. The performance of 3D-Jury was analysed for three aspects. First, we examined the correlation between the 3D-Jury score and a model quality measure: the number of correctly predicted residues. The 3D-Jury score was shown to correlate significantly with the number of correctly predicted residues, the correlation is good enough to be used for prediction. 3D-Jury was also found to improve upon the competing servers' choice of the best structure model in most cases. The value of the 3D-Jury score as a generic reliability measure was also examined. We found that the 3D-Jury score separates bad models from good models better than the reliability score of the original server in 27 cases and falls short of it in only 5 cases out of a total of 38. We report the release of a new Meta Server feature: instant 3D-Jury scoring of uploaded user models. The 3D-Jury score continues to be a good indicator of structural model quality. It also provides a generic reliability score, especially important for models that were not assigned such by the original server. Individual structure modellers can also benefit from the 3D-Jury scoring system by testing their models in the new instant scoring feature http://meta.bioinfo.pl/compare_your_model_example.pl available in the Meta Server.

Evaluation of 3D-Jury on CASP7 models

PubMed Central

Kaján, László; Rychlewski, Leszek

2007-01-01

Background 3D-Jury, the structure prediction consensus method publicly available in the Meta Server , was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers. Results The performance of 3D-Jury was analysed for three aspects. First, we examined the correlation between the 3D-Jury score and a model quality measure: the number of correctly predicted residues. The 3D-Jury score was shown to correlate significantly with the number of correctly predicted residues, the correlation is good enough to be used for prediction. 3D-Jury was also found to improve upon the competing servers' choice of the best structure model in most cases. The value of the 3D-Jury score as a generic reliability measure was also examined. We found that the 3D-Jury score separates bad models from good models better than the reliability score of the original server in 27 cases and falls short of it in only 5 cases out of a total of 38. We report the release of a new Meta Server feature: instant 3D-Jury scoring of uploaded user models. Conclusion The 3D-Jury score continues to be a good indicator of structural model quality. It also provides a generic reliability score, especially important for models that were not assigned such by the original server. Individual structure modellers can also benefit from the 3D-Jury scoring system by testing their models in the new instant scoring feature available in the Meta Server. PMID:17711571

Novel CPR system that predicts return of spontaneous circulation from amplitude spectral area before electric shock in ventricular fibrillation.

PubMed

Nakagawa, Yoshihide; Amino, Mari; Inokuchi, Sadaki; Hayashi, Satoshi; Wakabayashi, Tsutomu; Noda, Tatsuya

2017-04-01

Amplitude spectral area (AMSA), an index for analysing ventricular fibrillation (VF) waveforms, is thought to predict the return of spontaneous circulation (ROSC) after electric shocks, but its validity is unconfirmed. We developed an equation to predict ROSC, where the change in AMSA (ΔAMSA) is added to AMSA measured immediately before the first shock (AMSA1). We examine the validity of this equation by comparing it with the conventional AMSA1-only equation. We retrospectively investigated 285 VF patients given prehospital electric shocks by emergency medical services. ΔAMSA was calculated by subtracting AMSA1 from last AMSA immediately before the last prehospital electric shock. Multivariate logistic regression analysis was performed using post-shock ROSC as a dependent variable. Analysis data were subjected to receiver operating characteristic curve analysis, goodness-of-fit testing using a likelihood ratio test, and the bootstrap method. AMSA1 (odds ratio (OR) 1.151, 95% confidence interval (CI) 1.086-1.220) and ΔAMSA (OR 1.289, 95% CI 1.156-1.438) were independent factors influencing ROSC induction by electric shock. Area under the curve (AUC) for predicting ROSC was 0.851 for AMSA1-only and 0.891 for AMSA1+ΔAMSA. Compared with the AMSA1-only equation, the AMSA1+ΔAMSA equation had significantly better goodness-of-fit (likelihood ratio test P<0.001) and showed good fit in the bootstrap method. Post-shock ROSC was accurately predicted by adding ΔAMSA to AMSA1. AMSA-based ROSC prediction enables application of electric shock to only those patients with high probability of ROSC, instead of interrupting chest compressions and delivering unnecessary shocks to patients with low probability of ROSC. Copyright © 2017 Elsevier B.V. All rights reserved.

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

NASA Astrophysics Data System (ADS)

Duan, Rui; Xu, Xianjin; Zou, Xiaoqin

2018-01-01

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.

PubMed

Yılmaz Isıkhan, Selen; Karabulut, Erdem; Alpar, Celal Reha

2016-01-01

Background/Aim . Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods . Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results . The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion . Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n = 25 as a cutoff point for RT bagging to outperform a single RT.

Topex Microwave Radiometer thermal control - Post-system-test modifications and on-orbit performance

NASA Technical Reports Server (NTRS)

Lin, Edward I.

1993-01-01

The Topex Microwave Radiometer has had an excellent thermal performance since launch. The instrument, however, went through a hardware modification right before launch to correct for a thermal design inadequacy that was uncovered during the spacecraft thermal vacuum test. This paper reports on how the initially obscure problem was tracked down, and how the thermal models were revised, validated, and utilized to investigate the solution options and guide the hardware modification decisions. Details related to test data interpretation, analytical uncertainties, and model-prediction vs. test-data correlation, are documented. Instrument/spacecraft interface issues, where the problem originated and where in general pitfalls abound, are dealt with specifically. Finally, on-orbit thermal performance data are presented, which exhibit good agreement with flight predictions, and lessons learned are discussed.

SWAT system performance predictions

NASA Astrophysics Data System (ADS)

Parenti, Ronald R.; Sasiela, Richard J.

1993-03-01

In the next phase of Lincoln Laboratory's SWAT (Short-Wavelength Adaptive Techniques) program, the performance of a 241-actuator adaptive-optics system will be measured using a variety of synthetic-beacon geometries. As an aid in this experimental investigation, a detailed set of theoretical predictions has also been assembled. The computational tools that have been applied in this study include a numerical approach in which Monte-Carlo ray-trace simulations of accumulated phase error are developed, and an analytical analysis of the expected system behavior. This report describes the basis of these two computational techniques and compares their estimates of overall system performance. Although their regions of applicability tend to be complementary rather than redundant, good agreement is usually obtained when both sets of results can be derived for the same engagement scenario.

Atomic force microscopy characterization of Zerodur mirror substrates for the extreme ultraviolet telescopes aboard NASA's Solar Dynamics Observatory

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

Soufli, Regina; Baker, Sherry L.; Windt, David L.

2007-06-01

The high-spatial frequency roughness of a mirror operating at extreme ultraviolet (EUV)wavelengths is crucial for the reflective performance and is subject to very stringent specifications. To understand and predict mirror performance, precision metrology is required for measuring the surface roughness. Zerodur mirror substrates made by two different polishing vendors for a suite of EUV telescopes for solar physics were characterized by atomic force microscopy (AFM). The AFM measurements revealed features in the topography of each substrate that are associated with specific polishing techniques. Theoretical predictions of the mirror performance based on the AFM-measured high-spatial-frequency roughness are in good agreement withmore » EUV reflectance measurements of the mirrors after multilayer coating.« less

What Predicts Skill in Lecture Note Taking?

ERIC Educational Resources Information Center

Peverly, Stephen T.; Ramaswamy, Vivek; Brown, Cindy; Sumowski, James; Alidoost, Moona; Garner, Joanna

2007-01-01

Despite the importance of good lecture notes to test performance, very little is known about the cognitive processes that underlie effective lecture note taking. The primary purpose of the 2 studies reported (a pilot study and Study 1) was to investigate 3 processes hypothesized to be significantly related to quality of notes: transcription…

Cryogenic liquid-level detector

NASA Technical Reports Server (NTRS)

Hamlet, J.

1978-01-01

Detector is designed for quick assembly, fast response, and good performance under vibratory stress. Its basic parallel-plate open configuration can be adapted to any length and allows its calibration scale factor to be predicted accurately. When compared with discrete level sensors, continuous reading sensor was found to be superior if there is sloshing, boiling, or other disturbance.

Factors Predicting a Good Symptomatic Outcome After Prostate Artery Embolisation (PAE).

PubMed

Maclean, D; Harris, M; Drake, T; Maher, B; Modi, S; Dyer, J; Somani, B; Hacking, N; Bryant, T

2018-02-26

As prostate artery embolisation (PAE) becomes an established treatment for benign prostatic obstruction, factors predicting good symptomatic outcome remain unclear. Pre-embolisation prostate size as a predictor is controversial with a handful of papers coming to conflicting conclusions. We aimed to investigate if an association existed in our patient cohort between prostate size and clinical benefit, in addition to evaluating percentage volume reduction as a predictor of symptomatic outcome following PAE. Prospective follow-up of 86 PAE patients at a single institution between June 2012 and January 2016 was conducted (mean age 64.9 years, range 54-80 years). Multiple linear regression analysis was performed to assess strength of association between clinical improvement (change in IPSS) and other variables, of any statistical correlation, through Pearson's bivariate analysis. No major procedural complications were identified and clinical success was achieved in 72.1% (n = 62) at 12 months. Initial prostate size and percentage reduction were found to have a significant association with clinical improvement. Multiple linear regression analysis (r 2  = 0.48) demonstrated that percentage volume reduction at 3 months (r = 0.68, p < 0.001) had the strongest correlation with good symptomatic improvement at 12 months after adjusting for confounding factors. Both the initial prostate size and percentage volume reduction at 3 months predict good symptomatic outcome at 12 months. These findings therefore aid patient selection and counselling to achieve optimal outcomes for men undergoing prostate artery embolisation.

Prospective comparison of severity scores for predicting mortality in community-acquired pneumonia.

PubMed

Luque, Sonia; Gea, Joaquim; Saballs, Pere; Ferrández, Olivia; Berenguer, Nuria; Grau, Santiago

2012-06-01

Specific prognostic models for community acquired pneumonia (CAP) to guide treatment decisions have been developed, such us the Pneumonia Severity Index (PSI) and the Confusion, Urea nitrogen, Respiratory rate, Blood pressure and age ≥ 65 years index (CURB-65). Additionally, general models are available such as the Mortality Probability Model (MPM-II). So far, which score performs better in CAP remains controversial. The objective was to compare PSI and CURB-65 and the general model, MPM-II, for predicting 30-day mortality in patients admitted with CAP. Prospective observational study including all consecutive patients hospitalised with a confirmed diagnosis of CAP and treated according to the hospital guidelines. Comparison of the overall discriminatory power of the models was performed by calculating the area under a receiver operator characteristic curve (AUC ROC curve) and calibration through the Goodness-of-fit test. One hundred and fifty two patients were included (mean age 73.0 years; 69.1% male; 75.0% with more than one comorbid condition). Seventy-five percent of the patients were classified as high-risk subjects according to the PSI, versus 61.2% according to the CURB-65. The 30-day mortality rate was 11.8%. All three scores obtained acceptable and similar values of the AUCs of the ROC curve for predicting mortality. Despite all rules showed good calibration, this seemed to be better for CURB-65. CURB-65 also revealed the highest positive likelihood ratio. CURB-65 performs similar to PSI or MPMII for predicting 30-day mortality in patients with CAP. Consequently, this simple model can be regarded as a valid alternative to the more complex rules.

A Numerical Analysis of Heat Transfer and Effectiveness on Film Cooled Turbine Blade Tip Models

NASA Technical Reports Server (NTRS)

Ameri, A. A.; Rigby, D. L.

1999-01-01

A computational study has been performed to predict the distribution of convective heat transfer coefficient on a simulated blade tip with cooling holes. The purpose of the examination was to assess the ability of a three-dimensional Reynolds-averaged Navier-Stokes solver to predict the rate of tip heat transfer and the distribution of cooling effectiveness. To this end, the simulation of tip clearance flow with blowing of Kim and Metzger was used. The agreement of the computed effectiveness with the data was quite good. The agreement with the heat transfer coefficient was not as good but improved away from the cooling holes. Numerical flow visualization showed that the uniformity of wetting of the surface by the film cooling jet is helped by the reverse flow due to edge separation of the main flow.

Unsteady aerodynamic analysis of space shuttle vehicles. Part 2: Steady and unsteady aerodynamics of sharp-edged delta wings

NASA Technical Reports Server (NTRS)

Ericsson, L. E.; Reding, J. P.

1973-01-01

An analysis of the steady and unsteady aerodynamics of sharp-edged slender wings has been performed. The results show that slender wing theory can be modified to give the potential flow static and dynamic characteristics in incompressible flow. A semiempirical approximation is developed for the vortex-induced loads, and it is shown that the analytic approximation for sharp-edged slender wings gives good prediction of experimentally determined steady and unsteady aerodynamics at M = 0 and M = 1. The predictions are good not only for delta wings but also for so-called arrow and diamond wings. The results indicate that the effects of delta planform lifting surfaces can be included in a simple manner when determining elastic launch vehicle dynamic characteristics. For Part 1 see (N73-32763).

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

NASA Astrophysics Data System (ADS)

Mohan Reddy, M.; Gorin, Alexander; Abou-El-Hossein, K. A.

2011-02-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Prioritization of candidate disease genes by combining topological similarity and semantic similarity.

PubMed

Liu, Bin; Jin, Min; Zeng, Pan

2015-10-01

The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Use of the color trails test as an embedded measure of performance validity.

PubMed

Henry, George K; Algina, James

2013-01-01

One hundred personal injury litigants and disability claimants referred for a forensic neuropsychological evaluation were administered both portions of the Color Trails Test (CTT) as part of a more comprehensive battery of standardized tests. Subjects who failed two or more free-standing tests of cognitive performance validity formed the Failed Performance Validity (FPV) group, while subjects who passed all free-standing performance validity measures were assigned to the Passed Performance Validity (PPV) group. A cutscore of ≥45 seconds to complete Color Trails 1 (CT1) was associated with a classification accuracy of 78%, good sensitivity (66%) and high specificity (90%), while a cutscore of ≥84 seconds to complete Color Trails 2 (CT2) was associated with a classification accuracy of 82%, good sensitivity (74%) and high specificity (90%). A CT1 cutscore of ≥58 seconds, and a CT2 cutscore ≥100 seconds was associated with 100% positive predictive power at base rates from 20 to 50%.

Can somatosensory and visual evoked potentials predict neurological outcome during targeted temperature management in post cardiac arrest patients?

PubMed

Choi, Seung Pill; Park, Kyu Nam; Wee, Jung Hee; Park, Jeong Ho; Youn, Chun Song; Kim, Han Joon; Oh, Sang Hoon; Oh, Yoon Sang; Kim, Soo Hyun; Oh, Joo Suk

2017-10-01

In cardiac arrest patients treated with targeted temperature management (TTM), it is not certain if somatosensory evoked potentials (SEPs) and visual evoked potentials (VEPs) can predict neurological outcomes during TTM. The aim of this study was to investigate the prognostic value of SEPs and VEPs during TTM and after rewarming. This retrospective cohort study included comatose patients resuscitated from cardiac arrest and treated with TTM between March 2007 and July 2015. SEPs and VEPs were recorded during TTM and after rewarming in these patients. Neurological outcome was assessed at discharge by the Cerebral Performance Category (CPC) Scale. In total, 115 patients were included. A total of 175 SEPs and 150 VEPs were performed. Five SEPs during treated with TTM and nine SEPs after rewarming were excluded from outcome prediction by SEPs due to an indeterminable N20 response because of technical error. Using 80 SEPs and 85 VEPs during treated with TTM, absent SEPs yielded a sensitivity of 58% and a specificity of 100% for poor outcome (CPC 3-5), and absent VEPs predicted poor neurological outcome with a sensitivity of 44% and a specificity of 96%. The AUC of combination of SEPs and VEPs was superior to either test alone (0.788 for absent SEPs and 0.713 for absent VEPs compared with 0.838 for the combination). After rewarming, absent SEPs and absent VEPs predicted poor neurological outcome with a specificity of 100%. When SEPs and VEPs were combined, VEPs slightly increased the prognostic accuracy of SEPs alone. Although one patient with absent VEP during treated with TTM had a good neurological outcome, none of the patients with good neurological outcome had an absent VEP after rewarming. Absent SEPs could predict poor neurological outcome during TTM as well as after rewarming. Absent VEPs may predict poor neurological outcome in both periods and VEPs may provide additional prognostic value in outcome prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a design space and predictive statistical model for capsule filling of low-fill-weight inhalation products.

PubMed

Faulhammer, E; Llusa, M; Wahl, P R; Paudel, A; Lawrence, S; Biserni, S; Calzolari, V; Khinast, J G

2016-01-01

The objectives of this study were to develop a predictive statistical model for low-fill-weight capsule filling of inhalation products with dosator nozzles via the quality by design (QbD) approach and based on that to create refined models that include quadratic terms for significant parameters. Various controllable process parameters and uncontrolled material attributes of 12 powders were initially screened using a linear model with partial least square (PLS) regression to determine their effect on the critical quality attributes (CQA; fill weight and weight variability). After identifying critical material attributes (CMAs) and critical process parameters (CPPs) that influenced the CQA, model refinement was performed to study if interactions or quadratic terms influence the model. Based on the assessment of the effects of the CPPs and CMAs on fill weight and weight variability for low-fill-weight inhalation products, we developed an excellent linear predictive model for fill weight (R(2 )= 0.96, Q(2 )= 0.96 for powders with good flow properties and R(2 )= 0.94, Q(2 )= 0.93 for cohesive powders) and a model that provides a good approximation of the fill weight variability for each powder group. We validated the model, established a design space for the performance of different types of inhalation grade lactose on low-fill weight capsule filling and successfully used the CMAs and CPPs to predict fill weight of powders that were not included in the development set.

Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

PubMed

Jampathong, Nampet; Laopaiboon, Malinee; Rattanakanokchai, Siwanon; Pattanittum, Porjai

2018-03-09

Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke. We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values. We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85). The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

Universality, Limits and Predictability of Gold-Medal Performances at the Olympic Games

PubMed Central

Radicchi, Filippo

2012-01-01

Inspired by the Games held in ancient Greece, modern Olympics represent the world’s largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics–including running, jumping, and throwing–and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games. PMID:22808137

Comparison of Orbiter STS-2 development flight instrumentation data with thermal math model predictions

NASA Technical Reports Server (NTRS)

Norman, I.; Rochelle, W. C.; Kimbrough, B. S.; Ritrivi, C. A.; Ting, P. C.; Dotts, R. L.

1982-01-01

Thermal performance verification of Reusable Surface Insulation (RSI) has been accomplished by comparisons of STS-2 Orbiter Flight Test (OFT) data with Thermal Math Model (TMM) predictions. The OFT data was obtained from Development Flight Instrumentation RSI plug and gap thermocouples. Quartertile RSI TMMs were developed using measured flight data for surface temperature and pressure environments. Reference surface heating rates, derived from surface temperature data, were multiplied by gap heating ratios to obtain tile sidewall heating rates. This TMM analysis resulted in good agreement of predicted temperatures with flight data for thermocouples located in the RSI, Strain Isolation Pad, filler bar and structure.

[Application of exponential smoothing method in prediction and warning of epidemic mumps].

PubMed

Shi, Yun-ping; Ma, Jia-qi

2010-06-01

To analyze the daily data of epidemic Mumps in a province from 2004 to 2008 and set up exponential smoothing model for the prediction. To predict and warn the epidemic mumps in 2008 through calculating 7-day moving summation and removing the effect of weekends to the data of daily reported mumps cases during 2005-2008 and exponential summation to the data from 2005 to 2007. The performance of Holt-Winters exponential smoothing is good. The result of warning sensitivity was 76.92%, specificity was 83.33%, and timely rate was 80%. It is practicable to use exponential smoothing method to warn against epidemic Mumps.

Clinical profile of responders to buprenorphine as a substitution treatment in heroin addicts: results of a multicenter study of 73 patients.

PubMed

Poirier, Marie-France; Laqueille, Xavier; Jalfre, Valérie; Willard, Dominique; Bourdel, Marie Chantal; Fermanian, Jacques; Olié, Jean Pierre

2004-03-01

In France, high-dosage buprenorphine (HDB) is the main substitution treatment for narcotic addiction. Few data have been published concerning clinical factors predicting a good response to this treatment in a daily practice. A hospital-based multicenter clinical research program (PHRC) was undertaken in heroin-addicted patients, diagnosed according to DSM-III-R, to detect clinical criteria susceptible of predicting a good response to HDB administered during a 3-month treatment period. At the inclusion time in the study, a diagnostic structured interview (DIGS) was performed, and the Addiction Severity Index (ASI), Zuckerman scale, depression scale from Jouvent, and CGI were scored. MMPI was also administered. Good response was defined as an ongoing participation in the study, with absence of opiate detected in 75% of urine collected during the last month of treatment. Only subjects treated for at least 1 month were eligible for analyses. One hundred fifteen patients were recruited and 73 were analyzed. Patients received 8.5+/-2.6 mg (m+/-S.D.) of buprenorphine for 1 to 3 months. A forward stepwise logistic regression showed that six clinical parameters may predict a good response to treatment: probability to respond to buprenorphine was higher in subjects having a high psychopathology (ASI) subscore, low disinhibition and boredom susceptibility factor scores (Zuckerman scale), no alcohol dependence, no family history of addiction or mood disorder, and duration of opiate dependence less than 10 years. Only the MMPI D subscale was a psychological pattern correlated to a good response to substitution treatment. These findings are important to consider when making the decision to prescribe HDB substitution treatment in opiate addiction.

An Introduction to Goodness of Fit for PMU Parameter Estimation

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

Riepnieks, Artis; Kirkham, Harold

2017-10-01

New results of measurements of phasor-like signals are presented based on our previous work on the topic. In this document an improved estimation method is described. The algorithm (which is realized in MATLAB software) is discussed. We examine the effect of noisy and distorted signals on the Goodness of Fit metric. The estimation method is shown to be performing very well with clean data and with a measurement window as short as a half a cycle and as few as 5 samples per cycle. The Goodness of Fit decreases predictably with added phase noise, and seems to be acceptable evenmore » with visible distortion in the signal. While the exact results we obtain are specific to our method of estimation, the Goodness of Fit method could be implemented in any phasor measurement unit.« less

The value of forceps biopsy and core needle biopsy in prediction of pathologic complete remission in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy.

PubMed

Tang, Jing-Hua; An, Xin; Lin, Xi; Gao, Yuan-Hong; Liu, Guo-Chen; Kong, Ling-Heng; Pan, Zhi-Zhong; Ding, Pei-Rong

2015-10-20

Patients with pathological complete remission (pCR) after treated with neoadjuvant chemoradiotherapy (nCRT) have better long-term outcome and may receive conservative treatments in locally advanced rectal cancer (LARC). The study aimed to evaluate the value of forceps biopsy and core needle biopsy in prediction of pCR in LARC treated with nCRT. In total, 120 patients entered this study. Sixty-one consecutive patients received preoperative forceps biopsy during endoscopic examination. Ex vivo core needle biopsy was performed in resected specimens of another 43 consecutive patients. The accuracy for ex vivo core needle biopsy was significantly higher than forceps biopsy (76.7% vs. 36.1%; p < 0.001). The sensitivity for ex vivo core needle biopsy was significantly lower in good responder (TRG 3) than poor responder (TRG ≤ 2) (52.9% vs. 94.1%; p = 0.017). In vivo core needle biopsy was further performed in 16 patients with good response. Eleven patients had residual cancer cells in final resected specimens, among whom 4 (36.4%) patients were biopsy positive. In conclusion, routine forceps biopsy was of limited value in identifying pCR after nCRT. Although core needle biopsy might further identify a subset of patients with residual cancer cells, the accuracy was not substantially increased in good responders.

The value of forceps biopsy and core needle biopsy in prediction of pathologic complete remission in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy

PubMed Central

Gao, Yuan-Hong; Liu, Guo-Chen; Kong, Ling-Heng; Pan, Zhi-Zhong; Ding, Pei-Rong

2015-01-01

Patients with pathological complete remission (pCR) after treated with neoadjuvant chemoradiotherapy (nCRT) have better long-term outcome and may receive conservative treatments in locally advanced rectal cancer (LARC). The study aimed to evaluate the value of forceps biopsy and core needle biopsy in prediction of pCR in LARC treated with nCRT. In total, 120patients entered this study. Sixty-one consecutive patients received preoperative forceps biopsy during endoscopic examination. Ex vivo core needle biopsy was performed in resected specimens of another 43 consecutive patients. The accuracy for ex vivo core needle biopsy was significantly higher than forceps biopsy (76.7% vs. 36.1%; p < 0.001). The sensitivity for ex vivo core needle biopsy was significantly lower in good responder (TRG 3) than poor responder (TRG ≤ 2) (52.9% vs. 94.1%; p = 0.017). In vivo core needle biopsy was further performed in 16 patients with good response. Eleven patients had residual cancer cells in final resected specimens, among whom 4 (36.4%) patients were biopsy positive. In conclusion, routine forceps biopsy was of limited value in identifying pCR after nCRT. Although core needle biopsy might further identify a subset of patients with residual cancer cells, the accuracy was not substantially increased in good responders. PMID:26416245

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED).

PubMed

Li, ZhiLiang; Wu, ShiRong; Chen, ZeCong; Ye, Nancy; Yang, ShengXi; Liao, ChunYang; Zhang, MengJun; Yang, Li; Mei, Hu; Yang, Yan; Zhao, Na; Zhou, Yuan; Zhou, Ping; Xiong, Qing; Xu, Hong; Liu, ShuShen; Ling, ZiHua; Chen, Gang; Li, GenRong

2007-10-01

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

A Method to Test Model Calibration Techniques

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then themore » calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.« less

A Method to Test Model Calibration Techniques: Preprint

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then themore » calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.« less

Innovative High-Performance Deposition Technology for Low-Cost Manufacturing of OLED Lighting

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

Hamer, John; Scott, David

In this project, OLEDWorks developed and demonstrated the innovative high-performance deposition technology required to deliver dramatic reductions in the cost of manufacturing OLED lighting in production equipment. The current high manufacturing cost of OLED lighting is the most urgent barrier to its market acceptance. The new deposition technology delivers solutions to the two largest parts of the manufacturing cost problem – the expense per area of good product for organic materials and for the capital cost and depreciation of the equipment. Organic materials cost is the largest expense item in the bill of materials and is predicted to remain somore » through 2020. The high-performance deposition technology developed in this project, also known as the next generation source (NGS), increases material usage efficiency from 25% found in current Gen2 deposition technology to 60%. This improvement alone results in a reduction of approximately 25 USD/m 2 of good product in organic materials costs, independent of production volumes. Additionally, this innovative deposition technology reduces the total depreciation cost from the estimated value of approximately 780 USD/m 2 of good product for state-of-the-art G2 lines (at capacity, 5-year straight line depreciation) to 170 USD/m 2 of good product from the OLEDWorks production line.« less

Predicting capillarity of mudrocks for geological storage of CO2

NASA Astrophysics Data System (ADS)

Busch, Andreas; Amann-Hildenbrand, Alexandra

2013-04-01

Various rock types were investigated, with the main focus on the determination and prediction of the capillary breakthrough and snap-off pressure in mudrocks (e.g. shales, siltstones, mudstones). Knowledge about these two critical pressures is important for the prediction of the capillary sealing capacity of CO2 storage sites. Capillary pressure experiments, when performed on low-permeable core plugs, are difficult and time consuming. Laboratory measurements on core plugs under in-situ conditions are mostly performed using nitrogen, but also with methane and carbon dioxide. Therefore, mercury porosimetry measurements (MIP) are preferably used in the industry to determine an equivalent value for the capillary breakthrough pressure. These measurements have the advantage to be quick and cheap and only require cuttings or trim samples. When evaluating the database in detail we find that (1) MIP data plot well with the drainage breakthrough pressures determined on sample plugs, while the conversion of the system Hg/air to CO2/brine using interfacial and wettability data does not provide a uniform match, potentially caused by non fully water-wet conditions; (2) brine permeability versus capillary breakthrough pressure determined on sample plugs shows a good match and could provide a first estimate of Pc-values since permeability is easier to determine than capillary breakthrough pressures. For imbibition snap-off pressures a good correlation was found for CH4 measured on sample plugs only; (3) porosity shows a fairly good correlation with permeability for sandstone only, and with plug-derived capillary breakthrough pressures for sandstones, carbonates and evaporates. No such correlations exist for mudrocks; (4) air and brine-derived permeabilities show an excellent correlation and (5) from the data used we do not infer any direct correlations between specific surface area (SSA), mineralogy or organic carbon content with permeability or capillary pressure however were able to predict permeabilities using a more sophisticated model that relies on several of these parameters.

Iranian risk model as a predictive tool for retinopathy in patients with type 2 diabetes.

PubMed

Azizi-Soleiman, Fatemeh; Heidari-Beni, Motahar; Ambler, Gareth; Omar, Rumana; Amini, Masoud; Hosseini, Sayed-Mohsen

2015-10-01

Diabetic retinopathy (DR) is the leading cause of blindness in patients with type 1 or type 2 diabetes. The gold standard for the detection of DR requires expensive equipment. This study was undertaken to develop a simple and practical scoring system to predict the probability of DR. A total of 1782 patients who had first-degree relatives with type II diabetes were selected. Eye examinations were performed by an expert ophthalmologist. Biochemical and anthropometric predictors of DR were measured. Logistic regression was used to develop a statistical model that can be used to predict DR. Goodness of fit was examined using the Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve. The risk model demonstrated good calibration and discrimination (ROC area=0.76) in the validation sample. Factors associated with DR in our model were duration of diabetes (odds ratio [OR]=2.14, confidence interval [CI] 95%=1.87 to 2.45); glycated hemoglobin (A1C) (OR=1.21, CI 95%=1.13 to 1.30); fasting plasma glucose (OR=1.83, CI 95%=1.28 to 2.62); systolic blood pressure (OR=1.01, CI 95%= 1.00 to 1.02); and proteinuria (OR=1.37, CI 95%=1.01 to 1.85). The only factor that had a protective effect against DR were body mass index and education level (OR=0.95, CI 95%=0.92 to 0.98). The good performance of our risk model suggests that it may be a useful risk-prediction tool for DR. It consisted of the positive predictors like A1C, diabetes duration, sex (male), fasting plasma glucose, systolic blood pressure and proteinuria, as well as negative risk factors like body mass index and education level. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

Comparison Between Simulated and Experimentally Measured Performance of a Four Port Wave Rotor

NASA Technical Reports Server (NTRS)

Paxson, Daniel E.; Wilson, Jack; Welch, Gerard E.

2007-01-01

Performance and operability testing has been completed on a laboratory-scale, four-port wave rotor, of the type suitable for use as a topping cycle on a gas turbine engine. Many design aspects, and performance estimates for the wave rotor were determined using a time-accurate, one-dimensional, computational fluid dynamics-based simulation code developed specifically for wave rotors. The code follows a single rotor passage as it moves past the various ports, which in this reference frame become boundary conditions. This paper compares wave rotor performance predicted with the code to that measured during laboratory testing. Both on and off-design operating conditions were examined. Overall, the match between code and rig was found to be quite good. At operating points where there were disparities, the assumption of larger than expected internal leakage rates successfully realigned code predictions and laboratory measurements. Possible mechanisms for such leakage rates are discussed.

Phasic dopamine release in the rat nucleus accumbens predicts approach and avoidance performance

PubMed Central

Gentry, Ronny N.; Lee, Brian; Roesch, Matthew R.

2016-01-01

Dopamine (DA) is critical for reward processing, but significantly less is known about its role in punishment avoidance. Using a combined approach-avoidance task, we measured phasic DA release in the nucleus accumbens (NAc) of rats during presentation of cues that predicted reward, punishment or neutral outcomes and investigated individual differences based on avoidance performance. Here we show that DA release within a single microenvironment is higher for reward and avoidance cues compared with neutral cues and positively correlated with poor avoidance behaviour. We found that DA release delineates trial-type during sessions with good avoidance but is non-selective during poor avoidance, with high release correlating with poor performance. These data demonstrate that phasic DA is released during cued approach and avoidance within the same microenvironment and abnormal processing of value signals is correlated with poor performance. PMID:27786172

Predicting beta-turns in proteins using support vector machines with fractional polynomials

PubMed Central

2013-01-01

Background β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. Results We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. Conclusions In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods. PMID:24565438

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

PubMed

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

PubMed

Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

The importance of understanding: Model space moderates goal specificity effects.

PubMed

Kistner, Saskia; Burns, Bruce D; Vollmeyer, Regina; Kortenkamp, Ulrich

2016-01-01

The three-space theory of problem solving predicts that the quality of a learner's model and the goal specificity of a task interact on knowledge acquisition. In Experiment 1 participants used a computer simulation of a lever system to learn about torques. They either had to test hypotheses (nonspecific goal), or to produce given values for variables (specific goal). In the good- but not in the poor-model condition they saw torque depicted as an area. Results revealed the predicted interaction. A nonspecific goal only resulted in better learning when a good model of torques was provided. In Experiment 2 participants learned to manipulate the inputs of a system to control its outputs. A nonspecific goal to explore the system helped performance when compared to a specific goal to reach certain values when participants were given a good model, but not when given a poor model that suggested the wrong hypothesis space. Our findings support the three-space theory. They emphasize the importance of understanding for problem solving and stress the need to study underlying processes.

What predicts performance during clinical psychology training?

PubMed

Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

2014-06-01

While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Longitudinal cross-sectional study using prospective and retrospective data. Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. © 2013 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of the British Psychological Society.

External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

PubMed

Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2018-01-01

The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample. The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles. Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0.639). Across the 5 risk quintiles, the VSGNE model predicted observed mortality significantly with great accuracy. This simple VSGNE AAA risk predictive model showed very high discriminative ability in predicting mortality after elective AAA repair among a large external independent sample of AAA cases performed by a diverse array of physicians nationwide. The risk score based on this simple VSGNE model can reliably stratify patients according to their risk of mortality after elective AAA repair better than other established models. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Clinical risk scoring for predicting non-alcoholic fatty liver disease in metabolic syndrome patients (NAFLD-MS score).

PubMed

Saokaew, Surasak; Kanchanasuwan, Shada; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Charatcharoenwitthaya, Phunchai; Phisalprapa, Pochamana; Chaiyakunapruk, Nathorn

2017-10-01

Non-alcoholic fatty liver disease (NAFLD) can progress from simple steatosis to hepatocellular carcinoma. None of tools have been developed specifically for high-risk patients. This study aimed to develop a simple risk scoring to predict NAFLD in patients with metabolic syndrome (MetS). A total of 509 patients with MetS were recruited. All were diagnosed by clinicians with ultrasonography-confirmed whether they were patients with NAFLD. Patients were randomly divided into derivation (n=400) and validation (n=109) cohort. To develop the risk score, clinical risk indicators measured at the time of recruitment were built by logistic regression. Regression coefficients were transformed into item scores and added up to a total score. A risk scoring scheme was developed from clinical predictors: BMI ≥25, AST/ALT ≥1, ALT ≥40, type 2 diabetes mellitus and central obesity. The scoring scheme was applied in validation cohort to test the performance. The scheme explained, by area under the receiver operating characteristic curve (AuROC), 76.8% of being NAFLD with good calibration (Hosmer-Lemeshow χ 2 =4.35; P=.629). The positive likelihood ratio of NAFLD in patients with low risk (scores below 3) and high risk (scores 5 and over) were 2.32 (95% CI: 1.90-2.82) and 7.77 (95% CI: 2.47-24.47) respectively. When applied in validation cohort, the score showed good performance with AuROC 76.7%, and illustrated 84%, and 100% certainty in low- and high-risk groups respectively. A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Cytologic anaplasia is a prognostic factor in osteosarcoma biopsies, but mitotic rate or extent of spontaneous tumor necrosis are not: a critique of the College of American Pathologists Bone Biopsy template.

PubMed

Cates, Justin Mm; Dupont, William D

2017-01-01

The current College of American Pathologists cancer template for reporting biopsies of bone tumors recommends including information that is of unproven prognostic significance for osteosarcoma, such as the presence of spontaneous tumor necrosis and mitotic rate. Conversely, the degree of cytologic anaplasia (degree of differentiation) is not reported in this template. This retrospective cohort study of 125 patients with high-grade osteosarcoma was performed to evaluate the prognostic impact of these factors in diagnostic biopsy specimens in predicting the clinical outcome and response to neoadjuvant chemotherapy. Multivariate Cox regression was performed to adjust survival analyses for well-established prognostic factors. Multivariate logistic regression was used to determine odds ratios for good chemotherapy response (≥90% tumor necrosis). Osteosarcomas with severe anaplasia were independently associated with increased overall and disease-free survival, but mitotic rate and spontaneous necrosis had no prognostic impact after controlling for other confounding factors. Mitotic rate showed a trend towards increased odds of a good histologic response, but this effect was diminished after controlling for other predictive factors. Neither spontaneous necrosis nor the degree of cytologic anaplasia observed in biopsy specimens was predictive of a good response to chemotherapy. Mitotic rate and spontaneous tumor necrosis observed in pretreatment biopsy specimens of high-grade osteosarcoma are not strong independent prognostic factors for clinical outcome or predictors of response to neoadjuvant chemotherapy. Therefore, reporting these parameters for osteosarcoma, as recommended in the College of American Pathologists Bone Biopsy template, does not appear to have clinical utility. In contrast, histologic grading schemes for osteosarcoma based on the degree of cytologic anaplasia may have independent prognostic value and should continue to be evaluated.

Why do Reservoir Computing Networks Predict Chaotic Systems so Well?

NASA Astrophysics Data System (ADS)

Lu, Zhixin; Pathak, Jaideep; Girvan, Michelle; Hunt, Brian; Ott, Edward

Recently a new type of artificial neural network, which is called a reservoir computing network (RCN), has been employed to predict the evolution of chaotic dynamical systems from measured data and without a priori knowledge of the governing equations of the system. The quality of these predictions has been found to be spectacularly good. Here, we present a dynamical-system-based theory for how RCN works. Basically a RCN is thought of as consisting of three parts, a randomly chosen input layer, a randomly chosen recurrent network (the reservoir), and an output layer. The advantage of the RCN framework is that training is done only on the linear output layer, making it computationally feasible for the reservoir dimensionality to be large. In this presentation, we address the underlying dynamical mechanisms of RCN function by employing the concepts of generalized synchronization and conditional Lyapunov exponents. Using this framework, we propose conditions on reservoir dynamics necessary for good prediction performance. By looking at the RCN from this dynamical systems point of view, we gain a deeper understanding of its surprising computational power, as well as insights on how to design a RCN. Supported by Army Research Office Grant Number W911NF1210101.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Prediction of breakdown strength of cellulosic insulating materials using artificial neural networks

NASA Astrophysics Data System (ADS)

Singh, Sakshi; Mohsin, M. M.; Masood, Aejaz

In this research work, a few sets of experiments have been performed in high voltage laboratory on various cellulosic insulating materials like diamond-dotted paper, paper phenolic sheets, cotton phenolic sheets, leatheroid, and presspaper, to measure different electrical parameters like breakdown strength, relative permittivity, loss tangent, etc. Considering the dependency of breakdown strength on other physical parameters, different Artificial Neural Network (ANN) models are proposed for the prediction of breakdown strength. The ANN model results are compared with those obtained experimentally and also with the values already predicted from an empirical relation suggested by Swanson and Dall. The reported results indicated that the breakdown strength predicted from the ANN model is in good agreement with the experimental values.

Modeling of video compression effects on target acquisition performance

NASA Astrophysics Data System (ADS)

Cha, Jae H.; Preece, Bradley; Espinola, Richard L.

2009-05-01

The effect of video compression on image quality was investigated from the perspective of target acquisition performance modeling. Human perception tests were conducted recently at the U.S. Army RDECOM CERDEC NVESD, measuring identification (ID) performance on simulated military vehicle targets at various ranges. These videos were compressed with different quality and/or quantization levels utilizing motion JPEG, motion JPEG2000, and MPEG-4 encoding. To model the degradation on task performance, the loss in image quality is fit to an equivalent Gaussian MTF scaled by the Structural Similarity Image Metric (SSIM). Residual compression artifacts are treated as 3-D spatio-temporal noise. This 3-D noise is found by taking the difference of the uncompressed frame, with the estimated equivalent blur applied, and the corresponding compressed frame. Results show good agreement between the experimental data and the model prediction. This method has led to a predictive performance model for video compression by correlating various compression levels to particular blur and noise input parameters for NVESD target acquisition performance model suite.

Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application.

PubMed

Tkachenko, Pavlo; Kriukova, Galyna; Aleksandrova, Marharyta; Chertov, Oleg; Renard, Eric; Pereverzyev, Sergei V

2016-10-01

Nocturnal hypoglycemia (NH) is common in patients with insulin-treated diabetes. Despite the risk associated with NH, there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data and none has been validated for clinical use. Here we propose a method of combining several predictors into a new one that will perform at the level of the best involved one, or even outperform all individual candidates. The idea of the method is to use a recently developed strategy for aggregating ranking algorithms. The method has been calibrated and tested on data extracted from clinical trials, performed in the European FP7-funded project DIAdvisor. Then we have tested the proposed approach on other datasets to show the portability of the method. This feature of the method allows its simple implementation in the form of a diabetic smartphone app. On the considered datasets the proposed approach exhibits good performance in terms of sensitivity, specificity and predictive values. Moreover, the resulting predictor automatically performs at the level of the best involved method or even outperforms it. We propose a strategy for a combination of NH predictors that leads to a method exhibiting a reliable performance and the potential for everyday use by any patient who performs self-monitoring of blood glucose. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comprehensive computational model for combining fluid hydrodynamics, light transport and biomass growth in a Taylor vortex algal photobioreactor: Lagrangian approach.

PubMed

Gao, Xi; Kong, Bo; Vigil, R Dennis

2017-01-01

A comprehensive quantitative model incorporating the effects of fluid flow patterns, light distribution, and algal growth kinetics on biomass growth rate is developed in order to predict the performance of a Taylor vortex algal photobioreactor for culturing Chlorella vulgaris. A commonly used Lagrangian strategy for coupling the various factors influencing algal growth was employed whereby results from computational fluid dynamics and radiation transport simulations were used to compute numerous microorganism light exposure histories, and this information in turn was used to estimate the global biomass specific growth rate. The simulations provide good quantitative agreement with experimental data and correctly predict the trend in reactor performance as a key reactor operating parameter is varied (inner cylinder rotation speed). However, biomass growth curves are consistently over-predicted and potential causes for these over-predictions and drawbacks of the Lagrangian approach are addressed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

Comparison of Flight Measured, Predicted and Wind Tunnel Measured Winglet Characteristics on a KC-135 Aircraft

NASA Technical Reports Server (NTRS)

Dodson, R. O., Jr.

1982-01-01

One of the objectives of the KC-135 Winglet Flight Research and Demonstration Program was to obtain experimental flight test data to verify the theoretical and wind tunnel winglet aerodynamic performance prediction methods. Good agreement between analytic, wind tunnel and flight test performance was obtained when the known differences between the tests and analyses were accounted for. The flight test measured fuel mileage improvements for a 0.78 Mach number was 3.1 percent at 8 x 10(5) pounds W/delta and 5.5 percent at 1.05 x 10(6) pounds W/delta. Correcting the flight measured data for surface pressure differences between wind tunnel and flight resulted in a fuel mileage improvement of 4.4 percent at 8 x 10(5) pounds W/delta and 7.2 percent at 1.05 x 10(6) pounds W/delta. The performance improvement obtained was within the wind tunnel test data obtained from two different wind tunnel models. The buffet boundary data obtained for the baseline configuration was in good agreement with previous established data. Buffet data for the 15 deg cant/-4 deg incidence configuration showed a slight improvement, while the 15 deg cant/-2 deg incidence and 0 deg cant/-4 deg incidence data showed a slight deterioration.

Genome-based prediction of test cross performance in two subsequent breeding cycles.

PubMed

Hofheinz, Nina; Borchardt, Dietrich; Weissleder, Knuth; Frisch, Matthias

2012-12-01

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.

Performance of magnetic resonance imaging in the evaluation of first-time and reoperative primary hyperparathyroidism.

PubMed

Kluijfhout, Wouter P; Venkatesh, Shriya; Beninato, Toni; Vriens, Menno R; Duh, Quan-Yang; Wilson, David M; Hope, Thomas A; Suh, Insoo

2016-09-01

Preoperative imaging in patients with primary hyperparathyroidism and a previous parathyroid operation is essential; however, performance of conventional imaging is poor in this subgroup. Magnetic resonance imaging appears to be a good alternative, though overall evidence remains scarce. We retrospectively investigated the performance of magnetic resonance imaging in patients with and without a previous parathyroid operation, with a separate comparison for dynamic gadolinium-enhanced magnetic resonance imaging. All patients undergoing magnetic resonance imaging prior to parathyroidectomy for primary hyperparathyroidism (first time or recurrent) between January 2000 and August 2015 at a high-volume, tertiary care, referral center for endocrine operations were included. We compared the sensitivity and positive predictive value of magnetic resonance imaging with conventional ultrasound and sestamibi on a per-lesion level. A total of 3,450 patients underwent parathyroidectomy, of which 84 patients with recurrent (n = 10) or persistent (n = 74) disease and 41 patients with a primary operation were included. Magnetic resonance imaging had a sensitivity and positive predictive value of 79.9% and 84.7%, respectively, and performance was good in both patients with and without a previous parathyroid operation. Adding magnetic resonance imaging to the combination of ultrasound and sestamibi resulted in a significant increase in sensitivity from 75.2% to 91.5%. Dynamic magnetic resonance imaging produced excellent results in the reoperative group, with sensitivity and a positive predictive value of 90.1%. Technologic advances have enabled faster and more accurate magnetic resonance imaging protocols, making magnetic resonance imaging an excellent alternative modality without associated ionizing radiation. Our study shows that the sensitivity of multimodality imaging for parathyroid adenomas improved significantly with the use of conventional and dynamic magnetic resonance imaging, even in the case of recurrent or persistent disease. Published by Elsevier Inc.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

PubMed

Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

2017-07-10

In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

Prognostic indices for early mortality in ischaemic stroke - meta-analysis.

PubMed

Mattishent, K; Kwok, C S; Mahtani, A; Pelpola, K; Myint, P K; Loke, Y K

2016-01-01

Several models have been developed to predict mortality in ischaemic stroke. We aimed to evaluate systematically the performance of published stroke prognostic scores. We searched MEDLINE and EMBASE in February 2014 for prognostic models (published between 2003 and 2014) used in predicting early mortality (<6 months) after ischaemic stroke. We evaluated discriminant ability of the tools through meta-analysis of the area under the curve receiver operating characteristic curve (AUROC) or c-statistic. We evaluated the following components of study validity: collection of prognostic variables, neuroimaging, treatment pathways and missing data. We identified 18 articles (involving 163 240 patients) reporting on the performance of prognostic models for mortality in ischaemic stroke, with 15 articles providing AUC for meta-analysis. Most studies were either retrospective, or post hoc analyses of prospectively collected data; all but three reported validation data. The iSCORE had the largest number of validation cohorts (five) within our systematic review and showed good performance in four different countries, pooled AUC 0.84 (95% CI 0.82-0.87). We identified other potentially useful prognostic tools that have yet to be as extensively validated as iSCORE - these include SOAR (2 studies, pooled AUC 0.79, 95% CI 0.78-0.80), GWTG (2 studies, pooled AUC 0.72, 95% CI 0.72-0.72) and PLAN (1 study, pooled AUC 0.85, 95% CI 0.84-0.87). Our meta-analysis has identified and summarized the performance of several prognostic scores with modest to good predictive accuracy for early mortality in ischaemic stroke, with the iSCORE having the broadest evidence base. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Analysis of Discrete-Source Damage Progression in a Tensile Stiffened Composite Panel

NASA Technical Reports Server (NTRS)

Wang, John T.; Lotts, Christine G.; Sleight, David W.

1999-01-01

This paper demonstrates the progressive failure analysis capability in NASA Langley s COMET-AR finite element analysis code on a large-scale built-up composite structure. A large-scale five stringer composite panel with a 7-in. long discrete source damage was analyzed from initial loading to final failure including the geometric and material nonlinearities. Predictions using different mesh sizes, different saw cut modeling approaches, and different failure criteria were performed and assessed. All failure predictions have a reasonably good correlation with the test result.

Intelligent Monitoring? Assessing the ability of the Care Quality Commission's statistical surveillance tool to predict quality and prioritise NHS hospital inspections.

PubMed

Griffiths, Alex; Beaussier, Anne-Laure; Demeritt, David; Rothstein, Henry

2017-02-01

The Care Quality Commission (CQC) is responsible for ensuring the quality of the health and social care delivered by more than 30 000 registered providers in England. With only limited resources for conducting on-site inspections, the CQC has used statistical surveillance tools to help it identify which providers it should prioritise for inspection. In the face of planned funding cuts, the CQC plans to put more reliance on statistical surveillance tools to assess risks to quality and prioritise inspections accordingly. To evaluate the ability of the CQC's latest surveillance tool, Intelligent Monitoring (IM), to predict the quality of care provided by National Health Service (NHS) hospital trusts so that those at greatest risk of providing poor-quality care can be identified and targeted for inspection. The predictive ability of the IM tool is evaluated through regression analyses and χ 2 testing of the relationship between the quantitative risk score generated by the IM tool and the subsequent quality rating awarded following detailed on-site inspection by large expert teams of inspectors. First, the continuous risk scores generated by the CQC's IM statistical surveillance tool cannot predict inspection-based quality ratings of NHS hospital trusts (OR 0.38 (0.14 to 1.05) for Outstanding/Good, OR 0.94 (0.80 to -1.10) for Good/Requires improvement, and OR 0.90 (0.76 to 1.07) for Requires improvement/Inadequate). Second, the risk scores cannot be used more simply to distinguish the trusts performing poorly-those subsequently rated either 'Requires improvement' or 'Inadequate'-from the trusts performing well-those subsequently rated either 'Good' or 'Outstanding' (OR 1.07 (0.91 to 1.26)). Classifying CQC's risk bandings 1-3 as high risk and 4-6 as low risk, 11 of the high risk trusts were performing well and 43 of the low risk trusts were performing poorly, resulting in an overall accuracy rate of 47.6%. Third, the risk scores cannot be used even more simply to distinguish the worst performing trusts-those subsequently rated 'Inadequate'-from the remaining, better performing trusts (OR 1.11 (0.94 to 1.32)). Classifying CQC's risk banding 1 as high risk and 2-6 as low risk, the highest overall accuracy rate of 72.8% was achieved, but still only 6 of the 13 Inadequate trusts were correctly classified as being high risk. Since the IM statistical surveillance tool cannot predict the outcome of NHS hospital trust inspections, it cannot be used for prioritisation. A new approach to inspection planning is therefore required. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Assessing Soldier Individual Differences to Enable Tailored Training

DTIC Science & Technology

2010-04-01

upon effective and efficient training. However, there is ample evidence that learning-related individual differences exist ( Thorndike , 1985; Jensen...in both civilian and military settings (Schmidt, Hunter, & Outerbridge, 1986; Thorndike , 1985). Prior knowledge or knowledge of facts and...predictive power ( Thorndike , 1985; Jensen, 1998). Further, there is a good deal of evidence that general mental ability impacts performance largely

Dynamic Fatigue of a Titanium Silicate Glass

NASA Technical Reports Server (NTRS)

Tucker, Dennis S.; Nettles, Alan T.; Cagle, Holly A.; Smith, W. Scott (Technical Monitor)

2002-01-01

A dynamic fatigue study was performed on a Titanium Silicate Glass in order to assess its susceptibility to delayed failure. Fracture mechanics techniques were used to analyze the results for the purpose of making lifetime predictions for optical elements made from this material. The material has reasonably good resistance (N=23 to stress corrosion in ambient conditions).

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.

PubMed

Hilkens, N A; Algra, A; Greving, J P

2016-01-01

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis.

Computation of large-scale statistics in decaying isotropic turbulence

NASA Technical Reports Server (NTRS)

Chasnov, Jeffrey R.

1993-01-01

We have performed large-eddy simulations of decaying isotropic turbulence to test the prediction of self-similar decay of the energy spectrum and to compute the decay exponents of the kinetic energy. In general, good agreement between the simulation results and the assumption of self-similarity were obtained. However, the statistics of the simulations were insufficient to compute the value of gamma which corrects the decay exponent when the spectrum follows a k(exp 4) wave number behavior near k = 0. To obtain good statistics, it was found necessary to average over a large ensemble of turbulent flows.

Evaluation of soft rubber goods. [for use as O-rings, and seals on space shuttle

NASA Technical Reports Server (NTRS)

Merz, P. L.

1974-01-01

The performance of rubber goods suitable for use as O-rings, seals, gaskets, bladders and diaphragms under conditions simulating those of the space shuttle were studied. High reliability throughout the 100 flight missions planned for the space shuttle was considered of overriding importance. Accordingly, in addition to a rank ordering of the selected candidate materials based on prolonged fluid compatibility and sealability behavior, basic rheological parameters (such as cyclic hysteresis, stress relaxation, indicated modulus, etc.) were determined to develop methods capable of predicting the cumulative effect of these multiple reuse cycles.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

NASA Astrophysics Data System (ADS)

Bazzazi, Abbas Aghajani; Esmaeili, Mohammad

2012-12-01

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

Performance prediction of a ducted rocket combustor

NASA Astrophysics Data System (ADS)

Stowe, Robert

2001-07-01

The ducted rocket is a supersonic flight propulsion system that takes the exhaust from a solid fuel gas generator, mixes it with air, and burns it to produce thrust. To develop such systems, the use of numerical models based on Computational Fluid Dynamics (CFD) is increasingly popular, but their application to reacting flow requires specific attention and validation. Through a careful examination of the governing equations and experimental measurements, a CFD-based method was developed to predict the performance of a ducted rocket combustor. It uses an equilibrium-chemistry Probability Density Function (PDF) combustion model, with a gaseous and a separate stream of 75 nm diameter carbon spheres to represent the fuel. After extensive validation with water tunnel and direct-connect combustion experiments over a wide range of geometries and test conditions, this CFD-based method was able to predict, within a good degree of accuracy, the combustion efficiency of a ducted rocket combustor.

A LES-based Eulerian-Lagrangian approach to predict the dynamics of bubble plumes

NASA Astrophysics Data System (ADS)

Fraga, Bruño; Stoesser, Thorsten; Lai, Chris C. K.; Socolofsky, Scott A.

2016-01-01

An approach for Eulerian-Lagrangian large-eddy simulation of bubble plume dynamics is presented and its performance evaluated. The main numerical novelties consist in defining the gas-liquid coupling based on the bubble size to mesh resolution ratio (Dp/Δx) and the interpolation between Eulerian and Lagrangian frameworks through the use of delta functions. The model's performance is thoroughly validated for a bubble plume in a cubic tank in initially quiescent water using experimental data obtained from high-resolution ADV and PIV measurements. The predicted time-averaged velocities and second-order statistics show good agreement with the measurements, including the reproduction of the anisotropic nature of the plume's turbulence. Further, the predicted Eulerian and Lagrangian velocity fields, second-order turbulence statistics and interfacial gas-liquid forces are quantified and discussed as well as the visualization of the time-averaged primary and secondary flow structure in the tank.

Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

NASA Astrophysics Data System (ADS)

Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

2015-09-01

Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Forecasting stochastic neural network based on financial empirical mode decomposition.

PubMed

Wang, Jie; Wang, Jun

2017-06-01

In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a processing technique introduced to extract all the oscillatory modes embedded in a series, and the STNN model is established for considering the weight of occurrence time of the historical data. The linear regression performs the predictive availability of the proposed model, and the effectiveness of EMD-STNN is revealed clearly through comparing the predicted results with the traditional models. Moreover, a new evaluated method (q-order multiscale complexity invariant distance) is applied to measure the predicted results of real stock index series, and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparative study of turbulence models in predicting hypersonic inlet flows

NASA Technical Reports Server (NTRS)

Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

1992-01-01

A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared wery well with the experimental data, and performed better than the Thomas model near the walls.

Comparative study of turbulence models in predicting hypersonic inlet flows

NASA Technical Reports Server (NTRS)

Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

1992-01-01

A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared very well with the experimental data, and performed better than the Thomas model near the walls.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy

NASA Astrophysics Data System (ADS)

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-01

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R2 and pseudo R2 were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R2 ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R2 = 0.31), but there was still large variability between patients in R2. The R2 from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

PubMed

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-07

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

YouTube as a Potential Training Resource for Laparoscopic Fundoplication.

PubMed

Frongia, Giovanni; Mehrabi, Arianeb; Fonouni, Hamidreza; Rennert, Helga; Golriz, Mohammad; Günther, Patrick

To analyze the surgical proficiency and educational quality of YouTube videos demonstrating laparoscopic fundoplication (LF). In this cross-sectional study, a search was performed on YouTube for videos demonstrating the LF procedure. The surgical and educational proficiency was evaluated using the objective component rating scale, the educational quality rating score, and total video quality score. Statistical significance was determined by analysis of variance, receiver operating characteristic curve, and odds ratio analysis. A total of 71 videos were included in the study; 28 (39.4%) videos were evaluated as good, 23 (32.4%) were moderate, and 20 (28.2%) were poor. Good-rated videos were significantly longer (good, 22.0 ± 5.2min; moderate, 7.8 ± 0.9min; poor, 8.5 ± 1.0min; p = 0.007) and video duration was predictive of good quality (AUC, 0.672 ± 0.067; 95% CI: 0.541-0.802; p = 0.015). For good quality, the cut-off video duration was 7:42 minute. This cut-off value had a sensitivity of 67.9%, a specificity of 60.5%, and an odds ratio of 3.23 (95% CI: 1.19-8.79; p = 0.022) in predicting good quality. Videos uploaded from industrial sources and with a higher views/days online ratio had a higher objective component rating scale and total video quality score. In contrast, the likes/dislikes ratio was not predictive of video quality. Many videos showing the LF procedure have been uploaded to YouTube with varying degrees of quality. A process for filtering LF videos with high surgical and educational quality is feasible by evaluating the video duration, uploading source, and the views/days online ratio. However, alternative videos platforms aimed at professionals should also be considered for educational purposes. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

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

Folkvord, Sigurd; Flatmark, Kjersti; Department of Cancer and Surgery, Norwegian Radium Hospital, Oslo University Hospital

2010-10-01

Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatmentmore » as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.« less

A validation study of a clinical prediction rule for screening asymptomatic chlamydia and gonorrhoea infections among heterosexuals in British Columbia.

PubMed

Falasinnu, Titilola; Gilbert, Mark; Gustafson, Paul; Shoveller, Jean

2016-02-01

One component of effective sexually transmitted infections (STIs) control is ensuring those at highest risk of STIs have access to clinical services because terminating transmission in this group will prevent most future cases. Here, we describe the results of a validation study of a clinical prediction rule for identifying individuals at increased risk for chlamydia and gonorrhoea infection derived in Vancouver, British Columbia (BC), against a population of asymptomatic patients attending sexual health clinics in other geographical settings in BC. We examined electronic records (2000-2012) from clinic visits at seven sexual health clinics in geographical locations outside Vancouver. The model's calibration and discrimination were examined by the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow (H-L) statistic, respectively. We also examined the sensitivity and proportion of patients that would need to be screened at different cut-offs of the risk score. The prevalence of infection was 5.3% (n=10 425) in the geographical validation population. The prediction rule showed good performance in this population (AUC, 0.69; H-L p=0.26). Possible risk scores ranged from -2 to 27. We identified a risk score cut-off point of ≥8 that detected cases with a sensitivity of 86% by screening 63% of the geographical validation population. The prediction rule showed good generalisability in STI clinics outside of Vancouver with improved discriminative performance compared with temporal validation. The prediction rule has the potential for augmenting triaging services in STI clinics and enhancing targeted testing in population-based screening programmes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Prediction of Outcome after Moderate and Severe Traumatic Brain Injury: External Validation of the IMPACT and CRASH Prognostic Models

PubMed Central

Roozenbeek, Bob; Lingsma, Hester F.; Lecky, Fiona E.; Lu, Juan; Weir, James; Butcher, Isabella; McHugh, Gillian S.; Murray, Gordon D.; Perel, Pablo; Maas, Andrew I.R.; Steyerberg, Ewout W.

2012-01-01

Objective The International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict outcome after traumatic brain injury (TBI) but have not been compared in large datasets. The objective of this is study is to validate externally and compare the IMPACT and CRASH prognostic models for prediction of outcome after moderate or severe TBI. Design External validation study. Patients We considered 5 new datasets with a total of 9036 patients, comprising three randomized trials and two observational series, containing prospectively collected individual TBI patient data. Measurements Outcomes were mortality and unfavourable outcome, based on the Glasgow Outcome Score (GOS) at six months after injury. To assess performance, we studied the discrimination of the models (by AUCs), and calibration (by comparison of the mean observed to predicted outcomes and calibration slopes). Main Results The highest discrimination was found in the TARN trauma registry (AUCs between 0.83 and 0.87), and the lowest discrimination in the Pharmos trial (AUCs between 0.65 and 0.71). Although differences in predictor effects between development and validation populations were found (calibration slopes varying between 0.58 and 1.53), the differences in discrimination were largely explained by differences in case-mix in the validation studies. Calibration was good, the fraction of observed outcomes generally agreed well with the mean predicted outcome. No meaningful differences were noted in performance between the IMPACT and CRASH models. More complex models discriminated slightly better than simpler variants. Conclusions Since both the IMPACT and the CRASH prognostic models show good generalizability to more recent data, they are valid instruments to quantify prognosis in TBI. PMID:22511138

Prediction of Classroom Reverberation Time using Neural Network

NASA Astrophysics Data System (ADS)

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 < 0.005 and R > 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.

Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets.

PubMed

Valenzuela, Loreto M; Knight, Doyle D; Kohn, Joachim

2016-01-01

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R (2) > 0.78 for test set) but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R (2) = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.

Large-scale wind-tunnel investigation of a close-coupled canard-delta-wing fighter model through high angles of attack

NASA Technical Reports Server (NTRS)

Stoll, F.; Koenig, D. G.

1983-01-01

Data obtained through very high angles of attack from a large-scale, subsonic wind-tunnel test of a close-coupled canard-delta-wing fighter model are analyzed. The canard delays wing leading-edge vortex breakdown, even for angles of attack at which the canard is completely stalled. A vortex-lattice method was applied which gave good predictions of lift and pitching moment up to an angle of attack of about 20 deg, where vortex-breakdown effects on performance become significant. Pitch-control inputs generally retain full effectiveness up to the angle of attack of maximum lift, beyond which, effectiveness drops off rapidly. A high-angle-of-attack prediction method gives good estimates of lift and drag for the completely stalled aircraft. Roll asymmetry observed at zero sideslip is apparently caused by an asymmetry in the model support structure.

Prediction of pelvic organ prolapse using an artificial neural network.

PubMed

Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

2008-08-01

The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

The predictive value of cerebrospinal fluid tap-test in normal pressure hydrocephalus.

PubMed

Damasceno, B P; Carelli, E F; Honorato, D C; Facure, J J

1997-06-01

Eighteen patients (mean age of 66.5 years) with normal pressure hydrocephalus (NPH) underwent a ventriculo-peritoneal shunt surgery. Prior to operation a cerebrospinal fluid tap-test (CSF-TT) was performed with measurements of gait pattern and psychometric functions (memory, visuo-motor speed and visuo-constructive skills) before and after the removal of 50 ml CSF by lumbar puncture (LP). Fifteen patients improved and 3 were unchanged after surgery. Short duration of disease, gait disturbance preceding mental deterioration, wide temporal horns and small sulci on CT-scan were associated with good outcome after shunting. There was a good correlation between the results of CSF-TT and shunt surgery (chi 2 = 4.11, phi = 0.48, p < 0.05), with gait test showing highest correlation (r = 0.99, p = 0.01). In conclusion, this version of CSF-TT proved to be an effective test to predict improvement after shunting in patients with NPH.

Aerodynamic performance investigation on waverider with variable blunt radius in hypersonic flows

NASA Astrophysics Data System (ADS)

Li, Shibin; Wang, Zhenguo; Huang, Wei; Xu, Shenren; Yan, Li

2017-08-01

Waverider is an important candidate for the design of hypersonic vehicles. However, the ideal waverider cannot be manufactured because of its sharp leading edge, so the leading edge should be blunted. In the paper, the HMB solver and laminar flow model have been utilized to obtain the flow field properties around the blunt waverider with the freestream Mach number being 8.0, and several novel strategies have been suggested to improve the aerodynamic performance of blunt waverider. The numerical method has been validated against experimental data, and the Stanton number(St) of the predicted result has been analyzed. The obtained results show good agreement with the experimental data. Stmax decreases by 58% and L/D decreases by 8.2% when the blunt radius increases from 0.0002 m to 0.001 m. ;Variable blunt waverider; is a good compromise for aerodynamic performance and thermal insulation. The aero-heating characteristics are very sensitive to Rmax. The position of the smallest blunt radius has a great effect on the aerodynamic performance. In addition, the type of blunt leading edge has a great effect on the aero-heating characteristics when Rmax is fixed. Therefore, out of several designs, Type 4is the best way to achieve the good overall performance. The ;Variable blunt waverider; not only improves the aerodynamic performance, but also makes the aero-heating become evenly-distributed, leading to better aero-heating characteristics.

Large-scale three-dimensional phase-field simulations for phase coarsening at ultrahigh volume fraction on high-performance architectures

NASA Astrophysics Data System (ADS)

Yan, Hui; Wang, K. G.; Jones, Jim E.

2016-06-01

A parallel algorithm for large-scale three-dimensional phase-field simulations of phase coarsening is developed and implemented on high-performance architectures. From the large-scale simulations, a new kinetics in phase coarsening in the region of ultrahigh volume fraction is found. The parallel implementation is capable of harnessing the greater computer power available from high-performance architectures. The parallelized code enables increase in three-dimensional simulation system size up to a 5123 grid cube. Through the parallelized code, practical runtime can be achieved for three-dimensional large-scale simulations, and the statistical significance of the results from these high resolution parallel simulations are greatly improved over those obtainable from serial simulations. A detailed performance analysis on speed-up and scalability is presented, showing good scalability which improves with increasing problem size. In addition, a model for prediction of runtime is developed, which shows a good agreement with actual run time from numerical tests.

Data mining of tree-based models to analyze freeway accident frequency.

PubMed

Chang, Li-Yen; Chen, Wen-Chieh

2005-01-01

Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.

Development of demi-span equations for predicting height among the Malaysian elderly.

PubMed

Ngoh, H J; Sakinah, H; Harsa Amylia, M S

2012-08-01

This study aimed to develop demi-span equations for predicting height in the Malaysian elderly and to explore the applicability of previous published demi-span equations derived from adult populations to the elderly. A cross-sectional study was conducted on Malaysian elderly aged 60 years and older. Subjects were residents of eight shelter homes in Peninsular Malaysia; 204 men and 124 women of Malay, Chinese and Indian ethnicity were included. Measurements of weight, height and demi-span were obtained using standard procedures. Statistical analyses were performed using SPSS version 18.0. The demi-span equations obtained were as follows: Men: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age) + 4.13; Women: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age). Height predicted from these new equations demonstrated good agreement with measured height and no significant differences were found between the mean values of predicted and measured heights in either gender (p>0.05). However, the heights predicted from previous published adult-derived demi-span equations failed to yield good agreement with the measured height of the elderly; significant over-estimation and underestimation of heights tended to occur (p>0.05). The new demi-span equations allow prediction of height with sufficient accuracy in the Malaysian elderly. However, further validation on other elderly samples is needed. Also, we recommend caution when using adult-derived demi-span equations to predict height in elderly people.

Rapid learning in practice: A lung cancer survival decision support system in routine patient care data

PubMed Central

Dekker, Andre; Vinod, Shalini; Holloway, Lois; Oberije, Cary; George, Armia; Goozee, Gary; Delaney, Geoff P.; Lambin, Philippe; Thwaites, David

2016-01-01

Background and purpose A rapid learning approach has been proposed to extract and apply knowledge from routine care data rather than solely relying on clinical trial evidence. To validate this in practice we deployed a previously developed decision support system (DSS) in a typical, busy clinic for non-small cell lung cancer (NSCLC) patients. Material and methods Gender, age, performance status, lung function, lymph node status, tumor volume and survival were extracted without review from clinical data sources for lung cancer patients. With these data the DSS was tested to predict overall survival. Results 3919 lung cancer patients were identified with 159 eligible for inclusion, due to ineligible histology or stage, non-radical dose, missing tumor volume or survival. The DSS successfully identified a good prognosis group and a medium/poor prognosis group (2 year OS 69% vs. 27/30%, p < 0.001). Stage was less discriminatory (2 year OS 47% for stage I–II vs. 36% for stage IIIA–IIIB, p = 0.12) with most good prognosis patients having higher stage disease. The DSS predicted a large absolute overall survival benefit (~40%) for a radical dose compared to a non-radical dose in patients with a good prognosis, while no survival benefit of radical radiotherapy was predicted for patients with a poor prognosis. Conclusions A rapid learning environment is possible with the quality of clinical data sufficient to validate a DSS. It uses patient and tumor features to identify prognostic groups in whom therapy can be individualized based on predicted outcomes. Especially the survival benefit of a radical versus non-radical dose predicted by the DSS for various prognostic groups has clinical relevance, but needs to be prospectively validated. PMID:25241994

Tree-based flood damage modeling of companies: Damage processes and model performance

NASA Astrophysics Data System (ADS)

Sieg, Tobias; Vogel, Kristin; Merz, Bruno; Kreibich, Heidi

2017-07-01

Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data.

PubMed

Houssaïni, Allal; Assoumou, Lambert; Marcelin, Anne Geneviève; Molina, Jean Michel; Calvez, Vincent; Flandre, Philippe

2012-01-01

Background. Many statistical models have been tested to predict phenotypic or virological response from genotypic data. A statistical framework called Super Learner has been introduced either to compare different methods/learners (discrete Super Learner) or to combine them in a Super Learner prediction method. Methods. The Jaguar trial is used to apply the Super Learner framework. The Jaguar study is an "add-on" trial comparing the efficacy of adding didanosine to an on-going failing regimen. Our aim was also to investigate the impact on the use of different cross-validation strategies and different loss functions. Four different repartitions between training set and validations set were tested through two loss functions. Six statistical methods were compared. We assess performance by evaluating R(2) values and accuracy by calculating the rates of patients being correctly classified. Results. Our results indicated that the more recent Super Learner methodology of building a new predictor based on a weighted combination of different methods/learners provided good performance. A simple linear model provided similar results to those of this new predictor. Slight discrepancy arises between the two loss functions investigated, and slight difference arises also between results based on cross-validated risks and results from full dataset. The Super Learner methodology and linear model provided around 80% of patients correctly classified. The difference between the lower and higher rates is around 10 percent. The number of mutations retained in different learners also varys from one to 41. Conclusions. The more recent Super Learner methodology combining the prediction of many learners provided good performance on our small dataset.

Advances in Rotor Performance and Turbulent Wake Simulation Using DES and Adaptive Mesh Refinement

NASA Technical Reports Server (NTRS)

Chaderjian, Neal M.

2012-01-01

Time-dependent Navier-Stokes simulations have been carried out for a rigid V22 rotor in hover, and a flexible UH-60A rotor in forward flight. Emphasis is placed on understanding and characterizing the effects of high-order spatial differencing, grid resolution, and Spalart-Allmaras (SA) detached eddy simulation (DES) in predicting the rotor figure of merit (FM) and resolving the turbulent rotor wake. The FM was accurately predicted within experimental error using SA-DES. Moreover, a new adaptive mesh refinement (AMR) procedure revealed a complex and more realistic turbulent rotor wake, including the formation of turbulent structures resembling vortical worms. Time-dependent flow visualization played a crucial role in understanding the physical mechanisms involved in these complex viscous flows. The predicted vortex core growth with wake age was in good agreement with experiment. High-resolution wakes for the UH-60A in forward flight exhibited complex turbulent interactions and turbulent worms, similar to the V22. The normal force and pitching moment coefficients were in good agreement with flight-test data.

Structural Analysis and Testing of an Erectable Truss for Precision Segmented Reflector Application

NASA Technical Reports Server (NTRS)

Collins, Timothy J.; Fichter, W. B.; Adams, Richard R.; Javeed, Mehzad

1995-01-01

This paper describes analysis and test results obtained at Langley Research Center (LaRC) on a doubly curved testbed support truss for precision reflector applications. Descriptions of test procedures and experimental results that expand upon previous investigations are presented. A brief description of the truss is given, and finite-element-analysis models are described. Static-load and vibration test procedures are discussed, and experimental results are shown to be repeatable and in generally good agreement with linear finite-element predictions. Truss structural performance (as determined by static deflection and vibration testing) is shown to be predictable and very close to linear. Vibration test results presented herein confirm that an anomalous mode observed during initial testing was due to the flexibility of the truss support system. Photogrammetric surveys with two 131-in. reference scales show that the root-mean-square (rms) truss-surface accuracy is about 0.0025 in. Photogrammetric measurements also indicate that the truss coefficient of thermal expansion (CTE) is in good agreement with that predicted by analysis. A detailed description of the photogrammetric procedures is included as an appendix.

Microstructure Evolution and Flow Stress Model of a 20Mn5 Hollow Steel Ingot during Hot Compression.

PubMed

Liu, Min; Ma, Qing-Xian; Luo, Jian-Bin

2018-03-21

20Mn5 steel is widely used in the manufacture of heavy hydro-generator shaft due to its good performance of strength, toughness and wear resistance. However, the hot deformation and recrystallization behaviors of 20Mn5 steel compressed under high temperature were not studied. In this study, the hot compression experiments under temperatures of 850-1200 °C and strain rates of 0.01/s-1/s are conducted using Gleeble thermal and mechanical simulation machine. And the flow stress curves and microstructure after hot compression are obtained. Effects of temperature and strain rate on microstructure are analyzed. Based on the classical stress-dislocation relation and the kinetics of dynamic recrystallization, a two-stage constitutive model is developed to predict the flow stress of 20Mn5 steel. Comparisons between experimental flow stress and predicted flow stress show that the predicted flow stress values are in good agreement with the experimental flow stress values, which indicates that the proposed constitutive model is reliable and can be used for numerical simulation of hot forging of 20Mn5 hollow steel ingot.

Implementation of algebraic stress models in a general 3-D Navier-Stokes method (PAB3D)

NASA Technical Reports Server (NTRS)

Abdol-Hamid, Khaled S.

1995-01-01

A three-dimensional multiblock Navier-Stokes code, PAB3D, which was developed for propulsion integration and general aerodynamic analysis, has been used extensively by NASA Langley and other organizations to perform both internal (exhaust) and external flow analysis of complex aircraft configurations. This code was designed to solve the simplified Reynolds Averaged Navier-Stokes equations. A two-equation k-epsilon turbulence model has been used with considerable success, especially for attached flows. Accurate predicting of transonic shock wave location and pressure recovery in separated flow regions has been more difficult. Two algebraic Reynolds stress models (ASM) have been recently implemented in the code that greatly improved the code's ability to predict these difficult flow conditions. Good agreement with Direct Numerical Simulation (DNS) for a subsonic flat plate was achieved with ASM's developed by Shih, Zhu, and Lumley and Gatski and Speziale. Good predictions were also achieved at subsonic and transonic Mach numbers for shock location and trailing edge boattail pressure recovery on a single-engine afterbody/nozzle model.

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies

PubMed Central

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-01-01

Background The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study Aim To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. Design and setting A three-part longitudinal predictive validity study of selection into training for UK general practice. Method In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Results Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. Conclusion In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered. PMID:24267856

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies.

PubMed

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-11-01

The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. A three-part longitudinal predictive validity study of selection into training for UK general practice. In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered.

Effects of pupil filter patterns in line-scan focal modulation microscopy

NASA Astrophysics Data System (ADS)

Shen, Shuhao; Pant, Shilpa; Chen, Rui; Chen, Nanguang

2018-03-01

Line-scan focal modulation microscopy (LSFMM) is an emerging imaging technique that affords high imaging speed and good optical sectioning at the same time. We present a systematic investigation into optimal design of the pupil filter for LSFMM in an attempt to achieve the best performance in terms of spatial resolutions, optical sectioning, and modulation depth. Scalar diffraction theory was used to compute light propagation and distribution in the system and theoretical predictions on system performance, which were then compared with experimental results.

SATCOM antenna siting study on P-3C aircraft, volume 1

NASA Technical Reports Server (NTRS)

Bensman, D. A.; Marhefka, R. J.

1991-01-01

The NEC-BSC (Basic Scattering Code) was used to study the performance of a SATCOM antenna on a P-3C aircraft. After plate cylinder fields are added to version 3.1 of the NEC-BSC, it is shown that the NEC-BSC can be used to accurately predict the performance of a SATCOM antenna system on a P-3C aircraft. The study illustrates that the NEC-BSC gives good results when compared with scale model measurements provided by Boeing and Lockheed.

Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: a prospective, multicenter, European study.

PubMed

Taylor, Fiona G M; Quirke, Philip; Heald, Richard J; Moran, Brendan; Blomqvist, Lennart; Swift, Ian; Sebag-Montefiore, David J; Tekkis, Paris; Brown, Gina

2011-04-01

To assess local recurrence, disease-free survival, and overall survival in magnetic resonance imaging (MRI)-predicted good prognosis tumors treated by surgery alone. The MERCURY study reported that high-resolution MRI can accurately stage rectal cancer. The routine policy in most centers involved in the MERCURY study was primary surgery alone in MRI-predicted stage II or less and in MRI "good prognosis" stage III with selective avoidance of neoadjuvant therapy. Data were collected prospectively on all patients included in the MERCURY study who were staged as MRI-defined "good" prognosis tumors. "Good" prognosis included MRI-predicted safe circumferential resection margins, with MRI-predicted T2/T3a/T3b (less than 5 mm spread from muscularis propria), regardless of MRI N stage. None received preoperative or postoperative radiotherapy. Overall survival, disease-free survival, and local recurrence were calculated. Of 374 patients followed up in the MERCURY study, 122 (33%) were defined as "good prognosis" stage III or less on MRI. Overall and disease-free survival for all patients with MRI "good prognosis" stage I, II and III disease at 5 years was 68% and 85%, respectively. The local recurrence rate for this series of patients predicted to have a good prognosis tumor on MRI was 3%. The preoperative identification of good prognosis tumors using MRI will allow stratification of patients and better targeting of preoperative therapy. This study confirms the ability of MRI to select patients who are likely to have a good outcome with primary surgery alone.

Prediction of shallow landslide occurrence: Validation of a physically-based approach through a real case study.

PubMed

Schilirò, Luca; Montrasio, Lorella; Scarascia Mugnozza, Gabriele

2016-11-01

In recent years, physically-based numerical models have frequently been used in the framework of early-warning systems devoted to rainfall-induced landslide hazard monitoring and mitigation. For this reason, in this work we describe the potential of SLIP (Shallow Landslides Instability Prediction), a simplified physically-based model for the analysis of shallow landslide occurrence. In order to test the reliability of this model, a back analysis of recent landslide events occurred in the study area (located SW of Messina, northeastern Sicily, Italy) on October 1st, 2009 was performed. The simulation results have been compared with those obtained for the same event by using TRIGRS, another well-established model for shallow landslide prediction. Afterwards, a simulation over a 2-year span period has been performed for the same area, with the aim of evaluating the performance of SLIP as early warning tool. The results confirm the good predictive capability of the model, both in terms of spatial and temporal prediction of the instability phenomena. For this reason, we recommend an operating procedure for the real-time definition of shallow landslide triggering scenarios at the catchment scale, which is based on the use of SLIP calibrated through a specific multi-methodological approach. Copyright © 2016 Elsevier B.V. All rights reserved.

A simplified approach to predict performance degradation of a solid oxide fuel cell anode

NASA Astrophysics Data System (ADS)

Khan, Muhammad Zubair; Mehran, Muhammad Taqi; Song, Rak-Hyun; Lee, Jong-Won; Lee, Seung-Bok; Lim, Tak-Hyoung

2018-07-01

The agglomeration of nickel (Ni) particles in a Ni-cermet anode is a significant degradation phenomenon for solid oxide fuel cells (SOFCs). This work aims to predict the performance degradation of SOFCs due to Ni grain growth by using a simplified approach. Accelerated aging of Ni-scandia stabilized zirconia (SSZ) as an SOFC anode is carried out at 900 °C and subsequent microstructural evolution is investigated every 100 h up to 1000 h using scanning electron microscopy (SEM). The resulting morphological changes are quantified using a two-dimensional image analysis technique that yields the particle size, phase proportion, and triple phase boundary (TPB) point distribution. The electrochemical properties of an anode-supported SOFC are characterized using electrochemical impedance spectroscopy (EIS). The changes of particle size and TPB length in the anode as a function of time are in excellent agreement with the power-law coarsening model. This model is further combined with an electrochemical model to predict the changes in the anode polarization resistance. The predicted polarization resistances are in good agreement with the experimentally obtained values. This model for prediction of anode lifetime provides deep insight into the time-dependent Ni agglomeration behavior and its impact on the electrochemical performance degradation of the SOFC anode.

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

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Design and performance of an analysis-by-synthesis class of predictive speech coders

NASA Technical Reports Server (NTRS)

Rose, Richard C.; Barnwell, Thomas P., III

1990-01-01

The performance of a broad class of analysis-by-synthesis linear predictive speech coders is quantified experimentally. The class of coders includes a number of well-known techniques as well as a very large number of speech coders which have not been named or studied. A general formulation for deriving the parametric representation used in all of the coders in the class is presented. A new coder, named the self-excited vocoder, is discussed because of its good performance with low complexity, and because of the insight this coder gives to analysis-by-synthesis coders in general. The results of a study comparing the performances of different members of this class are presented. The study takes the form of a series of formal subjective and objective speech quality tests performed on selected coders. The results of this study lead to some interesting and important observations concerning the controlling parameters for analysis-by-synthesis speech coders.

A study on the predictability of the transition day from the dry to the rainy season over South Korea

NASA Astrophysics Data System (ADS)

Lee, Sang-Min; Nam, Ji-Eun; Choi, Hee-Wook; Ha, Jong-Chul; Lee, Yong Hee; Kim, Yeon-Hee; Kang, Hyun-Suk; Cho, ChunHo

2016-08-01

This study was conducted to evaluate the prediction accuracies of THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data at six operational forecast centers using the root-mean square difference (RMSD) and Brier score (BS) from April to July 2012. And it was performed to test the precipitation predictability of ensemble prediction systems (EPS) on the onset of the summer rainy season, the day of withdrawal in spring drought over South Korea on 29 June 2012 with use of the ensemble mean precipitation, ensemble probability precipitation, 10-day lag ensemble forecasts (ensemble mean and probability precipitation), and effective drought index (EDI). The RMSD analysis of atmospheric variables (geopotential-height at 500 hPa, temperature at 850 hPa, sea-level pressure and specific humidity at 850 hPa) showed that the prediction accuracies of the EPS at the Meteorological Service of Canada (CMC) and China Meteorological Administration (CMA) were poor and those at the European Center for Medium-Range Weather Forecasts (ECMWF) and Korea Meteorological Administration (KMA) were good. Also, ECMWF and KMA showed better results than other EPSs for predicting precipitation in the BS distributions. It is also evaluated that the onset of the summer rainy season could be predicted using ensemble-mean precipitation from 4-day leading time at all forecast centers. In addition, the spatial distributions of predicted precipitation of the EPS at KMA and the Met Office of the United Kingdom (UKMO) were similar to those of observed precipitation; thus, the predictability showed good performance. The precipitation probability forecasts of EPS at CMA, the National Centers for Environmental Prediction (NCEP), and UKMO (ECMWF and KMA) at 1-day lead time produced over-forecasting (under-forecasting) in the reliability diagram. And all the ones at 2˜4-day lead time showed under-forecasting. Also, the precipitation on onset day of the summer rainy season could be predicted from a 4-day lead time to initial time by using the 10-day lag ensemble mean and probability forecasts. Additionally, the predictability for withdrawal day of spring drought to be ended due to precipitation on onset day of summer rainy season was evaluated using Effective Drought Index (EDI) to be calculated by ensemble mean precipitation forecasts and spreads at five EPSs.

Application of SWAT-HS, a lumped hillslope model to simulate hydrology in the Cannonsville Reservoir watershed, New York

NASA Astrophysics Data System (ADS)

Hoang, Linh; Schneiderman, Elliot; Mukundan, Rajith; Moore, Karen; Owens, Emmet; Steenhuis, Tammo

2017-04-01

Surface runoff is the primary mechanism transporting substances such as sediments, agricultural chemicals, and pathogens to receiving waters. In order to predict runoff and pollutant fluxes, and to evaluate management practices, it is essential to accurately predict the areas generating surface runoff, which depend on the type of runoff: infiltration-excess runoff and saturation-excess runoff. The watershed of Cannonsville reservoir is part of the New York City water supply system that provides high quality drinking water to nine million people in New York City (NYC) and nearby communities. Previous research identified saturation-excess runoff as the dominant runoff mechanism in this region. The Soil and Water Assessment Tool (SWAT) is a promising tool to simulate the NYC watershed given its broad application and good performance in many watersheds with different scales worldwide, for its ability to model water quality responses, and to evaluate the effect of management practices on water quality at the watershed scale. However, SWAT predicts runoff based mainly on soil and land use characteristics, and implicitly considers only infiltration-excess runoff. Therefore, we developed a modified version of SWAT, referred to as SWAT-Hillslope (SWAT-HS), which explicitly simulates saturation-excess runoff by redefining Hydrological Response Units (HRUs) based on wetness classes with varying soil water storage capacities, and by introducing a surface aquifer with the ability to route interflow from "drier" to "wetter" wetness classes. SWAT-HS was first tested at Town Brook, a 37 km2 headwater watershed draining to the Cannonsville reservoir using a single sub-basin for the whole watershed. SWAT-HS performed well, and predicted streamflow yielded Nash-Sutcliffe Efficiencies of 0.68 and 0.87 at the daily and monthly time steps, respectively. More importantly, it predicted the spatial distribution of saturated areas accurately. Based on the good performance in the Town Brook watershed, we scale-up the application of SWAT-HS to the 1160 km2 Cannonsville watershed utilizing a setup of multiple sub-basins, and evaluate the model performance on flow simulation at different gauged locations in the watershed. Results from flow predictions will be used as a basis for evaluating the ability of SWAT-HS to make sediment and nutrient loading estimates.

CFD-RANS prediction of individual exposure from continuous release of hazardous airborne materials in complex urban environments

NASA Astrophysics Data System (ADS)

Efthimiou, G. C.; Andronopoulos, S.; Bartzis, J. G.; Berbekar, E.; Harms, F.; Leitl, B.

2017-02-01

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict individual exposure (maximum dosages) of an airborne material which is released continuously from a point source. The present work addresses the question whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict individual exposure for various exposure times. This is feasible by providing the two RANS concentration moments (mean and variance) and a turbulent time scale to a deterministic model. The whole effort is focused on the prediction of individual exposure inside a complex real urban area. The capabilities of the proposed methodology are validated against wind-tunnel data (CUTE experiment). The present simulations were performed 'blindly', i.e. the modeller had limited information for the inlet boundary conditions and the results were kept unknown until the end of the COST Action ES1006. Thus, a high uncertainty of the results was expected. The general performance of the methodology due to this 'blind' strategy is good. The validation metrics fulfil the acceptance criteria. The effect of the grid and the turbulence model on the model performance is examined.

The use of presurgical psychological screening to predict the outcome of spine surgery.

PubMed

Block, A R; Ohnmeiss, D D; Guyer, R D; Rashbaum, R F; Hochschuler, S H

2001-01-01

Several previous studies have shown that psychosocial factors can influence the outcome of elective spine surgery. The purpose of the current study was to determine how well a presurgical screening instrument could predict surgical outcome. The study was conducted by staff of a psychologist's office. They performed preoperative screening for spine surgery candidates and collected the follow-up data. Presurgical screening and follow-up data collection was performed on 204 patients who underwent laminectomy/discectomy (n=118) or fusion (n=86) of the lumbar spine. The outcome measures used in the study were visual analog pain scales, the Oswestry Disability Questionnaire, and medication use. A semi-structured interview and psychometric testing were used to identify specific, quantifiable psychological, and "medical" risk factors for poor surgical outcome. A presurgical psychological screening (PPS) scorecard was completed for each patient, assessing whether the patient had a high or low level of risk on these psychological and medical dimensions. Based on the scorecard, an overall surgical prognosis of "good," "fair," or "poor" was generated. Results showed spine surgery led to significant overall improvements in pain, functional ability, and medication use. Medical and psychological risk levels were significantly related to outcome, with the poorest results obtained by patients having both high psychological and medical risk. Further, the accuracy of PPS surgical prognosis in predicting overall outcome was 82%. Only 9 of 53 patients predicted to have poor outcome achieved fair or good results from spine surgery. These findings suggest that PPS should become a more routine part of the evaluation of chronic pain patients in whom spine surgery is being considered.

Comparison of ANN and RKS approaches to model SCC strength

NASA Astrophysics Data System (ADS)

Prakash, Aravind J.; Sathyan, Dhanya; Anand, K. B.; Aravind, N. R.

2018-02-01

Self compacting concrete (SCC) is a high performance concrete that has high flowability and can be used in heavily reinforced concrete members with minimal compaction segregation and bleeding. The mix proportioning of SCC is highly complex and large number of trials are required to get the mix with the desired properties resulting in the wastage of materials and time. The research on SCC has been highly empirical and no theoretical relationships have been developed between the mixture proportioning and engineering properties of SCC. In this work effectiveness of artificial neural network (ANN) and random kitchen sink algorithm(RKS) with regularized least square algorithm(RLS) in predicting the split tensile strength of the SCC is analysed. Random kitchen sink algorithm is used for mapping data to higher dimension and classification of this data is done using Regularized least square algorithm. The training and testing data for the algorithm was obtained experimentally using standard test procedures and materials available. Total of 40 trials were done which were used as the training and testing data. Trials were performed by varying the amount of fine aggregate, coarse aggregate, dosage and type of super plasticizer and water. Prediction accuracy of the ANN and RKS model is checked by comparing the RMSE value of both ANN and RKS. Analysis shows that eventhough the RKS model is good for large data set, its prediction accuracy is as good as conventional prediction method like ANN so the split tensile strength model developed by RKS can be used in industries for the proportioning of SCC with tailor made property.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

The Five Key Questions of Human Performance Modeling.

PubMed

Wu, Changxu

2018-01-01

Via building computational (typically mathematical and computer simulation) models, human performance modeling (HPM) quantifies, predicts, and maximizes human performance, human-machine system productivity and safety. This paper describes and summarizes the five key questions of human performance modeling: 1) Why we build models of human performance; 2) What the expectations of a good human performance model are; 3) What the procedures and requirements in building and verifying a human performance model are; 4) How we integrate a human performance model with system design; and 5) What the possible future directions of human performance modeling research are. Recent and classic HPM findings are addressed in the five questions to provide new thinking in HPM's motivations, expectations, procedures, system integration and future directions.

A Quantitative Model of Expert Transcription Typing

DTIC Science & Technology

1993-03-08

side of pure psychology, several researchers have argued that transcription typing is a particularly good activity for the study of human skilled...phenomenon with a quantitative METT prediction. The first, quick and dirty analysis gives a good prediction of the copy span, in fact, it is even...typing, it should be demonstrated that the mechanism of the model does not get in the way of good predictions. If situations occur where the entire

Predicting survival across chronic interstitial lung disease: the ILD-GAP model.

PubMed

Ryerson, Christopher J; Vittinghoff, Eric; Ley, Brett; Lee, Joyce S; Mooney, Joshua J; Jones, Kirk D; Elicker, Brett M; Wolters, Paul J; Koth, Laura L; King, Talmadge E; Collard, Harold R

2014-04-01

Risk prediction is challenging in chronic interstitial lung disease (ILD) because of heterogeneity in disease-specific and patient-specific variables. Our objective was to determine whether mortality is accurately predicted in patients with chronic ILD using the GAP model, a clinical prediction model based on sex, age, and lung physiology, that was previously validated in patients with idiopathic pulmonary fibrosis. Patients with idiopathic pulmonary fibrosis (n=307), chronic hypersensitivity pneumonitis (n=206), connective tissue disease-associated ILD (n=281), idiopathic nonspecific interstitial pneumonia (n=45), or unclassifiable ILD (n=173) were selected from an ongoing database (N=1,012). Performance of the previously validated GAP model was compared with novel prediction models in each ILD subtype and the combined cohort. Patients with follow-up pulmonary function data were used for longitudinal model validation. The GAP model had good performance in all ILD subtypes (c-index, 74.6 in the combined cohort), which was maintained at all stages of disease severity and during follow-up evaluation. The GAP model had similar performance compared with alternative prediction models. A modified ILD-GAP Index was developed for application across all ILD subtypes to provide disease-specific survival estimates using a single risk prediction model. This was done by adding a disease subtype variable that accounted for better adjusted survival in connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The GAP model accurately predicts risk of death in chronic ILD. The ILD-GAP model accurately predicts mortality in major chronic ILD subtypes and at all stages of disease.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

PubMed Central

Tang, Haijing; Liang, Yu; Huang, Zhongnan; Wang, Taoyi; He, Lin; Du, Yicong; Ding, Gangyi

2016-01-01

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction. PMID:27872637

Prediction of R-curves from small coupon tests

NASA Technical Reports Server (NTRS)

Yeh, J. R.; Bray, G. H.; Bucci, R. J.; Macheret, Y.

1994-01-01

R-curves were predicted for Alclad 2024-T3 and C188-T3 sheet using the results of small-coupon Kahn tear tests in combination with two-dimensional elastic-plastic finite element stress analyses. The predictions were compared to experimental R-curves from 6.3, 16 and 60-inch wide M(T) specimens and good agreement was obtained. The method is an inexpensive alternative to wide panel testing for characterizing the fracture toughness of damage-tolerant sheet alloys. The usefulness of this approach was demonstrated by performing residual strength calculations for a two-bay crack in a representative fuselage structure. C188-T3 was predicted to have a 24 percent higher load carrying capability than 2024-T3 in this application as a result of its superior fracture toughness.

Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors.

PubMed

Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

2016-10-01

Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK-signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r 2 Pred ) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Selection for Surgical Training: An Evidence-Based Review.

PubMed

Schaverien, Mark V

2016-01-01

The predictive relationship between candidate selection criteria for surgical training programs and future performance during and at the completion of training has been investigated for several surgical specialties, however there is no interspecialty agreement regarding which selection criteria should be used. Better understanding the predictive reliability between factors at selection and future performance may help to optimize the process and lead to greater standardization of the surgical selection process. PubMed and Ovid MEDLINE databases were searched. Over 560 potentially relevant publications were identified using the search strategy and screened using the Cochrane Collaboration Data Extraction and Assessment Template. 57 studies met the inclusion criteria. Several selection criteria used in the traditional selection demonstrated inconsistent correlation with subsequent performance during and at the end of surgical training. The following selection criteria, however, demonstrated good predictive relationships with subsequent resident performance: USMLE examination scores, Letters of Recommendation (LOR) including the Medical Student Performance Evaluation (MSPE), academic performance during clinical clerkships, the interview process, displaying excellence in extracurricular activities, and the use of unadjusted rank lists. This systematic review supports that the current selection process needs to be further evaluated and improved. Multicenter studies using standardized outcome measures of success are now required to improve the reliability of the selection process to select the best trainees. Published by Elsevier Inc.

Validation of the DRAGON Score in a Chinese Population to Predict Functional Outcome of Intravenous Thrombolysis-Treated Stroke Patients.

PubMed

Zhang, Xinmiao; Liao, Xiaoling; Wang, Chunjuan; Liu, Liping; Wang, Chunxue; Zhao, Xingquan; Pan, Yuesong; Wang, Yilong; Wang, Yongjun

2015-08-01

The DRAGON score predicts functional outcome of ischemic stroke patients treated with intravenous thrombolysis. Our aim was to evaluate its utility in a Chinese stroke population. Patients with acute ischemic stroke treated with intravenous thrombolysis were prospectively registered in the Thrombolysis Implementation and Monitor of acute ischemic Stroke in China. We excluded patients with basilar artery occlusion and missing data, leaving 970 eligible patients. We calculated the DRAGON score, and the clinical outcome was measured by the modified Rankin Scale at 3 months. Model discrimination was quantified by calculating the C statistic. Calibration was assessed using Pearson correlation coefficient. The C statistic was .73 (.70-.76) for good outcome and .75 (.70-.79) for miserable outcome. Proportions of patients with good outcome were 94%, 83%, 70%, and 0% for 0 to 1, 2, 3, and 8 to 10 score points, respectively. Proportions of patients with miserable outcome were 0%, 3%, 9%, and 50% for 0 to 1, 2, 3, and 8 to 10 points, respectively. There was high correlation between predicted and observed probability of 3-month favorable and miserable outcome in the external validation cohort (Pearson correlation coefficient, .98 and .98, respectively, both P < .0001). The DRAGON score showed good performance to predict functional outcome after tissue-type plasminogen activator treatment in the Chinese population. This study demonstrated the accuracy and usability of the DRAGON score in the Chinese population in daily practice. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Is electromyography a predictive test of patient response to biofeedback in the treatment of fecal incontinence?

PubMed

Lacima, Gloria; Pera, Miguel; González-Argenté, Xavier; Torrents, Abiguei; Valls-Solé, Josep; Espuña-Pons, Montserrat

2016-03-01

Biofeedback is effective in more than 70% of patients with fecal incontinence. However, reliable predictors of successful treatment have not been identified. The aim was to identify clinical variables and diagnostic tests, particularly electromyography, that could predict a successful outcome. We included 135 consecutive women with fecal incontinence treated with biofeedback. Clinical evaluation, manometry, ultrasonography, electromyography, and pudendal nerve terminal motor latency were performed before therapy. Treatment outcome was assessed using a symptoms diary, Wexner incontinence score and the patient's subjective perception. According to the symptoms diaries, 106 (78.5%) women had a good clinical result and 29 (21.5%) had a poor result. There were no differences in age, severity and type of fecal incontinence. Maximum resting pressure (39.3 ± 19.1 mmHg vs. 33.7 ± 20.2 mmHg; P = 0.156) and maximum squeeze pressure (91.8 ± 33.2 mmHg vs. 79.8 ± 31.2 mmHg; P = 0.127) were higher in patients having good clinical outcome although the difference was not significant. There were no differences in the presence of sphincter defects or abnormalities in electromyographic recordings. Logistic regression analysis found no independent predictive factor for good clinical outcome. Biofeedback is effective in more than 75% of patients with fecal incontinence. Clinical characteristics of patients and results of baseline tests have no predictive value of response to therapy. Specifically, we found no association between severity of electromyographic deficit and clinical response. © 2015 Wiley Periodicals, Inc.

The developmental dynamics of children's academic performance and mothers' homework-related affect and practices.

PubMed

Silinskas, Gintautas; Kiuru, Noona; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Nurmi, Jari-Erik

2015-04-01

This study investigated the longitudinal associations between children's academic performance and their mothers' affect, practices, and perceptions of their children in homework situations. The children's (n = 2,261) performance in reading and math was tested in Grade 1 and Grade 4, and the mothers (n = 1,476) filled out questionnaires on their affect, practices, and perceptions while their children were in Grades 2, 3, and 4. The results showed, first, that the more help in homework the mothers reported, the slower was the development of their children's academic performance from Grade 1 to Grade 4. This negative association was true especially if mothers perceived their children not to be able to work autonomously. Second, children's good academic performance in Grade 1 predicted mothers' perception of child's ability to be autonomous and positive affect in homework situations later on, whereas poor performance predicted mothers' negative affect, help, and monitoring. Finally, mothers' negative affect mediated the association between children's poor performance, maternal practices, and perceptions of their children. (c) 2015 APA, all rights reserved).

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

PubMed Central

Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

2014-01-01

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

Analytical performance, agreement and user-friendliness of six point-of-care testing urine analysers for urinary tract infection in general practice.

PubMed

Schot, Marjolein J C; van Delft, Sanne; Kooijman-Buiting, Antoinette M J; de Wit, Niek J; Hopstaken, Rogier M

2015-05-18

Various point-of-care testing (POCT) urine analysers are commercially available for routine urine analysis in general practice. The present study compares analytical performance, agreement and user-friendliness of six different POCT urine analysers for diagnosing urinary tract infection in general practice. All testing procedures were performed at a diagnostic centre for primary care in the Netherlands. Urine samples were collected at four general practices. Analytical performance and agreement of the POCT analysers regarding nitrite, leucocytes and erythrocytes, with the laboratory reference standard, was the primary outcome measure, and analysed by calculating sensitivity, specificity, positive and negative predictive value, and Cohen's κ coefficient for agreement. Secondary outcome measures were the user-friendliness of the POCT analysers, in addition to other characteristics of the analysers. The following six POCT analysers were evaluated: Uryxxon Relax (Macherey Nagel), Urisys 1100 (Roche), Clinitek Status (Siemens), Aution 11 (Menarini), Aution Micro (Menarini) and Urilyzer (Analyticon). Analytical performance was good for all analysers. Compared with laboratory reference standards, overall agreement was good, but differed per parameter and per analyser. Concerning the nitrite test, the most important test for clinical practice, all but one showed perfect agreement with the laboratory standard. For leucocytes and erythrocytes specificity was high, but sensitivity was considerably lower. Agreement for leucocytes varied between good to very good, and for the erythrocyte test between fair and good. First-time users indicated that the analysers were easy to use. They expected higher productivity and accuracy when using these analysers in daily practice. The overall performance and user-friendliness of all six commercially available POCT urine analysers was sufficient to justify routine use in suspected urinary tract infections in general practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Construct validity of tests that measure kick performance for young soccer players based on cluster analysis: exploring the relationship between coaches rating and actual measures.

PubMed

Palucci Vieira, Luiz H; de Andrade, Vitor L; Aquino, Rodrigo L; Moraes, Renato; Barbieri, Fabio A; Cunha, Sérgio A; Bedo, Bruno L; Santiago, Paulo R

2017-12-01

The main aim of this study was to verify the relationship between the classification of coaches and actual performance in field tests that measure the kicking performance in young soccer players, using the K-means clustering technique. Twenty-three U-14 players performed 8 tests to measure their kicking performance. Four experienced coaches provided a rating for each player as follows: 1: poor; 2: below average; 3: average; 4: very good; 5: excellent as related to three parameters (i.e. accuracy, power and ability to put spin on the ball). The scores interval established from k-means cluster metric was useful to originating five groups of performance level, since ANOVA revealed significant differences between clusters generated (P<0.01). Accuracy seems to be moderately predicted by the penalty kick, free kick, kicking the ball rolling and Wall Volley Test (0.44≤r≤0.56), while the ability to put spin on the ball can be measured by the free kick and the corner kick tests (0.52≤r≤0.61). Body measurements, age and PHV did not systematically influence the performance. The Wall Volley Test seems to be a good predictor of other tests. Five tests showed reasonable construct validity and can be used to predict the accuracy (penalty kick, free kick, kicking a rolling ball and Wall Volley Test) and ability to put spin on the ball (free kick and corner kick tests) when kicking in soccer. In contrast, the goal kick, kicking the ball when airborne and the vertical kick tests exhibited low power of discrimination and using them should be viewed with caution.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

PubMed

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

2014-01-01

In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Prognostic value of resident clinical performance ratings.

PubMed

Williams, Reed G; Dunnington, Gary L

2004-10-01

This study investigated the concurrent and predictive validity of end-of-rotation (EOR) clinical performance ratings. Surgeon EOR ratings of residents were collected and compared with end-of-year (EOY) progress decisions and to EOR and EOY confidential judgments of resident ability to provide patient care without direct supervision. Eighty percent to 85% of EOR ratings were Excellent or Very Good. Five percent or fewer were Fair or Poor. Almost all residents receiving Excellent or Very Good EOR ratings also received positive EOR judgments about ability to provide patient care without direct supervision. Residents rated Fair or Poor received negative EOR judgments about ability to provide patient care without direct supervision. As the cumulative percent of Good, Fair, and Poor EOR ratings increased, the number of residents promoted without stipulations at the end of the year decreased and the percentage of faculty members who judged the residents capable of providing effective patient care without direct supervision at the end of the year declined. All residents receiving 40% or more EOR ratings below Very Good had stipulations associated with their promotion. Despite use of descriptive anchors on the scale, clinical performance ratings have no direct meaning. Their meaning needs to be established in the same manner as is done in setting normal values for diagnostic tests, ie, by establishing the relationship between EOR ratings and practice outcomes.

Assessment of performance and utility of mortality prediction models in a single Indian mixed tertiary intensive care unit.

PubMed

Sathe, Prachee M; Bapat, Sharda N

2014-01-01

To assess the performance and utility of two mortality prediction models viz. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in a single Indian mixed tertiary intensive care unit (ICU). Secondary objectives were bench-marking and setting a base line for research. In this observational cohort, data needed for calculation of both scores were prospectively collected for all consecutive admissions to 28-bedded ICU in the year 2011. After excluding readmissions, discharges within 24 h and age <18 years, the records of 1543 patients were analyzed using appropriate statistical methods. Both models overpredicted mortality in this cohort [standardized mortality ratio (SMR) 0.88 ± 0.05 and 0.95 ± 0.06 using APACHE II and SAPS II respectively]. Patterns of predicted mortality had strong association with true mortality (R (2) = 0.98 for APACHE II and R (2) = 0.99 for SAPS II). Both models performed poorly in formal Hosmer-Lemeshow goodness-of-fit testing (Chi-square = 12.8 (P = 0.03) for APACHE II, Chi-square = 26.6 (P = 0.001) for SAPS II) but showed good discrimination (area under receiver operating characteristic curve 0.86 ± 0.013 SE (P < 0.001) and 0.83 ± 0.013 SE (P < 0.001) for APACHE II and SAPS II, respectively). There were wide variations in SMRs calculated for subgroups based on International Classification of Disease, 10(th) edition (standard deviation ± 0.27 for APACHE II and 0.30 for SAPS II). Lack of fit of data to the models and wide variation in SMRs in subgroups put a limitation on utility of these models as tools for assessing quality of care and comparing performances of different units without customization. Considering comparable performance and simplicity of use, efforts should be made to adapt SAPS II.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Cold flow testing of the Space Shuttle Main Engine high pressure fuel turbine model

NASA Technical Reports Server (NTRS)

Hudson, Susan T.; Gaddis, Stephen W.; Johnson, P. D.; Boynton, James L.

1991-01-01

In order to experimentally determine the performance of the Space Shuttle Main Engine (SSME) High Pressure Fuel Turbopump (HPFTP) turbine, a 'cold' air flow turbine test program was established at NASA's Marshall Space Flight Center. As part of this test program, a baseline test of Rocketdyne's HPFTP turbine has been completed. The turbine performance and turbine diagnostics such as airfoil surface static pressure distributions, static pressure drops through the turbine, and exit swirl angles were investigated at the turbine design point, over its operating range, and at extreme off-design points. The data was compared to pretest predictions with good results. The test data has been used to improve meanline prediction codes and is now being used to validate various three-dimensional codes. The data will also be scaled to engine conditions and used to improve the SSME steady-state performance model.

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

PubMed

Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

2002-01-01

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

Validation of the solar heating and cooling high speed performance (HISPER) computer code

NASA Technical Reports Server (NTRS)

Wallace, D. B.

1980-01-01

Developed to give a quick and accurate predictions HISPER, a simplification of the TRNSYS program, achieves its computational speed by not simulating detailed system operations or performing detailed load computations. In order to validate the HISPER computer for air systems the simulation was compared to the actual performance of an operational test site. Solar insolation, ambient temperature, water usage rate, and water main temperatures from the data tapes for an office building in Huntsville, Alabama were used as input. The HISPER program was found to predict the heating loads and solar fraction of the loads with errors of less than ten percent. Good correlation was found on both a seasonal basis and a monthly basis. Several parameters (such as infiltration rate and the outside ambient temperature above which heating is not required) were found to require careful selection for accurate simulation.

Multimodel predictive system for carbon dioxide solubility in saline formation waters.

PubMed

Wang, Zan; Small, Mitchell J; Karamalidis, Athanasios K

2013-02-05

The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO(2) solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304-433 K, pressure range 74-500 bar, and salt concentration range 0-7 m (NaCl equivalent), using 173 published CO(2) solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO(2) solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO(2) solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications.

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder.

PubMed

Shamshirband, Shahaboddin; Banjanovic-Mehmedovic, Lejla; Bosankic, Ivan; Kasapovic, Suad; Abdul Wahab, Ainuddin Wahid Bin

2016-01-01

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Implementation and Validation of the Viscoelastic Continuum Damage Theory for Asphalt Mixture and Pavement Analysis in Brazil

NASA Astrophysics Data System (ADS)

Nascimento, Luis Alberto Herrmann do

This dissertation presents the implementation and validation of the viscoelastic continuum damage (VECD) model for asphalt mixture and pavement analysis in Brazil. It proposes a simulated damage-to-fatigue cracked area transfer function for the layered viscoelastic continuum damage (LVECD) program framework and defines the model framework's fatigue cracking prediction error for asphalt pavement reliability-based design solutions in Brazil. The research is divided into three main steps: (i) implementation of the simplified viscoelastic continuum damage (S-VECD) model in Brazil (Petrobras) for asphalt mixture characterization, (ii) validation of the LVECD model approach for pavement analysis based on field performance observations, and defining a local simulated damage-to-cracked area transfer function for the Fundao Project's pavement test sections in Rio de Janeiro, RJ, and (iii) validation of the Fundao project local transfer function to be used throughout Brazil for asphalt pavement fatigue cracking predictions, based on field performance observations of the National MEPDG Project's pavement test sections, thereby validating the proposed framework's prediction capability. For the first step, the S-VECD test protocol, which uses controlled-on-specimen strain mode-of-loading, was successfully implemented at the Petrobras and used to characterize Brazilian asphalt mixtures that are composed of a wide range of asphalt binders. This research verified that the S-VECD model coupled with the GR failure criterion is accurate for fatigue life predictions of Brazilian asphalt mixtures, even when very different asphalt binders are used. Also, the applicability of the load amplitude sweep (LAS) test for the fatigue characterization of the asphalt binders was checked, and the effects of different asphalt binders on the fatigue damage properties of the asphalt mixtures was investigated. The LAS test results, modeled according to VECD theory, presented a strong correlation with the asphalt mixtures' fatigue performance. In the second step, the S-VECD test protocol was used to characterize the asphalt mixtures used in the 27 selected Fundao project test sections and subjected to real traffic loading. Thus, the asphalt mixture properties, pavement structure data, traffic loading, and climate were input into the LVECD program for pavement fatigue cracking performance simulations. The simulation results showed good agreement with the field-observed distresses. Then, a damage shift approach, based on the initial simulated damage growth rate, was introduced in order to obtain a unique relationship between the LVECD-simulated shifted damage and the pavement-observed fatigue cracked areas. This correlation was fitted to a power form function and defined as the averaged reduced damage-to-cracked area transfer function. The last step consisted of using the averaged reduced damage-to-cracked area transfer function that was developed in the Fundao project to predict pavement fatigue cracking in 17 National MEPDG project test sections. The procedures for the material characterization and pavement data gathering adopted in this step are similar to those used for the Fundao project simulations. This research verified that the transfer function defined for the Fundao project sections can be used for the fatigue performance predictions of a wide range of pavements all over Brazil, as the predicted and observed cracked areas for the National MEPDG pavements presented good agreement, following the same trends found for the Fundao project pavement sites. Based on the prediction errors determined for all 44 pavement test sections (Fundao and National MEPDG test sections), the proposed framework's prediction capability was determined so that reliability-based solutions can be applied for flexible pavement design. It was concluded that the proposed LVECD program framework has very good fatigue cracking prediction capability.

Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

NASA Astrophysics Data System (ADS)

Pôças, Isabel; Gonçalves, João; Costa, Patrícia Malva; Gonçalves, Igor; Pereira, Luís S.; Cunha, Mario

2017-06-01

In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.

Predicting mortality after congenital heart surgeries: evaluation of the Aristotle and Risk Adjustement in Congenital Heart Surgery-1 risk prediction scoring systems: a retrospective single center analysis of 1150 patients.

PubMed

Joshi, Shreedhar S; Anthony, G; Manasa, D; Ashwini, T; Jagadeesh, A M; Borde, Deepak P; Bhat, Seetharam; Manjunath, C N

2014-01-01

To validate Aristotle basic complexity and Aristotle comprehensive complexity (ABC and ACC) and risk adjustment in congenital heart surgery-1 (RACHS-1) prediction models for in hospital mortality after surgery for congenital heart disease in a single surgical unit. Patients younger than 18 years, who had undergone surgery for congenital heart diseases from July 2007 to July 2013 were enrolled. Scoring for ABC and ACC scoring and assigning to RACHS-1 categories were done retrospectively from retrieved case files. Discriminative power of scoring systems was assessed with area under curve (AUC) of receiver operating curves (ROC). Calibration (test for goodness of fit of the model) was measured with Hosmer-Lemeshow modification of χ2 test. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were applied to assess reclassification. A total of 1150 cases were assessed with an all-cause in-hospital mortality rate of 7.91%. When modeled for multivariate regression analysis, the ABC (χ2 = 8.24, P = 0.08), ACC (χ2 = 4.17 , P = 0.57) and RACHS-1 (χ2 = 2.13 , P = 0.14) scores showed good overall performance. The AUC was 0.677 with 95% confidence interval (CI) of 0.61-0.73 for ABC score, 0.704 (95% CI: 0.64-0.76) for ACC score and for RACHS-1 it was 0.607 (95%CI: 0.55-0.66). ACC had an improved predictability in comparison to RACHS-1 and ABC on analysis with NRI and IDI. ACC predicted mortality better than ABC and RCAHS-1 models. A national database will help in developing predictive models unique to our populations, till then, ACC scoring model can be used to analyze individual performances and compare with other institutes.

Predicting stress urinary incontinence during pregnancy: combination of pelvic floor ultrasound parameters and clinical factors.

PubMed

Chen, Ling; Luo, Dan; Yu, Xiajuan; Jin, Mei; Cai, Wenzhi

2018-05-12

The aim of this study was to develop and validate a predictive tool that combining pelvic floor ultrasound parameters and clinical factors for stress urinary incontinence during pregnancy. A total of 535 women in first or second trimester were included for an interview and transperineal ultrasound assessment from two hospitals. Imaging data sets were analyzed offline to assess for bladder neck vertical position, urethra angles (α, β, and γ angles), hiatal area and bladder neck funneling. All significant continuous variables at univariable analysis were analyzed by receiver-operating characteristics. Three multivariable logistic models were built on clinical factor, and combined with ultrasound parameters. The final predictive model with best performance and fewest variables was selected to establish a nomogram. Internal and external validation of the nomogram were performed by both discrimination represented by C-index and calibration measured by Hosmer-Lemeshow test. A decision curve analysis was conducted to determine the clinical utility of the nomogram. After excluding 14 women with invalid data, 521 women were analyzed. β angle, γ angle and hiatal area had limited predictive value for stress urinary incontinence during pregnancy, with area under curves of 0.558-0.648. The final predictive model included body mass index gain since pregnancy, constipation, previous delivery mode, β angle at rest, and bladder neck funneling. The nomogram based on the final model showed good discrimination with a C-index of 0.789 and satisfactory calibration (P=0.828), both of which were supported by external validation. Decision curve analysis showed that the nomogram was clinical useful. The nomogram incorporating both the pelvic floor ultrasound parameters and clinical factors has been validated to show good discrimination and calibration, and could be an important tool for stress urinary incontinence risk prediction at an early stage of pregnancy. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Evaluation of CFD to Determine Two-Dimensional Airfoil Characteristics for Rotorcraft Applications

NASA Technical Reports Server (NTRS)

Smith, Marilyn J.; Wong, Tin-Chee; Potsdam, Mark; Baeder, James; Phanse, Sujeet

2004-01-01

The efficient prediction of helicopter rotor performance, vibratory loads, and aeroelastic properties still relies heavily on the use of comprehensive analysis codes by the rotorcraft industry. These comprehensive codes utilize look-up tables to provide two-dimensional aerodynamic characteristics. Typically these tables are comprised of a combination of wind tunnel data, empirical data and numerical analyses. The potential to rely more heavily on numerical computations based on Computational Fluid Dynamics (CFD) simulations has become more of a reality with the advent of faster computers and more sophisticated physical models. The ability of five different CFD codes applied independently to predict the lift, drag and pitching moments of rotor airfoils is examined for the SC1095 airfoil, which is utilized in the UH-60A main rotor. Extensive comparisons with the results of ten wind tunnel tests are performed. These CFD computations are found to be as good as experimental data in predicting many of the aerodynamic performance characteristics. Four turbulence models were examined (Baldwin-Lomax, Spalart-Allmaras, Menter SST, and k-omega).

Development of PRIME for irradiation performance analysis of U-Mo/Al dispersion fuel

NASA Astrophysics Data System (ADS)

Jeong, Gwan Yoon; Kim, Yeon Soo; Jeong, Yong Jin; Park, Jong Man; Sohn, Dong-Seong

2018-04-01

A prediction code for the thermo-mechanical performance of research reactor fuel (PRIME) has been developed with the implementation of developed models to analyze the irradiation behavior of U-Mo dispersion fuel. The code is capable of predicting the two-dimensional thermal and mechanical performance of U-Mo dispersion fuel during irradiation. A finite element method was employed to solve the governing equations for thermal and mechanical equilibria. Temperature- and burnup-dependent material properties of the fuel meat constituents and cladding were used. The numerical solution schemes in PRIME were verified by benchmarking solutions obtained using a commercial finite element analysis program (ABAQUS). The code was validated using irradiation data from RERTR, HAMP-1, and E-FUTURE tests. The measured irradiation data used in the validation were IL thickness, volume fractions of fuel meat constituents for the thermal analysis, and profiles of the plate thickness changes and fuel meat swelling for the mechanical analysis. The prediction results were in good agreement with the measurement data for both thermal and mechanical analyses, confirming the validity of the code.

Off-design computer code for calculating the aerodynamic performance of axial-flow fans and compressors

NASA Technical Reports Server (NTRS)

Schmidt, James F.

1995-01-01

An off-design axial-flow compressor code is presented and is available from COSMIC for predicting the aerodynamic performance maps of fans and compressors. Steady axisymmetric flow is assumed and the aerodynamic solution reduces to solving the two-dimensional flow field in the meridional plane. A streamline curvature method is used for calculating this flow-field outside the blade rows. This code allows for bleed flows and the first five stators can be reset for each rotational speed, capabilities which are necessary for large multistage compressors. The accuracy of the off-design performance predictions depend upon the validity of the flow loss and deviation correlation models. These empirical correlations for the flow loss and deviation are used to model the real flow effects and the off-design code will compute through small reverse flow regions. The input to this off-design code is fully described and a user's example case for a two-stage fan is included with complete input and output data sets. Also, a comparison of the off-design code predictions with experimental data is included which generally shows good agreement.

Estimation and prediction under local volatility jump-diffusion model

NASA Astrophysics Data System (ADS)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Influence of Wind Model Performance on Wave Forecasts of the Naval Oceanographic Office

NASA Astrophysics Data System (ADS)

Gay, P. S.; Edwards, K. L.

2017-12-01

Significant discrepancies between the Naval Oceanographic Office's significant wave height (SWH) predictions and observations have been noted in some model domains. The goal of this study is to evaluate these discrepancies and identify to what extent inaccuracies in the wind predictions may explain inaccuracies in SWH predictions. A one-year time series of data is evaluated at various locations in Southern California and eastern Florida. Correlations are generally quite good, ranging from 73% at Pendleton to 88% at both Santa Barbara, California, and Cape Canaveral, Florida. Correlations for month-long periods off Southern California drop off significantly in late spring through early autumn - less so off eastern Florida - likely due to weaker local wind seas and generally smaller SWH in addition to the influence of remotely-generated swell, which may not propagate accurately into and through the wave models. The results of this study suggest that it is likely that a change in meteorological and/or oceanographic conditions explains the change in model performance, partially as a result of a seasonal reduction in wind model performance in the summer months.

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

NASA Astrophysics Data System (ADS)

Sulea, Traian; Hogues, Hervé; Purisima, Enrico O.

2012-05-01

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

The use of neural network technology to model swimming performance.

PubMed

Silva, António José; Costa, Aldo Manuel; Oliveira, Paulo Moura; Reis, Victor Machado; Saavedra, José; Perl, Jurgen; Rouboa, Abel; Marinho, Daniel Almeida

2007-01-01

to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).

Development of a reactive-dispersive plume model

NASA Astrophysics Data System (ADS)

Kim, Hyun S.; Kim, Yong H.; Song, Chul H.

2017-04-01

A reactive-dispersive plume model (RDPM) was developed in this study. The RDPM can consider two main components of large-scale point source plume: i) turbulent dispersion and ii) photochemical reactions. In order to evaluate the simulation performance of newly developed RDPM, the comparisons between the model-predicted and observed mixing ratios were made using the TexAQS II 2006 (Texas Air Quality Study II 2006) power-plant experiment data. Statistical analyses show good correlation (0.61≤R≤0.92), and good agreement with the Index of Agreement (0.70≤R≤0.95). The chemical NOx lifetimes for two power-plant plumes (Monticello and Welsh power plants) were also estimated.

Comparison of Repeatability and Agreement between Swept-Source Optical Biometry and Dual-Scheimpflug Topography.

PubMed

Jung, Soyeon; Chin, Hee Seung; Kim, Na Rae; Lee, Kang Won; Jung, Ji Won

2017-01-01

To assess the repeatability and agreement of parameters obtained with two biometers and to compare the predictability. Biometry was performed on 101 eyes with cataract using the IOLMaster 700 and the Galilei G6. Three measurements were obtained per eye with each device, and repeatability was evaluated. The axial length (AL), anterior chamber depth (ACD), keratometry (K), white-to-white (WTW) corneal diameter, central corneal thickness (CCT), and lens thickness (LT) were measured and postoperative predictability was compared. Measurements could not be obtained with the IOLMaster 700 in one eye and in seven eyes with the Galilei G6 due to dense cataract. Both the IOLMaster 700 and Galilei G6 showed good repeatability, although the IOLMaster 700 showed better repeatability than the Galilei G6. There were no statistically significant differences in AL, ACD, steepest K, WTW, and LT ( P > 0.050), although flattest K, mean K, and CCT differed ( P < 0.050). The proportion of eyes with an absolute prediction error within 0.5 D was 85.0% for the IOLMaster 700 and was 80.0% for the Galilei G6 based on the SRK/T formula. Two biometers showed high repeatability and relatively good agreements. The swept-source optical biometer demonstrated better repeatability, penetration, and an overall lower prediction error.

Analysis backpropagation methods with neural network for prediction of children's ability in psychomotoric

NASA Astrophysics Data System (ADS)

Izhari, F.; Dhany, H. W.; Zarlis, M.; Sutarman

2018-03-01

A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.

Evaluating the complementary roles of an SJT and academic assessment for entry into clinical practice.

PubMed

Cousans, Fran; Patterson, Fiona; Edwards, Helena; Walker, Kim; McLachlan, John C; Good, David

2017-05-01

Although there is extensive evidence confirming the predictive validity of situational judgement tests (SJTs) in medical education, there remains a shortage of evidence for their predictive validity for performance of postgraduate trainees in their first role in clinical practice. Moreover, to date few researchers have empirically examined the complementary roles of academic and non-academic selection methods in predicting in-role performance. This is an important area of enquiry as despite it being common practice to use both types of methods within a selection system, there is currently no evidence that this approach translates into increased predictive validity of the selection system as a whole, over that achieved by the use of a single selection method. In this preliminary study, the majority of the range of scores achieved by successful applicants to the UK Foundation Programme provided a unique opportunity to address both of these areas of enquiry. Sampling targeted high (>80th percentile) and low (<20th percentile) scorers on the SJT. Supervisors rated 391 trainees' in-role performance, and incidence of remedial action was collected. SJT and academic performance scores correlated with supervisor ratings (r = .31 and .28, respectively). The relationship was stronger between the SJT and in-role performance for the low scoring group (r = .33, high scoring group r = .11), and between academic performance and in-role performance for the high scoring group (r = .29, low scoring group r = .11). Trainees with low SJT scores were almost five times more likely to receive remedial action. Results indicate that an SJT for entry into trainee physicians' first role in clinical practice has good predictive validity of supervisor-rated performance and incidence of remedial action. In addition, an SJT and a measure of academic performance appeared to be complementary to each other. These initial findings suggest that SJTs may be more predictive at the lower end of a scoring distribution, and academic attainment more predictive at the higher end.

A Hybrid Model for Spatially and Temporally Resolved Ozone Exposures in the Continental United States

PubMed Central

Di, Qian; Rowland, Sebastian; Koutrakis, Petros; Schwartz, Joel

2017-01-01

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term. PMID:27332675

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Relation between ultrasonic properties, rheology and baking quality for bread doughs of widely differing formulation.

PubMed

Peressini, Donatella; Braunstein, Dobrila; Page, John H; Strybulevych, Anatoliy; Lagazio, Corrado; Scanlon, Martin G

2017-06-01

The objective was to evaluate whether an ultrasonic reflectance technique has predictive capacity for breadmaking performance of doughs made under a wide range of formulation conditions. Two flours of contrasting dough strength augmented with different levels of ingredients (inulin, oil, emulsifier or salt) were used to produce different bread doughs with a wide range of properties. Breadmaking performance was evaluated by conventional large-strain rheological tests on the dough and by assessment of loaf quality. The ultrasound tests were performed with a broadband reflectance technique in the frequency range of 0.3-6 MHz. Principal component analysis showed that ultrasonic attenuation and phase velocity at frequencies between 0.3 and 3 MHz are good predictors for rheological and bread scoring characteristics. Ultrasonic parameters had predictive capacity for breadmaking performance for a wide range of dough formulations. Lower frequency attenuation coefficients correlated well with conventional quality indices of both the dough and the bread. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Comparison of simplified models in the prediction of two phase flow in pipelines

NASA Astrophysics Data System (ADS)

Jerez-Carrizales, M.; Jaramillo, J. E.; Fuentes, D.

2014-06-01

Prediction of two phase flow in pipelines is a common task in engineering. It is a complex phenomenon and many models have been developed to find an approximate solution to the problem. Some old models, such as the Hagedorn & Brown (HB) model, have been highlighted by many authors to give very good performance. Furthermore, many modifications have been applied to this method to improve its predictions. In this work two simplified models which are based on empiricism (HB and Mukherjee and Brill, MB) are considered. One mechanistic model which is based on the physics of the phenomenon (AN) and it still needs some correlations called closure relations is also used. Moreover, a drift flux model defined in steady state that is flow pattern dependent (HK model) is implemented. The implementation of these methods was tested using published data in the scientific literature for vertical upward flows. Furthermore, a comparison of the predictive performance of the four models is done against a well from Campo Escuela Colorado. Difference among four models is smaller than difference with experimental data from the well in Campo Escuela Colorado.

SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2017-02-01

Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub-compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub-organelle localizations is crucial for large-scale functional studies. In this paper we present SChloro, a novel machine-learning approach to predict protein sub-chloroplastic localization, based on targeting signal detection and membrane protein information. The proposed approach performs multi-label predictions discriminating six chloroplastic sub-compartments that include inner membrane, outer membrane, stroma, thylakoid lumen, plastoglobule and thylakoid membrane. In comparative benchmarks, the proposed method outperforms current state-of-the-art methods in both single- and multi-compartment predictions, with an overall multi-label accuracy of 74%. The results demonstrate the relevance of the approach that is eligible as a good candidate for integration into more general large-scale annotation pipelines of protein subcellular localization. The method is available as web server at http://schloro.biocomp.unibo.it gigi@biocomp.unibo.it.

Predictive factors for survival and correlation to toxicity in advanced Stage III non-small cell lung cancer patients with concurrent chemoradiation.

PubMed

Kim, Yong-Hyub; Ahn, Sung-Ja; Kim, Young-Chul; Kim, Kyu-Sik; Oh, In-Jae; Ban, Hee-Jung; Chung, Woong-Ki; Nam, Taek-Keun; Yoon, Mee Sun; Jeong, Jae-Uk; Song, Ju-Young

2016-02-01

Concurrent chemoradiotherapy is the standard treatment for locally advanced Stage III non-small cell lung cancer in patients with a good performance status and minimal weight loss. This study aimed to define subgroups with different survival outcomes and identify correlations with the radiation-related toxicities. We retrospectively reviewed 381 locally advanced Stage III non-small cell lung cancer patients with a good performance status or weight loss of <10% who received concurrent chemoradiotherapy between 2004 and 2011. Three-dimensional conformal radiotherapy was administered once daily, combined with weekly chemotherapy. The Kaplan-Meier method was used for survival comparison and Cox regression for multivariate analysis. Multivariate analysis was performed using all variables with P values <0.1 from the univariate analysis. Median survival of all patients was 24 months. Age > 75 years, the diffusion lung capacity for carbon monoxide ≤80%, gross tumor volume ≥100 cm(3) and subcarinal nodal involvement were the statistically significant predictive factors for poor overall survival both in univariate and multivariate analyses. Patients were classified into four groups according to these four predictive factors. The median survival times were 36, 29, 18 and 14 months in Groups I, II, III and IV, respectively (P < 0.001). Rates of esophageal or lung toxicity ≥Grade 3 were 5.9, 14.1, 12.5 and 22.2%, respectively. The radiotherapy interruption rate differed significantly between the prognostic subgroups; 8.8, 15.4, 22.7 and 30.6%, respectively (P = 0.017). Severe toxicity and interruption of radiotherapy were more frequent in patients with multiple adverse predictive factors. To maintain the survival benefit in patients with concurrent chemoradiotherapy, strategies to reduce treatment-related toxicities need to be deeply considered. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data.

PubMed

Groenendijk, Piet; Heinen, Marius; Klammler, Gernot; Fank, Johann; Kupfersberger, Hans; Pisinaras, Vassilios; Gemitzi, Alexandra; Peña-Haro, Salvador; García-Prats, Alberto; Pulido-Velazquez, Manuel; Perego, Alessia; Acutis, Marco; Trevisan, Marco

2014-11-15

The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test for 2005-2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009-2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates. Copyright © 2014 Elsevier B.V. All rights reserved.

Low Cost Gas Turbine Off-Design Prediction Technique

NASA Astrophysics Data System (ADS)

Martinjako, Jeremy

This thesis seeks to further explore off-design point operation of gas turbines and to examine the capabilities of GasTurb 12 as a tool for off-design analysis. It is a continuation of previous thesis work which initially explored the capabilities of GasTurb 12. The research is conducted in order to: 1) validate GasTurb 12 and, 2) predict off-design performance of the Garrett GTCP85-98D located at the Arizona State University Tempe campus. GasTurb 12 is validated as an off-design point tool by using the program to predict performance of an LM2500+ marine gas turbine. Haglind and Elmegaard (2009) published a paper detailing a second off-design point method and it includes the manufacturer's off-design point data for the LM2500+. GasTurb 12 is used to predict off-design point performance of the LM2500+ and compared to the manufacturer's data. The GasTurb 12 predictions show good correlation. Garrett has published specification data for the GTCP85-98D. This specification data is analyzed to determine the design point and to comment on off-design trends. Arizona State University GTCP85-98D off-design experimental data is evaluated. Trends presented in the data are commented on and explained. The trends match the expected behavior demonstrated in the specification data for the same gas turbine system. It was originally intended that a model of the GTCP85-98D be constructed in GasTurb 12 and used to predict off-design performance. The prediction would be compared to collected experimental data. This is not possible because the free version of GasTurb 12 used in this research does not have a module to model a single spool turboshaft. This module needs to be purchased for this analysis.

Load-related brain activation predicts spatial working memory performance in youth aged 9–12 and is associated with executive function at earlier ages

PubMed Central

Huang, Anna S.; Klein, Daniel N.; Leung, Hoi-Chung

2015-01-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9–12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. PMID:26562059

Load-related brain activation predicts spatial working memory performance in youth aged 9-12 and is associated with executive function at earlier ages.

PubMed

Huang, Anna S; Klein, Daniel N; Leung, Hoi-Chung

2016-02-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9-12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling and Analysis of Structural Dynamics for a One-Tenth Scale Model NGST Sunshield

NASA Technical Reports Server (NTRS)

Johnston, John; Lienard, Sebastien; Brodeur, Steve (Technical Monitor)

2001-01-01

New modeling and analysis techniques have been developed for predicting the dynamic behavior of the Next Generation Space Telescope (NGST) sunshield. The sunshield consists of multiple layers of pretensioned, thin-film membranes supported by deployable booms. Modeling the structural dynamic behavior of the sunshield is a challenging aspect of the problem due to the effects of membrane wrinkling. A finite element model of the sunshield was developed using an approximate engineering approach, the cable network method, to account for membrane wrinkling effects. Ground testing of a one-tenth scale model of the NGST sunshield were carried out to provide data for validating the analytical model. A series of analyses were performed to predict the behavior of the sunshield under the ground test conditions. Modal analyses were performed to predict the frequencies and mode shapes of the test article and transient response analyses were completed to simulate impulse excitation tests. Comparison was made between analytical predictions and test measurements for the dynamic behavior of the sunshield. In general, the results show good agreement with the analytical model correctly predicting the approximate frequency and mode shapes for the significant structural modes.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Rainfall Prediction of Indian Peninsula: Comparison of Time Series Based Approach and Predictor Based Approach using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Dash, Y.; Mishra, S. K.; Panigrahi, B. K.

2017-12-01

Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prognostic scores in cirrhotic patients admitted to a gastroenterology intensive care unit.

PubMed

Freire, Paulo; Romãozinho, José M; Amaro, Pedro; Ferreira, Manuela; Sofia, Carlos

2011-04-01

prognostic scores have been validated in cirrhotic patients admitted to general Intensive Care Units. No assessment of these scores was performed in cirrhotics admitted to specialized Gastroenterology Intensive Care Units (GICUs). to assess the prognostic accuracy of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Sequential Organ Failure Assessment (SOFA), Model for End-stage Liver Disease (MELD) and Child-Pugh-Turcotte (CPT) in predicting GICU mortality in cirrhotic patients. the study involved 124 consecutive cirrhotic admissions to a GICU. Clinical data, prognostic scores and mortality were recorded. Discrimination was evaluated with area under receiver operating characteristic curves (AUC). Calibration was assessed with Hosmer-Lemeshow goodness-of-fit test. GICU mortality was 9.7%. Mean APACHE II, SAPS II, SOFA, MELD and CPT scores for survivors (13.6, 25.4, 3.5,18.0 and 8.6, respectively) were found to be significantly lower than those of non-survivors (22.0, 47.5, 10.1, 30.7 and 12.5,respectively) (p < 0.001). All the prognostic systems showed good discrimination, with AUC = 0.860, 0.911, 0.868, 0.897 and 0.914 for APACHE II, SAPS II, SOFA, MELD and CPT, respectively. Similarly, APACHE II, SAPS II, SOFA, MELD and CPT scores achieved good calibration, with p = 0.146, 0.120, 0.686,0.267 and 0.120, respectively. The overall correctness of prediction was 81.9%, 86.1%, 93.3%, 90.7% and 87.7% for the APA-CHE II, SAPS II, SOFA, MELD and CPT scores, respectively. in cirrhotics admitted to a GICU, all the tested scores have good prognostic accuracy, with SOFA and MELD showing the greatest overall correctness of prediction.

CFD analysis of the STME nozzle flowfield

NASA Technical Reports Server (NTRS)

Krishnan, Anantha; Tucker, Kevin

1992-01-01

The Space Transportation Main Engine (STME) uses a gas generator cycle to cool the nozzle wall by a film-dump of the turbine exhaust. The ability to cool the skirt is a key concern in the design of the STME. CFD calculations were undertaken to predict the film cooling effectiveness and performance sensitivities for various configurations, operating points, and inlet conditions. The results presented here were obtained for the subscale nozzle. The computations were performed using REFLEQS. The computational analysis showed that a chemical equilibrium model was necessary to obtain correct predictions of the specific impulse. The frozen composition model underpredicts the ISP by about 6 percent. It was also observed that the coolant film was successful in maintaining the nozzle wall well below the stagnation temperature of the core flow. The effect of the coolant flow on the performance of the engine was found to be negligible. The computed heat fluxes at the wall were in good agreement with the empirical data obtained by Pratt and Whitney. Further test data from Pratt and Whitney are forthcoming for the subscale nozzle. Calculations will be performed to determine cooling efficiencies and nozzle performance over a range of conditions, and model predictions will be compared with experimental data. Information is given in viewgraph form.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

A Novel Model for Predicting Incident Moderate to Severe Anemia and Iron Deficiency in Patients with Newly Diagnosed Ulcerative Colitis.

PubMed

Khan, Nabeel; Patel, Dhruvan; Shah, Yash; Yang, Yu-Xiao

2017-05-01

Anemia and iron deficiency are common complications of ulcerative colitis (UC). We aimed to develop and internally validate a prediction model for the incidence of moderate to severe anemia and iron deficiency anemia (IDA) in newly diagnosed patients with UC. Multivariable logistic regression was performed among a nationwide cohort of patients who were newly diagnosed with UC in the VA health-care system. Model development was performed in a random two-third of the total cohort and then validated in the remaining one-third of the cohort. As candidate predictors, we examined routinely available data at the time of UC diagnosis including demographics, medications, laboratory results, and endoscopy findings. A total of 789 patients met the inclusion criteria. For the outcome of moderate to severe anemia, age, albumin level and mild anemia at UC diagnosis were predictors selected for the model. The AUC for this model was 0.69 (95% CI 0.64-0.74). For the outcome of moderate to severe anemia with evidence of iron deficiency, the predictors included African-American ethnicity, mild anemia, age, and albumin level at UC diagnosis. The AUC was 0.76, (95% CI 0.69-0.82). Calibration was consistently good in all models (Hosmer-Lemeshow goodness of fit p > 0.05). The models performed similarly in the internal validation cohort. We developed and internally validated a prognostic model for predicting the risk of moderate to severe anemia and IDA among newly diagnosed patients with UC. This will help identify patients at high risk of these complications, who could benefit from surveillance and preventive measures.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Evaluation of WRF-Predicted Near-Hub-Height Winds and Ramp Events over a Pacific Northwest Site with Complex Terrain

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

Yang, Qing; Berg, Larry K.; Pekour, Mikhail

The WRF model version 3.3 is used to simulate near hub-height winds and power ramps utilizing three commonly used planetary boundary-layer (PBL) schemes: Mellor-Yamada-Janjic (MYJ), University of Washington (UW), and Yonsei University (YSU). The predicted winds have small mean biases compared with observations. Power ramps and step changes (changes within an hour) consistently show that the UW scheme performed better in predicting up ramps under stable conditions with higher prediction accuracy and capture rates. Both YSU and UW scheme show good performance predicting up- and down- ramps under unstable conditions with YSU being slightly better for ramp durations longer thanmore » an hour. MYJ is the most successful simulating down-ramps under stable conditions. The high wind speed and large shear associated with low-level jets are frequently associated with power ramps, and the biases in predicted low-level jet explain some of the shown differences in ramp predictions among different PBL schemes. Low-level jets were observed as low as ~200 m in altitude over the Columbia Basin Wind Energy Study (CBWES) site, located in an area of complex terrain. The shear, low-level peak wind speeds, as well as the height of maximum wind speed are not well predicted. Model simulations with 3 PBL schemes show the largest variability among them under stable conditions.« less

Formulation of aerodynamic prediction techniques for hypersonic configuration design

NASA Technical Reports Server (NTRS)

1979-01-01

An investigation of approximate theoretical techniques for predicting aerodynamic characteristics and surface pressures for relatively slender vehicles at moderate hypersonic speeds was performed. Emphasis was placed on approaches that would be responsive to preliminary configuration design level of effort. Supersonic second order potential theory was examined in detail to meet this objective. Shock layer integral techniques were considered as an alternative means of predicting gross aerodynamic characteristics. Several numerical pilot codes were developed for simple three dimensional geometries to evaluate the capability of the approximate equations of motion considered. Results from the second order computations indicated good agreement with higher order solutions and experimental results for a variety of wing like shapes and values of the hypersonic similarity parameter M delta approaching one.

QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

PubMed

Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

2013-03-01

A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.

Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension.

PubMed

Ybarra Sagarduy, José Luis; Camacho Mata, Dacia Yurima; Moral de la Rubia, José; Piña López, Julio Alfonso; Yunes Zárraga, José Luis Masud

2018-01-01

It is widely known that physical activity is the key to the optimal management and clinical control of hypertension. This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension. This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior. Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data ( p BS =0.235, χ 2 / gl =1.519, Jöreskog and Sörbom's Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen's Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053). The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so that quality of life of patients with hypertension improves and they might be able to manage and control their disease well.

Poor performances of EuroSCORE and CARE score for prediction of perioperative mortality in octogenarians undergoing aortic valve replacement for aortic stenosis.

PubMed

Chhor, Vibol; Merceron, Sybille; Ricome, Sylvie; Baron, Gabriel; Daoud, Omar; Dilly, Marie-Pierre; Aubier, Benjamin; Provenchere, Sophie; Philip, Ivan

2010-08-01

Although results of cardiac surgery are improving, octogenarians have a higher procedure-related mortality and more complications with increased length of stay in ICU. Consequently, careful evaluation of perioperative risk seems necessary. The aims of our study were to assess and compare the performances of EuroSCORE and CARE score in the prediction of perioperative mortality among octogenarians undergoing aortic valve replacement for aortic stenosis and to compare these predictive performances with those obtained in younger patients. This retrospective study included all consecutive patients undergoing cardiac surgery in our institution between November 2005 and December 2007. For each patient, risk assessment for mortality was performed using logistic EuroSCORE, additive EuroSCORE and CARE score. The main outcome measure was early postoperative mortality. Predictive performances of these scores were assessed by calibration and discrimination using goodness-of-fit test and area under the receiver operating characteristic curve, respectively. During this 2-year period, we studied 2117 patients, among whom 134/211 octogenarians and 335/1906 nonoctogenarians underwent an aortic valve replacement for aortic stenosis. When considering patients with aortic stenosis, discrimination was poor in octogenarians and the difference from nonoctogenarians was significant for each score (0.58, 0.59 and 0.56 vs. 0.82, 0.81 and 0.77 for additive EuroSCORE, logistic EuroSCORE and CARE score in octogenarians and nonoctogenarians, respectively, P < 0.05). Moreover, in the whole cohort, logistic EuroSCORE significantly overestimated mortality among octogenarians. Predictive performances of these scores are poor in octogenarians undergoing cardiac surgery, especially aortic valve replacement. Risk assessment and therapeutic decisions in octogenarians should not be made with these scoring systems alone.

Risk prediction models of breast cancer: a systematic review of model performances.

PubMed

Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

2012-05-01

The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

PubMed

Muley, Vijaykumar Yogesh; Ranjan, Akash

2012-01-01

Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.

Strategic Adaptation to Task Characteristics, Incentives, and Individual Differences in Dual-Tasking

PubMed Central

Janssen, Christian P.; Brumby, Duncan P.

2015-01-01

We investigate how good people are at multitasking by comparing behavior to a prediction of the optimal strategy for dividing attention between two concurrent tasks. In our experiment, 24 participants had to interleave entering digits on a keyboard with controlling a randomly moving cursor with a joystick. The difficulty of the tracking task was systematically varied as a within-subjects factor. Participants were also exposed to different explicit reward functions that varied the relative importance of the tracking task relative to the typing task (between-subjects). Results demonstrate that these changes in task characteristics and monetary incentives, together with individual differences in typing ability, influenced how participants choose to interleave tasks. This change in strategy then affected their performance on each task. A computational cognitive model was used to predict performance for a wide set of alternative strategies for how participants might have possibly interleaved tasks. This allowed for predictions of optimal performance to be derived, given the constraints placed on performance by the task and cognition. A comparison of human behavior with the predicted optimal strategy shows that participants behaved near optimally. Our findings have implications for the design and evaluation of technology for multitasking situations, as consideration should be given to the characteristics of the task, but also to how different users might use technology depending on their individual characteristics and their priorities. PMID:26161851

Prediction of the mass gain during high temperature oxidation of aluminized nanostructured nickel using adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Hayati, M.; Rashidi, A. M.; Rezaei, A.

2012-10-01

In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.

Associations between meteorological parameters and influenza activity in a subtropical country: Case of five sentinel sites in Yaoundé-Cameroon

PubMed Central

Monamele, Gwladys C.; Vernet, Marie-Astrid; Nsaibirni, Robert F. J.; Bigna, Jean Joel R.; Kenmoe, Sebastien; Njankouo, Mohamadou Ripa

2017-01-01

Influenza is associated with highly contagious respiratory infections. Previous research has found that influenza transmission is often associated with climate variables especially in temperate regions. This study was performed in order to fill the gap of knowledge regarding the relationship between incidence of influenza and three meteorological parameters (temperature, rainfall and humidity) in a tropical setting. This was a retrospective study performed in Yaoundé-Cameroon from January 2009 to November 2015. Weekly proportions of confirmed influenza cases from five sentinel sites were considered as dependent variables, whereas weekly values of mean temperature, average relative humidity and accumulated rainfall were considered as independent variables. A univariate linear regression model was used in determining associations between influenza activity and weather covariates. A time-series method was used to predict on future values of influenza activity. The data was divided into 2 parts; the first 71 months were used to calibrate the model, and the last 12 months to test for prediction. Overall, there were 1173 confirmed infections with influenza virus. Linear regression analysis showed that there was no statistically significant association observed between influenza activity and weather variables. Very weak relationships (-0.1 < r < 0.1) were observed. Three prediction models were obtained for the different viral types (overall positive, Influenza A and Influenza B). Model 1 (overall influenza) and model 2 (influenza A) fitted well during the estimation period; however, they did not succeed to make good forecasts for predictions. Accumulated rainfall was the only external covariate that enabled good fit of both models. Based on the stationary R2, 29.5% and 41.1% of the variation in the series can be explained by model 1 and 2, respectively. This study laid more emphasis on the fact that influenza in Cameroon is characterized by year-round activity. The meteorological variables selected in this study did not enable good forecast of future influenza activity and certainly acted as proxies to other factors not considered, such as, UV radiation, absolute humidity, air quality and wind. PMID:29088290

Predicting links based on knowledge dissemination in complex network

NASA Astrophysics Data System (ADS)

Zhou, Wen; Jia, Yifan

2017-04-01

Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method-knowledge dissemination based link prediction (KDLP)-is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

Confronting uncertainty in flood damage predictions

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno

2015-04-01

Reliable flood damage models are a prerequisite for the practical usefulness of the model results. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005 and 2006, in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Bispectral index to predict neurological outcome early after cardiac arrest.

PubMed

Stammet, Pascal; Collignon, Olivier; Werer, Christophe; Sertznig, Claude; Devaux, Yvan

2014-12-01

To address the value of continuous monitoring of bispectral index (BIS) to predict neurological outcome after cardiac arrest. In this prospective observational study in adult comatose patients treated by therapeutic hypothermia after cardiac arrest we measured bispectral index (BIS) during the first 24 hours of intensive care unit stay. A blinded neurological outcome assessment by cerebral performance category (CPC) was done 6 months after cardiac arrest. Forty-six patients (48%) had a good neurological outcome at 6-month, as defined by a cerebral performance category (CPC) 1-2, and 50 patients (52%) had a poor neurological outcome (CPC 3-5). Over the 24h of monitoring, mean BIS values over time were higher in the good outcome group (38 ± 9) compared to the poor outcome group (17 ± 12) (p<0.001). Analysis of BIS recorded every 30 minutes provided an optimal prediction after 12.5h, with an area under the receiver operating characteristic curve (AUC) of 0.89, a specificity of 89% and a sensitivity of 86% using a cut-off value of 23. With a specificity fixed at 100% (sensitivity 26%) the cut-off BIS value was 2.4 over the first 271 minutes. In multivariable analyses including clinical characteristics, mean BIS value over the first 12.5h was a predictor of neurological outcome (p = 6E-6) and provided a continuous net reclassification index of 1.28% (p = 4E-10) and an integrated discrimination improvement of 0.31 (p=1E-10). Mean BIS value calculated over the first 12.5h after ICU admission potentially predicts 6-months neurological outcome after cardiac arrest. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Validation of the Canada Acute Coronary Syndrome Risk Score for Hospital Mortality in the Gulf Registry of Acute Coronary Events-2.

PubMed

AlFaleh, Hussam F; Alsheikh-Ali, Alawi A; Ullah, Anhar; AlHabib, Khalid F; Hersi, Ahmad; Suwaidi, Jassim Al; Sulaiman, Kadhim; Saif, Shukri Al; Almahmeed, Wael; Asaad, Nidal; Amin, Haitham; Al-Motarreb, Ahmed; Kashour, Tarek

2015-09-01

Several risk scores have been developed for acute coronary syndrome (ACS) patients, but their use is limited by their complexity. The new Canada Acute Coronary Syndrome (C-ACS) risk score is a simple risk-assessment tool for ACS patients. This study assessed the performance of the C-ACS risk score in predicting hospital mortality in a contemporary Middle Eastern ACS cohort. The C-ACS score accurately predicts hospital mortality in ACS patients. The baseline risk of 7929 patients from 6 Arab countries who were enrolled in the Gulf RACE-2 registry was assessed using the C-ACS risk score. The score ranged from 0 to 4, with 1 point assigned for the presence of each of the following variables: age ≥75 years, Killip class >1, systolic blood pressure <100 mm Hg, and heart rate >100 bpm. The discriminative ability and calibration of the score were assessed using C statistics and goodness-of-fit tests, respectively. The C-ACS score demonstrated good predictive values for hospital mortality in all ACS patients with a C statistic of 0.77 (95% confidence interval [CI]: 0.74-0.80) and in ST-segment elevation myocardial infarction and non-ST-segment elevation acute coronary syndrome patients (C statistic: 0.76, 95% CI: 0.73-0.79; and C statistic: 0.80, 95% CI: 0.75-0.84, respectively). The discriminative ability of the score was moderate regardless of age category, nationality, and diabetic status. Overall, calibration was optimal in all subgroups. The new C-ACS score performed well in predicting hospital mortality in a contemporary ACS population outside North America. © 2015 Wiley Periodicals, Inc.

Influences on emergency department length of stay for older people.

PubMed

Street, Maryann; Mohebbi, Mohammadreza; Berry, Debra; Cross, Anthony; Considine, Julie

2018-02-14

The aim of this study was to examine the influences on emergency department (ED) length of stay (LOS) for older people and develop a predictive model for an ED LOS more than 4 h. This retrospective cohort study used organizational data linkage at the patient level from a major Australian health service. The study population was aged 65 years or older, attending an ED during the 2013/2014 financial year. We developed and internally validated a clinical prediction rule. Discriminatory performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. An integer-based risk score was developed using multivariate logistic regression. The risk score was evaluated using ROC analysis. There were 33 926 ED attendances: 57.5% (n=19 517) had an ED LOS more than 4 h. The area under ROC for age, usual accommodation, triage category, arrival by ambulance, arrival overnight, imaging, laboratory investigations, overcrowding, time to be seen by doctor, ED visits with admission and access block relating to ED LOS more than 4 h was 0.796, indicating good performance. In the validation set, area under ROC was 0.80, P-value was 0.36 and prediction mean square error was 0.18, indicating good calibration. The risk score value attributed to each risk factor ranged from 2 to 68 points. The clinical prediction rule stratified patients into five levels of risk on the basis of the total risk score. Objective identification of older people at intermediate and high risk of an ED LOS more than 4 h early in ED care enables targeted approaches to streamline the patient journey, decrease ED LOS and optimize emergency care for older people.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim.

PubMed

Sjögren, Erik; Westergren, Jan; Grant, Iain; Hanisch, Gunilla; Lindfors, Lennart; Lennernäs, Hans; Abrahamsson, Bertil; Tannergren, Christer

2013-07-16

Oral drug delivery is the predominant administration route for a major part of the pharmaceutical products used worldwide. Further understanding and improvement of gastrointestinal drug absorption predictions is currently a highly prioritized area of research within the pharmaceutical industry. The fraction absorbed (fabs) of an oral dose after administration of a solid dosage form is a key parameter in the estimation of the in vivo performance of an orally administrated drug formulation. This study discloses an evaluation of the predictive performance of the mechanistic physiologically based absorption model GI-Sim. GI-Sim deploys a compartmental gastrointestinal absorption and transit model as well as algorithms describing permeability, dissolution rate, salt effects, partitioning into micelles, particle and micelle drifting in the aqueous boundary layer, particle growth and amorphous or crystalline precipitation. Twelve APIs with reported or expected absorption limitations in humans, due to permeability, dissolution and/or solubility, were investigated. Predictions of the intestinal absorption for different doses and formulations were performed based on physicochemical and biopharmaceutical properties, such as solubility in buffer and simulated intestinal fluid, molecular weight, pK(a), diffusivity and molecule density, measured or estimated human effective permeability and particle size distribution. The performance of GI-Sim was evaluated by comparing predicted plasma concentration-time profiles along with oral pharmacokinetic parameters originating from clinical studies in healthy individuals. The capability of GI-Sim to correctly predict impact of dose and particle size as well as the in vivo performance of nanoformulations was also investigated. The overall predictive performance of GI-Sim was good as >95% of the predicted pharmacokinetic parameters (C(max) and AUC) were within a 2-fold deviation from the clinical observations and the predicted plasma AUC was within one standard deviation of the observed mean plasma AUC in 74% of the simulations. GI-Sim was also able to correctly capture the trends in dose- and particle size dependent absorption for the study drugs with solubility and dissolution limited absorption, respectively. In addition, GI-Sim was also shown to be able to predict the increase in absorption and plasma exposure achieved with nanoformulations. Based on the results, the performance of GI-Sim was shown to be suitable for early risk assessment as well as to guide decision making in pharmaceutical formulation development. Copyright © 2013 Elsevier B.V. All rights reserved.

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

PubMed

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-01

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

Parallel protein secondary structure prediction based on neural networks.

PubMed

Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

2004-01-01

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

Application of GA-SVM method with parameter optimization for landslide development prediction

NASA Astrophysics Data System (ADS)

Li, X. Z.; Kong, J. M.

2013-10-01

Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

Evaluation of icing drag coefficient correlations applied to iced propeller performance prediction

NASA Technical Reports Server (NTRS)

Miller, Thomas L.; Shaw, R. J.; Korkan, K. D.

1987-01-01

Evaluation of three empirical icing drag coefficient correlations is accomplished through application to a set of propeller icing data. The various correlations represent the best means currently available for relating drag rise to various flight and atmospheric conditions for both fixed-wing and rotating airfoils, and the work presented here ilustrates and evaluates one such application of the latter case. The origins of each of the correlations are discussed, and their apparent capabilities and limitations are summarized. These correlations have been made to be an integral part of a computer code, ICEPERF, which has been designed to calculate iced propeller performance. Comparison with experimental propeller icing data shows generally good agreement, with the quality of the predicted results seen to be directly related to the radial icing extent of each case. The code's capability to properly predict thrust coefficient, power coefficient, and propeller efficiency is shown to be strongly dependent on the choice of correlation selected, as well as upon proper specificatioon of radial icing extent.

Prediction of Cognitive Performance and Subjective Sleepiness Using a Model of Arousal Dynamics.

PubMed

Postnova, Svetlana; Lockley, Steven W; Robinson, Peter A

2018-04-01

A model of arousal dynamics is applied to predict objective performance and subjective sleepiness measures, including lapses and reaction time on a visual Performance Vigilance Test (vPVT), performance on a mathematical addition task (ADD), and the Karolinska Sleepiness Scale (KSS). The arousal dynamics model is comprised of a physiologically based flip-flop switch between the wake- and sleep-active neuronal populations and a dynamic circadian oscillator, thus allowing prediction of sleep propensity. Published group-level experimental constant routine (CR) and forced desynchrony (FD) data are used to calibrate the model to predict performance and sleepiness. Only the studies using dim light (<15 lux) during alertness measurements and controlling for sleep and entrainment before the start of the protocol are selected for modeling. This is done to avoid the direct alerting effects of light and effects of prior sleep debt and circadian misalignment on the data. The results show that linear combination of circadian and homeostatic drives is sufficient to predict dynamics of a variety of sleepiness and performance measures during CR and FD protocols, with sleep-wake cycles ranging from 20 to 42.85 h and a 2:1 wake-to-sleep ratio. New metrics relating model outputs to performance and sleepiness data are developed and tested against group average outcomes from 7 (vPVT lapses), 5 (ADD), and 8 (KSS) experimental protocols, showing good quantitative and qualitative agreement with the data (root mean squared error of 0.38, 0.19, and 0.35, respectively). The weights of the homeostatic and circadian effects are found to be different between the measures, with KSS having stronger homeostatic influence compared with the objective measures of performance. Using FD data in addition to CR data allows us to challenge the model in conditions of both acute sleep deprivation and structured circadian misalignment, ensuring that the role of the circadian and homeostatic drives in performance is properly captured.

Theoretical branching ratios for the 5I7 to 5I7 levels of Ho(3+) in the garnets A3B2C3O12 (A = Y,La,Lu,Gd; B = Al,Lu,Sc,Ga; C = Al,Ga)

NASA Technical Reports Server (NTRS)

Filer, Elizabeth D.; Morrison, Clyde A.; Turner, Gregory A.; Barnes, Norman P.

1991-01-01

Results are reported from an experimental study investigating triply ionized holmium in 10 garnets using the point-change model to predict theoretical energy levels and temperature-dependent branching ratios for the 5I7 to 5I8 manifolds for temperatures between 50 and 400 K. Plots were made for the largest lines at 300 K. YScAG was plotted twice, once for each set of X-ray data available. Energy levels are predicted based on theoretical crystal-field parameters, and good agreement to experiment is found. It is suggested that the present set of theoretical crystal-field parameters provides good estimates of the energy levels for the other hosts on which there are no experimental optical data. X-ray and index-of-refraction data are used to evaluate the performance of 10 lasers via a quantum mechanical model to predict the position of the energy levels and the temperature-dependent branching rations of the 5I7 to 5I8 levels of holmium. The fractional population inversion required for threshold is also evaluated.

Microstructure Evolution and Flow Stress Model of a 20Mn5 Hollow Steel Ingot during Hot Compression

PubMed Central

Liu, Min; Ma, Qing-Xian; Luo, Jian-Bin

2018-01-01

20Mn5 steel is widely used in the manufacture of heavy hydro-generator shaft due to its good performance of strength, toughness and wear resistance. However, the hot deformation and recrystallization behaviors of 20Mn5 steel compressed under high temperature were not studied. In this study, the hot compression experiments under temperatures of 850–1200 °C and strain rates of 0.01/s–1/s are conducted using Gleeble thermal and mechanical simulation machine. And the flow stress curves and microstructure after hot compression are obtained. Effects of temperature and strain rate on microstructure are analyzed. Based on the classical stress-dislocation relation and the kinetics of dynamic recrystallization, a two-stage constitutive model is developed to predict the flow stress of 20Mn5 steel. Comparisons between experimental flow stress and predicted flow stress show that the predicted flow stress values are in good agreement with the experimental flow stress values, which indicates that the proposed constitutive model is reliable and can be used for numerical simulation of hot forging of 20Mn5 hollow steel ingot. PMID:29561826

Protein single-model quality assessment by feature-based probability density functions.

PubMed

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

PubMed Central

Genders, Tessa S. S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; van Mieghem, Carlos A. G.; de Feyter, Pim J.

2010-01-01

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. PMID:20559838

Modelling of a stirling cryocooler regenerator under steady and steady - periodic flow conditions using a correlation based method

NASA Astrophysics Data System (ADS)

Kishor Kumar, V. V.; Kuzhiveli, B. T.

2017-12-01

The performance of a Stirling cryocooler depends on the thermal and hydrodynamic properties of the regenerator in the system. CFD modelling is the best technique to design and predict the performance of a Stirling cooler. The accuracy of the simulation results depend on the hydrodynamic and thermal transport parameters used as the closure relations for the volume averaged governing equations. A methodology has been developed to quantify the viscous and inertial resistance terms required for modelling the regenerator as a porous medium in Fluent. Using these terms, the steady and steady - periodic flow of helium through regenerator was modelled and simulated. Comparison of the predicted and experimental pressure drop reveals the good predictive power of the correlation based method. For oscillatory flow, the simulation could predict the exit pressure amplitude and the phase difference accurately. Therefore the method was extended to obtain the Darcy permeability and Forchheimer’s inertial coefficient of other wire mesh matrices applicable to Stirling coolers. Simulation of regenerator using these parameters will help to better understand the thermal and hydrodynamic interactions between working fluid and the regenerator material, and pave the way to contrive high performance, ultra-compact free displacers used in miniature Stirling cryocoolers in the future.

Diamond Tool Specific Wear Rate Assessment in Granite Machining by Means of Knoop Micro-Hardness and Process Parameters

NASA Astrophysics Data System (ADS)

Goktan, R. M.; Gunes Yılmaz, N.

2017-09-01

The present study was undertaken to investigate the potential usability of Knoop micro-hardness, both as a single parameter and in combination with operational parameters, for sawblade specific wear rate (SWR) assessment in the machining of ornamental granites. The sawing tests were performed on different commercially available granite varieties by using a fully instrumented side-cutting machine. During the sawing tests, two fundamental productivity parameters, namely the workpiece feed rate and cutting depth, were varied at different levels. The good correspondence observed between the measured Knoop hardness and SWR values for different operational conditions indicates that it has the potential to be used as a rock material property that can be employed in preliminary wear estimations of diamond sawblades. Also, a multiple regression model directed to SWR prediction was developed which takes into account the Knoop hardness, cutting depth and workpiece feed rate. The relative contribution of each independent variable in the prediction of SWR was determined by using test statistics. The prediction accuracy of the established model was checked against new observations. The strong prediction performance of the model suggests that its framework may be applied to other granites and operational conditions for quantifying or differentiating the relative wear performance of diamond sawblades.

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

PubMed

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (p<.001), associations with productivity (r=.51), mental health (r=.48), and distress (r=.47). The screener (WFS-H) had good predictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

Relatedness predicts multiple measures of investment in cooperative nest construction in sociable weavers

PubMed Central

Leighton, Gavin M.; Echeverri, Sebastian; Heinrich, Dirk; Kolberg, Holger

2015-01-01

Although communal goods are often critical to society, they are simultaneously susceptible to exploitation and are evolutionarily stable only if mechanisms exist to curtail exploitation. Mechanisms such as punishment and kin selection have been offered as general explanations for how communal resources can be maintained. Evidence for these mechanisms comes largely from humans and social insects, leaving their generality in question. To assess how communal resources are maintained, we observed cooperative nest construction in sociable weavers (Philetairus socius). The communal nest of sociable weavers provides thermal benefits for all individuals but requires continual maintenance. We observed cooperative nest construction and also recorded basic morphological characteristics. We also collected blood samples, performed next-generation sequencing, and isolated 2358 variable single nucleotide polymorphisms (SNPs) to estimate relatedness. We find that relatedness predicts investment in cooperative nest construction, while no other morphological characters significantly explain cooperative output. We argue that indirect benefits are a critical fitness component for maintaining the cooperative behavior that maintains the communal good. PMID:26726282

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Flowfield Analysis of a Small Entry Probe (SPRITE) Tested in an Arc Jet

NASA Technical Reports Server (NTRS)

Prabhu, Dinesh K.

2011-01-01

Results of simulations of flow of an arc-heated stream around a 14-inch diameter 45 sphere-cone configuration are presented. Computations are first benchmarked against pressure and heat flux measurements made using copper slug calorimeters of different shapes and sizes. The influence of catalycity of copper on computed results is investigated. Good agreements between predictions and measurements are obtained by assuming the copper slug to be partially catalytic to atomic recombination. With total enthalpy estimates obtained from these preliminary computations, calculations are then performed for the test article, with the nozzle and test article considered as an integrated whole the same procedure adopted for calorimeter simulations. The resulting heat fluxes at select points on the test article (points at which fully instrumented plugs were placed) are used in material thermal response code calculations. Predicted time histories of temperature are compared against thermocouple data from the instrumented plugs, and recession determined. Good agreement is obtained for in-depth thermocouples.

Development of a modified independent parallel reactions kinetic model and comparison with the distributed activation energy model for the pyrolysis of a wide variety of biomass fuels.

PubMed

Sfakiotakis, Stelios; Vamvuka, Despina

2015-12-01

The pyrolysis of six waste biomass samples was studied and the fuels were kinetically evaluated. A modified independent parallel reactions scheme (IPR) and a distributed activation energy model (DAEM) were developed and their validity was assessed and compared by checking their accuracy of fitting the experimental results, as well as their prediction capability in different experimental conditions. The pyrolysis experiments were carried out in a thermogravimetric analyzer and a fitting procedure, based on least squares minimization, was performed simultaneously at different experimental conditions. A modification of the IPR model, considering dependence of the pre-exponential factor on heating rate, was proved to give better fit results for the same number of tuned kinetic parameters, comparing to the known IPR model and very good prediction results for stepwise experiments. Fit of calculated data to the experimental ones using the developed DAEM model was also proved to be very good. Copyright © 2015 Elsevier Ltd. All rights reserved.

New Thiazolyl-triazole Schiff Bases: Synthesis and Evaluation of the Anti-Candida Potential.

PubMed

Stana, Anca; Enache, Alexandra; Vodnar, Dan Cristian; Nastasă, Cristina; Benedec, Daniela; Ionuț, Ioana; Login, Cezar; Marc, Gabriel; Oniga, Ovidiu; Tiperciuc, Brîndușa

2016-11-22

In the context of the dangerous phenomenon of fungal resistance to the available therapies, we present here the chemical synthesis of a new series of thiazolyl-triazole Schiff bases B1 - B15 , which were in vitro assessed for their anti- Candida potential. Compound B10 was found to be more potent against Candida spp. when compared with the reference drugs Fluconazole and Ketoconazole. A docking study of the newly synthesized Schiff bases was performed, and results showed good binding affinity in the active site of co-crystallized Itraconazole-lanosterol 14α-demethylase isolated from Saccharomyces cerevisiae . An in silico ADMET (absorption, distribution, metabolism, excretion, toxicity) study was done in order to predict some pharmacokinetic and pharmacotoxicological properties. The Schiff bases showed good drug-like properties. The results of in vitro anti- Candida activity, a docking study and ADMET prediction revealed that the newly synthesized compounds have potential anti- Candida activity and evidenced the most active derivative, B10 , which can be further optimized as a lead compound.

Validation of High Frequency (HF) Propagation Prediction Models in the Arctic region

NASA Astrophysics Data System (ADS)

Athieno, R.; Jayachandran, P. T.

2014-12-01

Despite the emergence of modern techniques for long distance communication, Ionospheric communication in the high frequency (HF) band (3-30 MHz) remains significant to both civilian and military users. However, the efficient use of the ever-varying ionosphere as a propagation medium is dependent on the reliability of ionospheric and HF propagation prediction models. Most available models are empirical implying that data collection has to be sufficiently large to provide good intended results. The models we present were developed with little data from the high latitudes which necessitates their validation. This paper presents the validation of three long term High Frequency (HF) propagation prediction models over a path within the Arctic region. Measurements of the Maximum Usable Frequency for a 3000 km range (MUF (3000) F2) for Resolute, Canada (74.75° N, 265.00° E), are obtained from hand-scaled ionograms generated by the Canadian Advanced Digital Ionosonde (CADI). The observations have been compared with predictions obtained from the Ionospheric Communication Enhanced Profile Analysis Program (ICEPAC), Voice of America Coverage Analysis Program (VOACAP) and International Telecommunication Union Recommendation 533 (ITU-REC533) for 2009, 2011, 2012 and 2013. A statistical analysis shows that the monthly predictions seem to reproduce the general features of the observations throughout the year though it is more evident in the winter and equinox months. Both predictions and observations show a diurnal and seasonal variation. The analysed models did not show large differences in their performances. However, there are noticeable differences across seasons for the entire period analysed: REC533 gives a better performance in winter months while VOACAP has a better performance for both equinox and summer months. VOACAP gives a better performance in the daily predictions compared to ICEPAC though, in general, the monthly predictions seem to agree more with the observations compared to the daily predictions.

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

PubMed Central

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

2014-01-01

Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

Ratio of ovarian stroma and total ovarian area by ultrasound in prediction of hyperandrogenemia in reproductive-aged Thai women with polycystic ovary syndrome: a diagnostic test.

PubMed

Leerasiri, Pichai; Wongwananuruk, Thanyarat; Rattanachaiyanont, Manee; Indhavivadhana, Suchada; Techatraisak, Kitirat; Angsuwathana, Surasak

2015-02-01

To evaluate the performance of ovarian stromal area to total ovarian area (S/A) ratio for the prediction of biochemical hyperandrogenism in Thai women with polycystic ovary syndrome (PCOS). A cross-sectional study was performed in 222 reproductive-aged Thai women with PCOS attending the Gynecologic Endocrinology Unit (GEU), Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital from May 2007 to January 2009. The patients were interviewed for medical history and examined for anthropometry and clinical hyperandrogenism. Venous blood samples were obtained for androgen profiles. An ovarian ultrasonogram was obtained via transvaginal or transrectal ultrasonography. The prevalences of clinical and biochemical hyperandrogenism were 48.6% and 81.1%, respectively. The S/A ratio at a cut-off point of 0.33 had modest predictability for hyperandrogenism, namely, 0.537 area under the receiver-operator curve, 36.6% sensitivity, 72.1% specificity, 83.8% positive predictive value (PPV) and 20.9% negative predictive value (NPV). The combination of clinical hyperandrogenism and S/A ratio improved the predictability for biochemical hyperandrogenism, with sensitivity, specificity, PPV and NPV of 72.1%, 58.1%, 87.8% and 33.3%, respectively. The S/A ratio alone is not a good predictor for biochemical hyperandrogenism in Thai PCOS women attending GEU for menstrual dysfunction. The combination of S/A ratio and clinical hyperandrogenism has better performance than the S/A ratio alone to predict biochemical hyperandrogenism. © 2014 The Authors. Journal of Obstetrics and Gynaecology Research © 2014 Japan Society of Obstetrics and Gynecology.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Temperature-dependent poroelastic and viscoelastic effects on microscale-modelling of seismic reflections in heavy oil reservoirs

NASA Astrophysics Data System (ADS)

Ciz, Radim; Saenger, Erik H.; Gurevich, Boris; Shapiro, Serge A.

2009-03-01

We develop a new model for elastic properties of rocks saturated with heavy oil. The heavy oil is represented by a viscoelastic material, which at low frequencies and/or high temperatures behaves as a Newtonian fluid, and at high frequencies and/or low temperatures as a nearly elastic solid. The bulk and shear moduli of a porous rock saturated with such viscoelastic material are then computed using approximate extended Gassmann equations of Ciz and Shapiro by replacing the elastic moduli of the pore filling material with complex and frequency-dependent moduli of the viscoelastic pore fill. We test the proposed model by comparing its predictions with numerical simulations based on a direct finite-difference solution of equations of dynamic viscoelasticity. The simulations are performed for the reflection coefficient from an interface between a homogeneous fluid and a porous medium. The numerical tests are performed both for an idealized porous medium consisting of alternating solid and viscoelastic layers, and for a more realistic 3-D geometry of the pore space. Both sets of numerical tests show a good agreement between the predictions of the proposed viscoelastic workflow and numerical simulations for relatively high viscosities where viscoelastic effects are important. The results confirm that application of extended Gassmann equations in conjunction with the complex and frequency-dependent moduli of viscoelastic pore filling material, such as heavy oil, provides a good approximation for the elastic moduli of rocks saturated with such material. By construction, this approximation is exactly consistent with the classical Gassmann's equation for sufficiently low frequencies or high temperature when heavy oil behaves like a fluid. For higher frequencies and/or lower temperatures, the predictions are in good agreement with the direct numerical solution of equations of dynamic viscoelasticity on the microscale. This demonstrates that the proposed methodology provides realistic estimates of elastic properties of heavy oil rocks.

Effects of Cerebral Blood Flow and Vessel Conditions on Speech Recognition in Patients With Postlingual Adult Cochlear Implant: Predictable Factors for the Efficacy of Cochlear Implant.

PubMed

Ishino, Takashi; Ragaee, Mahmoud Ali; Maruhashi, Tatsuya; Kajikawa, Masato; Higashi, Yukihito; Sonoyama, Toru; Takeno, Sachio; Hirakawa, Katsuhiro

Cochlear implantation (CI) has been the most successful procedure for restoring hearing in a patient with severe and profound hearing loss. However, possibly owing to the variable brain functions of each patient, its performance and the associated patient satisfaction are widely variable. The authors hypothesize that peripheral and cerebral circulation can be assessed by noninvasive and globally available methods, yielding superior presurgical predictive factors of the performance of CI in adult patients with postlingual hearing loss who are scheduled to undergo CI. Twenty-two adult patients with cochlear implants for postlingual hearing loss were evaluated using Doppler sonography measurement of the cervical arteries (reflecting cerebral blood flow), flow-mediated dilation (FMD; reflecting the condition of cerebral arteries), and their pre-/post-CI best score on a monosyllabic discrimination test (pre-/post-CI best monosyllabic discrimination [BMD] score). Correlations between post-CI BMD score and the other factors were examined using univariate analysis and stepwise multiple linear regression analysis. The prediction factors were calculated by examining the receiver-operating characteristic curve between post-CI BMD score and the significantly positively correlated factors. Age and duration of deafness had a moderately negative correlation. The mean velocity of the internal carotid arteries and FMD had a moderate-to-strong positive correlation with the post-CI BMD score in univariate analysis. Stepwise multiple linear regression analysis revealed that only FMD was significantly positively correlated with post-CI BMD score. Analysis of the receiver-operating characteristic curve showed that a FMD cutoff score of 1.8 significantly predicted post-CI BMD score. These data suggest that FMD is a convenient, noninvasive, and widely available tool for predicting the efficacy of cochlear implants. An FMD cutoff score of 1.8 could be a good index for determining whether patients will hear well with cochlear implants. It could also be used to predict whether cochlear implants will provide good speech recognition benefits to candidates, even if their speech discrimination is poor. This FMD index could become a useful predictive tool for candidates with poor speech discrimination to determine the efficacy of CI before surgery.

Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies.

PubMed

Dalessandro, Brian; Hook, Rod; Perlich, Claudia; Provost, Foster

2015-06-01

Online systems promise to improve advertisement targeting via the massive and detailed data available. However, there often is too few data on exactly the outcome of interest, such as purchases, for accurate campaign evaluation and optimization (due to low conversion rates, cold start periods, lack of instrumentation of offline purchases, and long purchase cycles). This paper presents a detailed treatment of proxy modeling, which is based on the identification of a suitable alternative (proxy) target variable when data on the true objective is in short supply (or even completely nonexistent). The paper has a two-fold contribution. First, the potential of proxy modeling is demonstrated clearly, based on a massive-scale experiment across 58 real online advertising campaigns. Second, we assess the value of different specific proxies for evaluating and optimizing online display advertising, showing striking results. The results include bad news and good news. The most commonly cited and used proxy is a click on an ad. The bad news is that across a large number of campaigns, clicks are not good proxies for evaluation or for optimization: clickers do not resemble buyers. The good news is that an alternative sort of proxy performs remarkably well: observed visits to the brand's website. Specifically, predictive models built based on brand site visits-which are much more common than purchases-do a remarkably good job of predicting which browsers will make a purchase. The practical bottom line: evaluating and optimizing campaigns using clicks seems wrongheaded; however, there is an easy and attractive alternative-use a well-chosen site-visit proxy instead.

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.

PubMed

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

2015-12-01

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests. The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips. The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

Validating a benchmarking tool for audit of early outcomes after operations for head and neck cancer.

PubMed

Tighe, D; Sassoon, I; McGurk, M

2017-04-01

INTRODUCTION In 2013 all UK surgical specialties, with the exception of head and neck surgery, published outcome data adjusted for case mix for indicator operations. This paper reports a pilot study to validate a previously published risk adjustment score on patients from separate UK cancer centres. METHODS A case note audit was performed of 1,075 patients undergoing 1,218 operations for head and neck squamous cell carcinoma under general anaesthesia in 4 surgical centres. A logistic regression equation predicting for all complications, previously validated internally at sites A-C, was tested on a fourth external validation sample (site D, 172 operations) using receiver operating characteristic curves, Hosmer-Lemeshow goodness of fit analysis and Brier scores. RESULTS Thirty-day complication rates varied widely (34-51%) between the centres. The predictive score allowed imperfect risk adjustment (area under the curve: 0.70), with Hosmer-Lemeshow analysis suggesting good calibration. The Brier score changed from 0.19 for sites A-C to 0.23 when site D was also included, suggesting poor accuracy overall. CONCLUSIONS Marked differences in operative risk and patient case mix captured by the risk adjustment score do not explain all the differences in observed outcomes. Further investigation with different methods is recommended to improve modelling of risk. Morbidity is common, and usually has a major impact on patient recovery, ward occupancy, hospital finances and patient perception of quality of care. We hope comparative audit will highlight good performance and challenge underperformance where it exists.

Validating a benchmarking tool for audit of early outcomes after operations for head and neck cancer

PubMed Central

Sassoon, I; McGurk, M

2017-01-01

INTRODUCTION In 2013 all UK surgical specialties, with the exception of head and neck surgery, published outcome data adjusted for case mix for indicator operations. This paper reports a pilot study to validate a previously published risk adjustment score on patients from separate UK cancer centres. METHODS A case note audit was performed of 1,075 patients undergoing 1,218 operations for head and neck squamous cell carcinoma under general anaesthesia in 4 surgical centres. A logistic regression equation predicting for all complications, previously validated internally at sites A–C, was tested on a fourth external validation sample (site D, 172 operations) using receiver operating characteristic curves, Hosmer–Lemeshow goodness of fit analysis and Brier scores. RESULTS Thirty-day complication rates varied widely (34–51%) between the centres. The predictive score allowed imperfect risk adjustment (area under the curve: 0.70), with Hosmer–Lemeshow analysis suggesting good calibration. The Brier score changed from 0.19 for sites A–C to 0.23 when site D was also included, suggesting poor accuracy overall. CONCLUSIONS Marked differences in operative risk and patient case mix captured by the risk adjustment score do not explain all the differences in observed outcomes. Further investigation with different methods is recommended to improve modelling of risk. Morbidity is common, and usually has a major impact on patient recovery, ward occupancy, hospital finances and patient perception of quality of care. We hope comparative audit will highlight good performance and challenge underperformance where it exists. PMID:27917662

Advanced Stirling Radioisotope Generator Thermal Power Model in Thermal Desktop SINDA/FLUINT Analyzer

NASA Technical Reports Server (NTRS)

Wang, Xiao-Yen; Fabanich, William A.; Schmitz, Paul C.

2012-01-01

This paper presents a three-dimensional Advanced Stirling Radioisotope Generator (ASRG) thermal power model that was built using the Thermal Desktop SINDA/FLUINT thermal analyzer. The model was correlated with ASRG engineering unit (EU) test data and ASRG flight unit predictions from Lockheed Martin's Ideas TMG thermal model. ASRG performance under (1) ASC hot-end temperatures, (2) ambient temperatures, and (3) years of mission for the general purpose heat source fuel decay was predicted using this model for the flight unit. The results were compared with those reported by Lockheed Martin and showed good agreement. In addition, the model was used to study the performance of the ASRG flight unit for operations on the ground and on the surface of Titan, and the concept of using gold film to reduce thermal loss through insulation was investigated.

Noise characteristics of upper surface blown configurations: Summary

NASA Technical Reports Server (NTRS)

Reddy, N. N.; Gibson, J. S.

1978-01-01

A systematic experimental program was conducted to develop a data base for the noise and related flow characteristics of upper surface blown configurations. The effect of various geometric and flow parameters was investigated experimentally. The dominant noise was identified from the measured flow and noise characteristics to be generated downstream of the trailing edge. The possibilities of noise reduction techniques were explored. An upper surface blown (USB) noise prediction program was developed to calculate noise levels at any point and noise contours (footprints). Using this noise prediction program and a cruise performance data base, aircraft design studies were conducted to integrate low noise and good performance characteristics. A theory was developed for the noise from the highly sheared layer of a trailing edge wake. Theoretical results compare favorably with the measured noise of the USB model.

Impulse propagation over a complex site: a comparison of experimental results and numerical predictions.

PubMed

Dragna, Didier; Blanc-Benon, Philippe; Poisson, Franck

2014-03-01

Results from outdoor acoustic measurements performed in a railway site near Reims in France in May 2010 are compared to those obtained from a finite-difference time-domain solver of the linearized Euler equations. During the experiments, the ground profile and the different ground surface impedances were determined. Meteorological measurements were also performed to deduce mean vertical profiles of wind and temperature. An alarm pistol was used as a source of impulse signals and three microphones were located along a propagation path. The various measured parameters are introduced as input data into the numerical solver. In the frequency domain, the numerical results are in good accordance with the measurements up to a frequency of 2 kHz. In the time domain, except a time shift, the predicted waveforms match the measured waveforms with a close agreement.

The etiology of mathematical self-evaluation and mathematics achievement: understanding the relationship using a cross-lagged twin study from age 9 to 12

PubMed Central

Luo, Yu L.L.; Kovas, Yulia; Haworth, Claire M.A.; Plomin, Robert

2011-01-01

The genetic and environmental origins of individual differences in mathematical self-evaluation over time and its association with later mathematics achievement were investigated in a UK sample of 2138 twin pairs at ages 9 and 12. Self-evaluation indexed how good children think they are at mathematical activities and how much they like those activities. Mathematics achievement was assessed by teachers based on UK National Curriculum standards. At both ages self-evaluation was approximately 40% heritable, with the rest of the variance explained by non-shared environment. The results also suggested moderate reciprocal associations between self-evaluation and mathematics achievement across time, with earlier self-evaluation predicting later performance and earlier performance predicting later self-evaluation. These cross-lagged relationships were genetically rather than environmentally mediated. PMID:22102781

Structural Acoustic Prediction and Interior Noise Control Technology

NASA Technical Reports Server (NTRS)

Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)

2001-01-01

This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

Bayesian-based estimation of acoustic surface impedance: Finite difference frequency domain approach.

PubMed

Bockman, Alexander; Fackler, Cameron; Xiang, Ning

2015-04-01

Acoustic performance for an interior requires an accurate description of the boundary materials' surface acoustic impedance. Analytical methods may be applied to a small class of test geometries, but inverse numerical methods provide greater flexibility. The parameter estimation problem requires minimizing prediction vice observed acoustic field pressure. The Bayesian-network sampling approach presented here mitigates other methods' susceptibility to noise inherent to the experiment, model, and numerics. A geometry agnostic method is developed here and its parameter estimation performance is demonstrated for an air-backed micro-perforated panel in an impedance tube. Good agreement is found with predictions from the ISO standard two-microphone, impedance-tube method, and a theoretical model for the material. Data by-products exclusive to a Bayesian approach are analyzed to assess sensitivity of the method to nuisance parameters.

Off-Ice Anaerobic Power Does Not Predict On-Ice Repeated Shift Performance in Hockey.

PubMed

Peterson, Ben J; Fitzgerald, John S; Dietz, Calvin C; Ziegler, Kevin S; Baker, Sarah E; Snyder, Eric M

2016-09-01

Peterson, BJ, Fitzgerald, JS, Dietz, CC, Ziegler, KS, Baker, SE, and Snyder, EM. Off-ice anaerobic power does not predict on-ice repeated shift performance in hockey. J Strength Cond Res 30(9): 2375-2381, 2016-Anaerobic power is a significant predictor of acceleration and top speed in team sport athletes. Historically, these findings have been applied to ice hockey although recent research has brought their validity for this sport into question. As ice hockey emphasizes the ability to repeatedly produce power, single bout anaerobic power tests should be examined to determine their ability to predict on-ice performance. We tested whether conventional off-ice anaerobic power tests could predict on-ice acceleration, top speed, and repeated shift performance. Forty-five hockey players, aged 18-24 years, completed anthropometric, off-ice, and on-ice tests. Anthropometric and off-ice testing included height, weight, body composition, vertical jump, and Wingate tests. On-ice testing consisted of acceleration, top speed, and repeated shift fatigue tests. Vertical jump (VJ) (r = -0.42; r = -0.58), Wingate relative peak power (WRPP) (r = -0.32; r = -0.43), and relative mean power (WRMP) (r = -0.34; r = -0.48) were significantly correlated (p ≤ 0.05) to on-ice acceleration and top speed, respectively. Conversely, none of the off-ice tests correlated with on-ice repeated shift performance, as measured by first gate, second gate, or total course fatigue; VJ (r = 0.06; r = 0.13; r = 0.09), WRPP (r = 0.06; r = 0.14; r = 0.10), or WRMP (r = -0.10; r = -0.01; r = -0.01). Although conventional off-ice anaerobic power tests predict single bout on-ice acceleration and top speed, they neither predict the repeated shift ability of the player, nor are good markers for performance in ice hockey.

How Well Do Trait Measures of Achievement Predict Students' Perceptions of the Link between Personal Effort and Academic Performance?

ERIC Educational Resources Information Center

Gray, Jennifer P.; Mannahan, Kimberly Kinsey

2017-01-01

The concept of Grit has gained momentum in the last several years as a better predictor of achievement than traditional measures, such as IQ. Duckworth, et al. (2007) found grit to be positively correlated to the Big Five personality dimension of conscientiousness, but not to IQ, causing the authors to hypothesize that grit is a good noncognitive…

Arrhythmia Evaluation in Wearable ECG Devices

PubMed Central

Sadrawi, Muammar; Lin, Chien-Hung; Hsieh, Yita; Kuo, Chia-Chun; Chien, Jen Chien; Haraikawa, Koichi; Abbod, Maysam F.; Shieh, Jiann-Shing

2017-01-01

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. PMID:29068369

Anthropometric predictors of body fat in a large population of 9-year-old school-aged children.

PubMed

Almeida, Sílvia M; Furtado, José M; Mascarenhas, Paulo; Ferraz, Maria E; Silva, Luís R; Ferreira, José C; Monteiro, Mariana; Vilanova, Manuel; Ferraz, Fernando P

2016-09-01

To develop and cross-validate predictive models for percentage body fat (%BF) from anthropometric measurements [including BMI z -score (zBMI) and calf circumference (CC)] excluding skinfold thickness. A descriptive study was carried out in 3,084 pre-pubertal children. Regression models and neural network were developed with %BF measured by Bioelectrical Impedance Analysis (BIA) as the dependent variables and age, sex and anthropometric measurements as independent predictors. All %BF grade predictive models presented a good global accuracy (≥91.3%) for obesity discrimination. Both overfat/obese and obese prediction models presented respectively good sensitivity (78.6% and 71.0%), specificity (98.0% and 99.2%) and reliability for positive or negative test results (≥82% and ≥96%). For boys, the order of parameters, by relative weight in the predictive model, was zBMI, height, waist-circumference-to-height-ratio (WHtR) squared variable (_Q), age, weight, CC_Q and hip circumference (HC)_Q (adjusted r 2  = 0.847 and RMSE = 2.852); for girls it was zBMI, WHtR_Q, height, age, HC_Q and CC_Q (adjusted r 2  = 0.872 and RMSE = 2.171). %BF can be graded and predicted with relative accuracy from anthropometric measurements excluding skinfold thickness. Fitness and cross-validation results showed that our multivariable regression model performed better in this population than did some previously published models.

Ratio of urine and blood urea nitrogen concentration predicts the response of tolvaptan in congestive heart failure.

PubMed

Shimizu, Keisuke; Doi, Kent; Imamura, Teruhiko; Noiri, Eisei; Yahagi, Naoki; Nangaku, Masaomi; Kinugawa, Koichiro

2015-06-01

This study was conducted to evaluate the performance of the ratio of urine and blood urea nitrogen concentration (UUN/BUN) as a new predictive factor for the response of an arginine vasopressin receptor 2 antagonist tolvaptan (TLV) in decompensated heart failure patients. This study enrolled 70 decompensated heart failure patients who were administered TLV at University of Tokyo Hospital. We collected the data of clinical parameters including UUN/BUN before administering TLV. Two different outcomes were defined as follows: having over 300 mL increase in urine volume on the first day (immediate urine output response) and having any decrease in body weight within one week after starting TLV treatment (subsequent clinical response). Among the 70 enrolled patients, 37 patients (52.9%) showed immediate urine output response; 51 patients (72.9%) showed a subsequent clinical response of body weight decrease. Receiver operating characteristics (ROC) analysis showed good prediction by UUN/BUN for the immediate response (AUC-ROC 0.86 [0.75-0.93]) and a significantly better prediction by UUN/BUN for the subsequent clinical response compared with urinary osmolality (AUC-ROC 0.78 [0.63-0.88] vs. 0.68 [0.52-0.80], P < 0.05). We demonstrated that a clinical parameter of UUN/BUN can predict the response of TLV even when measured before TLV administration. UUN/BUN might enable identification of good responders for this new drug. © 2015 Asian Pacific Society of Nephrology.

Reliability of simulated robustness testing in fast liquid chromatography, using state-of-the-art column technology, instrumentation and modelling software.

PubMed

Kormány, Róbert; Fekete, Jenő; Guillarme, Davy; Fekete, Szabolcs

2014-02-01

The goal of this study was to evaluate the accuracy of simulated robustness testing using commercial modelling software (DryLab) and state-of-the-art stationary phases. For this purpose, a mixture of amlodipine and its seven related impurities was analyzed on short narrow bore columns (50×2.1mm, packed with sub-2μm particles) providing short analysis times. The performance of commercial modelling software for robustness testing was systematically compared to experimental measurements and DoE based predictions. We have demonstrated that the reliability of predictions was good, since the predicted retention times and resolutions were in good agreement with the experimental ones at the edges of the design space. In average, the retention time relative errors were <1.0%, while the predicted critical resolution errors were comprised between 6.9 and 17.2%. Because the simulated robustness testing requires significantly less experimental work than the DoE based predictions, we think that robustness could now be investigated in the early stage of method development. Moreover, the column interchangeability, which is also an important part of robustness testing, was investigated considering five different C8 and C18 columns packed with sub-2μm particles. Again, thanks to modelling software, we proved that the separation was feasible on all columns within the same analysis time (less than 4min), by proper adjustments of variables. Copyright © 2013 Elsevier B.V. All rights reserved.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis.

PubMed

Li, Yanjie; Hou, Changjun; Lei, Jincan; Deng, Bo; Huang, Jing; Yang, Mei

2016-01-01

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

Validation of Heat Transfer and Film Cooling Capabilities of the 3-D RANS Code TURBO

NASA Technical Reports Server (NTRS)

Shyam, Vikram; Ameri, Ali; Chen, Jen-Ping

2010-01-01

The capabilities of the 3-D unsteady RANS code TURBO have been extended to include heat transfer and film cooling applications. The results of simulations performed with the modified code are compared to experiment and to theory, where applicable. Wilcox s k-turbulence model has been implemented to close the RANS equations. Two simulations are conducted: (1) flow over a flat plate and (2) flow over an adiabatic flat plate cooled by one hole inclined at 35 to the free stream. For (1) agreement with theory is found to be excellent for heat transfer, represented by local Nusselt number, and quite good for momentum, as represented by the local skin friction coefficient. This report compares the local skin friction coefficients and Nusselt numbers on a flat plate obtained using Wilcox's k-model with the theory of Blasius. The study looks at laminar and turbulent flows over an adiabatic flat plate and over an isothermal flat plate for two different wall temperatures. It is shown that TURBO is able to accurately predict heat transfer on a flat plate. For (2) TURBO shows good qualitative agreement with film cooling experiments performed on a flat plate with one cooling hole. Quantitatively, film effectiveness is under predicted downstream of the hole.

Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

PubMed

Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

2018-02-01

Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

Investigation of improving MEMS-type VOA reliability

NASA Astrophysics Data System (ADS)

Hong, Seok K.; Lee, Yeong G.; Park, Moo Y.

2003-12-01

MEMS technologies have been applied to a lot of areas, such as optical communications, Gyroscopes and Bio-medical components and so on. In terms of the applications in the optical communication field, MEMS technologies are essential, especially, in multi dimensional optical switches and Variable Optical Attenuators(VOAs). This paper describes the process for the development of MEMS type VOAs with good optical performance and improved reliability. Generally, MEMS VOAs have been fabricated by silicon micro-machining process, precise fibre alignment and sophisticated packaging process. Because, it is composed of many structures with various materials, it is difficult to make devices reliable. We have developed MEMS type VOSs with many failure mode considerations (FMEA: Failure Mode Effect Analysis) in the initial design step, predicted critical failure factors and revised the design, and confirmed the reliability by preliminary test. These predicted failure factors were moisture, bonding strength of the wire, which wired between the MEMS chip and TO-CAN and instability of supplied signals. Statistical quality control tools (ANOVA, T-test and so on) were used to control these potential failure factors and produce optimum manufacturing conditions. To sum up, we have successfully developed reliable MEMS type VOAs with good optical performances by controlling potential failure factors and using statistical quality control tools. As a result, developed VOAs passed international reliability standards (Telcodia GR-1221-CORE).

Investigation of improving MEMS-type VOA reliability

NASA Astrophysics Data System (ADS)

Hong, Seok K.; Lee, Yeong G.; Park, Moo Y.

2004-01-01

MEMS technologies have been applied to a lot of areas, such as optical communications, Gyroscopes and Bio-medical components and so on. In terms of the applications in the optical communication field, MEMS technologies are essential, especially, in multi dimensional optical switches and Variable Optical Attenuators(VOAs). This paper describes the process for the development of MEMS type VOAs with good optical performance and improved reliability. Generally, MEMS VOAs have been fabricated by silicon micro-machining process, precise fibre alignment and sophisticated packaging process. Because, it is composed of many structures with various materials, it is difficult to make devices reliable. We have developed MEMS type VOSs with many failure mode considerations (FMEA: Failure Mode Effect Analysis) in the initial design step, predicted critical failure factors and revised the design, and confirmed the reliability by preliminary test. These predicted failure factors were moisture, bonding strength of the wire, which wired between the MEMS chip and TO-CAN and instability of supplied signals. Statistical quality control tools (ANOVA, T-test and so on) were used to control these potential failure factors and produce optimum manufacturing conditions. To sum up, we have successfully developed reliable MEMS type VOAs with good optical performances by controlling potential failure factors and using statistical quality control tools. As a result, developed VOAs passed international reliability standards (Telcodia GR-1221-CORE).

Does taking endurance into account improve the prediction of weaning outcome in mechanically ventilated children?

PubMed Central

Noizet, Odile; Leclerc, Francis; Sadik, Ahmed; Grandbastien, Bruno; Riou, Yvon; Dorkenoo, Aimée; Fourier, Catherine; Cremer, Robin; Leteurtre, Stephane

2005-01-01

Introduction We conducted the present study to determine whether a combination of the mechanical ventilation weaning predictors proposed by the collective Task Force of the American College of Chest Physicians (TF) and weaning endurance indices enhance prediction of weaning success. Method Conducted in a tertiary paediatric intensive care unit at a university hospital, this prospective study included 54 children receiving mechanical ventilation (≥6 hours) who underwent 57 episodes of weaning. We calculated the indices proposed by the TF (spontaneous respiratory rate, paediatric rapid shallow breathing, rapid shallow breathing occlusion pressure [ROP] and maximal inspiratory pressure during an occlusion test [Pimax]) and weaning endurance indices (pressure-time index, tension-time index obtained from P0.1 [TTI1] and from airway pressure [TTI2]) during spontaneous breathing. Performances of each TF index and combinations of them were calculated, and the best single index and combination were identified. Weaning endurance parameters (TTI1 and TTI2) were calculated and the best index was determined using a logistic regression model. Regression coefficients were estimated using the maximum likelihood ratio (LR) method. Hosmer–Lemeshow test was used to estimate goodness-of-fit of the model. An equation was constructed to predict weaning success. Finally, we calculated the performances of combinations of best TF indices and best endurance index. Results The best single TF index was ROP, the best TF combination was represented by the expression (0.66 × ROP) + (0.34 × Pimax), and the best endurance index was the TTI2, although their performance was poor. The best model resulting from the combination of these indices was defined by the following expression: (0.6 × ROP) – (0.1 × Pimax) + (0.5 × TTI2). This integrated index was a good weaning predictor (P < 0.01), with a LR+ of 6.4 and LR+/LR- ratio of 12.5. However, at a threshold value <1.3 it was only predictive of weaning success (LR- = 0.5). Conclusion The proposed combined index, incorporating endurance, was of modest value in predicting weaning outcome. This is the first report of the value of endurance parameters in predicting weaning success in children. Currently, clinical judgement associated with spontaneous breathing trials apparently remain superior. PMID:16356229

Performance testing of asphalt concrete containing crumb rubber modifier and warm mix additives

NASA Astrophysics Data System (ADS)

Ikpugha, Omo John

Utilisation of scrap tire has been achieved through the production of crumb rubber modified binders and rubberised asphalt concrete. Terminal and field blended asphalt rubbers have been developed through the wet process to incorporate crumb rubber into the asphalt binder. Warm mix asphalt technologies have been developed to curb the problem associated with the processing and production of such crumb rubber modified binders. Also the lowered production and compaction temperatures associated with warm mix additives suggests the possibility of moisture retention in the mix, which can lead to moisture damage. Conventional moisture sensitivity tests have not effectively discriminated good and poor mixes, due to the difficulty of simulating field moisture damage mechanisms. This study was carried out to investigate performance properties of crumb rubber modified asphalt concrete, using commercial warm mix asphalt technology. Commonly utilised asphalt mixtures in North America such as dense graded and stone mastic asphalt were used in this study. Uniaxial Cyclic Compression Testing (UCCT) was used to measure permanent deformation at high temperatures. Indirect Tensile Testing (IDT) was used to investigate low temperature performance. Moisture Induced Sensitivity Testing (MiST) was proposed to be an effective method for detecting the susceptibility of asphalt mixtures to moisture damage, as it incorporates major field stripping mechanisms. Sonnewarm(TM), Sasobit(TM) and Evotherm(TM) additives improved the resistance to permanent deformation of dense graded mixes at a loading rate of 0.5 percent by weight of the binder. Polymer modified mixtures showed superior resistance to permanent deformation compared to asphalt rubber in all mix types. Rediset(TM) WMX improves low temperature properties of dense graded mixes at 0.5 percent loading on the asphalt cement. Rediset LQ and Rediset WMX showed good anti stripping properties at 0.5 percent loading on the asphalt cement. The American Association of State Highway and Transportation Official's Mechanistic-Empirical Pavement Design Guide (AASHTO MEPDG) software was used to predict long term low temperature performance of the mixtures in various areas of Ontario. Sasobit, Rediset LQ and Rediset WMX gave good 15 years prediction with stone mastic asphalt mixtures but the performance of dense graded mixtures was less satisfactory.

Symbolic Number Comparison Is Not Processed by the Analog Number System: Different Symbolic and Non-symbolic Numerical Distance and Size Effects

PubMed Central

Krajcsi, Attila; Lengyel, Gábor; Kojouharova, Petia

2018-01-01

HIGHLIGHTS We test whether symbolic number comparison is handled by an analog noisy system.Analog system model has systematic biases in describing symbolic number comparison.This suggests that symbolic and non-symbolic numbers are processed by different systems. Dominant numerical cognition models suppose that both symbolic and non-symbolic numbers are processed by the Analog Number System (ANS) working according to Weber's law. It was proposed that in a number comparison task the numerical distance and size effects reflect a ratio-based performance which is the sign of the ANS activation. However, increasing number of findings and alternative models propose that symbolic and non-symbolic numbers might be processed by different representations. Importantly, alternative explanations may offer similar predictions to the ANS prediction, therefore, former evidence usually utilizing only the goodness of fit of the ANS prediction is not sufficient to support the ANS account. To test the ANS model more rigorously, a more extensive test is offered here. Several properties of the ANS predictions for the error rates, reaction times, and diffusion model drift rates were systematically analyzed in both non-symbolic dot comparison and symbolic Indo-Arabic comparison tasks. It was consistently found that while the ANS model's prediction is relatively good for the non-symbolic dot comparison, its prediction is poorer and systematically biased for the symbolic Indo-Arabic comparison. We conclude that only non-symbolic comparison is supported by the ANS, and symbolic number comparisons are processed by other representation. PMID:29491845

Thermal response of Space Shuttle wing during reentry heating

NASA Technical Reports Server (NTRS)

Gong, L.; Ko, W. L.; Quinn, R. D.

1984-01-01

A structural performance and resizing (SPAR) finite element thermal analysis computer program was used in the heat transfer analysis of the space shuttle orbiter that was subjected to reentry aerodynamic heatings. One wing segment of the right wing (WS 240) and the whole left wing were selected for the thermal analysis. Results showed that the predicted thermal protection system (TPS) temperatures were in good agreement with the space transportation system, trajectory 5 (STS-5) flight-measured temperatures. In addition, calculated aluminum structural temperatures were in fairly good agreement with the flight data up to the point of touchdown. Results also showed that the internal free convection had a considerable effect on the change of structural temperatures after touchdown.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients.

PubMed

Won, Young-Woong; Joo, Jungnam; Yun, Tak; Lee, Geon-Kook; Han, Ji-Youn; Kim, Heung Tae; Lee, Jin Soo; Kim, Moon Soo; Lee, Jong Mog; Lee, Hyun-Sung; Zo, Jae Ill; Kim, Sohee

2015-05-01

Development of brain metastasis results in a significant reduction in overall survival. However, there is no an effective tool to predict brain metastasis in non-small cell lung cancer (NSCLC) patients. We conducted this study to develop a feasible nomogram that can predict metastasis to the brain as the first relapse site in patients with curatively resected NSCLC. A retrospective review of NSCLC patients who had received curative surgery at National Cancer Center (Goyang, South Korea) between 2001 and 2008 was performed. We chose metastasis to the brain as the first relapse site after curative surgery as the primary endpoint of the study. A nomogram was modeled using logistic regression. Among 1218 patients, brain metastasis as the first relapse developed in 87 patients (7.14%) during the median follow-up of 43.6 months. Occurrence rates of brain metastasis were higher in patients with adenocarcinoma or those with a high pT and pN stage. Younger age appeared to be associated with brain metastasis, but this result was not statistically significant. The final prediction model included histology, smoking status, pT stage, and the interaction between adenocarcinoma and pN stage. The model showed fairly good discriminatory ability with a C-statistic of 69.3% and 69.8% for predicting brain metastasis within 2 years and 5 years, respectively. Internal validation using 2000 bootstrap samples resulted in C-statistics of 67.0% and 67.4% which still indicated good discriminatory performances. The nomogram presented here provides the individual risk estimate of developing metastasis to the brain as the first relapse site in patients with NSCLC who have undergone curative surgery. Surveillance programs or preventive treatment strategies for brain metastasis could be established based on this nomogram. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The potential of at-home prediction of the formation of urolithiasis by simple multi-frequency electrical conductivity of the urine and the comparison of its performance with urine ion-related indices, color and specific gravity.

PubMed

Silverio, Angelito A; Chung, Wen-Yaw; Cheng, Cheanyeh; Wang, Hai-Lung; Kung, Chien-Min; Chen, Jun; Tsai, Vincent F S

2016-04-01

It is important to control daily diet, water intake and life style as well as monitor the quality of urine for urolithiasis prevention. For decades, many ion-related indices have been developed for predicting the formation of urinary stones or urolithiasis, such as EQUILs, relative supersaturation (RSS), Tiselius indices (TI), Robertson risk factor algorithms (RRFA) and more recently, the Bonn risk index. However, they mostly demand robust laboratory analysis, are work-intensive, and even require complex computational programs to get the concentration patterns of several urine analytes. A simple and fast platform for measuring multi-frequency electrical conductivity (MFEC) of morning spot urine (random urine) to predict the onset of urolithiasis was implemented in this study. The performance thereof was compared to ion-related indices, urine color and specific gravity. The concentrations of relevant ions, color, specific gravity (SG) and MFEC (MFEC tested at 1, 10, 100, 5001 KHz and 1 MHz) of 80 random urine samples were examined after collection. Then, the urine samples were stored at 4 °C for 24 h to determine whether sedimentation would occur or not. Ion-activity product index of calcium oxalate (AP(CaOx) EQ2) was calculated. The correlation between AP(CaOx) EQ2, urine color, SG and MFEC were analyzed. AP(CaOx) EQ2, urine color and MFEC (at 5 frequencies) all demonstrated good prediction (p = 0.01, 0.01, 0.01, respectively) for stone formation. The positive correlation between AP(CaOx) EQ2 and MFEC is also significant (p = 0.01). MFEC provides a good metric for predicting the onset of urolithiasis, which is comparable to conventional ion-related indices and urine color. This technology can be implemented with much ease for objectively monitoring the quality of urine at points-of-care or at home.

Orbiter windward surface entry Heating: Post-orbital flight test program update

NASA Technical Reports Server (NTRS)

Harthun, M. H.; Blumer, C. B.; Miller, B. A.

1983-01-01

Correlations of orbiter windward surface entry heating data from the first five flights are presented with emphasis on boundary layer transition and the effects of catalytic recombination. Results show that a single roughness boundary layer transition correlation developed for spherical element trips works well for the orbiter tile system. Also, an engineering approach for predicting heating in nonequilibrium flow conditions shows good agreement with the flight test data in the time period of significant heating. The results of these correlations, when used to predict orbiter heating for a high cross mission, indicate that the thermal protection system on the windward surface will perform successfully in such a mission.

An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

PubMed Central

2009-01-01

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down. PMID:20596382

An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube.

PubMed

Ahadian, Samad; Kawazoe, Yoshiyuki

2009-06-04

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down.

Aerodynamic prediction techniques for hypersonic configuration design

NASA Technical Reports Server (NTRS)

1981-01-01

An investigation of approximate theoretical techniques for predicting aerodynamic characteristics and surface pressures for relatively slender vehicles at moderate hypersonic speeds was performed. Emphasis was placed on approaches that would be responsive to preliminary configuration design level of effort. Potential theory was examined in detail to meet this objective. Numerical pilot codes were developed for relatively simple three dimensional geometries to evaluate the capability of the approximate equations of motion considered. Results from the computations indicate good agreement with higher order solutions and experimental results for a variety of wing, body, and wing-body shapes for values of the hypersonic similarity parameter M delta approaching one.

The Growth of Multi-Site Fatigue Damage in Fuselage Lap Joints

NASA Technical Reports Server (NTRS)

Piascik, Robert S.; Willard, Scott A.

1999-01-01

Destructive examinations were performed to document the progression of multi-site damage (MSD) in three lap joint panels that were removed from a full scale fuselage test article that was tested to 60,000 full pressurization cycles. Similar fatigue crack growth characteristics were observed for small cracks (50 microns to 10 mm) emanating from counter bore rivets, straight shank rivets, and 100 deg counter sink rivets. Good correlation of the fatigue crack growth data base obtained in this study and FASTRAN Code predictions show that the growth of MSD in the fuselage lap joint structure can be predicted by fracture mechanics based methods.

Homogenization kinetics of a nickel-based superalloy produced by powder bed fusion laser sintering

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

Zhang, Fan; Levine, Lyle E.; Allen, Andrew J.

2017-04-01

Additively manufactured (AM) metal components often exhibit fine dendritic microstructures and elemental segregation due to the initial rapid solidification and subsequent melting and cooling during the build process, which without homogenization would adversely affect materials performance. In this letter, we report in situ observation of the homogenization kinetics of an AM nickel-based superalloy using synchrotron small angle X-ray scattering. The identified kinetic time scale is in good agreement with thermodynamic diffusion simulation predictions using microstructural dimensions acquired by ex situ scanning electron microscopy. These findings could serve as a recipe for predicting, observing, and validating homogenization treatments in AM materials.

Homogenization Kinetics of a Nickel-based Superalloy Produced by Powder Bed Fusion Laser Sintering.

PubMed

Zhang, Fan; Levine, Lyle E; Allen, Andrew J; Campbell, Carelyn E; Lass, Eric A; Cheruvathur, Sudha; Stoudt, Mark R; Williams, Maureen E; Idell, Yaakov

2017-04-01

Additively manufactured (AM) metal components often exhibit fine dendritic microstructures and elemental segregation due to the initial rapid solidification and subsequent melting and cooling during the build process, which without homogenization would adversely affect materials performance. In this letter, we report in situ observation of the homogenization kinetics of an AM nickel-based superalloy using synchrotron small angle X-ray scattering. The identified kinetic time scale is in good agreement with thermodynamic diffusion simulation predictions using microstructural dimensions acquired by ex situ scanning electron microscopy. These findings could serve as a recipe for predicting, observing, and validating homogenization treatments in AM materials.

Wave propagation modeling in composites reinforced by randomly oriented fibers

NASA Astrophysics Data System (ADS)

Kudela, Pawel; Radzienski, Maciej; Ostachowicz, Wieslaw

2018-02-01

A new method for prediction of elastic constants in randomly oriented fiber composites is proposed. It is based on mechanics of composites, the rule of mixtures and total mass balance tailored to the spectral element mesh composed of 3D brick elements. Selected elastic properties predicted by the proposed method are compared with values obtained by another theoretical method. The proposed method is applied for simulation of Lamb waves in glass-epoxy composite plate reinforced by randomly oriented fibers. Full wavefield measurements conducted by the scanning laser Doppler vibrometer are in good agreement with simulations performed by using the time domain spectral element method.

What do we gain with Probabilistic Flood Loss Models?

NASA Astrophysics Data System (ADS)

Schroeter, K.; Kreibich, H.; Vogel, K.; Merz, B.; Lüdtke, S.

2015-12-01

The reliability of flood loss models is a prerequisite for their practical usefulness. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions which are cast in a probabilistic framework. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

PubMed Central

Laback, Bernhard; Savel, Sophie; Ystad, Sølvi; Balazs, Peter; Meunier, Sabine; Kronland-Martinet, Richard

2016-01-01

Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli. PMID:27875575

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli.

PubMed

Necciari, Thibaud; Laback, Bernhard; Savel, Sophie; Ystad, Sølvi; Balazs, Peter; Meunier, Sabine; Kronland-Martinet, Richard

2016-01-01

Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli.

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

PubMed

Wickens, Christopher Dow; Gutzwiller, Robert S; Vieane, Alex; Clegg, Benjamin A; Sebok, Angelia; Janes, Jess

2016-03-01

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. The multiattribute decision model provided a good fit to the data. The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive. © 2016, Human Factors and Ergonomics Society.

Stretched size of atrial septal defect predicted by intracardiac echocardiography.

PubMed

Lin, Ming-Chih; Fu, Yun-Ching; Jan, Sheng-Ling; Ho, Chi-Lin; Hwang, Betau

2010-01-01

The stretched size of an atrial septal defect (ASD) is important for device selection during transcatheter closure. However, balloon sizing carries potential risks such as hypotension, bradycardia, or laceration of the atrial septum. The aim of the present study was to investigate the accuracy of the predicted stretched size of ASD by intracardiac echocardiography (ICE). From December 2004 to November 2007, 136 consecutive patients with single secundum type ASD undergoing transcatheter closure of their defect using the Amplatzer septal occluder under ICE guidance were enrolled for analysis. There were 43 males and 93 females. The age ranged from 2.2 to 79.1 years with a median age of 13.4 years. The body weight ranged from 12.1 to 93.2 kg with a median body weight of 45.8 kg. The stretched size of ASD measured by a sizing plate was considered as the standard. ASD sizes measured by ICE in bicaval and short-axis views predicted the stretched size by formulae derived from linear regressions. The predicted stretched sizes obtained using 2 formulae, 1.34 x radicalbicaval xshort axis (formula 1) and 1.22 x larger diameter (formula 2), exhibited good agreement with the standard stretched size with Kappa values of 0.91 and 0.90, respectively. The accuracy rate of predicted stretched sizes within 2 mm, 3 mm, and 4 mm range of the standard size were 32.8%, 45.4%, and 57.7% (formula 1) and 33.1%, 50%, and 63.2% (formula 2). The stretched size of ASD predicted by ICE exhibited good agreement with the standard stretched size. This prediction provides helpful information, especially if balloon sizing cannot be adequately performed.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Evaluation of the NICE mini-GRACE risk scores for acute myocardial infarction using the Myocardial Ischaemia National Audit Project (MINAP) 2003-2009: National Institute for Cardiovascular Outcomes Research (NICOR).

PubMed

Simms, Alexander D; Reynolds, Stephanie; Pieper, Karen; Baxter, Paul D; Cattle, Brian A; Batin, Phillip D; Wilson, John I; Deanfield, John E; West, Robert M; Fox, Keith A A; Hall, Alistair S; Gale, Christopher P

2013-01-01

To evaluate the performance of the National Institute for Health and Clinical Excellence (NICE) mini-Global Registry of Acute Coronary Events (GRACE) (MG) and adjusted mini-GRACE (AMG) risk scores. Retrospective observational study. 215 acute hospitals in England and Wales. 137 084 patients discharged from hospital with a diagnosis of acute myocardial infarction (AMI) between 2003 and 2009, as recorded in the Myocardial Ischaemia National Audit Project (MINAP). Model performance indices of calibration accuracy, discriminative and explanatory performance, including net reclassification index (NRI) and integrated discrimination improvement. Of 495 263 index patients hospitalised with AMI, there were 53 196 ST elevation myocardial infarction and 83 888 non-ST elevation myocardial infarction (NSTEMI) (27.7%) cases with complete data for all AMG variables. For AMI, AMG calibration was better than MG calibration (Hosmer-Lemeshow goodness of fit test: p=0.33 vs p<0.05). MG and AMG predictive accuracy and discriminative ability were good (Brier score: 0.10 vs 0.09; C statistic: 0.82 and 0.84, respectively). The NRI of AMG over MG was 8.1% (p<0.05). Model performance was reduced in patients with NSTEMI, chronic heart failure, chronic renal failure and in patients aged ≥85 years. The AMG and MG risk scores, utilised by NICE, demonstrated good performance across a range of indices using MINAP data, but performed less well in higher risk subgroups. Although indices were better for AMG, its application may be constrained by missing predictors.

Song and speech: examining the link between singing talent and speech imitation ability.

PubMed

Christiner, Markus; Reiterer, Susanne M

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of "speech" on the productive level and "music" on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory.

Song and speech: examining the link between singing talent and speech imitation ability

PubMed Central

Christiner, Markus; Reiterer, Susanne M.

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of “speech” on the productive level and “music” on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory. PMID:24319438

Small Engine Technology. Task 4: Advanced Small Turboshaft Compressor (ASTC) Performance and Range Investigation

NASA Technical Reports Server (NTRS)

Hansen, Jeff L.; Delaney, Robert A.

1997-01-01

This contact had two main objectives involving both numerical and experimental investigations of a small highly loaded two-stage axial compressor designated Advanced Small Turboshaft Compressor (ASTC) winch had a design pressure ratio goal of 5:1 at a flowrate of 10.53 lbm/s. The first objective was to conduct 3-D Navier Stokes multistage analyses of the ASTC using several different flow modelling schemes. The second main objective was to complete a numerical/experimental investigation into stall range enhancement of the ASTC. This compressor was designed wider a cooperative Space Act Agreement and all testing was completed at NASA Lewis Research Center. For the multistage analyses, four different flow model schemes were used, namely: (1) steady-state ADPAC analysis, (2) unsteady ADPAC analysis, (3) steady-state APNASA analysis, and (4) steady state OCOM3D analysis. The results of all the predictions were compared to the experimental data. The steady-state ADPAC and APNASA codes predicted similar overall performance and produced good agreement with data, however the blade row performance and flowfield details were quite different. In general, it can be concluded that the APNASA average-passage code does a better job of predicting the performance and flowfield details of the highly loaded ASTC compressor.

Combine experimental and theoretical investigation on an alkaloid-Dimethylisoborreverine

NASA Astrophysics Data System (ADS)

Singh, Swapnil; Singh, Harshita; Karthick, T.; Agarwal, Parag; Erande, Rohan D.; Dethe, Dattatraya H.; Tandon, Poonam

2016-01-01

A combined experimental (FT-IR, 1H and 13C NMR) and theoretical approach is used to study the structure and properties of antimalarial drug dimethylisoborreverine (DMIB). Conformational analysis, has been performed by plotting one dimensional potential energy curve that was computed using density functional theory (DFT) with B3LYP/6-31G method and predicted conformer A1 as the most stable conformer. After full geometry optimization, harmonic wavenumbers were computed for conformer A1 at the DFT/B3LYP/6-311++G(d,P) level. A complete vibrational assignment of all the vibrational modes have been performed on the bases of the potential energy distribution (PED) and theoretical results were found to be in good agreement with the observed data. To predict the solvent effect, the UV-Vis spectra were calculated in different solvents by polarizable continuum model using TD-DFT method. Molecular docking studies were performed to test the biological activity of the sample using SWISSDOCK web server and Hex 8.0.0 software. The molecular electrostatic potential (MESP) was plotted to identify the reactive sites of the molecule. Natural bond orbital (NBO) analysis was performed to get a deep insight of intramolecular charge transfer. Thermodynamical parameters were calculated to predict the direction of chemical reaction.

Performance of chromatographic systems to model soil-water sorption.

PubMed

Hidalgo-Rodríguez, Marta; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2012-08-24

A systematic approach for evaluating the goodness of chromatographic systems to model the sorption of neutral organic compounds by soil from water is presented in this work. It is based on the examination of the three sources of error that determine the overall variance obtained when soil-water partition coefficients are correlated against chromatographic retention factors: the variance of the soil-water sorption data, the variance of the chromatographic data, and the variance attributed to the dissimilarity between the two systems. These contributions of variance are easily predicted through the characterization of the systems by the solvation parameter model. According to this method, several chromatographic systems besides the reference octanol-water partition system have been selected to test their performance in the emulation of soil-water sorption. The results from the experimental correlations agree with the predicted variances. The high-performance liquid chromatography system based on an immobilized artificial membrane and the micellar electrokinetic chromatography systems of sodium dodecylsulfate and sodium taurocholate provide the most precise correlation models. They have shown to predict well soil-water sorption coefficients of several tested herbicides. Octanol-water partitions and high-performance liquid chromatography measurements using C18 columns are less suited for the estimation of soil-water partition coefficients. Copyright © 2012 Elsevier B.V. All rights reserved.

The Predictive Value of Ultrasound Learning Curves Across Simulated and Clinical Settings.

PubMed

Madsen, Mette E; Nørgaard, Lone N; Tabor, Ann; Konge, Lars; Ringsted, Charlotte; Tolsgaard, Martin G

2017-01-01

The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. Twenty midwives completed a simulation-based training program in transvaginal sonography. The training was conducted on a VR simulator as well as on a physical mannequin. A subgroup of 6 participants underwent subsequent clinical training. During each of the 3 steps, the participants' performance was assessed using instruments with established validity evidence, and they advanced to the next level only after attaining predefined levels of performance. The number of repetitions and time needed to achieve predefined performance levels were recorded along with the performance scores in each setting. Finally, the outcomes were correlated across settings. A good correlation was found between time needed to achieve predefined performance levels on the VR simulator and the physical mannequin (Pearson correlation coefficient .78; P < .001). Performance scores on the VR simulator correlated well to the clinical performance scores (Pearson correlation coefficient .81; P = .049). No significant correlations were found between numbers of attempts needed to reach proficiency across the 3 different settings. A post hoc analysis found that the 50% fastest trainees at reaching proficiency during simulation-based training received higher clinical performance scores compared to trainees with scores placing them among the 50% slowest (P = .025). Performances during simulation-based sonography training may predict performance in related tasks and subsequent clinical learning curves. © 2016 by the American Institute of Ultrasound in Medicine.

Prediction of mortality rates using a model with stochastic parameters

NASA Astrophysics Data System (ADS)

Tan, Chon Sern; Pooi, Ah Hin

2016-10-01

Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.

Scaling Analysis of Alloy Solidification and Fluid Flow in a Rectangular Cavity

NASA Astrophysics Data System (ADS)

Plotkowski, A.; Fezi, K.; Krane, M. J. M.

A scaling analysis was performed to predict trends in alloy solidification in a side-cooled rectangular cavity. The governing equations for energy and momentum were scaled in order to determine the dependence of various aspects of solidification on the process parameters for a uniform initial temperature and an isothermal boundary condition. This work improved on previous analyses by adding considerations for the cooling bulk fluid flow. The analysis predicted the time required to extinguish the superheat, the maximum local solidification time, and the total solidification time. The results were compared to a numerical simulation for a Al-4.5 wt.% Cu alloy with various initial and boundary conditions. Good agreement was found between the simulation results and the trends predicted by the scaling analysis.

Noise reduction as affected by the extent and distribution of acoustic treatment in a turbofan engine inlet

NASA Technical Reports Server (NTRS)

Minner, G. L.; Homyak, L.

1976-01-01

An inlet noise suppressor for a TF-34 engine designed to have three acoustically treated rings was tested with several different ring arrangements. The configurations included: all three rings; two outer rings; single outer ring; single intermediate ring, and finally no rings. It was expected that as rings were removed, the acoustic performance would be degraded considerably. While a degradation occurred, it was not as large as predictions indicated. The prediction showed good agreement with the data only for the full-ring inlet configuration. The underpredictions which occurred with ring removal were believed a result of ignoring the presence of spinning modes which are known to damp more rapidly in cylindrical ducts than would be predicted by least attenuated mode or plane wave analysis.

Discrete Spring Model for Predicting Delamination Growth in Z-Fiber Reinforced DCB Specimens

NASA Technical Reports Server (NTRS)

Ratcliffe, James G.; OBrien, T. Kevin

2004-01-01

Beam theory analysis was applied to predict delamination growth in Double Cantilever Beam (DCB) specimens reinforced in the thickness direction with pultruded pins, known as Z-fibers. The specimen arms were modeled as cantilever beams supported by discrete springs, which were included to represent the pins. A bi-linear, irreversible damage law was used to represent Z-fiber damage, the parameters of which were obtained from previous experiments. Closed-form solutions were developed for specimen compliance and displacements corresponding to Z-fiber row locations. A solution strategy was formulated to predict delamination growth, in which the parent laminate mode I critical strain energy release rate was used as the criterion for delamination growth. The solution procedure was coded into FORTRAN 90, giving a dedicated software tool for performing the delamination prediction. Comparison of analysis results with previous analysis and experiment showed good agreement, yielding an initial verification for the analytical procedure.

Discrete Spring Model for Predicting Delamination Growth in Z-Fiber Reinforced DCB Specimens

NASA Technical Reports Server (NTRS)

Ratcliffe, James G.; O'Brien, T. Kevin

2004-01-01

Beam theory analysis was applied to predict delamination growth in DCB specimens reinforced in the thickness direction with pultruded pins, known as Z-fibers. The specimen arms were modeled as cantilever beams supported by discrete springs, which were included to represent the pins. A bi-linear, irreversible damage law was used to represent Z-fiber damage, the parameters of which were obtained from previous experiments. Closed-form solutions were developed for specimen compliance and displacements corresponding to Z-fiber row locations. A solution strategy was formulated to predict delamination growth, in which the parent laminate mode I fracture toughness was used as the criterion for delamination growth. The solution procedure was coded into FORTRAN 90, giving a dedicated software tool for performing the delamination prediction. Comparison of analysis results with previous analysis and experiment showed good agreement, yielding an initial verification for the analytical procedure.

Gradient retention prediction of acid-base analytes in reversed phase liquid chromatography: a simplified approach for acetonitrile-water mobile phases.

PubMed

Andrés, Axel; Rosés, Martí; Bosch, Elisabeth

2014-11-28

In previous work, a two-parameter model to predict chromatographic retention of ionizable analytes in gradient mode was proposed. However, the procedure required some previous experimental work to get a suitable description of the pKa change with the mobile phase composition. In the present study this previous experimental work has been simplified. The analyte pKa values have been calculated through equations whose coefficients vary depending on their functional group. Forced by this new approach, other simplifications regarding the retention of the totally neutral and totally ionized species also had to be performed. After the simplifications were applied, new prediction values were obtained and compared with the previously acquired experimental data. The simplified model gave pretty good predictions while saving a significant amount of time and resources. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

PubMed

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Advances in modeling sorption and diffusion of moisture in porous reactive materials.

PubMed

Harley, Stephen J; Glascoe, Elizabeth A; Lewicki, James P; Maxwell, Robert S

2014-06-23

Water-vapor-uptake experiments were performed on a silica-filled poly(dimethylsiloxane) (PDMS) network and modeled by using two different approaches. The data was modeled by using established methods and the model parameters were used to predict moisture uptake in a sample. The predictions are reasonably good, but not outstanding; many of the shortcomings of the modeling are discussed. A high-fidelity modeling approach is derived and used to improve the modeling of moisture uptake and diffusion. Our modeling approach captures the physics and kinetics of diffusion and adsorption/desorption, simultaneously. It predicts uptake better than the established method; more importantly, it is also able to predict outgassing. The material used for these studies is a filled-PDMS network; physical interpretations concerning the sorption and diffusion of moisture in this network are discussed. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A mathematical model for the interactive behavior of sulfate-reducing bacteria and methanogens during anaerobic digestion.

PubMed

Ahammad, S Ziauddin; Gomes, James; Sreekrishnan, T R

2011-09-01

Anaerobic degradation of waste involves different classes of microorganisms, and there are different types of interactions among them for substrates, terminal electron acceptors, and so on. A mathematical model is developed based on the mass balance of different substrates, products, and microbes present in the system to study the interaction between methanogens and sulfate-reducing bacteria (SRB). The performance of major microbial consortia present in the system, such as propionate-utilizing acetogens, butyrate-utilizing acetogens, acetoclastic methanogens, hydrogen-utilizing methanogens, and SRB were considered and analyzed in the model. Different substrates consumed and products formed during the process also were considered in the model. The experimental observations and model predictions showed very good prediction capabilities of the model. Model prediction was validated statistically. It was observed that the model-predicted values matched the experimental data very closely, with an average error of 3.9%.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models

PubMed Central

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance. PMID:26926235

An Investigation of Transonic Resonance in a Mach 2.2 Round Convergent-Divergent Nozzle

NASA Technical Reports Server (NTRS)

Dippold, Vance F., III; Zaman, Khairul B. M. Q.

2015-01-01

Hot-wire and acoustic measurements were taken for a round convergent nozzle and a round convergent-divergent (C-D) nozzle at a jet Mach number of 0.61. The C-D nozzle had a design Mach number of 2.2. Compared to the convergent nozzle jet flow, the Mach 2.2 nozzle jet flow produced excess broadband noise (EBBN). It also produced a transonic resonance tone at 1200 Herz. Computational simulations were performed for both nozzle flows. A steady Reynolds-Averaged Navier-Stokes simulation was performed for the convergent nozzle jet flow. For the Mach 2.2 nozzle flow, a steady RANS simulation, an unsteady RANS (URANS) simulation, and an unsteady Detached Eddy Simulation (DES) were performed. The RANS simulation of the convergent nozzle showed good agreement with the hot-wire velocity and turbulence measurements, though the decay of the potential core was over-predicted. The RANS simulation of the Mach 2.2 nozzle showed poor agreement with the experimental data, and more closely resembled an ideally-expanded jet. The URANS simulation also showed qualitative agreement with the hot-wire data, but predicted a transonic resonance at 1145 Herz. The DES showed good agreement with the hot-wire velocity and turbulence data. The DES also produced a transonic tone at 1135 Herz. The DES solution showed that the destabilization of the shock-induced separation region inside the nozzle produced increased levels of turbulence intensity. This is likely the source of the EBBN.

Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Deng, T.; Chen, Y.; Wan, Q.

2017-12-01

The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.

Prediction of SFL Interruption Performance from the Results of Arc Simulation during High-Current Phase

NASA Astrophysics Data System (ADS)

Lee, Jong-Chul; Lee, Won-Ho; Kim, Woun-Jea

2015-09-01

The design and development procedures of SF6 gas circuit breakers are still largely based on trial and error through testing although the development costs go higher every year. The computation cannot cover the testing satisfactorily because all the real processes arc not taken into account. But the knowledge of the arc behavior and the prediction of the thermal-flow inside the interrupters by numerical simulations are more useful than those by experiments due to the difficulties to obtain physical quantities experimentally and the reduction of computational costs in recent years. In this paper, in order to get further information into the interruption process of a SF6 self-blast interrupter, which is based on a combination of thermal expansion and the arc rotation principle, gas flow simulations with a CFD-arc modeling are performed during the whole switching process such as high-current period, pre-current zero period, and current-zero period. Through the complete work, the pressure-rise and the ramp of the pressure inside the chamber before current zero as well as the post-arc current after current zero should be a good criterion to predict the short-line fault interruption performance of interrupters.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

PubMed

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Direct Methods for Predicting Movement Biomechanics Based Upon Optimal Control Theory with Implementation in OpenSim.

PubMed

Porsa, Sina; Lin, Yi-Chung; Pandy, Marcus G

2016-08-01

The aim of this study was to compare the computational performances of two direct methods for solving large-scale, nonlinear, optimal control problems in human movement. Direct shooting and direct collocation were implemented on an 8-segment, 48-muscle model of the body (24 muscles on each side) to compute the optimal control solution for maximum-height jumping. Both algorithms were executed on a freely-available musculoskeletal modeling platform called OpenSim. Direct collocation converged to essentially the same optimal solution up to 249 times faster than direct shooting when the same initial guess was assumed (3.4 h of CPU time for direct collocation vs. 35.3 days for direct shooting). The model predictions were in good agreement with the time histories of joint angles, ground reaction forces and muscle activation patterns measured for subjects jumping to their maximum achievable heights. Both methods converged to essentially the same solution when started from the same initial guess, but computation time was sensitive to the initial guess assumed. Direct collocation demonstrates exceptional computational performance and is well suited to performing predictive simulations of movement using large-scale musculoskeletal models.

Determination of quantitative retention-activity relationships between pharmacokinetic parameters and biological effectiveness fingerprints of Salvia miltiorrhiza constituents using biopartitioning and microemulsion high-performance liquid chromatography.

PubMed

Gao, Haoshi; Huang, Hongzhang; Zheng, Aini; Yu, Nuojun; Li, Ning

2017-11-01

In this study, we analyzed danshen (Salvia miltiorrhiza) constituents using biopartitioning and microemulsion high-performance liquid chromatography (MELC). The quantitative retention-activity relationships (QRARs) of the constituents were established to model their pharmacokinetic (PK) parameters and chromatographic retention data, and generate their biological effectiveness fingerprints. A high-performance liquid chromatography (HPLC) method was established to determine the abundance of the extracted danshen constituents, such as sodium danshensu, rosmarinic acid, salvianolic acid B, protocatechuic aldehyde, cryptotanshinone, and tanshinone IIA. And another HPLC protocol was established to determine the abundance of those constituents in rat plasma samples. An experimental model was built in Sprague Dawley (SD) rats, and calculated the corresponding PK parameterst with 3P97 software package. Thirty-five model drugs were selected to test the PK parameter prediction capacities of the various MELC systems and to optimize the chromatographic protocols. QRARs and generated PK fingerprints were established. The test included water/oil-soluble danshen constituents and the prediction capacity of the regression model was validated. The results showed that the model had good predictability. Copyright © 2017. Published by Elsevier B.V.

Exploring the performance of the SEDD model to predict sediment yield in eucalyptus plantations. Long-term results from an experimental catchment in Southern Italy

NASA Astrophysics Data System (ADS)

Porto, P.; Cogliandro, V.; Callegari, G.

2018-01-01

In this paper, long-term sediment yield data, collected in a small (1.38 ha) Calabrian catchment (W2), reafforested with eucalyptus trees (Eucalyptus occidentalis Engl.) are used to validate the performance of the SEdiment Delivery Distributed Model (SEDD) in areas with high erosion rates. At first step, the SEDD model was calibrated using field data collected in previous field campaigns undertaken during the period 1978-1994. This first phase allowed the model calibration parameter β to be calculated using direct measurements of rainfall, runoff, and sediment output. The model was then validated in its calibrated form for an independent period (2006-2016) for which new measurements of rainfall, runoff and sediment output are also available. The analysis, carried out at event and annual scale showed good agreement between measured and predicted values of sediment yield and suggested that the SEDD model can be seen as an appropriate means of evaluating erosion risk associated with manmade plantations in marginal areas. Further work is however required to test the performance of the SEDD model as a prediction tool in different geomorphic contexts.

Development of PRIME for irradiation performance analysis of U-Mo/Al dispersion fuel

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

Jeong, Gwan Yoon; Kim, Yeon Soo; Jeong, Yong Jin

A prediction code for the thermo-mechanical performance of research reactor fuel (PRIME) has been developed with the implementation of developed models to analyze the irradiation behavior of U-Mo dispersion fuel. The code is capable of predicting the two-dimensional thermal and mechanical performance of U-Mo dispersion fuel during irradiation. A finite element method was employed to solve the governing equations for thermal and mechanical equilibria. Temperature-and burnup-dependent material properties of the fuel meat constituents and cladding were used. The numerical solution schemes in PRIME were verified by benchmarking solutions obtained using a commercial finite element analysis program (ABAQUS).The code was validatedmore » using irradiation data from RERTR, HAMP-1, and E-FUTURE tests. The measured irradiation data used in the validation were IL thickness, volume fractions of fuel meat constituents for the thermal analysis, and profiles of the plate thickness changes and fuel meat swelling for the mechanical analysis. The prediction results were in good agreement with the measurement data for both thermal and mechanical analyses, confirming the validity of the code. (c) 2018 Elsevier B.V. All rights reserved.« less

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Motivation: Most functions within the cell emerge thanks to protein–protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23812985

Verification of directed self-assembly (DSA) guide patterns through machine learning

NASA Astrophysics Data System (ADS)

Shim, Seongbo; Cai, Sibo; Yang, Jaewon; Yang, Seunghune; Choi, Byungil; Shin, Youngsoo

2015-03-01

Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n-dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology.

A novel highly differentially expressed gene in wheat endosperm associated with bread quality

PubMed Central

Furtado, A.; Bundock, P. C.; Banks, P. M.; Fox, G.; Yin, X.; Henry, R. J.

2015-01-01

Analysis of gene expression in developing wheat seeds was used to identify a gene, wheat bread making (wbm), with highly differential expression (~1000 fold) in the starchy endosperm of genotypes varying in bread making quality. Several alleles differing in the 5’-upstream region (promoter) of this gene were identified, with one present only in genotypes with high levels of wbm expression. RNA-Seq analysis revealed low or no wbm expression in most genotypes but high expression (0.2-0.4% of total gene expression) in genotypes that had good bread loaf volume. The wbm gene is predicted to encode a mature protein of 48 amino acids (including four cysteine residues) not previously identified in association with wheat quality, possibly because of its small size and low frequency in the wheat gene pool. Genotypes with high wbm expression all had good bread making quality but not always good physical dough qualities. The predicted protein was sulphur rich suggesting the possibility of a contribution to bread loaf volume by supporting the crossing linking of proteins in gluten. Improved understanding of the molecular basis of differences in bread making quality may allow more rapid development of high performing genotypes with acceptable end-use properties and facilitate increased wheat production. PMID:26011437

A novel highly differentially expressed gene in wheat endosperm associated with bread quality.

PubMed

Furtado, A; Bundock, P C; Banks, P M; Fox, G; Yin, X; Henry, R J

2015-05-26

Analysis of gene expression in developing wheat seeds was used to identify a gene, wheat bread making (wbm), with highly differential expression (~1000 fold) in the starchy endosperm of genotypes varying in bread making quality. Several alleles differing in the 5'-upstream region (promoter) of this gene were identified, with one present only in genotypes with high levels of wbm expression. RNA-Seq analysis revealed low or no wbm expression in most genotypes but high expression (0.2-0.4% of total gene expression) in genotypes that had good bread loaf volume. The wbm gene is predicted to encode a mature protein of 48 amino acids (including four cysteine residues) not previously identified in association with wheat quality, possibly because of its small size and low frequency in the wheat gene pool. Genotypes with high wbm expression all had good bread making quality but not always good physical dough qualities. The predicted protein was sulphur rich suggesting the possibility of a contribution to bread loaf volume by supporting the crossing linking of proteins in gluten. Improved understanding of the molecular basis of differences in bread making quality may allow more rapid development of high performing genotypes with acceptable end-use properties and facilitate increased wheat production.

Measurement properties of a screening questionnaire of obstructive sleep apnea risk: Little information, great prediction?(☆).

PubMed

Sargento, Paulo; Perea, Victoria; Ladera, Valentina; Lopes, Paulo; Oliveira, Jorge

2014-06-01

Previous research had shown the suitability of several questionnaires predicting the obstructive sleep apnea syndrome. Measurement properties of an online screening questionnaire were studied. The sample consisted of 184 Portuguese adults (89 men and 95 women); 46 of them were polysomnographically diagnosed with the untreated obstructive sleep apnea syndrome. The participants were assessed with an online questionnaire of sleep apnea risk, from University of Maryland. A principal component factor analysis was performed, revealing a single factor (49.24% of the total variance). Internal consistency was minimally adequate (α=0.74). The mean of inter-item correlation was of 0.35 (0.120.61), whereas the item-total correlations were considered good (0.520.81). The total score for patients was significantly higher than for healthy participants (p<0.000), but no significant statistical differences between severity groups of patients were found (p>0.05). Furthermore, the ability of the measure in discriminating between healthy subjects and OSA subjects was good. Overall data from the Rasch analysis was consistent with the guidelines of Linacre, scores show good model fit and psychometric adequacy. The measure showed an adequate structural, internal and criterion validity, suggesting this as a useful and effective screening for sleep apnea risk in Portuguese adults.

Alphabet Writing and Allograph Selection as Predictors of Spelling in Sentences Written by Spanish-Speaking Children Who Are Poor or Good Keyboarders.

PubMed

Peake, Christian; Diaz, Alicia; Artiles, Ceferino

This study examined the relationship and degree of predictability that the fluency of writing the alphabet from memory and the selection of allographs have on measures of fluency and accuracy of spelling in a free-writing sentence task when keyboarding. The Test Estandarizado para la Evaluación de la Escritura con Teclado ("Spanish Keyboarding Writing Test"; Jiménez, 2012) was used as the assessment tool. A sample of 986 children from Grades 1 through 3 were classified according to transcription skills measured by keyboard ability (poor vs. good) across the grades. Results demonstrated that fluency in writing the alphabet and selecting allographs mediated the differences in spelling between good and poor keyboarders in the free-writing task. Execution in the allograph selection task and writing alphabet from memory had different degrees of predictability in each of the groups in explaining the level of fluency and spelling in the free-writing task sentences, depending on the grade. These results suggest that early assessment of writing by means of the computer keyboard can provide clues and guidelines for intervention and training to strengthen specific skills to improve writing performance in the early primary grades in transcription skills by keyboarding.

Screening of depression in cardiology: A study on 617 cardiovascular patients.

PubMed

Tesio, Valentina; Marra, Sebastiano; Molinaro, Stefania; Torta, Riccardo; Gaita, Fiorenzo; Castelli, Lorys

2017-10-15

Depression screening in the cardiovascular disease (CVD) care setting is under-performed, also because the issue of the optimal screening tools cut-off is still open. We analysed which HADS (Hospital Anxiety and Depression Scale) total score cut-off value shows the best properties in two groups of 357 Acute Coronary Syndrome (ACS) and 260 Chronic Coronary Artery Disease (CAD) hospitalized patients. A Receiver Operating Characteristics (ROC) curve was plotted for both groups using the Montgomery-Asberg Depression Rating Scale (MADRS) as the criterion. Accuracy, positive (PPV) and negative (NPV) predictive values were computed for different cut-off scores. The ROC curves confirmed the excellent/very good accuracy of the HADS in both groups, with an area under the curve of 0.911 for the ACS and 0.893 for the CAD patients. The cut-off of 14 showed the best compromise between high sensitivity and good specificity in both groups, with high negative predicted values (95.5% and 92.4%, respectively). Using a cut-off value of 14, the HADS could be considered a good screening tool to identify hospitalized CAD and ACS patients requiring a more accurate depression assessment, in order to promptly plan the most appropriate treatment strategies and prevent the negative effects of depression in CVD patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of free modeling targets in CASP11 and ROLL.

PubMed

Kinch, Lisa N; Li, Wenlin; Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Grishin, Nick V

2016-09-01

We present an assessment of 'template-free modeling' (FM) in CASP11and ROLL. Community-wide server performance suggested the use of automated scores similar to previous CASPs would provide a good system of evaluating performance, even in the absence of comprehensive manual assessment. The CASP11 FM category included several outstanding examples, including successful prediction by the Baker group of a 256-residue target (T0806-D1) that lacked sequence similarity to any existing template. The top server model prediction by Zhang's Quark, which was apparently selected and refined by several manual groups, encompassed the entire fold of target T0837-D1. Methods from the same two groups tended to dominate overall CASP11 FM and ROLL rankings. Comparison of top FM predictions with those from the previous CASP experiment revealed progress in the category, particularly reflected in high prediction accuracy for larger protein domains. FM prediction models for two cases were sufficient to provide functional insights that were otherwise not obtainable by traditional sequence analysis methods. Importantly, CASP11 abstracts revealed that alignment-based contact prediction methods brought about much of the CASP11 progress, producing both of the functionally relevant models as well as several of the other outstanding structure predictions. These methodological advances enabled de novo modeling of much larger domain structures than was previously possible and allowed prediction of functional sites. Proteins 2016; 84(Suppl 1):51-66. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

Analysis of electric and thermal behaviour of lithium-ion cells in realistic driving cycles

NASA Astrophysics Data System (ADS)

Tourani, Abbas; White, Peter; Ivey, Paul

2014-12-01

A substantial part of electric vehicles (EVs) powertrain is the battery cell. The cells are usually connected in series, and failure of a single cell can deactivate an entire module in the battery pack. Hence, understanding the cell behaviour helps to predict and improve the battery performance and leads to design a cost effective thermal management system for the battery pack. A first principle thermo electrochemical model is applied to study the cell behaviour. The model is in good agreement with the experimental results and can predict the heat generation and the temperature distribution across the cell for different operating conditions. The operating temperature effect on the cell performance is studied and the operating temperature for the best performance is verified. In addition, EV cells are examined in a realistic driving cycle from the Artemis class. The study findings lead to the proposal of some crucial recommendation to design cost effective thermal management systems for the battery pack.

Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance.

PubMed

McCauley, Peter; Kalachev, Leonid V; Mollicone, Daniel J; Banks, Siobhan; Dinges, David F; Van Dongen, Hans P A

2013-12-01

Recent experimental observations and theoretical advances have indicated that the homeostatic equilibrium for sleep/wake regulation--and thereby sensitivity to neurobehavioral impairment from sleep loss--is modulated by prior sleep/wake history. This phenomenon was predicted by a biomathematical model developed to explain changes in neurobehavioral performance across days in laboratory studies of total sleep deprivation and sustained sleep restriction. The present paper focuses on the dynamics of neurobehavioral performance within days in this biomathematical model of fatigue. Without increasing the number of model parameters, the model was updated by incorporating time-dependence in the amplitude of the circadian modulation of performance. The updated model was calibrated using a large dataset from three laboratory experiments on psychomotor vigilance test (PVT) performance, under conditions of sleep loss and circadian misalignment; and validated using another large dataset from three different laboratory experiments. The time-dependence of circadian amplitude resulted in improved goodness-of-fit in night shift schedules, nap sleep scenarios, and recovery from prior sleep loss. The updated model predicts that the homeostatic equilibrium for sleep/wake regulation--and thus sensitivity to sleep loss--depends not only on the duration but also on the circadian timing of prior sleep. This novel theoretical insight has important implications for predicting operator alertness during work schedules involving circadian misalignment such as night shift work.

Validation of the DRAGON score in 12 stroke centers in anterior and posterior circulation.

PubMed

Strbian, Daniel; Seiffge, David J; Breuer, Lorenz; Numminen, Heikki; Michel, Patrik; Meretoja, Atte; Coote, Skye; Bordet, Régis; Obach, Victor; Weder, Bruno; Jung, Simon; Caso, Valeria; Curtze, Sami; Ollikainen, Jyrki; Lyrer, Philippe A; Eskandari, Ashraf; Mattle, Heinrich P; Chamorro, Angel; Leys, Didier; Bladin, Christopher; Davis, Stephen M; Köhrmann, Martin; Engelter, Stefan T; Tatlisumak, Turgut

2013-10-01

The DRAGON score predicts functional outcome in the hyperacute phase of intravenous thrombolysis treatment of ischemic stroke patients. We aimed to validate the score in a large multicenter cohort in anterior and posterior circulation. Prospectively collected data of consecutive ischemic stroke patients who received intravenous thrombolysis in 12 stroke centers were merged (n=5471). We excluded patients lacking data necessary to calculate the score and patients with missing 3-month modified Rankin scale scores. The final cohort comprised 4519 eligible patients. We assessed the performance of the DRAGON score with area under the receiver operating characteristic curve in the whole cohort for both good (modified Rankin scale score, 0-2) and miserable (modified Rankin scale score, 5-6) outcomes. Area under the receiver operating characteristic curve was 0.84 (0.82-0.85) for miserable outcome and 0.82 (0.80-0.83) for good outcome. Proportions of patients with good outcome were 96%, 93%, 78%, and 0% for 0 to 1, 2, 3, and 8 to 10 score points, respectively. Proportions of patients with miserable outcome were 0%, 2%, 4%, 89%, and 97% for 0 to 1, 2, 3, 8, and 9 to 10 points, respectively. When tested separately for anterior and posterior circulation, there was no difference in performance (P=0.55); areas under the receiver operating characteristic curve were 0.84 (0.83-0.86) and 0.82 (0.78-0.87), respectively. No sex-related difference in performance was observed (P=0.25). The DRAGON score showed very good performance in the large merged cohort in both anterior and posterior circulation strokes. The DRAGON score provides rapid estimation of patient prognosis and supports clinical decision-making in the hyperacute phase of stroke care (eg, when invasive add-on strategies are considered).

Benchmarking hydrological model predictive capability for UK River flows and flood peaks.

NASA Astrophysics Data System (ADS)

Lane, Rosanna; Coxon, Gemma; Freer, Jim; Wagener, Thorsten

2017-04-01

Data and hydrological models are now available for national hydrological analyses. However, hydrological model performance varies between catchments, and lumped, conceptual models are not able to produce adequate simulations everywhere. This study aims to benchmark hydrological model performance for catchments across the United Kingdom within an uncertainty analysis framework. We have applied four hydrological models from the FUSE framework to 1128 catchments across the UK. These models are all lumped models and run at a daily timestep, but differ in the model structural architecture and process parameterisations, therefore producing different but equally plausible simulations. We apply FUSE over a 20 year period from 1988-2008, within a GLUE Monte Carlo uncertainty analyses framework. Model performance was evaluated for each catchment, model structure and parameter set using standard performance metrics. These were calculated both for the whole time series and to assess seasonal differences in model performance. The GLUE uncertainty analysis framework was then applied to produce simulated 5th and 95th percentile uncertainty bounds for the daily flow time-series and additionally the annual maximum prediction bounds for each catchment. The results show that the model performance varies significantly in space and time depending on catchment characteristics including climate, geology and human impact. We identify regions where models are systematically failing to produce good results, and present reasons why this could be the case. We also identify regions or catchment characteristics where one model performs better than others, and have explored what structural component or parameterisation enables certain models to produce better simulations in these catchments. Model predictive capability was assessed for each catchment, through looking at the ability of the models to produce discharge prediction bounds which successfully bound the observed discharge. These results improve our understanding of the predictive capability of simple conceptual hydrological models across the UK and help us to identify where further effort is needed to develop modelling approaches to better represent different catchment and climate typologies.

Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension

PubMed Central

Ybarra Sagarduy, José Luis; Camacho Mata, Dacia Yurima; Moral de la Rubia, José; Piña López, Julio Alfonso; Yunes Zárraga, José Luis Masud

2018-01-01

Background It is widely known that physical activity is the key to the optimal management and clinical control of hypertension. Purpose This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension. Methods This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior. Results Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data (pBS =0.235, χ2/gl =1.519, Jöreskog and Sörbom’s Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen’s Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053). Conclusion The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so that quality of life of patients with hypertension improves and they might be able to manage and control their disease well. PMID:29379276

DeepQA: improving the estimation of single protein model quality with deep belief networks.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Comparison of Three Contemporary Risk Scores for Mortality Following Elective Abdominal Aortic Aneurysm Repair

PubMed Central

Grant, S.W.; Hickey, G.L.; Carlson, E.D.; McCollum, C.N.

2014-01-01

Objective/background A number of contemporary risk prediction models for mortality following elective abdominal aortic aneurysm (AAA) repair have been developed. Before a model is used either in clinical practice or to risk-adjust surgical outcome data it is important that its performance is assessed in external validation studies. Methods The British Aneurysm Repair (BAR) score, Medicare, and Vascular Governance North West (VGNW) models were validated using an independent prospectively collected sample of multicentre clinical audit data. Consecutive, data on 1,124 patients undergoing elective AAA repair at 17 hospitals in the north-west of England and Wales between April 2011 and March 2013 were analysed. The outcome measure was in-hospital mortality. Model calibration (observed to expected ratio with chi-square test, calibration plots, calibration intercept and slope) and discrimination (area under receiver operating characteristic curve [AUC]) were assessed in the overall cohort and procedural subgroups. Results The mean age of the population was 74.4 years (SD 7.7); 193 (17.2%) patients were women and the majority of patients (759, 67.5%) underwent endovascular aneurysm repair. All three models demonstrated good calibration in the overall cohort and procedural subgroups. Overall discrimination was excellent for the BAR score (AUC 0.83, 95% confidence interval [CI] 0.76–0.89), and acceptable for the Medicare and VGNW models, with AUCs of 0.78 (95% CI 0.70–0.86) and 0.75 (95% CI 0.65–0.84) respectively. Only the BAR score demonstrated good discrimination in procedural subgroups. Conclusion All three models demonstrated good calibration and discrimination for the prediction of in-hospital mortality following elective AAA repair and are potentially useful. The BAR score has a number of advantages, which include being developed on the most contemporaneous data, excellent overall discrimination, and good performance in procedural subgroups. Regular model validations and recalibration will be essential. PMID:24837173

Comparison of three contemporary risk scores for mortality following elective abdominal aortic aneurysm repair.

PubMed

Grant, S W; Hickey, G L; Carlson, E D; McCollum, C N

2014-07-01

A number of contemporary risk prediction models for mortality following elective abdominal aortic aneurysm (AAA) repair have been developed. Before a model is used either in clinical practice or to risk-adjust surgical outcome data it is important that its performance is assessed in external validation studies. The British Aneurysm Repair (BAR) score, Medicare, and Vascular Governance North West (VGNW) models were validated using an independent prospectively collected sample of multicentre clinical audit data. Consecutive, data on 1,124 patients undergoing elective AAA repair at 17 hospitals in the north-west of England and Wales between April 2011 and March 2013 were analysed. The outcome measure was in-hospital mortality. Model calibration (observed to expected ratio with chi-square test, calibration plots, calibration intercept and slope) and discrimination (area under receiver operating characteristic curve [AUC]) were assessed in the overall cohort and procedural subgroups. The mean age of the population was 74.4 years (SD 7.7); 193 (17.2%) patients were women and the majority of patients (759, 67.5%) underwent endovascular aneurysm repair. All three models demonstrated good calibration in the overall cohort and procedural subgroups. Overall discrimination was excellent for the BAR score (AUC 0.83, 95% confidence interval [CI] 0.76-0.89), and acceptable for the Medicare and VGNW models, with AUCs of 0.78 (95% CI 0.70-0.86) and 0.75 (95% CI 0.65-0.84) respectively. Only the BAR score demonstrated good discrimination in procedural subgroups. All three models demonstrated good calibration and discrimination for the prediction of in-hospital mortality following elective AAA repair and are potentially useful. The BAR score has a number of advantages, which include being developed on the most contemporaneous data, excellent overall discrimination, and good performance in procedural subgroups. Regular model validations and recalibration will be essential. Copyright © 2014 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Local mechanical properties of LFT injection molded parts: Numerical simulations versus experiments

NASA Astrophysics Data System (ADS)

Desplentere, F.; Soete, K.; Bonte, H.; Debrabandere, E.

2014-05-01

In predictive engineering for polymer processes, the proper prediction of material microstructure from known processing conditions and constituent material properties is a critical step forward properly predicting bulk properties in the finished composite. Operating within the context of long-fiber thermoplastics (LFT, length < 15mm) this investigation concentrates on the prediction of the local mechanical properties of an injection molded part. To realize this, the Autodesk Simulation Moldflow Insight 2014 software has been used. In this software, a fiber breakage algorithm for the polymer flow inside the mold is available. Using well known micro mechanic formulas allow to combine the local fiber length with the local orientation into local mechanical properties. Different experiments were performed using a commercially available glass fiber filled compound to compare the measured data with the numerical simulation results. In this investigation, tensile tests and 3 point bending tests are considered. To characterize the fiber length distribution of the polymer melt entering the mold (necessary for the numerical simulations), air shots were performed. For those air shots, similar homogenization conditions were used as during the injection molding tests. The fiber length distribution is characterized using automated optical method on samples for which the matrix material is burned away. Using the appropriate settings for the different experiments, good predictions of the local mechanical properties are obtained.

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Training to improve manual control in 7-8 and 10-12 year old children: Training eliminates performance differences between ages.

PubMed

Snapp-Childs, Winona; Fath, Aaron J; Watson, Carol A; Flatters, Ian; Mon-Williams, Mark; Bingham, Geoffrey P

2015-10-01

Many children have difficulty producing movements well enough to improve in perceptuo-motor learning. We have developed a training method that supports active movement generation to allow improvement in a 3D tracing task requiring good compliance control. We previously tested 7-8 year old children who exhibited poor performance and performance differences before training. After training, performance was significantly improved and performance differences were eliminated. According to the Dynamic Systems Theory of development, appropriate support can enable younger children to acquire the ability to perform like older children. In the present study, we compared 7-8 and 10-12 year old school children and predicted that younger children would show reduced performance that was nonetheless amenable to training. Indeed, the pre-training performance of the 7-8 year olds was worse than that of the 10-12 year olds, but post-training performance was equally good for both groups. This was similar to previous results found using this training method for children with DCD and age-matched typically developing children. We also found in a previous study of 7-8 year old school children that training in the 3D tracing task transferred to a 2D drawing task. We now found similar transfer for the 10-12 year olds. Copyright © 2015 Elsevier B.V. All rights reserved.

Performance of Smagorinsky and dynamic models in near surface turbulence

NASA Astrophysics Data System (ADS)

Brasseur, James G.; Juneja, Anurag

1997-11-01

In LES of high-Reynolds-number wall bounded turbulence such as the atmospheric boundary layer (ABL), a viscous sublayer either does not exist or is within the first grid cell, and some integral scale motions are necessarily under-resolved at the first few grid locations. Here the subgrid terms dominate the evolution of resolved velocity and the SGS model performance becomes crucial. To develop improved closures for surface layer turbulence (under-resolved and anisotropic), we explore (a) why current SGS closures fail and (b) what needs to be fixed. We evaluate the performance of the Smagorinsky and dynamic models using DNS data from shear- and buoyancy-driven turbulence as a function of filter cutoff location. We find that the underlying assumption of good alignment between the subgrid stress and resolved strain-rate tensors is not correct in general. More importantly, the Smagorinsky model incorrectly predicts a strong preference in the direction of the SGS stress divergence vector, a spurious prediction that is directly related to the anisotropic structure of the resolved turbulence field. This, and its under-estimation of the SGS pressure gradient, are likely sources of the errors observed in LES of the ABL. Whereas the dynamic formulations do a better job predicting some SGS dynamics, the model fails when the filter cutoff is near an integral scale, and predicts unreasonable fluctuation levels-- although performance is sensitive to type of averaging. *supported by ARO grant DAAL03-92-0117.

Thermal Performance of LANDSAT-7 ETM+ Instruments During First Year in Flight

NASA Technical Reports Server (NTRS)

Choi, Michael K.

2000-01-01

Landsat-7 was successfully launched into orbit on April 15, 1999. After devoting three months to the t bakeout and cool-down of the radiative cooler, and on- t orbit checkout, the Enhanced Thematic Mapper Plus (ETM+) began the normal imaging phase of the mission in mid-July 1999. This paper presents the thermal performance of the ETM+ from mid-July 1999 to mid-May 2000. The flight temperatures are compared to the yellow temperature limits, and worst cold case and worst hot case flight temperature predictions in the 15-orbit mission design profile. The flight temperature predictions were generated by a thermal model, which was correlated to the observatory thermal balance test data. The yellow temperature limits were derived from the flight temperature predictions, plus some margins. The yellow limits work well in flight, so that only several minor changes to them were needed. Overall, the flight temperatures and flight temperature predictions have good agreement. Based on the ETM+ thermal vacuum qualification test, new limits on the imaging time are proposed to increase the average duty cycle, and to resolve the problems experienced by the Mission Operation Team.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Executive functioning performance predicts subjective and physiological acute stress reactivity: preliminary results.

PubMed

Hendrawan, Donny; Yamakawa, Kaori; Kimura, Motohiro; Murakami, Hiroki; Ohira, Hideki

2012-06-01

Individual differences in baseline executive functioning (EF) capacities have been shown to predict state anxiety during acute stressor exposure. However, no previous studies have clearly demonstrated the relationship between EF and physiological measures of stress. The present study investigated the efficacy of several well-known EF tests (letter fluency, Stroop test, and Wisconsin Card Sorting Test) in predicting both subjective and physiological stress reactivity during acute psychosocial stress exposure. Our results show that letter fluency served as the best predictor for both types of reactivity. Specifically, the higher the letter fluency score, the lower the acute stress reactivity after controlling for the baseline stress response, as indicated by lower levels of state anxiety, negative mood, salivary cortisol, and skin conductance. Moreover, the predictive power of the letter fluency test remained significant for state anxiety and cortisol indices even after further adjustments for covariates by adding the body mass index (BMI) as a covariate. Thus, good EF performance, as reflected by high letter fluency scores, may dampen acute stress responses, which suggests that EF processes are directly associated with aspects of stress regulation. Copyright © 2012 Elsevier B.V. All rights reserved.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Measuring drug absorption improves interpretation of behavioral responses in a larval zebrafish locomotor assay for predicting seizure liability.

PubMed

Cassar, Steven; Breidenbach, Laura; Olson, Amanda; Huang, Xin; Britton, Heather; Woody, Clarissa; Sancheti, Pankajkumar; Stolarik, DeAnne; Wicke, Karsten; Hempel, Katja; LeRoy, Bruce

2017-11-01

Unanticipated effects on the central nervous system are a concern during new drug development. A larval zebrafish locomotor assay can reveal seizure liability of experimental molecules before testing in mammals. Relative absorption of compounds by larvae is lacking in prior reports of such assays; having those data may be valuable for interpreting seizure liability assay performance. Twenty-eight reference drugs were tested at multiple dose levels in fish water and analyzed by a blinded investigator. Responses of larval zebrafish were quantified during a 30min dosing period. Predictive metrics were calculated by comparing fish activity to mammalian seizure liability for each drug. Drug level analysis was performed to calculate concentrations in dose solutions and larvae. Fifteen drug candidates with neuronal targets, some having preclinical convulsion findings in mammals, were tested similarly. The assay has good predictive value of established mammalian responses for reference drugs. Analysis of drug absorption by larval fish revealed a positive correlation between hyperactive behavior and pro-convulsive drug absorption. False negative results were associated with significantly lower compound absorption compared to true negative, or true positive results. The predictive value for preclinical toxicology findings was inferior to that suggested by reference drugs. Disproportionately low exposures in larvae giving false negative results demonstrate that drug exposure analysis can help interpret results. Due to the rigorous testing commonly performed in preclinical toxicology, predicting convulsions in those studies may be more difficult than predicting effects from marketed drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

PubMed

Pearce, J; Ferrier, S; Scotts, D

2001-06-01

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

A nonproprietary, nonsecret program for calculating Stirling cryocoolers

NASA Technical Reports Server (NTRS)

Martini, W. R.

1985-01-01

A design program for an integrated Stirling cycle cryocooler was written on an IBM-PC computer. The program is easy to use and shows the trends and itemizes the losses. The calculated results were compared with some measured performance values. The program predicts somewhat optimistic performance and needs to be calibrated more with experimental measurements. Adding a multiplier to the friction factor can bring the calculated rsults in line with the limited test results so far available. The program is offered as a good framework on which to build a truly useful design program for all types of cryocoolers.

High-performance heat pipes for heat recovery applications

NASA Technical Reports Server (NTRS)

Saaski, E. W.; Hartl, J. H.

1980-01-01

Methods to improve the performance of reflux heat pipes for heat recovery applications were examined both analytically and experimentally. Various models for the estimation of reflux heat pipe transport capacity were surveyed in the literature and compared with experimental data. A high transport capacity reflux heat pipe was developed that provides up to a factor of 10 capacity improvement over conventional open tube designs; analytical models were developed for this device and incorporated into a computer program HPIPE. Good agreement of the model predictions with data for R-11 and benzene reflux heat pipes was obtained.

Composite panel development at JPL

NASA Technical Reports Server (NTRS)

Mcelroy, Paul; Helms, Rich

1988-01-01

Parametric computer studies can be use in a cost effective manner to determine optimized composite mirror panel designs. An InterDisciplinary computer Model (IDM) was created to aid in the development of high precision reflector panels for LDR. The materials properties, thermal responses, structural geometries, and radio/optical precision are synergistically analyzed for specific panel designs. Promising panels designs are fabricated and tested so that comparison with panel test results can be used to verify performance prediction models and accommodate design refinement. The iterative approach of computer design and model refinement with performance testing and materials optimization has shown good results for LDR panels.

Wavefront control performance modeling with WFIRST shaped pupil coronagraph testbed

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Nemati, Bijian; Krist, John; Cady, Eric; Kern, Brian; Poberezhskiy, Ilya

2017-09-01

NASA's WFIRST mission includes a coronagraph instrument (CGI) for direct imaging of exoplanets. Significant improvement in CGI model fidelity has been made recently, alongside a testbed high contrast demonstration in a simulated dynamic environment at JPL. We present our modeling method and results of comparisons to testbed's high order wavefront correction performance for the shaped pupil coronagraph. Agreement between model prediction and testbed result at better than a factor of 2 has been consistently achieved in raw contrast (contrast floor, chromaticity, and convergence), and with that comes good agreement in contrast sensitivity to wavefront perturbations and mask lateral shear.

Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

USGS Publications Warehouse

Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

2012-01-01

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

PubMed Central

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods. PMID:26880879

Predicting violence and recidivism in a large sample of males on probation or parole.

PubMed

Prell, Lettie; Vitacco, Michael J; Zavodny, Denis

This study evaluated the utility of items and scales from the Iowa Violence and Victimization Instrument in a sample of 1961 males from the state of Iowa who were on probation or released from prison to parole supervision. This is the first study to examine the potential of the Iowa Violence and Victimization Instrument to predict criminal offenses. The males were followed for 30months immediately following their admission to probation or parole. AUC analyses indicated fair to good predictive power for the Iowa Violence and Victimization Instrument for charges of violence and victimization, but chance predictive power for drug offenses. Notably, both scales of the instrument performed equally well at the 30-month follow-up. Items on the Iowa Violence and Victimization Instrument not only predicted violence, but are straightforward to score. Violence management strategies are discussed as they relate to the current findings, including the potential to expand the measure to other jurisdictions and populations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accuracy statistics in predicting Independent Activities of Daily Living (IADL) capacity with comprehensive and brief neuropsychological test batteries.

PubMed

Karzmark, Peter; Deutsch, Gayle K

2018-01-01

This investigation was designed to determine the predictive accuracy of a comprehensive neuropsychological and brief neuropsychological test battery with regard to the capacity to perform instrumental activities of daily living (IADLs). Accuracy statistics that included measures of sensitivity, specificity, positive and negative predicted power and positive likelihood ratio were calculated for both types of batteries. The sample was drawn from a general neurological group of adults (n = 117) that included a number of older participants (age >55; n = 38). Standardized neuropsychological assessments were administered to all participants and were comprised of the Halstead Reitan Battery and portions of the Wechsler Adult Intelligence Scale-III. A comprehensive test battery yielded a moderate increase over base-rate in predictive accuracy that generalized to older individuals. There was only limited support for using a brief battery, for although sensitivity was high, specificity was low. We found that a comprehensive neuropsychological test battery provided good classification accuracy for predicting IADL capacity.